System and method for validating and generating a battery pass

Abstract

The present invention relates to the field of sustainability, in particular to the field of sustainable industrialization. The present disclosure relates to methods, apparatuses, systems and computer elements for confirming an amount of data accessible for generating and / or updating a battery passport associated with a battery. The present disclosure further relates to methods, apparatuses, systems and computer elements for generating or updating a battery passport associated with a battery. The present disclosure further relates to a battery associated with a battery passport as generated and / or updated as disclosed herein, and to a battery passport associated with a data integrity measure as generated as disclosed herein. The present disclosure further relates to the use of a data integrity measure as generated as disclosed for generating and / or updating a battery passport, and to the use of a battery passport as generated and / or updated as disclosed herein for further processing a battery associated with the battery passport.

Description

Technical Field

[0001] This invention relates to the field of sustainability, and more particularly to the field of sustainable industrialization. This disclosure relates to methods, apparatus, systems, and computer elements for confirming the amount of data accessible for generating and / or updating battery passes associated with a battery. This disclosure further relates to methods, apparatus, systems, and computer elements for generating or updating battery passes associated with a battery. This disclosure further relates to a battery associated with a battery pass generated and / or updated as disclosed herein, and a battery pass associated with a data integrity metric generated as disclosed herein. This disclosure further relates to the use of the data integrity metric generated as disclosed herein for generating and / or updating battery passes, and the use of the battery pass generated and / or updated as disclosed herein for further processing of the battery associated with the battery pass. Background Technology

[0002] The supply and production of products require compliance with various regulatory requirements, which vary from product to product. Meeting these requirements may necessitate the generation of battery permits. Battery permits contain a variety of data, including data associated with the input materials used in battery production. This data needs to be provided by the producers of these input materials for the generation and / or renewal of battery permits. A lack of transparency regarding the data available for generating and / or renewing battery permits can hinder the provision of accurate and reliable battery permits required for efficient further processing and / or recycling of the batteries. Summary of the Invention

[0003] On one hand, a computer-implemented method is disclosed for confirming the amount of data accessible for generating and / or updating battery credentials associated with a battery, wherein the battery is produced from one or more input materials through a production chain, the method comprising:

[0004] • Provide data associated with the battery, including at least one digital battery identifier associated with the battery;

[0005] • Provide data associated with the data source that indicates the data available for generating and / or updating battery passes associated with the battery;

[0006] • Collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers;

[0007] • Generate a data integrity metric associated with the data available for generating and / or updating the battery pass by determining the degree of correlation between the data associated with these data sources and the data associated with the data model.

[0008] • Provides a measure of the integrity of the generated data.

[0009] On the other hand, a computer-implemented method is disclosed for confirming the amount of data accessible for generating and / or updating battery credentials associated with a battery, wherein the battery is produced from one or more input materials through a production chain, the method comprising:

[0010] • Provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0011] • Based on the at least one digital battery identifier, confirmed data associated with the production of the battery is provided, wherein the confirmed data is generated by: distributed data consuming nodes collecting production-related data from distributed data providing nodes associated with data related to the production of the battery based on the provided digital battery identifier, and using a rule-based engine to confirm at least a portion of the collected data, the rule-based engine including one or more rules related to the data model associated with the battery pass.

[0012] • Collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers;

[0013] • Generate a data integrity metric that associates the confirmed data with the data relevant to the data model, and links it to the data available for generating and / or updating the battery pass.

[0014] • Provides a measure of the integrity of the generated data.

[0015] In another aspect, an apparatus for confirming the amount of data accessible for generating and / or updating battery credentials associated with a battery is disclosed, wherein the battery is produced from one or more input materials through a production chain, the apparatus comprising:

[0016] • A data providing interface configured to provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0017] • Data storage device configured to provide data associated with a data source, the data indicating data accessible for generating and / or updating battery-associated passes;

[0018] • Data collection unit, configured to collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers;

[0019] • A metric generator configured to generate data integrity metrics associated with the data accessible for generating and / or updating the battery pass by determining the degree of correlation between data associated with these data sources and data associated with the data model.

[0020] • A data provider interface that is configured to provide the generated data integrity metrics.

[0021] In another aspect, an apparatus for confirming the amount of data accessible for generating and / or updating battery credentials associated with a battery is disclosed, wherein the battery is produced from one or more input materials through a production chain, the apparatus comprising:

[0022] • A data providing interface configured to provide data associated with the battery, including at least one digital battery identifier associated with the battery;

[0023] • Data verification unit, configured to provide verified data associated with the production of the battery based on the at least one digital battery identifier, wherein the verified data is generated by: a distributed data consumption node collecting data associated with the production from a distributed data providing node based on the provided digital battery identifier, and using a rule-based engine to verify at least a portion of the collected data, the rule-based engine including one or more rules related to the data model associated with the battery pass;

[0024] • Data collection unit, configured to collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers;

[0025] • A metric generator configured to generate data integrity metrics associated with the data accessible for generating and / or updating the battery pass by determining the degree of correlation between data associated with these data sources and data associated with the data model.

[0026] • A data provider interface that is configured to provide the generated data integrity metrics.

[0027] In another aspect, a computer-implemented method for generating battery tokens associated with a battery is disclosed, wherein the battery is produced from one or more input materials through a production chain, the method comprising:

[0028] • Provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0029] • Generate a data integrity measure that is associated with the data used to generate the battery pass, using the methods or apparatus disclosed herein.

[0030] • The determined data integrity measure is confirmed by comparing it with a given data integrity threshold.

[0031] • Collect data related to the production of the battery based on confirmed data integrity metrics, data associated with the battery, and / or generated data integrity metrics; generate a battery pass based on the collected data related to the production of the battery; and provide the generated battery pass and optionally the data integrity metrics for access. Optionally, the battery pass and optionally the generated data integrity metrics are provided for access under the control of the data owner of the battery pass.

[0032] In another aspect, an apparatus for generating a battery pass associated with a battery, wherein the battery is produced from one or more input materials through a production chain, the apparatus comprising:

[0033] • A data providing interface configured to provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0034] • A metric generator configured to generate, according to the methods disclosed herein or by the apparatus disclosed herein, a data integrity metric associated with the data accessible for generating the battery pass.

[0035] • An acknowledgment engine, configured to confirm a determined data integrity measure by comparing it to a given data integrity threshold.

[0036] • A pass generator configured to collect data related to the production of the battery based on a verified data integrity metric, data associated with the battery, and / or a generated data integrity metric; the pass generator configured to generate a battery pass based on the collected data related to the production of the battery; and the pass generator configured to provide the generated battery pass and optionally the data integrity metric for access; optionally, the battery pass and optionally the generated data integrity metric are provided for access under the control of the data owner of the battery pass.

[0037] In another aspect, a computer-implemented method for updating a battery pass associated with a battery is disclosed, wherein the battery is produced from one or more input materials through a production chain, the method comprising:

[0038] • Provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0039] • Provide the battery pass, which includes a distributed identifier and at least one of these digital battery identifiers.

[0040] • Generate a data integrity measure that is associated with the data accessible for updating the battery pass, using the methods or apparatus disclosed herein.

[0041] • Collect data related to the production of the battery based on data associated with the battery and / or data integrity metrics.

[0042] • Update the battery pass based on the collected data associated with the production of the battery, and provide the updated battery pass and optionally the data integrity metric for access, wherein the battery pass and optionally the generated data integrity metric are provided for access under the control of the data owner of the battery pass.

[0043] In another aspect, an apparatus for updating a battery pass associated with a battery, wherein the battery is produced from one or more input materials through a production chain, the apparatus comprising:

[0044] • A data providing interface configured to provide data associated with the battery, including at least one digital battery identifier associated with the battery.

[0045] • A pass provider configured to provide the battery pass, which includes a distributed identifier and at least one of these digital battery identifiers.

[0046] • A metric generator configured to generate, according to the methods disclosed herein or by the apparatus disclosed herein, a data integrity metric associated with data accessible for updating the battery pass.

[0047] • A data collection unit configured to collect data related to the production of the battery based on data associated with the battery and / or the data integrity metric.

[0048] • A pass updater configured to update the battery pass based on collected data associated with the production of the battery, and to provide the updated battery pass and optionally the data integrity metric for access, wherein the battery pass and optionally the generated data integrity metric are provided for access under the control of the data owner of the battery pass.

[0049] In another aspect, a battery associated with a battery pass generated and / or updated as disclosed herein by the method or apparatus disclosed herein is disclosed.

[0050] In another aspect, a battery pass is disclosed, which includes a distributed identifier and pass data associated with the battery, wherein the battery pass is associated with a data integrity metric generated as disclosed herein by the method or by the apparatus disclosed herein.

[0051] In another aspect, the use of data integrity metrics generated by the methods or apparatus disclosed herein for generating and / or updating battery passes associated with batteries produced from one or more input materials is disclosed.

[0052] In another aspect, the use of battery passes generated and / or updated for batteries, such as by the methods or apparatus disclosed herein, for further processing of batteries associated with the battery passes is disclosed.

[0053] In another aspect, a computer element, particularly a computer program product or computer-readable medium, is disclosed having instructions that, when executed on one or more computing nodes, are configured to perform steps of any of the methods disclosed herein.

[0054] In another aspect, this disclosure relates to a computer element having instructions that, when executed on one or more computing nodes, are configured to perform the steps of the methods of this disclosure or are configured to be performed by means of this disclosure.

[0055] Any disclosures, embodiments, and examples described herein relate to the methods, apparatus, systems, uses, and computer elements listed above and below. Advantageously, the benefits provided by any embodiment and example also apply to all other embodiments and examples. Example

[0056] Embodiments of this disclosure will be outlined below through examples and / or embodiments. It should be understood that this disclosure is not limited to the embodiments and / or examples described.

[0057] To enable efficient further processing and / or recycling of the battery, a battery pass can be provided. This battery pass can be generated and / or updated by collecting data related to the battery's production from multiple participants in the production chain associated with its production. However, it is necessary to ensure the completeness and accuracy of this collected data, as a low level of completeness and accuracy can negatively impact the further processing and / or recycling of the battery—due to incomplete and / or inaccurate data contained in the battery pass generated from and / or updated using this collected data.

[0058] By verifying the integrity of the data accessible for generating and / or updating battery tokens associated with batteries, the completeness of the token data included in the battery token, and therefore the reliability of this token data, becomes transparent to the battery token generator and / or battery token users. Determining the integrity level based on data associated with the data source allows for efficient transparency regarding the available data used to generate and / or update battery tokens, without having to collect any data related to battery production via a distributed network. Therefore, the integrity level can be efficiently determined without involving data traffic via a distributed network, thereby reducing overall data transfer rates and improving the stability of the distributed network. Determining the integrity level based on verified data provides finer-grained transparency regarding the integrity of the data available for generating tokens and / or updating existing battery tokens. This transparency allows battery tokens to be generated or updated based on a given or predefined integrity level, ensuring that the token data included in the battery token has sufficient integrity and reliability. This ensures that the generated and / or updated battery passes include the pass data necessary to ensure efficient further processing of batteries (such as efficient recycling of end-of-life batteries or their components). Efficient further processing can reduce the environmental impact of the battery ecosystem and / or improve the circularity of materials and byproducts used within the battery ecosystem. Transparency further allows for the identification of participants in the battery production chain who have not yet generated data to be included in the battery pass according to the data model associated with it, and / or who have not yet provided access to that data. Participants can then be requested to generate and / or provide access to such data, ensuring a high degree of completeness in the generated and / or updated battery passes.

[0059] By generating a request indicating missing data and providing it to participants in the production chain, missing data can be requested without requiring complete transparency about the production chain. Instead, transparency regarding at least a portion of these participants is required via input material identifiers associated with input material data provided by the production chain participants, without needing to know the identity of each individual participant in the production chain. This ensures that missing data can be reliably requested without requiring full knowledge of the production chain, thus avoiding privacy concerns of data providers and preventing situations where data providers refuse to provide data due to privacy concerns.

[0060] By generating battery passes based on a given or defined degree of completeness of the data required for generation (e.g., data defined by a data model used to generate battery passes), it is ensured that the data included in the battery pass meets a certain degree of completeness. This allows for the generation and delivery of battery passes that contain at least the data necessary for efficient further processing and / or recycling of the battery, and avoids delivering battery passes that do not contain the data required for further processing and / or recycling, or contain less data than required.

[0061] By generating and providing data integrity metrics that indicate the degree of integrity of pass data relative to data defined by the data model used to generate the pass, the flow of batteries associated with a battery pass can be monitored and / or controlled based on the data integrity metrics associated with the battery pass, leading to downstream production processes that use these batteries to produce one or more additional products. Battery flow can be controlled and / or monitored by verifying the battery pass based on the associated data integrity metrics. Verification may include comparing the data integrity metrics associated with the battery pass to a target data integrity metric. Based on a verified battery pass, the batteries associated with the verified battery pass can be supplied to downstream production to produce additional products. Batteries associated with a non-verified battery pass may be rejected, for example, they may not be supplied to downstream production. This ensures that additional products are produced more efficiently and reliably based on the data included in the battery pass.

[0062] By updating battery passes, data accessible for updating and provisioning after the battery pass is generated can be included in it. This allows the pass data to be "populated" after its generation, when data providers (such as input material producers) begin providing data for the pass. Based on data integrity metrics, the flow of batteries associated with the battery pass to downstream production processes that use these batteries to produce one or more additional products can be monitored and / or controlled. This ensures more efficient and reliable production of additional products based on the data included in the battery pass. Furthermore, the update level and data provision schedule can be made transparent, for example, to the entity that generated the battery pass. This allows the entity generating the battery pass to request missing data for higher quality and reliability of the pass data.

[0063] These methods, or any step thereof, may be performed, implemented, operated, and / or controlled, for example, by a computing device (e.g., a server, server cloud, computer system, or part thereof). The computing device may be included in a local computing and storage environment associated with a participant in the production chain. The computing device may be included in a local computing and storage environment associated with the entity that generates the battery pass.

[0064] Various units, entities, nodes, or other computing components can be described as being "configured to" perform one or more tasks. "Configured to" should be interpreted as meaning "having a circuit system that performs one or more tasks during operation." Units, circuits, entities, nodes, or other computing components can be configured to perform tasks even when the unit / circuit / component is not operational. Units, circuits, entities, nodes, or other computing components forming the structure corresponding to "configured to" may include hardware circuitry and / or memory storing executable program instructions to perform the operation. For convenience in the description, units, circuits, entities, nodes, or other computing components can be described as performing one or more tasks. This description should be interpreted as including the phrase "configured to."

[0065] Generally, the methods, apparatuses, systems, computer elements, nodes, or other computing components described herein may include memory, software components, and hardware components. Memory may include volatile memory (such as static or dynamic random access memory) and / or non-volatile memory (such as optical or magnetic disk storage devices, flash memory, programmable read-only memory, etc.). Hardware components may include any combination of the following: combinational logic circuit systems, clock storage devices (such as flip-flops, registers, latches, etc.), finite state machines, memory (such as static random access memory or embedded dynamic random access memory), custom-designed circuit systems, programmable logic arrays, etc.

[0066] Data accessible for generating and / or updating battery passes may include data accessible from one or more data sources and includes at least one data point defined by a data model associated with the battery pass. This data may be collected from such data sources during the generation and / or updating of the battery pass. This data may be stored in a dedicated storage device associated with the data source. This data may be provided under the control of the data owner. The data owner may be a producer of input materials used to produce batteries and / or a battery producer. This allows for the secure and reliable exchange of this data for the generation and / or updating of battery passes.

[0067] A battery pass can be a dataset with a defined semantic structure. A battery pass can be a dataset organized in a data format based on the defined semantic structure. A battery pass can be a single dataset. A product battery does not necessarily need to be a database containing multiple datasets. The semantic structure can be defined by a data model associated with the battery pass. The data model can define one or more data points included in the battery pass. The data model can define such data points as mandatory or optional data points. The data model can define a semantic description of the corresponding battery pass. The semantic description can include at least a portion of the structure and / or characteristics of the battery pass. Characteristics of a battery pass can include data types. Characteristics of a battery pass can include possible or allowed values ​​and / or value ranges. Characteristics of a battery pass can be physical units of parameters described by values ​​included in the battery pass. Characteristics of a set of battery passes can include one or more attributes. The data model can define data points to be included in the battery pass, such as data types and values ​​and / or value ranges.

[0068] A battery pass may include at least one decentralized identifier and pass data. The decentralized identifier may include any unique identifier uniquely associated with the battery. The decentralized identifier may include one or more Universally Unique Identifiers (UUIDs) or Digital Identifiers (DIDs). The decentralized identifier may be issued by a centralized or decentralized identity issuing authority. The decentralized identifier may include authentication information. Access to the pass data can be controlled by the data owner of the pass data (such as the battery manufacturer), the entity that generates the battery pass including the pass data, or the entity that generates the battery pass including the pass data on its behalf, through the decentralized identifier and its unique association with the battery. This contrasts with a centralized authority scheme, where the identifier is provided by the centralized authority and access to the data is controlled by the centralized authority. In this context, decentralized refers to the control of the use of the decentralized identifier by the data owner of the pass data.

[0069] Passport data can include data points defined by a data model used to generate the corresponding battery passport. Passport data can include data related to battery production and a battery identifier associated with the battery. In this way, a battery passport can be uniquely associated with the battery.

[0070] A battery pass may include one or more authentication mechanisms associated with a distributed identifier and pass data. A battery pass may involve one or more authorization mechanisms associated with a distributed identifier and pass data. One or more authorization mechanisms may include authorization rules that determine whether to grant access to at least a portion of the pass data. The battery pass and / or distributed identifier may be associated with one or more digital representations of the pass data. A digital representation may be considered an access element providing access to the battery pass or a portion thereof. The digital representation may include a distributed identifier and access data. A distributed identifier included in a digital representation may correspond to or be associated with a distributed identifier included in the battery pass. Access data may include a locator or pointer, such as a URL or URI, pointing to a dedicated storage device (e.g., a dedicated storage address) associated with the data owner of the battery pass. The pointer or locator may directly point to the dedicated storage device storing the battery pass. The pointer or locator may point to a data-providing network node associated with the dedicated storage device storing the battery pass. Access elements may include one or more authentication mechanisms associated with a distributed identifier and access data. Access elements can be associated with one or more authentication mechanisms linked to decentralized identifiers and access data. Access elements can be provided to a decentralized registry storing the access elements. The decentralized registry can be associated with data-providing network nodes. This allows control over access to such a registry and access to the access elements stored in such a registry via the data-providing network nodes. The decentralized registry can be associated with participants in the battery ecosystem. The decentralized registry can be associated with the data owner of a chemical battery pass. The decentralized registry can be part of a decentralized network but may not be associated with a specific participant in the production chain; for example, it can be considered an infrastructure node of the decentralized network.

[0071] Updating a battery pass can include modifying the pass data included in such a battery pass. Pass data can be modified by adding, deleting, and / or modifying data points existing within the pass data. Pass data can be modified by adding, deleting, and / or modifying values ​​and / or value ranges.

[0072] The data model associated with a battery pass can be a data model used to generate and / or update battery passes. The data model can be associated with a battery type identifier. The battery type identifier can be associated with the battery type associated with the battery. Based on the battery type identifier associated with the battery, such a data model and related data can be collected, for example, from a database storing this data. A mapping table can be used to determine the battery type identifier associated with the battery type. The mapping table can include battery identifiers that are mutually associated with the corresponding battery type identifiers of the battery type associated with the battery. The battery type identifier can be determined based on data included in the battery identifier. For example, the battery identifier can include the battery type or the battery name indicating the battery type.

[0073] A production chain for producing batteries may include one or more production steps. At least some of the production steps in the production chain may be performed by different participants in the battery ecosystem. The battery ecosystem may be associated with the production, use, and / or recycling of batteries. One or more input materials may be used to produce one or more intermediate products or batteries in the production steps. The input materials used within a production step may correspond to intermediate products produced by upstream participants of the participant performing the production step.

[0074] Input materials can refer to any goods purchased from a supplier and brought to the production plant of a participant in the production chain. Input materials can include starting materials used in the production process at the production plant to produce batteries. Input materials can be used at any production step in the production chain used to produce batteries. This means that the product of one production step can be used as input material for a subsequent production step. Input materials can include recycled materials. Input materials can include or may be any input material entering the production associated with a production step in the production chain. Input materials can include or may be any input material provided at any entry point in the production associated with a production step in the production chain.

[0075] A battery can be produced from one or more different input materials. Input materials can be components and / or chemical products. The component can be a discrete component. The component can be an electrode, a cell, a module, a battery management system, a cooling system, a casing, a separator, or a mechanical subsystem. The component can be used in at least one production step associated with the production of the battery. The component can be produced from one or more chemical products. The battery can be produced via one or more production steps. Production steps can involve chemical reactions and / or physical processes and / or assembly processes. Input materials can be used in one or more such production steps. The battery can be used as input material to produce one or more additional products, such as a final product. One or more batteries can be produced by one or more downstream participants who can use materials produced by one or more upstream participants as input materials. The battery can be associated with a battery identifier. The battery identifier can be a digital or virtual battery identifier. The battery identifier can uniquely identify a battery within an entity that produces the battery. The battery identifier can uniquely identify a battery within a distributed network. The battery identifier can be associated with an identifier element physically connected to the battery. The identifier element can encode the digital battery identifier. Battery identifiers may include battery name, battery number, LOT number, batch number, serial number, identification number, etc.

[0076] Data integrity metrics can indicate the degree of relevance between data associated with a data source (e.g., data indicating the amount of data accessible for generating and / or updating Battery Passes) and data associated with a data model associated with the corresponding Battery Pass (e.g., data indicating the amount of data defined by the data model). Data integrity metrics can also indicate the degree of relevance between verified data (e.g., the amount of data accessible for generating and / or updating Battery Passes) and data associated with a data model associated with the corresponding Battery Pass. A high degree of relevance indicates a larger amount of data accessible for generating and / or updating Battery Passes, and therefore a higher degree of integrity of the pass data included in the generated and / or updated Battery Passes, while a low degree of relevance indicates a smaller amount of data accessible for generating and / or updating Battery Passes, and therefore a lower degree of integrity of the pass data. Therefore, data integrity metrics can indicate the reliability of the pass data included in Battery Passes and can allow Battery Pass users or consumers to judge the quality or reliability of pass data based on the associated data integrity metrics. Data integrity metrics can indicate the overall degree of integrity (e.g., the proportion of data available for generating and / or updating battery passes relative to the amount of data defined by the data model associated with such battery passes). Data integrity metrics can indicate the degree of integrity for each participant who needs to provide data according to the data model, and optionally the overall degree of integrity.

[0077] Input materials and batteries can be part of a battery ecosystem. A battery ecosystem can include chemical products. A battery ecosystem can include a production chain for producing batteries. A battery ecosystem can include a processing chain for handling old batteries resulting from the use of products containing batteries. A processing chain can include a recycling chain for recovering at least a portion of products containing batteries (e.g., end-of-life products) or components thereof. A processing chain can include a reuse chain for reusing old batteries. A battery ecosystem can include various participants, such as producers of original input materials, producers of chemical intermediates, producers of chemical products, users of chemical products (e.g., component producers and / or component assembly producers (e.g., battery producers)), producers of end-of-life products, users of end-of-life products, collectors of end-of-life products, and recyclers. A battery ecosystem can allow the use of recycled materials resulting from the recycling of end-of-life products to produce new products, such as chemical intermediates and / or chemical products. A battery ecosystem can be associated with the production and / or reuse and / or recycling of physical products such as batteries.

[0078] Participants in the battery ecosystem can connect via a decentralized network. The decentralized network can include one or more decentralized network nodes configured to perform data transactions. At least a portion of the decentralized network nodes can be associated with participants in the battery ecosystem. Data transactions can be based on transaction protocols that include authentication and / or authorization mechanisms. Based on authentication and / or authorization mechanisms, a peer-to-peer network can be established between decentralized network nodes in the decentralized network. One or more authentication mechanisms can be associated with or linked to a decentralized identifier, such as a decentralized identifier included in a battery pass and / or a decentralized identifier included in a digital representation associated with such a battery pass. One or more authentication mechanisms associated with a decentralized identifier can be provided to the decentralized network nodes. One or more authentication mechanisms associated with a decentralized identifier can be accessed by the decentralized network nodes. Decentralized configuration allows for more efficient use of computing resources and strengthens each data owner's control over the data associated with the corresponding decentralized identifier.

[0079] A distributed data providing network node may include computer-executable instructions for providing and / or processing data (such as data related to battery production) requested by a distributed data consuming network node within the distributed network. A distributed data providing network node may be associated with or connected to one or more dedicated data storage devices that store data related to battery production. A distributed data providing network node may be directly or indirectly connected to the data storage device storing data related to battery production. Therefore, a distributed data providing network node may be associated with data related to battery production. The dedicated data storage device may be under the control of the data owner of the data related to battery production. The data owner may be an entity authorized to access the data related to battery production and to control access to the data related to battery production through the data consumption service of the distributed network. The data owner may be an input material producer or a battery producer. Access to the data related to battery production may be controlled by the data owner through a distributed identifier and its unique association with the data owner and the data related to battery production. The data owner may access the data related to battery production. Therefore, the data owner may directly or indirectly possess the data related to battery production. The data related to battery production may be stored in the data owner's database or a database associated with the data owner. Data related to battery production can be stored in a database accessible to the data owner. The data owner can control access to this data by providing services based on their data. Data related to battery production can be associated with a data owner. The data owner can be the owner of the data related to battery production.

[0080] Decentralized data consuming network nodes may include computer-executable instructions for accessing and / or processing data (such as data associated with battery production) provided by decentralized data providing network nodes within the decentralized network. Decentralized data consuming network nodes may be controlled, owned, or associated with a consumer of data associated with battery production (such as an entity generating battery tokens). A data consumer may be any entity that processes data associated with battery production, for example, by generating and / or updating battery tokens. A data consumer may be any entity operating production configured to process batteries associated with data related to battery production. Processing may include using batteries as input materials to produce additional byproducts (such as byproducts containing batteries). Processing may include performing one or more recycling steps on the battery or its components. Data consumers may be downstream participants of battery producers in the battery ecosystem.

[0081] A rule-based engine can be used to validate at least a portion of battery production-related data collected via a distributed network. The rule-based engine can be software or software components that apply one or more rules to at least a portion of the collected battery production-related data. The rule-based engine for validating at least a portion of the collected battery production-related data may include one or more rules associated with a data model associated with a battery pass. One or more rules may be associated with or related to one or more data points defined by the data model. Therefore, one or more rules can allow determination of whether the collected data includes one or more data points defined according to the data model. One or more rules may be associated with mandatory data points defined according to the data model. One or more rules may be generated based on mandatory data points defined by the data model. This allows validation of whether the data points required by the data model are included in the collected data, thereby ensuring that data integrity metrics are reliably generated based on such validated data. The rule-based engine can operate at the level of individual data points, combinations of data points, or the entire collected data. The operation of the rule-based engine can be defined by one or more rules. Rules associated with individual data points may include one or more rules that define conditions for individual data points present in the collected data. Using this rule allows verification that the data points required for the data model associated with the Battery Pass are included in the collected data. Rules associated with multiple data points can include one or more rules defining the required combination of data points. Using this rule allows verification that the combination of data points required for the data model associated with the Battery Pass is included in the collected data.

[0082] A rule-based engine can be used to validate generated data integrity metrics. A rule-based engine can be software or a software component that applies one or more rules to the generated data integrity metrics. A rule-based engine used to validate generated data integrity metrics may include one or more rules associated with a given integrity threshold. One or more rules may be associated with or correlated with the overall integrity level. Therefore, one or more rules can allow determination of whether the generated data integrity metric meets a given or predefined overall integrity level. One or more rules may be associated with or correlated with the integrity level of each participant in the production chain that needs to provide production-related data according to the data model associated with the battery pass. This allows validation of whether the integrity level of the amount of data provided by a given participant meets a given or predefined integrity threshold. Thus, the correlation of data provided by different participants can be considered during validation of the generated data integrity metric. A rule-based engine can operate at the level of individual data points, combinations of data points, or the entire data integrity metric. The operation of a rule-based engine can be defined by one or more rules. Rules associated with individual data points may include one or more rules that define conditions for individual data points present in the generated data integrity metric. Rules associated with multiple data points may include one or more rules that define the required combination of data points.

[0083] A rule-based engine can be used to validate data associated with a data source or validated data. A rule-based engine can be software or software components that apply one or more rules to data associated with a data source or validated data. A rule-based engine for validating data associated with a data source or validated data may include one or more rules configured to validate the data associated with the data source or the validated data based on data related to a data model associated with a battery pass. One or more rules may relate to input material producers that produce input materials associated with input material data defined by the data model. One or more rules may relate to data points defined by the data model. One or more rules may relate to data points defined by the data model that are associated with at least a portion of the input materials and the battery. One or more rules may relate to data points to be provided to each input material producer and / or battery producer according to the data model. A rule-based engine can operate at the level of individual data points, combinations of data points, or the entire data. The operation of a rule-based engine can be defined by one or more rules. Rules associated with individual data points may include one or more rules that define conditions for individual data points present in the data. Rules associated with multiple data points may include one or more rules that define desired combinations of data points. Rule-based engines can operate at the level of individual data points, combinations of data points, or the entire data integrity metric. The operation of a rule-based engine can be defined by one or more rules. Rules associated with an individual data point can include one or more rules defining conditions for that individual data point present in the generated data integrity metric. Rules associated with multiple data points can include one or more rules defining the desired combination of data points.

[0084] In embodiments, the amount of data accessible for generating and / or updating battery credentials includes input material data associated with at least a portion of the input materials used to produce the battery and / or battery data associated with the battery. Input material data may include at least one measured chemical and / or physical property of the respective input material, and / or at least one chemical and / or physical property determined based on data collected during the production and / or use of the respective input material. Battery data may include at least one measured chemical and / or physical property of the battery, and / or at least one chemical and / or physical property determined based on data collected during the production and / or use of the battery. Examples of chemical properties include heat of combustion, enthalpy of formation, toxicity, chemical stability in a given environment, flammability, oxidation state, corrosiveness, combustibility, acidity and alkalinity, chemical composition, recyclable content, bio-based content, renewable content, carbon footprint, biodegradability, and / or pH value. Examples of physical properties include absorption, brittleness, boiling point, capacitance, color, concentration, density, ductility, distribution, efficacy, elasticity, charge, conductivity, electrical impedance, potential, flow rate, fluidity, hardness, heat capacity, inductance, intrinsic impedance, brightness, luminosity, gloss, mass, melting point, opacity, permeability, permittivity, plasticity, pressure, emissivity, resistivity, reflectivity, refractive index, solubility, specific heat, strength, stiffness, temperature, tensile strength, thermal conductivity, thermal resistance, viscosity, volume and / or wave impedance.

[0085] In an embodiment, the data associated with the battery further includes the battery type associated with the battery and, optionally, an input material identifier associated with at least a portion of the input material. The battery type can be defined as a category or classification to which the battery belongs based on one or more characteristics of the battery, the purpose of the battery, and / or the usage of the battery. The battery type can allow defined battery physical entities to be classified or grouped into battery types, thereby allowing for dimensionality reduction and avoiding data multiplication for each individual battery.

[0086] In an embodiment, the data associated with the data source includes a dataset identifier associated with a dataset including data related to battery production, and data source data indicating the data source associated with such dataset identifier. The dataset may include input material data associated with input materials used to produce batteries and / or battery data associated with batteries. The dataset identifier may be stored in storage associated with a system that performs verification of the amount of data accessible for generating and / or updating battery passes. The dataset identifier may be stored within one or more registries in a distributed network. One or more registries may be associated with a provider node configured to provide such a dataset. The data source associated with such dataset identifier may provide the dataset associated with such dataset identifier upon request from a data consumer node. The data source data may include endpoints associated with such data source. The data source data may further include a distributed participant identifier associated with the data source and / or a participant role associated with or controlling the data source within the battery ecosystem. The data source may include a network node configured to provide data (e.g., data accessible for generating and / or updating battery passes) upon request from another network node (e.g., a consumer node). A data source can be associated with a dedicated storage device that stores the provided data. The data source can be configured to exchange data based on a transaction protocol that includes authentication and / or authorization mechanisms as described above. Based on the authentication and / or authorization mechanisms, a peer-to-peer network can be established between the data source and consumer nodes. The data source can be configured to provide data based on an identifier associated with this data (e.g., a dataset identifier). The data source can be a distributed network providing nodes. The data source can be associated with input material producers and / or battery producers that produce batteries.

[0087] The data associated with a data source may not contain datasets associated with dataset identifiers included in such data associated with a data source. Therefore, the database storing such data may not be considered a central repository for datasets. Instead, access control to the datasets is maintained by the respective data owners of such datasets, and the database only allows locating datasets and requesting access to them for generating and / or updating battery passes.

[0088] The data associated with the data source may further include a relational representation specifying the relationship between a given dataset identifier (e.g., a parent identifier) ​​and other dataset identifiers (e.g., child identifiers) associated with a dataset used to produce intermediate batteries or batteries associated with a given dataset identifier (e.g., the parent identifier). This relational representation allows the production chain to be parsed and the dataset identifiers associated with the input materials used to produce such batteries to be determined based on the dataset identifiers associated with a given battery. Therefore, this relational representation based on a given battery identifier (e.g., a dataset identifier associated with a battery dataset) can be used to determine the number of child identifiers associated with data accessible for generating and / or updating battery credentials.

[0089] In an embodiment, the data associated with the data model includes participant roles of participants in the battery production chain and the number of data points to be provided by such participant roles according to the data model. The data associated with the data model may further include digital battery identifiers, such as battery type identifiers. Thus, the data associated with the data model can be linked to a battery or battery type. Participants associated with battery production may include participants performing at least one production step in the production chain for battery production. The number of data points to be provided by such participant roles according to the data model can be determined by assigning participant roles to at least a portion of the data points defined by the data model and summing the data points for each assigned participant role. Data points defined by the data model may be assigned to such participant roles based on the characteristics of intermediate batteries or batteries produced by the participant roles. For example, data points defining battery characteristics within the data model may be assigned to the participant role "Battery Producer". Similarly, data points defining the characteristics of the cathode active material contained within the electrodes of the battery may be assigned to the participant role "Cathode Active Material (CAM) Producer".

[0090] In an embodiment, providing data related to the data model may include providing one or more rules (also referred to as confirmation rules in this disclosure) that include such data associated with the data model.

[0091] In this embodiment, the data associated with battery production includes input material data associated with the input materials used to produce the battery and / or battery data associated with the battery. Input material data may exist in the form of input material datasets associated with the respective input materials. Similarly, battery data may exist in the form of battery datasets.

[0092] In this embodiment, the distributed data providing node determines the dataset identifier associated with the input materials used to produce the battery by using a relational representation included in data associated with data sources that indicates access to data used to generate and / or update battery credentials, based on at least one of the battery identifiers. This relational representation can define parent identifiers and associated child identifiers. Based on this relational representation, the relationships between a given battery identifier and all child identifiers associated with that battery identifier can be resolved.

[0093] In an embodiment, the collected data related to battery production is provided to one or more input nodes, which are configured to collect data and provide the collected data as a dataset to one or more downstream nodes. The downstream nodes are configured to verify at least a portion of the data included in the dataset provided by the input nodes. Verification can be performed using a rule-based engine comprising one or more rules associated with or related to data associated with a data model associated with a battery pass. The downstream nodes may be further configured to associate the verified data with at least one battery identifier, determine the storage location of the verified data, and provide the verified data associated with the battery identifier to the determined storage location. Input nodes may represent computing nodes that collect input material data from one or more distributed data consumption nodes. One or more input nodes may be configured to generate data packets, such as messages or events, based on the collected data. The generated data packets can be sent downstream from the input nodes to one or more downstream nodes. Downstream nodes may be configured to retrieve data packets provided by the input nodes. Input nodes may be configured to provide data packets to persistent or non-persistent logs. Downstream nodes may be configured to retrieve data packets provided to persistent or non-persistent logs. Downstream nodes can be configured to receive data packets provided to persistent or non-persistent logs. Input nodes can be associated with a distributed network. For example, an input node can be associated with a distributed data consumption network node that is part of a distributed network. A downstream node can refer to a compute node that consumes data from a compute node that exists upstream relative to the data flow. Consuming data can include receiving data or retrieving data packets from input nodes or persistent or non-persistent logs. For example, data "flows" downstream from input nodes to downstream nodes. Downstream nodes can be considered output nodes. Requests for data can be sent from downstream nodes upstream to input nodes.

[0094] In embodiments, the rule-based engine operates on individual data points, multiple data points, or the entire dataset that exist within at least a portion of the collected data or dataset. The rule-based engine can be configured to validate data based on one or more rules included in the rule-based engine. The rule-based engine can apply one or more rules to individual data points, multiple data points, and / or the entire dataset to generate validated data. If the rule-based engine determines that an individual data point, multiple data points, and / or the entire dataset matches one or more conditions in a rule, then such an individual data point, multiple data points, or the entire dataset can be considered validated. Validated data can be associated with a classifier indicating successful validation of the corresponding data point, combination of data points, or the entire dataset. The classifier can indicate that the data has passed one or more applied rules. Validating data at the data point level can allow determining the number of data points included in the collected data, defined by the data model associated with the battery pass.

[0095] In this embodiment, one or more rules are generated from a rule template that includes unstructured data associated with instructions related to a confirmation operation. The confirmation operation may involve one or more data points and / or combinations of data points to be present within the collected data. The rule template can be used to generate executable logic that can be executed by a rule-based engine. Using a rule template facilitates the generation of one or more rules because the instructions used to generate the executable logic can be formulated in natural language. The rule template can be a predefined rule template. Rule templates can be generated to match a data model associated with the battery.

[0096] In an embodiment, the one or more rules define data points and / or combinations of data points to be present within the collected data. This allows for determining the number of data points, as defined by the data model, to be included in the collected data, thereby allowing for the determination of the completeness of the collected data by comparing the determined number of data points with the target number of data points defined by the data model. One or more rules can define data points and / or combinations of data points based on data defined by the data model. Therefore, one or more rules can be generated based on the data model associated with the battery pass.

[0097] In an embodiment, one or more rules are associated with or derived from a data model associated with a battery pass, and in particular, the one or more rules are associated with data points defined as mandatory by the data model.

[0098] In an embodiment, the rule-based engine verifies that the collected data includes identifying one or more rules applicable to the collected data and provides the applicable rules to the rule-based engine. Applicable rules can be identified based on identifiers associated with each applicable rule that match or relate to at least one identifier included in the collected data. Applicable rules can be included in rule templates. Therefore, verifying the collected data can include identifying applicable rule templates. Applicable rules and / or applicable rule templates can be stored on a database connected to the rule-based engine. The identifier associated with each applicable rule or rule template can correspond to a numeric identifier included in the collected data. Applicable rules or rule templates can be collected based on mapping data, which includes identifiers associated with the rules or rule templates and associated dataset identifiers, and optionally type identifiers.

[0099] In this embodiment, the collected data is validated in response to satisfying one or more rules applied to the collected data by a rule-based engine. This ensures that the collected data is validated only if the rule-based engine can successfully apply the rules to the collected data (e.g., when there are no errors due to, for example, missing data, incorrect data, incomplete data, etc. when the rules are applied). This allows for a reliable determination of the number of data points accessible for generating and / or updating battery passes, and the generation of a data integrity metric indicating the degree of integrity of the validated data relative to the data defined by the data model.

[0100] In one embodiment, the degree of relevance is determined by a separate rule-based engine, which includes one or more verification rules configured to verify the integrity of accessible data used to generate and / or update the Battery Pass based on data related to the data model associated with the Battery Pass. In another embodiment, the degree of relevance is determined by a rule-based engine, which further includes one or more verification rules configured to verify the integrity of accessible data used to generate and / or update the Battery Pass based on data related to the data model associated with the Battery Pass. This separate rule-based engine or the rule-based engine may operate on individual data points included in the verified data, combinations of data points included in the verified data, or the verified data itself.

[0101] In this embodiment, the degree of relevance is determined in the following manner:

[0102] • Based on the digital battery identifier and the data associated with these data sources, determine the data source data that indicates the data source providing the dataset associated with the digital battery identifier.

[0103] • Based on the digital battery identifier and the data associated with these data sources, determine the input material identifier and the associated data source data indicating the data source providing the dataset associated with the input material identifier.

[0104] • The determined indication provides data from the data source of the dataset associated with the input material identifier to data associated with the data model of the battery pass.

[0105] Mapping identified data sources to data associated with a data model related to the battery can include mapping participant roles included in the identified data sources to participant roles included in the data associated with the data model. The degree of matching of participant roles can represent the degree of relevance. Therefore, a higher degree of participant role matching can indicate a higher degree of relevance to the amount of data accessible for generating and / or updating battery passes compared to a lower degree of matching, and thus indicates a higher degree of completeness. The degree of relevance can correspond to the proportion of matched participant roles relative to the total number of participant roles included in the data associated with the data model. Determining relevance based on participant roles allows for the efficient determination of the completeness of the amount of data accessible for generating and / or updating battery passes without requiring access to such data.

[0106] In this embodiment, the degree of relevance is determined by comparing the number of confirmed data points with the number of data points included in the data related to the data model associated with the battery pass. Comparing the number of confirmed data points with the number of data points included in the data related to the data model may include:

[0107] • Determine participant roles associated with identified data points based on data linked to the data source.

[0108] • Map the identified participant roles and the number of associated confirmed data points to the number of participant roles and associated data points included in the data related to the data model.

[0109] The participant roles associated with confirmed data points can be determined using dataset identifiers associated with those data points. For example, the dataset identifier associated with a confirmed data point can be compared to a dataset identifier included in the data associated with the data source. Confirmation allows determining the number of accessible data points that match the data points defined in the data model. Determining relevance based on the number of confirmed data points allows for a more accurate assessment of the completeness of the data accessible for generating and / or updating Battery Passes, since the data points have been confirmed against the data model associated with the Battery Pass.

[0110] In this embodiment, the data integrity metric includes the digital battery identifier and the overall integrity of the data accessible for generating and / or updating battery passes. The overall integrity level can correspond to the proportion of the amount of data accessible for generating and / or updating battery passes to the amount of data defined by the data model. The overall integrity level can be given as a percentage based on the total amount of data defined by the data model. Therefore, an integrity level of 100% indicates that all data defined by the data model is accessible for generating and / or updating battery passes, while an integrity level of 0% indicates that none of the data defined by the data model is accessible for generating and / or updating battery passes.

[0111] In another embodiment, the data integrity metric includes a digital battery identifier, an input material identifier associated with input materials used to produce the battery, a participant role associated with the input material identifier and / or battery identifier, and the degree of integrity of data accessible for generating and / or updating battery passes for at least a portion of the input material identifier and / or digital battery identifier. The integrity degree of each digital battery identifier may correspond to the total integrity degree as described above. The integrity degree of a given input material identifier may correspond to the integrity degree of a dataset associated with that input material identifier. The integrity degree of the dataset may be determined based on participant roles defined for that dataset (e.g., by comparing participant roles defined for an accessible dataset with participant roles defined by a data model). The integrity degree of the dataset may be determined based on the number of confirmed data points included in such a dataset (e.g., by comparing the number of such confirmed data points for participant roles associated with confirmed data points with the number of data points defined by a data model for such participant roles). The integrity degree of each input material identifier allows identification of participant roles associated with missing data, such as participants in the production chain for whom access to data has not yet been provided. Based on this data, a request for providing the missing data can be generated and provided. Because data integrity metrics only provide participant roles to ensure adequate protection of the privacy of production chain participants, requests for providing missing data cannot be directly offered to participants who have not yet provided data for access. Instead, requests can be offered to participants associated with upstream participants who have not yet provided data for access. Participants associated with upstream participants can be identified using data integrity metrics by determining participants who provide datasets associated with sub-identifiers for which data accessible for generating and / or updating battery tokens has not yet been provided. Because such participants have information about their upstream participants, they can forward requests to those upstream participants.

[0112] In an embodiment, the data integrity metric further includes the number of accessible data points for at least a portion of the input material identifiers and / or digital battery identifiers, the number of data points for each input material identifier and digital battery identifier as defined by the data model, and optionally the total integrity of the data accessible for generating and / or updating battery credentials. This allows for more granular transparency regarding the amount of data accessible for generating and / or updating battery credentials. Additionally, this allows for the identification of missing data and the request for such missing data as previously described.

[0113] In an embodiment, a given integrity threshold defines the degree of correlation between the data used to generate the battery pass and one or more data points defined by the data model associated with the battery pass.

[0114] The degree of relevance can be defined as the overall relevance between the accessible data used to generate the battery pass and the data points defined by the data model associated with the battery pass. The degree of relevance can also be defined for data points that need to be provided by one or more participants in the battery production chain according to the data model. Furthermore, the degree of relevance can be defined for at least a subset of participants in the battery production chain who need to provide data according to the data model associated with the battery pass. This allows for different degrees of relevance to be defined for different participants in the production chain. Thus, the distinction in the relevance of data points provided by different production chain participants for generating and / or updating the battery pass can be reflected in the threshold. Therefore, data points that are relevant to downstream participants of battery producers who use batteries to produce other products (e.g., data points relevant to the handling and / or disposal of end-of-life (EoL) batteries (e.g., sorting and / or collecting and / or recycling EoL batteries or their components)) can be associated with a higher relevance threshold compared to data points that are less relevant to such downstream participants.

[0115] In this embodiment, the confirmation includes accepting the degree of relevance indicated by the determined data integrity metric. Accepting the degree of relevance can indicate that the amount of data available for generating the battery pass is sufficient to make the battery pass generated based on such available data accurate enough to control further processing of the battery based on the pass data included in such generated battery pass.

[0116] In one embodiment, confirmation includes rejecting the relevance level indicated by the determined data integrity metric, and triggering a request to provide at least a portion of the data missing according to the generated data integrity metric. Rejecting the relevance level may indicate insufficient access to the amount of data used to generate the battery pass, and that the battery pass generated based on such accessible data may not be considered reliable.

[0117] In one embodiment, generating the battery pass includes: providing a decentralized identifier associated with the collected data and a data model associated with the battery pass; applying the data model to the collected data to generate pass data; and generating the battery pass including the decentralized identifier and the pass data. The generated battery pass may be stored on a dedicated storage device associated with or under the control of the data owner of the battery pass. The generated battery pass may be associated with a digital representation pointing to a storage location where the battery pass is stored. The digital representation may be stored in a decentralized registry of a decentralized network associated with or under the control of the data owner of the battery pass. This enables other participants in the decentralized network authorized to query the decentralized registry to collect the digital representation and access the battery pass based on it.

[0118] In this embodiment, a battery pass and optionally the generated data integrity metric are provided for access under the control of a data owner of the battery pass and the associated data integrity metric. The data owner may include an entity that generates and / or updates the battery pass. The data owner may include an entity that generates and / or updates the battery pass on its behalf. The data owner may be a battery producer. The data owner may be a battery user, for example, an entity that uses batteries to produce additional products. For example, the data owner may be a final product producer that uses batteries to produce a final product. The battery pass may be stored in the data owner's database or in a database associated with the data owner. The battery pass may be stored in the data owner's database or under the data owner's control. The battery pass may be stored in a database accessible to the data owner. The battery pass may be associated with the data owner. The data owner may be the owner of the battery pass or the battery pass data owner. In this sense, the data owner should be broadly interpreted as an entity authorized to access the battery pass, which controls access to the battery pass or a portion thereof by distributed data consuming network nodes in the distributed network. Therefore, access to the digital twin or a portion thereof can be under the control of the data owner. This allows the data owner to retain full control over the battery pass, but at the same time enables the sharing of the battery pass or a portion thereof under controlled conditions, for example, through the use of appropriate authorization and authentication mechanisms or schemes.

[0119] Providing a battery pass under the control of the data owner may include: collecting access policy data associated with the battery pass based on a request from a data consumer to access the battery pass, and applying the access policy data to the battery pass based on the data included in the received request. The request may include a consumer identifier. The request may further include one or more actions to be performed on data points included in the battery pass, such as a read action.

[0120] Access policy data can be generated in the following ways:

[0121] • Generate group access data associated with the Battery Pass, which identifies the access control group including the data consumer identifier associated with the data consumer granted access to the Battery Pass.

[0122] • Based on the generated group access data, generate consumer access data associated with one or more data points existing within the battery pass. This consumer access data identifies the data consumer authorized to access one or more data points, and one or more actions that the data consumer associated with the data consumer identifier is allowed to perform on the one or more data points.

[0123] • Generate access policy data associated with the Battery Pass based on the generated group access data and generated consumer access data. This access policy data identifies the group access data and the associated consumer access data.

[0124] Access policy data can be generated in the following ways:

[0125] • Generate group access data associated with the Battery Pass, which identifies the access control group including the data consumer identifier associated with the data consumer granted access to the Battery Pass.

[0126] • Generate consumer access data associated with the battery pass based on the generated group access data. This consumer access data identifier allows one or more actions to be performed on the pass data by the data consumer associated with the data consumer identifier.

[0127] • Generate access policy data associated with the Battery Pass based on the generated group access data and generated consumer access data. This access policy data identifies the group access data and the associated consumer access data.

[0128] Group access data includes collecting one or more data consumer identifiers associated with data consumers who are granted access to the battery pass.

[0129] Applying access policy data to Battery Pass can include:

[0130] • Confirm whether the data consumer is granted access to the battery pass.

[0131] • In response to confirming the data consumer's access to the battery pass, confirm whether the data consumer is permitted to perform the requested action on the battery pass.

[0132] Access policy data and / or group access data can be associated with a battery pass, for example, via a distributed identifier and / or a battery identifier. Thus, access policy data and / or group access data can be obtained based on one of the distributed identifier and / or the battery identifier.

[0133] Confirming whether a data consumer is permitted to perform the requested action on the battery pass may include:

[0134] • Obtain access control list data associated with one or more data points existing within the Battery Pass.

[0135] • Identify one or more actions to be performed on one or more data points existing within the Battery Pass based on the data contained in the received request.

[0136] • Determine whether a data consumer is permitted to perform one or more actions on one or more data points requested by comparing the access control list entries present in the access control list data with the data included in the received request, and in response.

[0137] • Execute one or more actions requested to be performed on one or more data points, or provide battery passes to data consumers based on the actions requested by one or more data points.

[0138] By filtering data consumers requesting access to a Battery Pass based on group-based policies used to access the pass, Battery Passes can be securely exchanged and shared under the sovereignty of the data owner, preventing unauthorized access. This allows participants in the battery ecosystem—such as upstream participants of battery producers, pass generators, or entities that generate passes on their behalf—to have controlled access to the Battery Pass or portions thereof. Furthermore, access to the Battery Pass or portions thereof by multiple data consumers can be controlled by the data owner using group-based policies and distributed identifiers included in the Battery Pass. The combination of group-based access and fine-grained access control at the data point level allows for the avoidance of generating multiple copies of the Battery Pass tailored to the access permissions associated with each data consumer granted access. Therefore, the amount of data associated with the Battery Pass can be reduced because access can be controlled at the data point level, eliminating the need for generating multiple copies of the Battery Pass.

[0139] Access policy data can identify access control groups, which include data consumer identifiers associated with data consumers granted access to battery passes. Application access policy data may include:

[0140] • The consumer identifier is determined based on the user data contained in the received request.

[0141] • Access to battery data is determined based on group access control data associated with data consumers who are granted access to battery data.

[0142] • If the identified consumer identifier is granted access to battery data, then the usage data associated with the confirmed data used to generate the battery pass is determined, the usage data determined based on the identified consumer identifier is applied to the battery pass, and the battery pass is provided based on the applied usage data.

[0143] In another example, application access policy data may include:

[0144] • The consumer identifier is determined based on the user data contained in the received request.

[0145] • Identify group access data that is associated with the Battery Pass and identifies the consumer identifier associated with the data consumer who is granted access to the Battery Pass.

[0146] • Apply group access data determined based on the identified consumer identifier to the battery pass, and provide the battery pass based on the applied group access data.

[0147] A consumer identifier can be determined based on data indicating the relationship between the consumer identifier and user data. User data may include usernames. This relationship indicates the user data associated with a given consumer identifier. This relationship can be stored within the data associated with the data source. In this way, a user can be associated with data from a data source that includes the consumer identifier.

[0148] Use may include rules and / or conditions associated with the use of input material data used for pass generation and pass use. Pass use may include providing generated battery passes to pass consumers. Use rules may include rules that restrict access to the input material data to qualified data consumers. Rules may restrict access based on consumer identifiers.

[0149] In one embodiment, updating the battery pass includes generating a dataset by applying a data model associated with the battery pass to the collected data, and using the generated dataset to update the battery pass. Attached Figure Description

[0150] The disclosure will be further described below with reference to the accompanying drawings. In the drawings and the disclosure, the same reference numerals are intended to refer to the same or similar elements, components, and / or portions. The description of the drawings is provided for illustrative purposes and should not be considered limiting. Embodiments and examples are illustrative examples intended to further illustrate the concepts set forth herein. The drawings include schematic representations and should not be considered limiting. Other embodiments and examples falling within the scope of the concepts outlined herein are possible and may not be explicitly described herein.

[0151] Figure 1 An example of a participant network in a product ecosystem is shown, which includes material recycling and is associated with a decentralized peer-to-peer network for exchanging data related to raw materials, chemical products, discrete products, final products, and recycled materials.

[0152] Figure 2 An example of a participant network in the battery ecosystem is shown, which includes material recycling and is associated with a decentralized peer-to-peer network for exchanging data related to raw materials, chemicals, batteries, end products, and recycled materials.

[0153] Figure 3A and Figure 3B A schematic block diagram illustrating the battery manufacturing process is shown.

[0154] Figure 4 The illustration shows a battery with battery identification elements as a product.

[0155] Figure 5 The illustration schematically shows a battery component with identification elements, which is used as an input material for battery production.

[0156] Figure 6 An example architecture of a system configured to verify the integrity of data that can be accessed for generating and / or updating battery passes associated with the battery is shown.

[0157] Figure 7A An example block diagram is shown of an asset generation system configured to generate data associated with battery production and provide the generated data for access within a decentralized network.

[0158] Figure 7B A diagram is shown illustrating an example of a system configured to collect output product data and transform at least a portion of the collected output product data using a rule-based engine.

[0159] Figure 8Examples of systems and associated methods for generating data related to battery production and providing the generated data for access within a distributed network are shown.

[0160] Figure 9 A flowchart is shown illustrating an example method for generating an output product dataset associated with the produced output products.

[0161] Figure 10 Demonstrates according to one embodiment, Figure 9 Examples of the methods shown.

[0162] Figure 11 A sequence diagram illustrating example methods for generating an output product dataset associated with the produced output products is shown.

[0163] Figure 12 A distributed system for accessing data associated with the produced output products is demonstrated.

[0164] Figure 13 An example of a battery pass service configured to verify the integrity of data used to generate and / or update battery passes associated with a battery is shown.

[0165] Figure 14A Showing the storage Figure 13 The example shown is a database structure for the Battery Pass service.

[0166] Figure 14B Showing the storage Figure 13 The example shown is a relational representation of the Battery Pass service database.

[0167] Figure 14C Demonstrated for use to Figure 13 The example shown illustrates the user interface for the Battery Pass service, which provides asset data and associated data sources for storage.

[0168] Figure 15 Showing Figure 13 An example of a data quality analyzer is shown.

[0169] Figure 16 Showing Figure 15 An example of a metric generator is shown.

[0170] Figure 17A This demonstrates the first example of a data integrity metric.

[0171] Figure 17B Further examples of data integrity metrics are shown.

[0172] Figure 17CThis demonstrates yet another example of a data integrity metric.

[0173] Figure 18 A flowchart illustrating an example of a method for verifying the integrity of data accessible for generating and / or updating battery passes associated with a battery.

[0174] Figure 19A A block diagram of an example system for verifying data associated with battery production is shown.

[0175] Figure 19B The demonstration shows the use of including Figure 19A The diagram illustrates an example of a rule-based engine within a data validation unit, described in the context of the data validation process, used to validate data associated with battery production.

[0176] Figure 20 A flowchart illustrating a further example of a method for verifying the integrity of data accessible for generating and / or updating battery passes associated with the battery.

[0177] Figure 21 Showing Figure 20 Examples of the methods shown.

[0178] Figure 22 A flowchart illustrating an example method for verifying data associated with battery production is shown.

[0179] Figure 23 A sequence diagram illustrating example methods for verifying data associated with battery production is shown.

[0180] Figure 24 A system for generating battery passes associated with batteries was demonstrated.

[0181] Figure 25 An example of a method for generating battery passes associated with a battery is shown.

[0182] Figure 26 An example of a method for updating a battery pass associated with a battery is shown. Detailed Implementation

[0183] Figure 1 An example of a participant network in a product ecosystem is shown, which is associated with a decentralized peer-to-peer network for exchanging data related to raw materials, chemical products, discrete products, final products, and recycled materials.

[0184] The associated product ecosystem participant network 130 may be associated with a decentralized peer-to-peer network 134 to exchange data related to raw materials, chemical products, discrete products, final products, recycled materials, and / or their respective production. Participant network 130 may include one or more network participants, such as decentralized participants 102 to 114. Network participants may be part of a product ecosystem that includes chemical products. The product ecosystem may include a production chain that produces the final product. The product ecosystem may include a recycling chain to recover at least a portion of the end-of-life products generated from the use of the final product.

[0185] The product ecosystem may include raw material producers 104, chemical product producers 102, chemical product users 106, final product producers 108, final product users 110, EOL product collectors 112, and recyclers 114. Figure 1 The product ecosystem shown is merely an example and may include more or fewer network participants. For example, a product ecosystem may include a chain of chemical product producers, rather than just a single chemical product producer 102. A decentralized network of participants 130 may include a chemical supply chain. A product ecosystem may allow the production of new products, such as chemical products, using recycled materials generated from the recycling of end-of-life products. A product ecosystem may be associated with the production and / or recycling of physical products. Products may be chemical products, intermediate chemical products, components, component assemblies, final products, end-of-life products, or recycled materials.

[0186] Participant network 130 may be associated with the production of products and / or the recycling of end-of-life products generated from the use of products by product users (such as end-of-life user 110). Network participants 102 to 114 may refer to manufacturers of physical products, such as raw material producers 104, chemical product producers 102, chemical product users 106, end-of-life product producers 108, users of physical goods (such as end-of-life user 110), and / or participants in the recycling chain associated with physical products (such as EOL product collectors 112 and recyclers 114). Network participants may be associated with participant nodes 116 to 128 and with a distributed participant identifier associated with the associated participant nodes 116 to 128. The distributed participant identifier can uniquely identify the distributed network participant and its associated distributed participant nodes within the distributed peer-to-peer network 134.

[0187] Participants in the participant network 130 can be associated with or participate in the generation and / or updating of existing battery passes. Battery passes can be generated and / or updated by entities representing participants in the product ecosystem, for example, as in... Figures 24 to 2As described in the context of 7. The generation and / or updating of Battery Passes may include verifying the integrity of the data to be used to generate and / or update Battery Passes, for example, as in... Figures 13 to 23 The generated battery pass can be provided for access via a decentralized network 130. For example, recycler 114 can access such a battery pass to determine the composition of the end-of-life product, thereby allowing the recycling process to be adapted based on the composition determined based on the accessed battery pass. The generated or updated battery pass can be provided under the control of the data owner of the battery pass. The data owner of the battery pass can be the entity that generates the battery pass. The data owner of the battery pass can be the entity that generates the battery pass on its behalf. The data owner of the battery pass can be a product producer. The data owner of the battery pass can be a final product producer that uses the product to produce the final product.

[0188] Participants in participant network 130 can be connected via material flows. A material flow can be a circular material flow 136. A circular material flow 136 can be a closed-loop material flow. A closed-loop material flow can refer to a material cycle from which recycled material is used to produce the same final product, the recycled material being obtained from that material cycle via recycling. A circular material flow 136 can also be an open-loop material flow. An open-loop material flow can refer to a material cycle from which recycled material is used to produce a different final product compared to the material flow from which recycled material is obtained. A material flow can be a linear material flow (e.g., excluding recycling). Material flows 136, 138 can correspond to the flow of product from one participant in the decentralized participant network 130 to a downstream participant in the decentralized participant network 130. Material flows 136, 138 can refer to continuous or discontinuous flow of product. The flow of product can include any mode of transport suitable for transporting product from a participant to a downstream participant. Transport modes can include pipes, containers, barrels, and packaging. Material flow 138 can be associated with raw materials (e.g., virgin raw materials) used to produce the chemical product. Raw materials can be supplied to chemical product producer 102 for the production of chemical products and / or intermediate chemical products. A circular material stream 136 can be associated with both chemical products and discrete products. Chemical products can be supplied from chemical product producer 102 to chemical product user 106 for the production of discrete products. The discrete products produced are distinct units sold as individual products, as opposed to chemical production. The circular material stream 136 can be associated with recycled materials. Recycled materials can be supplied from recycler 114 to chemical product producer 102 for the production of chemical products using the recycled materials.

[0189] At least some of the participants in the distributed participant network 130 may be associated with distributed participant network nodes 116 to 128. Distributed participant nodes 116 to 128 may be under the control of the corresponding distributed participant associated with the respective distributed participant node. Distributed participant nodes 116 to 128 may form a distributed network 134. Distributed network 134 may be a peer-to-peer communication network. Distributed network 134 may be configured to execute data transactions 132. Data transactions 132 may be based on a transaction protocol including authentication and / or authorization mechanisms. Based on authentication and / or authorization mechanisms, peer-to-peer communication may be established between distributed network nodes 116 to 128 associated with distributed network participants 102 to 114. One or more authentication mechanisms may be associated with or linked to an identifier, such as in... Figure 12 The context described above. One or more authentication mechanisms associated with the identifier can be accessed by distributed participant nodes, as in... Figure 12 Decentralized configurations allow for more efficient use of computing resources and strengthen the control of data owners in decentralized networks by implementing data sovereignty, as described in the context of [the previous sentence].

[0190] Data transactions between participating nodes in a decentralized network can be based on identifiers associated with the corresponding data to be accessed, for example, as in... Figure 12 Described in the context of [the relevant context]. Identifiers can be uniquely associated with the physical entity of the corresponding product and the associated product data. Identifiers can be decentralized identifiers that uniquely identify products within a decentralized network. Identifiers can be associated with other identifiers (such as identifiers of production inputs used to produce the product, e.g., input materials, hereinafter referred to as sub-identifiers)). This allows tracking of product inputs used to produce products (such as chemical products, components, component assemblies, or final products). Identifiers can be included, for example, in the output product dataset associated with the product, e.g., as in [the relevant context]. Figure 9 Described in the context of [the relevant context]. Sub-identifiers can be provided to be associated with an identifier, for example, as in [the relevant context]. Figures 13 to 14C Described in the context of.

[0191] Data flows 132 (e.g., transactions) between distributed network participant nodes can be directly or indirectly associated with material flows 136, 138 between distributed network participants. For example, if data associated with input materials supplied from raw material producer 104 to chemical product producer 102 is accessed by a distributed participant node 118 associated with said chemical product producer 102, then data flow 132 can be directly associated with material flows 136, 138. For example, if data associated with chemical products produced by chemical product producer 102 is accessed by a distributed participant node 128 associated with recycler 114, then data flow 132 can be indirectly associated with material flows 136, 138.

[0192] Distributed participant nodes 116 to 128 may be distributed computing nodes. A distributed computing node may be any device or system comprising at least one physical tangible processor and physical tangible memory capable of having computer-executable instructions executed by the processor thereon. The memory may take any form and depends on the nature and form of the computing node.

[0193] At least some of the distributed participant nodes 116 to 128 may be distributed data providing network nodes. At least some of the participant nodes 116 to 128 may be distributed data consuming network nodes. Participants in the distributed participant network 130 may be associated with distributed data providing network nodes and / or distributed data consuming network nodes, depending on whether the data is provided to downstream participants or consumed from upstream participants. For example, final product producer 108 may be associated with a distributed data providing network node configured to provide product data to downstream participants (e.g., recycler 114). Alternatively, final product producer 108 may be associated with a distributed data consuming network node configured to access data associated with discrete products produced by upstream participants (e.g., chemical product user 106), for example, in Figure 12 Described in the context of.

[0194] The distributed network 134 may include additional distributed network nodes. These additional distributed network nodes may be distributed infrastructure service nodes (...). Figure 1(Not shown in the image). Distributed infrastructure service nodes may not be associated with participants in the product ecosystem. Distributed infrastructure service nodes can provide services to distributed participant nodes 116 to 128, such as verifying the identity of distributed network participant nodes 116 to 128 before performing data exchange. Distributed network participant nodes 116 to 128 may be associated with or include certificates, such as X.509 certificates. Certificates may be associated with distributed infrastructure service nodes that include, for example, certificate issuance services and / or dynamic provisioning services that provide dynamic attribute tokens (e.g., OAuth access tokens). Thus, distributed network participant nodes 116 to 124 have a unique identifier embedded in the X.509 certificate that identifies the corresponding distributed network participant node 116 to 128. The information required to verify the certificate can be provided via a certification registry associated with the certificate issuance service and / or dynamic provisioning service. For example, in the IDSA Reference Architecture Model version 3.0 of April 2019, decentralized data provisioning network nodes associated with data owners, Certificate Authorities (CAs), and Dynamic Attribute Provisioning Services (DAPS), as well as decentralized data consuming network nodes associated with data consumers, verify identities before performing data exchange (not shown, see reference). Figure 12 ).

[0195] Figure 2 An example of a participant network in the battery ecosystem is shown, which includes material recycling and is associated with a decentralized peer-to-peer network for exchanging data related to raw materials, chemicals, batteries, end products, and recycled materials.

[0196] Participant network 226 may include one or more network participants, such as network participants 202, 204, and 214 through 224. The battery ecosystem may include a production chain to produce end products, such as machines containing batteries. The battery ecosystem may include a recycling chain to recycle at least a portion of end-of-life batteries generated from the use of end products. The battery ecosystem may include miners 214, refiners 216, precursor cathode active material (PCAM) and cathode active material (CAM) producers 218, battery producers 220, end-of-life product producers 202, EOL product collectors 204, black powder producers 222, and metal extractors 224. The battery ecosystem may allow the use of recycled materials (such as recycled metals and metal salts) generated from recycling end-of-life batteries or their components to produce new products, such as PCAM and CAM. The product ecosystem may be associated with battery production and / or recycling.

[0197] Participants in participant network 226 may be associated with the production of end products containing batteries and / or the recycling of end-of-life batteries or their components. Decentralized network participants may be manufacturers of physical products, such as miners 214, refiners 216, PCAM and CAM producers 218, battery producers 220, end-of-life producers 202, and / or participants in recycling chains associated with end-of-life batteries or their components (such as EOL product collectors 204, black powder producers 220, and metal extractors 224). For example, metal extractor 224 and PCAM and CAM producers 218 may be single entities performing recycling operations to obtain recycled materials (such as recycled metals and / or metal salts) and using the recycled metals and / or metal salts to produce new chemical products (such as PCAM and / or CAM). Network participants may be associated with decentralized participant identifiers. Decentralized participant identifiers can uniquely identify network participants within participant network 226.

[0198] At least some of the participants in participant network 226 may be associated with the generation of battery passes. Such participants may not be associated with the production of end products containing batteries and / or the recycling of end-of-life batteries or components. Such participants may collect input material data (e.g., as in...). Figure 19A (as described in the context), and at least a portion of the collected input material data can be used to generate a battery pass. The generated battery pass can be provided by such a participant for access via a decentralized network 134. For example, black powder producer 222 can access such a battery pass to determine the chemical composition of end-of-life batteries or their components, thereby allowing the recycling process to be adapted based on the determined composition.

[0199] Participants in participant network 226 can be connected via material flow, such as in Figure 1 As described in the context. Raw materials (such as metals) can be supplied to refiner 216 for refining. Refined metals can be supplied to PCAM and CAM producers 218 for use in the production of cathode active materials. CAM can be used, for example, by battery producer 220 for the production of battery cells (see also...). Figure 3A , Figure 3B Individual cells can be used to produce batteries or battery packs (see also...). Figure 3B Batteries or battery packs can be supplied to end-product producer 202 to produce end products containing batteries, such as electric vehicles. Waste generated from battery production can be supplied to black powder producer 222.

[0200] At least some of the participants in participant network 226 may be associated with distributed participant network nodes 228 to 242, as in Figure 1The context is described below. Distributed participant nodes 228 to 242 can form a distributed network 134. The distributed network 134 can be as follows: Figure 1 The context describes a peer-to-peer communication network. Decentralized configurations allow for more efficient use of computing resources and strengthen the control of data owners in decentralized networks.

[0201] Data transactions between participating nodes in a decentralized network can be based on identifiers associated with the corresponding data to be accessed, for example, as in... Figure 12 Described in the context of [the relevant context]. Identifiers can be uniquely associated with the physical entity of the corresponding product and the associated product data. Identifiers can be decentralized identifiers that uniquely identify products within a decentralized network. Identifiers can be local identifiers used by the corresponding product producer to uniquely identify the corresponding product. Identifiers can be associated with other identifiers, such as identifiers for production inputs (e.g., input materials) used to produce output products. This allows tracking of product inputs used to produce products (e.g., final products). Identifiers can, for example, be included in input material data associated with input materials, such as [example data]. Figure 19A Described in the context of.

[0202] Data flows 206 (e.g., transactions) between network participant nodes can be directly or indirectly associated with material flows 210, 212 between network participants, as in... Figure 1 Described in the context of.

[0203] Distributed participant nodes 228 to 242 can be distributed computing nodes, such as in... Figure 1 Described in the context of.

[0204] At least some of the distributed participant nodes 228 to 242 may be distributed data providing network nodes. At least some of the participant nodes 228 to 242 may be distributed data consuming network nodes. Participants in the distributed participant network 134 may be associated with distributed data providing network nodes and / or distributed data consuming network nodes, depending on whether the data is provided to downstream participants or consumed from upstream participants (see also...). Figure 1 ).

[0205] The distributed network 134 may include additional distributed network nodes, such as in Figure 1 Described in the context of.

[0206] Figure 3A and Figure 3BA schematic block diagram illustrating the battery manufacturing process is shown. Batteries can be used in machines (such as automobiles). Batteries can be used until the end of their lifespan. Discarded batteries may have a state of health (SoH) below a predefined threshold. SoH can be a measurement indicating the level of battery degradation and remaining capacity. It can be defined as the ratio of maximum battery capacity to its rated capacity. SoH can be determined using the battery's battery management system (BMS). SoH can also be determined using battery capacity determination (BCD).

[0207] refer to Figure 4 The battery 402 may include a battery management system 408 and multiple battery cells 410 disposed within a battery housing 412. The battery cells 410 may be arranged in a battery pack or module comprising multiple battery cells 410. Each battery cell 410 may include an electrolyte 414, an anode element 416, a cathode element 418, and a separator 420. Depending on the application, different components of the battery may include different material compositions. For example, through... Figure 3A and Figure 3B The batteries produced in the demonstrated production process can be lithium-ion batteries.

[0208] Continue to refer to Figure 4 Cathode elements 318 and 418 may include cathode active material (CAM) 342 coated on current collector foil 344 (such as aluminum foil or copper foil). CAM 342 may contain layered oxides (LiMO2, where M = Co, Ni, Mn, Al, such as LCO (LiCoO2) or NCM (LiNiO2). x Mn y Co z O2), NCA (LiNi) x Co y Al z O2); spinel (LiM2O4, where M = Mn, Ni, e.g., LMO (LiMnO4)); or phosphate (LiMPO4, where M = Fe, Mn, Co, Ni, e.g., LiFePO4). CAM 342 can be produced from lithium salts (e.g., LiOH and Li2CO3). CAM 342 can be produced from precursor materials via solid-state synthesis. Precursor materials can be produced by co-precipitation of transition metal sulfates or hydroxides (e.g., NiSO4, MnSO4, CoSO4, Ni(OH)2, Mn(OH)2, Co(OH)2). Co-precipitation can be achieved by adding sodium hydroxide and ammonia to a solution of the transition metal sulfate or hydroxide material. Precursor materials (e.g., Ni) can be produced by co-precipitation of transition metal sulfates or hydroxides. x Co y Mn 1-x-y(OH)2) is mixed with lithium salt and calcined. The calcined material is ground and sorted to obtain CAM 342. CAM may further comprise binder 338, polyvinylidene fluoride (PVDF), and carbon as a conductive agent. Binder 338 may be a polymer produced from one or more monomers (e.g., raw material binder 306).

[0209] Continue to refer to Figure 4 The anode elements 316 and 416 may include an anode active material 334 coated on a current collector foil 336 (such as aluminum or copper foil). The anode active material may comprise artificial graphite (e.g., synthetically produced graphite), natural graphite, or mixtures thereof. Further, the anode active material may comprise silicon, SiO2, lithium titanate (LTO), or combinations thereof. Further, the anode active material may include a binder 338, such as styrene-butadiene rubber (SBR), a polymer thickener (such as carboxymethyl cellulose (CMC)), and carbon as a conductive agent. The binder 338 may be a polymer produced from one or more monomers (e.g., raw material binder 306).

[0210] Continue to refer to Figure 4 Electrolytes 324 and 414 may include a salt 354 for providing conductivity, a solvent 352 (such as a carbonate, ester, or ether), and additives (such as alkyl sulfites and sulpholipids, for example, vinyl sulfite and propylene sulpholipid) for supporting the formation of the SEI layer. The solvent may include cyclic carbonates (such as ethylene carbonate (EC) or propylene carbonate (PC)), open-chain carbonates (such as dimethyl carbonate (DMC) and / or ethyl methyl carbonate (EMC)) or mixtures thereof. Salt 354 may include conductive lithium salts (such as lithium hexafluorophosphate (LiPF6), lithium bis(trifluoromethyl)sulfonylimide (LiTFSI)) and their derivatives (e.g., lithium bis(fluorosulfonyl)imide (LiFSI) or [tri(pentafluoroethyl)-trifluorophosphate] (LiFAP), lithium 4,5-dicyano-2-trifluoromethyl-imidazolium (LiTDI), lithium bis(oxalate)borate (LiBOB)). Salt 354, solvent 352 and other additives can be prepared from the corresponding raw materials 364, 366 and 368.

[0211] Continue to refer to Figure 4 The separators 320 and 420 may include microporous membranes, coated separators, nonwoven pads, solid inorganic materials, or polymer materials. Coated separators may include polyolefin-based membranes coated with, for example, PVDF or ceramic. Polyolefin-based membranes may be produced from the corresponding raw material 346. Separators 320 and 420 can separate spaces between electrodes and are permeable to ions.

[0212] A single battery cell (such as a lithium-ion battery cell 370) can be manufactured from an anode element 316, a cathode element 318, a separator 320, a casing 322, and an electrolyte 324. The casing can be a metal casing made of steel or aluminum 348. The casing can have various shapes, such as prismatic, circular, or pouch-shaped. The anode element 316, cathode element 318, and separator 320 can be placed inside the casing 322. The casing 322, including the anode element, cathode element, and separator, can be filled with electrolyte 324. The casing can be filled with electrolyte after it has been sealed and emptied. The casing can also be filled with electrolyte before it has been sealed.

[0213] refer to Figure 5 Component identification elements 404 and 406 can be associated with the manufactured battery cell 410. Identification elements 404 and 406 can be physically attached to component 410. Identification elements 404 and 406 can be arranged inside or outside the product component 410. Identification elements 404 and 406 can be passive identification elements. Passive elements 404 and 406 can be arranged on the outer surface of the battery cell housing lithium salt 310. Passive elements 404 and 406 can be based on markings embedded in the cell housing. Passive elements 404 and 406 can include printed codes (such as barcodes or QR codes) and / or printed numbers (such as serial numbers) and / or combinations of printed numbers and letters (including symbols). Identification elements 404 and 406 can also be active identification elements. Active identification elements 404 and 406 can be transmitter or transceiver tags, such as RFID tags capable of communication via, for example, NFC, Bluetooth, Zigbee, or other suitable short-to-mid-range communication protocols. Identification elements 404 and 406 can be associated with digital component identifiers (e.g., digital battery cell identifiers). Digital component identifiers may include or relate to at least one input material identifier associated with a production input used to manufacture the component (e.g., the battery cell). Specifically, identification elements 404 and 406 can be configured to provide digital component identifiers, such as digital battery cell identifiers, for accessing component data associated with the corresponding component (e.g., the battery cell). Component data may include, for example, cathode active material composition data, anode active material composition data, and / or electrolyte composition data. Composition data may include data related to key compounds present within the CAM, anode active material, and / or electrolyte.

[0214] The manufactured battery cells (such as lithium-ion battery cell 370) can be used to produce battery packs, such as lithium-ion battery pack 372. Battery pack production can involve both module production and battery pack production. For module production, electrically insulating liquid or solid adhesives can be used, for example, to bond the battery cells. Examples of adhesives may include polyurethane-based adhesives. The bonded or stacked cells can be pressed to create a defined stack geometry and minimize expansion during charging and discharging. Plastic sheets or foils can be applied to the bonded or stacked cells for heat dissipation and electrical insulation. Stacked cells can be wired via electrical connections of contact tabs / current collectors. Depending on the module voltage, these cells can be contacted to form one or more parallel strings. A slave circuit board of the Battery Management System (BMS) 326 or a complete contact unit for processing data and controlling sensors can be bonded to the module via soldering and / or threaded connections. A controller, as well as a cooling system 332 (if required) later connected to the BMS host, can be bonded to the module. The manufactured modules can undergo testing procedures. Test procedures may include control of external irregularities (optical tolerances), communication and sensor functionality (software testing), cell voltage, cell variability (balance), module state of charge (SOC), high voltage resistance (HV) measurement, gas leak testing, overvoltage testing, vacuum testing, etc. Modules may also be associated with identification elements, as described in the context of battery cells. Identification elements may also be configured to provide a digital module identifier for accessing module data associated with the corresponding module. Module data may include, for example, adhesive data associated with the adhesive used to bond the battery cells, data associated with the slave circuit board, and / or data associated with the cooling system. Module data may further include identifiers associated with the input materials used to produce the module, such as battery cell identifiers, slave circuit board identifiers, and / or cooling system identifiers.

[0215] For battery pack production, cooling elements can be installed in the bottom of the battery pack tray or housing 328 for cooling and / or heating the modules. The produced battery modules can then be secured inside the battery pack housing. A cooling system 332 can be attached to and connected to the cooling elements in the battery pack housing. High-voltage modules can be installed and connected to the modules. The high-voltage modules may include relays, fuses, pre-charge and current measurement systems, insulation monitoring, etc. A battery management system (BMS host) can be installed and wired to control the cooling system, modules, slave circuit boards, and high-voltage modules. The final battery pack 372 can be obtained by applying sealant to the edge of the housing or cover, placing the cover of the housing on the sealant, and attaching it to the housing.

[0216] refer to Figure 4Identification elements 404 and 406 can be physically associated with battery 402. Identification elements 404 and 406 can be physically attached to battery housing 412. Identification elements 404 and 406 can be arranged inside or outside battery housing 412. Identification elements 404 and 406 can be passive identification elements 404 and 406 as described above, or active identification elements 404 and 406. For example, if the battery identifier associated with the battery is stored in BMS 408, then identification elements 404 and 406 can be part of battery management system 408.

[0217] Battery 402 may be associated with a digital battery identifier. The digital battery identifier may be associated with identification elements 404 and 406. The digital battery identifier may be stored within BMS 408. The digital battery identifier may be unique to the physical entity of battery 402. The digital battery identifier may be associated with battery data. This data may include any data collected during the production and / or lifespan of battery 402. For example, this data may include input material identifiers associated with production inputs used to produce the battery, data collected before, during, and / or after battery production, and / or monitoring data collected during the use of battery 402.

[0218] A digital battery identifier may be associated with or include at least one decentralized battery identifier. A decentralized battery identifier may include any unique identifier uniquely associated with the battery data and the identified battery 402. The decentralized battery identifier may further be associated with the data owner of the battery data. The decentralized battery identifier may include at least one Universally Unique Identifier (UUID) and / or at least one Digital Identifier (DID). The decentralized battery identifier may be issued by a centralized or decentralized identity issuing authority. The decentralized battery identifier may include authentication information used to authenticate the battery data. Access to the battery data can be controlled by the data owner of the battery data through the decentralized battery identifier and its unique association with battery 402. This contrasts with a centralized authority scheme, where identifiers are provided by a centralized authority and access to the data is controlled by such a centralized authority. In this context, decentralized refers to the use of the identifier being controlled by the data owner. Identification elements 404 and 406 may be configured to provide a decentralized battery identifier for accessing the battery data.

[0219] A data owner can include any entity that generates data, particularly data related to the identified battery. A generating node can be coupled to an entity that owns a physical product from which data (particularly data related to the identified battery) is generated. This data (particularly data related to the identified battery) can be generated by a third-party entity representing an entity that owns a physical product from which data is generated. The data owner can be a producer of materials and / or components contained in the battery, a battery manufacturer, or a producer of a final product containing the battery. Access to the corresponding data can be controlled by the data owner through a decentralized identifier and its unique association with the data owner and the data related to the identified product. The data related to the identified product can be accessible to the data owner. Therefore, the data owner can directly or indirectly own or control the data related to the identified product. The data related to the identified product can be stored in the data owner's storage environment or a storage environment associated with the data owner. The data related to the identified product can be stored in a storage environment accessible to the data owner. The data owner can control access to the data related to the identified product by providing services through the data owner's data. A data owner can control access to data related to the identified product. Data related to the identified product can be associated with a data owner. A data owner can be the owner or controller of data related to the identified product or data related to the owner of the identified product. Data related to the identified product can be stored in the data owner's storage environment or a storage environment controlled by the data owner. In this sense, a data owner can refer to an entity capable of accessing data related to the identified product or its parts and controlling access to data related to the identified product or its parts provided by network nodes through distributed data in a distributed computing environment.

[0220] Specifically, the distributed identifier may relate to material data specifying the material composition of one or more components of the product. The distributed product identifier may be associated with battery 402, and the material data may specify the material composition of one or more components of battery 402.

[0221] The battery packs produced (such as lithium-ion battery pack 372) can be used to produce machines powered by electric motors, such as electric vehicles or vehicles that include both combustion engines and electric motors.

[0222] Figures 3A to 5 The use of batteries in production should not be interpreted as a limitation on the basic concepts outlined in these figures. Figures 3A to 5 The concepts shown also apply to the production of other discrete products from one or more chemical products and / or components, as well as the use of such discrete products to produce component assemblies and / or final products. Figures 3A to 5 The concept shown also applies to the production of chemical products from one or more input materials.

[0223] Figure 6 An example architecture of a system configured to verify the integrity of accessible data used to generate and / or update battery passes associated with a battery is presented. The integrity of accessible data used to generate and / or update battery passes can be verified, such as in... Figures 13 to 23 Described in the context of [the relevant context]. Integrity can represent the degree of relevance between accessible data and data defined by a data model associated with a battery pass. The higher the degree of relevance, the smaller the deviation between the accessible data and the data defined by the data model, and therefore the higher the integrity of the accessible data and the pass data generated from such accessible data. This data may include data associated with the production of batteries from one or more input materials. The production-related data may include input material data associated with at least a portion of the input materials used to produce the battery and battery data associated with the battery. The system may be further configured to generate battery passes and / or update existing battery passes, for example, as in [the relevant context]. Figures 24 to 26 As described in the context, the generation of Battery Passes can be based on verification of the integrity of the data accessible for generating the Battery Passes.

[0224] Batteries can be manufactured from one or more input materials through production steps, for example, as in... Figure 3A and Figure 3B As described in the context of [the previous sentence]. Batteries can be produced from input materials via a production chain that includes one or more production steps. At least some of the production steps in the production chain can be performed by different participants in the battery ecosystem. For example, and refer to [the previous sentence]. Figure 2 The battery production chain can involve several production steps (e.g., crude metal mining, crude metal refining, PCAM and CAM production, cell production, and battery production). These production steps can be undertaken by different participants in the battery ecosystem (e.g., ...). Figure 2 (As shown by the participants). The output produced by one participant in the battery ecosystem can be used as input material by downstream participants in the battery ecosystem. For example, and refer to... Figure 2 The crude metal produced by miner 214 as an output product can be used as input material by downstream participants (e.g., refiner 216) to produce refined metals or metal products, such as metal salts.

[0225] A battery pass can be a dataset with a defined semantic structure. This defined semantic structure can be obtained by applying a semantic model (such as a data model) to data associated with battery production. The data model can define the semantic description of the corresponding battery pass. The semantic description can include at least a portion of the structure and / or characteristics of the battery pass. Characteristics of a battery pass can include data types. Characteristics of a battery pass can include possible or allowed values ​​and / or value ranges. Characteristics of a battery pass can be physical units of parameters described by values ​​included in the battery pass. Characteristics of a set of battery passes can include one or more attributes. The data model can define the data points to be included in the battery pass, such as data types and values ​​and / or value ranges.

[0226] A battery pass may include at least one decentralized identifier and pass data. The decentralized identifier may include any unique identifier uniquely associated with the battery. The decentralized identifier may include one or more Universally Unique Identifiers (UUIDs) or Digital Identifiers (DIDs). The decentralized identifier may be issued by a centralized or decentralized identity issuing authority. The decentralized identifier may include authentication information. Access to the pass data can be controlled by the data owner of the pass data (such as the battery manufacturer), the entity that generates the battery pass including the pass data, or the entity that generates the battery pass including the pass data on its behalf, through the decentralized identifier and its unique association with the battery. This contrasts with a centralized authority scheme, where the identifier is provided by the centralized authority and access to the data is controlled by the centralized authority. In this context, decentralized refers to the control of the use of the decentralized identifier by the data owner of the pass data.

[0227] Passport data may include data points defined by a data model used to generate the corresponding battery passport. Passport data may include data associated with battery production and a battery identifier associated with the battery. In this way, a battery passport can be uniquely associated with a battery. Data associated with battery production may include input material data associated with at least a portion of the input materials used to produce the battery. Input materials used to produce the battery may include input materials used in one or more production steps in the battery production chain. Data associated with battery production may include battery data associated with the battery.

[0228] Input material data and / or battery data may each include one or more chemical and / or physical properties associated with the input material or battery. One or more chemical and / or physical properties may include at least one measured chemical and / or physical property of the input material, and / or at least one chemical and / or physical property determined based on collected data associated with the production and / or use of the input material. One or more chemical and / or physical properties may include at least one measured chemical and / or physical property of the battery, and / or at least one chemical and / or physical property determined based on collected data associated with the production and / or use of the battery. The collected data may be used to determine at least one physical and / or chemical property. For example, at least one physical and / or chemical property may be determined based on sensor data obtained from a sensor. Data may be collected using a suitable sensor configured to measure chemical and / or physical properties. The measured at least one physical and / or chemical property may be obtained by a sensor configured to measure such property. The sensor may be included in a measuring device. The sensor may correspond to a measuring device.

[0229] A battery pass may include one or more authentication mechanisms associated with a distributed identifier and pass data. A battery pass may involve one or more authorization mechanisms associated with a distributed identifier and pass data. One or more authorization mechanisms may include authorization rules that determine whether to grant access to at least a portion of the pass data. The battery pass and / or distributed identifier may be associated with one or more digital representations of the pass data. A digital representation may be considered an access element providing access to the battery pass or a portion thereof. The digital representation may include a distributed identifier and access data. A distributed identifier included in a digital representation may correspond to or be associated with a distributed identifier included in the battery pass. Access data may include a locator or pointer, such as a URL or URI, pointing to a dedicated storage device (e.g., a dedicated storage address) associated with the data owner of the battery pass. The pointer or locator may directly point to the dedicated storage device storing the battery pass. The pointer or locator may point to a data-providing network node associated with the dedicated storage device storing the battery pass. Access elements may include one or more authentication mechanisms associated with a distributed identifier and access data. Access elements can be associated with one or more authentication mechanisms linked to decentralized identifiers and access data. Access elements can be provided to a decentralized registry storing access elements. The decentralized registry can be associated with data-providing network nodes. This allows control over access to such a registry and access to the access elements stored in such a registry via the data-providing network nodes. The decentralized registry can be associated with participants in the product ecosystem. The decentralized registry can be associated with the data owner of a chemical battery pass. The decentralized registry can be part of a decentralized network but may not be associated with a specific participant in the production chain; for example, it can be considered an infrastructure node of the decentralized network.

[0230] This architecture can connect participants in the battery ecosystem to the battery token service 628 via a distributed network 134. Participants in the battery ecosystem can provide access to data used to generate and / or update battery tokens, for example, as in... Figures 19A to 26The data is described in the context of [the above description]. This data can be provided via one or more decentralized participant nodes (e.g., nodes 228 to 232). This data can be provided to asset generation service 3 636 via asset generation service (e.g., asset generation service 1 604). The asset generation service can be configured to generate input material data or battery data and provide this generated data for access via decentralized network 134 under the control of the data owner of the input material data or battery data (e.g., the input material producer or battery producer, respectively). The input material data or battery data can be generated by asset generation service 1 604, asset generation service 2 606, and / or asset generation service 3 636 based on data stored in databases (e.g., databases 612 to 622) associated with the respective participants. The database can store the corresponding input material data or battery data and can be accessed by the corresponding data providing nodes (e.g., nodes 228 to 232). Access to the database can be controlled by the data owner of the data stored in such database, for example, via the corresponding data providing nodes 228 to 232. Participants can use the asset generation service to provide input material data or battery data. Alternatively, participants can generate input material data or battery data without using this asset generation service.

[0231] Battery pass service 628 can be configured to store data associated with a data source that provides access to data for generating and / or updating battery passes. This data associated with the data source may relate to or be associated with provider nodes 228 to 232 that provide data for generating and / or updating battery passes associated with batteries, for example, as in... Figure 13 The context described herein. Data associated with a data source may include endpoints of that data source (e.g., endpoints of provider nodes 228 to 232), distributed participant identifiers associated with the data source, participant roles associated with the data source, and identifiers of the input material data or battery data provided by that data source, such as... Figure 14A As shown, participant roles can indicate the respective roles of a participant within the battery ecosystem. Therefore, data associated with a data source can indicate the data accessible for generating and / or updating battery passes. Decentralized participant identifiers can uniquely identify the corresponding participant within a decentralized network (such as decentralized network 134). Decentralized participant identifiers can include, for example, […]. Figure 12 The identifier is described in the context of the distributed participant identifier. Figure 12 This is associated with the verifiable credentials described in the context. Verifiable credentials may include decentralized participant identifiers. Verifiable credentials may further include data indicating the participant's role within the battery ecosystem.

[0232] Identifiers for input material data and / or battery data can be associated with corresponding sub-identifiers indicating the input materials used to produce a given intermediate or finished product, for example, such as... Figure 14B As shown, the association of identifiers allows for the determination of input materials used to produce a given battery based on the battery identifier associated with the battery, for example, such as... Figure 14B As shown, this allows us to determine which participants have not yet provided the data required for the Battery Pass based on data associated with the corresponding Battery Pass data model.

[0233] Data associated with a data source can be generated by participants in a decentralized network (e.g., Figure 1 and Figure 2 (The participants shown) provide the data. Data associated with the data source can be provided by, for example, in... Figures 13 to 14C The participants are provided in the context described.

[0234] Battery credential service 628 can be configured to validate input material data or battery data collected from data provider nodes based on data associated with a data source, for example, as in Figure 13 as well as Figures 19A to 23 As described in the context, this confirms that data defined by the data model associated with this battery pass is included in the collected data, thereby allowing for a reduction in the complexity associated with dataset generation on the provider side by avoiding the use of complex data models during the generation of such a dataset. This enables the reliable sharing of such data regardless of the existence of a data model for such intermediate products, as the rules required to transform the collected data can be readily derived from the data model associated with the battery pass.

[0235] Battery Passport Service 628 can be configured to generate data integrity metrics based on data associated with a data source or verified data (e.g., verified input material data and verified battery data), such as in... Figure 13 as well as Figures 15 to 23The Battery Pass service 628 can be configured to provide generated data integrity metrics. Data integrity metrics can indicate the degree of relevance between data stored in data associated with a data source and data associated with a data model associated with the corresponding Battery Pass. Data integrity metrics can indicate the degree of relevance between confirmed data and data associated with a data model associated with the corresponding Battery Pass. A high degree of relevance can indicate a larger amount of data accessible for generating and / or updating Battery Passes, while a low degree of relevance can indicate a smaller amount of data accessible for generating and / or updating Battery Passes. Therefore, data integrity metrics can indicate the reliability of the pass data included in the Battery Pass and can allow Battery Pass users or consumers to judge the quality or reliability of such pass data based on the associated data integrity metrics. Additionally, data integrity metrics can be used by the entity generating the Battery Pass or by a third party generating the Battery Pass on its behalf to determine the amount of data accessible for generating and / or updating a specific Battery Pass. In this way, data integrity metrics can be used by such an entity or third party to identify participants who have not yet provided data defined by the data model associated with the battery pass, and to request such data for access. Additionally, data integrity metrics can be used by such an entity or third party to control the generation of battery passes, ensuring that the generated battery passes contain a minimum amount of pass data. This allows for ensuring a high degree of integrity in battery passes and therefore high reliability of the pass data included within them, thereby enabling efficient further processing of the batteries associated with such battery passes based on processing data generated using the pass data included in such battery passes.

[0236] Battery Pass Service 628 can be configured to generate battery passes and / or update existing battery passes based on input material data and battery data collected from data provider nodes, for example, as in Figures 24 to 26 The generated and / or updated battery passes can be persistently stored in an archive to allow retrieval of such battery passes without having to regenerate them. Alternatively, the generated battery passes can be provided to users requesting such battery passes without persistently storing the generated battery passes. Battery pass service 628 can be configured to generate one or more digital representations of pass data and provide such representations to a distributed registry for access by data consuming nodes under the control of distributed nodes associated with the entity operating battery pass service 628 or on behalf of the entity operating battery pass service 628.

[0237] The use of the battery ecosystem by participants should not be interpreted as limiting the basic concepts outlined in the diagram. Figure 6The concepts presented also apply to other battery ecosystems associated with products other than batteries and products containing batteries.

[0238] By verifying the integrity of the data accessible for generating and / or updating battery tokens associated with batteries, the completeness of the token data included in the battery token, and therefore the reliability of this token data, becomes transparent to the battery token generator and / or battery token users. Determining the completeness level based on data associated with the data source allows for efficient transparency regarding the available data used to generate and / or update battery tokens, without having to collect any data related to battery production via a decentralized network. Therefore, the completeness level can be efficiently determined without involving data traffic via a decentralized network, thereby reducing overall data transfer rates and improving the stability of the decentralized network. This transparency allows battery tokens to be generated or updated based on a given or predefined completeness level, ensuring that the token data included in the battery token has sufficient completeness and reliability. This ensures that the generated and / or updated battery tokens include the token data necessary to ensure efficient further processing of the battery (such as efficient recycling of end-of-life batteries or their components). Efficient further processing can reduce the environmental impact of the battery ecosystem and / or improve the circularity of materials and byproducts used within the battery ecosystem. Transparency further allows for the identification of participants in the battery production chain who have not yet generated data to be included in the battery pass according to the data model associated with it, and / or who have not yet provided access to that data. In this way, participants can be requested to generate such data and / or provide access to it, ensuring a high degree of completeness in the generated and / or updated battery passes.

[0239] Figure 7A A block diagram illustrates an example of an asset generation system configured to generate data associated with battery production and provide the generated data for access within a decentralized network. The battery can be in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A distributed network can be like [the previous sentence]. Figure 1 and Figure 2 The context describes a decentralized network. The asset generation system can be configured to perform... Figure 9 and Figure 10 The method shown. Data associated with battery production may include input material data associated with the input materials used to produce the battery and / or battery data associated with the battery. At least a portion of the input materials may be included in Figure 7A The output product described in the context of .

[0240] refer to Figure 8Output product 806 may be produced using one or more input materials 802. Output products may include or be produced by production 804 and provided at any exit point of production 804. Output products may correspond to batteries. Output products may correspond to intermediate products used in the production of batteries. Input materials may include starting materials used in the production process performed within production 804 to produce the output product. Input materials may be used in any process step of the production process. This means that an intermediate output product of one production plant of production 804 may correspond to input materials of a subsequent production plant of production 804. Input materials may include recycled materials. Input materials may include or be any input material entering production 804. Input materials may include or be any input material provided at any entry point of production 804.

[0241] The output product can be produced from input material 802 within production 804 via one or more process steps. These process steps may involve chemical reactions and / or physical processes, and / or assembly processes involving the assembly of different discrete input materials, and / or processes involving both chemical and discrete input materials, such as filling processes. The input material can be used in one or more of these production steps. Input material 802 can enter the system boundary 814 of production 804 at an entry point, such as a production plant or material storage facility associated with production 804. The amount of input material entering the system boundary 814 of production 804 can be measured, for example, using sensor 808a. Sensor 808a may include a sensor configured to measure the amount (e.g., weight and / or volume) of the input material, and / or a sensor configured to detect an input material identifier associated with the input material. The input material may include a physical identifier element associated with the input material identifier. The identifier element may encode the input material identifier. The input material identifier may be a numerical identifier. Data collected by sensor 808a can be stored in a database associated with the operating system 816 of production 804.

[0242] As the input material passes through the system boundary 814 of production 804, the chemical and / or physical properties of the input material can be measured, for example, using sensor 808b. The measured data can be used to determine at least one chemical and / or physical property of the corresponding input material. Examples of chemical properties include heat of combustion, enthalpy of formation, toxicity, chemical stability in a given environment, flammability, oxidation state, corrosiveness, combustibility, acidity and alkalinity, chemical composition, content of recycled materials used in the production or manufacture of the input material, content of bio-based materials used in the production or manufacture of the input material, content of renewable materials used in the production or manufacture of the input material, biodegradability, and / or pH value. Examples of physical properties include absorption, brittleness, boiling point, capacitance, color, concentration, density, ductility, distribution, efficacy, elasticity, charge, conductivity, electrical impedance, potential, flow rate, fluidity, hardness, heat capacity, inductance, intrinsic impedance, brightness, luminosity, gloss, mass, melting point, opacity, permeability, permittivity, plasticity, pressure, emissivity, resistivity, reflectivity, refractive index, solubility, specific heat, strength, stiffness, temperature, tensile strength, thermal conductivity, thermal resistance, viscosity, volume and / or wave impedance.

[0243] The operating system 816 for production 804 can monitor and / or control production 804 based on operating parameters of different processes. The operating system 816 can receive production demand data associated with the production plan of production 804. Production demand data can be generated from the target production capacity of one or more output products produced by production 804. Production demand data can be generated from predefined production capacity or from a data-driven model that links production capacity to market demand data or the quantity consumed at a consumption location. Production demand data may include the target capacity of the output products produced by production 804. The operating system 816 can further receive a bill of materials associated with the output products to be produced. The bill of materials may include material data associated with the input materials used to produce the output products, process data associated with the production chain used to produce the output products, and / or output product data associated with the output products (such as product specification data or data about the quantity of output products to be produced).

[0244] Based on the received production demand data and bill of materials, material demand data can be determined. Material demand data may include data on the amount of input materials required for the target capacity of the produced output. Material demand data may include input material identifiers associated with the input materials required for the produced output, and data on the quantity of the corresponding input materials. Material demand data may include one or more material specifiers representing each input material identifier. Material demand data may include data on the quantity of material for each input material identifier representing the quantity of material to be supplied. Material demand data may specify the production chain for producing 804. Material demand data may include a bill of materials for one or more production chains used to produce 804. Material demand data may include one or more formulations specifying one or more materials used in the production process for producing 804. The determined material demand data may be made accessible to supplier systems associated with suppliers outside the physical system boundaries of 804 production. Material supply can be triggered by accessing the material demand data through the supplier system.

[0245] Sensors (such as sensor 808b) can be used to measure the amount of output product produced by the process performed within production 804. The measured data can be stored in one or more databases associated with operating system 816. Furthermore, sensors (such as sensor 808b) can be used to monitor the process performed within production 804, and the generated monitoring data can be stored in one or more databases associated with operating system 816. The monitoring data can be correlated with a digital output product identifier associated with the corresponding output product. The physical and / or chemical properties of the produced output product can be measured by sensors (such as sensor 808a). Physical and / or chemical properties can include the properties previously described. The measured and / or determined data of the produced output product can be stored in one or more databases associated with operating system 816. The measured and / or determined data of the produced output product can be correlated with a digital output product identifier associated with the corresponding output product. The digital output product identifier can be correlated with physical identifier elements present on the output product or packaging units including the output product.

[0246] The produced output product 806 can be provided at one or more exit points of production 804. The output product 806 can leave the system boundary 814 of production 804.

[0247] Operating system 816 may include an asset generation service, such as asset generation service 2 606. Operating system 816 may be associated with such an asset generation service (not shown). Asset generation service 2 606 may be configured to generate a dataset (e.g., assets) associated with the output products produced by production 804, such as as in... Figure 9 and Figure 10 Described in the context of.

[0248] return Figure 7A And continue to refer to Figure 8 Asset generation service 2606 can generate a dataset (e.g., an output product dataset) associated with the output product in response to receiving a trigger signal. The trigger signal can be associated with or may include trigger data. Trigger data may include data associated with the output product produced by production 804. The trigger signal can be generated by trigger generator 812. Trigger generator 812 may be part of a packaging line or may reside in a storage location (e.g., a warehouse). Trigger generator 812 may include sensors configured to detect the produced output product and / or the packaged output product. Sensors may be configured to detect identification elements physically attached to the produced or packaged output product. Trigger generator 812 can use sensor data to generate trigger data that includes data associated with the produced output product. Data associated with the produced output product may include a digital output product identifier associated with the produced output product. The trigger signal can be generated by a user, for example, using a front-end application that allows the generation of trigger data. The front-end application can display data associated with the produced output product, such as the output product name, output product quantity, output product identifier, etc. This data can be collected from one or more databases that store data associated with the output products. The user can select the output product displayed by the front-end application. In response to the selection of the output product, a back-end application connected to the front-end application can generate trigger data by collecting digital output product identifiers associated with the selected output product. The collected digital output product identifiers can be used to generate the trigger data.

[0249] Triggering events or triggering data can be received by the data collection unit 722 of the asset generation service 2 606. The data collection unit 722 can be connected to the data source layer 720. The data source layer 720 can include one or more databases, such as DB1 616, DB2 618, and DB3 620. The database can be a distributed data source. A distributed data source can be a data lake that includes output artifact data from multiple distributed data sources. A distributed data source can be a collection of data stored at different sites on a computer network. Each site may have a degree of autonomy, serving the execution of a local application but also participating in the execution of a global application. For example, a distributed data source can be a distributed database. A distributed database can be created by splitting data from an existing database and distributing it across different sites or by combining multiple existing databases. Each data source may contain only a fragment of data associated with the corresponding output artifact. This results in the segmentation of the data. Two common types of data segmentation are: horizontal segmentation, where (potentially overlapping) subsets of data tuples are stored at different sites; and vertical segmentation, where (potentially overlapping) sub-tuples of data tuples are stored at different sites. More generally, data associated with output products can be partitioned into a set of relations (tables in a relational database distributed across multiple sites). One or more distributed data sources can contain output product data associated with the produced output products. Output product data can include at least one measured physical and / or chemical property of the produced output products, and / or at least one physical and / or chemical property determined based on collected data associated with the production and / or use of the produced output products, as previously described. At least one of the distributed data sources can contain data instances relating to output products for which the asset generation service 2 606 is configured to generate output product datasets. Data source layer 720 can be owned or controlled by the data owner of the data associated with the output product data. Data source layer 720 can be associated with the data owner of the output product data. Data collection unit 722 can be configured to collect output product data based on received trigger data (e.g., data associated with the produced output products from data source layer 720), for example, as in Figure 9 Described in the context of.

[0250] The output product data collected by the data collection unit 722 can be provided to the data transformation unit 724. The data transformation unit 724 can be configured to transform the output product data collected by the data collection unit 722 based on one or more rules retrieved from the rule database 712 to generate an output product dataset, for example, as in... Figure 7BThe transformations described in the context of [the document / parameter] are as follows: Filtering the collected output product data; aggregating output product data collected from multiple data sources into a given format; constructing attributes based on existing attributes to create or add new attributes to the output product data; discretizing to transform continuous output product data values ​​into a set of data intervals with specific values; generalizing the collected output product data to transform low-level data attributes into high-level data attributes; manipulating to change or modify the collected output product data; and / or normalizing to transform the output product data into another format to limit the occurrence of duplicate data. Rules can define key-value pairs to be included in the generated output product data. Rules can define values ​​to be included in the generated output product data. Rules can define the units of data points and / or one or more transformations used to transform the units associated with data points into another unit. The collected data can be aggregated and filtered to extract values ​​from the aggregated data that match the keys defined in the rules. The generated output product dataset can include one or more data points. The output product dataset can include one or more key-value pairs. The output product dataset can include output product data in tabular form. Using rules associated with products produced from such outputs ensures that the required output data points, based on the semantic model associated with the product (e.g., an aspect model), are included in the output dataset, thus avoiding missing data points during the generation of battery passes associated with the product using the semantic model. This transformation allows the collected outputs to be aggregated into a given data format (e.g., a tabular format) without using complex semantic models, thereby facilitating the generation and sharing of output datasets within the battery ecosystem. This sharing enables more efficient production and / or recycling processes based on shared output data (e.g., output composition data). The tabular format can be easily derived from... Figure 6 The Battery Pass service 628 described in the context is consumed via a distributed network.

[0251] The data transformation unit 724 can be further configured to provide the generated output product dataset (hereinafter also referred to as assets) to the data provider unit 744. The data provider unit 744 can be configured to provide the received output product dataset to a database (e.g., asset DB 704) for storage. Assets may include or be associated with a digital output product identifier related to or associated with that digital output product identifier. This allows assets to be retrieved based on the digital output product identifier. Assets may further include input material identifiers for the input materials used to produce the output product. This allows the input materials used to produce the output product to be identified. Assets can be persistently stored in the asset DB 704 by the data transformation unit 724. The asset DB 704 can be configured to store the output product dataset generated by the data transformation unit 724. The asset DB 704 can be configured to provide the output product dataset to the data transmission service 702.

[0252] The data provider unit 744 can be further configured to provide data (such as digital output product identifiers) included in the generated output product dataset to the asset publisher service 708.

[0253] Asset publisher service 708 can be configured to generate group access data associated with a corresponding output product dataset. The group access data can identify access control groups, which include decentralized participant identifiers associated with decentralized network participants granted access to the corresponding output product dataset. The group access data can include a group access data identifier. The group access data can further identify one or more authorization rules defining access to and / or use of the output product dataset. Asset publisher service 708 can be further configured to generate access control data including digital output product identifiers and group access data identifiers. Therefore, access control data can be associated with the output product dataset and the corresponding group access data. The access control data can further include access data associated with asset DB 704. The access data can include locators or pointers to asset DB 704, where the corresponding output product dataset is stored. This can allow access to the corresponding output product dataset based on data included in the access control data (e.g., digital output product identifiers and access data). The group access data and access control data can be generated by asset publisher service 708 for each piece of data included in the output product dataset (e.g., for each digital output product identifier). The generated group access data and access control data can be provided to the distributed data provider node 230 connected to the asset generation service 2606.

[0254] Distributed data provider node 230 can be part of a distributed network, such as in... Figure 1 and Figure 2The distributed network 134 is described in the context of the distributed data provider node 230. The distributed data provider node 230 can be configured to provide output product datasets in response to requests received from distributed data consumer nodes, for example, as in... Figure 12 Decentralized data provider node 230 can be configured to control access to the output artifact dataset based on associated group access data and access control data generated by asset publisher service 708, for example, as described in... Figure 12 As described in the context, distributed data provider node 230 can be associated with participants in the product ecosystem, such as producers of output products.

[0255] Data transmission service 702 can be configured to collect output product datasets from asset DB 704 in response to requests from distributed data provider node 230. Data transmission service 702 can be configured to retrieve asset metadata from asset DB 704 based on data received from distributed data provider node 230. Data transmission service 702 can be configured to parse the retrieved metadata to determine group access data associated with the corresponding output product dataset and / or the storage location of the corresponding output product dataset. Data transmission service 702 can be configured to collect the corresponding output product dataset from asset DB 704 based on the retrieved metadata. Data transmission service 702 can be configured to provide the collected output product datasets to distributed data provider node 230.

[0256] The system may exclude the storage of a digital representation associated with the identifier of the generated output product dataset and allow access to a decentralized registry of such output product datasets via identifiers and endpoints included in the digital representation. Therefore, instead of locating the output product dataset by querying the decentralized network 134 using the digital output product identifier, the output product dataset can be accessed directly from the decentralized data provider node 230 using only the digital output product identifier. Therefore, location data pointing to a dedicated storage device storing the output product dataset and the output product identifier may be required to access the corresponding output product dataset. Combining the location data with the digital output product identifier of the output product dataset can produce a unique identifier that allows unique identification of a given output product dataset within the decentralized network. The location data can be provided to the battery pass service 628 by the entity operating the asset generation service 2 606, for example, as in... Figure 13 Described in the context of.

[0257] Figure 7BA diagram illustrates an example of a system configured to collect output product data and transform at least a portion of the collected output product data using a rule-based engine. Transforming at least a portion of the collected output product data can result in the generation of an output product dataset, as shown in... Figure 7A The system can be configured to execute within the context described above. Figure 9 and Figure 10 The method described in the context.

[0258] Data transformation unit 724 may include rule-based engine 728. Rule-based engine 728 can operate on individual data points, multiple data points, and / or collected output product data. Rule-based engine 728 can operate individually on data collected for each output product identifier. This allows for transformation of the collected output product data for each output product, thus allowing for finer-grained transformation of the output product data. Rule-based engine 728 can receive requests to transform the collected output product data. These requests may include at least a portion of the collected output product data. Output product data may be collected by data collection unit 722 from data source layer 720 (see operation 726), as in... Figure 7A The rule-based engine 728 is described in the context of [the previous sentence]. It can be configured to determine an output product identifier associated with the output product data. For example, the rule-based engine 728 can parse received output product data to determine the corresponding output product identifier. The rule-based engine 728 can access one or more rules. The rule-based engine 728 can include one or more rules. One or more rules can exist within a rule template. One or more rules can be stored in a data storage device (such as rule DB 712). One or more rules or rule templates can be provided to rule DB 712 by a user. One or more rules can correspond to unstructured data associated with instructions associated with transformation operations. Rule templates can correspond to unstructured data associated with instructions associated with transformation operations. One or more rules or rule templates can be included in a file provided by a user. One or more rules or rule templates can be associated with an output product type identifier and / or an output product data identifier. The rule-based engine 728 can generate a request to obtain one or more rules from rule DB 712. This request can contain the corresponding output product identifier.

[0259] One or more rules (e.g., one or more applicable rules) associated with the output product data to be transformed can be provided to the rule-based engine 728 in response to a request. One or more rules can be associated with a product produced from the output product associated with the output product data to be transformed, for example, the output product associated with the output product data to be transformed can be used as input material to produce the product. The product can be a component. The product can be a component assembly. The product can be a final product. The product can be a chemical product. One or more rules can be associated with or derived from a semantic model of the product. Thus, one or more rules can ensure that data points of such output products required according to the semantic model can be included in the generated output product dataset. One or more rules can be defined by mandatory output product data points existing within the semantic model. One or more rules can be generated based on mandatory output product data points existing within the semantic model. This ensures that the output product data required by the semantic model is included in the generated output product dataset. One or more rules can be associated with output product identifiers and / or output product type identifiers. A mapping table can be used to map output product identifiers to corresponding output product type identifiers. Output product type identifiers can be associated with output product types. The mapping table can be stored in a separate database (not shown). The rule-based engine 728 can be configured to collect output product type identifiers based on the determined output product identifiers, and to request rules based on the collected output product type identifiers. This allows rules associated with output product type identifiers to be collected based on the output product identifiers, thereby avoiding the generation of rules for each output product identifier and reducing the number of rules that need to be generated, stored, and maintained in the rule DB 712.

[0260] One or more rules can define aggregation rules for aggregating output product data collected from multiple data sources into a given format. The given format can be a tabular representation. Therefore, aggregation can populate the collected output product data into a tabular format. One or more rules can define filters for filtering the collected output product data. Filters can be associated with or related to the semantic model of the product. This ensures that the output product data required by the semantic model is included in the generated output product dataset. One or more rules can define attribute construction for creating new attributes or adding new attributes to the collected output product data. One or more rules can define manipulations for transforming output product data points. For example, data points existing within the collected output product data can be transformed from one unit to another. This ensures that the generated output product dataset includes data points associated with the units required by the semantic model of the product. One or more rules can include triggering conditions and a corresponding set of one or more actions. Triggering conditions can involve a set of variables, sometimes called "working memory," which can contain data representing the state of relevant real-world items (sometimes called "facts" or "data tuples"). One or more actions can include attribute construction, filtering, and / or manipulation. A given output product identifier and / or output type identifier can be associated with the following: rules defining aggregations, rules defining filters, rules defining attribute constructions, rules defining manipulations, and / or rules including triggering conditions and actions. Rules can be associated with a given order.

[0261] The rule-based engine 728 can be configured to initialize the rule engine (see operation 732). Initialization of the rule engine may include generating rule data executable by a processor included in the rule-based engine 728. The rule data may include or correspond to executable logic. This allows rules or rule templates existing in unstructured data form to be transformed into code executable by the processor. Execution of the code may cause the executable rule data to be applied to collected output product data (see operation 734) to transform the output product data according to the obtained rules. Execution of the logic may cause the collected output product data to be matched against conditions included in the executable logic, the conditions to be evaluated with the matched output product data, and the execution of rule actions to be triggered based on the condition evaluation results. Transforming the output product data may include filtering the collected output product data. Filtering the collected output product data may include comparing individual data points present in the collected output product data with data points defined by the filter to determine output product data points to be included in the output product dataset. Transforming output product data may include comparing attributes of the collected or filtered output product data with attributes defined in rules to determine whether new attributes must be created or added to the collected or filtered output product data. Alternatively, transformation may include comparing attributes included in the collected or filtered output product data, or in output product data including added or created attributes, with attributes included in modification rules. Modification rules may include trigger conditions that can be associated with one or more actions, such as unit conversion if the trigger condition is met, and / or error handling if applying a filter produces empty data points due to missing data points in the collected output product data. Trigger conditions may be met if attributes included in the collected or filtered output product data, or in output product data including added or created attributes, do not match attributes defined in modification rules. Operations performed by the rule-based engine 728 may be determined by rules collected from rule DB 712. For example, rules collected from rule DB 712 can determine whether the rule-based engine 728 operates on individual data points and / or multiple data points and / or the entire collected output product data.

[0262] The rule-based engine 728 can be configured to determine whether the collected output product data can be transformed by one or more applied rules (see Operation 736). If the collected output product data does not include the data points required according to one or more obtained rules, the collected output product data may not be transformed or may only be partially transformed (e.g., transformation may result in at least one error associated with applying one or more obtained rules to the collected output product data). The rule-based engine 728 may provide the generated output product dataset to the asset DB 704 in response to successful transformation of the collected output product data (e.g., in response to generating an output product dataset by applying one or more obtained rules without generating an error).

[0263] If at least a portion of the collected output product data cannot be transformed, the rule-based engine 728 can proceed to operation 738. In operation 738, the rule-based engine 728 can generate message data indicating that at least a portion of the collected output product data cannot be transformed. The message data may include an indication of which data points(s) of the collected output product data cannot be transformed. The message data can be provided to a display device configured to display data received from the rule-based engine 728. The display device may include a graphical user interface 740. The display device may display the message in response to receiving the message data from the rule-based engine 728. This allows triggering correction or updating of data points identified as not matching one or more of the acquired rules. After correcting or updating the correspondingly identified data points, collection and transformation of the updated or corrected output product data can be initiated.

[0264] Figure 9 A flowchart illustrating an example method for generating data associated with battery production is provided. The battery can be... Figures 3A to 5 The battery described in the context of [the context]. Figure 9 The methods shown can be derived from Figures 7A to 8 The system shown is used for implementation. Data associated with battery production can include an output product dataset associated with the output products. The output products can correspond to the input materials used in battery production. The output products can be, for example... Figure 8 The production shown is for production.

[0265] refer to Figure 11 It can provide data associated with the output products. This data can be connected to the execution... Figure 9 The system of methods (e.g. in) Figures 7A to 8 The computational system (provided within the context of the asset generation service described in the original text) is responsible for this. Data can be provided by the user to the execution system via a communication interface. Figure 9The system uses methods such as asset generation services. Data can be provided to the data collection unit 722 of the asset generation service. The provided data can be generated in response to a received trigger signal, for example, as in... Figure 8 The data associated with the output product may include a digital output product identifier. The digital output product identifier may include the output product name, output product number, output product ID, serial number, identification number, LOT number, batch number, or a combination thereof.

[0266] Continue to refer to Figure 11 Output product data can be collected based on the provided data. This can be done from the data source layer (e.g., in...). Figure 7A and Figure 7B The data source layer 720, described in the context of [the previous sentence], collects output product data. Output product data can be collected from the data source layer 720 based on a numerical output product identifier included in the data provided in box 902. Output product data can be collected by the data collection unit 722. Output product data may include the physical and / or chemical properties of the produced output product and / or production data associated with the production of the output product, such as [the following information is missing from the original text]. Figure 7A Described in the context of.

[0267] Output product datasets can be generated by transforming the collected output product data using a rule-based engine, which includes one or more rules related to a data model associated with the battery pass for the product. Batteries can be produced from the output products by using the output products as input materials in at least one production step of a production chain used to produce batteries. The battery production process may include one or more production steps. These production steps may be performed by one or more entities within the battery ecosystem. The output products can be used as input materials in at least one of these production steps.

[0268] refer to Figure 7B , Figure 10 and Figure 11The output product data collected by the rule-based engine transformation may include one or more rules that identify the applicable output product data. The collected output product data can be transformed by the data transformation unit 724, which can identify applicable rules based on identifiers associated with each applicable rule that matches or relates to an output product identifier included in the collected output product data. The identifier associated with each applicable rule may include an output product identifier that matches an output product identifier included in the collected output product data. The identifier associated with each applicable rule may include an output product type identifier related to an output product identifier included in the collected output product data. The output product type identifier related to the output product identifier included in the collected output product data can be determined based on mapping data, such as in... Figure 7B Described in the context of.

[0269] Continue to refer to Figure 7B , Figure 10 and Figure 11 Executable logic can be generated from one or more applicable rules. The executable logic can be generated by the data transformation unit 724. The executable logic may include machine code, interpretable code, bytecode, and / or code running on a virtual machine. Logic included in the applicable rules can be encoded in the executable logic. Logic included in the applicable rules can be reflected in the executable logic. Upon completion of the generation of the executable logic, the data transformation unit 724 can send a response to the data collection unit 722 instructing the completion of the generation of the executable logic.

[0270] Continue to refer to Figure 7B , Figure 10 and Figure 11 The output product dataset can be generated by transforming the collected output product data through executable logic generated by a rule-based engine under the constraints of rule execution criteria. The data collection unit 722 can send a request to the data transformation unit 724 to transform the output product data based on executable logic generated for applicable rules. Rule execution criteria may include rule execution order, exemptions, and conditions. For example, a rule specified in the conditions is executed first because it triggers an execution or exemption action when executing its associated rules. The generated output product dataset can be provided from the data transformation unit 724 to the data provider unit 744.

[0271] It can be verified whether the collected output product data can be transformed according to one or more applicable rules (e.g., one or more rules collected from rule DB 712 based on the collected output product data). Verification can be performed as follows: Figure 7BThe method executes as described in the context. If the collected output data can be transformed according to applicable rules, for example, applying such rules does not result in any errors, then the method can proceed to box 912. Otherwise, message data can be generated and provided, for example, as in... Figure 7B Described in the context of.

[0272] The generated output dataset can be provided via a decentralized network (e.g., in...) under the control of the data owner of the output dataset. Figure 1 and Figure 2 Access is made through the distributed network 134 described in the context of [reference]. Figure 11 This can include storing the generated output dataset in a data storage device (such as Assets DB 704), for example, as in Figure 7A Described in the context of [the above]. See also [the relevant documentation / reference]. Figure 11 This can further include generating group access data and access control data, such as in Figure 7A Described in the context of [the relevant context]. Group access data and access control data can be used to control access to associated output product datasets, for example, as in [the relevant context]. Figure 12 Described in the context of.

[0273] Using rules associated with the data model linked to the Battery Pass allows for ensuring that the output product data points required according to the data model are included in the output product dataset, thus avoiding missing data points during the generation and / or updating of Battery Passes using the data model. This transformation allows for the aggregation of collected output product data into a given data format (such as a table) without using complex data models, thereby facilitating the generation and sharing of output product datasets within the product ecosystem. This sharing enables more efficient production and / or recycling processes based on shared output product data (e.g., output product composition data). The table format can be easily provided by Battery Pass services (e.g., in... Figure 6 and Figure 13 The Battery Pass service 628 described in the context of [context missing] is consumed via a distributed network. This Battery Pass service is configured to generate data integrity metrics based on consumed data and / or to generate and / or update Battery Passes. Verification performed on the consumer side ensures that the Battery Pass contains data defined by the data model associated with the Battery Pass, thereby allowing for reduced complexity associated with generating the output product dataset on the provider side by avoiding the use of complex data models during the generation of the output product dataset. This enables reliable sharing of output product data regardless of the existence of a data model for such output products, as the rules required to transform the collected output product data can be readily derived from the data model associated with the corresponding Battery Pass for batteries produced using such output products.

[0274] Figure 12 An example of a decentralized system for accessing data associated with battery production is shown. The battery can be located in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A distributed system can be like [the previous sentence]. Figure 1 and Figure 2 The decentralized peer-to-peer network 134 is described in the context of this. Data associated with battery production may include input material data associated with input materials used to produce the battery and battery data associated with the battery. Input materials may be used within production steps of the production chain used to produce the battery. Input materials may be included in Figure 8 The output product described in the context of .

[0275] Input materials can be associated with input material data. Input material data can be, for example, in... Figures 7A to 8 It is generated as described in the context. Batteries can be associated with battery data. Battery data can be generated as described in... Figures 7A to 8 It is generated as described in the context.

[0276] Input material data or battery data can be stored in an asset database 704 associated with data transmission service 702. Asset database 704 can be associated with distributed provider node 230. Distributed provider node can be associated with distributed network participants. Distributed network participants can be producers of input materials or batteries. Distributed network participants can be data owners of the input material or battery data stored in asset database 704. The input material data or battery data can each represent at least a portion of a digital twin of the physical entity of the input material or battery. The digital twin can be a digital representation of the physical entity. Therefore, a digital twin can represent a digital version of the physical entity. Once created, the digital twin can be used to represent the physical entity of the input material or battery in a digital representation of a real-world system.

[0277] Distributed systems can be used to collect input material data associated with the input materials used in battery production, as well as battery data, such as in... Figure 19A and Figure 23 Described in the context of.

[0278] A distributed system can include one or more distributed participants, such as data consumers and data providers. Data consumers and data providers can communicate via distributed networks (such as distributed peer-to-peer networks

[134] (see example)). Figure 1 and Figure 2The connection is as follows: A data provider can offer data to a data consumer, such as input material data or battery data. A data consumer can request input material data or battery data from a data provider. The data provider can correspond to the entity that produces the corresponding input material or battery. The data consumer can correspond to the entity that operates the battery pass service, such as... Figure 6 and Figure 13 As shown, a data provider can be associated with data provider environment 1208. A data consumer can be associated with data consumer environment 1202. These environments can include one or more nodes. Nodes can be connected via peer-to-peer communication channels to allow data transfer between nodes, as shown in... Figure 1 and Figure 2 Decentralized networks can include more than [the context described]. Figure 12 The environment shown is more or less.

[0279] Participants in a decentralized network can be associated with decentralized participant identifiers. Each participant in a decentralized network can be associated with one or more decentralized participant identifiers. Decentralized participant identifiers can include any identifier that uniquely associates with a participant in the decentralized network and / or with a production site of a participant in the decentralized network. Decentralized participant identifiers can include letters and / or numbers. Decentralized participant identifiers can include one or more Universally Unique Identifiers (UUIDs) and / or one or more Distributed Identifiers (DIDs). Decentralized participant identifiers can be associated with or include verifiable claims or credentials. Verifiable claims can be issued by a centralized or decentralized identity issuing authority that makes one or more claims to a subject, such as an entity that is a trusted participant in the decentralized network. For example, an issuing authority can make claims about consumers (e.g., entities operating data-consuming network nodes) or providers (e.g., entities operating data-providing network nodes) associated with a decentralized participant identifier. Verifiable claims can include these claims along with proof instructions demonstrating that the claims have not been tampered with and were indeed issued by the claim issuing authority. Verifiable claims may also include duration information metadata, which defines the valid period of use of the verifiable claim or the specific number of times the verifiable claim is authorized for use. Verifiable claims may also include the DID of the claim issuing authority and / or subject (such as an entity). Verifiable claims may be signed by the claim issuing authority. The claim issuing authority may provide verifiable claims to claim holders (such as entities) to present them to any dependent parties that rely on the authenticity of these claims, such as decentralized data providers. The signature of the verifiable claim can be verified using the public key associated with the claim issuing authority to determine that the corresponding entity is a trusted entity within the decentralized network. Verifiable credentials may be presented by decentralized data consuming network nodes and may be used by decentralized data providing network nodes to verify that the decentralized participant associated with the decentralized data consuming network node is a trusted entity within the decentralized network before providing access to data (such as output product data), thereby ensuring that input material data and / or battery data can be exchanged securely and in a controlled manner within the decentralized network.

[0280] The data consumer environment 1202 may include a consumer node 632 (e.g., a distributed data consumption network node 632) and a battery pass service 628. The battery pass service 628 may include or correspond to... Figure 6 and Figure 13The services shown are as follows: Consumer node 632 can be configured to communicate with Battery Passport Service 628. Consumer node 632 can be configured to receive data from Battery Passport Service 628 and provide data to Battery Passport Service 628. Consumer node 632 can be configured to receive data from provider nodes (such as provider node 230). Consumer node 632 can be configured to request data from provider nodes (such as provider node 230). Consumer node 632 can be configured to receive data from Battery Passport Service 628, provide data to Battery Passport Service 628, and receive and / or request data from provider nodes (such as provider node 230). Consumer node 632 can be configured to request input material data or battery data stored in Asset DB 704 associated with the corresponding provider node (such as provider node 230). Battery Passport Service 628 can be configured to send data requests to consumer node 632, for example, as in... Figures 19A to 23 The context described above. Battery pass service 628 can be configured to process data received from consumer node 632 (such as input material data and battery data), for example, as in... Figures 19A to 23 Described in the context of.

[0281] Data consumer environment 1202 can be integrated with the battery ecosystem (e.g. Figure 1 and Figure 2 The data consumer environment 1202 can be associated with participants in the battery ecosystem shown in the diagram. For example, the data consumer environment 1202 can be associated with input material producers (such as chemical product producers 102) or battery producers (such as chemical product users 106) who receive input materials and use them to produce batteries, or with battery users (such as end-product producers 108) who produce end products containing batteries.

[0282] Data consumer environment 1202 can be associated with the entity operating battery pass service 628. Data consumer environment 1202 can be associated with the entity executing... Figure 18 and / or Figures 20 to 23 and / or Figure 25 and Figure 26 The entities associated with the presented method are as follows: The entity implementing this method can be a participant in the battery ecosystem. Alternatively, the entity implementing this method may not be a participant in the battery ecosystem, but rather a service provider that can act as one. The service provider can generate and provide data integrity metrics. The service provider can generate and / or update battery passes based on data collected from a data provider environment associated with the input material data and battery data.

[0283] The data provider environment 1208 may include a provider node 230, a data transmission service 702, and an asset database 704. The data provider environment 1208 may include an asset generation system, such as... Figure 7A , Figure 7B and Figure 8 The asset generation service 2606 is shown. The data provider environment 1208 can be associated with a data owner (such as an input material producer or battery producer). The data provider environment 1208 may include an asset DB 704 storing input material data for battery production or battery data associated with the battery. The asset DB 704 may be a dedicated storage device associated with the data owner. The data owner may own such a dedicated storage device. The data owner may access such a dedicated storage device. The data owner may control access to such a storage device. The provider node 230 can be configured to provide data, such as input material data or battery data stored in the asset DB 704. The provider node 230 can be configured to perform authentication and authorization steps before providing data, for example, as described later. Figure 19A The provider node 230 may be coupled to a data transmission service 702, which is configured to collect input material data or battery data from the asset DB 704 in response to a request received from the provider node 230, and to provide the collected data to the provider node 230.

[0284] Figure 13 An example of a Battery Password service configured to verify the integrity of data accessible for generating and / or updating battery-associated passes is shown. The Battery Password service can be further configured to generate and / or update battery-associated passes. The Battery Password service can be configured to perform... Figure 18 as well as Figures 20 to 22 The method shown. The Battery Passport service can be further configured to perform... Figure 25 and Figure 26 The method shown. The battery can be in Figures 3A to 5 The battery described in the context of [the context].

[0285] Battery Passport Service 628 can include different modules or units. Figure 13 In the illustrated embodiment, the battery pass service 628 includes an asset registration and association module 1304, an asset verification module 1306, a data quality analyzer 1308, and a battery pass generator 1310. The battery pass service 628 may further include one or more databases, such as a registry 1312. At least some of these modules or units may be configured to communicate with each other. Using a modular service allows for flexible adjustment of these modules or units, for example, by adding, removing, or modifying individual modules or units.

[0286] Battery Password Service 628 and one or more modules included therein can be accessed via a communication interface. Access to Battery Password Service 628 may involve authentication and / or authorization steps. For example, a user may provide authentication data and may only be granted access to Battery Password Service 628 upon successful authentication. Access to modules included in Battery Password Service 628 may require successful authorization. For example, some modules may only be accessible to defined user groups. For instance, only the user group defined as Battery Password Generator can access Battery Password Generator 1310.

[0287] The asset registration and association module 1304 can be configured to provide an interface (such as a digital user interface or a computing interface) for providing asset data and associated data source data related to the data source providing such asset data. The data source can be provider nodes, which are configured to provide data (such as input material data and battery data) stored in dedicated storage devices associated with such provider nodes, for example, as in... Figure 12 The context described herein. Data source data may include endpoints associated with such a data source. Data source data may further include decentralized participant identifiers associated with the data source, and / or participant roles associated with or controlling the data source and related to participants in the battery ecosystem. Asset data may include asset identifiers associated with assets provided by such a data source (e.g., input material data or battery data). Asset data may further include relationship representations specifying the relationship between asset identifiers (e.g., parent identifiers) associated with asset data and additional asset identifiers (e.g., child identifiers) associated with assets used to produce intermediate products or input materials of the battery associated with the parent identifier, such as... Figure 14B What is shown.

[0288] The asset registration and association module 1304 can be further configured to store data received via the interface in the registry 1312. Before storage, the received data can be processed. For example, the provided data can be transformed, for instance, through aggregation, cross-referencing, etc. (See reference) Figure 14AData acquired by the asset registration and association module 1304 can be stored in registry 1312. Registry 1312 can store asset identifiers associated with assets (such as input material data and battery data) provided by data sources associated with participants in the battery ecosystem. Asset identifiers can be associated with participant roles of participants associated with the data source providing the asset, decentralized participant identifiers associated with the data source providing the asset, and endpoints of the data source providing the asset. Asset identifiers can be further associated with sub-identifiers associated with input materials used to produce the output product associated with the asset. Since these associations may not apply to all input materials used in battery production, [further details are needed]. Figure 14A The dashed lines in the diagram represent the lines connecting asset IDs and sub-IDs. Output products can correspond to the input materials used in battery production.

[0289] To reliably determine the integrity of the data accessible for generating and / or updating battery credentials, an identifier associated with the input materials used to produce the battery can be associated with or related to an output product identifier associated with or related to the output product produced from that input material. The output product can be an intermediate product used in battery production or the battery itself. Thus, an asset identifier associated with the input materials used to produce a given battery can be determined based on the battery identifier associated with that battery. (Reference) Figure 14B Register 1312 may include associating asset identifiers with battery identifiers for all input materials used in battery production (see [link to register 1312]). Figure 14B (Left side). Alternatively, registry 1312 may include associating asset identifiers with child identifiers in a tree structure, where the battery identifier is the top node of the tree (see left side). Figure 14B (on the right side). Figure 14B The relationships shown can be generated from asset data included in such a registry by parsing the relationships between asset identifiers included in registry 1312.

[0290] refer to Figure 14CAsset data and associated data source data can be provided as entries to a digital user interface, such as a graphical user interface 1408. In another embodiment (not shown), data source data and associated asset data can be provided as entries to a computing interface. The graphical user interface 1408 may include a field 1412 for entering an asset ID and a field 1418 for entering data source data associated with the asset ID. Field 1418 may include a list of data sources and may allow the user to select the appropriate data source for providing the asset associated with the asset ID. The graphical user interface 1408 may further include one or more data fields for entering a sub-identifier of the asset ID entered in field 1412. The graphical user interface 1408 may provide an option to upload such a sub-identifier using button 1420. Storage of data entered into the graphical user interface 1408 in the registry 1312 can be triggered using button 1424. Based on entries generated in the digital user interface or data provided to the computing interface, data associated with the data source can be generated and stored or persistently stored in the registry 1312.

[0291] In an alternative embodiment, the interface may include a communication interface configured to receive data source data and associated asset data. For example, the data source data and associated asset data may be provided to the asset registration and association module 1304 via such an interface.

[0292] The asset verification module 1306 can be configured to collect data related to battery production from one or more data sources (such as provider nodes) and verify the collected data, for example, as in Figures 19A to 23 As described in the context, the asset verification module 1306 can be configured to persistently store verified data in one or more databases. The asset verification module 1306 can also be configured to provide verified data to the data quality analyzer 1308 and / or the battery pass generator 1310.

[0293] Data quality analyzer 1308 can be configured to determine the amount of data accessible for generating and / or updating battery credentials (such as input material data associated with input materials used to produce the battery and / or battery data associated with the battery). Data quality analyzer 1308 may include a metric generator 1504 configured to generate data integrity metrics, for example, as in... Figure 15 The data quality analyzer 1308 can be configured to perform [the task] as described in the context of [the previous sentence]. Figure 18 The method shown. The data quality analyzer 1308 can be configured to generate data integrity metrics, such as... Figures 17A to 17CAs shown, the data quality analyzer 1308 can be configured to provide the generated data integrity metrics to the battery pass generator 1310. By confirming the quality of the data in terms of the degree of relevance between the data included in the data model associated with the battery pass and the data stored in the registry 1312 or the confirmed data generated by the asset confirmation module 1306, transparency regarding the number of data points accessible for generating and / or updating battery passes can be achieved. This transparency allows for determining the reliability of pass data included in the generated and / or updated battery passes and allows for the use of defined integrity thresholds to control the generation and / or updating of battery passes. Furthermore, this transparency allows for the identification of input material producers who have not yet generated and / or provided access to input material data defined by the data model. In this way, missing data can be requested, and it can be ensured that the pass data has a sufficient degree of integrity to facilitate efficient further processing of the batteries associated with such battery passes.

[0294] The battery pass generator 1310 can be configured to generate battery passes associated with batteries and / or update existing battery passes based on data integrity metrics received from the data quality analyzer 1308 and data related to battery production. The battery pass generator 1310 can be configured to perform... Figure 25 and / or Figure 26 The method shown. The battery pass generator 1310 can be configured to persistently store the generated battery passes in a file. The battery pass generator 1310 can be configured to provide the generated battery passes for data consumers to access. The generated battery passes can be provided via a distributed network, such as... Figure 1 and Figure 2 As shown, the generated battery pass can be provided under the control of the battery pass's data owner.

[0295] Figure 15 Showing Figure 13 An example of a data quality analyzer is shown. Data quality analyzer 1308 may include a metric generator 1504. Metric generator 1504 may be communicatively coupled to registry 1312 and asset verification module 1306. In one embodiment, metric generator 1504 may further communicate with a rules database 1508 that stores one or more verification rules configured to verify the integrity of accessible data used to generate and / or update corresponding battery passes based on data related to the data model associated with the battery pass.

[0296] refer to Figure 16The metric generator 1504 may include a rule-based engine 1620. The rule-based engine can be another rule-based engine. The rule-based engine 1620 may be included in a rule-based engine 728 (see [link to relevant documentation]). Figure 7B The rule-based engine 1620 can operate on individual data points, multiple data points, and / or the entire dataset (such as verified data provided by the asset verification module 1306 or data stored in the registry 1312). The rule-based engine 1620 can operate individually on verified data or data stored in the registry 1312 for each digital battery identifier. This allows for determining the degree of integrity of the data accessible for generating and / or updating a given battery pass, thereby achieving transparency regarding the data accessible for generating and / or updating a given battery pass at a more granular level (e.g., at the battery pass level). The rule-based engine 1620 can receive requests to verify the quality of the data accessible for generating and / or updating battery passes. These requests may include battery-related data, such as battery identifiers. Battery identifiers may include a unique battery ID. Battery identifiers may further include a battery type identifier that uniquely identifies the battery type or battery category. The rule-based engine 1620 can access one or more sets of verification rules that include one or more verification rules. The rule-based engine 1620 may include one or more sets of confirmation rules containing one or more confirmation rules. The one or more sets of confirmation rules may be stored in a data storage device (such as rule DB 1508). The one or more rules may correspond to unstructured data associated with instructions related to the confirmation operation. The confirmation rule sets may correspond to unstructured data associated with instructions related to the confirmation operation. One or more sets of confirmation rules and / or confirmation rules may be associated with a battery type identifier and / or a battery identifier. The rule-based engine 1620 may be configured to generate a request for obtaining at least one set of confirmation rules from rule DB 1508. This request may include the corresponding battery identifier.

[0297] In response to a request, one or more sets of verification rules can be provided to the rule-based engine 1620. The sets of verification rules can include rules configured to verify the integrity of data used to generate and / or update the corresponding battery pass based on data associated with the data model linked to the battery pass. Verification rules can relate to data associated with the data model. Verification rules can relate to input material producers that produce input materials associated with input material data defined by the data model. Verification rules can relate to one or more data points defined by the data model. Verification rules can relate to one or more data points, as defined by the data model, associated with input materials used to produce batteries and the batteries themselves. Verification rules can relate to one or more data points to be provided, according to the data model, for access by each participant in the battery ecosystem involved in battery production. Verification rules can be configured to determine input material identifiers and associated accessible data points based on data associated with the battery, optionally determining accessible data points associated with the battery based on data associated with the battery, and determining the degree of relevance by comparing target data points defined by the data model with accessible data points. Verification rules can be configured to determine the input material identifier associated with the battery identifier, determine the verified data associated with the determined input material identifier and optionally the battery identifier, and determine the degree of relevance by comparing the target data point defined by the data model with the data points included in the verified data. One or more verification rules can be configured to generate a data integrity metric for each battery identifier.

[0298] The rule-based engine 1620 can be configured to select at least one confirmation rule based on data associated with the battery. This selection, based on the data associated with the battery and the confirmation rule pair, can be dynamic and / or static. For example, the data associated with the battery (such as a battery type identifier) ​​and the confirmation rule pair can be predefined. One or more confirmation rules can be associated with a battery identifier and / or a battery type identifier. A mapping table can be used to map battery identifiers to corresponding battery type identifiers associated with the confirmation rules. The battery type identifier can be associated with a battery type. The mapping table can be stored in a separate database (not shown). In this way, the confirmation rule set can include confirmation rules associated with different battery type identifiers, thereby avoiding the generation of different confirmation rule sets for each battery type or each battery, and allowing for a reduction in the number of confirmation rule sets that need to be generated, stored, and maintained in the rule DB 1508.

[0299] The rule-based engine 1620 can be configured to initialize a rule engine (see operation 1624). Initialization of the rule engine may include generating rule data that can be executed by a processor included in the rule-based engine 1620, for example, as in... Figure 18 The context described herein. Rule data may include or correspond to executable logic. This allows verification rules, existing in the form of unstructured data, to be transformed into code that can be executed by a processor. Execution of the code may enable the application of executable rule data to data stored in registry 1312 or to verified data provided by asset verification module 1306 (see operation 1626) to verify access to data used for generating and / or updating battery-related passes according to the obtained rules. Execution of the logic may enable the matching of data stored in registry 1312 or verified data provided by asset verification module 1306 with conditions included in the executable logic, evaluating these conditions using the matched data, and triggering the execution of rule actions based on the condition evaluation results. Data verification may include determining input material identifiers and associated accessible data points based on data associated with the battery, optionally determining accessible data points associated with the battery based on data associated with the battery, and determining the degree of relevance by comparing target data points defined by the data model with the accessible data points. Verification rules can be configured to determine the input material identifier associated with the battery identifier, determine the verified data associated with the determined input material identifier and optionally the battery identifier, and determine the degree of relevance by comparing the target data point defined by the data model with the data points included in the verified data. The operations performed by the rule-based engine 1620 can be determined by the set of verification rules collected from rule DB 1508 and the included verification rules. For example, rules collected from rule DB 1508 can determine whether the rule-based engine 1620 operates on individual data points and / or multiple data points and / or the entire dataset.

[0300] The rule-based engine 1620 can be configured to provide confirmation results to the metric generator 1504. The metric generator 1504 can be configured to generate data integrity metrics based on the confirmation results received from the rule-based engine 1620. The metric generator 1504 can be configured to provide the generated data integrity metrics. Providing may include providing the metrics for display. Providing may include providing the metrics to a data storage device. Providing may include providing the metrics, for example, to another module (such as the battery pass generator 1310) of the battery pass service 628 via a communication interface.

[0301] refer to Figure 17AGenerating a data integrity metric may include determining the overall or total relevance between accessible data points and target data points based on confirmation results. The relevance may correspond to the proportion of accessible data points matching the target data point relative to the total number of target data points. The relevance may be determined for each battery identifier. The generated data integrity metric 1710 may include data associated with the battery (such as the battery identifier) ​​and the overall relevance of accessible data points associated with this data. The overall relevance may be displayed as an integrity bar 1708 within a graphical user interface. This metric provides an overview of the overall relevance (e.g., the total number of accessible and inaccessible data points, such as missing data points) and may allow for transparency regarding the overall quality of the pass data included in the battery pass generated from this accessible data.

[0302] In another embodiment and reference Figures 17B to 17C Generating data integrity metrics can include determining the relevance of accessible data points to target data points for at least a portion of the participants in the battery ecosystem involved in battery production (e.g., at least a portion of the participants in the battery production chain). The degree of integrity can be determined by comparing the target data points for each participant role with the accessible data points for each corresponding participant role. In one example and reference... Figure 17B The generated data integrity metric 1710 may include a battery identifier, available sub-identifiers associated with the battery identifier (e.g., an input material identifier associated with the input materials used to produce the battery), participant roles associated with such identifiers, and the degree of integrity of each participant role. Figure 17B The generated data integrity metric 1710 shown can be displayed within a graphical user interface. See another example and reference... Figure 17C The generated data integrity metrics can include information about Figure 17B The data mentioned, along with additional data points such as whether data points associated with input materials used in battery production are accessible, the number of accessible data points, and the number of target data points for each participant role, can be used to determine the overall correlation between the number of accessible data points and the number of target data points. Figure 17C The data integrity metric 1710 can be displayed within the graphical user interface. Additionally, the overall relevance level can be displayed as an integrity bar 1708 within the graphical user interface.

[0303] Figure 17B and Figure 17CThe presented metrics not only provide transparency regarding the completeness of the data points accessible for generating and / or updating battery passes, but also allow for the identification of participants in the battery ecosystem who have not yet generated input material data and / or battery data and / or have already provided access to such data. This transparency allows for requesting data from such participants to improve the quality (e.g., completeness) of the pass data included in the generated and / or updated battery passes.

[0304] Figure 18 A flowchart illustrating an example of a method for verifying the integrity of data accessible for generating and / or updating battery passes associated with a battery. The integrity of the data can be verified by the accessibility of this data used to generate battery passes and / or to update existing battery passes. A battery can be, for example, in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A battery can be produced from one or more input materials via a production chain. The production chain may include, for example, [the following text is missing from the original] Figure 6 One or more production steps described in the context of [the context]. Figure 18 The method shown can be derived from... Figure 6 and Figure 13 The Battery Pass service 628 is implemented in the context described in the document.

[0305] Data associated with the battery can be provided. This data may include at least one digital battery identifier associated with the battery. The digital battery identifier may include a batch number, LOT number, serial number, identification number, battery name, battery type identifier, or a combination thereof. The battery identifier may be provided from a code reader configured to read the physical code connected to the battery and determine the digital battery identifier encoded within the code. The battery identifier may also be provided from a device configured to acquire image data and determine the battery identifier based on the acquired image data.

[0306] Data associated with a data source can be provided, indicating data accessible for generating and / or updating battery passes. Data associated with a data source may include asset data provided by the data source and associated data from related data sources. A data source can be, for example, in... Figure 6 and Figure 13The provider node of the decentralized network described in the context of the data source. Data source data may include endpoints associated with such a data source. Data source data may further include decentralized participant identifiers associated with the data source, and / or participant roles associated with and related to the data source, which are also associated with participants in the production chain used to produce batteries. Asset data may include asset identifiers associated with assets provided by such a data source. Asset data may further include relational representations specifying the relationship between asset identifiers (e.g., parent identifiers) associated with asset data and additional asset identifiers (e.g., child identifiers) associated with assets used to produce intermediate products or input materials for batteries associated with the parent identifier. Data associated with a data source can be generated by associating asset identifiers associated with a given asset with endpoints of the data source, participant roles associated with the data source, decentralized participant identifiers, and optionally child identifiers associated with input material assets for producing intermediate products or asset identifiers for batteries, for example, as in Figure 14A Described in the context of [the document / context]. References Figure 13 Data associated with a data source and indicating accessible data can be provided from a storage device (such as registry 1312). Registry 1312 can store this data associated with the data source. Data associated with the data source can be, for example, in... Figure 13 and Figure 14C Generate and store as described in the context.

[0307] Data related to the data model associated with the battery can be collected based on the provided battery-related data. Data related to the data model can include participant roles of stakeholders involved in battery production and the number of data points provided by these participant roles according to the data model. Data related to the data model can further include digital battery identifiers, such as battery type identifiers. Thus, data related to the data model can be associated with the battery or battery type. Participant roles can include... Figure 1 and Figure 2The roles shown include, for example, miner 214, refiner 216, cell producer, and battery producer 220. Participants associated with battery production can include those performing at least one production step in the production chain used to produce the battery. The number of data points to be provided by such participant roles according to the data model can be determined by assigning participant roles to at least a portion of the data points defined by the data model and accumulating the data points for each assigned participant role. Data points defined by the data model can be assigned to such participant roles based on the characteristics of intermediate products or batteries produced by the participant roles. For example, data points defining battery characteristics within the data model can be assigned to the participant role "Battery Producer." Similarly, data points defining the composition of cathode active materials can be assigned to the participant role "Cathode Active Material (CAM) Producer." Data related to the data model can be included in one or more rules. Therefore, collecting data related to the data model can include collecting one or more rules that are associated with, related to, or include such data.

[0308] Data integrity metrics associated with the data accessible for generating and / or updating battery passes can be determined by assessing the degree of relevance between the data associated with the data source and the data associated with the data model. The degree of relevance can indicate the number of data points accessible based on the data associated with the data source, relative to the number of data points defined by the data model and included in the data associated with the data model. Data integrity metrics can be as follows: Figures 17A to 17C The data integrity metric described in the context of [the data integrity metric].

[0309] Data integrity metrics are generated by determining the degree of correlation between data associated with a data source and data associated with a data model. The degree of correlation can be determined in the following ways:

[0310] • Determine the sub-identifiers to be included in the data associated with the data source based on the provided battery-related data.

[0311] • The number of accessible data points is determined by matching the identified sub-identifiers and, optionally, the battery identifiers included in the data associated with the battery, with the identifiers included in the data associated with the data source.

[0312] • The degree of relevance is determined by comparing the number of accessible data points with the number of data points defined in the data related to the data model.

[0313] The degree of relevance can be determined by executing the generated executable logic on the provided data source-related data under the constraints of rule execution criteria. Rule logic can be generated by a rule-based engine (e.g., in...). Figure 16The rule-based engine (1620) described in the context of [the relevant context] generates and executes rules. Rule execution criteria may include rule execution order, exemptions, and conditions. For example, a rule specified in a condition is executed first because it triggers an execution or exemption action when the rule associated with it is executed. Executable logic can be generated from one or more rules configured to verify the integrity of data (e.g., accessible data) used to generate and / or update battery passes. One or more rules may include [the relevant context]. Figure 16 One or more validation rules are described in the context of the data source. One or more rules can be applied to data associated with the data source. One or more rules can be configured to perform the following operations:

[0314] • Determine the sub-identifiers to be included in the data associated with the data source based on the provided battery-related data.

[0315] • The number of accessible data points is determined by matching the identified sub-identifiers and, optionally, the battery identifier included in the data associated with the battery, with the identifiers included in the data associated with the data source.

[0316] • The degree of relevance is determined by comparing the number of accessible data points with the number of data points defined in the data related to the data model.

[0317] Rule-based engines can operate on individual data points existing within the data associated with a data source. Rule-based engines can also operate on combinations of data points existing within the data associated with a data source. Furthermore, rule-based engines can operate on both the complete data associated with the data source and the complete data associated with the data model.

[0318] The generated data integrity metrics may include, for example, in Figures 17A to 17C The data integrity metrics shown indicate the degree of relevance between accessible data and data defined by the data model associated with the Battery Pass. Specifically, data integrity metrics indicate the degree of relevance between data points defined by the data model (e.g., target data points) and data points included in the confirmed data. A higher degree of relevance indicates a greater number of data points included in the data associated with the data source that match or map to the target data points defined by the data model. Therefore, the degree of relevance indicates the completeness of the data provided by the data provider for generating and / or updating the Battery Pass relative to the data points defined by the data model.

[0319] Return to Figure 18The generated data integrity metric can be provided. Providing the generated data integrity metric may include providing the generated data integrity metric for display. Providing the generated data integrity metric for display may include generating a graphical interface rendering that includes instructions configured to allow the display of a graphical representation of the generated data integrity metric. The graphical representation may include an integrity bar that indicates the degree of integrity (e.g., the amount of accessible data compared to the data defined by the data model), such as... Figure 17A and Figure 17C The graphical representation shown may include, for example: Figures 17A to 17C The table shown. Graphical representations may include tabular format and integrity bars.

[0320] Providing the generated data integrity metrics may include providing the generated data integrity metrics via a computing interface. The data integrity metrics may be provided to a storage device used to store the data integrity metrics. Data integrity metrics may be associated with a battery identifier to allow associating the data integrity metrics with a battery. If data integrity metrics (including those associated with the battery identifier) ​​are provided to the storage device, the data integrity metrics stored in the storage device can be updated. Storing the generated data integrity metrics allows for the collection of such metrics for later use, such as when initiating the generation of a battery pass.

[0321] It is possible to generate and provide a request indicating missing data based on a generated data integrity metric. Missing data based on the data integrity metric can include data points defined according to the data model, provided by a data source but not included in the data associated with that data source. Therefore, missing data can include data points that should be provided by the data source but are not included in the provided data associated with that data source. Initiating a request can include determining:

[0322] • Provide a battery identifier and input material identifiers associated with the input materials used to produce the battery.

[0323] • Determine the data source associated with the input material identifier based on the provided data associated with the data source.

[0324] • Identify the data source associated with the input material identifier, where no data source is available for a sub-identifier of this input material identifier.

[0325] • Generate a request to provide the missing data and provide it to the identified data source.

[0326] The request may include a sub-identifier (e.g., an identifier for input materials used by a participant associated with the identified data source to produce intermediate batteries) and an indication of the data required from the upstream participant associated with the identified data source. The request can be provided to such participant using data source data associated with and included in the data associated with the data source. In response to receiving such a request, the participant can generate a request to the upstream participant for the provision of the missing data. This allows for requesting missing data from upstream participants without making the production chain (specifically, the identities of the participants in the production chain) transparent. In this way, requesting and receiving missing data can be ensured without disclosing confidential information (such as the identities of the participants in the production chain) to the entities that generate data integrity metrics and / or the entities that generate battery passes.

[0327] By verifying the integrity of the data accessible for generating and / or updating battery tokens associated with batteries, the completeness of the token data included in the battery token, and therefore the reliability of this token data, becomes transparent to the battery token generator and / or battery token users. Determining the completeness level based on data associated with the data source allows for efficient transparency regarding the available data used to generate and / or update battery tokens, without having to collect any data related to battery production via a decentralized network. Therefore, the completeness level can be efficiently determined without involving data traffic via a decentralized network, thereby reducing overall data transfer rates and improving the stability of the decentralized network. This transparency allows battery tokens to be generated or updated based on a given or predefined completeness level, ensuring that the token data included in the battery token has sufficient completeness and reliability. This ensures that the generated and / or updated battery tokens include the token data necessary to ensure efficient further processing of the battery (such as efficient recycling of end-of-life batteries or their components). Efficient further processing can reduce the environmental impact of the battery ecosystem and / or improve the circularity of materials and byproducts used within the battery ecosystem. Transparency further allows for the identification of participants in the battery production chain who have not yet generated data to be included in the battery pass according to the data model associated with it, and / or who have not yet provided access to that data. In this way, participants can be requested to generate such data and / or provide access to it, ensuring a high degree of completeness in the generated and / or updated battery passes.

[0328] By generating a request indicating missing data and providing it to participants in the production chain, missing data can be requested without requiring complete transparency about the production chain. Instead, transparency is only needed regarding the data source and associated participants with whom data has not yet been provided through identifier relationships, without needing to know every individual participant in the production chain. This ensures that missing data can be requested reliably without requiring full knowledge of the production chain, thereby increasing the credibility of the entity that generates battery passes based on data integrity metrics.

[0329] Figure 19A A block diagram of an example system for verifying data associated with battery production is shown. The battery can be, for example, in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A battery can be produced from one or more input materials via a production chain. Input materials may correspond to output products or intermediate products produced by participants in the production chain. The production chain may include, as in [the previous sentence]... Figure 6 The data may be one or more production steps described in the context of the battery production process. Data associated with battery production may include input material data (e.g., input material assets) associated with the input materials used to produce the battery. Data associated with battery production may further include battery data (e.g., battery assets) associated with the battery. Figure 19A The system shown can be configured to execute Figures 20 to 23 The method shown.

[0330] The asset verification system 1942 may include a data transmission service 1902, a streaming storage system 1912, and a data transformation unit 1904. Various databases (such as rule DB 1906, general archive 1948, and archive DPP product type A 1950) can be connected to the data transformation unit 1904.

[0331] Verification of data related to battery production can be initiated by a request to the Asset Verification System 1942. (Reference) Figure 23 This request can be received at data transfer service 1902. The request may include asset identifiers and associated data source data. Further reference... Figure 6 The request can be generated in response to receiving an instruction from a user confirming the integrity of the data used to generate and / or update a battery pass associated with the battery, or upon receiving an instruction from the user to generate a battery pass for the battery. The user instruction can trigger the collection of asset identifiers and associated data sources from registry 1312 based on the battery identifier included in the user instruction. The collected data can be used to generate a request to asset verification system 1942.

[0332] The asset verification system 1942 can connect to distributed data consumption nodes, such as consumer node 632. Distributed data consumption nodes can be part of a distributed network (e.g., in...). Figure 1 and Figure 2 This is part of the decentralized network 134 described in the context of [the previous sentence]. The decentralized data consumer node 632 can be associated with a decentralized participant. A decentralized participant can be a participant in a battery ecosystem associated with the battery. A decentralized participant may not be a participant in the battery ecosystem; for example, it could be a service provider that generates battery tokens on behalf of participants in the battery ecosystem. [Reference] Figure 12 The distributed data consumer node 632 can be configured to request access to data associated with a provider node related to battery production. This request may include an identifier associated with the corresponding data (e.g., an asset) and a distributed participant identifier associated with the distributed participant operating the consumer node 632. The request can be generated in response to data received from the system (e.g., data received from data transmission service 1902). The data received from the system may include an asset identifier and associated access data pointing to a distributed provider node (e.g., provider nodes 228 to 232). The access data may include an endpoint associated with the distributed provider node, such as a URI. A request can be generated for each asset identifier and associated access data. Authentication can be performed on requests received by the corresponding provider node. This authentication may be based on data related to an authentication mechanism. The authentication mechanism can be based on certificates and / or tokens associated with the corresponding distributed participant nodes (e.g., consumer node 632 and provider nodes (e.g., nodes 228 to 232)), such as device certificates (X.509v3), TLS connection certificates (X.509v3), and 'dynamic attribute tokens' (OAuth access tokens). If authentication fails, the corresponding data provider may not provide the output artifact dataset.

[0333] The provider node receiving the request (e.g., nodes 228 to 232) can initiate a data transaction according to the transaction protocol with consumer node 632. The provider node can provide a dataset including one or more data usage rules associated with a distributed identifier. The dataset can be provided to asset verification system 1942. Asset verification system 1942 can parse the received dataset to determine the data usage rules. The determined rules can be provided to the user for consent. Asset verification system 1942 can be configured to automatically accept such datasets provided by predefined provider nodes. Asset verification system 1942 can provide consumer node 632 with data indicating signature, such as a token and proof data (e.g., a digital signature) including the identifier associated with the dataset. If the rules are not accepted, asset verification system 1942 can also forward data indicating rejection of data usage rules included in the dataset to consumer node 632. Consumer node 632 can forward this data to the provider node. Upon rejection of data usage rules, the provider node can terminate the connection and may not provide any assets. Using a dataset including data usage rules ensures that consumer node 632 and other systems processing data comply with at least one data usage rule associated with the provided assets. This ensures that assets can be exchanged in a secure and controlled manner, thereby preventing unauthorized access to assets by decentralized network participants, while allowing assets to be provided to decentralized network participants who need to use such assets, for example, to generate battery tokens associated with batteries produced from input materials associated with such assets.

[0334] Continue to refer to Figure 12 Upon accepting usage rules, the data provider may provide assets associated with the identifier included in the request received from consumer node 632. These assets may be provided by the data provider from dedicated storage devices (such as...). Figure 7A and Figure 9 The assets described in the context (DB 704) are collected and provided to consumer nodes 632 via a peer-to-peer network.

[0335] Return to Figure 19A And continue to refer to Figure 23 Consumer node 632 can provide the received assets to data transmission service 1902. Data transmission service 1902 can be connected to streaming storage system 1912. Streaming storage system 1912 can be connected to data transformation unit 1904. Data transformation unit 1904 can be located upstream of data transmission service 1902. Assets collected via the distributed network can flow through data transmission service 1902 and streaming storage system 1912 to reach data transformation unit 1904.

[0336] Data transfer service 1902 can be configured to receive assets from consumer node 632. Assets can represent data streams. Such streams can be ordered sequences of records received relatively continuously from consumer node 632 (i.e., not in cumulative batches or blocks). Records can, for example, include input material data (e.g., assets) associated with a given input material via an input material identifier. Records can be represented tabularly. Records can be represented as objects, for example, using JSON or XML documents. Records can be defined as data that can be delivered continuously in small chunks or incrementally. Records can be chronologically ordered or not. Data transfer service 1902 can be configured to generate data packets including assets received from consumer node 632. Data packets can be generated for each received asset. Data packets can include additional data such as timestamps, datestamps, input material identifiers associated with the asset, distributed participant identifiers associated with the provider node, location data pointing to a dedicated storage device storing the asset, or combinations thereof. Data packets can represent messages or events. The generated data packets can be provided to streaming storage system 1912.

[0337] Streaming storage system 1912 can be configured to store data packets received (e.g., pushed) from data transmission service 1902. Streaming storage system 1912 can be configured to provide the stored data to data transformation unit 1904. Streaming storage system 1912 can be configured to provide the stored data to data consumption unit 1908 of data transformation unit 1904. Streaming storage system 1912 may include one or more persistent or non-persistent logs 1914, 1916. In this embodiment, streaming storage system 1912 includes two persistent or non-persistent logs 1914, 1916 (i.e., log 1 1914 and log 2 1916). Records stored in the logs can be sorted, for example, by using an ID. This allows identification of records within a specific log. Records may include data packets generated by data transmission service 1902.

[0338] The streaming storage system 1912 can provide streaming or streaming services between one or more streaming sources (e.g., data transmission service 1902) and one or more streaming receivers (e.g., log 1 1914 and log 2 1916). The streaming storage system 1912 can act as a persistent or non-persistent streaming receiver for input material datasets received from consumer node 632. For example, open-source software systems such as Apache Kafka (“Kafka”) or Azure Event Hubs can act as persistent streaming receivers.

[0339] The streaming storage system 1912 can be configured to pull data packets from the data transmission service 1902. For example, the streaming storage system 1912 can be configured to request data packets from the data transmission service 1902 at fixed time intervals.

[0340] The streaming storage system 1912 can be configured to determine whether a received or retrieved data packet has already been included in one or more persistent or non-persistent logs. If the data packet is already included in one or more persistent or non-persistent logs, the streaming storage system 1912 may not store the received or retrieved data packet in the logs. If the data packet is not included in one or more persistent or non-persistent logs or is updated, the streaming storage system 1912 can be configured to store the received or retrieved data packet in one or more persistent or non-persistent logs, or update the data packet existing in the persistent or non-persistent logs with the updated data packet received or retrieved. This avoids storing the same input data packet multiple times in the persistent or non-persistent logs, thereby avoiding redundant acknowledgment operations on the data packets stored in the streaming storage system 1912.

[0341] Data transformation unit 1904 can be connected to streaming storage system 1912. Data consumption unit 1908 of data transformation unit 1904 can be connected to streaming storage system 1912. Data consumption unit 1908 can be connected to one or more persistent or non-persistent logs of streaming storage system 1912 (e.g., in this embodiment, connected to log 1 1914 and log 2 1916) to ingest and process data packets stored in the logs. Streaming storage system 1912 can have a publisher-subscriber relationship with data consumption unit 1908. For example, data in one or more logs can be periodically read (e.g., pulled) by data consumption unit 1908. To avoid consuming data packets stored in the logs multiple times, such data packets can be marked as consumed by streaming storage system 1912. To avoid consuming data packets stored in the logs multiple times, an integer can be used to indicate the offset of the next data packet to be consumed. For each persistent or non-persistent log, such integer can be just a number. Such integer can be periodically set as a checkpoint. Using this integer allows the data consumption unit 1908 to reconsume data packets by rolling back to the old offset.

[0342] Data consumption unit 1908 can be connected to data verification unit 1910 of data transformation unit 1904. Data consumption unit 1908 can be configured to provide data packets collected from streaming storage system 1912 to data verification unit 1910 for verifying data included in said data packets. Data consumption unit 1908 can be configured to extract data from the data packets and provide the extracted data, including input material identifiers or battery identifiers, to data verification unit 1910. Data verification unit 1910 can be configured to verify the extracted data based on one or more rules retrieved from rule DB 1906, for example, as in... Figure 19B The following is described in the context of [the document]. Validation may include applying one or more rules retrieved from rule DB 1906 to the extracted data. The extracted data can be validated if at least a portion of the applied rules are satisfied. Applying rules to the extracted data may include comparing a combination of identifiers and location data to a database storing such combinations of identifiers and associated location data. Applying rules to the extracted data may include comparing data points present in one or more rules to individual data points present in the extracted data to be validated. Applying rules to the extracted data may include comparing a combination of data points defined in a rule to a combination of data points present in the extracted data to be validated. Applying rules to the extracted data may include comparing data defined in one or more rules to the entire extracted data to be validated. One or more rules may be associated with a data model related to a battery pass associated with a battery. The data model may define the data points to be included in the battery pass. The data types and associated values ​​and / or value ranges included in the data model may be included in one or more rules. This allows verification that the collected data satisfies the data types and associated values ​​and / or value ranges defined by the data model. By verifying the extracted data against one or more rules generated based on the data model, the number of verified data points included in the collected assets (e.g., data points included in the assets and matching data points defined by the data model) can be determined. This allows for the determination of a data integrity metric by comparing the number of verified data points with the number of data points defined by the data model.

[0343] In an embodiment, the data verification unit 1910 may be further configured to determine the archive to which the verified data is to be persistently stored. This archive may be determined based on mapping data, which includes mappings between identifiers and associated archive data. The archive data may include endpoints associated with the archive. The archive may be identified by an archive identifier included in the archive data. This identifier can be used to collect endpoints associated with such archives. The mapping data may include a first mapping between an input material identifier and an input material type identifier, and a second mapping between the input material type identifier and the associated archive data. The mapping data may further include a first mapping between a battery identifier and a battery type identifier, and a second mapping between the battery type identifier and the associated archive data. The mapping data may be stored in a database (not shown) connected to the data verification unit 1910. Determining the archive containing the verified input material data allows the verified data to be persistently stored in an archive associated with a given application or system. For example, verified data associated with a given battery may be persistently stored in an archive associated with a given application (such as Battery Passport Service 628) that processes such verified data. The process may include, for example, generating a data integrity metric, generating a battery pass, and / or updating the battery pass using this verified data, for example, as in Figures 20 to 25 As described in the context, confirmed data that may not be mapped to a given application can be persistently stored in a generic archive (such as Generic Archive 1948).

[0344] The data verification unit 1910 can be further configured to provide verified data to the identified archive (such as General Archive General 1948 or Archive DPP Product Type A 1950) for storage.

[0345] Therefore, the data verification system allows for the verification of collected data related to the production of a battery, based on a data model associated with the battery's battery pass. This determines how many data points included in the collected data match the data points defined by the data model. This allows for the determination of the completeness of the collected production-related data relative to the data defined by the data model by comparing the number of verified data points with the number of data points defined by the data model. This enables transparency regarding the degree of correlation between accessible data points and verified data points with the data points defined by the data model, and therefore also transparency regarding the completeness of the pass data generated by applying the data model to the verified data.

[0346] Figure 19B The demonstration shows the use of including Figure 19AThe diagram illustrates an example of a rule-based engine within a data validation unit, described in the context of the data validation process, used to validate data associated with battery production.

[0347] Data verification unit 1920 may include rule-based engine 1922. Rule-based engine 1922 can operate on individual data points, multiple data points, and / or collected data. Rule-based engine 1922 can operate individually on data collected for each identifier. This allows verification of the collected data for each input material or each battery, thus allowing for more granular verification of the collected data. Rule-based engine 1922 can receive requests for verification of the collected data. These requests may contain at least a portion of the collected data. Data packets (e.g., messages or events) from one or more logs of streaming storage system 1912 (see Operation 1938) can be consumed by data consumption unit 1908, as in... Figure 19A The context is described below. Data consumption unit 1908 can extract consumed data packets (see operation 1946). The extracted identifiers can be provided to rule-based engine 1922. Rule-based engine 1922 can access one or more rules. Rule-based engine 1922 can include one or more rules. One or more rules can exist within a rule template. One or more rules and / or rule templates can be stored in a data storage device (e.g., rule DB 1918). One or more rules or rule templates can be provided to rule DB 1918 by a user. One or more rules can correspond to unstructured data associated with a confirmation operation. Rule templates can include unstructured data associated with instructions associated with a confirmation operation. One or more rules or rule templates can be included in a file provided by a user. One or more rules or rule templates can be associated with an input material type identifier and / or an input material identifier. One or more rules or rule templates can be associated with a battery type identifier and / or a battery identifier. Rule-based engine 1922 can generate a request to obtain one or more rules from rule DB 1918. The request can contain the corresponding input material identifier or battery identifier.

[0348] One or more rules (e.g., one or more applicable rules) associated with the data to be verified can be provided to the rule-based engine 1922 in response to a request. These one or more rules can define data points and / or combinations of data points to be present in the consumed data. One or more rules can be associated with one or more batteries produced from input materials associated with the input material data to be verified. One or more rules can be associated with or derived from a data model associated with a battery's battery pass. Therefore, one or more rules can ensure that data points associated with the input materials and batteries and defined according to the semantic model can be included in the consumed data. One or more rules can relate to mandatory data points defined by the data model. One or more rules can be generated based on mandatory input material data points present in the semantic model. This ensures that mandatory data defined by the data model is included in the consumed data. One or more rules can be associated with input material identifiers and / or input material type identifiers. One or more rules can be associated with battery identifiers and / or battery type identifiers. Mapping tables can be used to map input material identifiers to corresponding input material type identifiers and / or battery identifiers to corresponding battery type identifiers. Input material type identifiers can be associated with input material types. Battery type identifiers can be associated with battery types. The mapping table can be stored in a separate database (not shown). The rule-based engine 1922 can be configured to collect input material type identifiers based on determined input material identifiers and to request rules based on the collected input material type identifiers. The rule-based engine 1922 can be further configured to collect battery type identifiers based on determined battery identifiers and to request rules based on the collected battery type identifiers. This allows rules associated with input material type identifiers and / or battery identifiers to be collected based on the corresponding input material identifiers and / or battery identifiers, thereby avoiding the generation of rules for each identifier and reducing the number of rules that need to be generated, stored, and maintained in the rule DB 1918.

[0349] The rule-based engine 1922 can be configured to initialize the rule engine (see Operation 1926). Initialization of the rule engine may include generating rule data executable by a processor included in the rule-based engine 1922. The rule data may include or correspond to executable logic. This allows rules or rule templates existing as unstructured data to be transformed into code executable by the processor. Execution of the code may enable the application of the executable rule data to the extracted input material data (see Operation 1928) to validate the consumed data according to the obtained rules. Execution of the logic may enable: matching the consumed data with data points and / or combinations of data points included in the executable logic; evaluating the consumed data with such data points or combinations of data points; and generating validation result data based on the evaluation results. The validation result data may include a classifier and associated validated or unvalidated data points. The classifier may be a binary classifier that distinguishes validated data from unvalidated data. A consumed data point can be considered validated if one or more rules applied by the rule-based engine 1922 are satisfied. Applying rules to consumed data can include comparing individual data points present within the rule with individual data points present within the data to be verified to determine whether the data to be verified includes the individual data points required by the rule. Applying rules to consumed data can also include comparing combinations of data points defined in the rule with combinations of data points present within the data to be verified to determine whether the data contains the combination of data points required by the rule. Applying rules to consumed data can further include comparing data defined in the rule with the entire dataset to be verified to determine whether the dataset contains all the data required by the rule. Operations performed by the rule-based engine 1922 can be determined by rules collected from the rule database 1918. For example, rules collected from the rule database 1918 can determine whether the rule-based engine 1922 operates on individual data points and / or multiple data points and / or the entire dataset of the consumed data.

[0350] The rule-based engine 1922 can be configured to determine whether consumed data satisfies one or more applied rules (see Operation 1930). If the consumed data does not include the data points required according to one or more acquired rules, the consumed data may fail confirmation or only partially pass confirmation (e.g., confirmation may result in at least one error associated with applying one or more acquired rules to the consumed data). The rule-based engine 1922 can indicate to the data confirmation unit 1920 that the consumed data has been successfully confirmed (e.g., satisfies one or more acquired rules) in response to determining that the consumed data has successfully passed confirmation (e.g., satisfies one or more acquired rules). In response to receiving this indication, the data confirmation unit 1920 can determine the target file to which the confirmed data should be persistently stored (see Operation 1940). The target file may be as follows: Figure 19A The determination is made as described in the context. If the data verification unit 1920 determines that the verified data is not associated with a given application, the data verification unit 1920 may provide the verified data to a file (such as general file 1948) that is not associated with any application or service processing the verified data. If the data verification unit 1920 determines that the verified data is associated with a given application or service processing such data (e.g., battery pass service 628), the data verification unit 1920 may provide the verified data to a storage device associated with such an application or service that processes the verified data stored therein, for example, by generating a data integrity metric and / or by generating and / or updating a battery pass.

[0351] If at least a portion of the consumed data fails to pass acknowledgment, the rule-based engine 1922 can indicate to the data acknowledgment unit 1920 that at least a portion of the consumed data fails to pass acknowledgment. In response to receiving this indication, the data acknowledgment unit 1920 can generate message data (operation 1932). The message data can indicate that at least a portion of the consumed data failed to pass acknowledgment. The message data can be provided to a display device configured to display the data received from the data acknowledgment unit 1920. The display device can include a graphical user interface 1934. The display device can display the message in response to receiving the message data from the rule-based engine 1922. This allows triggering correction or updating of the failed-to-acknowledge data points, for example, by generating a request to provide updated data points to a data source that has provided failed-to-acknowledge data points.

[0352] The rule-based engine 1922 can be configured to provide unverified data to archives not associated with any application that processes verified data, such as general archives 1948. Unverified data can be provided to this storage device along with a classifier that categorizes such data as unverified. Unverified data can be provided to this archive along with classifiers and rule data indicating the applied rules that caused at least a portion of such data to fail verification.

[0353] Figure 20 A flowchart illustrating a further example of a method for verifying the integrity of data accessible for generating and / or updating battery passes associated with a battery. The integrity of the data can be verified by the accessibility of this data used to generate battery passes and / or to update existing battery passes. A battery can be, for example, in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A battery can be produced from one or more input materials via a production chain. Input materials may correspond to output products or intermediate products produced by participants in the production chain. The production chain may include, as in [the previous sentence]... Figure 6 One or more production steps described in the context of [the context]. Figure 20 The method shown can be derived from... Figure 6 and Figure 13 The Battery Pass service 628 is implemented in the context described in the document.

[0354] It can provide data related to the battery. This data can be displayed as follows: Figure 18 The data associated with the battery may include a battery identifier associated with the battery and, optionally, an input material identifier associated with the input materials used to produce the battery.

[0355] Verified data related to battery production can be collected based on the provided battery-related data. Verified data can be generated by collecting production-related data from a distributed network based on the battery-related data, and verifying this collected data using a rule-based engine that includes one or more rules related to the data model associated with the battery pass. Data related to battery production can be collected from distributed data-providing nodes based on data associated with a data source that indicates access to data used to generate battery-related passes, for example, as in... Figure 19A The data is described in the context of [the relevant information]. Figures 19A to 23The data is generated as described in the context. Collecting verified data may include determining input material identifiers based on the provided battery-related data. Input material identifiers can be determined based on a relational representation of the relationship between the battery identifier indicating the battery and the input material identifiers used to produce the battery. This relational representation may be included and stored in a database, for example, as described in... Figure 13 and Figure 14B Described in the context of [the above]. Confirmed data can be collected based on the provided battery-related data and the identified input material identifiers. Confirmed data can be collected from a storage device storing this data. The storage device can be associated with the entity that generates the battery pass. The storage device can be associated with the system that generates the battery pass. Confirmed data can be collected from one or more storage devices storing this confirmed data, for example, as in [the context of the above]. Figure 19A and Figure 19B Described in the context of.

[0356] refer to Figure 21 and Figure 22 Collecting data related to battery production may include identifying decentralized data provider nodes associated with input material data based on the provided battery-related data. Identifying such provider nodes may include providing data associated with a data source that indicates access to data used to generate and / or update battery passes. Data associated with a data source may include a dataset identifier (hereinafter also referred to as an asset identifier) ​​associated with a dataset (hereinafter also referred to as an asset) that includes data related to battery production, and data source data indicating the data source associated with such dataset identifier. The dataset may include input material data associated with input materials used to produce batteries and / or battery data associated with batteries. Data source data may include endpoints associated with such data source. Data source data may further include decentralized participant identifiers associated with the data source and / or participant roles associated with or controlling the data source within the battery ecosystem. The data source may include network nodes configured to provide data (such as access to data used to generate and / or update battery passes) upon request from other network nodes (e.g., consumer nodes), for example, as in... Figure 6 , Figure 12 and Figure 19A The context described herein. A data source can be associated with a dedicated storage device that stores the provided data, for example, as in... Figure 7A and Figure 8 The context described above. Data sources can be configured to be based on, for example, as described in... Figure 12 and Figure 19AThe transaction protocol for authentication and / or authorization mechanisms described in the context of the data source is used to exchange data. The data associated with the data source may further include relationships between battery identifiers and associated input material identifiers. These relationships can indicate the input material identifiers associated with a given battery identifier, for example, as in... Figure 14B The context described above illustrates this. Using this relational representation allows for the efficient identification of provider nodes associated with input material data and, optionally, battery data, based on a given battery identifier, thereby reducing latency associated with the collection of input material data and, optionally, battery data. This enables efficient verification of data related to battery production and allows for the processing of verified data, for example, by generating data integrity metrics and by generating and / or updating battery passes.

[0357] Data associated with a data source can be provided through a database that stores such data associated with the data source, indicating accessible data used to generate and / or update battery passes. The database can be, for example, in... Figure 13 , Figure 14A and Figure 24 The database described in the context (e.g., registry 1312).

[0358] Based on this data associated with the data source, indicating access to data used for generating and / or updating battery passes, and the provided production-related data, a target data source can be determined. Determining the target data source may include determining an input material identifier based on the battery identifier included in the provided battery-related data. The input material identifier can be determined by querying the data associated with the data source using the provided battery-related data. A target data source matching the battery identifier included in the battery-related data can be determined based on this data associated with the data source. Alternatively, a target data source matching the determined input material identifier can be determined based on this data associated with the data source.

[0359] Continue to refer to Figure 21 This allows us to determine the access data of the target data source. We can parse the data associated with this target data source to determine the access data associated with it (e.g., a provider node associated with a dedicated storage device that stores input material data associated with a provided input material identifier or battery data associated with a provided battery identifier).

[0360] Continue to refer to Figure 21It can request access to data related to battery production from a identified target data source based on the identified access data and the identified and / or provided identifiers. Access to data related to battery production can be as follows: Figure 12 and Figure 19A The request is made in the context described above. The corresponding data can be provided by the target data source in response to such a request, for example, as in... Figure 12 and Figure 19A Described in the context of.

[0361] Continue to refer to Figure 22 And further reference Figure 19B A rule-based engine can be like this: Figure 19B The rule-based engine is described in the context of [the relevant context]. The collected data can be generated by [the relevant data]. Figure 19A and Figure 19B The data verification unit 1910, described in the context of the above, is used for verification. Applicable rules can be identified based on identifiers associated with or matching identifiers included in the extracted data. Identifiers associated with each applicable rule can include identifiers matching identifiers included in the extracted input material data. Identifiers associated with each applicable rule can include type identifiers associated with identifiers included in the extracted data. Type identifiers associated with identifiers included in the extracted data can be determined based on mapping data, such as in... Figure 19B The executable logic is described in the context of [the relevant rules]. Executable logic can be generated from one or more applicable rules. Executable logic can be generated by data verification unit 1910. Executable logic may include machine code, interpretable code, bytecode, and / or code running on a virtual machine. Logic included in the applicable rules may be encoded in the executable logic. Logic included in the applicable rules may be reflected in the executable logic. Upon completion of the generation of executable logic, data verification unit 1910 may send a response to data consumption unit 1908 indicating the completion of the generation of executable logic.

[0362] In response to receiving an indication that the generation of executable logic has been completed, the data consumption unit 1908 can provide the extracted input material data to the data verification unit 1910. The extracted input material data can be verified by executing the generated executable logic. The executable logic can be constrained by rule execution criteria. The data consumption unit 1908 can send a request to the data verification unit 1910 to transform the extracted input material based on the executable logic generated for applicable rules. Rule execution criteria can include rule execution order, exemptions, and conditions. Verification data, including verification results, can be generated by the rule engine. Verification data can include the input material data points that have undergone the verification process and associated classifiers. Classifiers can indicate whether the corresponding input material data point has passed or failed the verification process. Verification data can further include rule data associated with data points that failed verification. The rule data can indicate the applied rule that caused the verification failure of such data points. By verifying the collected data by a rule-based engine that includes one or more rules associated with the battery, a data integrity metric can be generated because verification allows determination of which data points, as defined by the data model, are included in the collected data. By verifying the collected data on the consumer side, the provider side may not need to use complex data models to generate the collected data.

[0363] Continue to refer to Figure 22 This method can verify whether the extracted data passes validation according to one or more applicable rules (e.g., one or more rules collected from rule DB 1918). If the extracted data passes validation according to the applicable rules, for example, applying such rules does not result in any errors, the method can proceed to box 2220 or box 2222. If only a portion of the extracted data passes validation, message data can be generated, for example, as in... Figure 19B The context is described above. In box 2218, confirmation data, including data that failed confirmation, can be provided to the archive, as previously described. If the retrieved data fails confirmation, message data can be generated, for example, as in... Figure 19B Described in the context of [the document / framework]. Confirmation data, including data that failed confirmation, can be provided to the archive, for example, as in [the document / framework]. Figure 19B Described in the context of.

[0364] Confirmed data can be associated with battery identifiers. This allows confirmed data to be collected, for example, based on at least one of the battery identifiers when generating data integrity metrics.

[0365] Confirmed data can be provided. Providing confirmed data may include offering confirmed data to the archive for storage (see also...). Figure 19A and Figure 19BProviding verified data may include providing verified data for the purpose of generating data integrity metrics.

[0366] Return to Figure 20 This allows for the collection of data related to the battery and the data model associated with the battery pass, based on battery-related data. Data related to the data model can be used as follows: Figure 18 The data will be collected as described in the context of the data model. Data related to the data model may include the participant roles of the stakeholders associated with battery production and the number of data points to be provided by these participant roles according to the data model, such as in... Figure 18 The data described in the context of the data model may further include digital battery identifiers, such as battery type identifiers. Data related to the data model can be obtained from the database storing this data by querying the database using the provided battery-related data.

[0367] A data integrity metric can be generated by determining the degree of relevance between confirmed data and data related to the data model, based on the provided battery-related data, to the data accessible for generating and / or updating battery passes. The degree of relevance can indicate the number of data points included in the confirmed data relative to the number of data points defined by the data model and included in the data related to the data model. A data integrity metric can be, for example, as shown in... Figures 17A to 17C The data integrity metric described in the context of [the data integrity metric].

[0368] Data integrity metrics can be determined by a rule-based engine that includes one or more verification rules configured to verify the integrity of accessible data (e.g., accessible data) used to generate and / or update battery passes, for example, as in... Figure 18 The context described above. Confirmation rules can be configured to determine the degree of integrity in the following ways:

[0369] • The sub-identifier is determined based on the battery-related data provided.

[0370] • The number of confirmed data points is determined based on the identified sub-identifier and battery identifier.

[0371] • The degree of correlation is determined by comparing the number of identified data points with the number of data points defined in the data related to the data model.

[0372] It can provide the generated data integrity metrics. Data integrity metrics can be, for example, in... Figure 18 Provided as described in the context.

[0373] By verifying the integrity of accessible data used to generate and / or update battery-related passes based on collected data, the completeness of the pass data included in the battery pass, and therefore the reliability of this pass data, becomes transparent to battery pass generators and / or battery pass users. Determining the level of completeness based on verified data provides finer-grained transparency regarding the quality of data available for generating passes and / or updating existing battery passes. This transparency allows battery passes to be generated or updated based on a given or predefined level of completeness, ensuring that the pass data included in the battery pass has sufficient completeness and reliability. This ensures that the generated and / or updated battery passes include the pass data necessary to ensure efficient further processing of batteries (such as efficient recycling of end-of-life batteries or their components). Efficient further processing can reduce the environmental impact of the battery ecosystem and / or improve the circularity of materials and byproducts used within the battery ecosystem. Transparency further allows for the identification of participants in the battery production chain who have not yet generated data to be included in the battery pass according to the data model associated with it, and / or who have not yet provided access to that data. This allows participants to be requested to generate such data and / or provide access to it, ensuring a high degree of completeness in the generated and / or updated battery passes.

[0374] Figure 24 A system for generating battery passes associated with a battery is demonstrated. The battery can be, for example, in... Figures 3A to 5 The battery is described in the context of [the previous sentence]. A battery can be produced from one or more input materials via a production chain. The production chain may include, for example, [the following text is missing from the original] Figure 6 The battery pass generator 1310 can be configured to perform one or more production steps described in the context of [the preceding text]. Figure 25 and Figure 26 The methods shown. Battery passes can include, for example, in Figure 6 The context describes one or more distributed identifiers and pass data.

[0375] The system may include an application 2426 and a battery pass generator 1310. Application 2426 may connect to battery pass generator 1310 via a communication interface (e.g., via an API). Application 2426 may include an authentication module 2428. The authentication module may be configured to authenticate users. Authentication module 2428 may be configured to receive authentication data from users, such as username and password, and digital credentials indicating user identity. Authentication module 2428 may be configured to verify or initiate verification of the received authentication data. Verification may be performed using authentication protocols such as OAuth and / or OpenID Connect. Application 2426 may receive a token containing information about the user and the permissions granted to the user. The token may be passed along with a request to battery pass generator 1310, and may be used by battery pass generator 1310 to authenticate and authorize requests received from application 2426.

[0376] Application 2426 may further include an ID provider 2430. ID provider 2430 may be configured to determine a battery identifier associated with the battery. The battery identifier may uniquely identify the battery. The battery identifier may include a batch number and / or LOT number, part number, serial number, battery identification number, or vehicle identification number. ID provider 2430 may include a code reader configured to read codes present on the physical entity of the battery and determine the battery identifier based on the acquired data. ID provider 2430 may include a camera and a unit configured to acquire image data of the identification number present on the physical entity of the battery, and the unit configured to determine the identification number based on the acquired image data. ID provider 2430 may be further configured to provide the determined battery identifier to a database of application 2426 (e.g., local DB 2434) for storage. In this way, a list of all batteries and associated battery identifiers related to the user can be stored in the database.

[0377] Application 2426 may further include a data refresher 2432 (or data refresh unit). Data refresher 2432 may be configured to collect data (such as battery tokens) stored in the local database 2434 of application 2426, for example, based on a battery identifier received from ID provider 2430. Data refresher 2432 may be further configured to provide the collected data, for example, for display within the user interface of application 2426. Data refresher 2432 may be further configured to generate a request for a battery token in response to receiving a battery identifier from ID provider 2430, and provide the generated request to token file 630. The request may be generated after determining that the local database 2434 does not store a battery token matching the received battery identifier.

[0378] The request may include a battery identifier associated with the battery, authentication data, and optionally an indication of the date when application 2426 last accessed the battery pass. The request may be provided directly to pass file 630. The request may also be provided to pass file 630 (not shown) via battery pass generator 1310. Pass file 630 or battery pass generator 1310 may be configured to authenticate and optionally authorize the request. Upon successful authentication and optional authorization, pass file 630 or battery pass generator 1310 may be configured to determine whether pass file 630 contains a battery pass that matches the data included in the received request (e.g., matches the battery identifier included in the received request). Additionally, pass file 630 or battery pass generator 1310 may be further configured to determine whether the corresponding battery pass has been updated relative to the time when the application last accessed the battery pass. For example, the pass file 630 or battery pass generator 1310 can be configured to compare the date indicating the last access to the battery pass with the date indicating the last update of such a battery pass in the pass file 630. If the dates match, or if the update time of the pass stored in the pass file 630 is earlier than the data indicating the last access, the pass file 630 or battery pass generator 1310 can be configured to provide a battery pass matching the received battery identifier to the data refresher 2432 of application 2426.

[0379] Providing a battery pass may include determining access policy data associated with the battery pass and applying the access policy data to the battery pass. The access policy data may be determined by the battery pass generator 1310. The access policy data may be determined based on a battery identifier associated with the battery included in a received request. The access policy data may identify group access data associated with the battery pass and optionally associated consumer access data. The group access data may define a consumer identifier associated with a data consumer granted access to the battery pass. The determined access policy data may be applied to the battery pass, for example, by the battery pass generator 1310.

[0380] Application access policy data may include: confirming whether a data consumer is permitted to access the battery pass; and in response to confirming the data consumer's access to the battery pass, confirming whether the data consumer is permitted to perform the requested action on the battery pass.

[0381] In another example, the application access policy data may include: confirming whether a data consumer is permitted to access the Battery Pass; and in response to confirming the data consumer's access to the Battery Pass, determining usage data associated with the confirmed data used to generate the Battery Pass and applying the determined usage data to the Battery Pass.

[0382] In yet another example, application access policy data may include confirmation of whether a data consumer is permitted to access the battery pass.

[0383] Confirming whether a data consumer is granted access to the battery pass may include determining a consumer identifier based on received authentication data. The consumer identifier can be determined based on data stored in registry 1312 (e.g., based on a relational representation mapping authentication data to consumer identifiers stored in registry 1312). The determined consumer identifier can be mapped to a consumer identifier defined by group access data. Additionally, group access data associated with confirmed input material data related to input materials used in battery production can be determined. The determined consumer identifier can be matched against the consumer identifier defined by the group access data associated with the confirmed input material data. If the determined consumer identifier matches the consumer identifier defined by the group access data, the request can be authorized, and the battery pass can be provided to the data refresher 1520 of application 2426. If no match is found, the pass file 630 or the battery pass generator 1310 can be configured to generate message data indicating a lack of authorization to access the battery pass. The message data can be provided to application 2426.

[0384] If the battery pass stored in pass file 630 is updated later than the data indicating the last access, pass file 630 or battery pass generator 1310 can be configured to provide the battery pass, generate message data, and provide this message data to data refresher 2432. This message data can indicate that the battery pass has been updated since the last access. Data refresher 2432 can be configured to store the battery pass received from pass file 630 or battery pass generator 1310 in local database 2434, or update the battery pass stored in local database 2434 based on data received from pass file 630 or battery pass generator 1310.

[0385] If the data refresher 2432 receives message data from the pass file 630 or the battery pass generator 1310 indicating that no battery pass is available for the provided battery identifier, the data refresher 2432 can be configured to generate a request for generating such a battery pass. This request may include a battery identifier associated with the battery, authentication data, an application identifier, an endpoint of application 2426, or a combination thereof. This request can be provided to the battery pass generator 1310. This request can also be provided to the streaming storage system 2402 of the battery pass generator 1310.

[0386] Battery Pass Generator 1310 can be a battery pass service (e.g., as in...) Figure 6 This is part of the Battery Password Service 628 described in the context of [the application / system]. The Battery Password Generator 1310 can be configured to generate battery passes associated with a battery. The Battery Password Generator 1310 can be configured to update existing battery passes associated with a battery. Battery passes can be generated and / or updated in response to requests received from applications (such as application 2426).

[0387] The Battery Pass Generator 1310 can connect to an asset verification system, such as in... Figure 13 , Figure 19A and Figure 19B The asset verification system 1314 described in the context of [the above] is configured to verify data collected from data providers via a decentralized network, for example, as in [the context of] [the above]. Figures 20 to 23 As described in the context. The Battery Pass Generator 1310 can be connected to a data quality analyzer, such as in... Figure 13 , Figure 15 and Figure 16 The data quality analyzer 1308 described in the context of [the previous sentence] is configured to determine data integrity metrics, such as [the following]. Figures 18 to 18 As described in the context, the Battery Password Generator 1310 can connect to an asset verification system and / or a data quality analyzer via communication interfaces such as APIs.

[0388] The battery pass generator may include a streaming storage system 2402, a pass archive 630, and a data aggregator 2408. The pass archive 630 may store battery passes associated with batteries. Battery passes stored in the pass archive 630 may be generated by the battery pass generator 1310. Battery passes stored in the pass archive 630 may be updated by the battery pass generator 1310. The pass archive 630 may be configured to provide battery passes upon request (e.g., upon request from application 2426 and / or upon request from the battery pass generator 1310).

[0389] Streaming storage system 2402 can be configured to receive requests for battery passes from one or more applications (such as application 2426). Requests can be received via a communication interface such as an API. Streaming storage system 2402 can be configured to authenticate such requests, for example, based on authentication data contained in such requests. Requests can represent data streams. Such streams can be ordered sequences of records or data packets received relatively continuously from application 2426 (i.e., not in cumulative batches or chunks). Records can, for example, include requests for battery passes for a given battery. Records can be defined as data that can be delivered continuously in chunks or incrementally. Records can be chronologically ordered or not. Data refresher 2432 can be configured to generate data packets including battery identifiers received from ID provider 2430. Data packets can be generated for each battery. Data packets can include additional data such as timestamps, datestamps, authorization data, application identifiers, application endpoints, or combinations thereof. Data packets can represent messages or events. The generated data packets can be provided to streaming storage system 2402.

[0390] Streaming storage system 2402 can be configured to store data packets received (e.g., pushed) from data refresher 2432. Data packets can be stored upon successful authentication. Streaming storage system 2402 can be configured to provide the stored data to data aggregator 2408. Streaming storage system 2402 can also be configured to provide the stored data to data collection unit 2410 of data aggregator 2408. Streaming storage system 2402 may include one or more persistent or non-persistent logs 2404, 2406. In this embodiment, streaming storage system 2402 includes two persistent or non-persistent logs 2404, 2406 (i.e., log 1 2404 and log 2 2406). Records stored in the logs can be sorted, for example, by using an ID. This allows identification of records within a specific log. Records may include data packets generated by data refresher 2432. Streaming storage system 2402 can provide streaming or streaming services between one or more streaming sources (e.g., data refresher 2432) and one or more streaming receivers (e.g., log 1 2404 and log 2 2406). Streaming storage system 2402 can act as a persistent or non-persistent stream receiver for requests for battery tokens received from application 2426. For example, open-source software systems such as Apache Kafka (“Kafka”) or Azure Event Hubs can act as persistent stream receivers.

[0391] The streaming storage system 2402 can be configured to pull data packets from the application 2426. For example, the streaming storage system 2402 can be configured to request data packets from the application 2426 at fixed time intervals.

[0392] The streaming storage system 2402 can be configured to determine whether a received or retrieved data packet has been included in one or more persistent or non-persistent logs, such as in... Figure 19A Described in the context of.

[0393] Data aggregator 2408 can be connected to streaming storage system 2402. Data collection unit 2410 of data aggregator 2408 can be connected to streaming storage system 2402. Data collection unit 2410 can be connected to one or more persistent or non-persistent logs of streaming storage system 2402 (e.g., in this embodiment, connected to log 1 2404 and log 2 2406) to ingest and process data packets stored within the logs. Streaming storage system 2402 can have a publisher-subscriber relationship with data collection unit 2410. For example, data in one or more logs can be periodically read (e.g., pulled) by data collection unit 2410. To avoid consuming data packets stored in the logs multiple times, such data packets can be marked as consumed by streaming storage system 2402. To avoid consuming data packets stored in the logs multiple times, an integer can be used to indicate the offset of the next data packet to be consumed. For each persistent or non-persistent log, such integer can be simply a number. This integer can be periodically set as a checkpoint. Using this integer allows the data collection unit 2410 to reconsume data packets by rolling back the integer to the old offset.

[0394] Data collection unit 2410 can be configured to collect data related to battery production based on data ingested from streaming storage system 2402. Data collection unit 2410 can be configured to determine an input material identifier associated with the input materials used to produce the battery based on the battery identifier included in the ingested data. For example, data collection unit 2410 can query registry 1312 for the input material identifier based on the battery identifier included in the ingested data. Alternatively, data collection unit 2410 can be configured to initiate the determination of an input material identifier associated with the input materials used to produce the battery associated with the battery identifier included in the ingested data. This determination can be initiated by generating a request including the battery identifier and providing such a request to a distributed network node 632 associated with battery pass generator 1310. Distributed network node 632 can be configured to generate a request to another distributed network node configured to determine such an input material identifier based on the received battery identifier. The additional network node can be configured to determine, based on the received battery identifier, a distributed input material identifier associated with the input material used to produce the battery by querying the distributed network, and return the determined distributed input material identifier to network node 632. The determined input material identifier can then be returned to data collection unit 2410 via node 632.

[0395] The data collection unit 2410 can be further configured to collect verified data related to battery production from a database (such as file DPP product type A 1950) associated with the asset verification system 1314. The asset verification system 1314 may include or be associated with one or more databases storing verified data related to battery production. Verified data can be generated and stored, for example, in... Figures 19A to 23 In the context of this database, verified data can be collected based on battery identifiers associated with the battery and input material identifiers associated with the input materials used to produce the battery. Alternatively or additionally, the data collection unit 2410 can be configured to request the asset verification system 1314 to collect and verify data associated with the production of the battery. This request may include battery identifiers associated with the corresponding battery and, optionally, input material identifiers associated with the input materials used to produce the battery. In response to this request, the asset verification system 1314 can be configured to collect and verify data associated with the production of the battery, for example, as described in... Figures 19A to 23 Described in the context of.

[0396] Data collection unit 2410 can be further configured to initiate collection from a distributed network (e.g., in [location]) based on a battery identifier included in the ingested data and optionally based on a distributed input material identifier received from a distributed node as described above. Figure 1 and Figure 2 The distributed network described in the context of the data collection unit 632 collects data related to battery production. Collection can be initiated by generating a request and providing it to node 632. The request may include a battery identifier associated with the battery and, optionally, a distributed input material identifier associated with the input materials used to produce the battery. A request can be generated for each battery. Node 632 can be configured to: determine a provider node by querying nodes providing lookup services in the distributed network based on the identifier included in the request, searching for distributed participant identifiers and endpoints of data providers associated with such distributed participants based on the battery identifier. Node 632 can be configured to determine a distributed battery identifier associated with battery data. Node 632 can be configured to request access to input material data associated with the inp...

Claims

1. A computer-implemented method for confirming the amount of data accessible for generating and / or updating battery-related passes, wherein, The battery is produced from one or more input materials via a production chain, the method comprising: - Provide data associated with the battery, including at least one digital battery identifier associated with the battery; - Provides data associated with a data source that indicates access to data used to generate and / or update battery passes associated with the battery; - Collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers; - Generate a data integrity metric associated with the data available for generating and / or updating the battery pass by determining the degree of correlation between the data associated with these data sources and the data associated with the data model. - Provides a measure of the integrity of the generated data.

2. The method as described in claim 1, wherein, The data associated with the data source further indicates the data source that provides access to the data used to generate and / or update these battery passes.

3. The method as described in claim 1 or 2, wherein, The data associated with the data source includes a dataset identifier associated with a dataset that includes data related to the production of the battery, and data source data indicating the data source associated with such dataset identifier.

4. The method according to any one of claims 1 to 3, wherein, The degree of correlation was determined in the following way: - Based on the digital battery identifier and the data associated with these data sources, determine the data source data that indicates the data source providing the dataset associated with the digital battery identifier. - Based on the digital battery identifier and the data associated with these data sources, determine the input material identifier and the associated data source data indicating the data source providing the dataset associated with the input material identifier. - The determined indication provides data from the data source of the dataset associated with the input material identifier to data associated with the data model of the battery pass.

5. A computer-implemented method for confirming the amount of data accessible for generating and / or updating battery-related passes, wherein, The battery is produced from one or more input materials via a production chain, the method comprising: - Provide data associated with the battery, including at least one digital battery identifier associated with the battery. - Based on the at least one digital battery identifier, confirmed data associated with the production of the battery is provided, wherein the confirmed data is generated by: a distributed data consuming node collecting production-related data from a distributed data providing node associated with data related to the production of the battery based on the provided digital battery identifier, and using a rule-based engine to confirm at least a portion of the collected data, the rule-based engine including one or more rules related to the data model associated with the battery pass. - Collect data related to the data model associated with the battery pass based on at least one of these digital battery identifiers; - Generate a data integrity metric that associates the confirmed data with the data relevant to the data model, and links it to the data available for generating and / or updating the battery pass. - Provides a measure of the integrity of the generated data.

6. The method according to any one of claims 1 to 5, wherein, The amount of data accessible for generating and / or updating the battery pass includes input material data associated with at least a portion of the input materials used to produce the battery and / or battery data associated with the battery.

7. The method according to any one of claims 1 to 6, wherein, The data associated with this data model includes the participant roles of the participants in the production chain that produces the battery, and the number of data points to be provided by such participant roles according to the data model.

8. The method according to any one of claims 1 to 7, wherein, The degree of relevance is determined by a separate rule-based engine, which includes one or more confirmation rules configured to confirm the integrity of accessible data used to generate and / or update the Battery Pass based on data related to the data model associated with the Battery Pass, or wherein the degree of relevance is determined by the rule-based engine, which further includes one or more confirmation rules configured to confirm the integrity of accessible data used to generate and / or update the Battery Pass based on data related to the data model associated with the Battery Pass.

9. The method according to any one of claims 5 to 8, wherein, The degree of relevance is determined by comparing the number of confirmed data points with the number of data points included in the data model associated with the battery pass.

10. An apparatus for confirming the amount of data accessible for generating and / or updating battery passes associated with a battery, wherein, The battery is manufactured from one or more input materials via a production chain, and the device includes: - A data providing interface configured to provide data associated with the battery, including at least one digital battery identifier associated with the battery. - A data storage device configured to provide data associated with a data source, the data indicating data accessible for generating and / or updating battery-associated passes; - A data collection unit configured to collect data related to a data model associated with the battery pass based on at least one of these digital battery identifiers; - A metric generator configured to generate data integrity metrics associated with the data accessible for generating and / or updating the battery pass by determining the degree of correlation between data associated with these data sources and data associated with the data model. - A data provider interface configured to provide the generated data integrity metrics.

11. A computer-implemented method for generating a battery pass associated with a battery, wherein, The battery is produced from one or more input materials via a production chain, the method comprising: - Provide data associated with the battery, including at least one digital battery identifier associated with the battery. - Generate a data integrity measure associated with the data used to generate the battery pass, according to the method of any one of claims 1 to 9 or by the apparatus of claim 10. - The determined data integrity measure is confirmed by comparing it with a given data integrity threshold. - Based on the confirmed data integrity measure, collect data related to the production of the battery based on the data associated with the battery and / or the generated data integrity measure, generate the battery pass based on the collected data related to the production of the battery, and provide the generated battery pass and optionally the data integrity measure for access.

12. The method of claim 11, wherein, The given integrity threshold defines the degree of correlation between the data used to generate the battery pass and one or more data points defined by the data model associated with the battery pass.

13. The method of claim 12, wherein, The degree of relevance is defined as the total degree of relevance between the data available for generating the battery pass and the data points defined by the data model associated with the battery pass, and / or wherein the degree of relevance is defined for the data points to be provided by one or more participants in the production chain that produce the battery according to the data model, and / or wherein the degree of relevance is defined for each participant in the production chain that produces the battery who needs to provide data according to the data model associated with the battery pass.

14. The method according to any one of claims 11 to 13, wherein, Generating the battery pass includes: providing a decentralized identifier associated with the collected data and a data model associated with the battery pass; applying the data model to the collected data to generate pass data; and generating the battery pass including the decentralized identifier and the pass data.

15. A computer-implemented method for updating a battery pass associated with a battery, wherein, The battery is produced from one or more input materials via a production chain, the method comprising: - Provide data associated with the battery, including at least one digital battery identifier associated with the battery. - Provide the battery pass, which includes a distributed identifier and at least one of these digital battery identifiers. - Generate a data integrity metric associated with data accessible for updating the battery pass, according to the method of any one of claims 1 to 9 or by the apparatus of claim 10. - Collect data related to the production of the battery based on data associated with the battery and / or data integrity metrics. - Update the battery pass based on the collected data associated with the battery's production, and provide the updated battery pass, along with, optionally, the data integrity metric, for access.

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