A carbon metering and tracking method and system
By constructing a carbon metering and tracking system using multi-source sensing devices and blockchain technology, the problems of low metering accuracy, inconsistent tracking, and poor adaptability in existing technologies have been solved. This system realizes a closed-loop carbon management system covering the entire life cycle and provides a high-precision, traceable, and dynamic carbon management solution.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI PROVINCE COAL SCI RES INST
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-05
AI Technical Summary
Existing carbon measurement methods and systems suffer from low measurement accuracy, inconsistent carbon tracking, poor adaptability to multiple scenarios, and inability to achieve dynamic management and control throughout the entire life cycle. This results in data disconnect, unclear responsibility definition, and a lack of targeted emission reduction optimization during the carbon management process.
By using multi-source sensing devices to collect data throughout the entire lifecycle, and combining data preprocessing and blockchain technology, a carbon footprint traceability chain is constructed to achieve closed-loop management of the entire process of carbon measurement and tracking. Through multi-scenario carbon measurement models and dynamic control mechanisms, real-time monitoring and optimization suggestions are provided.
It improves the accuracy and adaptability of carbon measurement, enables full-process traceability and tamper-proof carbon footprint, supports carbon management needs in multiple scenarios, provides dynamic control and emission reduction optimization suggestions, and enhances the reliability and scalability of carbon management.
Smart Images
Figure CN122155109A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of carbon management technology, and in particular to a carbon metering and carbon tracking method and system. Background Technology
[0002] With the advancement of the "dual carbon" goals, various industries have increasingly urgent needs for accurate carbon emission measurement, efficient carbon footprint tracking, and full life-cycle carbon management. Carbon measurement is the foundation of carbon management, and carbon tracking is a key means to achieve carbon emission reduction and carbon source tracing. The combination of the two can provide reliable support for carbon management decisions at the enterprise, industrial park, and regional levels.
[0003] Existing carbon measurement methods are mainly divided into accounting methods and monitoring methods. Accounting methods are mostly based on industry-default emission factors for estimation, which suffers from problems such as mismatch between emission factors and actual scenarios and low accounting accuracy, making it difficult to adapt to the carbon measurement needs of different industries and target objects of different sizes. Monitoring methods mostly use single monitoring equipment, with limited coverage, and cannot achieve comprehensive monitoring of multiple emission sources and the entire life cycle. At the same time, existing carbon tracking technologies mostly adopt centralized storage methods, which have the defects of easy data tampering and discontinuous traceability links. They cannot accurately trace the entire process of carbon footprint generation, flow and emission, and it is difficult to achieve linkage and control between carbon measurement data and carbon tracking data. This leads to problems such as data disconnect, unclear responsibility definition and lack of targeted emission reduction optimization in the carbon management process.
[0004] In addition, most existing carbon metering and carbon tracking systems are single-function designs, either only capable of carbon metering or only capable of simple carbon tracking. They cannot achieve closed-loop management of the entire process of "collection-preprocessing-metering-tracking-control". Furthermore, the systems have poor compatibility and scalability, making it difficult to adapt to the carbon management needs of multiple industries and scenarios, thus limiting the promotion and application of carbon management technology.
[0005] Therefore, it is necessary to provide a carbon measurement and carbon tracking method and system to solve the above-mentioned technical problems. Summary of the Invention
[0006] This invention provides a carbon metering and carbon tracking method and system, which solves the problems of low carbon metering accuracy, inconsistent carbon tracking, poor adaptability to multiple scenarios, and inability to achieve dynamic management and control throughout the entire life cycle in the prior art.
[0007] To address the aforementioned technical problems, this invention provides a carbon measurement and carbon tracking method, comprising the following steps:
[0008] S1. Data Acquisition: Collect basic data of the target object throughout its entire life cycle through multi-source sensing devices. The basic data includes energy consumption data, material flow data, emission source parameter data, and environmental correlation data.
[0009] S2. Data preprocessing: The basic data collected in step S1 is cleaned, calibrated, and normalized to remove abnormal data and unify the data format to obtain standardized data.
[0010] S3. Carbon Measurement: Based on the preset multi-scenario carbon measurement model, combined with the standardized data obtained in step S2, the direct carbon emissions, indirect carbon emissions and implicit carbon emissions of the target object at each stage of its life cycle are calculated respectively, and the total carbon emissions of the target object are integrated to generate a carbon measurement report.
[0011] S4. Carbon Tracking: A carbon footprint traceability chain is constructed using blockchain technology. The carbon measurement data in step S3, the data collection records in step S1, and the data processing logs in step S2 are stored on the blockchain to achieve full traceability and immutability of the carbon footprint.
[0012] S5. Dynamic Management and Control: Real-time monitoring of the deviation between the carbon measurement data in step S3 and the preset threshold. When the deviation exceeds the allowable range, an early warning message is generated. Combined with the carbon footprint traceability link analysis, the cause of the deviation is analyzed, and targeted carbon emission reduction optimization suggestions are output.
[0013] Preferably, in step S1, the target object includes any one or more of the enterprise production system, industrial park, supply chain system and regional ecosystem; the multi-source sensing device includes smart meters, smart water meters, gas meters, carbon emission monitoring sensors, material weighing equipment, GPS positioning modules and environmental monitoring equipment. The multi-source sensing device establishes a connection with the data receiving terminal through wired or wireless communication to realize the real-time collection and transmission of basic data.
[0014] Preferably, in step S2, the specific process of data preprocessing is as follows: outliers, missing values, and duplicate data in the basic data are removed using the 3σ principle, and missing data are supplemented using linear interpolation; the collected basic data are calibrated and measurement errors are corrected by comparing them with measurement data from standard metrology equipment; the basic data of different magnitudes and units are converted into standardized data of the same magnitude using the Z-score standardization method; and the standardized data are converted into a preset format and stored in the data buffer.
[0015] Preferably, in step S3, the multi-scenario carbon measurement model includes a direct emission measurement sub-model, an indirect emission measurement sub-model, and an implicit carbon measurement sub-model, and the parameters of the multi-scenario carbon measurement model can be adjusted according to the type of the target object;
[0016] The calculation method of the direct emission measurement sub-model is as follows: Direct carbon emissions = Σ (consumption of a certain type of direct emission source × emission factor corresponding to the emission source);
[0017] The calculation method of the indirect emission measurement sub-model is as follows: Indirect carbon emissions = Σ (consumption of a certain type of indirect emission source × emission factor corresponding to the emission source).
[0018] The calculation method of the implicit carbon metering sub-model is as follows: Implicit carbon emissions = Σ (the turnover of a certain type of material × the implicit carbon factor corresponding to the material).
[0019] Preferably, the multi-scenario carbon measurement model can be connected to an industry carbon emission factor database to achieve real-time updates of emission factors, while also supporting user-defined emission factors and implicit carbon factors.
[0020] Preferably, in step S4, the specific process of carbon tracking is as follows: A blockchain node network is constructed, comprising data collection nodes, data preprocessing nodes, carbon measurement nodes, regulatory nodes, and user nodes. Each node synchronizes data via P2P communication. Data collection records, data processing logs, and carbon measurement data are packaged into blocks according to timestamps, with each block containing the hash value of the previous block, forming a chain structure. A unique traceability identifier is assigned to the carbon footprint of each lifecycle stage, and this identifier is associated with the corresponding carbon measurement data and collection records. Access control is set, allowing different nodes to access data only within their corresponding permission range.
[0021] Preferably, the blockchain node network introduces a consensus mechanism, including a proof-of-work mechanism or a proof-of-stake mechanism, and also supports the permission upgrade of regulatory nodes.
[0022] Preferably, in step S5, the preset threshold includes a warning threshold and an over-limit threshold, which can be customized according to the carbon emission reduction target of the target object and industry standards; when the carbon measurement data reaches the warning threshold, a reminder-type warning message is generated; when the carbon measurement data exceeds the over-limit threshold, an emergency warning message is generated and a linkage control mechanism is triggered.
[0023] Preferably, the dynamic control can also be connected to third-party carbon emission reduction equipment. When an emergency warning is generated, the linkage control of the carbon emission reduction equipment is automatically triggered to realize real-time regulation of carbon emissions.
[0024] To address the aforementioned issues, this invention also provides a carbon metering and carbon tracking system for implementing any of the carbon metering and carbon tracking methods described above, comprising a data acquisition module, a data preprocessing module, a carbon metering module, a carbon tracking module, a dynamic management and control module, and a storage module.
[0025] The data acquisition module is used to collect basic data of the target object throughout its entire life cycle through multi-source sensing devices, and transmit the collected basic data to the data preprocessing module.
[0026] The data preprocessing module is used to receive the basic data transmitted by the data acquisition module, clean, calibrate and normalize the basic data to obtain standardized data, and transmit the standardized data to the carbon metering module and the storage module respectively.
[0027] The carbon metering module is used to receive standardized data transmitted by the data preprocessing module, calculate the total carbon emissions based on the preset multi-scenario carbon metering model, generate a carbon metering report, and transmit the relevant data to the carbon tracking module, dynamic management and control module and storage module.
[0028] The carbon tracking module is used to build a carbon footprint traceability chain using blockchain technology, and stores carbon measurement data, collection records and processing logs on the chain to achieve full traceability and immutability of the carbon footprint.
[0029] The dynamic control module is used to monitor the deviation between carbon measurement data and preset thresholds in real time, generate early warning information, analyze the causes of deviations, output carbon emission reduction optimization suggestions, and push them to the user terminal.
[0030] The storage module is used to store basic data, standardized data, carbon measurement data, on-chain data, and dynamic management and control related information. It adopts a distributed storage method, combining blockchain storage and local storage, and is equipped with a data backup unit.
[0031] Compared with related technologies, the carbon metering and carbon tracking method and system provided by the present invention have the following beneficial effects:
[0032] This invention provides a carbon measurement and carbon tracking method and system with high carbon measurement accuracy and strong adaptability: it adopts multi-source sensing devices to achieve comprehensive collection of basic data, covering multiple types of data such as energy consumption, material flow, emission source parameters, and environmental correlations. Combined with data preprocessing steps to eliminate errors and improve data quality; at the same time, it constructs a multi-scenario carbon measurement model, which can adjust parameters according to the type of target object to calculate direct, indirect, and implicit carbon emissions respectively, adapting to the carbon measurement needs of enterprises, parks, supply chains, and other scenarios, and solving the problems of emission factor mismatch and low measurement accuracy of existing methods.
[0033] Carbon tracking is traceable and tamper-proof: The carbon footprint traceability chain is built using blockchain technology. All data collection records, processing logs, and carbon measurement data are stored on the chain. The chain structure and hash encryption ensure that the data is tamper-proof. At the same time, a unique traceability identifier is assigned to each carbon footprint, realizing full life-cycle traceability of the carbon footprint. This solves the defects of existing centralized data storage that is easy to tamper with and the traceability is not consistent, and facilitates the definition of responsibility and regulatory verification.
[0034] Achieving dynamic closed-loop management and control throughout the entire process: A complete system of "collection-preprocessing-metering-tracking-control" has been constructed to dynamically monitor the deviation between carbon metering data and preset thresholds, generate early warning information in a timely manner and analyze the causes of deviations, output targeted emission reduction optimization suggestions, and even link with third-party emission reduction equipment to achieve real-time control. This solves the problems of existing technologies such as single function, data disconnection and inability to dynamically control, and provides reliable support for carbon emission reduction decision-making.
[0035] The system boasts strong compatibility and scalability: the data acquisition module supports compatible access to multiple communication protocols and sensing devices, enabling hot-swapping and expansion of equipment; each module of the system adopts a modular design, allowing for flexible adjustment and upgrades based on actual needs; simultaneously, it supports custom emission factors, preset thresholds, and other parameters, adapting to the carbon management needs of different industries and target groups of varying sizes, facilitating widespread application. Attached Figure Description
[0036] Figure 1 This is a flowchart of a preferred embodiment of a carbon metering and carbon tracking method provided by the present invention;
[0037] Figure 2 This is a system block diagram of a preferred embodiment of a carbon metering and carbon tracking system provided by the present invention. Detailed Implementation
[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0039] Please refer to the following: Figure 1 and Figure 2 ,in, Figure 1 for Figure 1 This is a flowchart of a preferred embodiment of a carbon metering and carbon tracking method provided by the present invention; Figure 2 This is a system block diagram of a preferred embodiment of a carbon metering and carbon tracking system provided by the present invention.
[0040] A carbon measurement and carbon tracking method includes the following steps:
[0041] S1. Data Acquisition: Collect basic data of the target object throughout its entire life cycle through multi-source sensing devices. The basic data includes energy consumption data, material flow data, emission source parameter data, and environmental correlation data.
[0042] S2. Data preprocessing: The basic data collected in step S1 is cleaned, calibrated, and normalized to remove abnormal data and unify the data format to obtain standardized data.
[0043] S3. Carbon Measurement: Based on the preset multi-scenario carbon measurement model, combined with the standardized data obtained in step S2, the direct carbon emissions, indirect carbon emissions and implicit carbon emissions of the target object at each stage of its life cycle are calculated respectively, and the total carbon emissions of the target object are integrated to generate a carbon measurement report.
[0044] S4. Carbon Tracking: A carbon footprint traceability chain is constructed using blockchain technology. The carbon measurement data in step S3, the data collection records in step S1, and the data processing logs in step S2 are stored on the blockchain to achieve full traceability and immutability of the carbon footprint.
[0045] S5. Dynamic Management and Control: Real-time monitoring of the deviation between the carbon measurement data in step S3 and the preset threshold. When the deviation exceeds the allowable range, an early warning message is generated. Combined with the carbon footprint traceability link analysis, the cause of the deviation is analyzed, and targeted carbon emission reduction optimization suggestions are output.
[0046] In step S1, the target object includes any one or more of the enterprise production system, industrial park, supply chain system and regional ecosystem; the multi-source sensing device includes smart meters, smart water meters, gas meters, carbon emission monitoring sensors, material weighing equipment, GPS positioning modules and environmental monitoring equipment. The multi-source sensing device establishes a connection with the data receiving terminal through wired or wireless communication to realize the real-time collection and transmission of basic data.
[0047] Multi-source sensing devices cover the collection of data from multiple dimensions, including energy, materials, emission sources, and the environment, ensuring the comprehensiveness of basic data. The combination of wired and wireless communication adapts to the data transmission needs of different scenarios, ensuring data real-time performance and providing data support for subsequent accurate metering.
[0048] In step S2, the specific process of data preprocessing is as follows: outliers, missing values and duplicate data in the basic data are removed using the 3σ principle, and missing data are supplemented using linear interpolation; the collected basic data are calibrated and measurement errors are corrected by comparing with the measurement data of standard metrology equipment; the basic data of different magnitudes and units are converted into standardized data of the same magnitude using the Z-score standardization method; the standardized data are converted into a preset format and stored in the data buffer.
[0049] The 3σ principle accurately removes outlier data, linear interpolation ensures data integrity, the calibration process corrects measurement deviations, Z-score standardization achieves data uniformity, and multi-step preprocessing ensures data quality, preventing inferior data from affecting the accuracy of carbon measurement.
[0050] In step S3, the multi-scenario carbon measurement model includes a direct emission measurement sub-model, an indirect emission measurement sub-model, and an implicit carbon measurement sub-model. The parameters of the multi-scenario carbon measurement model can be adjusted according to the type of the target object.
[0051] The calculation method of the direct emission measurement sub-model is as follows: Direct carbon emissions = Σ (consumption of a certain type of direct emission source × emission factor corresponding to the emission source);
[0052] The calculation method of the indirect emission measurement sub-model is as follows: Indirect carbon emissions = Σ (consumption of a certain type of indirect emission source × emission factor corresponding to the emission source).
[0053] The calculation method of the implicit carbon metering sub-model is as follows: Implicit carbon emissions = Σ (the turnover of a certain type of material × the implicit carbon factor corresponding to the material).
[0054] The multi-scenario carbon measurement model can be connected to the industry carbon emission factor database to achieve real-time updates of emission factors, while also supporting user-defined emission factors and implicit carbon factors.
[0055] The multi-scenario carbon measurement model covers direct, indirect, and implicit carbon emissions, adapting to the measurement needs of different target groups; the combination of real-time emission factor updates and customization takes into account both industry universality and scenario specificity, improving measurement accuracy; the total emissions are calculated and integrated in stages to generate detailed measurement reports, providing clear data support for carbon management.
[0056] In step S4, the specific process of carbon tracking is as follows: A blockchain node network is constructed, comprising data collection nodes, data preprocessing nodes, carbon measurement nodes, regulatory nodes, and user nodes. Each node synchronizes data via P2P communication. Data collection records, data processing logs, and carbon measurement data are packaged into blocks according to timestamps, with each block containing the hash value of the previous block, forming a chain structure. A unique traceability identifier is assigned to the carbon footprint of each lifecycle stage, and this identifier is associated with the corresponding carbon measurement data and collection records. Access control is set, allowing different nodes to access data only within their corresponding permission range.
[0057] The blockchain node network introduces a consensus mechanism, including a proof-of-work mechanism or a proof-of-stake mechanism, and also supports the permission upgrade of regulatory nodes.
[0058] The chain structure of blockchain and its association with hash values ensure that data is immutable, and the unique traceability identifier enables full-process carbon footprint tracking; multi-node network and permission control ensure data security, consensus mechanism improves network stability, and the upgraded permissions of regulatory nodes adapt to regulatory needs, solving the trust problem of centralized storage.
[0059] In step S5, the preset threshold includes a warning threshold and an over-limit threshold, which can be customized according to the carbon emission reduction target of the target object and industry standards; when the carbon measurement data reaches the warning threshold, a reminder-type warning message is generated; when the carbon measurement data exceeds the over-limit threshold, an emergency warning message is generated and the linkage control mechanism is triggered.
[0060] The dynamic control system can also be connected to third-party carbon emission reduction equipment. When an emergency warning is generated, the system will automatically trigger the linkage control of the carbon emission reduction equipment to achieve real-time regulation of carbon emissions.
[0061] The tiered thresholds adapt to different control needs, and the early warning information promptly reminds users. The combination of emergency early warning and coordinated control enables rapid response. By analyzing the causes of deviations through source tracing, emission reduction recommendations become more targeted. By connecting to third-party devices, automated control is achieved, forming a closed-loop management system of "monitoring-early warning-analysis-optimization-control".
[0062] To address the aforementioned issues, this invention also provides a carbon metering and carbon tracking system for implementing any of the carbon metering and carbon tracking methods described above, comprising a data acquisition module, a data preprocessing module, a carbon metering module, a carbon tracking module, a dynamic management and control module, and a storage module.
[0063] The data acquisition module is used to collect basic data of the target object throughout its entire life cycle through multi-source sensing devices, and transmit the collected basic data to the data preprocessing module.
[0064] The data preprocessing module is used to receive the basic data transmitted by the data acquisition module, clean, calibrate and normalize the basic data to obtain standardized data, and transmit the standardized data to the carbon metering module and the storage module respectively.
[0065] The carbon metering module is used to receive standardized data transmitted by the data preprocessing module, calculate the total carbon emissions based on the preset multi-scenario carbon metering model, generate a carbon metering report, and transmit the relevant data to the carbon tracking module, dynamic management and control module and storage module.
[0066] The carbon tracking module is used to build a carbon footprint traceability chain using blockchain technology, and stores carbon measurement data, collection records and processing logs on the chain to achieve full traceability and immutability of the carbon footprint.
[0067] The dynamic control module is used to monitor the deviation between carbon measurement data and preset thresholds in real time, generate early warning information, analyze the causes of deviations, output carbon emission reduction optimization suggestions, and push them to the user terminal.
[0068] The storage module is used to store basic data, standardized data, carbon measurement data, on-chain data, and dynamic management and control related information. It adopts a distributed storage method, combining blockchain storage and local storage, and includes a data backup unit.
[0069] Compared with related technologies, the carbon measurement and carbon tracking method provided by this invention has the following beneficial effects:
[0070] High carbon measurement accuracy and strong adaptability: It adopts multi-source sensing devices to achieve comprehensive collection of basic data, covering multiple types of data such as energy consumption, material flow, emission source parameters and environmental correlations. Combined with data preprocessing steps to eliminate errors and improve data quality; at the same time, it constructs a multi-scenario carbon measurement model, which can adjust parameters according to the type of target object to calculate direct, indirect and implicit carbon emissions separately, adapting to the carbon measurement needs of enterprises, parks, supply chains and other scenarios, and solving the problems of emission factor mismatch and low measurement accuracy of existing methods.
[0071] Carbon tracking is traceable and tamper-proof: The carbon footprint traceability chain is built using blockchain technology. All data collection records, processing logs, and carbon measurement data are stored on the chain. The chain structure and hash encryption ensure that the data is tamper-proof. At the same time, a unique traceability identifier is assigned to each carbon footprint, realizing full life-cycle traceability of the carbon footprint. This solves the defects of existing centralized data storage that is easy to tamper with and the traceability is not consistent, and facilitates the definition of responsibility and regulatory verification.
[0072] Achieving dynamic closed-loop management and control throughout the entire process: A complete system of "collection-preprocessing-metering-tracking-control" has been constructed to dynamically monitor the deviation between carbon metering data and preset thresholds, generate early warning information in a timely manner and analyze the causes of deviations, output targeted emission reduction optimization suggestions, and even link with third-party emission reduction equipment to achieve real-time control. This solves the problems of existing technologies such as single function, data disconnection and inability to dynamically control, and provides reliable support for carbon emission reduction decision-making.
[0073] The system boasts strong compatibility and scalability: the data acquisition module supports compatible access to multiple communication protocols and sensing devices, enabling hot-swapping and expansion of equipment; each module of the system adopts a modular design, allowing for flexible adjustment and upgrades based on actual needs; simultaneously, it supports custom emission factors, preset thresholds, and other parameters, adapting to the carbon management needs of different industries and target groups of varying sizes, facilitating widespread application.
Claims
1. A method for carbon measurement and carbon tracking, characterized in that, Includes the following steps: S1. Data Acquisition: Collect basic data of the target object throughout its entire life cycle through multi-source sensing devices. The basic data includes energy consumption data, material flow data, emission source parameter data, and environmental correlation data. S2. Data preprocessing: The basic data collected in step S1 is cleaned, calibrated, and normalized to remove abnormal data and unify the data format to obtain standardized data. S3. Carbon Measurement: Based on the preset multi-scenario carbon measurement model, combined with the standardized data obtained in step S2, the direct carbon emissions, indirect carbon emissions and implicit carbon emissions of the target object at each stage of its life cycle are calculated respectively, and the total carbon emissions of the target object are integrated to generate a carbon measurement report. S4. Carbon Tracking: A carbon footprint traceability chain is constructed using blockchain technology. The carbon measurement data in step S3, the data collection records in step S1, and the data processing logs in step S2 are stored on the blockchain to achieve full traceability and immutability of the carbon footprint. S5. Dynamic Management and Control: Real-time monitoring of the deviation between the carbon measurement data in step S3 and the preset threshold. When the deviation exceeds the allowable range, an early warning message is generated. Combined with the carbon footprint traceability link analysis, the cause of the deviation is analyzed, and targeted carbon emission reduction optimization suggestions are output.
2. The carbon metering and carbon tracking method according to claim 1, characterized in that, In step S1, the target object includes any one or more of the enterprise production system, industrial park, supply chain system and regional ecosystem; the multi-source sensing device includes smart meters, smart water meters, gas meters, carbon emission monitoring sensors, material weighing equipment, GPS positioning modules and environmental monitoring equipment. The multi-source sensing device establishes a connection with the data receiving terminal through wired or wireless communication to realize the real-time collection and transmission of basic data.
3. The carbon metering and carbon tracking method according to claim 1, characterized in that, In step S2, the specific process of data preprocessing is as follows: outliers, missing values and duplicate data in the basic data are removed using the 3σ principle, and missing data are supplemented using linear interpolation; the collected basic data are calibrated and measurement errors are corrected by comparing with the measurement data of standard metrology equipment; the basic data of different magnitudes and units are converted into standardized data of the same magnitude using the Z-score standardization method; the standardized data are converted into a preset format and stored in the data buffer.
4. The carbon metering and carbon tracking method according to claim 1, characterized in that, In step S3, the multi-scenario carbon measurement model includes a direct emission measurement sub-model, an indirect emission measurement sub-model, and an implicit carbon measurement sub-model. The parameters of the multi-scenario carbon measurement model can be adjusted according to the type of the target object. The calculation method of the direct emission measurement sub-model is as follows: Direct carbon emissions = Σ (consumption of a certain type of direct emission source × emission factor corresponding to the emission source); The calculation method of the indirect emission measurement sub-model is as follows: Indirect carbon emissions = Σ (consumption of a certain type of indirect emission source × emission factor corresponding to the emission source). The calculation method of the implicit carbon metering sub-model is as follows: Implicit carbon emissions = Σ (the turnover of a certain type of material × the implicit carbon factor corresponding to the material).
5. A carbon metering and carbon tracking method according to claim 4, characterized in that, The multi-scenario carbon measurement model can be connected to the industry carbon emission factor database to achieve real-time updates of emission factors, while also supporting user-defined emission factors and implicit carbon factors.
6. The carbon metering and carbon tracking method according to claim 1, characterized in that, In step S4, the specific process of carbon tracking is as follows: A blockchain node network is constructed, comprising data collection nodes, data preprocessing nodes, carbon measurement nodes, regulatory nodes, and user nodes. Each node synchronizes data via P2P communication. Data collection records, data processing logs, and carbon measurement data are packaged into blocks according to timestamps, with each block containing the hash value of the previous block, forming a chain structure. A unique traceability identifier is assigned to the carbon footprint of each lifecycle stage, and this identifier is associated with the corresponding carbon measurement data and collection records. Access control is set, allowing different nodes to access data only within their corresponding permission range.
7. A carbon metering and carbon tracking method according to claim 6, characterized in that, The blockchain node network introduces a consensus mechanism, including a proof-of-work mechanism or a proof-of-stake mechanism, and also supports the permission upgrade of regulatory nodes.
8. The carbon metering and carbon tracking method according to claim 1, characterized in that, In step S5, the preset threshold includes a warning threshold and an over-limit threshold, which can be customized according to the carbon emission reduction target of the target object and industry standards; when the carbon measurement data reaches the warning threshold, a reminder warning message is generated. When carbon measurement data exceeds the threshold, an emergency warning is generated, and a coordinated control mechanism is triggered.
9. A carbon metering and carbon tracking method according to claim 8, characterized in that, The dynamic control system can also be connected to third-party carbon emission reduction equipment. When an emergency warning is generated, the system will automatically trigger the linkage control of the carbon emission reduction equipment to achieve real-time regulation of carbon emissions.
10. A carbon metering and carbon tracking system, used to implement the carbon metering and carbon tracking method according to any one of claims 1-9, characterized in that, It includes a data acquisition module, a data preprocessing module, a carbon metering module, a carbon tracking module, a dynamic management and control module, and a storage module; The data acquisition module is used to collect basic data of the target object throughout its entire life cycle through multi-source sensing devices, and transmit the collected basic data to the data preprocessing module. The data preprocessing module is used to receive the basic data transmitted by the data acquisition module, clean, calibrate and normalize the basic data to obtain standardized data, and transmit the standardized data to the carbon metering module and the storage module respectively. The carbon metering module is used to receive standardized data transmitted by the data preprocessing module, calculate the total carbon emissions based on the preset multi-scenario carbon metering model, generate a carbon metering report, and transmit the relevant data to the carbon tracking module, dynamic management and control module and storage module. The carbon tracking module is used to build a carbon footprint traceability chain using blockchain technology, and stores carbon measurement data, collection records and processing logs on the chain to achieve full traceability and immutability of the carbon footprint. The dynamic control module is used to monitor the deviation between carbon measurement data and preset thresholds in real time, generate early warning information, analyze the causes of deviations, output carbon emission reduction optimization suggestions, and push them to the user terminal. The storage module is used to store basic data, standardized data, carbon measurement data, on-chain data, and dynamic management and control related information. It adopts a distributed storage method, combining blockchain storage and local storage, and is equipped with a data backup unit.