Methods, equipment, and storage media for calculating the carbon footprint of batteries over their entire lifecycle

Abstract

This application discloses a method, device, and storage medium for calculating the carbon footprint of a battery throughout its entire life cycle, relating to the field of carbon footprint accounting technology. The method includes: analyzing the battery's production process based on the core production stage range determined by the accounting boundary, and generating a corresponding structured process hierarchy list; selecting key processes from the structured process hierarchy list based on the ratio of energy consumption to material consumption, and determining a list of key data collection objects; collecting key data from the list of key data collection objects, and matching it with auxiliary data according to timestamps to generate a measured raw dataset; analyzing and verifying the measured raw dataset, calculating the carbon emissions of each process and emission source item by item, and summarizing to obtain a life cycle emission inventory; and calculating the battery's entire life cycle carbon footprint based on the carbon emissions of each stage and process in the battery's entire life cycle in the life cycle emission inventory. This application achieves high-precision calculation of the battery's entire life cycle carbon footprint.

Description

Technical Field

[0001] This application relates to the field of carbon footprint accounting technology, and in particular to a method, device and storage medium for calculating the carbon footprint of a battery throughout its entire life cycle. Background Technology

[0002] In the context of global efforts to address climate change and promote a green and low-carbon transition, it is necessary to identify high-emission hotspots through carbon footprint accounting, and then deduce the emission reduction potential and cost savings achievable through process optimization or supply chain optimization. Related technologies employ fixed allocation mechanisms, distributing the total carbon emissions of the entire process or system based on the mass ratio of the product or its constituent materials. This ignores the actual carbon emission differences caused by variations in process complexity, production time, energy density, or value, leading to accounting results that deviate from reality.

[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 purpose of this application is to provide a method, device and storage medium for calculating the carbon footprint of a battery throughout its entire life cycle, aiming to solve the technical problem that the actual carbon emissions differ due to differences in process complexity, production time, energy density or value, causing the calculation results to deviate from the true situation.

[0005] To achieve the above objectives, this application proposes a method for calculating the carbon footprint of a battery throughout its entire life cycle, the method comprising:

[0006] Based on the ratio of energy consumption to material consumption, key processes in the structured process hierarchy list are selected, and a list of key data collection objects is determined.

[0007] Collect key data corresponding to the list of key data collection objects, and match and associate them with auxiliary data according to timestamps to generate the actual measurement raw dataset;

[0008] The measured raw dataset after analysis and verification is analyzed, and the carbon emissions of each process and emission source are calculated item by item to obtain the life cycle emission inventory.

[0009] The full life cycle carbon footprint of the battery is calculated based on the carbon emissions of each stage and process in the battery's entire life cycle emission inventory.

[0010] In one embodiment, the battery life cycle is divided according to preset defining conditions to determine the stage division results;

[0011] The direct emissions and indirect energy emissions of the corresponding organizational level in the stage division results are calculated using the hierarchical control method, and the initial accounting boundary is output.

[0012] The emissions of key upstream raw materials are included in the preliminary accounting boundary, and the final accounting boundary is output.

[0013] In one embodiment, the battery process knowledge base is invoked to match the core production stage range determined according to the accounting boundary, and a list of candidate processes within the core production stage range is determined.

[0014] Verify the production equipment corresponding to the processes in the candidate process list, and output a mapping table between equipment and processes;

[0015] Based on the mapping table between equipment and processes, a tree-like topology structure with processes, workstations, and equipment as hierarchical relationships is constructed to generate the structured process hierarchy list.

[0016] In one embodiment, a historical carbon emission database is invoked to calculate the energy consumption and material consumption ratio of each process in the structured process hierarchy list, thereby obtaining a process contribution ranking list.

[0017] By using preset contribution threshold rules, key processes are selected from the process contribution ranking list to determine the set of key processes;

[0018] The key process set is matched and associated with the sensor metadata in the IoT device registry to generate the key data collection object list.

[0019] In one embodiment, a data collection instruction set is generated based on the list of key data collection objects to control the IoT gateway to collect the key data with timestamps;

[0020] Extract the values ​​of the key data and their corresponding timestamps, and arrange the values ​​of the key data in order of timestamps to obtain standardized key data;

[0021] Based on a unified timestamp, the standardized key data and the auxiliary data are correlated and matched to generate the measured original dataset.

[0022] In one embodiment, a preset material balance verification algorithm is invoked to perform elemental mass balance calculations on the input and output materials in the measured original dataset, and output the theoretical content data of elemental flow for each material.

[0023] The relative error between the theoretical content data of the element flow and the measured output data is calculated, and the error analysis results are generated based on the comparison between the relative error and the preset tolerance.

[0024] Based on the error analysis results, a calibration process is triggered to correct the out-of-tolerance data in the measured output data, thereby obtaining the verified measured original dataset.

[0025] In one embodiment, the validated raw dataset is standardized to generate activity level data;

[0026] The activity level data is classified and matched with the corresponding emission factors in the database according to the material and energy consumption type. The activity level data and its corresponding emission factors are calculated item by item by matrix multiplication to obtain the initial emission list.

[0027] The co-consumption emissions corresponding to mixed-line production in the initial emission list are allocated and calculated in real time, and a complete process emission list including direct emissions and indirect emissions after allocation is output.

[0028] The complete process emission inventory is processed by cross-stage data aggregation, and the carbon emissions of each life cycle stage are added together to generate the life cycle emission inventory.

[0029] In one embodiment, the life cycle stage mapping rule is invoked to extract carbon emission data for each stage and process from the life cycle emission inventory, generating a carbon emission data sequence sorted by life cycle stage;

[0030] The carbon emission data sequence is accumulated across stages to calculate the total carbon emissions of the next stage.

[0031] The total carbon emissions of the upper-level stage are summed to generate the full life cycle carbon footprint of the battery.

[0032] In addition, to achieve the above objectives, this application also proposes a carbon footprint accounting device, 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 life cycle carbon footprint accounting method as described above.

[0033] 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 life cycle carbon footprint calculation method as described above.

[0034] This application provides a method for calculating the carbon footprint of a battery throughout its entire life cycle. The method includes analyzing the battery's production process based on the core production stage range determined by the calculation boundary and generating a corresponding structured process hierarchy list; selecting key processes based on the proportion of energy consumption and material consumption and determining a list of key data collection objects; collecting key data and auxiliary data and matching them according to timestamps to generate a measured raw dataset; calculating the carbon emissions of each process and emission source after parsing and verification, and summarizing them to obtain a life cycle emission list; and finally calculating the corresponding full life cycle carbon footprint of the battery based on the carbon emissions of each stage and process in the full life cycle list. This method overcomes the technical problems of low calculation accuracy and poor reliability in traditional carbon footprint calculations due to insufficient data collection granularity, lack of multi-dimensional data verification mechanisms, and inability to adapt to complex mixed-line production scenarios. It achieves a leap in carbon footprint calculation accuracy from the workshop statistical level to the process measurement level, establishes a data-driven high-precision carbon footprint calculation system, and provides accurate data support for optimizing carbon emission reduction in the battery production process.

[0035] In summary, this application overcomes the technical problem of deviation between the battery's full-cycle carbon footprint calculation results and the actual situation by constructing a production line-level measured data acquisition system, introducing a stoichiometric material balance verification algorithm, and establishing a dynamic allocation model. It improves the accuracy of data acquisition, the reliability of the verification mechanism, and the model's dynamic adaptability to production scenarios, thus achieving high-precision full-lifecycle carbon footprint calculation for batteries. Attached Figure Description

[0036] 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.

[0037] 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.

[0038] Figure 1 This is a flowchart illustrating the first embodiment of the method for calculating the carbon footprint of a battery throughout its entire life cycle in this application.

[0039] Figure 2 This is a flowchart of the carbon footprint accounting process for this application;

[0040] Figure 3 This is a flowchart illustrating the seventh embodiment of the method for calculating the carbon footprint of a battery throughout its entire life cycle in this application.

[0041] Figure 4 This is a schematic diagram of the carbon footprint accounting equipment used in this application.

[0042] 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

[0043] 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.

[0044] The relevant technologies employ a fixed allocation mechanism, which allocates the total carbon emissions of the entire process or system based on the mass ratio of the product or its constituent materials. This ignores the actual carbon emission differences caused by variations in process complexity, production time, energy density, or value of different products, resulting in accounting results that deviate from the true situation.

[0045] This application provides a solution: First, based on the core production stage range determined by the accounting boundary, the battery production process is analyzed to generate a corresponding structured process hierarchy list. Then, based on the ratio of energy consumption to material consumption, key processes in the structured process hierarchy list are selected to determine a list of key data collection objects. Next, key data corresponding to the list of key data collection objects is collected and matched with auxiliary data according to timestamps to generate a measured raw dataset. Then, the verified measured raw dataset is analyzed, and the carbon emissions of each process and emission source are calculated item by item to obtain a life cycle emission inventory. Finally, based on the carbon emissions of each stage and process in the battery's entire life cycle in the life cycle emission inventory, the full life cycle carbon footprint of the battery is calculated.

[0046] 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 an electronic device or carbon footprint accounting device capable of performing the above functions. The following description uses a carbon footprint accounting device as an example to illustrate this embodiment and the subsequent embodiments.

[0047] 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.

[0048] This application provides a method for calculating the carbon footprint of a battery over its entire lifecycle, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for calculating the carbon footprint of a battery throughout its entire lifecycle, as described in this application.

[0049] In this embodiment, the method for calculating the carbon footprint of the battery throughout its entire life cycle includes steps S10 to S50:

[0050] Step S10: Based on the core production stage range determined by the accounting boundary, analyze the battery production process and generate a corresponding structured process hierarchy list.

[0051] In this embodiment, the core production stage range defined by the accounting boundary refers to the range of battery production-related stages that require key attention during carbon footprint accounting. Analyzing the battery production process refers to breaking down and analyzing the entire battery production process and procedures. The structured process hierarchy list refers to a detailed list of production processes organized according to hierarchical structures such as procedures, workstations, and equipment.

[0052] As an optional implementation, the core production stage range defined by the accounting boundary is received as input. By calling the built-in production stage identification module, all production steps included in the core production stage range are extracted. Next, a pre-stored battery production process database is loaded, containing standard process flows corresponding to each production step. The extracted production steps are broken down into specific processes, and each process is further decomposed into several workstations required to complete that process. Each workstation is then associated with specific production equipment that performs the operation at that workstation. During the decomposition process, the machine compares in real time whether each decomposed process, workstation, and equipment belongs to the core production stage range; any items outside the range are automatically removed. Finally, the verified decomposition results are organized according to the hierarchical structure of processes, workstations, and equipment to generate a corresponding structured process hierarchy list. The list generated by this method has a clear hierarchy and is suitable for large-scale production lines with high standardization and stable production processes.

[0053] As an alternative implementation, the equipment map construction module is activated by taking the core production stage scope defined by the accounting boundary as input. This module scans all possible production equipment models and functional parameters within the core production stage scope. Based on these parameters, equipment with similar functions is grouped into the same operational unit, i.e., a workstation. Subsequently, the machine aggregates highly related workstations into complete processes based on the operational purpose and sequence of each workstation, ensuring that the process covers all production stages within the core production stage scope. A hierarchical verification algorithm is then invoked to check whether the aggregated processes fully encompass the corresponding workstations and whether each workstation is accurately associated with its corresponding equipment. If any hierarchical gaps exist, they are automatically filled and adjusted. Finally, the machine organizes the adjusted results according to the reverse hierarchical structure of equipment, workstation, and process, generating a corresponding structured process hierarchy list. This method generates a list that is highly targeted at equipment management, facilitates tracing the production process from the equipment perspective, and is suitable for flexible production scenarios with frequent equipment adjustments.

[0054] Step S20: Based on the ratio of energy consumption to material consumption, select the key processes in the structured process hierarchy list and determine the list of key data collection objects.

[0055] In this embodiment, the energy consumption to material consumption ratio standard refers to a preset quantitative benchmark used to determine the importance of a process. Key processes are defined by comparing the proportions of their energy consumption and material consumption to the total production energy consumption and total material consumption, respectively. A key process is a production process whose energy or material consumption ratio reaches the energy consumption to material consumption ratio standard and has a significant impact on carbon footprint accounting. The key data collection object list refers to a list recording the selected key processes and the corresponding workstations and equipment information for which data needs to be collected.

[0056] As an optional implementation, the workstation and equipment information corresponding to each process, as well as the energy consumption statistics and material consumption statistics associated with each process, are extracted layer by layer from the structured process hierarchy list. Then, pre-stored energy and material consumption ratio standards are loaded, and the energy and material correlation algorithm is invoked to calculate the energy consumption ratio of each extracted process by associating it with the total production energy consumption benchmark value, and to calculate the material consumption ratio of each process by associating it with the total material consumption benchmark value. By comparing the energy consumption ratio and material consumption ratio of each process with the ratio standards one by one, if the energy consumption ratio or material consumption ratio of a certain process reaches the standard, the process is marked as a critical process, and the corresponding workstation and equipment are recorded simultaneously. Finally, all marked critical processes and their corresponding workstations and equipment are organized according to the structure of critical process, associated workstation, and associated equipment to generate a list of key data collection objects. This method is suitable for scenarios with a small number of processes and relatively simple production processes, ensuring that the selected critical processes comprehensively cover high-energy-consuming and high-material-consuming links.

[0057] As an alternative implementation, the processes in the key data collection object list are sorted according to the production flow sequence, and the energy consumption type and material type of each process are extracted. The proportion standards for energy consumption and material consumption, along with a pre-stored association library of process, energy consumption, and material characteristics, are loaded. Based on this association library, the sorted processes are initially screened, and processes with high energy consumption and major material consumption are marked as candidate key processes. Next, the proportions of the candidate key processes are calculated. The proportion calculation module is called, and combined with the baseline data of total production energy consumption and total material consumption, the energy consumption proportion and material consumption proportion of each candidate key process are calculated. The calculated proportions are then compared with the preset proportion standards, retaining the candidate key processes that meet the standards and removing those that do not. Finally, the machine integrates the key processes that meet the standards and their corresponding workstations and equipment information to generate a key data collection object list. This method is suitable for scenarios with a large number of processes, complex production processes, and a complete association library. It can quickly screen out core key processes and improve overall process efficiency.

[0058] Step S30: Collect key data corresponding to the list of key data collection objects, and match it with auxiliary data according to timestamps to generate the actual test raw dataset.

[0059] In this embodiment, key data refers to energy consumption data and material flow data explicitly listed in the key data collection object list that are related to carbon footprint accounting. Auxiliary data refers to production auxiliary information that supports data correlation analysis, including production batch numbers, process hours, equipment operating status, etc., providing parameters for dynamic allocation. A timestamp is a time marker that records the moment the data was generated, used to ensure time consistency of data from different sources. The actual measured raw dataset refers to the unprocessed raw data set formed after key data and auxiliary data are correlated and matched using timestamps.

[0060] As an optional implementation, based on the equipment information in the list of key data acquisition objects, acquisition commands are sent to the sensors of the corresponding devices. The sensors acquire key data in real time according to preset rules. The acquired key data is automatically appended with a timestamp of the acquisition time and temporarily stored in an edge buffer. Simultaneously, auxiliary data related to the key data acquisition objects is acquired synchronously through the production control system interface. The auxiliary data is automatically generated with a corresponding timestamp and transmitted to the same edge buffer. Subsequently, the timestamps of the key data and auxiliary data in the buffer are compared, and key data with timestamp deviations within the allowable range are associated and bound with the auxiliary data. Finally, the associated and bound data is standardized in format, unifying data fields and structures, and integrated to form the measured original dataset. This method can efficiently process large-scale continuous acquisition data, is suitable for batch data acquisition under stable production conditions, and generates a dataset with wide coverage.

[0061] As an alternative implementation, the clocks of the sensors listed in the inventory are calibrated to the same time base as the clock of the production control system. Then, according to the workstation groups in the inventory, a data acquisition thread is initiated for each workstation: sensors collect the key data corresponding to that workstation and attach a high-precision timestamp; simultaneously, the production control system pushes auxiliary data for that workstation. Both types of data are transmitted to the local data processing node in real time. The data processing node parses the received key and auxiliary data in real time, extracts the timestamp field, sorts it chronologically, and uses a sliding window algorithm to filter out related data within the same time segment, completing dynamic association. The associated data from each workstation is then summarized to check if it covers all key objects in the inventory; if any are missing, a supplementary acquisition mechanism is triggered. Finally, the complete associated data after supplementary acquisition is packaged to generate the original measured dataset. This method generates a dataset with small association errors and high completeness, suitable for scenarios with strict data accuracy requirements.

[0062] Step S40: Analyze the verified raw dataset, calculate the carbon emissions of each process and emission source, and summarize to obtain the life cycle emission inventory.

[0063] In this embodiment, the verified measured raw dataset refers to the original collected data set containing key and auxiliary data after data verification to confirm its accuracy. Each process refers to the specific production steps in the battery production process. Each emission source refers to the specific source of carbon emissions. Carbon emissions refer to the emission values ​​measured in carbon equivalents from each emission source. The lifecycle emissions inventory is a structured list summarizing the carbon emissions of each stage, process, and emission source throughout the battery's entire lifecycle.

[0064] As an optional implementation, the dataset is split according to the process dimension, and the activity level data and associated emission source types corresponding to each process are extracted. Next, a preset emission factor matching rule is invoked to automatically match the corresponding emission factors based on the emission source types of each process. Subsequently, the activity level data of each emission source under each process is associated with the matched emission factors, and the carbon emissions of each emission source are calculated item by item, with the results temporarily stored according to the process and emission source hierarchy. After calculation, the emissions of each process are categorized according to the battery life cycle stage, and the emissions within the same stage are accumulated and summarized to obtain a summary result. Finally, the machine integrates the summary results of each stage to generate a life cycle emission inventory containing the carbon emissions of each stage, process, and emission source throughout the entire life cycle. The inventory generated by this method is more closely aligned with actual production operations and is suitable for process-level emission reduction analysis at the production end.

[0065] Step S50: Calculate the full life cycle carbon footprint of the battery based on the carbon emissions of each stage and process in the battery's full life cycle emission inventory.

[0066] In this embodiment, each stage of the battery's entire life cycle refers to the different segments of the complete process from raw material input to end-of-life recycling. The life cycle carbon footprint refers to the total carbon equivalent emission value obtained by combining the carbon emissions of each stage and process of the battery's entire life cycle, based on a unit battery product, reflecting the carbon emission level of the battery from production to recycling.

[0067] As an optional implementation, this method divides the entire lifecycle of a battery into stages: raw material acquisition, production, transportation, use, and recycling. It extracts the carbon emissions of all processes corresponding to each stage from the lifecycle emissions inventory, and then sums the carbon emissions of each process within the same stage to obtain the total carbon emissions for each lifecycle stage. Next, it compares the total carbon emissions of each stage with the initial statistical value for that stage in the inventory to confirm no duplicate calculations or omissions, and then temporarily stores the total carbon emissions of each stage in an intermediate data area. Subsequently, it calls the total carbon emissions of each stage from the intermediate data area and sums the total carbon emissions of all stages to obtain the total carbon emissions for the entire battery lifecycle. Finally, it extracts the corresponding battery product production data recorded in the inventory, and correlates the total lifecycle carbon emissions with the production volume to obtain the lifecycle carbon footprint per unit battery product. This method generates a lifecycle carbon footprint directly correlated with stage carbon emission data, making it more suitable for emission reduction optimization scenarios focusing on specific stages such as production and use.

[0068] As an alternative implementation, the emission source types corresponding to all carbon emissions are identified from the lifecycle emission inventory. The carbon emissions of each process in the inventory are then categorized according to emission source type, and the total carbon emissions for each emission source type over the entire lifecycle are calculated. Next, the total carbon emissions for each emission source type are matched with the corresponding lifecycle stage to obtain the correlation between emission sources and stages. Based on this correlation, the carbon emissions of all emission source types within each stage are summed to obtain the total carbon emissions for each lifecycle stage. Finally, the battery product specifications and production information corresponding to the lifecycle emission inventory are obtained, and the total carbon emissions for all stages throughout the lifecycle are converted into the carbon emissions per unit specification of battery product, thus obtaining the full lifecycle carbon footprint of the battery. This method generates a full lifecycle carbon footprint that includes data support from both emission sources and stages, making it more suitable for developing emission reduction plans from a whole-chain source control perspective.

[0069] For example, in the scenario of carbon footprint accounting for automotive power batteries, the machine first analyzes the battery production process based on the core production stages defined by the accounting boundary: "battery component production, single cell and pack production." This process is broken down into a three-level structure: "electrode preparation: slurry mixing, coating, etc. - corresponding workstations - equipment," generating a structured process hierarchy list. Then, based on the standard that "energy consumption accounts for more than 10% of total production energy consumption or material consumption accounts for more than 8% of total material input," key processes such as coating, electrolyte injection, and formation testing are selected, determining a list of key data collection objects. Subsequently, the machine collects the corresponding key data from the list through an IoT sensor network, such as coating machine power consumption and electrolyte input, and matches it with auxiliary data such as production batches and working hours according to timestamps to generate a measured raw dataset. After verifying that the material balance error is ≤±2%, the machine analyzes the verified measured raw dataset, calculates the carbon emissions from each process's hidden emission sources such as electricity and materials, and summarizes them to obtain a lifecycle emission inventory. Finally, based on the carbon emissions of each stage of raw material processing and production in the list, the carbon footprint of the vehicle power battery unit product throughout its entire life cycle is calculated.

[0070] Furthermore, referring to Figure 2 , Figure 2 This is a flowchart illustrating the carbon footprint accounting process for this application. In the carbon footprint accounting scenario for ternary lithium batteries, the core production stages are determined based on the accounting boundaries: "electrode preparation, cell assembly, and formation." The production processes are analyzed to generate a structured process hierarchy list including steps such as "cathode material mixing, electrode coating, cell winding, and charge / discharge." For example, based on the criterion that "energy / material consumption exceeding 15% is critical," key processes such as "cathode material mixing (nickel-cobalt-manganese raw material consumption 20%)," "electrode coating (power consumption 18%)," and "cell charge / discharge (energy consumption 16%)" are selected, and a list of key data collection objects is determined. By deploying an IoT sensor network, key data such as raw material usage and power consumption for these key processes are collected, and auxiliary data such as production line hours are correlated. The actual measured raw dataset is generated by matching timestamps. The dataset was validated using a stoichiometric material balance verification model (passing if the error is ≤ ±2%). The validated data was analyzed, and the carbon emissions of each process (e.g., emissions from cathode material mixing due to raw material production, and indirect emissions from coating due to electricity) and each emission source (direct combustion, indirect electricity generation) were calculated. A lifecycle emissions inventory covering the entire process from cradle to gate was then compiled. Based on the carbon emissions of each stage (total carbon dioxide emissions of 320 kg during the production stage) and each process in the inventory, the full lifecycle carbon footprint of the battery was calculated.

[0071] By constructing a production line-level measured data acquisition system, introducing a stoichiometric material balance verification algorithm, and establishing a dynamic allocation model, the technical problem of the battery full-cycle carbon footprint accounting results deviating from the actual situation has been overcome. This has improved the accuracy of data acquisition, the reliability of the verification mechanism, and the model's dynamic adaptability to production scenarios, thus achieving high-precision battery full-lifecycle carbon footprint accounting.

[0072] Based on any of the above embodiments, in Embodiment 2 of this application, before step S10, steps A11 to A13 are further included:

[0073] Step A11: Based on preset definition conditions, divide the battery's life cycle and determine the stage division results.

[0074] In this embodiment, the preset defining conditions refer to pre-defined standards or rules used to guide the division of battery lifecycle stages. The battery lifecycle refers to the complete process of a battery from the acquisition of raw materials, through production and use, until its end-of-life recycling. The stage division result refers to a structured list of each independent stage of the battery lifecycle after the stage division is completed, including the name, boundaries, and core content of each stage.

[0075] As an optional implementation, the division criteria and coverage range are extracted from the preset definition conditions. Then, a pre-stored battery lifecycle basic process library is loaded, which contains the complete process nodes of a battery from raw material input to recycling. According to the parsed division rules, the process nodes in the basic process library are grouped into raw material acquisition stage, production stage, usage stage, and recycling stage. After grouping, it is checked whether the stage boundaries of each group conform to the coverage range in the preset definition conditions. If there are process nodes that are exceeded or missing, the grouping is automatically adjusted. Finally, the adjusted grouping results are organized into structured information containing the names, boundaries, and core process nodes of each stage, generating the stage division results. The stage division results generated by this method are suitable for refined analysis of the material flow and energy flow of the battery lifecycle, especially applicable to scenarios requiring in-depth breakdown of the internal processes of each stage.

[0076] As an alternative implementation, based on the process node division criteria in the preset definition conditions, key nodes with demarcation significance are selected from a pre-stored general lifecycle node library for the battery industry. These key nodes are then sorted chronologically to form a node sequence. Subsequently, using adjacent key nodes as boundaries, the battery lifecycle is broken down into several stages. After decomposition, it is confirmed that all key nodes have been included as boundaries, and that there is no overlap or gap between stages. Finally, the names of each stage, the boundaries of key nodes, and the included process content are integrated to generate the stage division result. This method generates stage division results with clear boundaries and high division efficiency, suitable for scenarios where high precision of stage boundaries is not required but rapid adaptation to different battery types is necessary.

[0077] Step A12: Calculate the direct emissions and indirect energy emissions of the corresponding organizational level in the stage division results using the hierarchical control method, and output the initial calculation boundary.

[0078] In this embodiment, the hierarchical control method refers to a method of hierarchically controlling the scope and objects of carbon emission accounting according to organizational management or operational dimensions. The stage division result refers to the structured list output from the previous step, containing each stage of the battery lifecycle. Organizational level refers to the organizational management level used when calculating carbon emissions. Direct emissions refer to carbon emissions directly generated by an organization through its own activities within the corresponding stage and organizational level. Energy indirect emissions refer to carbon emissions indirectly generated by an organization consuming purchased energy within the corresponding stage and organizational level. The initial accounting boundary refers to the boundary definition result, including the accounting scope, emission type, and organizational level, determined after calculating direct and energy indirect emissions using the hierarchical control method.

[0079] Step A13: Incorporate upstream key raw material emissions into the preliminary accounting boundary and output the final accounting boundary.

[0080] In this embodiment, upstream critical raw material emissions refer to the implicit carbon emissions generated from the mining, processing, and transportation of critical raw materials required for battery production to the production end due to production, processing, and energy consumption. The final accounting boundary refers to the final result that fully defines the scope of carbon emission accounting for the battery lifecycle after including upstream critical raw material emissions.

[0081] As an optional implementation, the system reads the preliminary accounting boundary file and calls the upstream raw material classification database. This database categorizes raw materials into different priority categories, such as main raw materials, auxiliary materials, and consumables. The system iterates through the raw material list for each category according to a preset priority order. For each raw material in the list, the system automatically queries its corresponding background lifecycle database to check for available emission factor data. If it exists, the raw material and its corresponding emission source are automatically added to the accounting boundary. If it does not exist, the raw material is marked as temporarily excluded, and the reason is recorded. After iterating through all priority raw material categories, the system generates a new upstream emission source list, merges this list with the preliminary accounting boundary, and outputs the final accounting boundary file.

[0082] For example, in scenarios involving the segmentation of battery lifecycle stages and the determination of accounting boundaries, this module forms the foundation for the accounting work, aiming to establish a comprehensive and reasonable system boundary. Stage Segmentation: Adopting a "cradle-to-grave" full lifecycle perspective, the battery lifecycle is systematically divided into raw material acquisition and processing, battery component production (such as positive and negative electrodes, separators, and electrolytes), battery cell and pack production (core stage), distribution and transportation, usage stage (usually based on assumed charge-discharge cycle counts and energy efficiency models), and end-of-life and recycling. Boundary Determination: The accounting boundary is defined using a hierarchical control method, prioritizing the accounting of direct emissions (Scope 1) and indirect emissions (Scope 2) at the organizational level, and gradually incorporating emissions from upstream key raw materials (Scope 3).

[0083] By dividing the entire process into stages, calculating emissions at different levels, and allocating benefits in a closed loop, the system addresses the issues of traditional accounting methods that only cover the production stage, lack upstream emissions, and fail to quantify recycling benefits, thereby improving the accuracy of carbon footprint accounting.

[0084] Based on any of the above embodiments, in Embodiment 3 of this application, step S10 includes steps B11 to B13:

[0085] Step B11: Call the battery process knowledge base to match the core production stage range determined by the accounting boundary, and determine the candidate process list within the core production stage range.

[0086] In this embodiment, the battery process knowledge base refers to a structured database that pre-stores process information for the entire battery production process, including data such as the process name, process logic, applicable scenarios, and associated equipment corresponding to each production stage. The candidate process list refers to a set of processes that belong to the core production stage and may participate in carbon footprint accounting, selected by matching the battery process knowledge base, and presented in a structured form.

[0087] As an optional implementation, the scope marked as the core production stage is selected from the stage definition information of the accounting boundary, and the name, functional boundary, and key feature parameters of each core production stage are extracted. Next, a battery process knowledge base is loaded, which stores data according to the hierarchical structure of production stages and processes. Based on the extracted core production stage names, a forward matching query is performed in the knowledge base to filter out all processes belonging to each core production stage, forming an initial candidate process set. Then, the functional descriptions of the initial candidate processes are compared with the functional boundaries of the core production stages, and processes whose functions exceed the scope of the core stages are eliminated. Finally, the validated processes are categorized and organized according to the core production stages, generating a structured candidate process list containing core stages, candidate processes, and process functions. The candidate process list generated by this method has strong stage correlation and well-organized process classification, making it suitable for battery manufacturing processes with a high degree of standardization.

[0088] As an alternative implementation, process attributes for each core production stage are extracted from the core production stage range within the accounting boundary. A knowledge base reverse retrieval module is activated, calling the battery process knowledge base, which labels each process step with a corresponding process attribute tag. Then, based on the extracted core stage process attributes, all processes with matching attribute tags are retrieved from the knowledge base, forming a cross-stage candidate process pool. Next, a stage attribution verification module is activated to confirm whether the preset attribution stage of each process in the candidate process pool belongs to the core production stage within the accounting boundary, eliminating processes whose attribution stage is not a core stage. Finally, the verified processes are associated with their corresponding core stages according to their process attributes, generating a candidate process list containing process attributes, core stages, and candidate processes. This method generates a candidate process list with strong correlation of process attributes, covering more processes that meet the core stage process requirements, making it suitable for scenarios where core stage process attributes are dynamically adjusted in flexible production.

[0089] Step B12: Verify the production equipment corresponding to the processes in the candidate process list, and output a mapping table between equipment and processes.

[0090] In this embodiment, the production equipment corresponding to a process refers to the specific production equipment on which a certain process operation is actually performed. The equipment-process mapping table is a structured list that accurately presents the relationship between each process and its corresponding production equipment, clearly specifying which equipment performs each process.

[0091] As an optional implementation, the process name and functional description are extracted one by one from the candidate process list. A pre-stored process-equipment association library is invoked, which records the default equipment models and functional parameters corresponding to each process in regular production. Initial associated equipment is matched based on the extracted process names. Subsequently, the functional parameters of the initial associated equipment are compared with the functional descriptions of the corresponding processes. If the functions match, the association is retained; otherwise, it is marked as needing to be supplemented. For processes marked as needing supplementation, the equipment candidate library is invoked to retrieve other equipment with the corresponding process functions and re-verify them until a matching equipment is determined. Finally, all verified process-equipment associations are integrated and organized in the format of process name, equipment model, and functional matching degree to generate a mapping table of equipment and processes. This method generates a mapping table with processes as the core and accurate equipment associations, suitable for scenarios where equipment data is traced by process.

[0092] Step B13: Based on the mapping relationship table between equipment and process, construct a tree-like topology structure with process, workstation, and equipment as the hierarchical relationship, and generate the structured process hierarchy list.

[0093] In this embodiment, the hierarchical relationship between processes, workstations, and equipment refers to the superior-subordinate relationship of "process (core production step), workstation (operation unit broken down into processes), and equipment (tools performing workstation operations)" in the process structure. Workstations belong to processes, and equipment belongs to workstations. A tree-like topology structure refers to a structure that presents hierarchical relationships in a tree-like form. The top layer consists of process nodes, the middle layer consists of workstation nodes belonging to the process, and the bottom layer consists of equipment nodes belonging to the workstation, clearly demonstrating the superior-subordinate relationship. A structured process hierarchy list refers to a standardized process list organized according to the tree-like topology of processes, workstations, and equipment, including the name of each level, its relationships, and core attributes.

[0094] As an optional implementation, all independent process names are extracted from the equipment-process mapping table and sorted according to the production flow. A pre-stored process-workstation splitting library is called to match a dedicated workstation to each sorted process, forming a process-workstation association group. Subsequently, based on the equipment-process mapping table, the equipment corresponding to each workstation is extracted from the mapping table and bound to its respective workstation. Then, using the sorted processes as top-level nodes, each process has its corresponding workstation as a mid-level node, and each workstation has its corresponding equipment as a bottom-level node, generating an initial tree-like topology. Finally, it is checked whether there are processes without workstations or workstations without equipment. If so, alternative workstations or equipment libraries are called to supplement the associations. After confirming no omissions, the tree structure is transformed into a structured process hierarchy list with clear process, workstation, and equipment levels. The list generated by this method has a high degree of fit with the production flow and is suitable for production lines with stable processes and a high degree of standardization.

[0095] As an alternative implementation, all equipment models are extracted from the equipment-process mapping table and grouped by equipment function. Next, the equipment-workstation adaptation library is called to match the corresponding workstation for each type of equipment, forming equipment-workstation adaptation groups. Subsequently, the process to which each workstation belongs is queried in reverse according to the equipment-process mapping table, associating the workstation with the corresponding process. Then, the reverse tree structure construction module is activated, using equipment as the bottom-level node, each equipment with its corresponding workstation as the middle-level node, and each workstation with its corresponding process as the top-level node, generating a reverse tree topology. Finally, the machine initiates affiliation logic verification, checking whether workstations under the same process cover all equipment corresponding to that process, and whether all equipment under the same workstation meets the workstation's functional requirements. After correcting affiliation errors, the reverse tree structure is adjusted into a structured process hierarchy list presented in a forward hierarchy of processes, workstations, and equipment. The list generated by this method focuses more on the association between equipment and workstations, making it suitable for production lines where equipment is dominant and processes are frequently adjusted.

[0096] For example, in the scenario of battery manufacturing process division and data acquisition, this module is the core of improving the accuracy of calculations. By constructing a production line-level measured data acquisition system, it achieves a leap in data granularity from "workshop level" to "process level". Process level decomposition: The battery manufacturing system is deconstructed into three levels: process → workstation → equipment. The focus is on key quality and energy consumption processes such as electrode preparation (slurry stirring, coating, rolling, slitting), cell assembly (stacking / winding, electrolyte injection, encapsulation), and formation testing (activation, capacity testing, capacity grading). Multi-dimensional data acquisition: An IoT sensor network is deployed at the equipment end of each key process, including: Energy consumption data: Real-time collection of instantaneous and cumulative consumption of electricity, natural gas, and steam through smart meters. Material flow data: Tracking the input, output (qualified products), and loss (scrap, scrap) of major materials such as positive and negative electrode active materials, separators, and electrolytes through high-precision weighing sensors and flow meters. Auxiliary Data: Synchronously collect data on product specifications, production batches, working hours, and equipment status to provide parameters for dynamic allocation. Construct a production line-level measured data acquisition system to improve calculation accuracy. Data acquisition dimensions: Deploy IoT sensors in key battery production line processes (such as electrode preparation, cell assembly, and formation testing) to collect real-time process-level energy consumption data (electricity, natural gas, steam consumption, accuracy ±1%), material flow data (positive and negative electrode materials, electrolyte consumption, accuracy ±0.1kg), and product quality data (battery capacity decay rate, charge / discharge efficiency, accuracy ±0.5%). Real-time monitoring of unit product power consumption in the coating process using smart meters replaces traditional workshop-level statistical data; weighing sensors track the amount of electrode material fed in each batch to achieve accurate material balance verification. Data verification mechanism: Introduce material balance verification. Material balance: The error between raw material input and finished product / waste output is controlled within ±2%, ensuring data authenticity.

[0097] By quantifying contribution based on historical data, accurately filtering using thresholds, and matching with IoT metadata, the problems of traditional key process screening relying on experience and redundant data collection have been solved. This improves the accuracy of data collection for key processes.

[0098] Based on any of the above embodiments, in Embodiment 4 of this application, step S20 includes steps C11 to C13:

[0099] Step C11: Call the historical carbon emission database to calculate the energy consumption and material consumption ratio of each process in the structured process hierarchy list, and obtain the process contribution ranking list.

[0100] In this embodiment, the historical carbon emission database refers to a structured database that pre-stores energy consumption data, material consumption data, and corresponding carbon emission records for each process in the past battery production process, including data collection period, production batch, and other related information. The energy and material consumption ratio of each process refers to the proportion of energy consumption of a single process to the total energy consumption of all processes, and the proportion of material consumption of a single process to the total material consumption of all processes, used to quantify the contribution of each process to overall consumption. The process contribution ranking list is a structured list ranked from highest to lowest based on the comprehensive calculation of the energy and material consumption ratios of each process, including process name, energy consumption ratio, material consumption ratio, and overall contribution.

[0101] As an optional implementation, all independent process names are extracted from the process hierarchy of the structured process hierarchy list, and duplicate or redundant process entries are removed to form a process list to be calculated. Based on the process names in the process list, the energy consumption data and material consumption data for that process in the historical carbon emission database for a preset production cycle are forward matched. If a process has no matching historical data, it is marked as needing to be supplemented, and a secondary search is performed using the database backup data source. Subsequently, the energy consumption data of all processes in the process list to be calculated are accumulated to obtain the total energy consumption of the entire process, and the material consumption data of all processes are accumulated to obtain the total material consumption of the entire process. Then, the energy consumption ratio and material consumption ratio of each process are calculated separately, and the comprehensive contribution of each process is calculated according to preset weights. Afterwards, the machine starts the sorting module to sort all processes from high to low according to the comprehensive contribution value. For processes with the same contribution value, a secondary sort is performed according to the energy consumption ratio from high to low. Finally, the sorted process names, energy consumption ratios, material consumption ratios, and comprehensive contributions are integrated to generate a process contribution ranking list. The ranking list generated by this method comprehensively reflects the overall contribution of the processes and is suitable for selecting key processes from a global perspective.

[0102] Step C12: Select key processes from the process contribution ranking list by using a preset contribution threshold rule to determine the set of key processes.

[0103] In this embodiment, the preset contribution threshold rule refers to the pre-set contribution judgment standard used to determine whether a process is a key process. It can be divided into a single numerical threshold or a multi-dimensional composite condition threshold, which clarifies the contribution threshold for key processes.

[0104] As an optional implementation, a single fixed threshold rule is extracted from a pre-stored rule base and transformed into machine-recognizable judgment logic. Next, a traversal filtering module is activated, extracting the comprehensive contribution data of each process in the order of the sorted list and comparing it with a preset single fixed threshold. If the comprehensive contribution of a process is greater than or equal to the threshold, it is marked as a candidate key process and temporarily stored in a temporary set. After traversal, the machine activates a set verification module to check whether all candidate key processes in the temporary set originate from the process contribution sorting list, removing non-target processes mistakenly added due to data anomalies, and confirming that all processes in the sorting list with qualified comprehensive contributions have been included in the temporary set. Finally, the verified candidate key processes are sorted from highest to lowest comprehensive contribution and organized into a structured key process set containing process name, comprehensive contribution, and stage. This method generates a concise and clearly ordered key process set, suitable for production scenarios with high screening speed requirements and relatively balanced carbon emission impacts of processes.

[0105] Step C13: Match and associate the set of key processes with the sensor metadata in the IoT device registry to generate the list of key data collection objects.

[0106] In this embodiment, the IoT device registry refers to a database that stores basic information about all IoT sensors in the battery production scenario, including sensor model, installation location, associated production equipment, etc. Sensor metadata refers to the core descriptive information of the IoT sensor, covering sensor monitoring type, data acquisition accuracy, and corresponding monitoring parameters.

[0107] As an optional implementation, the core data types to be collected are extracted from each key process, forming a mapping table between key processes and core data types. Next, the IoT device registry is accessed, loading all sensor metadata and categorizing it by monitoring type. Then, based on the mapping table, sensors capable of monitoring the corresponding data types are filtered from the categorized sensor metadata. Simultaneously, it is verified whether the production equipment associated with the sensor belongs to the equipment corresponding to the key process. If both the sensor monitoring type and associated equipment match, an association between the key process and the sensor metadata is established. Finally, all associations are integrated and organized by key process name, core data type, sensor model, monitoring accuracy, and associated equipment structure to generate a list of key data collection objects. This method generates a list with clear data collection targets, suitable for scenarios with well-defined key process requirements and a moderate number of sensors.

[0108] As an alternative implementation, the sensor metadata loading module is first activated, which calls the IoT device registry to extract all sensor metadata and label its monitoring parameters, forming an index table of sensor metadata and monitoring parameters. Next, the process data requirement decomposition module is activated, breaking down the data collection requirements of each process in the key process set into specific monitoring parameters. Then, the reverse matching module is activated, using the decomposed monitoring parameters as keywords to search the index table of sensor metadata and monitoring parameters for matching sensor metadata. Simultaneously, it checks whether the installation location corresponding to the sensor metadata is within the production area of ​​the key process. If both location and parameter match, the correspondence between the sensor metadata and the key process is recorded. After matching, the redundancy removal module is activated, retaining the sensor with the highest data collection frequency from multiple sensor metadata monitoring the same parameter under the same key process. Finally, the data is integrated according to the structure of sensor metadata, monitoring parameters, key process name, and installation location to generate a list of key data collection objects, which is stored as input for the next stage. This method generates a list that captures potential data requirements of processes, providing richer basic data for subsequent carbon accounting, and is particularly suitable for battery production scenarios with complex processes and requiring multi-dimensional data support.

[0109] For example, in the scenario of ternary lithium battery production, the machine accesses a historical carbon emission database storing nearly two years of production data to calculate the energy and material consumption ratios of processes such as electrode preparation (slurry stirring, electrode coating), and cell assembly (stacking, electrolyte injection, and formation testing) in a structured process hierarchy list. This yields a ranking list of process contributions. For instance, electrode coating accounts for 28% of energy consumption, electrolyte injection accounts for 22% of material consumption, and formation testing accounts for 17% of energy consumption, ranking in the top three. By setting a contribution threshold rule of "energy or material consumption ratio ≥ 15%", the processes of electrode coating, electrolyte injection, and formation testing are selected from the ranking list to determine the set of key processes. This set is then matched and associated with sensor metadata in the IoT device registry: electrode coating machine power sensor, electrolyte injection machine electrolyte flow sensor, and formation cabinet energy consumption sensor, generating a list of key data collection objects.

[0110] By quantifying contribution based on historical data, accurately screening based on thresholds, and matching sensor metadata, the problem of relying on experience and data collection redundancy in traditional key process screening has been solved, thus improving the accuracy of data collection for key processes.

[0111] Based on any of the above embodiments, in Embodiment 5 of this application, step S30 includes steps D11 to D13:

[0112] Step D11: Generate a data collection instruction set based on the list of key data collection objects, and control the IoT gateway to collect the key data with timestamps.

[0113] In this embodiment, the data acquisition instruction set refers to a set of instructions generated based on the list of key data acquisition objects, containing acquisition parameters, sensor identifiers, and execution times, used to guide IoT devices to perform acquisition operations. The IoT gateway is an intermediary device connecting the IoT sensors and the data processing system, capable of receiving acquisition instructions, transmitting sensor data, and enabling communication and interaction between the device and the system. Timestamped key data refers to the dataset containing key data acquired by the sensors according to instructions, with the acquisition time stamp appended, ensuring the accuracy of the data's time dimension.

[0114] As an optional implementation, the monitoring parameters and associated key process information for each sensor are extracted from the list of key data acquisition objects. Individual acquisition instructions are generated according to the structure of sensor identification, monitoring parameters, acquisition frequency, and data format requirements. All individual instructions are integrated to form an initial data acquisition instruction set. Next, the instruction set is checked for abnormal instructions such as duplicate sensor identifications or acquisition frequencies exceeding the sensor's capability range. Abnormal instructions are automatically corrected to generate the target data acquisition instruction set. The machine then sends the final instruction set to the IoT gateway via a communication interface. After receiving the instructions, the gateway parses the sensor address corresponding to each instruction and sends an acquisition trigger signal to the target sensor. The sensor responds to the acquisition trigger signal, acquires key data, automatically adds a timestamp of the acquisition time, and sends the timestamped key data back to the IoT gateway. The gateway performs format verification on the data, temporarily stores it, and synchronously feeds back the acquisition status to the machine, completing one data acquisition cycle. This method generates an instruction set with strong versatility and a stable data acquisition rhythm, making it suitable for production scenarios with stable processes and balanced requirements for data real-time performance.

[0115] As an alternative implementation, the instruction layering module is first activated, dividing the data collection objects in the list into a core layer and a general layer according to the priority of key processes. The core layer corresponds to high-frequency collection instructions, and the general layer corresponds to low-frequency collection instructions. Subsets of core layer instructions and general layer instructions are generated separately and merged into a data collection instruction set. Then, the gateway configuration module is activated. When the instruction set is sent to the IoT gateway, the gateway's data caching and feedback rules are configured simultaneously. The gateway parses the instructions according to the configuration and sends collection instructions to the corresponding sensors. The sensors collect key data with timestamps and send it to the gateway. The gateway temporarily stores or sends back the data according to the rules. If data from a sensor times out, the gateway automatically triggers a second collection instruction until the data is successfully received or the collection is marked as failed. Finally, the gateway feeds back all valid data and collection status to the machine. The instruction set generated by this method is highly targeted and suitable for production scenarios that are sensitive to high-contribution process data and require key monitoring.

[0116] Step D12: Extract the values ​​of the key data and their corresponding timestamps, and arrange the values ​​of the key data in order of timestamps to obtain standardized key data.

[0117] In this embodiment, the numerical value of key data refers to the specific data content used for quantitative calculation within the key data. The corresponding timestamp refers to the time marker bound to the key data value, recording the moment the data was collected, ensuring the temporal correlation of the data. Arranging by timestamp order refers to the operation of organizing and sorting the key data values ​​according to their bound timestamps in chronological order. Standardized key data refers to the key dataset with a unified format and clear temporal logic after numerical extraction and timestamp sorting, facilitating subsequent data analysis and calculation.

[0118] As an optional implementation, the input key data is broken down based on key processes and sensor identifiers, grouping time-stamped key data collected from the same key process and sensor into independent data groups. For each data group, the numerical values ​​of the key data are extracted from each data entry, and entries with abnormal data formats are removed. Then, the extracted numerical values ​​and timestamp pairs within each data group are sorted according to the chronological order of the timestamps. If multiple values ​​correspond to the same timestamp, the average value is automatically taken as the final value for that timestamp. Finally, all sorted independent data groups are integrated according to key processes, and the data format is standardized to generate standardized key data. This method is easy to operate and suitable for carbon accounting scenarios centered on processes.

[0119] As an alternative implementation, a data type classification module is activated to group the input data according to the type of key data. All energy-related key data with timestamps are grouped into an energy-related data group, and all material-related key data with timestamps are grouped into a material-related data group. Next, for each data group, a batch extraction module is activated. Using preset data field identification rules, it batch extracts the key data values ​​and corresponding timestamps of all data within the group, generating a list of values ​​and timestamps, while also marking the key processes and sensor information associated with each data point. Subsequently, a global time sorting module is activated to globally sort the list of values ​​and timestamps within the same data group based on the timestamps, retaining the key process and sensor identifiers associated with each data point during the sorting process. Finally, the machine performs format validation on the sorted set of values, timestamps, and associated information, organizing it into standardized key data categorized by data type and ordered chronologically. This method offers high flexibility when filtering data by process or sensor as needed, making it suitable for scenarios requiring multi-dimensional data analysis.

[0120] Step D13: Based on the unified timestamp, associate and match the standardized key data with the auxiliary data to generate the measured original dataset.

[0121] In this embodiment, a unified timestamp refers to a pre-set, uniformly formatted time stamp benchmark used to eliminate time format differences between key data and auxiliary data, ensuring the accuracy of the association.

[0122] As an optional implementation, timestamps are extracted from standardized key data and their format is unified to a preset data format to generate key data with unified timestamps. Next, auxiliary data from the production system is loaded, invalid timestamps are removed, and the timestamps of the remaining auxiliary data are also unified to the same format to generate auxiliary data with unified timestamps. Then, using the key data with unified timestamps as a benchmark, the system iterates through the data in chronological order. For each key data point corresponding to a timestamp, it retrieves auxiliary data with the same timestamp or within the allowed time deviation range from the auxiliary data with unified timestamps, establishing association groups for key data, auxiliary data, and unified timestamps. If a key data point has no matching auxiliary data, it is marked as missing auxiliary data, and the key data is retained. Finally, all association groups and marked missing data are integrated and organized according to the structure of unified timestamps, standardized key data, auxiliary data, and missing data annotations to generate the actual test dataset. This method has a direct matching logic and is suitable for scenarios where auxiliary data has dense timestamps and is easy to retrieve.

[0123] For example, a dynamic allocation model is established to adapt to complex production scenarios. In the production stage: for multiple battery models produced on mixed production lines, the measured man-hour percentage allocation method replaces the traditional quality allocation method. The carbon emissions of a shared process allocated to a certain battery model = total carbon emissions of the shared process × (production man-hours for that model / total production man-hours). In the disposal stage: a tiered utilization allocation coefficient is used. Compared with the measured capacity decay rate Combination:

[0124] ;

[0125] in, This refers to the recycling ratio; This refers to the recycling ratio; The coefficient is corrected, and the real-time market price ratio is dynamically updated through data from the industry trading platform connected via the Internet of Things.

[0126] Reuse the allocation coefficient Compared with measured recovery efficiency Related:

[0127] ;

[0128] in, The actual recycling rate of recycled materials is measured on the production line.

[0129] Develop a real-time carbon management platform to enhance production decision-making linkage, data integration and algorithm embedding, and build a real-time carbon footprint monitoring platform for production lines, integrating the following functions: Real-time early warning module: Set carbon intensity thresholds for processes and push abnormal notifications through production line LED screens or mobile devices; Path optimization algorithm: Build a multi-objective optimization model (minimize carbon footprint + maximize capacity) based on measured data and use it to solve for the optimal production path.

[0130] By using instruction set-based data collection, timestamp sorting, and unified timestamp association, the problems of disordered data collection, chaotic timestamps, and loose data association in traditional data collection have been solved, thus improving the accuracy of carbon emission calculation.

[0131] Based on any of the above embodiments, in Embodiment Six of this application, before step S40, steps E11 to E13 are further included:

[0132] Step E11: Call the preset material balance verification algorithm to perform elemental mass balance calculation on the input and output materials in the measured original dataset, and output the elemental flow theoretical content data of each material.

[0133] In this embodiment, the preset material balance verification algorithm refers to the calculation logic pre-stored in the system to verify whether the mass of elements contained in the input and output materials during the production process is conserved. It includes core functions such as element identification, mass conversion, and deviation determination. Element mass balance calculation refers to the process of calculating the total mass of each element in the input material based on the principle of element conservation and allocating it to each output material to verify whether the mass of input and output elements matches. The theoretical content data of element flow refers to the data on the theoretical mass and proportion of each element that should be contained in each output material, obtained through element mass balance calculation, serving as the basis for verifying the accuracy of the measured data.

[0134] As an optional implementation, entries labeled as input and output materials are selected from the original measured dataset, and the name, actual usage, and preset material element composition information of each material category are extracted. Next, the machine calls a preset material balance verification algorithm, first classifying by element type, and for each element category, accumulating the mass of that element in all input materials to obtain the total mass of that element at the input end. Subsequently, the algorithm allocates the total mass of the input element to each output material proportionally according to the process allocation ratio of each output material, calculating the theoretical mass and proportion of that element in each type of output material. After the calculation is completed, the machine starts the data processing module, integrating the total input mass corresponding to each element and the theoretical content of each element in each output material according to the structure of element type, output material name, theoretical content, and proportion, outputting the element flow theoretical content data of each material. The element flow theoretical content data generated by this method covers all elements, has high data integrity, and is suitable for basic verification scenarios with comprehensive element balance requirements.

[0135] Step E12: Calculate the relative error between the theoretical content data of the element flow and the measured output data, and generate error analysis results based on the comparison between the relative error and the preset tolerance.

[0136] In this embodiment, the measured output data refers to the actual mass and proportion of each element in each output material, extracted from the original measured dataset and detected during the actual production process. Relative error is an index that quantifies the difference between theoretical and actual values ​​by calculating the deviation between the theoretical content data and the measured output data. The preset tolerance is a pre-defined maximum range that allows for deviations between theoretical and actual values, used to determine whether the data error is within an acceptable range. The error analysis results are structured conclusions based on the comparison between the relative error and the preset tolerance, clarifying the error status of each element and the direction for improvement of elements exceeding the tolerance.

[0137] As an optional implementation, output material entries corresponding to the theoretical content data of elemental flows are selected from the measured raw dataset, and the measured output data of each element in each output material are extracted. For each element of each type of output material, the relative error is calculated according to the ratio of (theoretical content data - measured output data) to the measured output data, and the element type and output material name corresponding to each error are marked. Subsequently, preset tolerance parameters are loaded, and the relative error of each element is compared with the corresponding preset tolerance: if the absolute value of the relative error is less than or equal to the preset tolerance, the error status of the element is marked as qualified. If it exceeds the preset tolerance, it is marked as exceeding the tolerance and the error exceeds the tolerance is recorded. Finally, the error calculation results and status markings of all elements are integrated and organized according to the structure of output material, element type, theoretical content, measured content, relative error, and error status to generate an error analysis result containing a list of elements exceeding the tolerance and a preliminary speculation on the cause of the error. This method can accurately locate the error problem of a single element, and the element-level tolerance matching is more in line with the difference in detection accuracy of different elements, making it suitable for scenarios with few element types and requiring precise investigation of errors of a single element.

[0138] Step E13: Based on the error analysis results, a calibration process is triggered to correct the out-of-tolerance data in the measured output data, thereby obtaining the verified measured original dataset.

[0139] In this embodiment, calibration processing refers to adjusting and correcting out-of-tolerance data in the measured output data based on error analysis results, so that the data meets the elemental mass balance requirements. Weighted correction refers to a correction method that assigns weights according to the element importance, error magnitude, or material type corresponding to the out-of-tolerance data, and adjusts the out-of-tolerance data values ​​according to the weight ratio. Out-of-tolerance data refers to abnormal data in the measured output data whose relative error exceeds the preset tolerance, including out-of-tolerance elements, corresponding materials, and original numerical information.

[0140] As an optional implementation, the system loads corresponding group weights according to the material groupings in the error analysis results, while simultaneously loading the basic weights of each element within the group. Next, the machine extracts out-of-tolerance data from the error analysis results by group, forming out-of-tolerance datasets for main products and by-products. For each group of out-of-tolerance data, a dual correction weight is calculated for both the group and the element, combining the group weights and the element's basic weights. Subsequently, based on the dual correction weights for the out-of-tolerance data within each group, the corresponding out-of-tolerance values ​​are adjusted in batches, while recording the total correction weight and average error reduction for each group. After correction, the relative error of the corrected data is checked group by group to see if it meets the preset tolerance for that group. If a group still has out-of-tolerance data, the weight of that group is increased and corrected again. Finally, the corrected data from all groups are integrated with the original qualified data, and the calibration completion status and correction statistics for each group are marked to generate a verified original dataset. This method offers high correction accuracy and can precisely match the calibration requirements of different elements, making it suitable for scenarios where element importance varies significantly and fine-grained calibration is required.

[0141] For example, a cross-validation algorithm for battery carbon emission related data: To ensure the authenticity and reliability of the collected data, this work introduces a data cross-validation mechanism based on physicochemical principles. The core algorithm is a stoichiometric material balance verification. This algorithm is based on the elemental balance (e.g., lithium, nickel, cobalt, manganese elemental flows) and mass conservation of electrode active materials. The system compares the total amount of raw materials input with the theoretical content in the final product, by-products, and waste, controlling the total error rate within ±2%. If the error exceeds this range, a data anomaly alarm is automatically triggered, prompting on-site verification and calibration, thus forming a self-improving closed-loop feedback system for data quality.

[0142] By employing elemental balance calculations, error analysis, and weighted calibration, the problem of large deviations in carbon accounting caused by the lack of verification of traditional measured data and the direct use of out-of-tolerance data has been solved, thus improving the accuracy of carbon footprint accounting.

[0143] Based on any of the above embodiments, in Embodiment Seven of this application, referring to Figure 3 , Figure 3 This is a flowchart illustrating the seventh embodiment of the battery lifecycle carbon footprint calculation method of this application. Step S40 includes steps F11-F14:

[0144] Step F11: Standardize the verified raw dataset to generate activity level data.

[0145] In this embodiment, activity level data refers to the quantitative data that characterizes the scale or intensity of production activities after standardization, and is the core basic data for calculating carbon emissions.

[0146] As an optional implementation, the data type classification module is first activated to categorize the data into direct and indirect activity data. The statistical period standardization module is then activated to summarize direct activity data at a fixed period and indirect activity data at the same period. Next, the outlier secondary filtering module is activated to identify potential outliers in the standardized data according to a preset standard deviation rule, and, in conjunction with auxiliary data, determines whether to retain them, removing outliers without reasonable explanation. Then, the data association and labeling module is activated to attach corresponding process parameter labels to each data point. Finally, the processed data is integrated according to the structure of data type, statistical period, standard value, and process parameter labels to generate activity level data. This method enhances the temporal comparability of the data and is suitable for cross-time period and cross-type activity level analysis.

[0147] Step F12: The activity level data is classified and matched with the corresponding emission factors in the database according to the material and energy consumption type. The activity level data and its corresponding emission factors are calculated item by item by matrix multiplication to obtain the initial emission list.

[0148] In this embodiment, the emission factor refers to the unit carbon emission coefficient pre-stored in the database that corresponds to a specific material or energy consumption type.

[0149] As an optional implementation, the data is divided into two main sets: material type and energy consumption type. Each set is then further subdivided by specific category, and each sub-category is assigned a unique classification code. An emission factor database is accessed, and emission factors corresponding to each sub-category are matched forward according to the classification code. If a sub-category has no directly matching factor, an alternative factor from the database is retrieved and marked as the substitute. The categorized activity level data is then arranged into a data matrix according to the sub-category order, and the matched emission factors are arranged in the same order to form a factor matrix. The two matrices are multiplied according to their corresponding positions to obtain the emission amounts for each sub-category. Finally, an initial emission list is generated by summarizing the data according to the structure of material / energy consumption, sub-category, activity level data, emission factor, and emission amount. This method has a simple matching logic, high computational efficiency, and is suitable for scenarios with few material and energy consumption types and small differences in emission factors across processes.

[0150] Step F13: Perform real-time allocation calculation on the co-consumption emissions corresponding to mixed-line production in the initial emission list, and output a complete process emission list including direct emissions and indirect emissions after allocation.

[0151] In this embodiment, mixed-line production refers to a production mode in which multiple production lines for different products share the same equipment, energy, or material resources. Shared emissions refer to indirect carbon emissions generated from shared resources in mixed-line production that cannot be directly attributed to a specific process. Real-time allocation calculation refers to the calculation process of dynamically allocating shared emissions to corresponding processes based on real-time operational data of mixed-line production. Direct emissions refer to carbon emissions directly generated during the production process. Allocated indirect emissions refer to indirect carbon emissions that are attributed to each process after real-time allocation calculation. A complete process emission inventory is a structured list that integrates direct and allocated indirect emissions from each process, clearly defining the total emissions and composition of each process.

[0152] As an optional implementation, emission items marked as shared resources are selected from the initial emission inventory, and the total shared emissions and corresponding shared resource types are extracted. Real-time product output data for each process in the mixed-line production is obtained, and the proportion of each process's output to the total mixed-line output is calculated. Then, the total shared emissions are allocated to the corresponding processes according to their output proportions, resulting in the allocated indirect emissions for each process. Next, the direct emissions data for each process are extracted, and the direct emissions are added to the allocated indirect emissions for the corresponding processes to obtain the total emissions for each process. Finally, the data is integrated according to the structure of process name, direct emissions, allocated indirect emissions, and total emissions. The sum of the allocated indirect emissions for each process is verified to be equal to the total shared emissions. Once confirmed, a complete process emission inventory is generated. This method has a simple and intuitive calculation logic, high allocation efficiency, and is suitable for scenarios with similar product types and stable output in mixed-line production.

[0153] Step F14: Perform cross-stage data aggregation processing on the complete process emission inventory, sum up the carbon emissions of each life cycle stage, and generate the life cycle emission inventory.

[0154] In this embodiment, cross-stage data aggregation processing refers to the processing operation of integrating and summarizing process emission data belonging to different stages of the battery life cycle according to the stage dimension, thereby eliminating data dispersion between stages.

[0155] As an optional implementation, all process names, corresponding total emissions, and production stage information of each process are extracted from the complete process emission inventory. A pre-stored mapping table between lifecycle stages and processes is loaded, and the extracted processes and total emissions are categorized into their corresponding lifecycle stages according to the mapping table, forming association groups between stages and process emissions. Subsequently, the total emissions of all associated groups under each lifecycle stage are summed to obtain the total emissions for each stage. Then, it is checked whether all processes have been categorized into their corresponding stages and whether the cumulative emissions of each stage equal the sum of the total emissions of all processes. If any omissions or discrepancies exist, the processes are recategorized and recalculated. Finally, the data is organized according to the structure of lifecycle stage, total stage emissions, and a list of processes to generate a lifecycle emission inventory.

[0156] For example, a cross-validation algorithm for battery carbon emission related data: To ensure the authenticity and reliability of the collected data, this work introduces a data cross-validation mechanism based on physicochemical principles. The core algorithm is a stoichiometric material balance verification. This algorithm is based on the elemental balance (e.g., lithium, nickel, cobalt, manganese elemental flows) and mass conservation of electrode active materials. The system compares the total amount of raw materials input with the theoretical content in the final product, by-products, and waste, controlling the total error rate within ±2%. If the error exceeds this range, a data anomaly alarm is automatically triggered, prompting on-site verification and calibration, thus forming a self-improving closed-loop feedback system for data quality.

[0157] By employing data standardization, factor matching calculation, co-consumption emission allocation, and cross-stage aggregation, the system addresses the problems of chaotic data in traditional emission inventories, lack of allocation for co-consumption emissions across different emission lines, and fragmented data throughout the entire life cycle. This improves the accuracy of carbon footprint accounting.

[0158] Based on any of the above embodiments, in Embodiment 8 of this application, step S50 includes steps G11 to G13:

[0159] Step G11: Invoke the life cycle stage mapping rules to extract carbon emission data for each stage and process from the life cycle emission inventory, and generate a carbon emission data sequence sorted by life cycle stage.

[0160] In this embodiment, the life cycle stage mapping rule refers to a pre-defined set of rules used to clarify the life cycle stage to which each process belongs and the stage sorting logic. The carbon emission data for each stage and process refers to the carbon emission information extracted from the life cycle emission inventory, belonging to different life cycle stages and specific processes, including the stage name, process name, and corresponding emission amount. The carbon emission data sequence sorted by life cycle stage refers to the extracted carbon emission data arranged in a pre-defined order according to the life cycle stages, with each stage containing an ordered set of data including the emissions of its respective process.

[0161] As an optional implementation, pre-stored lifecycle stage mapping rules are invoked to determine the preset order of each lifecycle stage and the correspondence between each process and stage. The name of each lifecycle stage, the list of its associated processes, and the carbon emission data of each process are extracted from the lifecycle emission inventory. Then, the process list under each stage is sorted from highest to lowest carbon emission. Next, the sorted stage data is arranged sequentially according to the preset stage order in the mapping rules. Finally, the completeness of the emission data for each stage and process is verified, and a carbon emission data sequence sorted by lifecycle stage is generated after confirming no omissions. This method has a simple sorting logic and intuitive data presentation, making it suitable for scenarios requiring rapid identification of emission reduction targets.

[0162] Step G12: Accumulate the carbon emission data sequence across stages to calculate the total carbon emissions of the upper stage.

[0163] In this embodiment, cross-stage accumulation refers to the operation of summing the carbon emissions of multiple basic stages, arranged according to their life cycle. A higher-level stage refers to a more macroscopic stage formed by aggregating multiple functionally or temporally related basic life cycle stages. Total carbon emissions refer to the sum of carbon emissions from all basic stages included in the higher-level stage, reflecting the overall carbon emission scale of the higher-level stage.

[0164] As an optional implementation, pre-stored functional attribute partitioning rules are used to aggregate the basic lifecycle stages into higher-level stages based on their functions. The name and corresponding total carbon emissions of each basic stage are extracted from the carbon emission data sequence, and invalid stage entries are removed. The extracted basic stages and emissions are then categorized into corresponding higher-level stages according to the functional attribute partitioning rules, forming association groups of higher-level stages, basic stages, and basic stage emissions. The emissions of all basic stages within each association group are then summed to obtain the total carbon emissions of each higher-level stage. Finally, the completeness of the basic stages included in each higher-level stage is verified, and the summation result is confirmed to equal the sum of the emissions of the corresponding basic stages. After verification, the data is organized according to the structure of higher-level stage name, included basic stages, and total carbon emissions of the higher-level stage, and the total carbon emissions of the higher-level stage are output. This method reflects the temporal distribution characteristics of carbon emissions, facilitates the planning of carbon reduction schedules in conjunction with time, and is suitable for time-oriented carbon management scenarios.

[0165] Step G13: The total carbon emissions of the upper stage are summed to generate the full life cycle carbon footprint of the battery.

[0166] In this embodiment, the total carbon emissions of the upper stage refer to the overall carbon emissions of the macro stage output from the previous step, which are formed by the aggregation of multiple basic life cycle stages.

[0167] As an optional implementation, the names and corresponding total carbon emissions of all higher-level stages are extracted from the total carbon emissions of the higher-level stages, and data of higher-level stages marked as needing supplementation or with abnormal values ​​are removed. The total carbon emissions of each stage are then summed sequentially according to the extracted higher-level stages to obtain the total carbon emissions for the entire battery lifecycle. The proportion of the total carbon emissions of each higher-level stage to the total carbon emissions for the entire lifecycle is then calculated to quantify the contribution of each stage to the overall carbon footprint. Finally, the data is integrated according to the structure of total carbon emissions for the entire lifecycle and the names of the higher-level stages. The sum of the emissions of all valid higher-level stages is verified to ensure it equals the total carbon emissions for the entire lifecycle. Once verified, the carbon footprint for the entire lifecycle of the battery is generated. This method can quickly obtain the total carbon emissions for the entire lifecycle and the proportion of each stage, making it suitable for scenarios requiring rapid output of core carbon footprint results.

[0168] As an optional method for optimizing carbon emissions, after calculating the carbon footprint of the battery throughout its entire life cycle, key parameters are selected from a preset parameter library based on the carbon footprint calculation logic. A single-factor perturbation analysis module sequentially perturbs each key parameter within a set range, and then the carbon footprint calculation model is called to recalculate the carbon footprint. Subsequently, a sensitivity coefficient calculation module compares the results before and after the perturbation to calculate the sensitivity coefficients of each key parameter. Then, a hotspot analysis module is activated to identify carbon footprint hotspots based on the sensitivity coefficients and carbon emissions at each stage / process. Next, an emission reduction suggestion generation module is activated to generate emission reduction optimization suggestions for process adjustments and parameter optimization based on the process knowledge base, targeting the hotspots. Finally, a feedback module is activated to transmit the emission reduction optimization suggestions to the production process design system and the production decision management system to guide subsequent process optimization and production decision-making.

[0169] For example, in the scenario of battery carbon footprint accounting, the battery emission inventory formulation and carbon footprint accounting algorithm in this module transforms verified raw activity data into carbon footprint results. Emission inventory construction: The verified activity level data is multiplied by the corresponding emission factors to calculate the carbon emissions of each process and emission source, and a complete lifecycle emission inventory is generated. Dynamic allocation algorithm: During the inventory aggregation process, for shared emissions from mixed-line production (such as workshop air conditioning, lighting, and utilities), a measured working hour ratio allocation method is adopted. Battery emission sensitivity analysis method: To identify carbon footprint hotspots and guide emission reduction optimization, this work introduces sensitivity analysis. Method: A single-factor perturbation analysis method is used. Based on the baseline scenario, key parameters (such as: electrical carbon intensity, unit consumption of electrode materials, coating and drying energy consumption, formation time, and proportion of recycled materials) are systematically perturbed within a certain range (e.g., ±10%), and their impact on the final carbon footprint results is observed.

[0170] By using stage mapping extraction, cross-stage accumulation, and total emission summation, the problems of disordered stage data, lack of standards for higher-level stage accounting, and inaccurate total emission aggregation in traditional full life cycle carbon footprint calculations have been solved, thus improving the accuracy of carbon footprint accounting.

[0171] This application provides a carbon footprint accounting device, which 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 life cycle carbon footprint accounting method in Embodiment 1 above.

[0172] The following is for reference. Figure 4 The diagram illustrates a structural schematic suitable for implementing the carbon footprint accounting device of the embodiments of this application. The carbon footprint accounting device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, data acquisition equipment, personal digital assistants (PDAs), tablet computers (PADs), portable media players (PMPs), programmable logic controllers, etc., as well as fixed terminals such as sensors, desktop computers, etc. Figure 4 The carbon footprint accounting device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0173] like Figure 4As shown, the carbon footprint accounting device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the carbon footprint accounting device. The processing unit 1001, the read-only memory 1002, and the 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 I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the carbon footprint accounting device to communicate wirelessly or wiredly with other devices to exchange data. Although carbon footprint accounting devices with various systems are 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.

[0174] 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.

[0175] The carbon footprint accounting device provided in this application adopts the battery life cycle carbon footprint accounting method in the above embodiments, which can solve the technical problem of accounting results deviating from the actual situation. Compared with the prior art, the beneficial effects of the carbon footprint accounting device provided in this application are the same as the beneficial effects of the battery life cycle carbon footprint accounting method provided in the above embodiments, and other technical features in the carbon footprint accounting device are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0176] 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.

[0177] 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.

[0178] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the battery lifecycle carbon footprint calculation method in the above embodiments.

[0179] 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 or flash memory), optical fiber, 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, radio frequency (RF), etc., or any suitable combination thereof.

[0180] The aforementioned computer-readable storage medium may be included in the carbon footprint accounting device; or it may exist independently and not be assembled into the carbon footprint accounting device.

[0181] The aforementioned computer-readable storage medium carries one or more programs. When the carbon footprint accounting device executes these programs, the carbon footprint accounting device performs the following actions: 1. Based on the core production stage range determined by the accounting boundary, analyzes the battery's production process and generates a corresponding structured process hierarchy list; 2. Based on the ratio of energy consumption to material consumption, selects key processes in the structured process hierarchy list and determines a list of key data collection objects; 3. Collects key data corresponding to the list of key data collection objects and matches it with auxiliary data according to timestamps to generate a measured raw dataset; 4. Analyzes and verifies the measured raw dataset, calculates the carbon emissions of each process and emission source item by item, and summarizes them to obtain a life cycle emission list; 5. Based on the carbon emissions of each stage and process in the battery's entire life cycle in the life cycle emission list, calculates the full life cycle carbon footprint corresponding to the battery.

[0182] 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 the "C" language 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).

[0183] 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.

[0184] 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.

[0185] 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 calculating the carbon footprint of a battery throughout its entire life cycle, thereby solving the technical problem of calculation results deviating from reality. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the method for calculating the carbon footprint of a battery throughout its entire life cycle provided in the above embodiments, and will not be repeated here.

[0186] 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 calculating the carbon footprint of a battery over its entire life cycle, characterized in that, The method includes: Based on the core production stage range determined by the accounting boundary, the battery production process is analyzed to generate a corresponding structured process hierarchy list; By calling the historical carbon emission database, the energy consumption and material consumption ratio of each process in the structured process hierarchy list are calculated to obtain the process contribution ranking list. The process contribution ranking list refers to the structured list that is ranked from high to low according to the contribution of each process in a comprehensive calculation of the energy consumption and material consumption ratio, and includes process name, energy consumption ratio, material consumption ratio, and comprehensive contribution. By using preset contribution threshold rules, key processes are selected from the process contribution ranking list to determine the set of key processes; The system calls the IoT device registry, extracts all sensor metadata, and labels its monitoring parameters to form an index table of sensor metadata and monitoring parameters. The data collection requirements for each process in the set of key processes are broken down into monitoring parameters; Using the disassembled monitoring parameters as keywords, search for matching sensor metadata in the index table of sensor metadata and monitoring parameters, and check whether the installation location corresponding to the sensor metadata is within the production area of ​​the key process. If the location and parameters match, record the correspondence between the sensor metadata and the key process. After matching is completed, for multiple sensor metadata that monitor the same parameter under the same key process, retain the metadata of the sensor with the highest data acquisition frequency; Based on the structure of sensor metadata, monitoring parameters, key process names, and installation locations, a list of key data acquisition objects is generated. Collect key data corresponding to the key data collection object list, and match and associate it with auxiliary data according to timestamp to generate the actual measurement raw dataset. The key data refers to energy consumption data and material flow data related to carbon footprint accounting in the key data collection object list. The auxiliary data refers to production auxiliary information that supports data association analysis, including production batch number, process time, and equipment operating status data. The measured raw dataset after analysis and verification is analyzed, and the carbon emissions of each process and emission source are calculated item by item to obtain the life cycle emission inventory. The full life cycle carbon footprint of the battery is calculated based on the carbon emissions of each stage and process in the battery's entire life cycle emission inventory.

2. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, Before the step of analyzing the battery manufacturing process based on the core production stage range determined by the accounting boundary and generating a corresponding structured process hierarchy list, the method for calculating the battery's full life cycle carbon footprint also includes: The battery life cycle is divided according to preset definition conditions, and the stage division results are determined. The direct emissions and indirect energy emissions of the corresponding organizational level in the stage division results are calculated using the hierarchical control method, and the initial accounting boundary is output. The emissions of key upstream raw materials are included in the preliminary accounting boundary, and the final accounting boundary is output.

3. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, The step of analyzing the battery manufacturing process and generating a corresponding structured process hierarchy list based on the core production stage range determined by the accounting boundary includes: The battery process knowledge base is invoked to match the core production stage range determined by the accounting boundary, and a list of candidate processes within the core production stage range is determined. Verify the production equipment corresponding to the processes in the candidate process list, and output a mapping table between equipment and processes; Based on the mapping table between equipment and processes, a tree-like topology structure with processes, workstations, and equipment as hierarchical relationships is constructed to generate the structured process hierarchy list.

4. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, The steps of collecting key data corresponding to the list of key data collection objects and associating and matching auxiliary data with them according to timestamps to generate the original measured dataset include: A data collection instruction set is generated based on the list of key data collection objects to control the IoT gateway to collect the key data with timestamps; Extract the values ​​of the key data and their corresponding timestamps, and arrange the values ​​of the key data in order of timestamps to obtain standardized key data; Based on a unified timestamp, the standardized key data and the auxiliary data are correlated and matched to generate the measured original dataset.

5. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, Before the step of calculating the carbon emissions of each process and emission source in the verified measured raw dataset and summarizing them to obtain the life cycle emission inventory, the method for calculating the carbon footprint of the battery throughout its entire life cycle also includes: The preset material balance verification algorithm is invoked to perform elemental mass balance calculations on the input and output materials in the measured original dataset, and output the theoretical content data of elemental flow for each material. The relative error between the theoretical content data of the element flow and the measured output data is calculated, and the error analysis results are generated based on the comparison between the relative error and the preset tolerance. Based on the error analysis results, a calibration process is triggered to correct the out-of-tolerance data in the measured output data, thereby obtaining the verified measured original dataset.

6. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, The steps for calculating the carbon emissions of each process and each emission source from the analyzed and verified raw dataset, and summarizing them to obtain the life cycle emission inventory, include: The validated raw dataset is standardized to generate activity level data. The activity level data is classified and matched with the corresponding emission factors in the database according to the material and energy consumption type. The activity level data and its corresponding emission factors are calculated item by item by matrix multiplication to obtain the initial emission list. The co-consumption emissions corresponding to mixed-line production in the initial emission list are allocated and calculated in real time, and a complete process emission list including direct emissions and indirect emissions after allocation is output. The complete process emission inventory is processed by cross-stage data aggregation, and the carbon emissions of each life cycle stage are added together to generate the life cycle emission inventory.

7. The method for calculating the carbon footprint of a battery throughout its entire life cycle as described in claim 1, characterized in that, The step of calculating the full life cycle carbon footprint of the battery based on the carbon emissions of each stage and process in the battery's full life cycle emission inventory includes: The life cycle stage mapping rules are invoked to extract carbon emission data for each stage and process from the life cycle emission inventory, generating a carbon emission data sequence sorted by life cycle stage. The carbon emission data sequence is accumulated across stages to calculate the total carbon emissions of the next stage. The total carbon emissions of the upper-level stage are summed to generate the full life cycle carbon footprint of the battery.

8. A carbon footprint accounting device, characterized in that, The carbon footprint accounting 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 calculating the carbon footprint of a battery throughout its entire life cycle as described in any one of claims 1 to 7.

9. 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 calculating the carbon footprint of the battery throughout its entire life cycle as described in any one of claims 1 to 7.

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