Carbon emission prediction method, device and equipment of electric power equipment and storage medium

Abstract

The embodiment of the invention discloses a carbon emission prediction method, device and equipment for electric power equipment and a storage medium, and relates to the technical field of carbon emission, and the method comprises the steps: obtaining the planned yield of a preset production line of target electric power equipment produced by a target production unit in a target production period in response to a condition of meeting a production stage prediction triggering condition; acquiring a current carbon emission prediction model corresponding to the target electric power equipment, and predicting the carbon emission generated by the target electric power equipment produced by the preset production line in the target production period based on the planned yield by using the current carbon emission prediction model to obtain a carbon emission prediction amount; and writing the identifier of the current carbon emission prediction model, the planned yield and the carbon emission prediction quantity into a block structure corresponding to a production stage in a carbon footprint life cycle chain of the target electric power equipment, so that an external service system provides corresponding business services based on the carbon footprint life cycle chain corresponding to the target electric power equipment. And a carbon emission prediction function and a data non-tampering and traceable function are realized.

Description

Technical Field

[0001] This application relates to the field of carbon emission technology, and in particular to a method, apparatus, equipment and storage medium for predicting carbon emissions from power equipment. Background Technology

[0002] Power equipment generates significant carbon emissions throughout its entire lifecycle. Currently, the carbon emissions for the production phase of power equipment are primarily determined manually. This involves manually calculating the energy consumption of each piece of equipment on the production line during power equipment production, and then determining the carbon emissions generated by the production line based on this energy consumption. The relevant data is then stored locally for subsequent carbon footprint calculations. However, this manual method is time-consuming, labor-intensive, and inefficient. Furthermore, it cannot predict the carbon emissions of the production line in future production cycles. Additionally, relying on local storage presents problems such as data tampering and difficulty in traceability. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and storage medium for predicting carbon emissions from power equipment, thereby enabling the prediction of carbon emissions from power equipment and solving the problems in the prior art of being unable to predict the carbon emissions of production lines in future production cycles and the ease with which data can be tampered with and difficult to trace.

[0004] In a first aspect, embodiments of this application provide a method for predicting carbon emissions from power equipment. The method includes: in response to meeting a production stage prediction trigger condition, obtaining the planned output of a preset production line of a target production unit producing the target power equipment within a target production cycle; obtaining the current carbon emission prediction model corresponding to the target power equipment, and using the current carbon emission prediction model to predict the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, thereby obtaining a carbon emission prediction amount; and writing the identifier of the current carbon emission prediction model, the planned output, and the carbon emission prediction amount into the block structure corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment, so that an external service system can provide corresponding business services based on the carbon footprint lifecycle chain corresponding to the target power equipment.

[0005] Secondly, embodiments of this application provide a carbon emission prediction device for power equipment. The device includes: a first acquisition module, configured to acquire the planned output of a preset production line of a target production unit producing the target power equipment within a target production cycle in response to meeting a production stage prediction trigger condition; a prediction module, configured to acquire the current carbon emission prediction model corresponding to the target power equipment, and use the current carbon emission prediction model to predict the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, thereby obtaining a carbon emission prediction amount; and a writing module, configured to write the identifier of the current carbon emission prediction model, the planned output, and the carbon emission prediction amount into the block structure corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment, so that an external service system can provide corresponding business services based on the carbon footprint lifecycle chain corresponding to the target power equipment.

[0006] Thirdly, embodiments of this application provide an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the carbon emission prediction method for power equipment according to any embodiment of this application.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a carbon emission prediction method for power equipment as described in any embodiment of this application.

[0008] In this embodiment, in response to meeting the production stage prediction trigger condition, the planned output of the preset production line of the target production unit producing the target power equipment within the target production cycle is obtained. Then, the current carbon emission prediction model corresponding to the target power equipment is obtained, and the carbon emission prediction model is used to predict the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, thus obtaining the carbon emission prediction amount. This allows for accurate prediction of the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, eliminating the need for manual statistics on the energy consumption of each device within the production line during power equipment production, saving manpower and resources, and thereby improving the efficiency and accuracy of carbon emission prediction. Then, the identifier of the current carbon emission prediction model, the planned output, and the carbon emission prediction amount are written into the block structure corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment, so that... The external service system provides corresponding business services based on the carbon footprint lifecycle chain of the target power equipment. The carbon footprint lifecycle chain is a consortium blockchain. By writing the carbon emission data predicted during the production stage into the carbon footprint lifecycle chain, and combining the characteristics of the consortium blockchain, it is impossible for any participant (i.e., the control node equipment at any lifecycle stage) to arbitrarily modify the data in the carbon footprint lifecycle chain. This achieves the immutability and traceability of the entire process of carbon emission prediction during the production stage. Furthermore, by combining the mechanism of writing carbon emission data from other lifecycle stages into the carbon footprint lifecycle chain, it achieves the reliable storage and traceability management of carbon emission data of the target power equipment throughout its entire lifecycle. This effectively solves the problems of easy data tampering and difficulty in traceability in existing technologies, significantly improves the credibility of the carbon footprint in the external service system, and thus significantly improves the intelligence level and operation optimization capability of carbon emission control of power equipment. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a carbon emission prediction method for power equipment provided in an embodiment of this application.

[0011] Figure 2 This is another schematic flowchart of the carbon emission prediction method for power equipment provided in the embodiments of this application;

[0012] Figure 3 This is a schematic diagram of a carbon emission prediction device for power equipment provided in an embodiment of this application;

[0013] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0015] It should be noted that the terms "first," "second," "target," and "original," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprising," "having," and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1 This is a flowchart illustrating a carbon emission prediction method for power equipment provided in this application embodiment. This embodiment can be applied to scenarios requiring the prediction of carbon emissions from the production of power equipment. The carbon emission prediction method for power equipment provided in this embodiment can be executed by a carbon emission prediction device for power equipment provided in this application embodiment. This device can be implemented through software and / or hardware. In a specific embodiment, the carbon emission prediction device for power equipment can be integrated into an electronic device, which is equipment in the target production unit, such as a computer. The executing entity of this method can be an electronic device. See also... Figure 1 The carbon emission prediction method for power equipment in this embodiment includes, but is not limited to, the following steps:

[0017] S110. In response to meeting the production stage prediction triggering conditions, obtain the planned output of the preset production line of the target production unit for producing the target power equipment within the target production cycle.

[0018] The target power equipment can be power devices such as transmission towers, transformers, or smart meters. The entire lifecycle of the target power equipment includes the raw material acquisition stage, raw material transportation stage, production stage, equipment transportation stage, equipment use stage, and equipment disposal stage. The production stage prediction trigger condition is the condition used to trigger electronic equipment to begin predicting the carbon emissions generated during the production stage of the target power equipment. The target production unit can be a factory that produces the target power equipment.

[0019] The preset production line is the production line in the target production unit that produces the target power equipment, and each production line produces only one piece of power equipment. The target production cycle is one production cycle of the target power equipment, and the target production cycle has not yet occurred; it belongs to a future production cycle of the target power equipment. The planned output is the number of target power equipment that the preset production line of the target production unit plans to produce within the target production cycle.

[0020] Specifically, before the production stage of the target power equipment, the control node equipment in the raw material acquisition stage calculates the carbon emissions of the target power equipment in the raw material acquisition stage, and writes the energy consumption data and carbon emissions of the raw material acquisition stage into the corresponding block structure of the raw material acquisition stage in the carbon footprint lifecycle chain of the target power equipment through a smart contract. Then, after detecting that the data of the raw material acquisition stage has been successfully written, the control node equipment in the raw material transportation stage calculates the carbon emissions of the target power equipment in the raw material transportation stage, and writes the energy consumption data and carbon emissions of the raw material transportation stage into the corresponding block structure of the raw material transportation stage in the carbon footprint lifecycle chain of the target power equipment through a smart contract. Here, the carbon footprint lifecycle chain is a consortium chain used to store the entire lifecycle of the target power equipment. The carbon footprint lifecycle chain includes carbon emission data (i.e., energy consumption data and carbon emissions) for each lifecycle stage within the entire lifecycle. It comprises multiple block structures, with one block structure corresponding to each lifecycle stage, and these block structures are linked sequentially throughout the entire lifecycle. Furthermore, control node devices at each lifecycle stage of the target power equipment can participate in the construction, maintenance, and use of the carbon footprint lifecycle chain; that is, a control node device at a lifecycle stage writes the carbon emission data for that stage into the corresponding block structure. Smart contracts are used to automatically execute operations such as data writing, version registration, predictive behavior verification, and data access permission control. They support whitelist mechanisms and multi-signature control to ensure that model and data writing actions are only effective after authorization.

[0021] After detecting that the data from the raw material transportation stage has been successfully written, the electronic equipment in the production stage (i.e., the control node equipment in the production stage) determines that the production stage prediction trigger condition has been met. At this time, in response to the meeting of the production stage prediction trigger condition, the planned output of the preset production line of the target power equipment produced by the target production unit in the target production cycle is obtained.

[0022] S120. Obtain the current carbon emission prediction model corresponding to the target power equipment, and use the current carbon emission prediction model to predict the carbon emissions generated by the preset production line in the target production cycle of the target power equipment based on the planned output, so as to obtain the carbon emission prediction amount.

[0023] The carbon emission prediction model is used to predict the carbon emissions generated by power equipment producing a specific output within a specific production cycle, and different power equipment corresponds to different carbon emission prediction models; the current carbon emission prediction model is the most recently trained carbon emission prediction model. The carbon emission prediction amount is the output result of the current carbon emission prediction model based on the planned output prediction.

[0024] Specifically, the latest carbon emission prediction model obtained from the training of the target power equipment can be acquired to obtain the current carbon emission prediction model. Then, the planned output is input into the current carbon emission prediction model. At this time, the current carbon emission prediction model can use the learned model parameters to analyze the planned output, predict the carbon emissions generated by the preset production line in producing the target power equipment within the target production cycle, obtain the output result, and determine the carbon emission prediction amount.

[0025] S130. Write the identifier, planned output, and carbon emission forecast of the current carbon emission prediction model into the block structure corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment, so that the external service system can provide corresponding business services based on the carbon footprint life cycle chain of the target power equipment.

[0026] The identifier for the current carbon emission prediction model is a unique identifier generated when the carbon emission prediction model is trained. External service systems can be carbon trading platforms, carbon auditing platforms, or carbon regulatory platforms, etc.

[0027] Optionally, the block structure includes a block header and a block body. The block header includes multiple fields, namely the hash value (PreHash) of the previous block, the hash value (Hash) of the current block body, and a timestamp (TimeStamp) field. The hash value of the previous block in the block header can link to the previous block through a hash pointer, thus forming a blockchain. This allows different blocks to be associated with each other through hash values, ensuring the continuity and immutability of the carbon footprint lifecycle chain. Furthermore, the hash value of the previous block in the block header can support an on-chain comparison mechanism to detect data tampering in the carbon footprint lifecycle chain, ensuring the verifiability and integrity of the carbon footprint lifecycle chain.

[0028] Specifically, the identifier, planned output, and predicted carbon emissions of the current carbon emission prediction model are written into the block structure corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment. For example, the identifier, planned output, and predicted carbon emissions of the current carbon emission prediction model can be encapsulated according to a preset format to obtain the first data. This first data is the data of the block body, and the preset format can be JS key-value pair data (JavaScript Objects). The first data is generated in JSON format and a hash function is used to calculate its hash value. This hash value is then used as the hash value of the current block. Next, the block structure corresponding to the raw material transportation stage in the carbon footprint lifecycle chain is obtained, i.e., the block structure of the previous block. The hash function is then used to calculate the hash value of the previous block. The hash value of the previous block, the hash value of the first data, and the current time are then encapsulated according to the block header format to obtain the second data. This second data is then used as the block header data. Finally, the first data and the second data are written into the block body and block header corresponding to the production stage in the carbon footprint lifecycle chain through a smart contract, ensuring that the data is immutable and auditable.

[0029] After detecting successful data writing during the production phase, the control node devices in the equipment transportation phase calculate the carbon emissions of the target power equipment during transportation and, via smart contract, write the energy consumption data and carbon emissions of the transportation phase into the corresponding block structure of the carbon footprint lifecycle chain for the target power equipment. Similarly, after detecting successful data writing during the equipment transportation phase, the control node devices in the equipment usage phase calculate the carbon emissions of the target power equipment during usage and, via smart contract, write the energy consumption data and carbon emissions of the usage phase into the corresponding block structure of the carbon footprint lifecycle chain for the target power equipment. Finally, after detecting successful data writing during the equipment disposal phase, the control node devices in the equipment disposal phase calculate the carbon emissions of the target power equipment during disposal and, via smart contract, write the energy consumption data and carbon emissions of the disposal phase into the corresponding block structure of the carbon footprint lifecycle chain for the target power equipment, thus completing the carbon footprint lifecycle chain for the target power equipment. The carbon footprint lifecycle chain supports interface queries and off-chain data callbacks, facilitating regulatory integration or trusted data service calls from external service systems.

[0030] Subsequently, external service systems can provide corresponding business services based on the carbon footprint lifecycle chain of the target power equipment. For example, a carbon trading platform system can query data in the carbon footprint lifecycle chain and provide carbon credit calculation services or tradable quota push services based on the data in the carbon footprint lifecycle chain; a carbon audit platform system can query data in the carbon footprint lifecycle chain and provide audit services based on the data in the carbon footprint lifecycle chain; and a carbon regulatory platform system can query data in the carbon footprint lifecycle chain and provide regulatory services based on the data in the carbon footprint lifecycle chain.

[0031] The technical solution of this application embodiment, in response to the fulfillment of the production stage prediction trigger condition, obtains the planned output of the preset production line of the target production unit producing the target power equipment within the target production cycle. Then, it obtains the current carbon emission prediction model corresponding to the target power equipment and uses this model to predict the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, thus obtaining the carbon emission prediction amount. This allows for accurate prediction of the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, eliminating the need for manual statistics on the energy consumption of each device within the production line during power equipment production, saving manpower and resources, and thereby improving the efficiency and accuracy of carbon emission prediction. Finally, the identifier of the current carbon emission prediction model, the planned output, and the carbon emission prediction amount are written into the block structure corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment. This enables external service systems to provide corresponding business services based on the carbon footprint lifecycle chain of the target power equipment. The carbon footprint lifecycle chain is a consortium blockchain. By writing the carbon emission data predicted during the production stage into the carbon footprint lifecycle chain, and combining the characteristics of the consortium blockchain, any participant (i.e., the control node equipment at any lifecycle stage) cannot arbitrarily modify the data in the carbon footprint lifecycle chain. This achieves the immutability and traceability of the entire process of carbon emission prediction during the production stage. Furthermore, by combining the mechanism of writing carbon emission data from other lifecycle stages into the carbon footprint lifecycle chain, the system achieves the reliable storage and traceability management of carbon emission data of the target power equipment throughout its entire lifecycle. This effectively solves the problems of data being easily tampered with and difficult to trace in existing technologies, significantly improves the credibility of the carbon footprint in external service systems, and thus significantly improves the intelligence level and operational optimization capabilities of carbon emission control of power equipment.

[0032] The following further describes a method for predicting carbon emissions from power equipment provided by an embodiment of this application. Figure 2 This is another schematic flowchart illustrating the carbon emission prediction method for power equipment provided in this application. This application's embodiment is an optimization based on the above embodiments. See also... Figure 2 The method in this embodiment includes, but is not limited to, the following steps:

[0033] S210. In response to meeting the production stage prediction triggering conditions, obtain the planned output of the preset production line of the target production unit for producing the target power equipment within the target production cycle.

[0034] S220. Obtain the current carbon emission prediction model corresponding to the target power equipment, and use the current carbon emission prediction model to predict the carbon emissions generated by the preset production line in the target production cycle of the target power equipment based on the planned output, so as to obtain the carbon emission prediction amount.

[0035] Specifically, obtain the current carbon emission prediction model corresponding to the target power equipment, including Sa1-Sa6:

[0036] Sa1. Determine whether the time difference between the current moment and the previous training moment is greater than the preset duration threshold.

[0037] Here, "previous training time" refers to the time of the most recent training of the carbon emission prediction model corresponding to the target power equipment, i.e., the training time of the previous carbon emission prediction model corresponding to the target power equipment; "previous carbon emission prediction model" refers to the carbon emission prediction model obtained in the most recent training. The preset duration threshold is a pre-set duration used to characterize the time interval between two adjacent training sessions of the carbon emission prediction model.

[0038] Specifically, if the time difference between the current moment and the previous training moment is greater than the preset duration threshold, it indicates that the previous carbon emission prediction model needs to be trained and optimized using the newly added sample data, and Sa2 can be executed in this case; otherwise, it indicates that the previous carbon emission prediction model does not need to be trained and optimized using the newly added sample data, and Sa6 can be executed in this case.

[0039] Sa2. When the time difference between the current moment and the previous training moment is greater than the preset duration threshold, the time range between the current moment and the previous training moment is determined as the training time range.

[0040] The training timeframe includes multiple newly added historical production cycles; the newly added historical production cycles are the production cycles of the target power equipment within the training timeframe.

[0041] Sa3. Obtain the actual operating data of the preset production line when producing target power equipment in each new historical production cycle, obtain the sample operating data of the corresponding new historical production cycle, and obtain the actual output of the preset production line in each new historical production cycle, obtain the sample output of the corresponding new historical production cycle.

[0042] Among them, the sample operation data is the actual operation data of the preset production line when producing target power equipment in the newly added historical production cycle.

[0043] Actual operating data may include energy consumption data of various energy sources for the preset production line during the production cycle, or actual operating data may include operating data of each piece of equipment in the preset production line during the production cycle, such as operating power and operating time, so that the energy consumption data of the preset production line for various energy sources in the corresponding production cycle can be determined based on the operating data of each piece of equipment in the preset production line during the production cycle; various energy sources may include electricity, water, gas and natural gas, etc.

[0044] It should be noted that if the energy consumption data of the preset production line for various energy sources during the production cycle cannot be directly obtained, the actual operating data includes the operating data of each piece of equipment in the preset production line during the production cycle; if the energy consumption data of the preset production line for various energy sources during the production cycle can be directly obtained, the actual operating data includes the energy consumption data of the preset production line for various energy sources during the production cycle.

[0045] The sample output is the number of target power equipment actually produced by the preset production line within the newly added historical production cycle.

[0046] Sa4. Calculate carbon emissions for the sample operation data of each new historical production cycle to obtain the carbon emission label generated by the preset production line in the corresponding new historical production cycle for the production of target power equipment. Combine the sample output of each new historical production cycle with the carbon emission label of the corresponding new historical production cycle to form new sample data.

[0047] The carbon emission label represents the actual carbon emissions generated by the pre-defined production line during the production of the target power equipment in the newly added historical production cycle. The new sample data includes the sample output for each newly added historical production cycle and the carbon emission label for each newly added historical production cycle.

[0048] Specifically, for the current newly added historical production cycle within each newly added historical production cycle, the sample operation data of the current newly added historical production cycle can be preprocessed to obtain intermediate operation data. That is, the sample operation data can be formatted to convert the units of the sample operation data into standard units. Based on the carbon emission factor library and the preset life cycle assessment rules, carbon emissions are calculated on the intermediate operation data to obtain the carbon emission label generated by the preset production line in the current newly added historical production cycle for the production of target power equipment. The carbon emission factor library includes carbon emission factors for various energy sources. The carbon emission factor represents the amount of greenhouse gases such as carbon dioxide emitted when performing one unit of a certain activity, producing or consuming one unit of a certain product, or providing one unit of a certain service under specific conditions. The preset life cycle assessment rules are the life cycle assessment rules followed by the target production unit when producing the target power equipment. For example, the life cycle assessment rules can quantify the climate impact of the target power equipment throughout its entire life cycle.

[0049] Specifically, when the intermediate operating data includes the operating data of each piece of equipment within the preset production line during the current newly added historical production cycle, the energy consumption data of the preset production line for various energy sources during the current newly added historical production cycle is determined based on the operating data of each piece of equipment within the preset production line during the current newly added historical production cycle. When the intermediate operating data includes the energy consumption data of the preset production line for various energy sources during the current newly added historical production cycle, no additional calculation is required. Then, the carbon emission factors corresponding to various energy sources under the preset life cycle assessment rules are obtained from the carbon emission factor library, and the product of the energy consumption data of each energy source and the corresponding carbon emission factor is calculated to obtain the carbon emissions of the preset production line for various energy sources during the current newly added historical production cycle. Finally, the sum of the carbon emissions of all energy types is calculated to obtain the carbon emission label generated by the preset production line in producing the target power equipment during the current newly added historical production cycle. This improves computational efficiency, reduces implementation complexity, and thus improves the accuracy and efficiency of carbon emission label determination, providing an accurate training foundation for subsequent carbon emission prediction model training.

[0050] Sa5. Using multiple newly added sample data, the previous carbon emission prediction model corresponding to the target power equipment is trained and optimized to obtain the current carbon emission prediction model.

[0051] Specifically, the sample output from the newly added sample data can be input into the previous carbon emission prediction model to obtain the carbon emission training data. The loss value between the carbon emission training data and the corresponding carbon emission label in the newly added sample data can be calculated. Then, with the goal of minimizing the loss value, the previous carbon emission prediction model can be trained and optimized to obtain the current carbon emission prediction model. The training optimization methods can include gradient descent, feature reweighting, and automatic model structure search, etc., to prevent model overfitting or performance degradation.

[0052] It is understandable that as long as the time difference between the current moment and the previous training moment is greater than the preset duration threshold, a carbon emission prediction model training operation is triggered. That is, the carbon emission prediction model obtained from the most recent training is trained and optimized using newly added sample data in order to improve the prediction accuracy of the carbon emission prediction model.

[0053] Sa6. When the time difference between the current moment and the previous training moment is not greater than the preset duration threshold, the current carbon emission prediction model is determined to be the previous carbon emission prediction model corresponding to the target power equipment.

[0054] In this embodiment, the current carbon emission prediction model can be the latest trained model, providing an accurate data foundation for determining the carbon emission prediction amount, thereby improving the prediction accuracy of the carbon emission prediction amount; by training and optimizing the previous carbon emission prediction model with new sample data, the prediction performance and adaptability of the carbon emission prediction model can be improved.

[0055] Optionally, a model training blockchain for carbon emission prediction models can be established. This blockchain can include multiple block structures, each storing data related to the carbon emission prediction model trained using sample data within a preset time threshold. These block structures are linked chronologically; that is, after training the initial carbon emission prediction model to obtain the first model, an identifier for that model can be generated. A hash function is then used to calculate the hash value corresponding to the first carbon emission prediction model, considering its local storage address, model structure, model parameters, and identifier. This hash value is then the hash value of the current block body within the block header. Finally, this hash value, along with the timestamp and... The hash value of the previous block (set to all zeros or a specific placeholder) is written into the block header of the first block in the model training blockchain. The local storage address of the first carbon emission prediction model, its model structure and parameters, and its identifier are written into the block body of the first block in the model training blockchain. Similarly, every preset time interval, when a new carbon emission prediction model is trained and optimized using new sample data, the relevant data of the new carbon emission prediction model is written into the model training blockchain using the same processing steps. This enables the traceability of the carbon emission prediction model, ensuring that each iteration of the model has a reliable record and audit basis, thereby guaranteeing the verifiability and integrity of the carbon emission prediction process. The initial carbon emission prediction model is a model framework that has not yet been trained.

[0056] S230. Obtain the actual operating data of the preset production line when producing the target power equipment within the target production cycle, and obtain the target operating data.

[0057] The target operating data may include energy consumption data of various types of energy for the preset production line within the target production cycle, or the target operating data may include the operating data of each piece of equipment in the preset production line within the target production cycle.

[0058] S240. Calculate carbon emissions from the target operating data to obtain the actual carbon emissions of the preset production line during the target production cycle.

[0059] The actual carbon emissions refer to the actual carbon emissions generated by the production line within the target production cycle when it produces the planned output of the target power equipment.

[0060] Specifically, carbon emissions can be calculated based on the carbon emission factor library and preset life cycle assessment rules to obtain the actual amount of carbon emissions of the preset production line during the target production cycle.

[0061] Optionally, after calculating carbon emissions from the target operating data to obtain the actual carbon emissions of the preset production line within the target production cycle, the deviation between the actual carbon emissions and the predicted carbon emissions can be calculated to obtain the prediction deviation value. When the preset deviation value is greater than the preset deviation threshold, a prediction risk warning is generated and displayed. This can be done by displaying the prediction risk warning on the screen of an electronic device or by sending the prediction risk warning to the user terminal of the management personnel. This allows the user terminal to display the prediction risk warning to the management personnel, reminding them that the prediction accuracy of the carbon emission prediction model is low, and enabling the management personnel to take timely countermeasures, such as retraining and optimizing the carbon emission prediction model.

[0062] S250. The current carbon emission prediction model's identifier, planned output, predicted carbon emissions, target operating data, and actual carbon emissions are encapsulated to obtain block data.

[0063] Specifically, the identifier, planned output, predicted carbon emissions, target operating data, and actual carbon emissions of the current carbon emission prediction model can be encapsulated according to a preset format to obtain block data.

[0064] Optionally, after using the current carbon emission prediction model to predict the carbon emissions generated by the production of target power equipment by the preset production line within the target production cycle based on the planned output, the carbon emission prediction can be classified and labeled. That is, the source credibility, data integrity, and processing method transparency of the carbon emission prediction input by the management personnel can be obtained, and the carbon emission prediction can be classified and labeled based on the source credibility, data integrity, and processing method transparency. Then, the source credibility, data integrity, and processing method transparency of the carbon emission prediction can be packaged together with the current carbon emission prediction model's identifier, planned output, carbon emission prediction, target operating data, and actual carbon emission to obtain block data, thereby enhancing data auditability.

[0065] S260. Perform hash calculation on the block body data to obtain the hash value corresponding to the block body data, and determine the block header data based on the current time, the hash value corresponding to the block body data, and the block structure corresponding to the previous life cycle stage of the production stage.

[0066] Among them, the previous life cycle stage is the life cycle stage above the production stage in the entire life cycle of the target power equipment, namely the raw material transportation stage.

[0067] Specifically, a hash function can be used to hash the block body data to obtain the hash value corresponding to the block body data. This hash value is then used to determine the hash value of the current block. Next, the block structure corresponding to the previous lifecycle stage (i.e., the raw material transportation stage) in the carbon footprint lifecycle chain is obtained; this is the block structure of the previous block. A hash function is then used to hash the block structure of the previous block to obtain its hash value. Finally, the hash value of the previous block, the hash value of the block body data, and the current time are encapsulated according to the block header format to obtain the block header data. It should be noted that for other fields in the block header (such as the version number field, difficulty target field, and random number field), the determination methods in existing technologies can be referenced.

[0068] S270. Write the block header data and block body data into the corresponding block header and block body of the production stage in the carbon footprint lifecycle chain of the target power equipment, so that the external service system can provide corresponding business services based on the carbon footprint lifecycle chain of the target power equipment.

[0069] Specifically, smart contracts are used to write the block header data and block body data into the corresponding block header and block body of the production stage in the carbon footprint lifecycle chain of the target power equipment, forming an auditable and predictable credible certificate.

[0070] Optionally, after using the current carbon emission prediction model to predict the carbon emissions generated by the production of target power equipment by the preset production line within the target production cycle based on the planned output, and obtaining the carbon emission prediction, a carbon reduction strategy for the preset production line can be generated based on the carbon emission prediction and the carbon emission control standards set by the target production unit for the preset production line. The carbon reduction strategy is a production plan formulated around the production of target power equipment by the preset production line to reduce greenhouse gas (especially carbon dioxide) emissions. That is, the standard carbon emission corresponding to the carbon emission control standards set by the target production unit for the preset production line is obtained, and when the carbon emission prediction is greater than the standard carbon emission, a carbon reduction strategy is formulated for the preset production line, such as reducing the rotation speed of each piece of equipment in the preset production line or reducing the planned output of the preset production line within the target production cycle. Then, the carbon reduction strategy is sent to the decision support terminal of the target production unit so that the decision support terminal can adjust the various pieces of equipment in the preset production line based on the carbon reduction strategy. The decision support terminal is a terminal device built on information technology, which aims to provide data support, analysis tools, and visualization display functions for the decision-making process. It can realize the function of formulating carbon emission reduction strategies for preset production lines based on carbon emission prediction, and thus guide the production rhythm of preset production lines within the target production cycle, so that the preset production lines of the target production unit meet the carbon emission control standards.

[0071] Optionally, after using the current carbon emission prediction model to predict the carbon emissions generated by the production of target power equipment by the preset production line within the target production cycle based on the planned output, and obtaining the carbon emission prediction amount, operation optimization suggestions for the preset production line can be generated based on the carbon emission prediction amount and the carbon emission control standards set by the target production unit for the preset production line. The operation optimization suggestions can be sent to the decision support terminal of the target production unit so that the decision support terminal can adjust the various equipment in the preset production line based on the operation optimization suggestions. In addition, a carbon asset assessment report can be generated based on the carbon emission prediction amount and the carbon emission control standards set by the target production unit for the preset production line, and the carbon asset assessment report can be displayed.

[0072] The technical solution of this application embodiment, in response to the fulfillment of the production stage prediction triggering condition, obtains the planned output of the preset production line of the target production unit producing the target power equipment within the target production cycle, then obtains the current carbon emission prediction model corresponding to the target power equipment, and uses the current carbon emission prediction model to predict the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, thus obtaining the carbon emission prediction amount. This allows for accurate prediction of the carbon emissions generated by the preset production line producing the target power equipment within the target production cycle based on the planned output, eliminating the need for manual statistics, saving manpower and resources, and thereby improving the efficiency and accuracy of carbon emission prediction determination; next, obtains the pre- The process involves using actual operating data from the production line during the target production cycle to produce the target power equipment. This yields the target operating data, and carbon emissions are calculated from this data to obtain the actual carbon emissions of the production line within the target production cycle. This enriches the subsequent on-chain data and provides an accurate data foundation for determining the prediction deviation of the current carbon emission prediction model. Then, the identifier, planned output, predicted carbon emissions, target operating data, and actual carbon emissions of the current carbon emission prediction model are encapsulated to obtain block data. A hash calculation is performed on the block data to obtain its corresponding hash value. Finally, based on the current time, the hash value of the block data, and the previous production stage... The block structure corresponding to each stage of the lifecycle determines the block header data. Then, the block header data and block body data are written into the block header and block body corresponding to the production stage in the carbon footprint lifecycle chain of the target power equipment. This allows external service systems to provide corresponding business services based on the carbon footprint lifecycle chain of the target power equipment. This enriches the block body data of the production stage, improves computational efficiency, reduces implementation complexity, and thus improves the data writing efficiency and accuracy of the carbon footprint lifecycle chain. It also achieves the immutability and traceability of the entire carbon emission prediction process during the production stage. Furthermore, the carbon footprint lifecycle chain is a consortium blockchain, which writes the predicted carbon emission data from the production stage into the carbon footprint... The lifecycle chain, combined with the characteristics of a consortium blockchain, prevents any participant (i.e., the control node device at any lifecycle stage) from arbitrarily modifying the data in the carbon footprint lifecycle chain. This achieves the immutability and traceability of carbon emission prediction throughout the entire production stage. Furthermore, by combining the mechanism of writing carbon emission data from other lifecycle stages into the carbon footprint lifecycle chain, it realizes the reliable storage and traceability management of carbon emission data of the target power equipment throughout its entire lifecycle. This effectively solves the problems of data being easily tampered with and difficult to trace in existing technologies, significantly improves the credibility of carbon footprint in external service systems, and thus significantly enhances the intelligence level and operational optimization capabilities of carbon emission control for power equipment.

[0073] Figure 3This is a schematic diagram of a carbon emission prediction device for power equipment provided in an embodiment of this application, with reference to... Figure 3 The carbon emission prediction device for this power equipment may include:

[0074] The first acquisition module 310 is used to acquire the planned output of the preset production line of the target power equipment of the target production unit within the target production cycle in response to the fulfillment of the production stage prediction triggering condition.

[0075] The prediction module 320 is used to obtain the current carbon emission prediction model corresponding to the target power equipment, and use the current carbon emission prediction model to predict the carbon emissions generated by the preset production line in the target production cycle of the target power equipment based on the planned output, so as to obtain the carbon emission prediction amount.

[0076] The writing module 330 is used to write the identifier of the current carbon emission prediction model, the planned output and the predicted carbon emission into the block structure corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment, so that the external service system can provide corresponding business services based on the carbon footprint life cycle chain of the target power equipment.

[0077] In one embodiment, the prediction module 320 acquires the current carbon emission prediction model corresponding to the target power equipment, including: when the time difference between the current time and the previous training time is greater than a preset duration threshold, determining the time range between the current time and the previous training time as the training time range; the training time range includes multiple newly added historical production cycles; acquiring the actual operating data of the preset production line when producing the target power equipment in each newly added historical production cycle, obtaining sample operating data of the corresponding newly added historical production cycle, and acquiring the actual output of the preset production line in each newly added historical production cycle, obtaining sample output of the corresponding newly added historical production cycle; calculating carbon emissions for the sample operating data of each newly added historical production cycle, obtaining the carbon emission label generated by the preset production line in producing the target power equipment in the corresponding newly added historical production cycle, and combining the sample output of each newly added historical production cycle and the carbon emission label of the corresponding newly added historical production cycle into new sample data; using multiple new sample data to train and optimize the previous carbon emission prediction model corresponding to the target power equipment, to obtain the current carbon emission prediction model.

[0078] In one embodiment, the prediction module 320 calculates carbon emissions from sample operation data for each newly added historical production cycle to obtain a carbon emission label for the preset production line producing the target power equipment in the corresponding newly added historical production cycle. This includes: preprocessing the sample operation data of the current newly added historical production cycle for each newly added historical production cycle to obtain intermediate operation data; and calculating carbon emissions from the intermediate operation data based on the carbon emission factor library and preset life cycle assessment rules to obtain a carbon emission label for the preset production line producing the target power equipment in the current newly added historical production cycle.

[0079] In one embodiment, the prediction module 320 obtains the current carbon emission prediction model corresponding to the target power equipment, including: when the time difference between the current time and the previous training time is not greater than a preset duration threshold, determining that the current carbon emission prediction model is the previous carbon emission prediction model corresponding to the target power equipment.

[0080] In one embodiment, the carbon emission prediction device for the power equipment further includes a second acquisition module, which is specifically used to: predict the carbon emissions generated by the preset production line in producing the target power equipment within the target production cycle based on the planned output using the current carbon emission prediction model, and obtain the predicted carbon emission amount; then acquire the actual operating data of the preset production line when producing the target power equipment within the target production cycle, and obtain the target operating data; and perform carbon emission calculation on the target operating data to obtain the actual carbon emission amount of the preset production line within the target production cycle.

[0081] Accordingly, the block structure includes a block header and a block body. The writing module 330 is specifically used to: encapsulate the identifier, planned output, predicted carbon emissions, target operating data, and actual carbon emissions of the current carbon emission prediction model to obtain block body data; perform hash calculation on the block body data to obtain the hash value corresponding to the block body data, and determine the block header data based on the current time, the hash value corresponding to the block body data, and the block structure corresponding to the previous life cycle stage of the production stage; and write the block header data and block body data into the block header and block body corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment.

[0082] In one embodiment, the carbon emission prediction device for the power equipment further includes a deviation determination module, which is specifically used to: calculate the carbon emission of the target operating data to obtain the actual carbon emission of the preset production line in the target production cycle, calculate the deviation between the actual carbon emission and the predicted carbon emission, and obtain a prediction deviation value; when the preset deviation value is greater than the preset deviation threshold, generate a prediction risk warning and display the prediction risk warning.

[0083] In one embodiment, the carbon emission prediction device for the power equipment further includes a strategy generation module, which is specifically used to: predict the carbon emissions generated by the production of target power equipment by the preset production line within the target production cycle based on the planned output using the current carbon emission prediction model, and after obtaining the carbon emission prediction amount, generate a carbon emission reduction strategy for the preset production line based on the carbon emission prediction amount and the carbon emission control standards set by the target production unit for the preset production line; and send the carbon emission reduction strategy to the decision support terminal of the target production unit so that the decision support terminal can adjust the various equipment in the preset production line based on the carbon emission reduction strategy.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0085] The carbon emission prediction device for power equipment provided in this embodiment can be applied to the carbon emission prediction method for power equipment provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0086] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 A block diagram is shown of an exemplary electronic device 11 suitable for implementing embodiments of the present application. Figure 4 The electronic device 11 shown is merely an example and should not impose any limitations on the functionality and scope of use of this embodiment.

[0087] like Figure 4 As shown, the electronic device 11 is represented in the form of a general-purpose computing electronic device. The components of the electronic device 11 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0088] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, industry-standard architecture buses, microchannel architecture buses, enhanced industry-standard architecture buses, Video Electronics Standards Association (VESA) local buses, and peripheral component interconnect buses.

[0089] Electronic device 11 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 11, including volatile and non-volatile media, removable and non-removable media.

[0090] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and / or cache memory 32. Electronic device 11 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 As not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., portable compact disk read-only memory, digital multifunction optical disc read-only memory, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0091] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this application.

[0092] Electronic device 11 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 11, and / or with any device that enables electronic device 11 to communicate with one or more other computing devices (e.g., network interface card and modem, etc.). Such communication can be performed through input / output interface 22. Furthermore, electronic device 11 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network) through network adapter 20.

[0093] like Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 11 via bus 18. It should be understood that, although... Figure 4As not shown, other hardware and / or software modules may be used in conjunction with electronic device 11, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, independent disk redundant array systems, tape drives, and data backup storage systems.

[0094] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing a carbon emission prediction method for power equipment provided in any embodiment of this application.

[0095] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a carbon emission prediction method for power equipment, such as that provided in any embodiment of this application.

[0096] The computer storage medium of this embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, flash memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0097] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0098] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0099] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] Those skilled in the art will understand that the modules or steps described above in this application can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0101] It should be noted that in the technical solutions of this application embodiment, the information collected is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data all comply with the relevant laws, regulations and standards of relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse. In addition, corresponding operation entry points are provided for users to choose to agree to or refuse the automated decision results. If the user chooses to refuse, the process enters the expert decision-making process.

[0102] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the inventive concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method of carbon emission prediction for power equipment, characterized in that, The method comprises: in response to satisfying a production stage prediction trigger condition, obtaining a planned yield of a preset production line of a target power equipment production target power equipment within a target production period; obtain the current carbon emission prediction model corresponding to the target power equipment, and use the current carbon emission prediction model to predict the carbon emission amount generated by the preset production line producing the target power equipment within the target production period based on the planned yield, to obtain a carbon emission prediction amount; write the identifier of the current carbon emission prediction model, the planned yield and the carbon emission prediction amount into the block structure corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment, so that the external service system provides corresponding business services based on the carbon footprint life cycle chain corresponding to the target power equipment.

2. The method of claim 1, wherein, obtain the current carbon emission prediction model corresponding to the target power equipment, comprising: when the time difference between the current time and the last training time is greater than the preset time threshold, determine the time range between the current time and the last training time as the to-be-trained time range; the to-be-trained time range includes a plurality of newly added historical production periods; obtain the actual running data of the preset production line when producing the target power equipment in each newly added historical production period to obtain sample running data corresponding to the newly added historical production period, and obtain the actual yield of the preset production line in each newly added historical production period to obtain sample yield corresponding to the newly added historical production period; carbon emission calculation is performed on the sample running data of each newly added historical production period to obtain the carbon emission label generated by the preset production line producing the target power equipment in the corresponding newly added historical production period, and the sample yield of each newly added historical production period and the carbon emission label of the corresponding newly added historical production period are combined into newly added sample data; use the plurality of newly added sample data to train and optimize the last carbon emission prediction model corresponding to the target power equipment to obtain the current carbon emission prediction model.

3. The method of claim 2, wherein, carbon emission calculation is performed on the sample running data of each newly added historical production period to obtain the carbon emission label generated by the preset production line producing the target power equipment in the corresponding newly added historical production period, comprising: for each newly added historical production period, the sample running data of the current newly added historical production period is preprocessed to obtain intermediate running data; based on the carbon emission factor library and the preset life cycle evaluation rule, the intermediate running data is subjected to carbon emission calculation to obtain the carbon emission label generated by the preset production line producing the target power equipment in the current newly added historical production period.

4. The method of claim 1, wherein, obtain the current carbon emission prediction model corresponding to the target power equipment, comprising: when the time difference between the current time and the last training time is not greater than the preset time threshold, the current carbon emission prediction model is determined as the last carbon emission prediction model corresponding to the target power equipment.

5. The method of claim 1, wherein, after using the current carbon emission prediction model to predict the carbon emission amount generated by the preset production line producing the target power equipment within the target production period based on the planned yield, to obtain the carbon emission prediction amount, further comprising: obtain the actual running data of the preset production line when producing the target power equipment within the target production period to obtain target running data; The target operation data is subjected to carbon emission calculation to obtain actual carbon emission of the preset production line in the target production period; Correspondingly, the block structure includes a block header and a block body, and the identification of the current carbon emission prediction model, the planned production, and the carbon emission prediction are written into the block structure corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment, including: The identification of the current carbon emission prediction model, the planned production, the carbon emission prediction, the target operation data, and the actual carbon emission are encapsulated to obtain block body data; The block body data is subjected to hash calculation to obtain a hash value corresponding to the block body data, and the block header data is determined based on the current time, the hash value corresponding to the block body data, and the block structure corresponding to the last life cycle stage of the production stage; The block header data and the block body data are written into the block header and the block body corresponding to the production stage in the carbon footprint life cycle chain of the target power equipment.

6. The method of claim 5, wherein, After the target operation data is subjected to carbon emission calculation to obtain actual carbon emission of the preset production line in the target production period, the method further includes: The deviation between the actual carbon emission and the carbon emission prediction is calculated to obtain a prediction deviation value; When the preset deviation value is greater than a preset deviation threshold, a prediction risk prompt is generated, and the prediction risk prompt is displayed.

7. The method of claim 1, wherein, After the carbon emission prediction of the preset production line in the target production period is predicted based on the planned production by using the current carbon emission prediction model, the carbon emission prediction value is obtained, and the method further includes: A carbon emission reduction strategy of the preset production line is generated based on the carbon emission prediction value and a carbon emission control standard set by the target production unit for the preset production line; The carbon emission reduction strategy is sent to a decision support terminal of the target production unit, so that the decision support terminal adjusts each device in the preset production line based on the carbon emission reduction strategy.

8. A carbon emission prediction device of power equipment characterized by, The apparatus includes: A first obtaining module configured to, in response to satisfying a production stage prediction trigger condition, obtain a planned production of a preset production line of a target production unit producing target power equipment in a target production period; A prediction module configured to obtain a current carbon emission prediction model corresponding to the target power equipment, and predict carbon emission generated by the preset production line in the target production period in producing the target power equipment based on the planned production by using the current carbon emission prediction model, to obtain a carbon emission prediction value; A writing module configured to write an identification of the current carbon emission prediction model, the planned production, and the carbon emission prediction value into a block structure corresponding to a production stage in a carbon footprint life cycle chain of the target power equipment, so that an external service system provides corresponding service based on the carbon footprint life cycle chain corresponding to the target power equipment.

9. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the carbon emission prediction method of the power equipment according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the carbon emission prediction method of the power equipment according to any one of claims 1 to 7.

Images