Power equipment green authentication method based on carbon emission data analysis

Abstract

The invention discloses a power equipment green authentication method based on carbon emission data analysis, and relates to the technical field of equipment green authentication, and the method comprises the steps: constructing a carbon emission environment influence relation based on carbon emission data, local environment data and global environment data under a historical time sequence; obtaining an equipment combination probability according to the similarity of the life cycle data of each equipment under the historical time sequence, and constructing an equipment carbon emission change model based on the equipment combination probability and the life cycle change factor thereof; obtaining carbon emission dynamic constraint data based on the current time sequence according to the carbon emission environmental influence relationship; based on the current equipment information, obtaining carbon emission prediction data according to an equipment carbon emission change model; and executing power equipment green authentication based on the carbon emission dynamic constraint data and the carbon emission prediction data. According to the method, the carbon emission constraint and the equipment combination probability are adaptively fluctuated according to the carbon emission absorption condition of the local environment and the global environment under the actual time sequence to compensate and correct the equipment carbon emission prediction data, so that the accuracy and the adaptability of power equipment authentication are improved.

Description

Technical Field

[0001] The present application relates to the technical field of equipment green certification, and in particular to a green certification method for power equipment based on carbon emission data analysis. Background Art

[0002] In the power industry, carbon emissions from power equipment are one of the important sources of greenhouse gas emissions. Therefore, green certification of power equipment to ensure that its carbon emissions meet environmental protection requirements is an important measure to promote low-carbon development in the power industry.

[0003] At present, green certification of power equipment mainly relies on traditional carbon emission measurement and assessment methods, which are usually estimated based on overall electricity consumption or energy consumption. The data granularity is coarse and it is difficult to accurately reflect the carbon emissions of different equipment and its carbon emissions under different environments and operating conditions.

[0004] In related technologies, power equipment is usually regarded as an independent individual, and the mutual influence between equipment is ignored, which causes repeated calculation or neglect of calculation of carbon emissions, resulting in deviations in the certification of whether the carbon emissions of power equipment meet green emissions under complex equipment combinations and dynamic environmental conditions, and the certification accuracy is poor.

[0005] The patent "A green electricity-CCER mutual recognition trading system based on dynamic emission reduction factors", publication number: CN119444242A, publication date: February 14, 2025, specifically discloses a green electricity-CCER mutual recognition system. The system consists of four modules: data interaction module, dynamic carbon emission reduction factor publicity module, CCER certificate issuance module, and circulation and cancellation module. It can systematically implement mutual recognition technology, making green electricity-CCER mutual recognition transparent and executable. This solution publishes the dynamic carbon emission reduction factors of each user node in real time, but users still base their load planning on fixed carbon emission constraints, and the environmental adaptability is poor.

[0006] The patent "A mechanism for linking green power consumption certification and carbon emission monitoring and accounting", publication number: CN118036882A, publication date: May 14, 2024, specifically discloses that the scope of carbon emission accounting is determined according to the logistics business activities of power grid enterprises; a carbon-green certificate joint trading model considering conditional risk value is established; a total carbon emission accounting model for power equipment logistics space is established; the green certificate management agency uses blockchain to verify the sampling data in the database, and if the sampling data is true, a green certificate is issued. Although this solution incorporates the total carbon emission accounting of power equipment logistics space, there are still fixed carbon emission constraints and it cannot adapt to the actual environmental carbon emission needs. Summary of the invention

[0007] This application aims to solve the technical problem in the prior art that there are deviations in the certification of whether the carbon emissions of power equipment meet the green emission requirements under complex equipment combinations and dynamic environmental conditions. The application provides a green certification method for power equipment based on carbon emission data analysis, constructs a carbon emission environmental impact relationship based on the respective impacts of carbon emission data on local environmental data and global environmental data in historical time series, and adaptively imposes fluctuating carbon emission constraints on the carbon emission absorption situation of the local environment and the global environment in actual time series, so that the carbon emission constraints adapt to the actual environment. At the same time, the carbon emission prediction data of the equipment is compensated and corrected based on the probability of equipment combination, and the equipment that may have a combination is included in the current equipment green certification considerations to avoid the problem of excessive carbon emissions caused by the subsequent addition of equipment, thereby improving the accuracy and adaptability of green certification of power equipment.

[0008] To achieve the above-mentioned technical objectives, a technical solution provided by the present application is a green certification method for power equipment based on carbon emission data analysis, comprising the following steps: constructing a carbon emission environmental impact relationship based on carbon emission data, local environmental data and global environmental data in a historical time series; obtaining the equipment combination probability based on the similarity of the life cycle data of each device in a historical time series, and constructing an equipment carbon emission change model based on the equipment combination probability and its life cycle change factor; obtaining carbon emission dynamic constraint data based on the carbon emission environmental impact relationship based on the current time series; obtaining carbon emission prediction data based on the equipment carbon emission change model based on the current equipment information; and performing green certification of power equipment based on the carbon emission dynamic constraint data and the carbon emission prediction data.

[0009] Furthermore, the construction of carbon emission environmental impact relationships based on carbon emission data, local environmental data and global environmental data in historical time series includes: obtaining local impact relationships based on the superposition values ​​of carbon emission data in historical time series and carbon emission impact data of the local environment; obtaining global impact relationships based on the superposition values ​​of carbon emission data in historical time series and carbon emission impact data of the global environment.

[0010] Furthermore, the carbon emission environmental impact relationship constructed based on the carbon emission data, local environmental data and global environmental data in the historical time series includes: obtaining local environmental data according to the preset area, and executing for each preset area: obtaining carbon emission data that changes synchronously with the local environmental data; obtaining the corresponding equipment spatial position according to the carbon emission data, and constructing a spatial impact relationship; constructing a temporal impact relationship according to the local environmental parameters in the local environmental data and the superposition value of the carbon emission data; and obtaining the local impact relationship based on the spatial impact relationship and the temporal impact relationship.

[0011] Furthermore, the spatial position of the corresponding equipment is obtained according to the carbon emission data, and the spatial influence relationship is constructed, including: obtaining carbon emission data having fluctuation similarity with the local environmental data; obtaining the spatial position of the equipment corresponding to the carbon emission data; and constructing the spatial influence relationship according to the spatial position of the equipment and the fluctuation value of the local environmental data and the fluctuation value of the carbon emission data.

[0012] Furthermore, the carbon emission environmental impact relationship is constructed based on the carbon emission data, local environmental data and global environmental data in the historical time series, including: obtaining the vegetation impact coefficient according to the proportion of animal species, biological abundance index, vegetation coverage rate and vegetation carbon absorption rate; obtaining the carbon emission environmental impact relationship according to the temperature, precipitation, wind speed, wind direction, humidity and vegetation impact coefficient combined with the superposition value of carbon emission data.

[0013] Furthermore, the method of obtaining the equipment combination probability based on the similarity of the life cycle data of each equipment in the historical time series includes: obtaining the transportation similarity of each equipment based on the transportation type, transportation sequence and transportation path of each equipment in the historical time series; obtaining the operation similarity of each equipment based on the operation dependency and operation sequence of each equipment in the historical time series; obtaining the scrapping similarity of each equipment based on the structural type, scrapping sequence and scrapping area of ​​each equipment in the historical time series; and obtaining the equipment combination probability based on multidimensional scaling analysis combined with equipment transportation similarity, equipment operation similarity and equipment scrapping similarity.

[0014] Furthermore, the construction of the equipment carbon emission change model based on the equipment combination probability and its life cycle change factor includes: constructing an equipment combination probability calculation layer based on the initial transportation weight, initial operation weight and initial scrapping weight; constructing a factor matching layer of equipment combination probability, initial probability threshold and life cycle change factor; constructing the carbon emission change initial model with the equipment combination probability calculation layer and the factor matching layer; iteratively updating the initial transportation weight, initial operation weight, initial scrapping weight and initial probability threshold in the initial carbon emission change model according to the carbon emission data of each equipment in the historical time series to obtain the carbon emission change model.

[0015] Furthermore, the construction of the equipment carbon emission change model based on the equipment combination probability and its life cycle change factor also includes: constructing a data compensation layer based on a merged unique strategy; constructing an initial model of equipment carbon emission changes with a neural network model architecture and a data compensation layer; and training the initial model of equipment carbon emission changes with processed carbon emission data to obtain the equipment carbon emission change model.

[0016] Furthermore, the data compensation layer performs: merging carbon emission data according to the equipment combination probability, outputting merged equipment information according to the carbon emission data merging result; and outputting equipment carbon emission compensation value according to the merged equipment information and equipment carbon emission calculation sequence.

[0017] Furthermore, the method of obtaining carbon emission dynamic constraint data based on the current time series according to the carbon emission environmental impact relationship includes: obtaining local environmental impact values ​​based on the current time series and the time impact relationship, the current device spatial position and the spatial impact relationship; obtaining global environmental impact values ​​based on the current time series and the global impact relationship; obtaining local environmental constraints and global environmental constraints according to the current device spatial position; obtaining local carbon emission constraints with local environmental constraints and local environmental impact values, and obtaining global carbon emission constraints with global environmental constraints and global environmental impact values.

[0018] Furthermore, the execution of green certification of power equipment based on carbon emission dynamic constraint data and carbon emission prediction data includes: if the carbon emission prediction data meets the carbon emission dynamic constraint data, the green certification of the power equipment passes; if the carbon emission prediction data does not meet the carbon emission dynamic constraint data, the green certification of the power equipment fails.

[0019] The beneficial effects of this application are as follows: 1. Constructing the environmental impact relationship of carbon emissions based on the impact of carbon emissions on the local and global environments in historical time series, and quantifying the continuous impact of carbon emissions on the local and global environments. At the same time, based on the equipment combination probability, a targeted equipment carbon emission change model is constructed according to each equipment type, and the carbon emission data of the entire process of power equipment is comprehensively calculated and monitored. The equipment combination probability is used to include the carbon emissions generated by the remaining equipment that may be synchronized with the equipment into the carbon emission prediction data of the equipment, and the impact of equipment combination operation is included. The overall equipment carbon emission status is considered, and the carbon emissions and energy-saving effects of a single individual are considered from a macro perspective, so as to improve the accuracy of the certification effect and avoid ignoring the global carbon emission impact. And based on the dynamic tolerance of the environment, the carbon emission dynamic constraint data is output as the certification standard to improve the temporal adaptability and spatial adaptability of green certification, and improve the reliability of green certification of power equipment.

[0020] 2. By using local environmental data and carbon emission data with similar fluctuations, the spatial location of equipment that may have an impact on the current local environment is obtained, and then the spatial impact relationship is constructed based on the correlation between the historical carbon emission data fluctuation values ​​and the local environmental data fluctuation values ​​of each device spatial location. This highlights the impact of different equipment on different local environments and avoids the single calculation of the impact of equipment carbon emissions.

[0021] 3. The impact of equipment carbon emissions on different local environments is obtained respectively through spatial impact relationships and temporal impact relationships, and the impact of equipment carbon emissions on the overall environment is obtained according to the global time impact. This can avoid ignoring the impact of equipment on other adjacent or related local environments when conducting green certification of power equipment, and avoid repeated calculation of environmental impacts when calculating overall carbon emissions. This improves targeting while avoiding repeated calculation of overall environmental considerations to ensure accuracy.

[0022] 4. The devices whose combined probability of two devices exceeds the probability threshold are recorded as merged devices. The carbon emission data of the combination of the two in the historical carbon emission data and the previously calculated device carbon emission data are retrieved according to the merged device information to output the compensation value of the device carbon emission data calculated later. If there is no online calculated device carbon emission data, the output device carbon emission data is the value calculated based on the merged carbon emission data. Therefore, when conducting green certification of power equipment, carbon emission reservations can be made in advance based on the necessity of equipment combination to avoid excessive carbon emissions from combined operation caused by ignoring the combination during the certification process.

[0023] 5. The overall carbon absorption capacity is obtained through vegetation carbon absorption rate and vegetation coverage rate, the grazing pressure of vegetation is obtained according to the ratio of carnivores to herbivores, the biodiversity in the region is reflected according to the biological abundance index, and the impact of vegetation carbon absorption is comprehensively calculated. The vegetation impact coefficient is used to reflect the vegetation carbon absorption capacity, ecological balance and biodiversity amplification effect, so as to quantify the carbon sink potential of vegetation, thereby accurately obtaining the current region's carbon absorption and processing capacity, and improving the accuracy of subsequent carbon emission constraint calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the green certification method for power equipment based on carbon emission data analysis in this application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] like Figure 1 As shown, as the first embodiment of the present application, the green certification method of power equipment based on carbon emission data analysis includes the following steps: Construct the carbon emission environmental impact relationship based on historical time series carbon emission data, local environmental data and global environmental data; The equipment combination probability is obtained based on the similarity of the life cycle data of each device in the historical time series, and the equipment carbon emission change model is constructed based on the equipment combination probability and its life cycle change factor; Obtain carbon emission dynamic constraint data based on the current time series according to the carbon emission environmental impact relationship; Obtain carbon emission prediction data based on the current equipment information and the equipment carbon emission change model; Perform green certification of power equipment based on carbon emission dynamic constraint data and carbon emission prediction data.

[0027] In this embodiment, the carbon emission environmental impact relationship is constructed based on the impact of carbon emissions on the local environment and the global environment in the historical time series, and the continuous impact of carbon emissions on the local environment and the global environment is quantified. At the same time, based on the equipment combination probability, a targeted equipment carbon emission change model is constructed according to each equipment type, and the carbon emission data of the entire process of the power equipment is comprehensively calculated and monitored. The equipment combination probability is used to retrieve the carbon emissions generated by the synchronous operation of the remaining equipment with the equipment as a life cycle change factor and included in the carbon emission prediction data of the equipment, and the impact of the equipment combination operation is included. The overall equipment carbon emission status is considered, and the carbon emissions and energy-saving effects of a single individual are considered from a macro perspective, so as to improve the accuracy of the certification effect and avoid ignoring the global carbon emission impact. And based on the dynamic tolerance of the environment, the carbon emission dynamic constraint data is output as the certification standard to improve the temporal adaptability and spatial adaptability of green certification, and improve the reliability of green certification of power equipment.

[0028] Specifically, the carbon emission environmental impact relationship is constructed based on the historical time series carbon emission data, local environmental data and global environmental data, including: Obtain the local impact relationship based on the superposition value of carbon emission data in historical time series and the carbon emission impact data of the local environment; Obtain the global impact relationship based on the carbon emission data superposition value in the historical time series and the carbon emission impact data of the global environment; The environmental impact relationship of carbon emissions is obtained through local impact relationship and global impact relationship.

[0029] The impact of carbon emissions from power equipment on the local environment is quantified through local impact relationships, and the impact of carbon emissions from power equipment on the global environment is quantified through global environmental impact relationships. Carbon emission constraints are considered from the local and global impacts respectively, thereby avoiding certification bias caused by macro data ignoring local impacts.

[0030] In some cases, the carbon emission environmental impact relationship is constructed based on historical carbon emission data, local environmental data, and global environmental data, including: Get local environment data based on the preset area and execute for each preset area: Obtain carbon emission data that changes synchronously with local environmental data; Obtain the corresponding equipment spatial location based on carbon emission data and build spatial impact relationships; Construct the time impact relationship based on the local environmental parameters in the local environmental data and the superposition value of the carbon emission data; The local influence relationship is obtained by using the spatial influence relationship and the temporal influence relationship.

[0031] The spatial impact is constructed by the carbon emission impact corresponding to the spatial location of the equipment, and the temporal impact relationship is constructed by superimposing the carbon emission data under time series changes, so as to obtain the local impact relationship corresponding to a single area, so as to quantify the impact relationship of carbon emissions of power equipment on various areas at different locations and different time series. In this case, it also includes: Construct global impact relationships based on global environmental parameters in global environmental data and superimposed values ​​of carbon emission data; The environmental impact relationship of carbon emissions is obtained through global impact relationship and local impact relationship.

[0032] The local environmental data at least include the carbon emission impact data and local environmental parameters of the local environment. The global environmental data at least include the carbon emission impact data and global environmental parameters of the global environment. The carbon emission impact data at least include temperature impact data, air quality impact data, water quality impact data, soil pollution impact data and ecosystem impact data. The local environmental parameters at least include local weather data and local ecological data. The global environmental parameters at least include global weather data and global ecological fluctuation data. The carbon emission impact data can obtain the impact value of carbon emissions on the environment based on expert experience excluding other interference factors. The local weather data at least include local temperature, local precipitation, local wind speed, local wind direction and local humidity. The global weather data at least include: temperature combination, precipitation combination, wind speed combination, wind direction combination and humidity combination. The local ecological data at least include local vegetation coverage and local vegetation carbon absorption rate. The global ecological data at least include global vegetation coverage and global vegetation carbon absorption rate. It is understandable that the positive effect of vegetation coverage on carbon emissions has always been considered based on the ability of plants and microorganisms to store carbon. Therefore, the vegetation coverage and the vegetation's ability to absorb carbon are used here to calculate the local or global vegetation carbon impact.

[0033] Among them, obtaining the corresponding equipment space location based on carbon emission data and building the spatial impact relationship include: Obtain carbon emission data that has similar fluctuations to local environmental data; Obtain the spatial location of the equipment corresponding to the carbon emission data; The spatial impact relationship is constructed based on the spatial location of the equipment and the fluctuation values ​​of local environmental data and carbon emission data.

[0034] By using local environmental data and carbon emission data with similar fluctuations, the spatial location of equipment that may have an impact on the current local environment is obtained, and then the spatial impact relationship is constructed based on the correlation between the historical carbon emission data fluctuation value and the local environmental data fluctuation value of each device spatial location, so as to highlight the impact of different equipment on different local environments and avoid the single calculation of the impact of equipment carbon emissions. It can be understood that this embodiment obtains the impact of equipment carbon emissions on different local environments through spatial impact relationships and temporal impact relationships, and obtains the impact of equipment carbon emissions on the overall environment based on global temporal impacts. This can avoid ignoring the impact of power equipment on other adjacent or related local environments when conducting green certification, and can also avoid repeated calculation of environmental impacts when calculating overall carbon emissions, thereby improving targetedness while avoiding repeated calculation of overall environmental considerations to ensure accuracy.

[0035] In some cases, the equipment combination probability is obtained based on the similarity of the life cycle data of each device in the historical time series, and the equipment carbon emission change model is constructed based on the equipment combination probability and its life cycle change factor, including: Obtain the transportation data, operation data and scrap data of each equipment in the historical time series; Based on the clustering algorithm, the equipment combination probability is obtained according to the similarity of transportation data, operation data and scrap data of each equipment in the historical time series; For each equipment type, carbon emission data correction processing is performed according to the equipment combination probability, and the equipment carbon emission change model is obtained according to the processed carbon emission data.

[0036] In this case, the carbon emissions of the equipment life cycle are calculated through the process of equipment transportation, operation, and scrapping. The transportation data at least includes transportation mode, transportation distance, transportation energy consumption, and transportation carbon emission data. The operation data at least includes operation energy consumption, operation time, load data, maintenance data, operation efficiency fluctuation data, and operation carbon emission data. The scrapping data at least includes scrapping time, scrapping factors, scrapping methods, and scrapping carbon emission data. The clustering algorithm can be K-means, DBSCAN, etc. According to the similarity of transportation data, operation data, and scrapping data of each device, the combined characteristics of each device in the entire life cycle of transportation, operation, and scrapping are obtained, such as whether the transportation of two devices can be transported at the same time, whether the operation of a certain device is accompanied by the operation of another device, and whether the scrapping of a certain device means the scrapping of another device, so as to perform carbon emission data correction processing according to each equipment type and the equipment combination probability, such as: for equipment combinations that can be transported at the same time, the carbon emissions of the combined transportation are calculated, and the carbon emission data of the single equipment is corrected accordingly; for equipment where operation and scrapping affect each other, the carbon emissions of the equipment are combined and calculated. By analyzing the transportation, operation and scrapping data of equipment, the carbon emissions of equipment throughout its life cycle can be comprehensively and accurately assessed, deviations and omissions in carbon emission data can be identified, and the accuracy of power equipment certification can be improved. In other cases, the full life cycle can also include processes such as raw material mining and production.

[0037] In this embodiment, obtaining the device combination probability according to the similarity of the life cycle data of each device in the historical time series includes: Obtain the transportation similarity of each device based on the transportation type, transportation sequence and transportation path of each device in the historical time series; Obtain the operation similarity of each device based on the operation dependency and operation sequence of each device under historical time sequence; Obtain the scrapping similarity of each device based on the structural type, scrapping sequence and scrapping area of ​​each device in the historical time series; The equipment combination probability is obtained based on multidimensional scaling analysis combined with equipment transportation similarity, equipment operation similarity and equipment scrapping similarity.

[0038] When two devices use the same method for transportation and the transportation sequence and transportation starting point are the same, the transportation between the two devices is considered to be the same, that is, the carbon emissions generated by the transportation are jointly generated by the two devices. When two devices have the same transportation route (the transportation sequence and transportation type on this transportation route are the same), the transportation between the two devices is considered to be similar, and the operation similarity is calculated based on the same proportion of the transportation route.

[0039] Operational dependency refers to the situation where devices are used in coordination. When two devices have an operational dependency and have the same operating sequence, it is considered that the two devices operate the same. At this time, the operation of one device must exist for the operation of another device, and the same is true for carbon emissions. When two devices have an operational dependency in a certain time sequence, the operation of one device must exist for the operation of another device in this time sequence. At this time, the carbon emissions of the two devices are correlated in this time sequence, and the two devices are considered to operate similarly. The operational similarity is calculated based on the proportion of the same operating sequence. Of course, in some cases, the operational dependency of two devices is not inevitable. In this case, the operational similarity is calculated based on the proportion of the operational dependency and the proportion of the same operating sequence. For example, in the past year, there were n A devices running, but only nk B devices running with A. At this time, the operational similarity of A and B devices is , if device B is only The operation similarity between A and B devices is , is the running sequence of device A. The running dependency can be determined according to the device type and the distance between the devices.

[0040] When two devices have the same structural type, and the same scrapping sequence and scrapping area, the two devices are considered to be scrapped in the same way. For example, if redundant devices are scrapped in the same sequence, the carbon emissions generated by the scrapping are generated by both devices. When two devices have partially the same structural type, the two devices are considered to be scrapped in a similar way. The scrapping similarity is calculated based on the same proportion of structural types. For example, the hardware structure of device C contains 30% metal materials and 70% non-metallic materials, and the hardware structure of device D contains 100% non-metallic materials. The scrapping similarity of devices C and D is In other cases, the scrapping sequence includes the actual scrapping sequence and the shutdown sequence. The actual scrapping sequence is the time point when the equipment is actually disassembled and recycled, and the shutdown sequence is the time point when the equipment stops running. Since some equipment has the same actual scrapping sequence but different shutdown sequences, the difference between the actual scrapping sequence and the shutdown sequence is included in the scrapping similarity consideration. For example, the shutdown sequence of device C is the same as the actual scrapping sequence, and the shutdown sequence of device D is earlier than that of device C, and the actual scrapping sequence is the same. At this time, the scrapping similarity of devices C and D is , Indicates the actual scrapping sequence of the equipment. Indicates the equipment shutdown sequence. Indicates the actual scrapping sequence of C equipment, Indicates the shutdown sequence of device D.

[0041] The transport similarity, operation similarity and scrapping similarity reflect the combination probability of equipment in the processes of transport, operation and scrapping. Multidimensional scaling analysis is used to integrate the combination probabilities of transport, operation and scrapping to obtain the equipment combination probability, so as to comprehensively evaluate the overall characteristics of the equipment combination. The equipment combination probability P is: ; in, represents the transport weight, represents the running weight, represents the scrap weight, , Indicates the transport similarity of the equipment transport dimension, Indicates the operation similarity of the device operation dimension, Represents the scrap similarity of equipment scrapping dimension.

[0042] In some cases, the equipment carbon emission change model is constructed based on the equipment combination probability and its life cycle change factor, including: Construct an equipment combination probability calculation layer based on initial transportation weight, initial operation weight and initial scrap weight; Construct a factor matching layer of device combination probability, initial probability threshold, and life cycle change factor; Construct an initial model of carbon emission changes using the equipment combination probability calculation layer and factor matching layer; According to the carbon emission data of each device in the historical time series, the initial transportation weight, initial operation weight, initial scrapping weight and initial probability threshold in the initial carbon emission change model are iteratively updated to obtain the carbon emission change model.

[0043] The carbon emission change model is trained through the carbon emission data of each device in the historical time series to obtain the transportation weight, operation weight, scrap weight and probability threshold that conform to the historical situation. When the equipment combination probability between two devices is greater than the probability threshold, the carbon emission data of the other device is taken into consideration as a life cycle change factor in the carbon emission data of the current device.

[0044] In other cases, the equipment carbon emission change model based on the equipment combination probability and its life cycle change factor also includes: Build a data compensation layer based on the merge unique strategy; Construct an initial model of equipment carbon emission changes using a neural network model architecture and a data compensation layer; The processed carbon emission data is used to train the initial model of equipment carbon emission changes to obtain the equipment carbon emission change model.

[0045] Execute in the data compensation layer: Combine carbon emission data according to the equipment combination probability, and output combined equipment information according to the carbon emission data combination result; The equipment carbon emission compensation value is output based on the combined equipment information and the equipment carbon emission calculation sequence.

[0046] When the probability of a device combination is greater than the probability threshold, the carbon emission data of the corresponding devices will be combined and considered, and only a unique carbon emission value will be output. By merging carbon emission data and building a data processing layer, the carbon emissions of the associated devices will only be calculated once, thereby avoiding repeated calculations of carbon emission data. In this embodiment, for example, when the probability of a device combination of E and F devices is greater than the probability threshold, the transportation carbon emission data of E and F devices are merged according to transportation similarity, the operation carbon emission data of E and F devices are merged according to operation similarity, and the scrap carbon emission data of E and F are merged according to scrap similarity. If the E device first performs green certification, the transportation carbon emission data of the E device for: ; in, Indicates the E-equipment transportation carbon emission data predicted based on historical data; E equipment operation carbon emission data for: ; in, Indicates the E equipment operation carbon emission data predicted based on historical data. Indicates the F equipment operation carbon emission data predicted based on historical data; E-device end-of-life carbon emissions data for: ; in, Indicates the E equipment scrapping carbon emission data predicted based on historical data; The transportation carbon emission data of F equipment for: ; in, It indicates the F equipment transportation carbon emission data predicted based on historical data. Indicates the transport similarity between E and F equipment, Indicates the actual transportation carbon emission data of E equipment; F Equipment Operation Carbon Emission Data for: ; in, It indicates the carbon emission data of F equipment operation predicted based on historical data. Indicates the actual operating carbon emission data of E equipment; F Equipment scrapping carbon emission data for: ; in, Indicates the carbon emission data of F equipment scrapping predicted based on historical data, Indicates the scrap similarity between E and F equipment, Indicates the actual scrapped carbon emission data of E equipment.

[0047] In this embodiment, the equipment whose equipment combination probability exceeds the probability threshold is recorded as a merged equipment, and the carbon emission data of the combination of the two in the historical carbon emission data and the previously calculated equipment carbon emission data are retrieved according to the merged equipment information to output the compensation value of the equipment carbon emission data calculated later, that is, the part of transportation, operation, and scrapping that has been calculated by the previous equipment, so as to ensure the uniqueness of the merger. If there is no previously calculated equipment carbon emission data, the output equipment carbon emission data is the value calculated based on the merged carbon emission data. Therefore, when conducting green certification of power equipment, carbon emission reservations can be made in advance according to the necessity of equipment combination to avoid excessive carbon emissions caused by ignoring the combination during the certification process. Since the later certified power equipment outputs the equipment carbon emission compensation value according to the carbon emission reservation of the previous equipment in the data compensation layer, the power equipment is certified to avoid repeated calculation of equipment carbon emissions and ensure the accuracy of carbon emission calculation under equipment combination.

[0048] It can be understood that in some cases, the probability threshold includes a transportation probability threshold, an operation probability threshold, a scrap probability threshold, and a comprehensive probability threshold. When any similarity exceeds the corresponding probability threshold, the corresponding process carbon emission of the corresponding equipment can also be included in the life cycle change factor of the current equipment. Although only the real-time situation of the combination of two equipments is given in this embodiment, in actual applications, the combination of multiple equipments can also be considered at the same time, that is, each process of multiple equipments can be included in the consideration of the life cycle change factor of the current equipment.

[0049] Furthermore, obtaining carbon emission dynamic constraint data based on the current time series according to the carbon emission environmental impact relationship includes: Obtaining a local environmental impact value based on the current time sequence and time impact relationship, the current device spatial position and spatial impact relationship; Obtain the global environmental impact value based on the current time series and the global impact relationship; Get local and global environment constraints based on the current device spatial position; Local carbon emission constraints are obtained using local environmental constraints and local environmental impact values, and global carbon emission constraints are obtained using global environmental constraints and global environmental impact values.

[0050] Among them, local environmental constraints and global environmental constraints can be obtained according to the carbon emission requirements of the actual region and the carbon emission requirements of the overall region. The absorption capacity of the local environment and the global environment for carbon emissions is obtained by using the local environmental impact value and the global environmental impact value of the current time series, so as to obtain the actual acceptable carbon emissions considering the environmental impact under the local environmental constraints and the global environmental constraints. At the same time, the changes in time series and spatial position are taken into account, so that carbon emission management can be dynamically adjusted with the changes in environmental conditions, and local and global carbon emission requirements can be adapted.

[0051] In this embodiment, carbon emission prediction data is obtained according to the equipment carbon emission change model based on the current equipment information. The carbon emission prediction data is calculated based on whether there is a corresponding merged device for the equipment and whether the corresponding merged device has been calculated in advance. If so, the carbon emission prediction data output by the equipment is the difference between the initial value of the prediction data calculated according to the neural network model and the carbon emission prediction data output by the merged device. If not, the carbon emission prediction data output by the equipment is the initial value of the prediction data calculated according to the neural network model.

[0052] Executing green certification of power equipment based on dynamic carbon emission constraint data and carbon emission forecast data includes: If the carbon emission prediction data meets the carbon emission dynamic constraint data, the green certification of the power equipment will be passed; If the carbon emission prediction data does not meet the carbon emission dynamic constraint data, the green certification of the power equipment will not pass.

[0053] Specifically, if the carbon emission prediction data meets both the local carbon emission constraints and the global carbon emission constraints, the green certification of the power equipment will be passed. If the carbon emission prediction data does not meet any of the local carbon emission constraints and the global carbon emission constraints, the green certification of the power equipment will not be passed.

[0054] By obtaining the local environmental impact value and the global environmental impact value, as well as the corresponding local environmental constraints and global environmental constraints, the impact of equipment carbon emissions on the local and global environment is comprehensively considered. At the same time, by calculating the difference between the initial value of the predicted data and the carbon emission predicted data output by the combined equipment, the prediction results are further corrected to ensure that the predicted carbon emission data can not only reserve the necessary carbon emission conditions that may exist in the combination, but also avoid repeated calculation of carbon emissions, thereby improving the accuracy of the prediction.

[0055] As a second embodiment of the present application, the local ecological data also includes the local animal species ratio and the local biological abundance index. The global ecological data at least includes the global animal species ratio and the global biological abundance index. The local animal species ratio is obtained according to the ratio of the number of carnivores to herbivores in the local environment, and the local biological abundance index is calculated according to the species richness in the local environment, which can be calculated according to the species counting method, the Shannon-Weller diversity index, etc.; the global animal species ratio is obtained according to the ratio of the number of carnivores to herbivores in the global environment, and the global biological abundance index is calculated according to the species richness in the global environment, which can also be calculated according to the species counting method, the Shannon-Weller diversity index, etc.

[0056] Among them, the carbon emission environmental impact relationship constructed based on historical time series carbon emission data, local environmental data and global environmental data also includes: The vegetation impact coefficient was obtained based on the animal species ratio, biological abundance index, vegetation coverage and vegetation carbon absorption rate; The environmental impact relationship of carbon emissions is obtained by combining the temperature, precipitation, wind speed, wind direction, humidity and vegetation influence coefficient with the superposition value of carbon emission data.

[0057] Specifically, the time impact relationship is constructed based on the local environmental parameters in the local environmental data and the superposition value of the carbon emission data, including: The local vegetation impact coefficient is obtained based on the local animal species ratio, local biological abundance index, local vegetation coverage rate and local vegetation carbon absorption rate; The influence weight of the time influence relationship is obtained based on the local temperature, local precipitation, local wind speed, local wind direction, local humidity and local vegetation influence coefficient combined with the superposition value of carbon emission data.

[0058] The global impact relationship is constructed based on the global environmental parameters in the global environmental data and the superposition values ​​of carbon emission data, including: The global vegetation impact coefficient is obtained based on the global animal species ratio, global biological abundance index, global vegetation coverage rate and global vegetation carbon absorption rate; The influence weight of the global influence relationship is obtained based on the temperature combination, precipitation combination, wind speed combination, wind direction combination, humidity combination and global vegetation influence coefficient combined with the superposition value of carbon emission data.

[0059] Specifically, the local vegetation impact coefficient is calculated according to the following formula: ; in, represents the local vegetation impact coefficient, I represents the number of local vegetation species, represents the vegetation coverage rate of the i-th type of vegetation at time t, represents the carbon absorption rate of the i-th type of vegetation, represents the number of carnivores at time t, represents the number of herbivores at time t, Represents the local biological abundance index.

[0060] The influence weight of the time influence relationship is obtained based on the local temperature, local precipitation, local wind speed, local wind direction, local humidity and local vegetation influence coefficient combined with the carbon emission data superposition value: ; in, represents the local carbon emission impact at time t; Represents the superposition value of carbon emission data at time t; represents the influence weight of local temperature, represents the local temperature at time t; represents the weight of local precipitation impact, represents the local precipitation at time t; represents the influence weight of local wind speed, represents the wind speed at time t; represents the influence weight of local wind direction, represents the wind direction coefficient at time t; represents the influence weight of local humidity, Indicates local humidity; Represents the local ecological impact coefficient.

[0061] In this embodiment, the overall carbon absorption capacity is obtained through the vegetation carbon absorption rate and vegetation coverage rate, the grazing pressure of vegetation is obtained according to the ratio of carnivores to herbivores, the biodiversity in the region is reflected according to the biological abundance index, the vegetation carbon absorption impact is comprehensively calculated, and the vegetation impact coefficient is used to reflect the vegetation carbon absorption capacity, ecological balance and biodiversity amplification effect, so as to quantify the vegetation carbon sink potential, thereby accurately obtaining the current region's absorption and processing capacity for carbon emissions and improving the calculation accuracy of subsequent carbon emission constraints. It is understandable that in other cases, the animal ratio and herbivorous pressure cannot show a linear relationship. The nonlinear relationship between the animal ratio and herbivorous pressure is converted by linear regression, and then the local vegetation impact coefficient and impact weight are calculated.

[0062] Similarly, the same temperature, precipitation, wind speed, wind direction and humidity in the global weather data are combined to obtain the temperature combination, precipitation combination, wind speed combination, wind direction combination and humidity combination, and the impact relationship between the global weather conditions and carbon emissions is further calculated according to the proportional changes of each value in the combination, and then the impact weight of the global impact relationship is obtained according to the temperature combination, precipitation combination, wind speed combination, wind direction combination, humidity combination and the global vegetation impact coefficient combined with the superposition value of carbon emission data.

[0063] By integrating multi-source data on ecology and climate, we can obtain the carbon emission absorption capacity under different weather and ecological factors, so as to accurately calculate the impact of carbon emissions that cannot be absorbed on the actual environment, improve the dynamic adaptability of carbon emission constraints, and ensure the accuracy of green certification of power equipment.

[0064] As a third embodiment of the present application, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the green certification method for power equipment based on carbon emission data analysis in any of the above embodiments. The computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0065] In this embodiment, an electronic device is also provided, which may include a processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the steps of the green certification method for power equipment based on carbon emission data analysis in any of the above embodiments by executing the executable instructions.

[0066] The specific implementation method described above is a preferred implementation method of the green certification method for power equipment based on carbon emission data analysis of this application, and is not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A green certification method for power equipment based on carbon emission data analysis, characterized by: The steps include: Construct the carbon emission environmental impact relationship based on historical time series carbon emission data, local environmental data and global environmental data; The equipment combination probability is obtained based on the similarity of the life cycle data of each device in the historical time series, and the equipment carbon emission change model is constructed based on the equipment combination probability and its life cycle change factor; Obtain carbon emission dynamic constraint data based on the current time series according to the carbon emission environmental impact relationship; Obtain carbon emission prediction data based on the current equipment information and the equipment carbon emission change model; Perform green certification of power equipment based on carbon emission dynamic constraint data and carbon emission prediction data.

2. The green certification method for electric power equipment based on carbon emission data analysis according to claim 1, characterized in that: The construction of carbon emission environmental impact relationship based on historical time series carbon emission data, local environmental data and global environmental data includes: Obtain the local impact relationship based on the superposition value of carbon emission data in historical time series and the carbon emission impact data of the local environment; The global impact relationship is obtained based on the superposition value of carbon emission data in historical time series and the carbon emission impact data of the global environment.

3. The green certification method for electric power equipment based on carbon emission data analysis according to claim 1, characterized in that: The construction of carbon emission environmental impact relationship based on historical time series carbon emission data, local environmental data and global environmental data includes: Get local environment data based on the preset area and execute for each preset area: Obtain carbon emission data that changes synchronously with local environmental data; Obtain the corresponding equipment spatial location based on carbon emission data and build spatial impact relationships; Construct the time impact relationship based on the local environmental parameters in the local environmental data and the superposition value of the carbon emission data; The local influence relationship is obtained by using the spatial influence relationship and the temporal influence relationship.

4. The green certification method for electric power equipment based on carbon emission data analysis according to claim 3 is characterized in that: Obtain the corresponding equipment space location based on carbon emission data and build the spatial impact relationship including: Obtain carbon emission data that has similar fluctuations to local environmental data; Obtain the spatial location of the equipment corresponding to the carbon emission data; The spatial impact relationship is constructed based on the spatial location of the equipment and the fluctuation values ​​of local environmental data and carbon emission data.

5. The green certification method for electric power equipment based on carbon emission data analysis according to claim 1, characterized in that: The carbon emission environmental impact relationship is constructed based on historical time series carbon emission data, local environmental data and global environmental data, including: The vegetation impact coefficient was obtained based on the animal species ratio, biological abundance index, vegetation coverage and vegetation carbon absorption rate; The environmental impact relationship of carbon emissions is obtained by combining the temperature, precipitation, wind speed, wind direction, humidity and vegetation influence coefficient with the superposition value of carbon emission data.

6. The green certification method for electric power equipment based on carbon emission data analysis according to claim 1, characterized in that: The method of obtaining the device combination probability according to the similarity of the life cycle data of each device in the historical time series includes: Obtain the transportation similarity of each device based on the transportation type, transportation sequence and transportation path of each device in the historical time series; Obtain the operation similarity of each device based on the operation dependency and operation sequence of each device under historical time sequence; Obtain the scrapping similarity of each device based on the structural type, scrapping sequence and scrapping area of ​​each device in the historical time series; The equipment combination probability is obtained based on multidimensional scaling analysis combined with equipment transportation similarity, equipment operation similarity and equipment scrapping similarity.

7. The green certification method for electric power equipment based on carbon emission data analysis according to claim 6, characterized in that: The equipment carbon emission change model based on equipment combination probability and its life cycle change factor is constructed as follows: Construct an equipment combination probability calculation layer based on initial transportation weight, initial operation weight and initial scrap weight; Construct a factor matching layer of device combination probability, initial probability threshold, and life cycle change factor; Construct an initial model of carbon emission changes using the equipment combination probability calculation layer and factor matching layer; According to the carbon emission data of each device in the historical time series, the initial transportation weight, initial operation weight, initial scrapping weight and initial probability threshold in the initial carbon emission change model are iteratively updated to obtain the carbon emission change model.

8. The green certification method for electric power equipment based on carbon emission data analysis according to claim 6, characterized in that: The equipment carbon emission change model constructed based on the equipment combination probability and its life cycle change factor also includes: Build a data compensation layer based on the merge unique strategy; Construct an initial model of equipment carbon emission changes using a neural network model architecture and a data compensation layer; The processed carbon emission data is used to train the initial model of equipment carbon emission changes to obtain the equipment carbon emission change model.

9. The green certification method for electric power equipment based on carbon emission data analysis according to claim 8, characterized in that: In the data compensation layer perform: Combine carbon emission data according to the equipment combination probability, and output combined equipment information according to the carbon emission data combination result; The equipment carbon emission compensation value is output based on the combined equipment information and the equipment carbon emission calculation sequence.

10. The green certification method for electric power equipment based on carbon emission data analysis according to claim 3, characterized in that: The obtaining of carbon emission dynamic constraint data based on the current time sequence and the carbon emission environmental impact relationship includes: Obtaining a local environmental impact value based on the current time sequence and time impact relationship, the current device spatial position and spatial impact relationship; Obtain the global environmental impact value based on the current time series and the global impact relationship; Get local and global environment constraints based on the current device spatial position; Local carbon emission constraints are obtained using local environmental constraints and local environmental impact values, and global carbon emission constraints are obtained using global environmental constraints and global environmental impact values.

11. The green certification method for electric power equipment based on carbon emission data analysis according to claim 1, characterized in that: The green certification of power equipment based on carbon emission dynamic constraint data and carbon emission prediction data includes: If the carbon emission prediction data meets the carbon emission dynamic constraint data, the green certification of the power equipment will be passed; If the carbon emission prediction data does not meet the carbon emission dynamic constraint data, the green certification of the power equipment will not pass.

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