Enterprise carbon emission data full life cycle management method and system

Abstract

The invention discloses an enterprise carbon emission data full life cycle management method and system, particularly relates to the technical field of carbon emission data management, and is used for solving the problem of insufficient carbon data consistency and disclosure compliance. According to the method, consistency gating, unit and time alignment and identity verification of multi-source data are realized through a unified time window, a credible carbon event frame is generated, a factor library matched with multi-dimensional rule screening and similarity is introduced, and path atlas construction and repeated measurement identification are supported in cooperation with confidence evaluation and uncertainty labels; the enterprise carbon data management method is characterized in that the enterprise carbon data management method combines the least adjustment principle to realize accurate allocation and double bookkeeping, then drives multi-template disclosure mapping through a field map to generate a difference description and backtracking link, and finally ensures rule iteration safety through a gray release and sandbox verification mechanism, thereby realizing high precision, strong compliance and traceability of enterprise carbon data management.

Description

Technical Field

[0001] This invention relates to the field of carbon emission data governance technology, and more specifically, to a method and system for the full lifecycle management of enterprise carbon emission data. Background Technology

[0002] Against the backdrop of corporate carbon verification and disclosure being gradually incorporated into regulatory and supply chain assessment systems, various enterprises have generally established carbon emission data collection, accounting, and reporting mechanisms to meet requirements such as carbon footprint certification, ESG assessment, and carbon market trading. Current mainstream practices typically involve connecting energy consumption, logistics, and procurement systems to periodically extract emission-related data, relying on fixed-format templates for manual processing and reporting. In this process, some solutions introduce automated tools to complete field mapping and calculation formula application, improving computational efficiency and compliance response speed. The technical implementation of these systems focuses on indicator model building and template reporting process optimization, emphasizing system integration capabilities with existing ERP, MES, and OA platforms.

[0003] However, existing technologies have significant shortcomings in data processing and accounting logic: First, business data from different sources have issues such as inconsistent granularity, misaligned timelines, and inconsistent object identification, leading to overlaps, conflicts, or omissions in the identification, unit conversion, and time alignment stages, making it difficult to form a unified carbon event flow; Second, the matching of carbon emission factors is often based on simple field filtering, lacking a multi-layered matching mechanism based on caliber fingerprints and device versions, resulting in large errors and high uncertainty in the referenced factors; Third, the calculation path does not construct a structural map based on the symmetrical relationship between activities and emissions, lacks a mechanism for identifying and adjusting duplicate measurements, and is difficult to avoid accounting deviations caused by path overlap and boundary penetration, making it difficult to support the unified requirements of corporate annual reports, project verification, and multi-dimensional disclosure, and limiting the credible application of carbon data in compliance verification, disclosure spot checks, and value assessment scenarios. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in the prior art, this invention provides a method and system for managing the entire lifecycle of enterprise carbon emission data, thereby addressing the problems of insufficient carbon data consistency and disclosure compliance in the aforementioned background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for managing the entire lifecycle of enterprise carbon emission data includes the following steps: Within the enterprise's organizational boundaries and accounting cycle, it connects production, energy, procurement, logistics, and travel data, performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames, and writes entry fingerprints and responsibility tags. Establish a factor library with versioning by region, year, medium, and device; perform rule filtering, similarity retrieval, and confidence integration matching for carbon event frames; output candidate factors and difference descriptions; establish factor citation records; and summarize uncertainty labels for error and fit. Using a dual accounting system with activities as debits and emissions as credits, an upstream and downstream path map is constructed. Identification of duplicate measurements is achieved through isomorphism and loops. Conflict resolution and anomaly isolation are performed according to the principle of minimum adjustment. Under constraints, the minimum allocation deviation is iteratively calculated according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. Based on a pre-defined caliber mapping map, carbon accounts and calculation records are organized into corporate annual reports, project verification, product carbon footprints, and supplier disclosures, and difference explanations and backtracking links are generated for version comparison and spot checks. The rules for calculation, allocation, completion, and caliber mapping are subject to gray-scale release and rollback, and take effect after confirmation by sandbox playback. It also provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, and triggers supplementary collection and review based on coverage and uncertainty thresholds.

[0006] In a preferred embodiment, consistency gating and mutual exclusion are performed on multi-source data of the same object within a unified time window to complete identity verification, unit and time alignment, generate carbon event frames, and write caliber fingerprints and responsibility tags. The specific process is as follows: Under the constraints of enterprise organizational boundaries and accounting cycles, a unified time window is set to aggregate production, energy, procurement, logistics and travel data into the same time axis according to object identifiers; Perform identity verification and unit standardization on the data of the same object within the window, map the object identifier to the unified enterprise master data system, unify energy consumption, material and emission data to the preset measurement unit, and align the timestamp to the unified time base; Consistency gating is performed on the output of the same object within the same time window. Consistency is triggered when the number of sources and the time overlap ratio both reach the preset judgment threshold. Once the consistent triggering condition is met, the mutual exclusion rule is executed to reject or downgrade combinations of source conflict, process reversal and physical impossibility. Carbon event frames are generated for records that pass the consistency gating and are not rejected. A caliber fingerprint and a responsibility label are written for each carbon event frame. The caliber fingerprint includes the region, unit of measurement, energy medium, boundary type and time base. The responsibility label includes the roles of reporting, reviewing and publishing.

[0007] In a preferred embodiment, a factor library is established, categorized by region, year, medium, and device version. The carbon event frame operation rules are then used for filtering, similarity retrieval, and confidence-based integrated matching. The specific process is as follows: Emission factors are versioned based on regional policies, accounting years, energy media and device models, and a factor library is built to record factor sources, applicable boundaries and effective times. The region, year, energy medium and device attributes marked in the carbon event frame are filtered by rules, and factor entries that do not meet the corresponding regional scope of application, time scope of effect, medium type and device version are removed from the factor library. In the set of factors filtered by rules, similarity retrieval is performed on the field labels of carbon event frames and factor field labels. Nearest neighbor matching is performed based on the consistency of unit structure, data field semantics and device parameters to form a set of candidate factors. Perform confidence integration on the candidate factor set, calculate confidence values ​​based on field matching consistency ratio, unit conversion consistency, and parameter proximity, determine priority factors by confidence score, and output factor difference descriptions; The selected factor and its corresponding difference description are written into the carbon event frame to form a factor reference record.

[0008] In a preferred embodiment, candidate factors and difference descriptions are output, factor citation records are established, and uncertainty labels for error and fit are summarized. The specific process is as follows: The candidate factor set, which integrates similarity retrieval and confidence, is sorted, and the top-ranked item is output as the recommendation factor. The fields of the recommended factors and the attributes identified by the carbon event frames are compared to extract regional offsets, time inconsistencies, medium mismatches, unit conversion errors, and device model inconsistencies. Difference descriptions are generated according to rules and associated with factor reference records. After the factor reference is established, the confidence value, number of field deviation items, and unit conversion ratio deviation of the selected factors are combined to form record-level uncertainty labels. Uncertainty labels and factor references are written together into the carbon event frame as reference indicators for determining computational validity and triggering supplementary mining.

[0009] In a preferred embodiment, a dual accounting system is used, with activities as debits and emissions as credits, to construct an upstream and downstream path map. Duplicate metering is then identified through isomorphism and loop detection. The specific process is as follows: The generated carbon event frames are assigned to the activity side and the emission side respectively, and an upstream and downstream path graph is constructed with business objects as nodes and carbon events as edges. The activity side edge points to the emission side edge, forming a borrowing and lending symmetrical structure. Based on object identifiers, timestamps, and accounting boundaries, upstream and downstream dependencies between nodes are established in the graph to form a set of paths, each path representing a transfer link from the original activity to the final emission. Perform isomorphism identification on the path set to determine whether there are paths with the same structure but duplicate sources. When at least two paths are found to be completely identical in terms of node and event structure, they are marked as duplicate paths. A loop detection algorithm is performed on the path sequence that forms a loop to identify closed paths that are repeatedly counted for the same object in similar time intervals, and the degree of redundancy is evaluated by a combination of path weight and node credibility. Paths or loops that are identified as duplicate measurements will be marked.

[0010] In a preferred embodiment, conflict resolution and anomaly isolation are performed according to the principle of minimum adjustment. The minimum amortization deviation is iteratively calculated under constraints based on product, process, order, and organizational dimensions, generating calculation records and carbon ledgers. The specific process is as follows: For the set of paths marked as duplicate measurement or having closed loops, a priority ranking is established according to the path credibility label, data integrity and business main line weight, and the path node with the smallest impact is selected as the deduction object. Based on the principle of minimum adjustment, deduction is only performed on necessary paths, and conflict resolution records are generated, recording the event identifiers involved, the amount deducted, the difference before and after adjustment, and the reasons for adjustment; Based on the effective path map after conflict resolution, and combined with product identification, process number, order attribution and organizational structure, a set of allocation targets is constructed. Using record-level carbon emissions as the total constraint, and combining driving factors such as historical output, resource consumption ratio, shift hours, and material allocation ratio, the iterative process is controlled by the constraint conditions. In each iteration, the amortization deviation is calculated, and it is determined whether the minimum change threshold or the maximum number of iterations is met. If either termination condition is met, the amortization result is output.

[0011] In a preferred embodiment, the carbon account and calculation records are compiled into corporate annual reports, project verification, product carbon footprints, and supplier disclosures based on a preset caliber mapping map, and discrepancy explanations and backtracking links are generated for version comparison and spot checks. The specific process is as follows: Establish a caliber mapping graph consisting of field nodes and transformation edges. Field nodes include carbon account field, calculation record field and disclosure template field. Transformation edges carry unit conversion, time base conversion, field aggregation and splitting and allocation rules. The graph is maintained by version number and effective time. Using the fields in the carbon account records and calculation records as source nodes, and combining them with the target field structure in the selected disclosure template, the mapping relationship is executed one by one according to the graph traversal path to generate a dataset with the corresponding disclosure scope. During the data mapping process, the transformation chain, transformation conditions, and field difference information between the source field and the target field are recorded, and difference description text is generated. Mark the differences and source field locations as backlinks and link them to the field output in the disclosure results; For objects that require version disclosure, retrieve historically disclosed versions, perform inter-version comparisons based on data from the same source, and mark fields with structural changes, boundary adjustments, and numerical offsets. The comparison results are subjected to sampling logic, which outputs the fields to be audited and their source paths, and generates disclosure audit records.

[0012] In a preferred embodiment, the calculation, allocation, completion, and caliber mapping rules are subject to canary release and rollback, and the specific process is as follows: Define a version number, an activation identifier, and an applicable scenario label for each rule. When a rule is updated, mark it as a grayscale version and limit its applicable scope and time period. Before the gray-scale version of the rules takes effect, the corresponding sample data is injected into the sandbox ledger to execute the complete replay process and compare the differences between the carbon event frames, factor references, calculation records and carbon ledger output by the old and new rules. When the sandbox playback output meets the preset consistency threshold and deviation threshold, the rule is set to be officially effective; otherwise, a one-click rollback operation is triggered and the failed version is recorded.

[0013] In a preferred embodiment, a lightweight submission and anonymous aggregation portal is provided for suppliers and carriers, triggering supplementary sampling and verification based on coverage and uncertainty thresholds. The specific process is as follows: Provides a unified interface template for external entities, allowing suppliers and carriers to submit energy consumption or emission data by time period and object type, and supports the inclusion of data into statistical categories through anonymous aggregation; The system calculates the coverage of data integrity within the disclosure dimensions and sets a threshold for the uncertainty label of each record. When the coverage is lower than the set standard or the proportion of uncertain records exceeds the preset threshold, a supplementary collection request and review process are triggered. After aligning the verified data with the original data, update the parameter items in the factor library based on the correction results, adjust the uncertainty threshold, and record the influence boundary.

[0014] A corporate carbon emission data lifecycle management system, used to implement the aforementioned corporate carbon emission data lifecycle management method, includes: The enterprise carbon emission data acquisition module is used to connect with production, energy, procurement, logistics and travel data within the enterprise's organizational boundaries and accounting cycle. It performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames and writes entry fingerprint and responsibility tags. The fit analysis module is used to establish a factor library that is versioned by region, year, medium and device, to filter carbon event frame operation rules, retrieve similarity and integrate confidence matching, output candidate factors and difference descriptions and establish factor reference records, and summarize the uncertainty labels of error and fit. The carbon ledger module is used for dual accounting with activities as debits and emissions as credits. It constructs upstream and downstream path maps, identifies duplicate measurements using isomorphism and loops, performs conflict resolution and anomaly isolation according to the principle of minimum adjustment, and iteratively calculates the minimum allocation deviation under constraints according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. The version comparison module is used to compile carbon accounts and calculation records into corporate annual reports, project verification, product carbon footprints, and supplier disclosures based on a preset caliber mapping map, and generate difference descriptions and backtracking links for version comparison and spot checks. The completion module is used to implement gray-scale release and rollback of calculation, allocation, completion and caliber mapping rules. It takes effect after confirmation by sandbox playback, and provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, triggering supplementary collection and review based on coverage and uncertainty thresholds.

[0015] The technical effects and advantages of this invention are as follows: This invention achieves refined management of enterprise carbon emission data throughout its entire lifecycle, from raw multi-source collection to disclosure output, by constructing carbon event frames within a unified time window, a multi-dimensional rule matching mechanism for factor libraries, and an upstream and downstream path map. During the data docking stage, consistency gating and mutual exclusion are performed on multi-source records of the same object. Combined with field standardization, time alignment, and unit unification, source conflicts and physically impossible events are effectively eliminated, ensuring the credibility of basic data.

[0016] By filtering and matching uncertainties based on region, medium, and device version dimensions in the factor library using rules, a confidence-based uncertainty label is generated and embedded in the dual accounting path. This enables the identification of duplicate measurements and the minimum allocation of deviations under the principle of least adjustment. Furthermore, a field graph drives the disclosure mapping, and discrepancy explanations and backtracking links are introduced to ensure traceability from the mapping of definitions to the disclosure results. Finally, through rule-based gray-scale release and sandbox verification mechanisms, linked with supply chain data aggregation and random audit strategies, a multi-source data-driven, transparent processing path, and consistent definition arrangement for the entire process of enterprise carbon emission management is achieved, significantly improving data compliance, accounting accuracy, and disclosure efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for managing the entire lifecycle of enterprise carbon emission data according to the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of a corporate carbon emission data lifecycle management system according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: As Figure 1 As shown, a method for managing the entire lifecycle of enterprise carbon emission data includes the following steps: Within the enterprise's organizational boundaries and accounting cycle, it connects production, energy, procurement, logistics, and travel data, performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames, and writes entry fingerprints and responsibility tags. Establish a factor library with versioning by region, year, medium, and device; perform rule filtering, similarity retrieval, and confidence integration matching for carbon event frames; output candidate factors and difference descriptions; establish factor citation records; and summarize uncertainty labels for error and fit. Using a dual accounting system with activities as debits and emissions as credits, an upstream and downstream path map is constructed. Identification of duplicate measurements is achieved through isomorphism and loops. Conflict resolution and anomaly isolation are performed according to the principle of minimum adjustment. Under constraints, the minimum allocation deviation is iteratively calculated according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. Based on a pre-defined caliber mapping map, carbon accounts and calculation records are organized into corporate annual reports, project verification, product carbon footprints, and supplier disclosures, and difference explanations and backtracking links are generated for version comparison and spot checks. The rules for calculation, allocation, completion, and caliber mapping are subject to gray-scale release and rollback, and take effect after confirmation by sandbox playback. It also provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, and triggers supplementary collection and review based on coverage and uncertainty thresholds.

[0021] Step 1: Within the enterprise's organizational boundaries and accounting cycle, integrate production, energy, procurement, logistics, and travel data. Within a unified time window, perform consistency gating and mutual exclusion on multi-source data for the same object, completing identity verification, unit and time alignment, generating carbon event frames, and writing entry fingerprints and responsibility tags. The specific implementation is as follows: Within the enterprise organizational boundaries and accounting cycle, the platform first sets a unified time window, a unified time benchmark, and a unified enterprise master data system. The organizational boundaries are numbered according to legal person, factory, workshop, and project team. The accounting cycle is set to the natural month and intermediate versions are generated daily. The unified time base adopts Coordinated Universal Time and records time zone offsets. The unified time window is configured as five minutes in the platform parameters. The unified enterprise master data system includes an object identifier dictionary and a field mapping dictionary. The object identifier is used to uniquely locate business objects and includes equipment number, order number, vehicle number, and personnel number. The field mapping is used to merge field names from various sources into platform standard field names. The platform establishes a reliable link with the production system, energy system, procurement system, logistics system, and travel system. All collected records have an object identifier, start time, end time, and original measurement value.

[0022] The object identifier dictionary maintains the mapping relationship between object identifiers and enterprise master data as follows: When a one-to-many or many-to-one conflict occurs, the platform takes the mapping last confirmed by the audit role as the standard. If there are more than two candidate mappings that take effect on the same day, the one with the higher source signature stability score will be given priority, and the unselected mapping will be set to a standby state with a limited standby validity period and triggering conditions. The field mapping dictionary is used to merge source field names to platform standard field names. When a conflict occurs where the same source field is mapped to multiple platform fields, the uniqueness constraint of the field in the disclosure template will take priority. If uniqueness cannot be achieved, a review will be submitted and the source field will be prohibited from entering the production chain. Both types of dictionaries maintain version numbers, effective times, and deactivation times. Historical versions can be replayed, and when changes occur, a difference description will be generated and written to the audit storage area. The source signature is used to uniquely indicate the data source and includes four items: source system identifier, interface version number, collection node identifier, and verification summary of the record. The four items are concatenated and written to the record in a fixed order, and a source signature file is established in the audit storage area for replay and stability scoring reference.

[0023] Within a unified time window, the platform performs identity verification and unit and time alignment on multi-source records of the same object. Identity verification maps object identifiers from each source to a unified enterprise master data system. When historical aliases exist in one-to-many or many-to-one relationships, they are merged according to the latest valid relationship in the master data. Unit and time alignment is completed in two steps. The first step is unit standardization: the platform sets a preset unit of measurement for each measurement field in the factor library. Electricity consumption uses kilowatt-hours, gas uses standard cubic meters, liquid fuels use liters and kilograms while retaining the density source, mileage uses kilometers, and time uses hours. When the source record is inconsistent with the preset unit of measurement, the original measurement value is converted to the preset unit of measurement step by step according to the conversion chain registered in the field mapping dictionary, and the conversion source and conversion step number are written into the record. The second step is time alignment: when the time granularity of the source record is inconsistent with the unified time window, the platform segments or merges the records using the window as the boundary. For the cumulative amount spanning multiple windows, it is allocated according to the proportion of coverage duration in each window; for power or flow with high frequency values ​​at the second level within a window, the instantaneous value is averaged over time according to the duration within the window. The specific process is as follows: first, the total duration of each sample within the window is calculated, and then the instantaneous value corresponding to each sample is multiplied by the proportion of the duration of that sample to the total value, and the sum is obtained to get the window average. After completing identity verification and unit and time alignment, the platform performs consistency gating and mutual exclusion on multiple source outputs of the same object within the same time window, generates candidate records, and determines whether to enter the carbon event frame. The consistency gating uses a combination of source quantity threshold and time overlap ratio threshold: the source quantity threshold is set to two, and the time overlap ratio threshold is set to 50%. The time overlap ratio is calculated as follows: Under the same object and the same window, take the time intervals of two source records, calculate the duration of the intersection of the two intervals, and then compare this duration with the shorter duration of the two records. If the duration of the intersection is not less than half of the shorter duration, it is determined that the time overlap ratio threshold is met. When at least two different sources output the same business conclusion for the same object in the same window and simultaneously meet the source quantity threshold and the time overlap ratio threshold, it is determined to be a consistent trigger. The mutual exclusion rejection rule rejects or downgrades physical or process contradictions. Its rule list includes: If the equipment is in a shutdown state and the same window continues to consume energy, the order is closed and the new consumption is added, or the warehouse outbound is not registered and the production usage is confirmed, the record will be rejected or downgraded to a review candidate when any of the mutually exclusive relationships are matched, and the triggered rule number and judgment time will be recorded. For records that pass the consistency gating and are not mutually exclusive, the platform generates carbon event frames by event and writes caliber fingerprints and responsibility tags. The carbon event frame includes a unique event identifier, object identifier, source signature, start time, end time, measurement value under the preset unit of measurement, alignment method, conversion source, window number, and evidence path. The caliber fingerprint is a record-level fingerprint, including region, unit of measurement, energy medium, boundary type, and time base. The region is used to identify the administrative region, the unit of measurement is the preset unit of measurement used in this event, the energy medium is used to indicate electricity, gas, or liquid fuel, the boundary type is used to identify whether it is a direct activity within the enterprise boundary, and the time base is used to indicate the time zone offset from the unified time base. The evidence path field is used to trace back from the event frame to the original data and intermediate processing steps, including the location index of the original source record, the window number sequence when splitting or merging, the source number sequence of the unit conversion chain, the time base conversion record number, the mutual exclusive rejection or consistency gating trigger record number, and the index of the audit role operation record, ensuring that playback can be gradually expanded to each processing stage.

[0024] The responsibility tags are written by the platform according to the process roles, including the reporting role, the review role, and the publishing role, which point to specific positions or responsible persons respectively. When the review is not completed, the publishing role is in a pending assignment state. After the event is generated, the platform writes the carbon event frame, the caliber fingerprint, and the responsibility tags into the auditable event storage area in window order, and generates a traceability link for spot checks. The traceability link points to the original source record and the process record of alignment and conversion. Through the above steps, the platform achieves data consistency gating, mutual exclusion, identity verification, unit and time alignment, and event solidification for the same object within the organizational boundary and accounting cycle under a unified time window, forming carbon event frames with evidence chains and caliber fingerprints, providing auditable and reproducible basic data for subsequent factor matching and calculation.

[0025] Step 2: Establish a factor library categorized by region, year, medium, and device version; perform rule-based filtering, similarity retrieval, and confidence-based integrated matching on carbon event frames; output candidate factors and difference descriptions; establish factor citation records; and summarize uncertainty labels for errors and fit. The specific implementation is as follows: When establishing the factor library, the platform uses region, year, energy medium, and device model as four main partition dimensions, and sets a version number and effective time for each factor record. Each factor record must contain at least: The factor database includes the following information: factor identifier, region, accounting year, energy medium, device model, applicable boundaries, unit of measurement, data source, effective date, expiration date, version number, caliber description, and quality mark. The factor source can be a publicly available national or regional standard, a third-party verification report, or an internal baseline audited by the enterprise. The platform verifies the authenticity of the source and records its number. The device model field is used to compare with device attributes in the carbon event frame. If a device has undergone hardware modifications that do not substantially affect emission characteristics, the platform registers it as a different version under the same device model in the factor database and clarifies its applicable boundaries. The unit of measurement field maintains consistency with the platform's preset units of measurement. When the source units differ, the platform performs unit conversion according to the field mapping dictionary before data entry and records the conversion chain and number of steps. After completion, the factor database is indexed by partition dimension, supporting subsequent rule filtering and similarity retrieval.

[0026] When a carbon event frame enters the factor matching process, the platform first performs rule filtering. Rule filtering takes the caliber fingerprint of the carbon event frame as input. The caliber fingerprint includes at least the region, unit of measurement, energy medium, boundary type, and time base. The platform filters out inapplicable factors in the following order: First, regional mismatch. Factors that are completely consistent with the region of the event frame are prioritized. If none are found, the upper-level regional factor is selected according to the regional mapping table, and the source and level of regional offset are recorded in the difference description. Second, factors whose accounting year is not within the range of the factor's effective time to expiration time. Third, factors with inconsistent energy mediums. Fourth, factors with inconsistent device models and no factors registered as different versions of the same model. Fifth, factors whose unit of measurement conflicts with the preset unit of measurement and cannot be consistent through the registered conversion chain. The regional mapping table maintains the hierarchical relationship and effective time by city and province. When a city-level factor does not exist or has expired, it is traced back to the provincial and national levels in sequence. When tracing back occurs, the tracing level, original region, and adopted region are recorded in the difference description, and the effective time of both is retained for audit playback. After filtering according to the above rules, a candidate set for similarity retrieval is formed. If the set is empty, the platform marks the event frame as a review candidate and writes it into the reason rule for no match. At the same time, an uncertainty label of high is generated and the process of supplementary collection and review is initiated.

[0027] During the similarity retrieval phase, the platform compares the field consistency and parameter proximity of each factor set filtered by the rules with the carbon event frames. Field consistency is verified in four aspects: region, accounting year, energy medium and device model. Each consistent aspect is recorded as consistent, and the ratio of the number of consistent aspects to the total number of aspects is used as the field matching consistency ratio.

[0028] Unit conversion consistency is categorized according to the conversion chain: no conversion required is considered completely consistent, single-step conversion is considered single-step consistent, and two or more steps are considered multi-step consistent. The conversion chain is recorded in the difference description. Parameter proximity is judged based on the proximity of key device parameters, such as rated power, rated load range, or combustion mode: the difference between the event frame parameter and the factor recorded parameter is compared with the allowable range. When the difference does not exceed half of the allowable range, it is considered approximately consistent. When the difference exceeds half but does not exceed the entire allowable range, it is considered critically consistent. When it exceeds the allowable range, it is considered inconsistent.

[0029] The platform uses a combination of confidence criteria based on the above three aspects: when the field matching consistency ratio reaches full consistency, the unit conversion consistency is complete or single-step consistency, and the parameter proximity is approximately consistent, it is marked as high confidence; when the field matching consistency ratio is missing only one consistent element, the unit conversion consistency is single-step consistency, and the parameter proximity is critically consistent, it is marked as medium confidence; all other cases are marked as low confidence. The platform outputs candidate factors in high, medium, and low order, and automatically generates a difference description for each candidate factor. The difference description includes at least the triggered items and their triggering reasons from regional offset, time inconsistency, energy medium mismatch, unit conversion error, and device model inconsistency.

[0030] The platform writes the top-ranked candidate factor and its differences into a carbon event frame, forming a factor reference record. The record includes the factor identifier, version number, effective time, expiration time, reason for selection, and main differences from the abandoned factor.

[0031] The platform generates and labels record-level uncertainty. The uncertainty label consists of three parts: measurement error, factor fit, and caliber difference. Measurement error is determined based on the unit conversion chain: no conversion required is recorded as low, single-step conversion is recorded as medium, and two or more steps are recorded as high. Factor fit is determined based on the confidence level: high confidence is recorded as low, medium confidence is recorded as medium, and low confidence is recorded as high. It should be noted that the unit conversion chains originate from national or regional standards, industry standards, and internal enterprise standards confirmed by verified roles. The platform registers the source number, effective date, discontinuation date, and conversion step description for each chain. When multiple conversion chains exist for the same pair of units, national or regional standards take precedence, followed by industry standards, and then internal enterprise standards. A lower-level source can only be used when the higher-level standard does not exist. Changes to conversion chains do not trace historical records, but historical records carry the conversion source and conversion steps to ensure consistency during playback.

[0032] The difference in caliber is determined based on the number and severity of the items in the difference description: When only a unit conversion item appears and the conversion is done in a single step, it is recorded as low. When any one of the following items appears, namely regional offset, time inconsistency, or device model inconsistency, and only one of them appears, it is recorded as medium. When two or more of the above items appear simultaneously, it is recorded as high.

[0033] A combined criterion is used to generate the final uncertainty label: when any two of the three parts are high, the final uncertainty label is high; when only one of the three parts is high or two parts are medium, the final uncertainty label is medium; otherwise, it is low. The final uncertainty label and factor citation record are written together into the carbon event frame as the basis for subsequent calculation validity determination and supplementary sampling triggering; when the final uncertainty label is high, the platform simultaneously sets a review mark and incorporates the event frame into the subsequent supplementary sampling and review process. Through the above process, the version management of the factor library, rule filtering, similarity retrieval and confidence integration, factor citation record and uncertainty label realize a complete, auditable and reproducible matching mechanism, providing reliable input for dual accounting and caliber mapping.

[0034] Specific examples of factor matching and uncertainty label generation are as follows: The scenario and input are as follows: the object is identified as smelting furnace E-750, the region is province A, city B, the accounting year is 2024, the energy medium is electricity, the boundary type is direct activity within the enterprise boundary, the time base is a unified time base, and the carbon event frame records that the power consumption of this device in the unified time window of "2024-05-01 08:00–08:05" is 1200 kWh (the unit and time have been aligned).

[0035] The selection criteria are as follows: The platform first removes entries from the factor library that do not match the region (excluding Province A or its superior mapping chain), retaining the 2024 electricity emission factor for Province A (version V2024.1, effective date 2024-01-01) and the 2024 national electricity emission factor (V2024.2). Then, it removes entries that do not match the energy medium or the device model, and for which there are no different versions of the same model registered. The unit of measurement is checked to be kilowatt-hours, consistent with the preset unit of measurement, requiring no conversion. Two candidate sets are obtained: Provincial V2024.1 and National V2024.2.

[0036] Field matching consistency ratio: Among the four items of region, year, medium, and device model or version, provincial V2024.1 is completely consistent in the region, year, medium, and device model or version without any restrictions, and is recorded as completely consistent; national V2024.2 is considered to be missing one consistent item because the region is a higher-level region; unit conversion consistency is completely consistent for both; parameter proximity: compared with the rated power and load range in the device technical manual, this event is within half of the allowable range, and is judged as approximately consistent. Therefore: provincial V2024.1 is of high confidence; national V2024.2 is of medium confidence. If the confidence levels are tied, the priority order is from the closest effective time (not later than the event time) to the newest version number and then to the source level. No trigger is required here. The platform outputs the first-ranked provincial-level V2024.1 and generates a difference description: the recorded region is completely consistent; unit conversion is not required; parameter similarity is approximately consistent; non-triggered time is inconsistent and there are differences in device model. The following information is written to the factor reference record: factor identifier, version number, effective / expired time, reason for selection, and main differences of the abandoned factor (national-level V2024.2 is sorted in descending order because the region is a higher-level region).

[0037] Uncertainty label consists of three parts: measurement error (no need to convert to low); factor fit (high confidence level is low); and caliber difference (no regional offset, no time inconsistency, no device model inconsistency, only unit inconsistency, no need to convert to low). Combined criterion: There are no high values ​​in the three parts, and two of them are low values. Therefore, the final uncertainty label is low. This uncertainty label and factor reference record are written into the carbon event frame.

[0038] Step 3 involves constructing an upstream and downstream path map using a dual accounting system with activities as debits and emissions as credits. This process identifies duplicate measurements based on isomorphism and loops, resolves conflicts and isolates anomalies according to the principle of minimum adjustment, and iteratively calculates the minimum allocation deviation under constraints at the product, process, order, and organizational dimensions. The resulting calculation records and carbon ledger are then generated. The specific implementation is as follows: After the carbon event frame is factor-matched, a record-level registry is established according to the double-entry bookkeeping principle: Using activities as debits and emissions as credits, each carbon event frame is split into two symmetrical records: an activity-side registration and an emission-side registration. The registration fields include event identifier, object identifier, start time, end time, measurement value under a preset unit of measurement, caliber fingerprint, factor reference record, and uncertainty label. Based on all registrations, an upstream and downstream pathway map is constructed. Using business objects as nodes and carbon events as edges, with activity-side edges pointing to emission-side edges, a path represents the transfer link from the original activity to the final emission. The platform maintains a path identifier, path length, set of involved nodes, set of involved events, and evidence chain link for each path, while providing two essential definitions for identification: Path isomorphism identification refers to the identification of two paths where the node types and event types correspond one-to-one and the source signatures are the same, provided that the node order and event order are consistent. Loop identification refers to the identification of the same object returning to the starting node through several edges within a similar time interval to form a closed path.

[0039] The path set is subjected to isomorphic identification and loop identification one by one, and candidates for duplicate measurement are marked. The redundancy is evaluated by a combination of path weight and node credibility. The path weight is derived from the business main line label and boundary compliance label, and the node credibility is derived from the uncertainty label level and source signature stability score. The combined criteria are prioritized in the following order: low path weight, low node credibility, short path length, and few events involved.

[0040] Source signature stability score reflects the availability and consistency of a source within a continuous window. The platform calculates three metrics across one hundred rolling unified time windows: missing rate, timestamp drift rate, and duplicate / conflict rate. Missing rate represents the proportion of records that should have been generated within the window but were not; timestamp drift rate represents the proportion of records within the window whose actual start time differs from the unified time reference beyond the allowed range; duplicate / conflict rate represents the proportion of the same object that is mutually rejected by that source within the same window. The platform assigns a high score to a combination of "low, medium, low," a medium score to "medium, medium, low" or "low, medium, medium," and a low score to any single metric that reaches a high score. The score results are fixed daily and written to the source signature archive, and are also referenced when calculating the node credibility of path nodes.

[0041] The specific process for establishing business mainline annotations and boundary compliance annotations for each path is as follows: Business mainline annotations are based on the enterprise's production process routing and bill of materials, prioritizing the process sequence, material destination, and standard operating procedures in the enterprise resource planning system. When multiple versions of the same route exist, the version with the closest effective time and no later than the event time is used. When enterprise resource planning records are missing, a temporary route confirmed by the auditing role is used as a substitute within the specified validity period, and the temporary source and expiration date are recorded. Boundary compliance annotations are determined based on the boundary type field in the caliber fingerprint and the enterprise's carbon accounting boundary list. Only when the boundary type of the event matches the list and the evidence path is complete is it marked as compliant. If there is a boundary type mismatch, a missing segment of the evidence path, or the boundary list is pending update, it is marked as pending audit. Both types of annotations are written to the path record and entered into the audit storage area for use in path weight calculation.

[0042] For example, within the same object and the same time window, after isomorphic identification and loop identification, the platform obtained four paths marked as candidates for duplicate measurement: path A, path B, path C, and path D. The attributes of the four paths are as follows: Path A is marked as the main business line and has compliant boundaries, with a high path weight; the uncertainty label of the node set is mainly low, the source signature stability score is high, and the node credibility is high; the path length is four edges, and the number of events involved is five. Path B is marked as a non-main line and requires review, with a low path weight; the uncertainty label of the node set is mainly medium, the source signature stability score is medium, the node credibility is medium, the path length is five edges, and the number of events involved is six. Path C is marked as a non-mainline and requires review, with a low path weight; the uncertainty labels in the node set are predominantly high, the source signature stability score is low, and the node credibility is low; the path length is three edges, and it involves three events. Path D is marked as a non-mainline and requires review, with a low path weight; the uncertainty labels in the node set are predominantly low, the source signature stability score is low, and the node credibility is low; the path length is three edges, and it involves four events. The platform prioritizes paths based on their weight: first, paths with low weight; second, nodes with low credibility; third, paths with shortest lengths; and fourth, those involving the fewest events. The first step compares path weights: paths B, C, and D have low weights, taking precedence over path A (which has a high weight), thus Path A is excluded from priority handling in this round. The second step compares node credibility among paths B, C, and D: paths C and D have low node credibility, taking precedence over path B (which has a medium credibility), thus Path B is prioritized later in this round. The third step compares path lengths between paths C and D; both have three edges, making them indistinguishable. The fourth step compares the number of events involved: path C has three events, while path D has four, meaning path C has fewer events than path D; therefore, path C is designated as the first target for handling in this round. The platform performs quantity reduction or abnormal isolation on path C based on the principle of minimum adjustment (the specific operation type is determined according to the aforementioned conflict type and the strength of the evidence chain), and generates a conflict resolution record or abnormal isolation record. The record includes the rule number, processing time, path identifier, involved event identifier, operation type, quantity of reduction or isolation, difference before and after reduction, and evidence chain link. Subsequently, the platform repeats the judgment in the remaining candidates in the same order until the repeated measurement candidate is resolved or the processing limit for this round is reached.

[0043] A specific example includes: For the same order A-2024-05 within the same disclosure period, after double accounting and upstream / downstream path mapping, three duplicate measurement candidates were identified: path P1, path P2 (isomorphic), and path P3 (closed loop). All three paths originate from the same business chain: natural gas procurement, storage tank delivery, combustion unit, and emissions. However, the node and event sequence of P1 and P2 are completely identical, and their source signatures are the same. P3, due to material return and refilling, forms a closed structure of storage tank delivery, refilling, and further delivery. The platform has calculated the path weight and node credibility for each path. P1: High path weight, meaning that the main business line and boundary compliance are both affirmative, node credibility is high, uncertainty label is low, and source signature stability score is high. P2: Low path weight, i.e., not the main line and marked as needing review; medium node credibility, i.e., medium uncertainty label and medium source signature stability score. P3: Low path weight, meaning that the return of materials leads to the need for boundary compliance verification, and low node credibility, meaning that the uncertainty label is high and the source signature stability score is low. Following the order of path weight (lowest), node credibility (lowest), path length (shortest), and number of events involved (lowest), P3 takes precedence over P2, and P2 takes precedence over P1.

[0044] Step 1: P3 Handling: Select the edge with the lowest node credibility in the loop (recharge edge) for anomaly isolation, transfer its measurement value to the anomaly pool and suspend its entry into allocation and disclosure, and generate an anomaly isolation record, recording the rule number, processing time, path identifier, involved event identifier, operation type and evidence chain link. If the lending symmetry is broken after isolation, the platform restores the symmetry by callback along the directly connected upstream and downstream edges of the recharge edge with the minimum amplitude, and writes the callback description and the list of affected edges.

[0045] The second step is to handle P2 (a subsequent path that is isomorphic to P1): the quantity of the emission registration of P2 is reduced, and the reduction amount is equal to the measurement value of the part that overlaps with P1; the reason for the reduction is recorded as a subsequent path that is isomorphic to P1, and a conflict resolution record is generated. At this time, P1 remains unchanged, the main business line is not affected, and it meets the principle of minimum adjustment.

[0046] After completing the above processing, the platform uses the recorded carbon emissions as the total constraint and initiates allocation iterations across product, process, order, and organizational dimensions: It constructs an allocation target set using historical output, resource consumption ratio, shift hours, and material allocation ratio as driving factors, initially allocating according to the center value of the target ratio range for each dimension; it calculates the allocation deviation, which is the difference between the result of each dimension and the center value of its target range, determining whether it falls within the range. If a dimension exceeds the range, it is set as a priority correction dimension. In the next round, only the unlocked dimensions are adjusted with minimal magnitude, allowing the priority correction dimension to gradually return to the range. The process stops when the minimum change threshold is met (the change in each dimension in two adjacent rounds is no higher than 0.5%) or the maximum number of iterations (one hundred rounds) is reached. The output is written into the calculation record and carbon ledger: including the final allocation ratio and quantity, total constraint verification results, stopping condition type, iteration round, and evidence chain link; and a symmetry verification is performed to confirm that the activity side and the emission side are symmetrical in quantity under the same object and the same window.

[0047] Step 4: Based on the preset caliber mapping map, the carbon account and calculation records are compiled into enterprise annual reports, project verification, product carbon footprints, and supplier disclosures, and discrepancy explanations and backtracking links are generated. Version comparison and spot checks are then conducted. The specific implementation is as follows: The platform pre-establishes a caliber mapping map for disclosure arrangement. The caliber mapping map consists of field nodes and transformation edges. Field nodes are divided into three categories: carbon account fields, calculation record fields, and disclosure template fields. Transformation edges carry four types of transformation information: unit conversion, time base conversion, field aggregation and splitting, and allocation rules, and record the rule number, effective time, discontinuation time, and applicable scenario tags.

[0048] The disclosure templates include corporate annual report templates, project verification templates, product carbon footprint templates, and supplier disclosure templates. Each template defines the disclosure target identifier, disclosure period, target field list, and field constraint descriptions. The caliber mapping map is maintained by version number and effective time. When a version is updated, the old version is retained and the reference relationship between versions is established to ensure that the mapping chain can be replayed. When the platform loads a disclosure task, it selects the target template and version number and locks the set of mapping rules used in this task. During orchestration, the platform uses carbon ledgers and calculation records as source nodes, traversing the caliber mapping map item by item according to the disclosure template fields. It sequentially performs unit conversion, time base conversion, and field aggregation and splitting along the transformation edges: When the source field unit differs from the target field unit, the platform completes the unit conversion step-by-step according to the conversion chain specified by the rule number, recording the conversion source and conversion step number at each step; when the time base of the source record differs from the template disclosure period, the platform rearranges the data to the template period according to the time base conversion rules, writing the period identifiers before and after conversion into the conversion record; when the target field needs to be aggregated from multiple source fields, the platform lists the participating fields and aggregation order according to the field aggregation rules; when the target field needs to be split into multiple disclosure records, the platform lists the splitting key and the method for determining the splitting ratio according to the field splitting rules. A difference description is generated for each target field, which includes at least the triggered items from the following categories: unit change, time base change, aggregation source change, boundary type change, and inconsistency with device model or version, and includes the triggering rule number, triggering reason, and processing result.

[0049] The platform also generates a backtracking link for each target field. The backtracking link points to the source field location, the carbon event frame identifier involved, the factor reference record identifier, the transformation chain number, and the evidence chain link in the audit storage area, ensuring that the path of the field from the disclosure result back to the source data and processing process is complete.

[0050] For entities with version disclosure requirements, the platform performs version comparison and random audits based on the template version number, provided that the data from the same source is locked. Version comparison is divided into three categories of identification: structural change identification, boundary adjustment identification, and numerical offset identification. Structural change identification refers to comparing the differences between the target field sets of two versions. If a new field is added or a field is removed, it is recorded as a structural change and a list of fields is provided. Boundary adjustment identification refers to comparing the differences in the caliber fingerprints of the fields in two versions. If the boundary type, unit, or time base changes, it is recorded as a boundary adjustment and a list of changes is provided. Numerical offset identification refers to using the mapping result of the previous version as a reference to calculate the numerical difference between the current version and the reference version in the same field and the same disclosure period. When the difference exceeds the preset deviation threshold, it is recorded as a numerical offset and the source of the difference value and the deviation threshold is listed. The deviation threshold is configured by the disclosure template and a fixed upper limit is set according to disclosure regulatory requirements or internal audit requirements. The platform determines the sample set for random audit based on a combination of uncertainty labels, the number of structural change entries, and the number of numerical offsets: when the record-level uncertainty label is high, or there is a structural change, or the number of numerical offsets exceeds the upper limit set in the template, it automatically enters the mandatory audit set; the remaining records enter the random audit set according to the sampling ratio defined in the template.

[0051] The platform outputs the fields to be audited, source paths, and differences for each item in the sample audit set, forming a disclosure audit record and submitting it for review.

[0052] After the disclosure results are generated, the platform saves and organizes the products and audit data according to the disclosure object. Each disclosure generates a disclosure record, which includes the disclosure object identifier, disclosure template identifier, template version number, effective time, disclosure cycle, field list, difference description list, backtracking link list, and index of the disclosure audit record. The disclosure record is bound to the version of the caliber mapping map. All conversion chains, difference descriptions, and sampling audit results are written to the replayable audit storage area. When it is necessary to reproduce the disclosure of data from the same source or to review the difference items, the platform can directly locate the carbon account, calculation record, carbon event frame, and factor reference record through the backtracking links in the disclosure record, and gradually unfold the conversion chain to ensure consistent disclosure caliber, transparent processing, and complete evidence chain. This meets the multi-caliber requirements of corporate annual reports, project verification, product carbon footprint, and supplier disclosure, and supports the feasibility and traceability of version comparison and sampling audit work.

[0053] Step 5 involves the canary release and rollback of the calculation, allocation, completion, and caliber mapping rules. These rules take effect after confirmation via sandbox replay. Lightweight submission and anonymous aggregation entry points are provided for suppliers and carriers. Supplementary sampling and verification are triggered based on coverage and uncertainty thresholds. The specific implementation is as follows: The platform implements unified gray-scale release and rollback control for calculation rules, allocation rules, completion rules, and caliber mapping rules. Each rule is registered with its version number, effective time, deactivation time, and applicable scenario tags when it is added to the database. The applicable scope and time period can also be configured. When modifications are made, a gray-scale version is generated while keeping the old version available. The gray-scale version is in a pending verification state by default. Before the gray-scale version is implemented, the platform extracts sample data of carbon event frames, factor reference records, calculation records, and carbon ledgers directly related to the rule into a sandbox ledger according to three dimensions: organization, product, and disclosure template. The source signature and time base of the sandbox ledger are frozen to ensure that the replay data and production data are consistent. The gray-scale version goes through the entire processing link from factor matching to caliber mapping in the sandbox ledger, generating comparison results and process records.

[0054] The sandbox playback pass criteria consist of a preset consistency threshold and a deviation threshold. The preset consistency threshold is used to determine whether the proportion of records with consistent results or changes within the allowable range meets the standard. The platform compares the record-level results before and after playback one by one. Records with unchanged values ​​or belonging to allowed change types (e.g., the displayed units differ only due to unit conversion in the caliber mapping, while the values ​​are the same in the target unit) are considered consistent. The proportion of consistent records to all playback records is calculated. When this proportion is not lower than a pre-configured threshold, the preset consistency threshold is met. The deviation threshold is used to determine whether the allowed changes in values ​​are within a controllable range: for records that have changed, the platform checks against the upper limit of allowed deviations registered in the disclosure template and caliber mapping map. When a single difference does not exceed this upper limit, it is recorded as a controllable deviation. If all changed records are controllable deviations, the deviation threshold is met. Only when both the preset consistency threshold and deviation threshold are met simultaneously is the grayscale version converted from pending verification to officially effective; otherwise, a rollback operation is performed, restoring the old version to the effective version. The failed version, triggering step, difference details, and backtracking link are registered in the audit storage area. Both effectiveness and rollback generate rule evolution records, recording the rule version number, applicable scenario tag, effectiveness or rollback time, and verification conclusion.

[0055] The rule evolution record and rollback condition log include: each time a rule goes from gray-scale to effective or rolls back from gray-scale, the platform generates a rule evolution record. Fields include the rule version number, applicable scenario tag, effective time or rollback time, sandbox sample range, preset consistency threshold, deviation threshold, achievement status, and a list of differences. The rollback condition log details the specific object triggering the rollback, the fields, the difference value, the source of the deviation threshold, and the evidence chain link, facilitating future replay and localization of individual anomalies.

[0056] The platform provides suppliers and carriers with a unified interface template for both lightweight submission and anonymous aggregation entry points. The lightweight submission entry point receives energy consumption or emission-related data using a fixed set of fields, including at least the submitter category, object type, time period, measurement value, unit of measurement, and location of supporting evidence. The platform performs identity verification and unit standardization on the submitted content and maps it to a unified enterprise master data system. The anonymous aggregation entry point receives aggregated data with low granularity or where source disclosure is inconvenient. The platform anonymizes the source identifier at the entry point, does not write the source signature, and rewrites it as an aggregation batch identifier. Data is then bucketed by region, energy medium, and disclosure cycle. Only when the number of independent submitters within the same bucket reaches the minimum participation limit set by the platform is a usable aggregation record generated and enters the subsequent processing chain. Records from both types of entry points are written to the audit storage area, and a backtracking link is generated for each record. The backtracking link for anonymous aggregation records stops at the aggregation batch level to protect anonymity.

[0057] A unified triggering mechanism for supplementary sampling and verification is implemented for coverage and uncertainty thresholds. The specific steps include: Coverage is used to describe the achievement of data integrity within the disclosure dimension, and is calculated as follows: For a specific disclosure period and target set, the platform compares the number of records collected with the number of records actually received. When the proportion of records actually received is lower than the coverage threshold pre-configured by the platform, a supplementary collection request is triggered. The record-level uncertainty label comes from the aforementioned factor matching process. The platform counts the proportion of high uncertainty labels within the disclosure period. When the proportion exceeds the uncertainty threshold, a review process is triggered.

[0058] It should be noted that the number of records to be collected for the coverage threshold is determined by the object set and disclosure period defined in the disclosure template. The platform compares the number of record types and times that the object set should generate within the disclosure period with the actual number of records of the same type received. The coverage threshold is given an initial value in the template and can be adjusted in the rule evolution record. Adjustments need to be verified through sandbox replay. The minimum number of participants for the anonymous aggregation entry is the minimum number of independent submitting entities within the same bucket. The initial value is set in the template. Changes are released in a gray-scale manner and are recorded. Aggregation batches that do not reach the minimum number of participants will not enter the subsequent chain.

[0059] Supplementary procurement requests are submitted through lightweight submission portals or anonymous aggregation portals by suppliers and carriers. The review process generates a review work order within the platform, assigns an auditing role to verify the target record, and verifies the factor reference record, unit conversion chain, time base conversion, and evidence attachments. After supplementary procurement and review are completed, the platform updates the parameter items with high reference frequency in the factor library based on the correction results, adjusts the uncertainty threshold and coverage threshold if necessary, and writes the changes into the rule version number and applicable scenario tag, entering a new round of gray release and sandbox replay verification. Once the verification is passed, it takes effect, and the relevant parameters are synchronously written back to the disclosure template and caliber mapping map to achieve a closed loop of rule governance and supply chain data collaboration.

[0060] It should be noted that the thresholds involved in the embodiments can be determined according to specific scenarios and needs.

[0061] This invention achieves refined management of enterprise carbon emission data throughout its entire lifecycle, from raw multi-source collection to disclosure output, by constructing carbon event frames within a unified time window, a multi-dimensional rule matching mechanism for factor libraries, and an upstream and downstream path map. During the data docking stage, consistency gating and mutual exclusion are performed on multi-source records of the same object. Combined with field standardization, time alignment, and unit unification, source conflicts and physically impossible events are effectively eliminated, ensuring the credibility of basic data.

[0062] By filtering and matching uncertainties based on region, medium, and device version dimensions in the factor library using rules, a confidence-based uncertainty label is generated and embedded in the dual accounting path. This enables the identification of duplicate measurements and the minimum allocation of deviations under the principle of least adjustment. Furthermore, a field graph drives the disclosure mapping, and discrepancy explanations and backtracking links are introduced to ensure traceability from the mapping of definitions to the disclosure results. Finally, through rule-based gray-scale release and sandbox verification mechanisms, linked with supply chain data aggregation and random audit strategies, a multi-source data-driven, transparent processing path, and consistent definition arrangement for the entire process of enterprise carbon emission management is achieved, significantly improving data compliance, accounting accuracy, and disclosure efficiency.

[0063] Example 2: A full lifecycle management system for enterprise carbon emission data, such as Figure 2 As shown, it specifically includes: The enterprise carbon emission data acquisition module is used to connect with production, energy, procurement, logistics and travel data within the enterprise's organizational boundaries and accounting cycle. It performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames and writes entry fingerprint and responsibility tags. The fit analysis module is used to establish a factor library that is versioned by region, year, medium and device, to filter carbon event frame operation rules, retrieve similarity and integrate confidence matching, output candidate factors and difference descriptions and establish factor reference records, and summarize the uncertainty labels of error and fit. The carbon ledger module is used for dual accounting with activities as debits and emissions as credits. It constructs upstream and downstream path maps, identifies duplicate measurements using isomorphism and loops, performs conflict resolution and anomaly isolation according to the principle of minimum adjustment, and iteratively calculates the minimum allocation deviation under constraints according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. The version comparison module is used to compile carbon accounts and calculation records into corporate annual reports, project verification, product carbon footprints, and supplier disclosures based on a preset caliber mapping map, and generate difference descriptions and backtracking links for version comparison and spot checks. The completion module is used to implement gray-scale release and rollback of calculation, allocation, completion and caliber mapping rules. It takes effect after confirmation by sandbox playback, and provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, triggering supplementary collection and review based on coverage and uncertainty thresholds.

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0065] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0069] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] 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.

Claims

1. A method for managing the entire lifecycle of enterprise carbon emission data, characterized in that, Includes the following steps: Within the enterprise's organizational boundaries and accounting cycle, it connects production, energy, procurement, logistics, and travel data, performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames, and writes entry fingerprints and responsibility tags. Establish a factor library with versioning by region, year, medium, and device; perform rule filtering, similarity retrieval, and confidence integration matching for carbon event frames; output candidate factors and difference descriptions; establish factor citation records; and summarize uncertainty labels for error and fit. Using a dual accounting system with activities as debits and emissions as credits, an upstream and downstream path map is constructed. Identification of duplicate measurements is achieved through isomorphism and loops. Conflict resolution and anomaly isolation are performed according to the principle of minimum adjustment. Under constraints, the minimum allocation deviation is iteratively calculated according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. Based on a pre-defined caliber mapping map, carbon accounts and calculation records are organized into corporate annual reports, project verification, product carbon footprints, and supplier disclosures, and difference explanations and backtracking links are generated for version comparison and spot checks. The rules for calculation, allocation, completion, and caliber mapping are subject to gray-scale release and rollback, and take effect after confirmation by sandbox playback. It also provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, and triggers supplementary collection and review based on coverage and uncertainty thresholds.

2. The method for full lifecycle management of enterprise carbon emission data according to claim 1, characterized in that: Within a unified time window, consistency gating and mutual exclusion are performed on multi-source data of the same object to complete identity verification, unit and time alignment, generate carbon event frames, and write entry fingerprints and responsibility tags. The specific process is as follows: Under the constraints of enterprise organizational boundaries and accounting cycles, a unified time window is set to aggregate production, energy, procurement, logistics and travel data into the same time axis according to object identifiers; Perform identity verification and unit standardization on the data of the same object within the window, map the object identifier to the unified enterprise master data system, unify energy consumption, material and emission data to the preset measurement unit, and align the timestamp to the unified time base; Consistency gating is performed on the output of the same object within the same time window. Consistency is triggered when the number of sources and the time overlap ratio both reach the preset judgment threshold. Once the consistent triggering condition is met, the mutual exclusion rule is executed to reject or downgrade combinations of source conflict, process reversal and physical impossibility. Carbon event frames are generated for records that pass the consistency gating and are not rejected. A caliber fingerprint and a responsibility label are written for each carbon event frame. The caliber fingerprint includes the region, unit of measurement, energy medium, boundary type and time base. The responsibility label includes the roles of reporting, reviewing and publishing.

3. The method for full lifecycle management of enterprise carbon emission data according to claim 2, characterized in that: A factor library was established, categorized by region, year, medium, and device version. The carbon event frame operation rules were then used for filtering, similarity retrieval, and confidence-based integrated matching. The specific process is as follows: Emission factors are versioned based on regional policies, accounting years, energy media and device models, and a factor library is built to record factor sources, applicable boundaries and effective times. The region, year, energy medium and device attributes marked in the carbon event frame are filtered by rules, and factor entries that do not meet the corresponding regional scope of application, time scope of effect, medium type and device version are removed from the factor library. In the set of factors filtered by rules, similarity retrieval is performed on the field labels of carbon event frames and factor field labels. Nearest neighbor matching is performed based on the consistency of unit structure, data field semantics and device parameters to form a set of candidate factors. Perform confidence integration on the candidate factor set, calculate confidence values ​​based on field matching consistency ratio, unit conversion consistency, and parameter proximity, determine priority factors by confidence score, and output factor difference descriptions; The selected factor and its corresponding difference description are written into the carbon event frame to form a factor reference record.

4. The method for full lifecycle management of enterprise carbon emission data according to claim 3, characterized in that: Output candidate factors and their differences, establish factor citation records, and summarize the uncertainty labels of error and fit. The specific process is as follows: The candidate factor set, which integrates similarity retrieval and confidence, is sorted, and the top-ranked item is output as the recommendation factor. The fields of the recommended factors and the attributes identified by the carbon event frames are compared to extract regional offsets, time inconsistencies, medium mismatches, unit conversion errors, and device model inconsistencies. Difference descriptions are generated according to rules and associated with factor reference records. After the factor reference is established, the confidence value, number of field deviation items, and unit conversion ratio deviation of the selected factors are combined to form record-level uncertainty labels. Uncertainty labels and factor references are written together into the carbon event frame as reference indicators for determining computational validity and triggering supplementary mining.

5. The method for full lifecycle management of enterprise carbon emission data according to claim 4, characterized in that: Using a dual accounting system with activities as debits and emissions as credits, an upstream and downstream path map is constructed. Duplicate metering is identified through isomorphism and loop detection. The specific process is as follows: The generated carbon event frames are assigned to the activity side and the emission side respectively, and an upstream and downstream path graph is constructed with business objects as nodes and carbon events as edges. The activity side edge points to the emission side edge, forming a borrowing and lending symmetrical structure. Based on object identifiers, timestamps, and accounting boundaries, upstream and downstream dependencies between nodes are established in the graph to form a set of paths, each path representing a transfer link from the original activity to the final emission. Perform isomorphism identification on the path set to determine whether there are paths with the same structure but duplicate sources. When at least two paths are found to be completely identical in terms of node and event structure, they are marked as duplicate paths. A loop detection algorithm is performed on the path sequence that forms a loop to identify closed paths that are repeatedly counted for the same object in similar time intervals, and the degree of redundancy is evaluated by a combination of path weight and node credibility. Paths or loops that are identified as duplicate measurements will be marked.

6. The method for full lifecycle management of enterprise carbon emission data according to claim 5, characterized in that: Conflict resolution and anomaly isolation are performed according to the principle of minimum adjustment. Under constraints, the minimum amortization deviation is calculated iteratively by product, process, order, and organization dimensions to generate calculation records and carbon ledgers. The specific process is as follows: For the set of paths marked as duplicate measurement or having closed loops, a priority ranking is established according to the path credibility label, data integrity and business main line weight, and the path node with the smallest impact is selected as the deduction object. Based on the principle of minimum adjustment, deduction is only performed on necessary paths, and conflict resolution records are generated, recording the event identifiers involved, the amount deducted, the difference before and after adjustment, and the reasons for adjustment; Based on the effective path map after conflict resolution, and combined with product identification, process number, order attribution and organizational structure, a set of allocation targets is constructed. Using record-level carbon emissions as the total constraint, and combining driving factors such as historical output, resource consumption ratio, shift hours, and material allocation ratio, the iterative process is controlled by the constraint conditions. In each iteration, the amortization deviation is calculated, and it is determined whether the minimum change threshold or the maximum number of iterations is met. If either termination condition is met, the amortization result is output.

7. The method for full lifecycle management of enterprise carbon emission data according to claim 6, characterized in that: Based on a pre-defined caliber mapping map, carbon accounts and calculation records are organized into corporate annual reports, project verification, product carbon footprints, and supplier disclosures. Discrepancy explanations and backtracking links are generated, and version comparisons and spot checks are conducted. The specific process is as follows: Establish a caliber mapping graph consisting of field nodes and transformation edges. Field nodes include carbon account field, calculation record field and disclosure template field. Transformation edges carry unit conversion, time base conversion, field aggregation and splitting and allocation rules. The graph is maintained by version number and effective time. Using the fields in the carbon account records and calculation records as source nodes, and combining them with the target field structure in the selected disclosure template, the mapping relationship is executed one by one according to the graph traversal path to generate a dataset with the corresponding disclosure scope. During the data mapping process, the transformation chain, transformation conditions, and field difference information between the source field and the target field are recorded, and difference description text is generated. Mark the differences and source field locations as backlinks and link them to the field output in the disclosure results; For objects that require version disclosure, retrieve historically disclosed versions, perform inter-version comparisons based on data from the same source, and mark fields with structural changes, boundary adjustments, and numerical offsets. The comparison results are subjected to sampling logic, which outputs the fields to be audited and their source paths, and generates disclosure audit records.

8. The method for full lifecycle management of enterprise carbon emission data according to claim 7, characterized in that: The canary release and rollback of calculation, allocation, completion, and caliber mapping rules are implemented as follows: Define a version number, an activation identifier, and an applicable scenario label for each rule. When a rule is updated, mark it as a grayscale version and limit its applicable scope and time period. Before the gray-scale version of the rules takes effect, the corresponding sample data is injected into the sandbox ledger to execute the complete replay process and compare the differences between the carbon event frames, factor references, calculation records and carbon ledger output by the old and new rules. When the sandbox playback output meets the preset consistency threshold and deviation threshold, the rule is set to be officially effective; otherwise, a one-click rollback operation is triggered and the failed version is recorded.

9. The method for full lifecycle management of enterprise carbon emission data according to claim 8, characterized in that: Provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, triggering supplementary sampling and review based on coverage and uncertainty thresholds. The specific process is as follows: Provides a unified interface template for external entities, allowing suppliers and carriers to submit energy consumption or emission data by time period and object type, and supports the inclusion of data into statistical categories through anonymous aggregation; The system calculates the coverage of data integrity within the disclosure dimensions and sets a threshold for the uncertainty label of each record. When the coverage is lower than the set standard or the proportion of uncertain records exceeds the preset threshold, a supplementary collection request and review process are triggered. After aligning the verified data with the original data, update the parameter items in the factor library based on the correction results, adjust the uncertainty threshold, and record the influence boundary.

10. A full lifecycle management system for enterprise carbon emission data, used to implement the full lifecycle management method for enterprise carbon emission data as described in any one of claims 1-9, characterized in that, include: The enterprise carbon emission data acquisition module is used to connect with production, energy, procurement, logistics and travel data within the enterprise's organizational boundaries and accounting cycle. It performs consistency gating and mutual exclusion on multi-source data of the same object in a unified time window, completes identity verification, unit and time alignment, generates carbon event frames and writes entry fingerprint and responsibility tags. The fit analysis module is used to establish a factor library that is versioned by region, year, medium and device, to filter carbon event frame operation rules, retrieve similarity and integrate confidence matching, output candidate factors and difference descriptions and establish factor reference records, and summarize the uncertainty labels of error and fit. The carbon ledger module is used for dual accounting with activities as debits and emissions as credits. It constructs upstream and downstream path maps, identifies duplicate measurements using isomorphism and loops, performs conflict resolution and anomaly isolation according to the principle of minimum adjustment, and iteratively calculates the minimum allocation deviation under constraints according to product, process, order, and organization dimensions to generate calculation records and carbon ledgers. The version comparison module is used to compile carbon accounts and calculation records into corporate annual reports, project verification, product carbon footprints, and supplier disclosures based on a preset caliber mapping map, and generate difference descriptions and backtracking links for version comparison and spot checks. The completion module is used to implement gray-scale release and rollback of calculation, allocation, completion and caliber mapping rules. It takes effect after confirmation by sandbox playback, and provides lightweight submission and anonymous aggregation entry points for suppliers and carriers, triggering supplementary collection and review based on coverage and uncertainty thresholds.

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