A method, system, device, and medium for carbon emission management of electrical equipment

Abstract

The application discloses a kind of carbon emission management method, system, equipment and medium of electric power equipment, it is characterized in that, comprising: obtaining the carbon emission related data corresponding to each stage of the whole life cycle of electric power equipment;Carbon emission related data is uploaded to the corresponding alliance block chain node of each stage respectively, to verify carbon emission related data by multi-node consensus mechanism, generate traceability mark, retrieve the target carbon emission related data corresponding to each stage from block chain based on traceability mark, and carry out carbon emission accounting to target carbon emission related data by preset carbon footprint calculation model, respectively obtain the carbon emission of each stage corresponding to carbon emission, carbon emission is input to preset time series prediction model, obtain the carbon emission trend of electric power equipment;Carbon emission management scheme is generated based on carbon emission trend, to manage the carbon emission of electric power equipment.The overall efficiency of carbon emission management of electric power equipment can be improved by the present application.

Description

Technical Field

[0001] This invention relates to the field of carbon emission management, and more particularly to a method, system, device, and medium for carbon emission management of electrical equipment. Background Technology

[0002] As a crucial physical carrier in power grid construction and operation, power equipment involves varying degrees of energy consumption and carbon emissions throughout its entire lifecycle, including raw material acquisition, manufacturing, transportation and storage, installation and maintenance, and recycling. Due to the diverse types of power equipment, its long service life, and the involvement of multiple stakeholders such as suppliers, manufacturers, power grid companies, and recycling organizations, its carbon emissions are characterized by a wide range of stages, dispersed data sources, and significant cumulative effects. Therefore, implementing systematic and comprehensive carbon emission management for power equipment is of great importance for fully understanding the composition of carbon emissions, identifying key high-emission links, and supporting the carbon reduction targets of the power industry.

[0003] However, existing carbon emission management technologies for power equipment still have shortcomings in terms of management efficiency and precision. On the one hand, carbon emission data related to power equipment is distributed across multiple entities and business systems, lacking unified data collection standards, standardized processing mechanisms, and reliable storage methods. This makes it difficult to efficiently integrate and continuously manage carbon emission data, affecting the accuracy and timeliness of carbon emission accounting and management decisions. On the other hand, existing carbon emission management methods are mostly based on static statistics and post-event analysis, making it difficult to dynamically assess carbon emissions by considering the differences in power equipment types and actual operating conditions. They also lack precise management and continuous optimization methods for different lifecycle stages. Furthermore, there is a lack of effective feedback and iteration mechanisms for the implementation of carbon emission management measures, making it difficult to form a data-driven closed-loop management process, thus hindering further improvement in the overall efficiency of carbon emission management for power equipment. Summary of the Invention

[0004] This invention provides a method, system, device, and medium for carbon emission management of electrical equipment, which can improve the overall efficiency of carbon emission management of electrical equipment.

[0005] In a first aspect, embodiments of the present invention provide a method for carbon emission management of electrical equipment, comprising: Obtain carbon emission data for each stage of the entire life cycle of electrical equipment; Each carbon emission-related data is uploaded to the corresponding consortium blockchain node for each stage, and the carbon emission-related data is verified through a multi-node consensus mechanism to generate a traceability identifier associated with the power equipment. Based on the traceability identifier, the target carbon emission-related data for each stage is retrieved from the blockchain, and the carbon emission is calculated for each target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emission amount for each stage. The carbon emission amount is then input into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. Based on the aforementioned carbon emission change trends, carbon emission management schemes are generated for each stage to manage the carbon emissions of electrical equipment.

[0006] This invention systematically collects carbon emission-related data at each stage of the entire lifecycle of power equipment, providing a complete and continuous data foundation for subsequent carbon footprint accounting and emission reduction decisions, thereby improving the comprehensiveness and data support efficiency of low-carbon management of power equipment. By utilizing a multi-node consensus mechanism to verify data consistency, it achieves reliable storage and tamper-proof management of carbon emission data in a multi-stakeholder environment, ensuring clear data sources and traceability, thus improving the reliability and collaborative efficiency of carbon emission data management for power equipment. Furthermore, by generating a unique traceability identifier for each piece of power equipment, carbon emission data at each stage of the lifecycle can be accurately and quickly retrieved, reducing the data processing costs of manual verification and repetitive organization, and improving the efficiency of retrieval and management of carbon emission data throughout the entire lifecycle. By obtaining the carbon emissions corresponding to each stage, the carbon emission assessment process is made standardized and comparable, avoiding the impact of inconsistent calculation methods on emission reduction analysis results and improving the applicability of carbon emission accounting results in low-carbon management. By constructing carbon emission time series according to the stage sequence of each life cycle stage and inputting it into a preset time series prediction model, the changing trend of carbon emissions of power equipment with the life cycle is predicted and analyzed, improving the efficiency of identifying carbon emission change patterns and the ability of forward-looking analysis. By combining the carbon emission change trend analysis results, targeted emission reduction optimization schemes are generated for different life cycle stages, so that emission reduction measures can be matched with specific high carbon emission stages and change characteristics, thereby improving the overall efficiency of low-carbon management and emission reduction optimization of power equipment.

[0007] Furthermore, the acquisition of carbon emission-related data for each stage of the entire life cycle of electrical equipment includes: Obtain data on the first carbon emissions at each stage of the entire life cycle of electrical equipment; Identify the data format of each of the first carbon emission related data, obtain the data format type, and extract fields from each of the first carbon emission related data using a data extraction method corresponding to the data format type to obtain the second carbon emission related data. By unifying the units of the second carbon emission-related data, the third carbon emission-related data is obtained. Outlier detection is performed on the third carbon emission-related data to obtain invalid data, and the invalid data is removed from the third carbon emission-related data to obtain the carbon emission-related data.

[0008] This invention achieves data standardization, integrity, and reliability by performing format recognition, field extraction, unit unification, and outlier removal on carbon emission data throughout its entire lifecycle. This improves the accuracy and operability of carbon emission accounting and management, thereby enhancing the overall efficiency of carbon emission management for power equipment.

[0009] Furthermore, the step of uploading each carbon emission-related data to the corresponding consortium blockchain node at each stage, so as to verify each carbon emission-related data through a multi-node consensus mechanism and generate a traceability identifier associated with the power equipment, includes: Each of the aforementioned carbon emission-related data is uploaded to each of the aforementioned consortium blockchain nodes through a preset interface, so as to verify each of the aforementioned carbon emission-related data through a multi-node consensus mechanism to obtain the fourth carbon emission-related data. The aforementioned consortium blockchain nodes are determined based on the type of the entity collecting the carbon emission-related data corresponding to each stage of the entire life cycle. Data hashes are generated based on the fourth carbon emission-related data, and the data hashes, historical data hashes, and corresponding timestamps are associated to obtain block records; Based on the block records, a traceability identifier associated with the power equipment is generated.

[0010] This invention uploads carbon emission data throughout its entire lifecycle to consortium blockchain nodes and uses multi-node consensus verification to generate tamper-proof block records and unique traceability identifiers, thereby achieving trusted storage and full traceability of carbon emission data and improving the overall efficiency of carbon emission management for power equipment.

[0011] Furthermore, the carbon emission-related data are verified through a multi-node consensus mechanism to obtain fourth carbon emission-related data, including: Each of the aforementioned consortium blockchain nodes performs consistency verification on the aforementioned carbon emission-related data, generating several node verification results; Determine whether the number of nodes that pass the verification in the verification results of each node meets the preset node number threshold. If it does, determine the fourth carbon emission related data.

[0012] The embodiments of the present invention ensure the authenticity and integrity of carbon emission-related data through multi-node consistency verification and preset threshold judgment, thereby generating reliable fourth carbon emission-related data and improving the overall efficiency of carbon emission management of power equipment.

[0013] Furthermore, the carbon emission calculation is performed on the carbon emission-related data of each target using a preset carbon footprint calculation model to obtain the carbon emission amount corresponding to each stage, including: The carbon emission-related data for each target are classified to extract energy consumption data and material consumption data for each stage of the entire life cycle. The energy consumption data, material consumption data, and preset emission factors are input into the carbon footprint calculation model to perform attribution calculations on the energy consumption and material consumption corresponding to each stage based on the life cycle assessment algorithm, so as to obtain the carbon emissions corresponding to each stage.

[0014] This invention accurately obtains carbon emissions at each stage through classification accounting and attribution calculation based on the entire life cycle, thereby achieving refined and quantitative management of carbon emissions from power equipment and improving the overall efficiency and decision-making accuracy of carbon emission management.

[0015] Furthermore, the step of inputting each of the carbon emissions into a preset time series prediction model to obtain the carbon emission change trend of electrical equipment over a preset time period includes: The carbon emissions are preprocessed to obtain a standardized carbon emission time series. The standardized carbon emission time series is input into the time series prediction model to learn the time series features of the standardized carbon emission time series based on the time series prediction model, so as to obtain the carbon emission prediction series of power equipment in the future preset time period. The first-order difference of the carbon emission prediction sequence is calculated to determine the carbon emission change trend of electrical equipment in a future preset time period based on the calculation results.

[0016] This invention enables dynamic identification of future carbon emission changes in power equipment by performing time-series feature learning and trend prediction on carbon emissions. This allows for the timely detection of high-emission-risk areas, thereby improving the foresight and overall efficiency of carbon emission management for power equipment.

[0017] Furthermore, the generation of carbon emission management schemes for each stage based on the carbon emission change trend to manage the carbon emissions of electrical equipment includes: Based on the carbon emission change trend, identify the corresponding life cycle stage where the carbon emission of the power equipment exceeds a preset threshold throughout its entire life cycle, so as to determine the carbon emission risk stage. Several carbon emission management schemes are generated based on the aforementioned carbon emission risk stage; The emission reduction benefit indicators of each of the carbon emission management schemes are calculated based on the expected emission reduction and implementation cost parameters, and the target carbon emission management scheme is determined based on the emission reduction benefit indicators. The carbon emission management scheme includes the expected emission reduction and the implementation cost parameters. The target carbon emission management scheme is applied to the actual management process of the corresponding life cycle stage to collect the actual carbon emissions of the corresponding stage, and compare and analyze the actual carbon emissions with the expected emission reduction. If the expected emission reduction is not achieved, the target carbon emission management scheme is adjusted to achieve closed-loop management of carbon emissions from power equipment.

[0018] This invention generates and dynamically optimizes carbon emission management schemes for each stage based on carbon emission change trends, achieving closed-loop management of the entire life cycle of power equipment. This not only enables effective emission reduction measures for high-emission-risk stages, but also continuously improves the precision and overall efficiency of carbon emission management through scheme adjustments and effect feedback.

[0019] Secondly, embodiments of the present invention provide a carbon emission management system for electrical equipment, the system comprising: an acquisition module, a calculation module, and a management module; The acquisition module is used to acquire carbon emission-related data for each stage of the entire life cycle of electrical equipment. The calculation module is used to upload the carbon emission-related data to the corresponding consortium blockchain nodes at each stage, so as to verify the carbon emission-related data through a multi-node consensus mechanism, generate traceability identifiers associated with the power equipment, retrieve the target carbon emission-related data corresponding to each stage from the blockchain based on the traceability identifiers, and perform carbon emission accounting on the target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emission amount corresponding to each stage. The carbon emission amount is then input into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. The management module is used to generate carbon emission management schemes for each stage based on the carbon emission change trend, so as to manage the carbon emissions of electrical equipment.

[0020] This invention, through the construction of an integrated carbon emission management system encompassing acquisition, calculation, and management modules, enables reliable collection, accurate calculation, and dynamic prediction of data throughout the entire lifecycle of power equipment. Based on the prediction results, targeted management solutions are generated, thereby improving the overall efficiency and sophistication of carbon emission management for power equipment.

[0021] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform operations as described in this application regarding a carbon emission management method for electrical equipment.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device or system where the computer-readable storage medium is located to perform a carbon emission management method for electrical equipment as described in this application.

[0023] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic flowchart of an embodiment of a carbon emission management method for electrical equipment provided in this application; Figure 2 This is a flowchart illustrating steps S201 to S203 provided in this application; Figure 3 This is a flowchart illustrating steps S301 to S302 provided in this application; Figure 4 This is a flowchart illustrating steps S401 to S403 provided in this application; Figure 5 This is a schematic diagram of an embodiment of a carbon emission management method for electrical equipment provided in this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0031] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0032] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0033] Power equipment generates energy consumption and carbon emissions at every stage of its life cycle, including raw material acquisition, manufacturing, transportation and warehousing, installation, operation and maintenance, and recycling. However, due to the variety of equipment types, long service life, and involvement of multiple stakeholders such as suppliers, manufacturers, power grid companies, and recycling organizations, carbon emission data sources are scattered and the cumulative effect is significant. Existing management technologies suffer from difficulties in data integration, insufficient standardization, and lack of reliable storage, making it difficult to achieve efficient, dynamic, and refined carbon emission accounting. At the same time, the lack of optimization and implementation effect feedback mechanisms for different life cycle stages restricts the accuracy, timeliness, and overall efficiency of carbon emission management.

[0034] See Figure 1 To improve the overall efficiency of carbon emission management for electrical equipment, an embodiment of the present invention provides a carbon emission management method for electrical equipment, including steps S101 to S103. Step S101: Obtain carbon emission data for each stage of the entire life cycle of electrical equipment; In some embodiments, acquiring carbon emission-related data for each stage of the entire lifecycle of power equipment includes multi-source collection and standardization of energy consumption data, material consumption data, and operating parameter data generated throughout the entire lifecycle of power equipment, from the raw material acquisition stage, manufacturing stage, transportation and warehousing stage, installation and maintenance stage, and recycling and disposal stage. Specifically, as follows: First, in the raw material acquisition stage, basic data such as the source information, mining location, and transportation distance of raw materials are collected by setting RFID tags on raw material batches. Combined with the supplier's enterprise resource planning (ERP) system, energy consumption data such as electricity consumption and fuel consumption during the raw material production process are obtained to form carbon emission-related data for the raw material stage. Second, in the manufacturing stage, by connecting to the manufacturing execution system (MES) of the manufacturing enterprise, equipment operating energy consumption data and key process parameter data corresponding to each production process of power equipment are collected, including but not limited to the electricity consumption of the transformer core lamination process and the equipment load of the cable insulation extrusion process. At the same time, by deploying IoT sensors in the production workshop, energy consumption data such as workshop electricity consumption and natural gas consumption are collected in real time to form carbon emission-related data for the manufacturing stage. Then, during the transportation and warehousing phase, mileage information of transport vehicles is collected via onboard GPS, and corresponding fuel consumption data is obtained by combining vehicle fuel consumption sensors or the transportation management system. Simultaneously, energy consumption data of equipment such as temperature-controlled warehouses and lighting systems in the warehousing process is collected through integration with the warehousing management system to construct carbon emission data for the transportation and warehousing phases. Further, during the installation and maintenance phase, operating parameter data of electrical equipment during actual operation is collected by integrating with the power grid dispatch automation system or Supervisory Control and Data Acquisition (SCADA) system, including transformer load rate, cable current carrying capacity, and equipment loss data. Energy consumption data of the station's cooling system and auxiliary equipment are aggregated and processed through edge computing nodes to obtain carbon emission data for the installation and maintenance phase. Finally, during the recycling and disposal phase, energy consumption data during the dismantling of recycling equipment is collected by integrating with the dismantling management system of recycling companies, and material recovery rate data and carbon emission data corresponding to waste treatment methods are obtained, thus forming carbon emission data for the recycling and disposal phase.

[0035] It should be noted that after the data collection at each of the above stages is completed, the carbon emission-related data undergoes unified data standardization processing, including: automatically identifying the data format type of the input data, and extracting core data items using field mapping parsing, path matching parsing, or optical character recognition methods based on the identification results; subsequently, according to ISO 14064 and GB / T 29158 standards, the extracted data is uniformly converted to units of measurement to obtain carbon emission basic data with consistent units; then, the carbon emission basic data is encapsulated according to a six-dimensional data structure of "life cycle stage, emission index type, value, unit, collection time, and collection subject" to form standardized carbon emission-related data. Simultaneously, based on preset outlier detection rules, the standardized carbon emission-related data undergoes anomaly detection. When data is detected to exceed the statistical distribution range or industry reasonable threshold, it is automatically marked as abnormal data and a manual verification process is triggered. After the abnormal data is confirmed or corrected, it is included in the data set of the corresponding life cycle stage, ultimately obtaining the carbon emission-related data corresponding to each stage of the entire life cycle of power equipment.

[0036] Through the above steps, it is possible to achieve multi-source collection, unified standardization, and anomaly verification of carbon emission-related data at all stages of the entire life cycle of power equipment, thereby ensuring the integrity, accuracy, and comparability of the data. This provides a reliable data foundation for subsequent carbon emission accounting, dynamic prediction, and refined management, and improves the overall efficiency and accuracy of carbon emission management for power equipment.

[0037] Step S102: Upload the carbon emission-related data to the corresponding consortium blockchain nodes for each stage to verify the carbon emission-related data through a multi-node consensus mechanism, generate traceability identifiers associated with the power equipment, retrieve the target carbon emission-related data for each stage from the blockchain based on the traceability identifiers, and perform carbon emission accounting on the target carbon emission-related data for each stage through a preset carbon footprint calculation model to obtain the carbon emission amount for each stage. Input the carbon emission amount into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. In some embodiments, acquiring carbon emission-related data corresponding to each stage of the entire life cycle of power equipment includes: acquiring first carbon emission-related data corresponding to each stage of the entire life cycle of power equipment; identifying the data format of each of the first carbon emission-related data to obtain a data format type, and extracting fields from each of the first carbon emission-related data using a data extraction method corresponding to the data format type to obtain second carbon emission-related data; unifying the units of the second carbon emission-related data to obtain third carbon emission-related data; performing outlier detection on the third carbon emission-related data to obtain invalid data, and removing the invalid data from the third carbon emission-related data to obtain the carbon emission-related data.

[0038] In some embodiments, acquiring the first carbon emission-related data corresponding to each stage of the entire life cycle of power equipment specifically involves: constructing a multi-source heterogeneous data acquisition system to collect carbon emission-related data for each stage of the entire life cycle of power equipment. Each stage of the entire life cycle includes at least the raw material acquisition stage, the manufacturing stage, the transportation and warehousing stage, the installation and maintenance stage, and the recycling and disposal stage. In the raw material acquisition stage, information on the mining location, mining energy consumption data, and transportation distance data of raw materials are collected using RFID tags, and the system is connected to the supplier's Enterprise Resource Planning (ERP) system to obtain energy data such as electricity and fuel consumed during the raw material production process. In the manufacturing stage, by connecting to the manufacturer's Manufacturing Execution System (MES), equipment operating energy consumption data and process parameter data corresponding to each production process of the power equipment are collected. The system collects real-time data on electricity and natural gas consumption in the production workshop through IoT sensors. During the transportation and warehousing phase, it collects mileage and fuel consumption data of transport vehicles through GPS positioning modules and connects to the warehousing management system to obtain energy consumption data of environmental control equipment during warehousing. During the installation and maintenance phase, it connects to the SCADA system to collect load rate, current carrying capacity, and operating loss data of electrical equipment during operation, and edge computing nodes preprocess the real-time energy consumption data in the substation. During the recycling and disposal phase, it connects to the dismantling system of recycling companies to collect energy consumption data of dismantling equipment, material recovery rate data, and carbon emission data generated during waste disposal, thereby forming the first carbon emission-related data corresponding to each stage of the entire life cycle of electrical equipment.

[0039] In some embodiments, the data format of each of the first carbon emission-related data is identified to obtain a data format type, and the fields of each of the first carbon emission-related data are extracted using a data extraction method corresponding to the data format type to obtain second carbon emission-related data. Specifically, the data format of the first carbon emission-related data is first identified, and the data format type is determined based on the file extension, data structure characteristics, and data content organization. The data format type includes at least structured data, semi-structured data, and unstructured data. When the data format type is structured data, the data fields are parsed and matched based on a preset field mapping table, and the original fields are mapped to unified standard fields. It supports extending field mapping rules through visual configuration; when the data format type is semi-structured data, a path matching parsing method is used to extract corresponding energy consumption data, material consumption data, and related parameter data from the semi-structured data by configuring the target data node path; when the data format type is unstructured data, the optical character recognition (OCR) module is called to recognize the text content, and key data related to carbon emissions are extracted through keyword matching and context semantic verification; through the above data extraction methods corresponding to the data format type, fields are extracted from each of the first carbon emission related data to obtain second carbon emission related data containing stage information, indicator type, and original values.

[0040] In some embodiments, the units of the second carbon emission-related data are unified to obtain the third carbon emission-related data. Specifically, the data units in the second carbon emission-related data are identified, and data in different unit formats are uniformly converted based on preset unit conversion rules. The unit conversion rules are set according to the ISO 14064 carbon footprint accounting standard and the GB / T 29158 power equipment life cycle assessment standard, uniformly converting energy consumption data to kilowatt-hours (kWh), mass data to tons, and distance data to kilometers (km). During the unit conversion process, non-standard unit data is automatically converted using built-in corresponding conversion formulas, and the validity of the conversion results is verified. After the unit unification conversion is completed, the second carbon emission-related data is encapsulated into a six-dimensional structured data containing stage, indicator, value, unit, collection time, and collection subject information, thereby obtaining the third carbon emission-related data.

[0041] In some embodiments, outlier detection is performed on the third carbon emission-related data to obtain invalid data, and the invalid data is removed from the third carbon emission-related data to obtain the carbon emission-related data. Specifically, this involves: performing outlier detection on the third carbon emission-related data based on statistical anomaly detection methods, and calculating the mean of the data corresponding to each indicator. and standard deviation and will exceed the range [ -3 , +3 Data is marked as abnormal; simultaneously, based on preset reasonable industry thresholds, core indicators at each stage are verified. When data exceeds the reasonable range for the corresponding stage, it is marked as abnormal. For data marked as abnormal, a manual verification process is triggered, and an abnormality alert is sent to the corresponding data collection entity. The data collector then manually selects whether to correct, confirm, or re-collect the abnormal data. Abnormal data that has been manually confirmed as invalid is removed from the third carbon emission-related data, while valid data is retained. Finally, carbon emission-related data for subsequent carbon footprint calculation is obtained.

[0042] Please refer to Figure 2 In some embodiments, the step of uploading each carbon emission-related data to the corresponding consortium blockchain node at each stage to verify each carbon emission-related data through a multi-node consensus mechanism and generate a traceability identifier associated with the power equipment includes: steps S201 to S203. Step S201: Upload each of the carbon emission-related data to each of the consortium blockchain nodes through a preset interface, so as to verify each of the carbon emission-related data through a multi-node consensus mechanism and obtain the fourth carbon emission-related data. The consortium blockchain nodes are determined based on the type of the subject collecting the carbon emission-related data corresponding to each stage of the entire life cycle. In some embodiments, based on a consortium blockchain architecture, raw material suppliers, manufacturers, transportation companies, power companies, recycling organizations, and regulatory authorities are configured as consortium blockchain nodes, and each node is assigned data read / write permissions corresponding to its business process. Specifically, raw material supplier nodes are used to upload and view carbon emission-related data during the raw material acquisition stage; manufacturer nodes are used to upload and view carbon emission-related data during the production and manufacturing stage; transportation company nodes are used to upload and view carbon emission-related data during the transportation and warehousing stage; power company nodes are used to upload and view carbon emission-related data during the installation and maintenance stage; recycling organization nodes are used to upload and view carbon emission-related data during the recycling and disposal stage; and regulatory authority nodes have read-only access to carbon emission-related data at each stage of the entire lifecycle. Each data collection entity accesses the data through a pre-defined application programming interface (API). The PI uploads standardized carbon emission-related data for the corresponding stage to its associated consortium blockchain node. After the data upload is completed, a multi-node consensus verification process is triggered, in which at least three pre-defined associated consortium blockchain nodes perform consistency and authenticity verification on the carbon emission-related data. The consistency verification includes at least data integrity verification and field standardization verification, and the authenticity verification includes at least data source entity legitimacy verification and data logic rationality verification. When a preset proportion of the participating consortium blockchain nodes output verification pass results, the carbon emission-related data is determined to meet the consensus conditions and is designated as the fourth carbon emission-related data. When the preset proportion of nodes output verification pass results, the carbon emission-related data is determined to have failed consensus verification, and a data re-collection or manual review process is triggered.

[0043] Step S202: Generate a data hash based on the fourth carbon emission related data, and associate the data hash, historical data hash and corresponding timestamp to obtain a block record; In some embodiments, a hash operation is performed on the fourth carbon emission-related data to generate a data hash value that uniquely corresponds to the data; the historical data hash value of the previous confirmed block in the blockchain is obtained, and the timestamp information corresponding to the current data writing time is obtained; the data hash value, the historical data hash value, and the timestamp are encapsulated according to a preset block structure to form a block record, wherein the block record includes at least the current block hash, the previous block hash, the timestamp, and data index information; after the block record is constructed, the block record is written into the consortium blockchain and synchronized to each consortium blockchain node participating in the consensus, thereby realizing the immutable evidence storage of the fourth carbon emission-related data.

[0044] Step S203: Generate a traceability identifier associated with the power equipment based on the block record.

[0045] In some embodiments, a traceability identifier uniquely corresponding to the fourth carbon emission-related data is generated based on the data hash value, timestamp, and corresponding collection stage identifier in the block record; the traceability identifier is associated and stored with the corresponding unique identifier of the power equipment, wherein the unique identifier of the power equipment includes the equipment asset code, equipment serial number, or preset equipment code; when a query request based on the unique identifier of the power equipment or the traceability identifier is received, the upload subject information, data collection time, and historical modification records corresponding to the block record are extracted from the consortium blockchain, and the query results are returned to the query terminal, thereby realizing the full-process traceability and auditing of carbon emission data of power equipment.

[0046] In some embodiments, the step of verifying each of the carbon emission-related data through a multi-node consensus mechanism to obtain the fourth carbon emission-related data includes: performing consistency verification on each of the carbon emission-related data according to each of the consortium blockchain nodes, generating a number of node verification results; determining whether the number of nodes that pass the verification in each of the node verification results meets a preset node number threshold, and if so, determining the fourth carbon emission-related data.

[0047] In some embodiments, each of the consortium blockchain nodes performs consistency verification on the carbon emission-related data to generate several node verification results. Specifically, when any data collection entity uploads carbon emission-related data for the corresponding stage to its consortium blockchain node through a preset interface, a multi-node consistency verification process is triggered. At least three pre-configured consortium blockchain nodes with business relationships with the data collection entity independently verify the carbon emission-related data. Each consortium blockchain node performs consistency verification operations according to its local verification rules. The consistency verification includes at least: verifying the data integrity of the carbon emission-related data to determine whether the data contains preset necessary fields; verifying the data format standardization of the carbon emission-related data to determine whether the data conforms to a unified data interface specification; and verifying the data logic rationality of the carbon emission-related data to determine whether the data meets the business rules and industry reasonable scope of the corresponding lifecycle stage. After completing the consistency verification, each consortium blockchain node generates a node verification result and feeds the node verification result back to the consensus verification module. The node verification result includes at least the identifier information of "verification passed" or "verification failed".

[0048] In some embodiments, it is determined whether the number of nodes that pass verification in each node verification result meets a preset node number threshold. If it does, the fourth carbon emission related data is determined. Specifically, the node verification results fed back by each consortium blockchain node are summarized and statistically analyzed to determine whether the number of nodes that output "verification passed" results meets a preset consensus condition. The preset consensus condition includes: the number of consortium blockchain nodes participating in the verification is not less than a preset minimum number of nodes, and the number of nodes that output "verification passed" results is not less than a preset proportion of the total number of nodes participating in the verification. When the node verification result meets the preset consensus condition, the carbon emission related data is determined to have passed multi-node consensus verification and is determined to be the fourth carbon emission related data. When the node verification result does not meet the preset consensus condition, the carbon emission related data is determined to have failed consensus verification and an abnormal verification result is generated to trigger a data re-collection process or a manual review process.

[0049] Please refer to Figure 3 In some embodiments, the step of calculating carbon emissions of each target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emissions corresponding to each stage includes: steps S301 to S302. Step S301: Classify the carbon emission-related data of each target to extract the energy consumption data and material consumption data corresponding to each stage of the entire life cycle. In some embodiments, carbon emission data for each stage of the entire lifecycle of electrical equipment are categorized according to stage identifiers, specifically: Raw material acquisition stage: Extracting raw material consumption data, including the consumption of raw materials such as ores and metal conductors, mining energy consumption, and transportation energy consumption; Manufacturing stage: Extracting energy consumption data for the production process, including the consumption of electricity and natural gas in each production step; Simultaneously extracting material consumption data, including the consumption of iron cores, winding materials, and insulation materials; Transportation and warehousing stage: Extracting fuel consumption of transport vehicles, transportation mileage, and energy consumption data for the warehousing environment; Installation and maintenance stage: Extracting energy consumption data of electrical equipment during operation, such as transformer no-load loss, load loss, and cooling system energy consumption; Recycling and disposal stage: Extracting energy and recycled material data consumed during recycling and dismantling, and calculating negative emissions (such as copper and iron recycling emission reductions). In this embodiment, for the data of each stage, a preset template is called according to the equipment type to perform unified field mapping and format standardization of energy consumption and material consumption data, so as to facilitate subsequent carbon footprint calculation model calls.

[0050] Step S302: Input the energy consumption data, the material consumption data, and the preset emission factor into the carbon footprint calculation model, and perform attribution calculation on the energy consumption and material consumption corresponding to each stage based on the life cycle assessment algorithm to obtain the carbon emissions corresponding to each stage.

[0051] In some embodiments, the energy consumption and material consumption of each stage extracted in step S301 are matched with corresponding preset emission factors, including: energy factors: such as grid power supply emission factors and natural gas emission factors; material factors: such as primary copper mining emission factors and recycled copper production emission factors; transportation factors: such as diesel truck emission factors per kilometer; in this embodiment, the carbon footprint calculation model multiplies the energy consumption and material consumption of each stage by the corresponding emission factors according to the configurable template of the equipment type, and performs stage attribution calculation according to the attribution formula, that is: carbon emissions of a certain stage = (Energy consumption / material consumption at this stage × corresponding emission factor). In this embodiment, the calculation results of each stage are summarized to generate the carbon emissions of the power equipment throughout its entire life cycle, and output according to the stage proportion and time trend dimensions to clarify the carbon emission contribution of each stage and the core emission reduction links in the operation and maintenance stage; at the same time, the system can automatically synchronize the latest national / industry emission factors according to the dynamic factor update mechanism to ensure that the accounting results are consistent with the latest standards.

[0052] Please refer to Figure 4 In some embodiments, the step of inputting each of the carbon emissions into a preset time series prediction model to obtain the carbon emission change trend of electrical equipment in a preset time period includes: steps S401 to S403. Step S401: Perform data preprocessing on each of the carbon emissions to obtain a standardized carbon emission time series; In some embodiments, multidimensional operating data and carbon emission-related data of power equipment within a historical preset time period are acquired. The multidimensional operating data includes at least: monthly average load rate, monthly average loss value, monthly average operating temperature, monthly average energy consumption, seasonal coefficient, equipment service life, ambient temperature, ambient humidity, grid voltage fluctuation amplitude, equipment maintenance frequency, material aging coefficient, and grid emission factor for the corresponding region for the past 12 months. The historical operating data and carbon emission-related data are then cleaned, specifically: missing data items are supplemented using linear interpolation; when operating data for a certain month is missing, linear fitting is performed based on the corresponding data of the preceding and following months to complete the data; abnormal fluctuation data are smoothed using a moving average method, with the moving average window size set to 3 to eliminate the impact of occasional interference on time-series feature learning; and the cleaned feature data for each dimension are standardized using the Z-Score standardization method, according to the formula... Normalize each feature, where The historical mean of the corresponding feature. The standard deviation of the corresponding features is used to map data of different dimensions to a unified numerical range, thereby improving the convergence speed and stability of subsequent model training. The standardized multidimensional time series data is reconstructed by converting the original one-dimensional time series data into a three-dimensional tensor input format, specifically "sample size × time step × feature dimension", where the time step is set to 6, indicating that a sample is constructed based on 6 consecutive months of historical operating data to predict future carbon emission changes. Through the above steps, standardized carbon emission time series data for time series prediction is obtained.

[0053] Step S402: Input the standardized carbon emission time series into the time series prediction model to learn the time series features of the standardized carbon emission time series based on the time series prediction model, and obtain the carbon emission prediction series of power equipment in the future preset time period. In some embodiments, the time series prediction model is a carbon emission prediction model based on a Long Short-Term Memory (LSTM) network. Its model structure and training process include: constructing an LSTM neural network model, which adopts an "input layer-hidden layer-output layer" network structure, wherein: the input layer has a feature dimension of 12, corresponding to the 12 standardized core operational features; the hidden layer is set as a two-layer LSTM network, with the first layer containing 64 neurons and the second layer containing 32 neurons, and each hidden layer uses the ReLU activation function to enhance the model's ability to express nonlinear time series features and avoid gradient vanishing; the output layer is set as a fully connected layer with an output dimension of 1, used to output the carbon emissions in a single future time step. The model is trained based on historical operating data. The training data consists of approximately 600,000 time-series samples from 1000 similar power equipment units over their 5-year lifespan. The dataset is divided into training, validation, and test sets in a 7:2:1 ratio. The Adam optimizer is used for training, with an initial learning rate of 0.001 and a learning rate decay strategy, halving the learning rate every 100 training cycles. The mean squared error (MSE) loss function is used, with 300 training epochs and a batch size of 32. After training, the model's predictive performance is evaluated using the test set, employing mean absolute error (MAE) and coefficient of determination (R²). 2 As an evaluation indicator, when MAE 5%, and R 2 When the value is 0.92, the model is deemed to meet the prediction accuracy requirements. The standardized carbon emission time series obtained in step S401 is input into the trained LSTM time series prediction model, and the carbon emission prediction sequence of power equipment in the future within a preset time period is output, wherein the preset time period is the next 6 months. In this embodiment, the carbon emission prediction sequence also includes a 95% confidence interval corresponding to the prediction value, which is used to characterize the uncertainty range of the prediction result.

[0054] Step S403: Calculate the first difference of the carbon emission prediction sequence to determine the carbon emission change trend of electrical equipment in a future preset time period based on the calculation results.

[0055] In some embodiments, calculating the first difference of the carbon emission prediction sequence includes: calculating the first difference between adjacent prediction values ​​in chronological order for the carbon emission prediction sequence for the next 6 months, specifically: ,in This represents the predicted carbon emissions for month n; the carbon emission trend is determined based on the first-order difference result, with the specific determination rule being: when... When emissions are greater than 0 for three consecutive months and the growth rate exceeds a preset threshold, it is judged as a "high emission growth trend"; when When the value is greater than 0 but the growth rate does not exceed 5%, it is judged as a "stable growth trend"; when When the value is less than 0, it is determined to be an "emission reduction trend". Combining the carbon emission change trend with the carbon emission ratio information of power equipment at each stage of its life cycle, high emission risk links are located. When the prediction results show that the carbon emission growth ratio of a certain stage exceeds 80%, that stage is marked as a core high emission risk link, and the corresponding risk factors are further associated, such as excessively high load rate, abnormal operating temperature, or insufficient maintenance frequency. Through the above steps, the dynamic identification of the future carbon emission change trend of power equipment and the accurate location of high emission risk links are achieved.

[0056] Through the above steps, it is possible to achieve multi-source collection, standardized processing, blockchain traceability and consensus verification of carbon emission data throughout the entire life cycle of power equipment. Based on the carbon footprint calculation model and time series prediction model, the carbon emission amount and future trend can be accurately calculated and predicted, thereby dynamically identifying high emission risk links, providing data support and decision-making basis for carbon emission management of power equipment, and significantly improving the accuracy, transparency and overall efficiency of carbon emission management.

[0057] Step S103: Generate carbon emission management schemes for each stage based on the carbon emission change trend, so as to manage the carbon emissions of electrical equipment.

[0058] In some embodiments, the step of generating carbon emission management schemes corresponding to each stage based on the carbon emission change trend to manage the carbon emissions of power equipment includes: identifying the corresponding life cycle stage where the carbon emissions of the power equipment exceed a preset threshold during its entire life cycle based on the carbon emission change trend, thereby determining the carbon emission risk stage; generating several carbon emission management schemes based on the carbon emission risk stage; calculating the emission reduction benefit index of each carbon emission management scheme based on the expected emission reduction and implementation cost parameters, and determining the target carbon emission management scheme based on the emission reduction benefit index, wherein the carbon emission management scheme includes the expected emission reduction and the implementation cost parameters; applying the target carbon emission management scheme to the actual management process of the corresponding life cycle stage to collect the actual carbon emissions of the corresponding stage, and comparing and analyzing the actual carbon emissions with the expected emission reduction; if the expected emission reduction is not achieved, adjusting the target carbon emission management scheme to achieve closed-loop management of the carbon emissions of the power equipment.

[0059] In some embodiments, based on the carbon emission change trend, the system identifies the corresponding life cycle stage where the carbon emissions of the power equipment exceed a preset threshold throughout its entire life cycle, thereby determining the carbon emission risk stage. Specifically, based on the carbon emission change trend obtained in step S403, the system divides the entire life cycle of the power equipment into a production stage, a transportation stage, an operation and maintenance stage, and a recycling stage, and calculates the predicted carbon emissions of each life cycle stage within a preset time window. The predicted carbon emissions of each life cycle stage are compared with the corresponding preset stage emission threshold, wherein the preset stage emission threshold is determined based on the industry benchmark emission value of similar power equipment in history or the reference limit issued by the regulatory authority. When the predicted carbon emissions of any life cycle stage exceed the corresponding preset stage emission threshold, the life cycle stage is determined to be a carbon emission risk stage. In this embodiment, if a certain life cycle stage shows a continuous increase in the carbon emission change trend and its carbon emission growth accounts for more than a preset proportion threshold (e.g., 80%) of the total life cycle carbon emission increment, then the life cycle stage is further marked as a high-priority carbon emission risk stage for the key generation and optimization of subsequent emission reduction schemes.

[0060] In some embodiments, several carbon emission management schemes are generated based on the carbon emission risk stages. Specifically, for each identified carbon emission risk stage, a multi-dimensional set of carbon emission management schemes is automatically generated based on the process characteristics, operating parameters, and historical emission reduction case library of the corresponding stage. In the production stage, the carbon emission management schemes include process parameter optimization schemes, such as adjustments to welding current, welding voltage, or process cycle time. In the operation and maintenance stage, the carbon emission management schemes include operation strategy adjustment schemes, such as grid topology adjustments, load diversion control, or operating time optimization schemes. In the recycling stage, the carbon emission management schemes include equipment upgrades or process modifications, such as upgrading and dismantling equipment to improve the metal material recycling rate. Each carbon emission management scheme is associated with at least the corresponding life cycle stage, scheme implementation content, implementation cost parameters, and expected emission reduction parameters predicted based on historical data or models.

[0061] In some embodiments, emission reduction benefit indicators for each carbon emission management scheme are calculated based on expected emission reductions and implementation cost parameters, and a target carbon emission management scheme is determined based on the emission reduction benefit indicators. The carbon emission management scheme includes the expected emission reductions and the implementation cost parameters. Specifically, for multiple carbon emission management schemes generated for the same carbon emission risk stage, their corresponding implementation cost parameters and expected emission reduction parameters are obtained respectively. Based on the implementation cost parameters and the expected emission reduction parameters, the emission reduction benefit indicators for each carbon emission management scheme are calculated according to a preset emission reduction benefit assessment model. The emission reduction benefit indicator is the cost-emission reduction benefit ratio, specifically calculated as follows: cost... Emission reduction benefit ratio Implementation costs The expected emission reduction is expressed in yuan per ton of carbon dioxide. In this embodiment, the carbon emission management schemes are ranked from low to high according to the emission reduction benefit index. The lower the emission reduction benefit index, the lower the unit emission reduction cost and the better the emission reduction economy. Based on the ranking results and combined with the company's budget constraints or implementation feasibility constraints, the carbon emission management scheme with the best emission reduction benefit index or that meets the preset constraints is selected as the target carbon emission management scheme.

[0062] In some embodiments, the target carbon emission management scheme is applied to the actual management process of the corresponding life cycle stage to collect the actual carbon emissions of the corresponding stage and compare and analyze the actual carbon emissions with the expected emission reduction. If the expected emission reduction is not achieved, the target carbon emission management scheme is adjusted to achieve closed-loop management of carbon emissions from electrical equipment. Specifically, the target carbon emission management scheme is issued to the actual production, operation, or recycling management system of the corresponding life cycle stage for execution, and operating parameters, energy consumption data, and actual carbon emission data of the corresponding stage are continuously collected during the scheme execution process. Based on the carbon emission data before and after the scheme execution, the actual carbon emissions are statistically analyzed to calculate the actual emission reduction achieved after the scheme execution. The system compares the actual emission reduction with the expected emission reduction in the target carbon emission management plan. When the actual emission reduction reaches or exceeds the expected emission reduction, the target carbon emission management plan is marked as a valid plan and stored in the emission reduction plan library. When the actual emission reduction is lower than the expected emission reduction, the system automatically triggers a plan iteration mechanism to adjust the target carbon emission management plan according to the reasons for the emission reduction deviation. The adjustment includes, but is not limited to, further optimizing process parameters, changing the type of emission reduction measures, or recombining multiple emission reduction methods. The adjusted carbon emission management plan re-enters the execution and monitoring process, thereby forming a dynamic emission reduction optimization closed-loop management mechanism of "data collection - trend prediction - plan generation - plan execution - effect evaluation - plan iteration".

[0063] In some embodiments, a visualization interaction layer is also included for multi-role customized display and early warning interaction of carbon emission management results of power equipment. The visualization interaction layer configures different data display views based on user role permissions to meet the management and supervision needs of different entities for carbon emission information. Specifically, the system classifies users into suppliers, power company users, and regulatory department users according to their identities, and configures corresponding data access permissions and display interfaces for different user roles. For supplier users, the visualization interaction layer includes a raw material carbon footprint contribution view, which displays the proportion of different raw materials in the carbon emissions of the equipment throughout its entire life cycle. It also shows the upload status and progress information of carbon emission-related data, allowing suppliers to self-check the integrity and compliance of their data. For power company users, the visualization interaction layer includes a comprehensive life-cycle carbon emission display view, which uses a combination of text and tables to show the carbon emission changes of power equipment from the production, transportation, operation and maintenance, to the recycling stage, clearly marking the corresponding carbon emission values ​​and high-emission nodes for each life cycle stage. The display view also includes a module for analyzing the implementation progress and cost-effectiveness of emission reduction schemes, which uses structured tables to display key indicators such as the implementation cost, expected emission reduction, actual emission reduction, and investment payback period of different emission reduction schemes, enabling intuitive comparative analysis of multiple schemes. For regulatory authorities, the visualization interaction layer includes a regional or industry carbon footprint statistics display view, which summarizes and statistically analyzes the carbon emissions of power equipment within a region or specific industry. For example, it displays the average carbon emission level of a 110kV transformer within an administrative region to support macro-level supervision and decision-making analysis.

[0064] It should be noted that the visualization interaction layer also includes a threshold early warning module. The system pre-stores industry benchmark carbon emission thresholds. When the carbon emissions of electrical equipment at any stage of its life cycle exceed the corresponding industry benchmark value, an early warning mechanism is automatically triggered. The early warning mechanism includes sending alarm information to the corresponding user via system messages or SMS. The alarm information clearly identifies the life cycle stage exceeding the standard, the actual carbon emission value, and the corresponding industry benchmark value, so as to remind the relevant user to take timely intervention or rectification measures, thereby improving the response efficiency and management level of carbon emission management.

[0065] In some embodiments, taking a "110kV oil-immersed transformer (equipment ID: T12345)" as an example, the implementation process is described in detail: Step 1: Data collection and standardization: Raw material stage: The silicon steel sheet supplier (Company A) collects data on the silicon steel sheet mining location (a mine in Shanxi), mining energy consumption (200 kWh / t), and transportation distance (1000 km) through RFID tags. After the data is converted into a standardized format by the ERP system, it is uploaded to the "supplier node" of the consortium blockchain; Production stage: The transformer manufacturer (Company B) connects to the MES system to collect data on core lamination (energy consumption 500 kWh / unit), winding (energy consumption 300 kWh / unit), and tank welding (energy consumption 200 kWh / unit). After verification by IoT sensors, it is uploaded to the "manufacturer node"; Transportation stage: The transportation company (Company C) collects data on truck mileage (500 km) and fuel consumption (80 kWh / t) through GPS. L), the data is automatically converted into the "Transportation Energy Consumption-Emissions" related format and uploaded to the "Transportation Node"; Operation and Maintenance Phase: The substation (D Power Company) collects the transformer load rate (70% daily average), no-load loss (0.5 kW), and load loss (2.5 kW) through the SCADA system, and uploads them to the "Power Company Node" after processing by the edge computing node; Recycling Phase: The recycling company (E Company) collects the dismantling energy consumption (100 kWh / unit), silicon steel sheet recycling rate (90%), and copper wire recycling rate (95%), and uploads them to the "Recycling Node". Step 2: Blockchain Evidence Storage and Traceability: After data is uploaded by each node, the "regulatory node" (local energy bureau), "power company node," and "producer node" jointly execute PBFT consensus to verify data consistency (e.g., the production energy consumption of company B matches the original records of the MES system). After successful verification, the data is written to the blockchain, generating a traceability ID "T12345-20240501-Full Lifecycle." Users can use this ID to query the uploading entity of data at each stage (e.g., silicon steel sheet data was uploaded by company A) and modification records (no modifications). Carbon Footprint Calculation: The system calls the "transformer calculation template" to automatically match the emission factors at each stage (e.g., the emission factor of the East China Power Grid in 2024 is 0.58 tCO2 / MWh, and the emission factor of diesel trucks is 0.27 kgCO2 / km). Stage-by-stage calculation: Raw material stage: emissions from silicon steel sheet mining. 200 kWh / t 0.58 tCO2 / MWh 1000 km 0.27 kgCO2 / km 0.116 tCO2 0.27 tCO2 0.386 tCO2; Production stage: Total energy consumption 500 300 200 1000 kWh, emissions 1000 kWh 0.58 tCO2 / MWh 0.58 tCO2; Operation and maintenance phase (calculated based on a 20-year lifespan): Annual energy consumption loss. (0.5) 2.5 × 0.7 2 )kW 8760 h 18396 kWh, annual emissions 18396 kWh 0.58 tCO2 / MWh 10.67 tCO2, total emissions over 20 years 213.4 tCO2; Recovery stage: dismantling and emission 100 kWh 0.58 tCO2 / MWh 0.058 tCO2, emission reduction through regeneration (Primary copper emissions - Recycled copper emissions) Recovery rate (8.5-0.3)tCO2 / t 0.95 t 7.79 tCO2, net emissions 0.058-7.79 -7.73 tCO2; Total carbon emissions over the entire life cycle 0.386 0.58 213.4-7.73 206.64 tCO2. Step 4: Dynamic Emission Reduction Optimization: Operating Condition Prediction: Based on the load data of Power Company D over the past year, the LSTM model predicts that the transformer load rate will rise to 85% in the summer (June-August), and maintenance emissions will increase by 15% (to 12.27 tCO2 / year); Solution Generation: The system generates two solutions: Solution 1: Adjust the grid load distribution to reduce the load rate to 65%, with an implementation cost of 30,000 yuan and an expected annual emission reduction of 1.8 tCO2. Cost-Emission Reduction Ratio 16,667 yuan / ton; Option 2: Replace with a low-loss transformer (no-load loss 0.3 kW, load loss 1.8 kW), implementation cost 450,000 yuan, expected annual emission reduction of 4.2 tCO2, cost-emission reduction ratio 10714 yuan / ton; Feedback: Power Company D selected Option 2. Six months later, the system collected data on the new transformer's load rate (65%) and loss (0.3%). 1.8 0.652 (1.15 kW), calculated actual annual emission reduction of 4.5 tCO2 (7% higher than expected), the system marks the solution as "effective" and no iteration is needed. Visualization and early warning: D Power Company's operation and maintenance personnel can view the following through the system interface: Emission trend information: 98% of the process is in the operation and maintenance phase (213.4 / 206.64), which is the core emission reduction link; Solution implementation progress: Low-loss transformers have been installed and the emission reduction effect has met the standard; No early warning information: The total life cycle emission is lower than the industry benchmark (220 tCO2 / unit).

[0066] Through the above steps, multi-dimensional carbon emission management plans can be automatically generated and optimized by combining expected emission reductions with implementation costs to achieve the best economic efficiency and feasibility of emission reduction plans. During the implementation of the plan, carbon emission data is collected in real time and compared with expected targets. If the targets are not met, automatic iterative adjustments are made to form a dynamic closed-loop management of "prediction-plan generation-execution-evaluation-iteration". At the same time, it provides multi-role visualization and early warning functions to achieve accurate quantification, dynamic optimization, cost-effectiveness and traceability of carbon emission management throughout its entire life cycle.

[0067] like Figure 5 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a schematic diagram of the structure of a carbon emission management system for electrical equipment, including: an acquisition module 100, a calculation module 200, and a management module 300; The acquisition module 100 is used to acquire carbon emission-related data corresponding to each stage of the entire life cycle of electrical equipment. The calculation module 200 is used to upload the carbon emission-related data to the corresponding consortium blockchain nodes at each stage, so as to verify the carbon emission-related data through a multi-node consensus mechanism, generate traceability identifiers associated with the power equipment, retrieve the target carbon emission-related data corresponding to each stage from the blockchain based on the traceability identifiers, and perform carbon emission accounting on the target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emission amount corresponding to each stage. The carbon emission amount is then input into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. The management module 300 is used to generate carbon emission management schemes for each stage based on the carbon emission change trend, so as to manage the carbon emissions of electrical equipment.

[0068] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the carbon emission management method for electrical equipment provided by any of the above-described method embodiments of the present invention. More detailed workflows and principles of this system can be found, but are not limited to, the relevant descriptions of the above methods.

[0069] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0070] Based on the above-described embodiment of a carbon emission management method for electrical equipment, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a carbon emission management method for electrical equipment according to any embodiment of the present invention.

[0071] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0072] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0073] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0074] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a carbon emission management method for electrical equipment as described in any of the above-described method embodiments of the present invention.

[0075] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0076] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for managing carbon emissions from electrical equipment, characterized in that, include: Obtain carbon emission data for each stage of the entire life cycle of electrical equipment; Each carbon emission-related data is uploaded to the corresponding consortium blockchain node for each stage, and the carbon emission-related data is verified through a multi-node consensus mechanism to generate a traceability identifier associated with the power equipment. Based on the traceability identifier, the target carbon emission-related data for each stage is retrieved from the blockchain, and the carbon emission is calculated for each target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emission amount for each stage. The carbon emission amount is then input into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. Based on the aforementioned carbon emission change trends, carbon emission management schemes are generated for each stage to manage the carbon emissions of electrical equipment.

2. The carbon emission management method for electrical equipment as described in claim 1, characterized in that, The acquisition of carbon emission data for each stage of the entire life cycle of electrical equipment includes: Obtain data on the first carbon emissions at each stage of the entire life cycle of electrical equipment; Identify the data format of each of the first carbon emission related data, obtain the data format type, and extract fields from each of the first carbon emission related data using a data extraction method corresponding to the data format type to obtain the second carbon emission related data. By unifying the units of the second carbon emission-related data, the third carbon emission-related data is obtained. Outlier detection is performed on the third carbon emission-related data to obtain invalid data, and the invalid data is removed from the third carbon emission-related data to obtain the carbon emission-related data.

3. The carbon emission management method for electrical equipment as described in claim 1, characterized in that, The process of uploading the carbon emission-related data to the corresponding consortium blockchain nodes at each stage, and verifying the carbon emission-related data through a multi-node consensus mechanism to generate traceability identifiers associated with electrical equipment, includes: Each of the aforementioned carbon emission-related data is uploaded to each of the aforementioned consortium blockchain nodes through a preset interface, so as to verify each of the aforementioned carbon emission-related data through a multi-node consensus mechanism to obtain the fourth carbon emission-related data. The aforementioned consortium blockchain nodes are determined based on the type of the entity collecting the carbon emission-related data corresponding to each stage of the entire life cycle. Data hashes are generated based on the fourth carbon emission-related data, and the data hashes, historical data hashes, and corresponding timestamps are associated to obtain block records; Based on the block records, a traceability identifier associated with the power equipment is generated.

4. The carbon emission management method for electrical equipment as described in claim 3, characterized in that, The process involves verifying each carbon emission-related data through a multi-node consensus mechanism to obtain the fourth carbon emission-related data, including: Each of the aforementioned consortium blockchain nodes performs consistency verification on the aforementioned carbon emission-related data, generating several node verification results; Determine whether the number of nodes that pass the verification in the verification results of each node meets the preset node number threshold. If it does, determine the fourth carbon emission related data.

5. The carbon emission management method for electrical equipment as described in claim 1, characterized in that, The carbon emission calculation is performed on the carbon emission-related data of each target using a preset carbon footprint calculation model to obtain the carbon emission amount corresponding to each stage, including: The carbon emission-related data for each target are classified to extract energy consumption data and material consumption data for each stage of the entire life cycle. The energy consumption data, material consumption data, and preset emission factors are input into the carbon footprint calculation model to perform attribution calculations on the energy consumption and material consumption corresponding to each stage based on the life cycle assessment algorithm, so as to obtain the carbon emissions corresponding to each stage.

6. The carbon emission management method for electrical equipment as described in claim 1, characterized in that, The step of inputting each of the aforementioned carbon emissions into a preset time series prediction model to obtain the carbon emission change trend of electrical equipment within a preset future time period includes: The carbon emissions are preprocessed to obtain a standardized carbon emission time series. The standardized carbon emission time series is input into the time series prediction model to learn the time series features of the standardized carbon emission time series based on the time series prediction model, so as to obtain the carbon emission prediction series of power equipment in the future preset time period. The first-order difference of the carbon emission prediction sequence is calculated to determine the carbon emission change trend of electrical equipment in a future preset time period based on the calculation results.

7. The carbon emission management method for electrical equipment as described in claim 1, characterized in that, The process of generating carbon emission management schemes for each stage based on the aforementioned carbon emission change trends to manage the carbon emissions of electrical equipment includes: Based on the carbon emission change trend, identify the corresponding life cycle stage where the carbon emission of the power equipment exceeds a preset threshold throughout its entire life cycle, so as to determine the carbon emission risk stage. Several carbon emission management schemes are generated based on the aforementioned carbon emission risk stage; The emission reduction benefit indicators of each of the carbon emission management schemes are calculated based on the expected emission reduction and implementation cost parameters, and the target carbon emission management scheme is determined based on the emission reduction benefit indicators. The carbon emission management scheme includes the expected emission reduction and the implementation cost parameters. The target carbon emission management scheme is applied to the actual management process of the corresponding life cycle stage to collect the actual carbon emissions of the corresponding stage, and compare and analyze the actual carbon emissions with the expected emission reduction. If the expected emission reduction is not achieved, the target carbon emission management scheme is adjusted to achieve closed-loop management of carbon emissions from power equipment.

8. A carbon emission management system for electrical equipment, characterized in that, The system includes: an acquisition module, a calculation module, and a management module; The acquisition module is used to acquire carbon emission-related data for each stage of the entire life cycle of electrical equipment. The calculation module is used to upload the carbon emission-related data to the corresponding consortium blockchain nodes at each stage, so as to verify the carbon emission-related data through a multi-node consensus mechanism, generate traceability identifiers associated with the power equipment, retrieve the target carbon emission-related data corresponding to each stage from the blockchain based on the traceability identifiers, and perform carbon emission accounting on the target carbon emission-related data through a preset carbon footprint calculation model to obtain the carbon emission amount corresponding to each stage. The carbon emission amount is then input into a preset time series prediction model to obtain the carbon emission change trend of the power equipment in a preset time period in the future. The management module is used to generate carbon emission management schemes for each stage based on the carbon emission change trend, so as to manage the carbon emissions of electrical equipment.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a carbon emission management method for electrical equipment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a carbon emission management method for electrical equipment as described in any one of claims 1-7.

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