Methods, equipment, and storage media for determining the carbon footprint of batteries

Abstract

This application discloses a method, device, and storage medium for determining the carbon footprint of a battery, relating to the field of carbon footprint accounting technology. The method involves: constructing a battery lifecycle knowledge graph based on enterprise nodes at each stage of the battery's entire lifecycle, multi-dimensional information of each enterprise node, and business relationships between enterprise nodes; responding to carbon footprint query requests for a target batch of batteries, performing multi-hop queries based on the battery lifecycle knowledge graph to extract carbon emission data for each stage associated with the target batch of batteries from the battery lifecycle knowledge graph; and filling a preset carbon footprint template based on the carbon emission data for each stage to generate the full-chain carbon footprint result corresponding to the target batch of batteries. This solves the technical problems of fragmented and inaccurate calculations of battery lifecycle carbon footprint data, improving the accuracy of full lifecycle carbon footprint accounting for battery products.

Description

Technical Field

[0001] This application relates to the field of carbon footprint accounting technology, and in particular to a method, apparatus and storage medium for determining the carbon footprint of a battery. Background Technology

[0002] A battery's carbon footprint refers to the greenhouse gas emissions it generates throughout its entire life cycle, typically expressed as carbon dioxide equivalent. Accurately quantifying the carbon footprint of battery products is crucial for achieving low-carbon manufacturing, meeting international carbon compliance requirements, and promoting green supply chain management. Existing solutions focus on specific stages of the battery life cycle when calculating the carbon footprint of battery products. Data from each stage is scattered across different companies or systems, and the data standards are inconsistent and updates are not synchronized. This makes it difficult to achieve refined management and dynamic traceability of carbon emissions throughout the entire life cycle, resulting in incomplete and inaccurate carbon footprint calculations that fail to reflect the true carbon emissions of batteries throughout their entire life cycle.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, and storage medium for determining the carbon footprint of a battery, aiming to solve the technical problem of inaccurate carbon footprint calculation results for specific batches of batteries due to the fragmentation and difficulty in tracing the carbon footprint data throughout the battery's entire life cycle.

[0005] To achieve the above objectives, this application proposes a method for determining the carbon footprint of a battery, the method comprising:

[0006] A battery lifecycle knowledge graph is constructed based on the enterprise nodes in each category of the battery lifecycle, the multidimensional information of each enterprise node, and the business relationships between the enterprise nodes. The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types.

[0007] In response to the carbon footprint query request of the target batch of batteries, a multi-hop query is performed based on the battery life cycle knowledge graph. Carbon emission data of each category stage associated with the target batch of batteries are extracted from the battery life cycle knowledge graph. The carbon emission data of each category stage is calculated based on the production activity level data of the corresponding category stage and the carbon emission factor corresponding to the production activity level data. The enterprise node type corresponds one-to-one with the category stage.

[0008] Based on the carbon emission data of each category stage, a preset carbon footprint template is filled to generate the full-chain carbon footprint result corresponding to the target batch of batteries. The category stages include the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage.

[0009] In one embodiment, the step of responding to a carbon footprint query request for a target batch of batteries and performing a multi-hop query based on the battery lifecycle knowledge graph to extract carbon emission data for each category and stage associated with the target batch of batteries from the battery lifecycle knowledge graph includes:

[0010] Based on the carbon footprint query request, the identification information of the target batch of batteries is determined, and based on the identification information, the battery manufacturer enterprise node that produced the target batch of batteries is located.

[0011] Based on the battery lifecycle knowledge graph, starting from the battery manufacturer enterprise node, the associated enterprise nodes of each category stage associated with the target batch of batteries are traversed along a preset relationship path.

[0012] The battery manufacturer enterprise node and each of the associated enterprise nodes are identified as target enterprise nodes. Based on the identification information, the target production activity level data and target carbon emission factor of the corresponding category stage are extracted from each of the target enterprise nodes.

[0013] Based on the target production activity level data and the target carbon emission factor corresponding to each target enterprise node, the carbon emission data of each category stage associated with the target batch of batteries is determined.

[0014] In one embodiment, the step of traversing the associated enterprise nodes of each category stage associated with the target batch of batteries along a preset relationship path includes:

[0015] The supply relationship is traced back to the raw material mining enterprise node associated with the raw material mining and production stage of the target batch of batteries, and the raw material production enterprise node associated with the raw material battery production stage of the target batch of batteries.

[0016] The recycling relationship can be traced back to the battery recycler enterprise node corresponding to the recycling stage associated with the target batch of batteries;

[0017] The transportation relationship is traced back to the transportation company node corresponding to the transportation stage associated with the target batch of batteries;

[0018] The raw material mining enterprise node, the raw material production enterprise node, the battery recycling enterprise node, and the transportation enterprise node are identified as the associated enterprise nodes.

[0019] In one embodiment, the step of extracting target production activity level data and target carbon emission factors for the corresponding category stage from each of the target enterprise nodes based on the identification information includes:

[0020] Using the identification information of the target batch of batteries as the starting point for the query, the production events directly associated with the identification information are matched in the battery manufacturer enterprise node, the production energy consumption data corresponding to the production events are extracted as the target production activity level data of the battery production stage, and the target carbon emission factor corresponding to the target production activity level data of the battery production stage is determined.

[0021] Along the preset relationship path, traverse the raw material supply events, transportation events, and recycling events associated with the production event;

[0022] Extract the quantity of raw materials corresponding to the raw material supply event as the target production activity level data of the raw material mining and production stage, and determine the target carbon emission factor corresponding to the production activity level data of the raw material mining and production stage;

[0023] Extract the transportation distance corresponding to the transportation event as the target production activity level data of the transportation stage, and determine the target carbon emission factor corresponding to the target production activity level data of the transportation stage;

[0024] Extract the production energy consumption data corresponding to the recycling event as the production activity level data of the recycling stage, and determine the target carbon emission factor corresponding to the production activity level data of the recycling stage.

[0025] In one embodiment, the step of filling a preset carbon footprint template with carbon emission data from each of the aforementioned categories to generate the full-chain carbon footprint result corresponding to the target batch of batteries includes:

[0026] The carbon emission data for each of the aforementioned categories are summed to obtain the total carbon emissions corresponding to the target batch of batteries;

[0027] The proportion of carbon emission data for each category stage is determined based on the carbon emission data for each category stage and the total carbon emissions.

[0028] The preset carbon footprint template is filled with carbon emission data for each category stage, the proportion of carbon emission data for each category stage, and the total carbon emission to generate the full-chain carbon footprint result corresponding to the target batch of batteries.

[0029] In one embodiment, after the step of generating the full-chain carbon footprint result corresponding to the target batch of batteries, the method further includes:

[0030] The full-chain carbon footprint results are input into a machine learning model, and the corresponding carbon footprint data of the target batch of batteries is analyzed based on the machine learning model to generate corresponding emission reduction strategies.

[0031] In one embodiment, the step of constructing a battery lifecycle knowledge graph based on enterprise nodes at each stage of the battery's entire lifecycle, multidimensional information of each enterprise node, and business relationships between the enterprise nodes includes:

[0032] The battery lifecycle is defined into multiple categorized stages, including the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage.

[0033] For each of the aforementioned categories and stages, the corresponding entity's enterprise node type attributes, basic attributes, and carbon data attributes are defined. The enterprise node type attributes include battery manufacturer enterprise nodes, raw material mining enterprise nodes, raw material producer enterprise nodes, transporter enterprise nodes, and battery recycler enterprise nodes.

[0034] Define a relationship chain connecting the entities corresponding to each of the aforementioned category stages, the relationship chain including supply relationships, transportation relationships, and recycling relationships;

[0035] The enterprise nodes of each category stage in the battery life cycle are instantiated as entities of the corresponding category stage. The enterprise node type attribute, the basic attribute and the carbon data attribute of the corresponding entity are filled with the multi-dimensional information of each enterprise node. The relationship chain between the entities is established according to the business relationship between the enterprise nodes, and the battery life cycle knowledge graph is constructed.

[0036] In one embodiment, it further includes:

[0037] Deploying a consortium blockchain in the battery industry chain, wherein the nodes of the consortium blockchain include the actual enterprises corresponding to the enterprise nodes of each category and stage in the battery life cycle knowledge graph, as well as third-party auditing agencies;

[0038] Collect production activity level data for each of the aforementioned enterprise nodes;

[0039] Based on each of the enterprise nodes and the third-party auditing agency, the production activity level data of each of the enterprise nodes is verified through a node consensus algorithm. After verification, the production activity level data of each of the enterprise nodes is packaged with the hash value of the previous block and the hash value of the current block to generate the corresponding block data of each of the enterprise nodes.

[0040] The corresponding block data of each enterprise node is shared to each node of the consortium blockchain, and the corresponding block data of each enterprise node is bound to the corresponding enterprise node or relationship chain in the battery life cycle knowledge graph.

[0041] Furthermore, to achieve the above objectives, this application also proposes a battery carbon footprint determination device, the battery carbon footprint determination device comprising:

[0042] The module is used to construct a battery lifecycle knowledge graph based on the enterprise nodes of each category stage of the battery lifecycle, the multidimensional information of each enterprise node, and the business relationships between the enterprise nodes. The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types.

[0043] The query module is used to respond to the carbon footprint query request of the target batch of batteries. It performs multi-hop queries based on the battery life cycle knowledge graph and extracts carbon emission data of each category stage associated with the target batch of batteries from the battery life cycle knowledge graph. The carbon emission data of each category stage is calculated based on the production activity level data of the corresponding category stage and the carbon emission factor corresponding to the production activity level data. The enterprise node type corresponds one-to-one with the category stage.

[0044] The determination module is used to fill the preset carbon footprint template based on the carbon emission data of each category stage, and generate the full-chain carbon footprint result corresponding to the target batch of batteries. The category stages include the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage.

[0045] In addition, to achieve the above objectives, this application also proposes a device for determining the carbon footprint of a battery, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery carbon footprint determination method as described above.

[0046] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the battery carbon footprint determination method described above.

[0047] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the battery carbon footprint determination method described above.

[0048] One or more technical solutions proposed in this application have at least the following technical effects:

[0049] First, based on the enterprise nodes at each stage of the battery's entire lifecycle, the multidimensional information of each enterprise node, and the business relationships between these enterprise nodes, a battery lifecycle knowledge graph is constructed. This graph structurally links the enterprise nodes scattered across each stage and their business relationships, forming a traceable data network. This structurally eliminates data fragmentation and provides underlying support for cross-enterprise and cross-stage data traceability. The multidimensional information includes production activity level data, the carbon emission factor corresponding to the production activity level data, and the enterprise node type. When a carbon footprint query request for a target batch of batteries is received, a multi-hop query is performed based on the battery lifecycle knowledge graph. Production activity data and carbon emission factors associated with each category and stage of the target batch of batteries are accurately extracted from the battery lifecycle knowledge graph. This ensures that the data used for carbon footprint accounting of the target batch of batteries truly reflects the actual production process of the target batch of batteries. Based on the production activity level data and corresponding carbon emission factors for each category and stage, carbon emission data for each category and stage is calculated. By automatically tracing the associated data (carbon emission factors of production activity level data for each stage) for each category and stage of the battery lifecycle, the problem of scattered and difficult-to-trace data in existing technologies is solved. The enterprise node type corresponds one-to-one with the category and stage, which includes the raw material mining and production stage, battery production stage, transportation stage, and recycling stage. Based on the carbon emission data for each category and stage, a preset carbon footprint template is populated to generate a uniformly formatted and clearly sourced full-chain carbon footprint result for the target batch of batteries. This application achieves cross-entity carbon data integration by constructing a battery lifecycle knowledge graph and structurally associating enterprise nodes and their business relationships in the raw material mining, production, transportation, and recycling stages. The battery lifecycle knowledge graph stores production activity levels, corresponding carbon emission factors, and enterprise types for each enterprise node, ensuring data traceability and correlation. When responding to a query request for a target batch of batteries, multi-hop queries are used to accurately locate the actual carbon emission data of that batch at each stage along the relationship chain, and the carbon emission data for each category stage is filled into a preset template to generate a full-chain carbon footprint result. Through the collaborative efforts of knowledge graph construction, multi-hop query tracing, and dynamic calculation and filling, the technical problems of fragmented and inaccurate calculation of battery lifecycle carbon footprint data are accurately solved, improving the accuracy of full lifecycle carbon footprint accounting for battery products. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating the method for determining the carbon footprint of the battery in this application (Example 1).

[0053] Figure 2 A flowchart illustrating Embodiment 2 of the method for determining the carbon footprint of the battery in this application;

[0054] Figure 3 This is a schematic diagram of the module structure of the battery carbon footprint determination device according to an embodiment of this application;

[0055] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for determining the carbon footprint of a battery in this application embodiment.

[0056] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0058] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0059] The main solution of this application embodiment is as follows: A battery lifecycle knowledge graph is constructed based on the enterprise nodes at each stage of the battery lifecycle, the multi-dimensional information of each enterprise node, and the business relationships between enterprise nodes. The multi-dimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types. In response to a carbon footprint query request for a target batch of batteries, a multi-hop query is performed based on the battery lifecycle knowledge graph. Carbon emission data for each stage associated with the target batch of batteries is extracted from the battery lifecycle knowledge graph. The carbon emission data for each stage is calculated based on the production activity level data and the carbon emission factors corresponding to the production activity level data for the corresponding stage. Enterprise node types correspond one-to-one with stage categories. Based on the carbon emission data for each stage category, a preset carbon footprint template is filled to generate the full-chain carbon footprint result for the target batch of batteries. The stage categories include the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage.

[0060] In this embodiment, for ease of description, the following description will focus on the system for determining the carbon footprint of a battery.

[0061] Because existing technologies focus on specific stages of the battery lifecycle when calculating the carbon footprint of battery products, the data for each stage is scattered across different companies or systems, with inconsistent standards and asynchronous updates. This makes it difficult to achieve refined management and dynamic traceability of carbon emissions throughout the entire lifecycle, resulting in incomplete and inaccurate carbon footprint calculations that fail to reflect the true carbon emissions of batteries throughout their entire lifecycle.

[0062] This application provides a solution that constructs a battery lifecycle knowledge graph to structurally link enterprise nodes and their business relationships across the stages of raw material extraction, production, transportation, and recycling, thereby achieving cross-entity carbon data integration. The battery lifecycle knowledge graph stores production activity level data, corresponding carbon emission factors, and enterprise types for each enterprise node, ensuring data traceability and correlation. When responding to a query request for a target batch of batteries, multi-hop queries are used to accurately locate the actual carbon emission data of that batch at each stage along the relationship chain, and the carbon emission data for each category stage is filled into a preset template to generate a full-chain carbon footprint result. Through the collaborative efforts of knowledge graph construction, multi-hop query tracing, and dynamic calculation filling, the technical problems of fragmented and inaccurate calculations of battery lifecycle carbon footprint data are accurately solved, improving the accuracy of full lifecycle carbon footprint accounting for battery products.

[0063] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a battery carbon footprint determination device capable of performing the above functions. The following description uses a battery carbon footprint determination system as an example to illustrate this embodiment and the subsequent embodiments.

[0064] Based on this, embodiments of this application provide a method for determining the carbon footprint of a battery, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for determining the carbon footprint of the battery in this application.

[0065] In this embodiment, the method for determining the carbon footprint of a battery includes steps 101-103:

[0066] Step 101: Construct a battery lifecycle knowledge graph based on the enterprise nodes in each category of the battery lifecycle, the multidimensional information of each enterprise node, and the business relationships between enterprise nodes.

[0067] The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, enterprise node types, and category stages including raw material mining and production stage, battery production stage, transportation stage, and recycling stage.

[0068] Specifically, enterprise nodes are actual enterprise entities participating in a certain life cycle stage of the battery industry chain, such as lithium mine miners, lithium mine producers, cathode material plants, battery manufacturers, logistics companies, and recycling companies. For example, enterprise nodes in the raw material mining and production stage can be lithium mine miners, enterprise nodes in the battery production stage can be cathode material plants and battery manufacturers, enterprise nodes in the transportation stage can be logistics companies, and enterprise nodes in the recycling stage can be recycling companies. Each enterprise exists as an entity or node in the battery life cycle knowledge graph. The enterprise node types in this application include battery manufacturer enterprise nodes, raw material miner enterprise nodes, raw material producer enterprise nodes, transportation enterprise nodes, and battery recycler enterprise nodes. Enterprise node types can be used to identify the battery life cycle stage to which an enterprise belongs, including the raw material mining and production stage, battery production stage, transportation stage, or recycling stage. Carbon emission factor refers to the amount of carbon released per unit level (quantity) of carbon emission activities (production activities). The multidimensional information of enterprise nodes consists of attribute data attached to the enterprise nodes, mainly including production activity level data, carbon emission factor corresponding to the production activity level data, enterprise node type, and other basic data. The business relationships between enterprise nodes are represented by edges connecting two enterprise nodes, used for actual business transactions between them. These business relationships can include supply relationships, transportation relationships, and recycling relationships. For example, the business relationship between lithium miner A and cathode material manufacturer B is a supply relationship, meaning lithium miner A supplies lithium raw materials to cathode material manufacturer B; the business relationship between cathode material manufacturer B and battery manufacturer C is a supply relationship, meaning cathode material manufacturer B supplies cathode materials to battery manufacturer C; the business relationship between logistics company D and battery manufacturer C is a transportation relationship, meaning logistics company D transports a batch of batteries from battery manufacturer C; and the business relationship between recycling company E and battery manufacturer C is a transportation relationship, meaning recycling company E recycles a batch of retired batteries manufactured by battery manufacturer C.

[0069] In the raw material mining and production stage, raw material mining companies and raw material production companies carry out mining and initial processing of minerals such as lithium, cobalt, and nickel. In the battery production stage, battery manufacturing companies, such as cathode material plants and battery manufacturers, carry out the production of electrodes, cells, and modules. In the transportation stage, transportation companies, such as logistics companies, carry out the production of logistics transportation of raw materials, battery semi-finished products, and battery finished products. In the recycling stage, battery recycling companies carry out the production of cascade utilization of retired batteries or material recycling.

[0070] For lithium mining companies, production activity data can be the annual lithium ore mining volume (tons). Other basic data can include company ID and name. The company node type is the raw material mining company node. The carbon emission factor corresponding to the production activity data can be the carbon emissions per ton of lithium ore mined. For cathode material manufacturers, production activity data can be the annual cathode material production volume. Other basic data can include company ID and name. The company node type is the raw material producer company node. The carbon emission factor corresponding to the production activity data can be the carbon emissions per ton of cathode material produced. For battery manufacturers, production activity data can be electricity consumption (kWh). Other basic data can include company ID and name. The company node type is the battery manufacturer company node. The carbon emission factor corresponding to the production activity data can be the carbon dioxide emissions per kWh of electricity. For logistics companies, production activity data can be the transportation mode (land / sea / air) and transportation distance. Other basic data can include company ID and name. The company node type is the transportation company node. The carbon emission factor corresponding to the production activity data can be the carbon dioxide emissions per kilometer of distance traveled. For recycling companies, their production activity level data can be electricity consumption (kWh), and other basic data can be company ID, name, etc. The company node type is battery recycler company node, and the carbon emission factor corresponding to the production activity level data can be the carbon dioxide emissions per kWh of electricity.

[0071] In some embodiments, based on the four key stages of the battery lifecycle—raw material mining and production, battery production, transportation, and recycling—corresponding enterprise node types are defined: raw material mining enterprise nodes, raw material production enterprise nodes, battery manufacturing enterprise nodes, transportation enterprise nodes, and battery recycling enterprise nodes. For each type of enterprise node, the multidimensional information to be collected is defined to populate the attribute data of each entity in the battery lifecycle knowledge graph, including basic information such as enterprise ID, name, and geographical location, and focusing on integrating production activity level data related to carbon accounting (such as annual lithium mining volume, production line energy consumption, and transportation mileage) and the corresponding carbon emission factors. Subsequently, based on the actual supply chain collaboration, the business relationship chain between enterprise nodes is defined, including supply relationships, transportation, and recycling relationship types, to support refined traceability. By connecting to the enterprise resource planning (ERP), manufacturing execution system (MES), logistics system, or manual input methods of various enterprises, the actual participating enterprises are injected as instantiated nodes into the battery lifecycle knowledge graph, and the association relationships between entities are established based on the business relationships between enterprise nodes. Finally, a graph database is used to store the aforementioned entities, attributes, and relationships, forming a structured knowledge graph of the entire battery lifecycle, providing a traceable and computable data foundation for subsequent batch-level carbon footprint queries.

[0072] In some embodiments, the steps for constructing a battery lifecycle knowledge graph based on enterprise nodes at each stage of the battery lifecycle, multidimensional information of each enterprise node, and business relationships between enterprise nodes include:

[0073] The battery lifecycle is divided into multiple categories and stages, including the raw material mining and production stage, the raw material manufacturer node, the battery production stage, the transportation stage, and the recycling stage.

[0074] For each category stage, define the corresponding entity's enterprise node type attributes, basic attributes, and carbon data attributes. The enterprise node type attributes include battery manufacturer enterprise nodes, raw material mining enterprise nodes, raw material producer enterprise nodes, transportation enterprise nodes, and battery recycler enterprise nodes.

[0075] Define the relationship chains connecting entities corresponding to each category stage. These relationship chains include supply relationships, transportation relationships, and recycling relationships.

[0076] The enterprise nodes of each category stage in the battery life cycle are instantiated as entities of the corresponding category stage. The enterprise node type attributes, basic attributes and carbon data attributes of the corresponding entities are filled with the multi-dimensional information of each enterprise node. The relationship chain between entities is established according to the business relationship between enterprise nodes, and a knowledge graph of the battery life cycle is constructed.

[0077] As an example, firstly, four key stages of battery product lifecycles are identified: raw material mining and production, battery production, transportation, and recycling. Based on the business characteristics of each stage, corresponding enterprise node type attributes are defined, specifically: battery manufacturer enterprise node, raw material mining enterprise node, raw material production enterprise node, transportation enterprise node, and battery recycler enterprise node. For each type of enterprise node, its attribute structure is further refined, including enterprise node type attributes, basic attributes, and carbon data attributes. The enterprise node type attribute identifies the lifecycle stage of the battery product to which the entity belongs; the basic attributes can include multi-dimensional information such as enterprise name, unified social credit code, geographical location, production capacity, and technology roadmap; the carbon data attributes can include production activity level data directly related to carbon accounting (such as monthly electricity consumption of a battery factory's production line, or annual transportation mileage of a transportation company) and corresponding carbon emission factors (such as carbon dioxide emissions per unit of electricity consumption, or carbon emission coefficient per unit of transportation distance). After determining the production activity level data, the carbon emission factors corresponding to each production activity level data can be retrieved from a pre-defined carbon emission factor database. Subsequently, the relationship chains connecting various enterprise nodes are defined to reflect the actual supply chain collaboration logic, including supply relationships, transportation relationships, and recycling relationships. Based on this, enterprises actually participating in the battery supply chain are instantiated entities and injected into their corresponding enterprise node types. By connecting to enterprise ERP, MES, energy management systems, or third-party data platforms, multi-dimensional information and real-time carbon data of each enterprise are obtained and used to populate the attribute fields of each entity. Simultaneously, based on the relationship chains between business entities, the connections in the constructed battery lifecycle knowledge graph accurately reflect the supply chain flow. Finally, all entities, attributes, and relationships are imported into a graph database to form a clearly structured and semantically clear battery lifecycle knowledge graph. This achieves structured integration of cross-enterprise and cross-stage carbon data, providing a traceable and computable data foundation for subsequent batch-level carbon footprint queries, supporting multi-hop queries, dynamic source tracing, and carbon emission attribution analysis, and helping to improve the accuracy and transparency of carbon accounting for battery products.

[0078] Step 102: Respond to the carbon footprint query request of the target batch of batteries, perform multi-hop query based on the battery life cycle knowledge graph, and extract carbon emission data of each category and stage associated with the target batch of batteries from the battery life cycle knowledge graph.

[0079] The carbon emission data for each category stage is calculated based on the production activity level data and the carbon emission factor corresponding to the production activity level data for the corresponding category stage, and the enterprise node type corresponds one-to-one with the category stage.

[0080] Specifically, the target batch of batteries specifies the particular battery product whose carbon footprint needs to be calculated, and has a unique batch number, such as B202506-001, which serves as the starting point for the query. The carbon footprint query request can be an instruction initiated by an external system or user, requesting the total and composition of carbon emissions throughout the entire lifecycle of the target batch of batteries. The carbon emission data for each category and stage represents the greenhouse gas emissions for that stage, calculated using the formula: Carbon Emissions = Production Activity Level Data × Carbon Emission Factor. For example, if a batch of batteries consumes 8,500 kWh of electricity during the battery production stage, and the local power grid carbon emission factor is 0.5 kg CO2 / kWh, then the carbon emissions during the battery production stage are 4,250 kg CO2.

[0081] In some embodiments, in response to a carbon footprint query request for a target batch of batteries, the system initiates a carbon footprint accounting process. First, it receives a query request containing a unique identifier (such as a batch number) for the target batch of batteries and locates the battery manufacturer node producing the target batch of batteries in the constructed battery lifecycle knowledge graph. It then obtains the target production activity level data and corresponding carbon emission factors for each stage of battery production. Subsequently, based on the graph structure, a multi-hop query is performed. Starting from the battery manufacturer node producing the target batch of batteries, the system traverses predefined relationship chains such as supply, transportation, and recycling. For example, through supply relationships, it jumps to the raw material mining node and the raw material production node to obtain the production activity level data for the raw material mining stage; through transportation relationships, it jumps to the transportation node to obtain the target production activity level data and corresponding carbon emission factors for the transportation stage; if the target batch of batteries has entered the recycling stage, it jumps to the battery recycler node through the recycling relationship to obtain the production activity level data and corresponding carbon emission factors for the recycling stage. For each category stage, the system reads production activity level data (such as mining volume, power consumption, and transportation distance) and its matching carbon emission factors (such as carbon emission intensity per unit of energy consumption) from the corresponding enterprise nodes. Based on the calculation model of "carbon emission amount = activity level × carbon emission factor", the system calculates the carbon emission data for each stage item by item, realizing accurate source tracing and dynamic aggregation of carbon emissions throughout the entire life cycle of a specific batch of batteries, and providing reliable data support for generating high-fidelity carbon footprint reports.

[0082] Step 103: Based on the carbon emission data of each category stage, fill the preset carbon footprint template to generate the full-chain carbon footprint result corresponding to the target batch of batteries.

[0083] The categories and stages include raw material extraction and production, battery production, transportation, and recycling. The preset carbon footprint template is a standardized data structure or document format that pre-defines the fields, structure, and presentation of the carbon footprint report, including basic product (battery) information, carbon emission data for each category and stage, percentage, total emissions, data source, calculation method, and time range. The full-chain carbon footprint result is a complete report covering the total carbon emissions and their composition throughout the entire process from raw materials to recycling, providing an end-to-end carbon emission view for the target batch of batteries.

[0084] In some embodiments, the system invokes a pre-configured preset carbon footprint template. This template is a structured data model that may include fields such as basic product (battery) information, carbon emission data for each category and stage, percentage, total carbon footprint, data source, calculation basis, and time range. Subsequently, the calculated carbon emission data for the raw material mining and production stage, battery production stage, transportation stage, and recycling stage are mapped one by one to the corresponding fields in the template according to the category. At the same time, the system automatically calculates the total carbon emissions (the sum of all stages) and the contribution percentage of each stage, and fills them into the areas in the template. In addition, the template can also integrate data traceability information, such as the enterprise node from which the data comes in each stage, the collection time, and the associated blockchain transaction hash (if supported), to enhance the credibility of the results. Finally, the system renders the filled data into a standard format output, which can generate a full-chain carbon footprint report (full-chain carbon footprint results) in Portable Document Format (PDF), JavaScript Object Notation (JSON), or HyperText Markup Language (HTML) format, which can be used by enterprises for green product certification, supply chain information disclosure, or international carbon compliance declaration. The above process enables the automated and standardized expression of carbon footprint results, ensuring the comparability and verifiability of carbon data across different batches and batteries, and significantly improving the efficiency and transparency of carbon management.

[0085] Based on the battery carbon footprint determination method provided in this application, a battery lifecycle knowledge graph is constructed according to the enterprise nodes at each stage of the battery's entire lifecycle, the multidimensional information of each enterprise node, and the business relationships between enterprise nodes. This structurally links the enterprise nodes and their business relationships scattered across each stage, forming a traceable data network. This eliminates data fragmentation structurally and provides underlying support for cross-enterprise and cross-stage data traceability. The multidimensional information includes production activity level data, the carbon emission factors corresponding to the production activity level data, and the enterprise node type. When a carbon footprint query request for a target batch of batteries is received, a multi-hop query is performed based on the battery lifecycle knowledge graph. This accurately extracts production activity data and carbon emission factors associated with each stage of the target batch of batteries from the knowledge graph. This ensures that the data used for carbon footprint accounting for the target batch of batteries truly reflects the actual production process. Based on the production activity level data and corresponding carbon emission factors for each stage, carbon emission data for each stage is calculated. By automatically tracing the associated data (production activity level data and carbon emission factors for each stage) across the battery lifecycle, the problem of scattered and difficult-to-trace data in existing technologies is solved. Enterprise node types correspond one-to-one with stage categories, which include raw material mining and production, battery production, transportation, and recycling. Based on the carbon emission data for each stage, a preset carbon footprint template is populated to generate a uniformly formatted and clearly sourced full-chain carbon footprint result for the target batch of batteries. This application achieves cross-entity carbon data integration by constructing a battery lifecycle knowledge graph that structurally links enterprise nodes and their business relationships across the raw material mining, production, transportation, and recycling stages. The battery lifecycle knowledge graph stores production activity levels, corresponding carbon emission factors, and enterprise types for each enterprise node, ensuring data traceability and correlation. When responding to a query request for a target batch of batteries, multi-hop queries are used to accurately locate the actual carbon emission data of that batch at each stage along the relationship chain, and the carbon emission data of each category stage is filled into a preset template to generate a full-chain carbon footprint result. Through the collaborative efforts of knowledge graph construction, multi-hop query tracing, and dynamic calculation and filling, the technical problems of fragmented and inaccurate calculation of battery lifecycle carbon footprint data are accurately solved, improving the accuracy of full lifecycle carbon footprint accounting for battery products.

[0086] In some embodiments, in response to a carbon footprint query request for a target batch of batteries, the steps of performing a multi-hop query based on a battery lifecycle knowledge graph and extracting carbon emission data for each category and stage associated with the target batch of batteries from the battery lifecycle knowledge graph include:

[0087] Based on the carbon footprint query request, the identification information of the target batch of batteries is determined, and based on the identification information, the battery manufacturer enterprise node that produced the target batch of batteries is located.

[0088] Based on the battery life cycle knowledge graph, starting from the battery manufacturer enterprise node, the associated enterprise nodes of each category and stage of the target batch of batteries are traversed along the preset relationship path.

[0089] Battery manufacturer enterprise nodes and related enterprise nodes are identified as target enterprise nodes. Based on the identification information, target production activity level data and target carbon emission factors for the corresponding category stage are extracted from each target enterprise node.

[0090] Based on the target production activity level data and target carbon emission factors corresponding to each target enterprise node, determine the carbon emission data of each category and stage associated with the target batch of batteries.

[0091] Specifically, the identification information for the target batch of batteries is a unique code used to identify the target batch of batteries, such as batch number, B202506-001, serial number or VIN code, which is the starting point for the query.

[0092] Battery manufacturer nodes are nodes in the knowledge graph representing companies producing the target batch of batteries, serving as the starting anchor for multi-hop queries. Predefined relationship paths are semantic connections between nodes in the battery lifecycle knowledge graph (e.g., "transportation relationship," "supply relationship"), defining the direction of data flow and standardizing query paths to ensure the accuracy of cross-stage data association. Target enterprise nodes include battery manufacturer nodes, raw material mining enterprise nodes, transportation enterprise nodes, and / or battery recycler enterprise nodes found in the query and related to the target batch of batteries; these are the objects of carbon data extraction. Target production activity level data are actual production data directly related to the target batch of batteries, such as power consumption, transportation mileage, and raw material usage. Target carbon emission factors are carbon emission coefficients that match the target production activity level data in terms of time, region, and process, such as the local power grid emission factor.

[0093] As an example, upon receiving a carbon footprint query request, which includes the unique identifier of the target batch of batteries (e.g., batch number B202506-001), the system matches and searches within the constructed battery lifecycle knowledge graph to locate the battery manufacturer node that produced the target batch of batteries. This node serves as the starting point for the multi-hop query. Subsequently, based on the structured relationships of the knowledge graph, the system traverses the preset relationship path from this battery manufacturer node. The preset relationship path follows the physical flow of the battery lifecycle, including supply relationships between raw material mining companies and battery manufacturers, between battery manufacturers, between transportation companies, and between battery recyclers. Through upstream and downstream traversal, the system connects to enterprise nodes at each stage of the target batch of batteries, including raw material mining and production, transportation, recycling, and battery production. Through this multi-hop traversal, the entire supply chain path of the target batch of batteries from raw materials to recycling is fully reconstructed. Next, the battery manufacturer node and all associated enterprise nodes obtained through the traversal are identified as target enterprise nodes. Based on batch identification information, target production activity level data and target carbon emission factors directly related to their business processes are extracted from each target enterprise node. Following the calculation model of "carbon emission data = target production activity level data × target carbon emission factor," the data of each target enterprise node are classified and calculated to obtain carbon emission data for the raw material mining and production stage, battery production stage, transportation stage, and recycling stage. This achieves accurate traceability and dynamic accounting of the carbon footprint of a specific batch of batteries, ensuring that the results are authentic, verifiable, and auditable.

[0094] In some embodiments, the step of traversing the associated enterprise nodes of each category stage associated with the target batch of batteries along a preset relationship path includes:

[0095] The supply relationship is traced back to the raw material mining enterprise node corresponding to the raw material mining and production stage associated with the target batch of batteries, and the raw material production enterprise node corresponding to the raw material battery production stage associated with the target batch of batteries.

[0096] The recycling relationship can be traced back to the battery recycler enterprise node corresponding to the recycling stage associated with the target batch of batteries;

[0097] The transportation relationship can be traced back to the corresponding transportation company node at the transportation stage associated with the target batch of batteries;

[0098] The raw material mining enterprise node, the raw material production enterprise node, the battery recycling enterprise node, and the transportation enterprise node are identified as related enterprise nodes.

[0099] Specifically, the associated enterprise nodes are external enterprise nodes that have business dealings with the target batch of batteries at various stages, traceable through recycling, transportation, and supply relationships. These nodes are the targets for carbon data extraction. Starting with the battery manufacturer, the system locates raw material mining enterprise nodes, raw material production enterprise nodes, battery recycler enterprise nodes, and transportation enterprise nodes along structured relationship paths through supply, recycling, and transportation relationships. This forms an associated network covering the entire chain from raw material mining to production, transportation, and recycling. This enables accurate traceability and integration of associated enterprise nodes at each stage of the battery's lifecycle, breaking down data barriers at each stage and ensuring the complete collection and accurate association of carbon emission data. This provides a complete and traceable range of enterprise nodes for subsequent extraction of carbon data for the target batch of batteries.

[0100] In some embodiments, the step of traversing the associated enterprise nodes of each category stage associated with the target batch of batteries along a preset relationship path further includes:

[0101] The supply relationship is traced back to the raw material mining enterprise node corresponding to the raw material mining and production stage associated with the target batch of batteries, and the raw material production enterprise node corresponding to the raw material mining and production stage associated with the target batch of batteries.

[0102] By tracing the supply relationship back to other battery manufacturers at the corresponding battery production stages associated with the target batch of batteries;

[0103] The recycling relationship can be traced back to the battery recycler enterprise node corresponding to the recycling stage associated with the target batch of batteries;

[0104] The transportation relationship can be traced back to the corresponding transportation company node at the transportation stage associated with the target batch of batteries;

[0105] The raw material mining enterprise nodes, raw material production enterprise nodes, battery recycler enterprise nodes, transportation enterprise nodes, and other battery manufacturing enterprise nodes are identified as affiliated enterprise nodes.

[0106] In some embodiments, the step of extracting target production activity level data and target carbon emission factors for the corresponding category stage from each target enterprise node based on identification information includes:

[0107] Starting with the identification information of the target batch of batteries, the system matches production events directly associated with the identification information in the battery manufacturer enterprise node, extracts the production energy consumption data corresponding to the production events as the target production activity level data for the battery production stage, and determines the target carbon emission factor corresponding to the target production activity level data for the battery production stage.

[0108] Following the preset relationship path, traverse the raw material supply events, transportation events, and recycling events associated with the production events;

[0109] Extract the raw material quantity corresponding to the raw material supply event as the target production activity level data for the raw material mining and production stage, and determine the target carbon emission factor corresponding to the production activity level data for the raw material mining and production stage.

[0110] Extract the transportation distance corresponding to the transportation event as the target production activity level data for the transportation phase, and determine the target carbon emission factor corresponding to the target production activity level data for the transportation phase;

[0111] Extract the production energy consumption data corresponding to the recycling event as the production activity level data for the recycling phase, and determine the target carbon emission factor corresponding to the production activity level data for the recycling phase.

[0112] Specifically, the target enterprise nodes include battery manufacturers, raw material miners, transporters, battery recyclers, and other enterprise entities directly related to the target batch of batteries.

[0113] As an example, starting with the unique identifier of the target batch of batteries (e.g., batch number B202506-001), the query retrieves production event records directly associated with that batch of batteries from the located battery manufacturer's management system (MES). These production events can include detailed production time, production line number, shift, process parameters, and energy consumption data. The actual energy consumption (e.g., 8,500 kWh) during the production of the target batch of batteries is extracted from these events and used as the target production activity level data for the battery production stage. Simultaneously, based on the time and geographical location of the production event, the corresponding target carbon emission factor is matched to ensure consistency between the production activity level data and the emission factor in both time and space. Subsequently, based on the timestamp and bill of materials of this production event, the associated business events can be traversed upstream and downstream along a pre-defined relationship path. Upstream, query related raw material supply events for the raw materials consumed in this production event. For example, a transaction record of a raw material mining enterprise node supplying x tons of lithium carbonate to a battery manufacturer enterprise node on a certain date in 20xx. Extract the quantity of raw materials related to the target batch in this supply event (e.g., 10 tons) as production activity level data for the raw material mining and production stage. Carbon emission factors (e.g., 8 t CO2 e / ton) can be matched based on the mining location and mining process of the raw materials to ensure that the target production activity level data for the raw material mining and production stage accurately reflects the actual supply chain path. In the transportation stage, query transportation events related to this production event. This can include the inbound transportation of raw materials from the supplier to the factory and the outbound transportation of finished batteries from the factory to the customer. Extract the transportation distance for each segment (e.g., 1,200 km for raw material transportation and 1,800 km for finished product transportation) as production activity level data for the transportation stage. Match the corresponding target carbon emission factor (e.g., 0.1 kg CO2 / km for road transportation) based on the transportation mode (road / rail / sea), vehicle type, or ship type to achieve cumulative calculation of carbon emissions from multiple transportation segments. If the target batch of batteries has entered the retirement stage, its processing records can be queried through recycling events. For example, the operation log of the recycling enterprise node in 20xx for dismantling and regenerating batch B202506-001 batteries can be extracted as the target production activity level data for the recycling stage. Combined with the carbon emission factor of the recycling plant's location (such as 0.4 kg CO2 / kWh), the processing emissions can be calculated, thereby calculating the carbon emissions for the recycling stage.By structurally linking the target production activity level data extracted from each of the above stages with the target carbon emission factors, a complete carbon accounting input dataset is formed, providing a high-fidelity and traceable data foundation for the accurate calculation of carbon emissions in subsequent stages. This achieves automated and refined extraction of carbon data from batch identification to the entire chain, ensuring that the final carbon footprint accounting results can truly reflect the actual carbon footprint of a specific batch of batteries.

[0114] In some embodiments, the step of filling a preset carbon footprint template with carbon emission data from each category stage to generate the full-chain carbon footprint result for the target batch of batteries includes:

[0115] The carbon emission data for each category stage are summed to obtain the total carbon emissions corresponding to the target batch of batteries;

[0116] The proportion of carbon emission data for each category and stage is determined based on the carbon emission data for each category and stage, as well as the total carbon emissions.

[0117] The carbon footprint template is filled with carbon emission data for each category and stage, the proportion of carbon emission data for each category and stage, and the total carbon emission to generate the full-chain carbon footprint result for the target batch of batteries.

[0118] Specifically, the carbon emission data from the raw material mining and production stage, battery production stage, transportation stage, and recycling stage are summed to calculate the total carbon emissions of the target batch of batteries throughout their entire lifecycle, which is taken as its total carbon footprint. Then, based on the numerical relationship between the carbon emission data of each category stage and the total carbon emissions, the proportion of each category stage is calculated. For example, the carbon emission data of the raw material stage is divided by the total carbon emissions to obtain its contribution percentage in the overall carbon footprint, used to identify carbon emission hotspots. Next, a preset carbon footprint template is invoked, and the carbon emission data of each stage, the calculated proportion of carbon emission data, and the total carbon emissions are filled into the corresponding positions in the template according to the field mapping relationship. Simultaneously, the template can also integrate data traceability information, such as the enterprise node from which the data comes, the collection time, and the associated blockchain hash value (if supported), to enhance the credibility and auditability of the results. Finally, the system renders the completed full-chain carbon footprint result into a standard output format, generating a full-chain carbon footprint report in PDF, JSON, or HTML format, supporting use in scenarios such as green product certification, supply chain information disclosure, and international carbon compliance declaration.

[0119] In some embodiments, after the step of generating the full-chain carbon footprint results corresponding to the target batch of batteries, the method further includes:

[0120] The carbon footprint results of the entire chain are input into the machine learning model, and the corresponding carbon footprint data of the target batch of batteries are analyzed based on the machine learning model to generate corresponding emission reduction strategies.

[0121] Specifically, the emission reduction strategies are optimization suggestions for high-carbon links based on the carbon footprint results of the entire supply chain, such as replacing materials with low-carbon ones, using green electricity, and optimizing logistics routes. The aforementioned machine learning model is an industry-specific model fine-tuned with industry knowledge. It can be a Bidirectional Encoder Representations from Transformers (BERT) model architecture, possessing carbon footprint analysis and strategy reasoning capabilities. To ensure the machine learning model can accurately understand the battery industry's carbon footprint data and generate scientific and feasible emission reduction strategies, it needs to be specifically fine-tuned. The specific fine-tuning process can be as follows: First, construct a high-quality domain-specific training dataset. This dataset contains two core types of samples: one type consists of the full-chain carbon footprint results of real battery products (including structured data and natural language descriptions), covering different technical routes such as lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC), production locations, and raw material sources; the other type contains expert-level emission reduction suggestions corresponding to the full-chain carbon footprint results. These suggestions can be written by professionals such as carbon management consultants and battery process engineers, and can cover aspects such as material substitution, green electricity applications, process optimization, and logistics improvements. Each sample is organized in a format where the full-chain carbon footprint results are input and emission reduction strategies are output, forming a supervised learning dataset. Then, a basic machine learning model, such as the BERT model, is selected as a pre-trained model, and the BERT model is trained sequentially using the supervised learning dataset. The model parameters are adjusted through multiple rounds of iterative training. During training, a validation set can be introduced to monitor overfitting, and the relevance, feasibility, and professionalism of the generated results are evaluated manually. Ultimately, a dedicated model with knowledge of carbon management in the battery industry was developed, which can accurately analyze carbon footprint reports and generate actionable emission reduction strategies, significantly improving the practicality and industry adaptability of the recommendations.

[0122] As an example, structured, end-to-end carbon footprint results are input into a finely tuned machine learning model. Based on its understanding of battery manufacturing processes, materials science, energy structure, and carbon management practices, the model performs semantic parsing and attribution analysis to identify high-carbon sources. Building on this, targeted strategies are generated by integrating industry practices. These strategies include, for example, changing the node of raw material mining companies, adopting hydrogen reduction low-carbon processes to reduce the carbon intensity of raw materials, optimizing supply chain layout, and replacing some road transport with rail transport to reduce logistics carbon emissions. Finally, the generated emission reduction strategies are appended to the carbon footprint report, forming an integrated output of accounting, diagnosis, and recommendations. This provides enterprises with actionable low-carbon transformation paths and enhances the decision-making support value of carbon data.

[0123] In some embodiments, a consortium blockchain is deployed in the battery industry chain. The nodes of the consortium blockchain include the actual enterprises corresponding to the enterprise nodes of each category and stage in the battery life cycle knowledge graph, as well as third-party auditing agencies.

[0124] Collect production activity level data for each enterprise node;

[0125] Based on each enterprise node and a third-party auditing agency, the production activity level data of each enterprise node is verified through a node consensus algorithm. After verification, the production activity level data of each enterprise node is packaged with the hash value of the previous block and the hash value of the current block to generate the corresponding block data of each enterprise node.

[0126] The corresponding block data of each enterprise node is shared with each node of the consortium blockchain, and the corresponding block data of each enterprise node is bound to the corresponding enterprise node or relationship chain in the battery life cycle knowledge graph.

[0127] Specifically, a consortium blockchain is a semi-open blockchain, jointly maintained by multiple pre-authorized enterprises or institutions, combining decentralization and controllability, and is suitable for collaborative scenarios in the battery industry chain. The aforementioned nodes refer to the entities participating in the consortium blockchain, including raw material miners, battery manufacturers, transporters, battery recyclers, and other industry chain enterprises, as well as third-party auditing agencies. The node consensus algorithm is the mechanism used in the consortium blockchain to verify and confirm data consistency, such as Practical Byzantine Fault Tolerance (PBFT) and Replicated State Machine Consensus Algorithm (Raft), to ensure that all nodes reach a consensus on the authenticity of the data. Block data is the basic unit of data storage in the blockchain, containing transaction data, timestamps, the hash of the previous block (forming a chain structure), and the hash of the current block (tamper-proof). The hash values ​​of the previous and current blocks are cryptographic digests of the previous and current blocks, respectively, to ensure that carbon emission data cannot be tampered with once written to the blockchain; otherwise, the hash chain would break.

[0128] As an example, a consortium blockchain network is established, jointly participated in by enterprises at all stages of the battery lifecycle and third-party auditing agencies. Nodes include raw material miners, battery manufacturers, transporters, battery recyclers, and qualified third-party auditing agencies. This consortium blockchain employs an access control mechanism, requiring each node to be authenticated before access, ensuring the authenticity and trustworthiness of the participants. Each enterprise node collects real-time production activity data based on unified data standards through IoT devices, Manufacturing Execution Systems (MES), or Application Programming Interfaces (APIs). This data includes key parameters such as raw material extraction volume, electricity / gas consumption during battery production, mileage and load during transportation, and the weight and energy consumption of recycled batteries. After collection, each enterprise node submits the production activity data to its local blockchain node, initiating the consensus verification process. Consensus algorithms suitable for consortium blockchain environments, such as PBFT or Raft, can be used. Other industry chain nodes and third-party auditing institutions can perform multi-dimensional cross-verification of the submitted data. For example, the energy consumption data for a batch of production uploaded by a battery manufacturer needs to be compared with the supply records of upstream raw material suppliers, the transportation time of logistics companies, and third-party energy audit reports to ensure data consistency, tamper-proof nature, and compliance with industry benchmarks. Only when a majority of nodes reach a consensus is the production activity level data marked as "verified." After verification, the production activity level data, timestamp, data source identifier, and third-party audit signature of the enterprise node are structured and encapsulated. This data is then packaged together with the hash value of the previous block (used for chaining) and the hash value of the current block generated by encryption algorithms such as Secure Hash Algorithm 256-bit (SHA-256) to form a new block. This block data can be shared to all nodes in the consortium blockchain via a P2P network. After verification by each node, the data is stored synchronously, achieving distributed and tamper-proof data storage. Simultaneously, the unique identifier of the block (such as a transaction hash or block number) is used as metadata and bound to the corresponding entity node or relationship chain in the battery lifecycle knowledge graph. For example, the energy consumption block hash of a battery manufacturer is bound to the production relationship chain, and the mileage data hash of a transporter is bound to the transportation relationship chain, achieving a deep integration of blockchain notarization and the semantic network of the knowledge graph. The multi-node consensus mechanism of the consortium blockchain ensures the credibility of the data source, the hash chain structure of the blockchain guarantees the immutability of data storage, and the binding with the knowledge graph enables precise association between business entities and trusted data. Ultimately, this constructs a full-chain trusted data system covering collection, verification, on-chain storage, and association, providing solid support for subsequent accurate carbon footprint calculation, dynamic traceability, and compliant disclosure.

[0129] Furthermore, in constructing the battery lifecycle knowledge graph, the system integrates standardized communication protocols such as Message Queuing Telemetry Transport (MQTT) and Open Platform Communications Unified Architecture (OPC UA) through a hardware abstraction layer. It is compatible with various industrial sensing devices and edge gateways such as RS485 and SIM7020, enabling plug-and-play access to production activity data across the raw material extraction, battery production, transportation, and recycling stages. Specifically, energy consumption data during battery production and transportation distances during transportation can be collected and uploaded in real time through a unified interface, ensuring the authenticity, continuity, and traceability of data sources for each enterprise node (such as raw material suppliers, manufacturers, and transporters) in the knowledge graph. Based on this, and relying on a low-code development platform, data collection rules and carbon management logic can be customized via a highly visual graphical user interface (GUI) using drag-and-drop functionality, adapting to different production processes without writing traditional code. For example, users can configure a rule: "When the real-time energy consumption of the coating machine (equipment ID: Moulding-01) in Workshop 1 exceeds 25 kWh for more than 5 minutes, an alarm will be automatically triggered, the production supervisor will be notified, and the abnormal event will be marked as a high-carbon risk operation and simultaneously recorded in the carbon data log," thereby achieving dynamic monitoring of key carbon emission nodes. The low-code development platform integrates a rule engine and a workflow engine, supporting complex logic orchestration, such as multi-condition judgments, timed aggregation analysis, and cross-process data association. It can automatically identify energy consumption anomalies, optimize production scheduling, and improve carbon data quality. The lightweight deployment characteristics of the low-code development platform allow the system to be quickly installed and run on edge devices or local servers, reducing reliance on high-performance IT infrastructure, making it particularly suitable for battery companies with multi-site, distributed layouts. Through the above architecture, a carbon data acquisition and response system adapted to the characteristics of its own production line can be quickly built, ensuring that the production activity level data in the knowledge graph is accurate and timely, and providing high-fidelity input for subsequent multi-hop queries, carbon emission calculation and full-chain carbon footprint report generation, significantly improving the automation, refinement and scalability of carbon footprint accounting, and helping the battery industry chain achieve efficient and reliable green transformation.

[0130] refer to Figure 2 , Figure 2This is a flowchart of a second embodiment of a method for determining the carbon footprint of a battery. The process involves acquiring production activity level data, node type, and other basic data for raw material mining, battery manufacturing, transportation, and battery recycling enterprise nodes. The carbon emission factors corresponding to the production activity level data of these nodes are then determined. In response to carbon footprint query requests for the target batch of batteries, a multi-hop query is performed based on the battery lifecycle knowledge graph. Carbon emission data for each category and stage associated with the target batch of batteries is extracted from the knowledge graph. Based on this data, a pre-defined carbon footprint template is populated to generate the full-chain carbon footprint result for the target batch of batteries. This full-chain carbon footprint result is then input into a machine learning model. The model analyzes the corresponding carbon footprint data for the target batch of batteries to generate appropriate emission reduction strategies.

[0131] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for determining the carbon footprint of the battery in this application. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0132] This application also provides a device for determining the carbon footprint of a battery, please refer to... Figure 3 The device for determining the carbon footprint of a battery includes:

[0133] Module 301 is used to construct a battery lifecycle knowledge graph based on the enterprise nodes of each category stage of the battery lifecycle, the multidimensional information of each enterprise node, and the business relationships between enterprise nodes. The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types.

[0134] The query module 302 is used to respond to the carbon footprint query request of the target batch of batteries. It performs multi-hop queries based on the battery life cycle knowledge graph and extracts carbon emission data of each category stage associated with the target batch of batteries from the battery life cycle knowledge graph. The carbon emission data of each category stage is calculated based on the production activity level data of the corresponding category stage and the carbon emission factor corresponding to the production activity level data. The enterprise node type corresponds one-to-one with the category stage.

[0135] The determination module 303 is used to fill the preset carbon footprint template based on the carbon emission data of each category stage, and generate the full-chain carbon footprint result corresponding to the target batch of batteries. The categories and stages include the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage.

[0136] The battery carbon footprint determination device provided in this application, employing the battery carbon footprint determination method described in the above embodiments, can solve the technical problem of inaccurate carbon footprint calculation results for specific batches of batteries due to fragmented and untraceable carbon footprint data throughout the battery's life cycle. Compared with the prior art, the beneficial effects of the battery carbon footprint determination device provided in this application are the same as those of the battery carbon footprint determination method provided in the above embodiments, and other technical features in the battery carbon footprint determination device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0137] This application provides a device for determining the carbon footprint of a battery. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the battery carbon footprint determination method in Embodiment 1 above.

[0138] The following is for reference. Figure 4 The diagram illustrates a structural schematic of a battery carbon footprint determination device suitable for implementing embodiments of this application. The battery carbon footprint determination device in the embodiments of this application may include, but is not limited to, mobile terminals such as laptops and fixed terminals such as digital TVs and desktop computers. Figure 4 The shown battery carbon footprint determination device is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0139] like Figure 4As shown, the battery carbon footprint determination device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the battery carbon footprint determination device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the battery carbon footprint determination device to communicate wirelessly or wiredly with other devices to exchange data. Although a battery carbon footprint determination device with various systems is shown in the figure, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0140] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0141] The battery carbon footprint determination device provided in this application, employing the battery carbon footprint determination method described in the above embodiments, can solve the technical problem of inaccurate carbon footprint calculation results for specific batches of batteries due to fragmented and untraceable carbon footprint data throughout the battery's entire life cycle. Compared with the prior art, the beneficial effects of the battery carbon footprint determination device provided in this application are the same as those of the battery carbon footprint determination method provided in the above embodiments, and other technical features in this battery carbon footprint determination device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0143] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0144] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the battery carbon footprint determination method in the above embodiments.

[0145] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0146] The aforementioned computer-readable storage medium may be included in a battery carbon footprint determination device; or it may exist independently in a carbon footprint determination device not assembled into a battery.

[0147] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a battery carbon footprint determination device, the battery carbon footprint determination device: constructs a battery lifecycle knowledge graph based on enterprise nodes at each stage of the battery's entire lifecycle, multidimensional information of each enterprise node, and business relationships between the enterprise nodes. The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types. Responding to a carbon footprint query request for a target batch of batteries, the device performs a multi-hop query based on the battery lifecycle knowledge graph, extracting carbon emission data for each stage associated with the target batch of batteries from the battery lifecycle knowledge graph. The carbon emission data for each stage is calculated based on the corresponding production activity level data and the carbon emission factors corresponding to the production activity level data. The enterprise node types correspond one-to-one with the stage categories. Based on the carbon emission data for each stage category, the device fills a preset carbon footprint template to generate a full-chain carbon footprint result for the target batch of batteries. The stage categories include raw material mining and production, battery production, transportation, and recycling.

[0148] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0150] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0151] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for determining the carbon footprint of batteries. This solves the technical problem of inaccurate carbon footprint calculation results for specific batches of batteries due to fragmented and untraceable carbon footprint data throughout the battery's lifecycle. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the battery carbon footprint determination method provided in the above embodiments, and will not be repeated here.

[0152] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery carbon footprint determination method described above.

[0153] The computer program product provided in this application can solve the technical problem of inaccurate carbon footprint calculation results for specific batches of batteries due to the fragmentation and difficulty in tracing the carbon footprint data throughout the battery's entire life cycle. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the battery carbon footprint determination method provided in the above embodiments, and will not be repeated here.

[0154] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for determining the carbon footprint of a battery, characterized in that, The method for determining the carbon footprint of the battery includes: A battery lifecycle knowledge graph is constructed based on the enterprise nodes in each category of the battery lifecycle, the multidimensional information of each enterprise node, and the business relationships between the enterprise nodes. The multidimensional information includes production activity level data, carbon emission factors corresponding to the production activity level data, and enterprise node types. Based on the carbon footprint query request of the target batch of batteries, the identification information of the target batch of batteries is determined, and based on the identification information, the battery manufacturer enterprise node that produced the target batch of batteries is located. Based on the battery lifecycle knowledge graph, starting from the battery manufacturer enterprise node, the associated enterprise nodes of each category stage associated with the target batch of batteries are traversed along a preset relationship path. The battery manufacturer enterprise node and each of the associated enterprise nodes are identified as target enterprise nodes. Based on the identification information, the target production activity level data and target carbon emission factor of the corresponding category stage are extracted from each of the target enterprise nodes. Based on the target production activity level data and the target carbon emission factor corresponding to each target enterprise node, the carbon emission data of each category stage associated with the target batch of batteries is determined; the carbon emission data of each category stage is calculated based on the production activity level data and the carbon emission factor corresponding to the production activity level data of the corresponding category stage, and the enterprise node type corresponds one-to-one with the category stage; Based on the carbon emission data of each category stage, the preset carbon footprint template is filled to generate the full-chain carbon footprint result corresponding to the target batch of batteries. The category stages include the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage. Deploying a consortium blockchain in the battery industry chain, wherein the nodes of the consortium blockchain include the actual enterprises corresponding to the enterprise nodes of each category and stage in the battery life cycle knowledge graph, as well as third-party auditing agencies; Collect production activity level data for each of the aforementioned enterprise nodes; Based on each of the enterprise nodes and the third-party auditing agency, the production activity level data of each of the enterprise nodes is verified through a node consensus algorithm. After verification, the production activity level data of each of the enterprise nodes is packaged with the hash value of the previous block and the hash value of the current block to generate the corresponding block data of each of the enterprise nodes. The corresponding block data of each enterprise node is shared to each node of the consortium blockchain, and the corresponding block data of each enterprise node is bound to the corresponding enterprise node or relationship chain in the battery life cycle knowledge graph.

2. The method for determining the carbon footprint of a battery as described in claim 1, characterized in that, The step of traversing the associated enterprise nodes of each category stage associated with the target batch of batteries along a preset relationship path includes: The supply relationship is traced back to the raw material mining enterprise node associated with the raw material mining and production stage of the target batch of batteries, and the raw material production enterprise node associated with the raw material mining and production stage of the target batch of batteries. The recycling relationship can be traced back to the battery recycler enterprise node corresponding to the recycling stage associated with the target batch of batteries; The transportation relationship is traced back to the transportation company node corresponding to the transportation stage associated with the target batch of batteries; The raw material mining enterprise node, the raw material production enterprise node, the battery recycling enterprise node, and the transportation enterprise node are identified as the associated enterprise nodes.

3. The method for determining the carbon footprint of a battery as described in claim 1, characterized in that, The step of extracting target production activity level data and target carbon emission factors for the corresponding category stage from each of the target enterprise nodes based on the identification information includes: Using the identification information of the target batch of batteries as the starting point for the query, the production events directly associated with the identification information are matched in the battery manufacturer enterprise node, the production energy consumption data corresponding to the production events are extracted as the target production activity level data of the battery production stage, and the target carbon emission factor corresponding to the target production activity level data of the battery production stage is determined. Along the preset relationship path, traverse the raw material supply events, transportation events, and recycling events associated with the production event; Extract the quantity of raw materials corresponding to the raw material supply event as the target production activity level data of the raw material mining and production stage, and determine the target carbon emission factor corresponding to the production activity level data of the raw material mining and production stage; Extract the transportation distance corresponding to the transportation event as the target production activity level data of the transportation stage, and determine the target carbon emission factor corresponding to the target production activity level data of the transportation stage; Extract the production energy consumption data corresponding to the recycling event as the production activity level data of the recycling stage, and determine the target carbon emission factor corresponding to the production activity level data of the recycling stage.

4. The method for determining the carbon footprint of a battery as described in claim 1, characterized in that, The step of filling a preset carbon footprint template with carbon emission data from each of the aforementioned categories to generate the full-chain carbon footprint result for the target batch of batteries includes: The carbon emission data for each of the aforementioned categories are summed to obtain the total carbon emissions corresponding to the target batch of batteries; The proportion of carbon emission data for each category stage is determined based on the carbon emission data for each category stage and the total carbon emissions. The preset carbon footprint template is filled with carbon emission data for each category stage, the proportion of carbon emission data for each category stage, and the total carbon emission to generate the full-chain carbon footprint result corresponding to the target batch of batteries.

5. The method for determining the carbon footprint of a battery as described in any one of claims 1 or 4, characterized in that, After the step of generating the full-chain carbon footprint results corresponding to the target batch of batteries, the method further includes: The full-chain carbon footprint results are input into a machine learning model, and the corresponding carbon footprint data of the target batch of batteries is analyzed based on the machine learning model to generate corresponding emission reduction strategies.

6. The method for determining the carbon footprint of a battery as described in claim 1, characterized in that, The steps for constructing a battery lifecycle knowledge graph based on enterprise nodes at each stage of the battery lifecycle, multidimensional information of each enterprise node, and business relationships between the enterprise nodes include: The battery lifecycle is defined into multiple categorized stages, including the raw material mining and production stage, the battery production stage, the transportation stage, and the recycling stage. For each of the aforementioned categories and stages, the corresponding entity's enterprise node type attributes, basic attributes, and carbon data attributes are defined. The enterprise node type attributes include battery manufacturer enterprise nodes, raw material mining enterprise nodes, raw material producer enterprise nodes, transporter enterprise nodes, and battery recycler enterprise nodes. Define a relationship chain connecting the entities corresponding to each of the aforementioned category stages, the relationship chain including supply relationships, transportation relationships, and recycling relationships; The enterprise nodes of each category stage in the battery life cycle are instantiated as entities of the corresponding category stage. The enterprise node type attribute, the basic attribute and the carbon data attribute of the corresponding entity are filled with the multidimensional information of each enterprise node. The relationship chain between the entities is established according to the business relationship between the enterprise nodes, and the battery life cycle knowledge graph is constructed.

7. A device for determining the carbon footprint of a battery, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for determining the carbon footprint of a battery as claimed in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for determining the carbon footprint of a battery as described in any one of claims 1 to 6.

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