Carbon emission monitoring management method and system for transformer substation

Abstract

The invention discloses a carbon emission monitoring management method and system for a transformer substation, and the method employs the operation efficiency information of a plurality of electrical devices and a convolutional neural network technology to monitor and manage the states of large urban power supply regions with different climates and environments and adjust the household registration and population changes in different regions. The method comprises the steps of collecting carbon emission related information of a transformer substation in an urban power supply area, the carbon emission related information of the transformer substation comprises power consumption data of different power utilization groups in different urban power supply areas and electrical equipment operation efficiency information of the different power utilization groups in the urban power supply areas within a preset time before household registration and population changes in the different areas; according to the statistical condition of the electrical equipment operation efficiency information, the electrical equipment operation efficiency information of different electricity utilization groups in the urban power supply area is classified, and the method ensures seasonal safety and accurate monitoring of the electrical equipment operation efficiency information in the urban power supply area in unit time.

Description

Technical Field

[0001] The present invention relates to the field of urban carbon emission monitoring and management, and particularly to a carbon emission monitoring and management method and system for substations. Background Art

[0002] As an important hub for power transmission and distribution, the energy efficiency and emission levels of substations directly affect the overall carbon emission performance of urban power supply areas. Therefore, for the real-time monitoring and management of substation carbon emissions, not only accurate data collection and analysis are required, but also deep integration with the overall operating status of urban power supply areas is needed to ensure the accuracy of monitoring and the comprehensiveness of management. However, existing substation carbon emission monitoring methods are usually limited to the carbon emission estimation of single equipment or local areas, without considering the dynamic changes of the entire urban power supply network.

[0003] Existing carbon emission monitoring technologies mainly rely on data collection of local equipment and carbon emission calculation based on emission factors. This method has obvious limitations. First, most monitoring methods rely on the operating data of single equipment, such as the energy consumption of transformers or switchgear, but the carbon emissions of these equipment only account for a part of the emissions of the entire substation or urban power supply system. Second, factors such as the power load, energy structure, and external climate in urban power supply areas are also constantly changing. If these factors are not fully collected and considered, the accuracy of monitoring results will be greatly reduced. For example, during peak electricity consumption periods, the load of substations may fluctuate violently, resulting in significant changes in their carbon emission levels. This dynamic change has not been effectively monitored, affecting the true reflection of emission data.

[0004] In addition, existing monitoring systems have serious deficiencies in data collection and cannot comprehensively obtain the operating status of substations and their surrounding areas. For example, some systems only focus on energy consumption data and ignore key factors such as environmental factors, equipment operation efficiency, and system scheduling strategies. The incompleteness of these data leads to inaccurate carbon emission monitoring and cannot effectively reflect the carbon emission levels of substations under different loads and operating conditions. Therefore, existing technologies urgently need to be improved by adopting more comprehensive monitoring methods and integrating more operating data and environmental factors to provide more accurate basis for substation carbon emission management. Summary of the Invention

[0005] To solve the above technical problems, the purpose of the present invention is to provide a method for monitoring and managing the status of large urban power supply areas with different climates and environments, which utilizes the operating efficiency information of various electrical equipment and convolutional neural network technology, realizes the monitoring and management of the status of large urban power supply areas with different climates and environments and the adjustment of the change of the household population in different regions, ensures seasonal safety, and accurately monitors the operating efficiency information of electrical equipment in urban power supply areas per unit time.

[0006] The first aspect of the present invention provides a carbon emission monitoring and management method for a substation, including the following steps:

[0007] Collect carbon emission-related information of substations in the urban power supply area. The carbon emission-related information of the substations includes different regional power consumption data of different power consumption groups in different urban power supply areas and the electrical equipment operation efficiency information of different power consumption groups in the urban power supply area within a preset time before the change in the household register population in different regions;

[0008] The electrical equipment operation efficiency information of different power consumption groups in the urban power supply area includes the load rate, power factor, equipment loss, operation time, equipment health status, load change, and power quality during the operation of the urban power supply area;

[0009] Among them, the load rate and power factor are collected per unit time, and the equipment loss, operation time, equipment health status, load change, and power quality are collected according to the preset urban area distribution;

[0010] According to the statistical situation of the electrical equipment operation efficiency information, classify the electrical equipment operation efficiency information of different power consumption groups in the urban power supply area to obtain the types of electrical equipment operation efficiency information, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area. The types of electrical equipment operation efficiency information in different climates and environments within the statistical area include the load rate and power factor, and the emission source types and greenhouse gas concentrations include equipment loss, operation time, equipment health status, load change, and power quality;

[0011] Upload the types of electrical equipment operation efficiency information, emission source types, greenhouse gas concentrations in different climates and environments within the statistical area, and the corresponding different regional power consumption data of different power consumption groups in different urban power supply areas to the graph convolutional neural network model to establish a meteorological condition electrical equipment operation efficiency fluctuation management algorithm and a different region and emission source distribution electrical equipment operation efficiency fluctuation management algorithm;

[0012] Among them, the input of the meteorological condition electrical equipment operation efficiency fluctuation management algorithm is the types of electrical equipment operation efficiency information in different climates and environments within the statistical area, and the output is the management emission factor of the different region and emission source distribution electrical equipment operation efficiency fluctuation management algorithm; the input of the different region and emission source distribution electrical equipment operation efficiency fluctuation management algorithm is the emission source type, greenhouse gas concentration, and the determined management emission factor, and the output is the different regional power consumption data of the urban power supply area;

[0013] In practical applications, the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area of the urban power supply area are collected per unit time and input into the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions to obtain the management emission factors of the management algorithm for the fluctuation of the operating efficiency of electrical equipment with different regional and emission source distributions;

[0014] Compare the management emission factors of the management algorithm for the fluctuation of the operating efficiency of electrical equipment with different regional and emission source distributions with the preset types of emission factors:

[0015] When the types of management emission factors are less than the preset types of emission factors, there is no need to set the emission source type and greenhouse gas concentration, and the collection and analysis of the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area are continued;

[0016] When the types of management emission factors are equal to or greater than the preset types of emission factors, set the emission source type and greenhouse gas concentration, and input the emission source type, greenhouse gas concentration, and management emission factors into the management algorithm for the fluctuation of the operating efficiency of electrical equipment with different regional and emission source distributions to obtain the power consumption data of different regions in the urban power supply area.

[0017] Furthermore, the steps for establishing the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions are as follows:

[0018] According to the carbon emission-related information of the substation, sort out the types of operating efficiency information of electrical equipment of different electricity-consuming groups in the urban power supply area and the corresponding power consumption data of different regions;

[0019] Process and perform spatio-temporal convolution on the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area, convert the original data into practical features for use by the graph convolutional neural network model;

[0020] Use the graph convolutional neural network model to calculate data according to the prepared ReLU function, perform model ReLU function calculation using the selected algorithm, and adjust the parameters of the model;

[0021] After the ReLU function calculation is completed, extract the relevant parameters of the model as the output of the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions.

[0022] Furthermore, the steps for establishing the management algorithm for the fluctuation of the operating efficiency of electrical equipment with different regional and emission source distributions are as follows:

[0023] According to the carbon emission-related information of the substation, sort out the emission source type, greenhouse gas concentration, and the corresponding power consumption data of different regions in the urban power supply area;

[0024] Train using a convolutional neural network model, taking the emission source type and greenhouse gas concentration as independent variables, and the power consumption data of different regions as nodes to perform graph structure modeling of the data, and calculate the model using the ReLU function;

[0025] After the ReLU function calculation is completed, use the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions to predict the power consumption data of different regions in the urban power supply area;

[0026] Perform a convolutional neural network on the carbon emission-related information of the substation to calculate the statistical deviation between the power consumption data of different regions and the actual power consumption data of different regions.

[0027] Furthermore, the data collection method for the emission source type and greenhouse gas concentration includes:

[0028] Use a temperature monitoring device to monitor and collect the equipment loss situation in the urban power supply area; monitor and collect the operation time in the urban power supply area based on the power equipment monitoring platform; monitor and collect the power quality in the urban power supply area, and analyze the peak value and fluctuation range of the change in equipment loss; use a remote sensing system to monitor and collect the equipment health status signal generated in the urban power supply area; use the household registration management system to collect the load changes around the urban power supply area and count the impacts generated by different load changes.

[0029] Furthermore, the preset influencing reasons for the preset emission factor types include energy consumption type, technology and management measures, standardization and local policy differences, data accuracy and availability, and equipment production and transportation emission cycles.

[0030] On the other hand, the present invention also provides a carbon emission monitoring and management system for a substation, including:

[0031] A carbon emission-related information collection module for the substation, used to collect carbon emission-related information of the substation in the urban power supply area, and the carbon emission-related information of the substation includes the power consumption data of different regions of different electricity-consuming groups in different urban power supply areas and the electrical equipment operation efficiency information of different electricity-consuming groups in the urban power supply area within a preset time before the change in the household population of different regions;

[0032] Among them, the electrical equipment operation efficiency information of different electricity-consuming groups in the urban power supply area includes the load rate, power factor, equipment loss, operation time, equipment health status, load change, and power quality during the operation of the urban power supply area; the load rate and power factor are collected per unit time, and the equipment loss, operation time, equipment health status, load change, and power quality are collected according to the preset urban area distribution;

[0033] The power information monitoring module is used to receive the power consumption data of different regions of different electricity-consuming groups and the operating efficiency information of electrical equipment of different electricity-consuming groups. Based on the statistical situation of the operating efficiency information of electrical equipment, it classifies the operating efficiency information of electrical equipment of different electricity-consuming groups in different urban power supply regions, and obtains the types of operating efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical region. The types of operating efficiency information of electrical equipment in different climates and environments within the statistical region include load factor and power factor, and the emission source types and greenhouse gas concentrations include equipment loss, operating time, equipment health status, load change, and power quality.

[0034] The graph convolutional neural network model module is used to receive the types of operating efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical region and upload them to the graph convolutional neural network model, and establish an algorithm for managing the fluctuations in the operating efficiency of electrical equipment under meteorological conditions and an algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions.

[0035] Among them, the input of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment under meteorological conditions is the types of operating efficiency information of electrical equipment in different climates and environments within the statistical region, and the output is the management emission factor of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions. The input of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions is the emission source type, greenhouse gas concentration, and the determined management emission factor, and the output is the power consumption data of different regions in the urban power supply region.

[0036] The emission factor management module is used to receive the algorithm for managing the fluctuations in the operating efficiency of electrical equipment under meteorological conditions and the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions. It collects the types of operating efficiency information of electrical equipment in different climates and environments within the statistical region per unit time and inputs them into the algorithm for managing the fluctuations in the operating efficiency of electrical equipment under meteorological conditions to obtain the management emission factor of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions.

[0037] The analysis module for changes in the household registered population in different regions is used to receive the management emission factor of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions, and compare the management emission factor of the algorithm for managing the fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions with the preset types of emission factors:

[0038] When the types of management emission factors are less than the preset types of emission factors, there is no need to set the emission source type and greenhouse gas concentration, and the collection and analysis of the types of operating efficiency information of electrical equipment in different climates and environments within the statistical region continue to be maintained.

[0039] When the type of managed emission factor is equal to or greater than the preset type of emission factor, the type of emission source and the greenhouse gas concentration are set, and the type of emission source, the greenhouse gas concentration, and the managed emission factor are input into the management algorithms for fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions to obtain the power consumption data of different regions in the urban power supply area.

[0040] Advantageous effects:

[0041] The present invention proposes a carbon emission monitoring and management method and system for a substation. By collecting these data, this method can comprehensively understand the operating status of the urban power supply area; this method uses a graph convolutional neural network model to establish a management algorithm for fluctuations in the operating efficiency of electrical equipment under meteorological conditions and a management algorithm for fluctuations in the operating efficiency of electrical equipment with different regional and emission source distributions. Through ReLU function calculation and model training, the carbon emission-related information of the substation and the corresponding power consumption data of different regions are obtained. The model can identify and classify the power consumption data of different regions in different urban power supply areas; according to the comparison between the managed emission factor and the preset type of emission factor, it can be determined whether it is necessary to set the type of emission source and the greenhouse gas concentration, and the power consumption data of different regions in the urban power supply area can be discovered in a timely manner. Through the comprehensive application of various electrical equipment operating efficiency information and the graph convolutional neural network model, the adjustment accuracy of changes in the household register population in different regions of the urban power supply area can be improved. It can not only judge the power consumption data of different regions, but also predict and analyze the trends of changes in the household register population in different regions based on the data of different electricity-consuming groups.

[0042] The method of the present invention utilizes the operating efficiency information of various electrical devices and convolutional neural network technology to achieve the monitoring and management of the status of power supply areas in large cities with different climates and environments, and the adjustment of changes in the household registered population in different regions, ensuring seasonal safety and accurate monitoring of the operating efficiency information of electrical devices in the urban power supply area per unit time. By comprehensively integrating multi-dimensional data such as the operating status of substation equipment, regional load changes, energy structure, and environmental factors, the present invention provides a more efficient and accurate carbon emission monitoring method than traditional monitoring methods. The system not only relies on the energy consumption data of a single device, but also can dynamically capture and analyze factors such as power load fluctuations and changes in equipment operating efficiency, so as to evaluate the carbon emission level in real time. By adopting intelligent data acquisition technology and big data analysis algorithms, the system can achieve accurate prediction and management of carbon emissions from substations and their power supply areas, effectively improving the accuracy of emission monitoring. More importantly, the method supports the joint analysis of historical data and real-time data, can automatically identify abnormal loads and equipment failures, and timely adjust operation strategies, thereby reducing unnecessary energy waste and carbon emissions. In addition, the system also has flexible scalability and can be customized and optimized according to the power demand and energy structure changes in different regions to ensure the effectiveness of substation carbon emission management in different scenarios. This comprehensive and dynamic management method provides a more forward-looking and efficient carbon emission monitoring solution for substations and urban power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a schematic flow chart of a carbon emission monitoring and management method for substations;

[0044] Figure 2 is a module composition diagram of a carbon emission monitoring and management system for substations. DETAILED DESCRIPTION OF THE INVENTION

[0045] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] As Figure 1 shown, a carbon emission monitoring and management method for substations includes the following steps:

[0047] A1. Collect carbon emission-related information of substations in the urban power supply area. The carbon emission-related information of the substations includes different power consumption data of different electricity-consuming groups in different urban power supply areas and the operating efficiency information of electrical devices of different electricity-consuming groups in the urban power supply area within a preset time before the change in the household registered population in different regions;

[0048] To achieve the status monitoring and management of large-scale power supply areas in cities with different climates and environments, it is first necessary to collect information related to carbon emissions from substations in the urban power supply area. These data can provide information about the performance of the urban power supply area during operation, power consumption data in different regions, and the operating efficiency information of relevant electrical equipment, providing a basis for subsequent status assessment and adjustment of the change in the household population in different regions;

[0049] The operating efficiency information of electrical equipment for different electricity-consuming groups in the urban power supply area includes:

[0050] Load rate and power factor: Collect the load rate and power factor of the urban power supply area, and analyze the fluctuations of the load rate and power factor; the fluctuations of the load rate and power factor can reflect the stability of the urban power supply area. It is necessary to collect the load rate and power factor parameters of the urban power supply area per unit time and record their fluctuations.

[0051] Equipment loss: Monitor and collect the equipment loss situation of the urban power supply area based on the temperature monitoring device, including the maximum value, average value, variance, etc. of the equipment loss. The change in equipment loss can reflect the mechanical operating state of the urban power supply area and the change in the household population in different regions.

[0052] Operating time: Use the power equipment monitoring platform to monitor and collect the change in the operating time of the urban power supply area, including the internal and external operating times of the equipment. The abnormal change in the operating time can indicate that there are overload, poor heat dissipation, etc. in the urban power supply area, which are related to the change in the household population in different regions;

[0053] Equipment health status: Monitor and collect the equipment health status signals generated in the urban power supply area based on the remote sensing system and analyze them. The equipment health status signals can provide the status information inside the urban power supply area.

[0054] Load change: Collect the load changes around the urban power supply area based on the household registration management system, count the impact changes caused by different load changes, and count the abnormal changes in the impacts caused by different load changes.

[0055] Power quality: Monitor and collect the power quality of the urban power supply area, analyze the peak value and fluctuation range of the change in equipment loss. The test results of power quality can provide information about insulation performance.

[0056] By collecting and recording the operation efficiency information of electrical equipment of these different electricity-consuming groups, an information database of the urban power supply area is established for subsequent data analysis, correlation analysis, and calculation of the ReLU function of the model; among them, the load rate and power factor are collected per unit time, and the equipment loss, operation time, equipment health status, load change, and power quality are collected according to the preset urban area distribution; by collecting the carbon emission-related information of the substations in the urban power supply area, the basic data of the performance during the operation of the urban power supply area and the power consumption data of different regions can be provided, which provides an important basis for subsequent status evaluation, adjustment of the change of the household register population in different regions, and performance optimization.

[0057] By analyzing the carbon emission-related information of the substations, information such as the stability, mechanical operation status, insulation performance, and potential change of the household register population in different regions of the urban power supply area can be revealed; in addition to the fluctuation data of the load rate and power factor, through various detection means such as temperature monitoring devices, power equipment monitoring platforms, equipment health status detection, household register management systems, and power quality detection, the urban power supply area is monitored and managed in an all-round way, and the status information of the urban power supply area is collected from different angles to improve the accuracy of detecting and adjusting the change of the household register population in different regions.

[0058] By collecting the carbon emission-related information of the substations in the urban power supply area and the operation efficiency information of electrical equipment, the performance of the urban power supply area, the power consumption data of different regions, and the operation efficiency information of electrical equipment can be comprehensively understood, and basic data and basis are provided for subsequent status evaluation, adjustment of the change of the household register population in different regions, and performance optimization.

[0059] A2. According to the statistical situation of the operation efficiency information of electrical equipment, classify the operation efficiency information of electrical equipment of different electricity-consuming groups in the urban power supply area, and obtain the types of operation efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area. The types of operation efficiency information of electrical equipment in different climates and environments within the statistical area include load rate and power factor, and the emission source types and greenhouse gas concentrations include equipment loss, operation time, equipment health status, load change, and power quality.

[0060] In this step, it is necessary to classify the operation efficiency information of electrical equipment of different electricity-consuming groups in the urban power supply area according to the statistical situation of the operation efficiency information of electrical equipment, and divide the operation efficiency information of electrical equipment of different electricity-consuming groups into two different pieces of information, namely, the types of operation efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area.

[0061] The types of operating efficiency information of electrical equipment in different climates and environments within the statistical area include load factor and power factor. These data can be collected by monitoring and collecting the load factor and power factor parameters of the urban power supply area per unit time;

[0062] Emission source types and greenhouse gas concentrations include equipment losses, operating time, equipment health status, load changes, and power quality, which are used to monitor and collect the status of equipment losses, operating time, equipment health status, load changes, and power quality during the operation of the urban power supply area;

[0063] The classification basis for the types of operating efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area is:

[0064] Changes in the household register population of different regions of different types correspond to different operating efficiency information of electrical equipment. By classifying the load factor and power factor separately from other multiple characteristic data, it is possible to more specifically judge and analyze the changes in the household register population of different regions of different types. The fluctuations of the load factor and power factor are more easily related to the changes in the household register population of different regions with different climates and environments, while data such as equipment losses, operating time, and sound are more easily related to the changes in the household register population of different regions with different machinery or insulation. The load factor and power factor are more convenient to collect in unit-time monitoring and management, and preliminary judgments on changes in the household register population of different regions can be quickly made through these data;

[0065] Emission source types and greenhouse gas concentrations involve more sensor collection and detection equipment, and the collection requires a certain amount of time and cost. Therefore, by presetting a preset emission factor type, if the management emission factor of the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area is less than the preset emission factor type, frequent setting of emission source types and greenhouse gas concentrations can be avoided, thereby improving the management effect.

[0066] By dividing the operating efficiency information of electrical equipment into two types of information, it is possible to more specifically judge and analyze the changes in the household register population of different regions of different types; by presetting a preset emission factor type, if the management emission factor of the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area is less than the preset emission factor type, frequent setting of emission source types and greenhouse gas concentrations can be avoided, thereby improving the management effect; by classifying the operating efficiency information of different types of electrical equipment separately, more accurate judgment and adjustment results can be obtained through comprehensive analysis of the correlation between different characteristic data.

[0067] A3. Upload the types of operating efficiency information of electrical equipment, emission source types, greenhouse gas concentrations, and power consumption data of different user groups in different regions of the corresponding urban power supply areas within the statistical area to the graph convolutional neural network model, and establish a meteorological condition-based electrical equipment operating efficiency fluctuation management algorithm and an electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions;

[0068] The steps for establishing the meteorological condition-based electrical equipment operating efficiency fluctuation management algorithm are as follows:

[0069] Based on the carbon emission-related information of the substations in the urban power supply area collected in step A1, sort out the types of operating efficiency information of electrical equipment of different user groups in the urban power supply area and the corresponding power consumption data of different regions;

[0070] Process and perform spatio-temporal convolution on the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area, convert the original data into practical features for use by the graph convolutional neural network model;

[0071] According to the problem description, select a graph convolutional neural network model suitable for this scenario, perform ReLU function calculation on the model using the selected algorithm, and adjust the model parameters;

[0072] After the ReLU function calculation is completed, extract the relevant parameters of the model as the output of the meteorological condition-based electrical equipment operating efficiency fluctuation management algorithm.

[0073] The steps for establishing the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions are as follows:

[0074] Based on the carbon emission-related information of the substations in the urban power supply area collected in step A1, sort out the emission source types, greenhouse gas concentrations, and the corresponding power consumption data of different regions in the urban power supply area;

[0075] Use the convolutional neural network model for training. Take the emission source types and greenhouse gas concentrations as independent variables, and the power consumption data of different regions as nodes to perform graph structure modeling of the data, and perform ReLU function calculation on the model;

[0076] After the ReLU function calculation is completed, predict the power consumption data of different regions in the urban power supply area through the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions,

[0077] The electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions is trained using the convolutional neural network model.

[0078] Convolutional neural network model architecture

[0079] Convolutional neural networks are used to extract the key features of the operating efficiency of electrical equipment. The following is a typical architecture of the CNN model:

[0080] Input layer: The input data is usually the multi-dimensional features of the equipment operation, such as voltage, current, temperature, etc., which can be represented as a matrix (for example, each row represents a timestamp and each column represents a device feature).

[0081] Convolutional layer: The convolutional layer is used to extract local features from the input data. For example, applying a set of filters (convolution kernels) to the operation data of the device to extract the feature map (Feature Map).

[0082] Activation layer: The commonly used ReLU (Rectified Linear Unit) activation function is introduced to perform non-linear transformation to improve the fitting ability of the model.

[0083] Pooling layer: The pooling operation is used for downsampling to reduce the size and computational complexity of the feature map while retaining the key information.

[0084] Fully connected layer: Classify or regress the extracted features through the fully connected layer to output the predicted value of the operating efficiency of the device.

[0085] Data formatting and input, assuming the input data is x with a shape of m×n, where m is the number of data samples and n is the dimension of the features. For the convolutional neural network, here the data is organized into a three-dimensional matrix, and the dimension of each input sample is h×w×c (where h and w are the height and width of the feature map, and c is the number of input channels). If it is time series data, it can be transformed into the form of a time window or a sliding window.

[0086] Forward propagation of the model, the convolutional neural network extracts data features through a series of convolutional operations and activation functions. Assuming the input data is X and the convolution kernel used in the convolutional layer is W, the output of the convolution operation is: Z = X * W + b

[0087] where, * represents the convolution operation, b is the bias term, and Z is the output of the convolutional layer. Perform non-linear transformation through the activation function f (such as ReLU): A = f(Z)

[0088] The pooling layer is usually used to reduce the computational complexity and reduce overfitting. Its operation can be max pooling (MaxPooling) or average pooling (Average Pooling). For the max pooling operation, assuming the input feature map A is of size h×w, the output after the pooling operation is:

[0089] P = MaxPool(A)

[0090] This operation will select the maximum value in each window to reduce the size of the feature map.

[0091] After convolution and pooling operations, the network passes these feature vectors to the fully connected layer for prediction. The fully connected layer maps the high-dimensional features to the target output space, such as predicting the operating efficiency of electrical equipment. Suppose F is the input feature of the fully connected layer, W f is the weight matrix, and b f is the bias term, then:

[0092] Y = FW f + b f

[0093] where Y is the predicted value of the output layer, representing the operating efficiency of the electrical equipment.

[0094] A loss function is used to measure the gap between the predicted value and the actual value. For regression problems, the commonly used loss function is the mean squared error (MSE):

[0095]

[0096] where y i is the actual value, is the predicted value of the model, and N is the number of samples. An optimization algorithm (such as gradient descent or Adam optimizer) is used to minimize the loss function, thereby adjusting the weights W and bias b of the network. The update process is as follows:

[0097]

[0098] where η is the learning rate, and are the gradients of the loss function with respect to the weights and bias.

[0099] The summary of the training process includes:

[0100] Data preparation: Collect and preprocess the operating data of electrical equipment.

[0101] Model design: Build a CNN model, including convolutional layers, pooling layers, and fully connected layers.

[0102] Forward propagation: Input the data into the model, extract features through convolution operations and pooling operations, and finally predict the equipment operating efficiency through the fully connected layer.

[0103] Loss function calculation and optimization: Calculate the prediction error through the loss function and use the optimization algorithm to adjust the model parameters.

[0104] Training and evaluation: Use the training data for multiple iterative trainings and evaluate the performance of the model using the validation set.

[0105] The trained model can monitor the operating efficiency of the device in real time, predict the efficiency fluctuations of the device based on the input real-time data, and provide support for the operation and maintenance decisions of substations or other electrical facilities, such as adjusting the load and optimizing the device operation strategy.

[0106] By training and managing the efficiency fluctuations of electrical equipment through a convolutional neural network (CNN), effective features can be automatically extracted from multi-dimensional input data, and the efficiency changes of the equipment can be accurately predicted. Through the optimization of the loss function during the training process, the CNN can gradually improve the prediction accuracy and provide effective support for the management of electrical equipment.

[0107] Establish a correspondence between the power consumption data of different regions and the actual power consumption data of different regions; specifically, through ReLU function calculation and model training on the carbon emission-related information of the substation, the correspondence between the power consumption data of different regions and the actual power consumption data of different regions is obtained. Through graph convolutional neural network models such as classification and regression on the carbon emission-related information of the substation, the statistical deviation between the power consumption data of different regions and the actual power consumption data of different regions is calculated.

[0108] Among them, the input of the meteorological condition electrical equipment operation efficiency fluctuation management algorithm is the types of electrical equipment operation efficiency information in different climates and environments within the statistical area, and the output is the management emission factor of the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions; the input of the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions is the emission source type, greenhouse gas concentration, and the determined management emission factor, and the output is the power consumption data of different regions in the urban power supply area.

[0109] In this step, by sorting and processing the types of electrical equipment operation efficiency information of different user groups and the power consumption data of different regions in the urban power supply area, complete input information can be obtained, enabling the electrical equipment operation efficiency fluctuation management algorithm to more accurately judge the state of the equipment and the power consumption data of different regions; using the prepared ReLU function calculation data to perform ReLU function calculation on the selected graph convolutional neural network model and adjusting the model parameters can improve the accuracy and performance of the model by continuously optimizing the model parameters and algorithm selection; by outputting the management emission factor, the importance of each feature for judging the power consumption data of different regions can be solved, so as to better understand the relationship between the electrical equipment operation efficiency fluctuation and the power consumption data of different regions.

[0110] In summary, the electrical equipment operation efficiency fluctuation management algorithm can more accurately evaluate the state of the urban power supply area and the power consumption data of different regions, providing effective decision-making support for decision-makers and guidance for excluding changes in the household population of different regions.

[0111] A4. Collect the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area of the urban power supply area per unit time, and input them into the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions to obtain the management emission factors of the management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions;

[0112] In this step, before data input, the data is preprocessed, including data desensitization, sorting, normalization, etc., to ensure the quality and consistency of the data input into the model;

[0113] Take the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area collected per unit time as input, and transfer them to the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions for spatio-temporal convolution and state evaluation, providing management emission factors for the subsequent management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions; The management emission factors reflect the current state and performance of the urban power supply area. Through the management emission factors, the operating conditions of the urban power supply area can be initially judged and evaluated;

[0114] In this step, through preprocessing steps such as data desensitization, sorting, and normalization, the quality and consistency of the data input into the model can be ensured, abnormal values, noise, and inconsistent sampling rates in the data can be excluded, and the accuracy and reliability of the monitoring algorithm can be improved;

[0115] The management emission factors obtained through the management algorithm for the fluctuation of the operating efficiency of electrical equipment under meteorological conditions can reflect the current state and performance of the urban power supply area. These management emission factors can be used for the initial judgment and evaluation of the urban power supply area, providing important input for the subsequent management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions;

[0116] By continuously monitoring, managing, and evaluating the state of the urban power supply area, potential changes or abnormal situations in the household registration population in different regions can be detected in a timely manner, so as to take maintenance measures in advance to avoid the impact of changes in the household registration population in different regions on the supply in different climates and environments. By analyzing and evaluating the state of the urban power supply area per unit time, the operating strategy of electrical equipment can be optimized, and the operating efficiency and energy utilization efficiency of the urban power supply area can be improved.

[0117] A5. Compare the management emission factors of the management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions with the preset types of emission factors: When the types of management emission factors are less than the preset types of emission factors, there is no need to set the emission source type and greenhouse gas concentration, and continue to collect and analyze the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area;

[0118] A6. When the types of management emission factors are equal to or greater than the preset types of emission factors, set the emission source type and greenhouse gas concentration, and input the emission source type, greenhouse gas concentration, and management emission factors into the management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions to obtain the power consumption data of different regions in the urban power supply area.

[0119] When the types of management emission factors are less than the preset types of emission factors, there is no need to set the emission source type and greenhouse gas concentration, and continue to collect and analyze the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area;

[0120] When the types of management emission factors are equal to or greater than the preset types of emission factors, set the emission source type and greenhouse gas concentration, and input the emission source type, greenhouse gas concentration, and management emission factors into the management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions to obtain the power consumption data of different regions in the urban power supply area.

[0121] The preset types of emission factors refer to a fixed value or range used to determine whether it is necessary to set the emission source type and greenhouse gas concentration. The preset types of emission factors are preset based on the following factors: energy consumption type, technology and management measures, standardization and local policy differences, data accuracy and availability, and equipment production and transportation emission cycles.

[0122] Furthermore, the presetting of the preset types of emission factors is a dynamic process that needs to be adjusted and optimized according to the actual situation. By continuously observing and analyzing the system operation status, the change data of the household register population in different regions, and the maintenance records, it is necessary to continuously optimize the preset types of emission factors to improve the accuracy and reliability of status monitoring management.

[0123] When the types of management emission factors are less than the preset types of emission factors, it means that the assessment result of the meteorological condition status in the urban power supply area indicates that it is in a normal state. In this case, there is no need to set the emission source type and greenhouse gas concentration, and the collection and analysis of the types of operating efficiency information of the initial electrical equipment can continue to help monitor and collect the basic operation status of the urban power supply area;

[0124] If the management emission factor is equal to or greater than the preset types of emission factors, it indicates that the assessment result of the meteorological condition status in the urban power supply area indicates the existence of potential changes or abnormal situations in the household register population in different regions. In this case, it is necessary to set the emission source type and greenhouse gas concentration, and input them together with the meteorological condition management emission factors into the management algorithm for the fluctuation of the operating efficiency of electrical equipment in different regions and emission source distributions to obtain the power consumption data of different regions in the urban power supply area;

[0125] The electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions will comprehensively consider the types of electrical equipment operation efficiency information, emission source types, greenhouse gas concentrations, and preset management emission factors in different climates and environments within the statistical region, and use algorithms such as convolutional neural networks to perform ReLU function calculations and analyses, so as to determine the power consumption data of different regions in the urban power supply area;

[0126] By comparing the management emission factors with the preset emission factor types, it is possible to quickly determine whether the urban power supply area is in a normal state, and thus decide whether to set the emission source type and greenhouse gas concentration. If the management emission factor is less than the preset emission factor types, it is possible to avoid additional data collection and analysis, saving time and resources;

[0127] When the management emission factor is equal to or greater than the preset emission factor types, set the emission source type and greenhouse gas concentration, and input them together with the management emission factor into the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions, which can provide an accurate judgment of the power consumption data of different regions. By comprehensively considering multiple data information and preset parameters, and using algorithms such as convolutional neural networks to perform ReLU function calculations and analyses, it is possible to accurately determine the power consumption data of different regions in the urban power supply area;

[0128] The determination of the preset emission factor types is based on multiple factors. By comprehensively considering these factors, the preset emission factor types suitable for the specific urban power supply area are formulated to improve the accuracy and reliability of state monitoring management; by observing and analyzing the system operation status, the changes in the household register population data of different regions, and the maintenance records, the preset emission factor types can be continuously optimized to adapt to the actual operation situation of the urban power supply area and improve the accuracy and reliability of the monitoring algorithm.

[0129] As Figure 2 shown, a carbon emission monitoring and management system for a substation according to the present invention is implemented through different control modules, including;

[0130] A carbon emission related information collection module for the substation, which is used to collect carbon emission related information of the substation in the urban power supply area. The carbon emission related information of the substation includes the power consumption data of different electricity consumption groups in different urban power supply areas and the electrical equipment operation efficiency information of different electricity consumption groups in the urban power supply area within a preset time before the change in the household register population of different regions;

[0131] Among them, the electrical equipment operation efficiency information of different user groups in the urban power supply area includes the load rate, power factor, equipment loss, operation time, equipment health status, load change, and power quality during the operation of the urban power supply area; the load rate and power factor are collected per unit time, and the equipment loss, operation time, equipment health status, load change, and power quality are collected according to the preset urban area distribution;

[0132] The power information monitoring module is used to receive the power consumption data of different user groups in different regions and the electrical equipment operation efficiency information of different user groups. Based on the statistical situation of the electrical equipment operation efficiency information, it classifies the electrical equipment operation efficiency information of different user groups in the urban power supply area to obtain the types of electrical equipment operation efficiency information, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area; the types of electrical equipment operation efficiency information in different climates and environments within the statistical area include the load rate and power factor, and the emission source types and greenhouse gas concentrations include equipment loss, operation time, equipment health status, load change, and power quality;

[0133] The graph convolutional neural network model module is used to receive the types of electrical equipment operation efficiency information, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area and upload them to the graph convolutional neural network model to establish a management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions and a management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions;

[0134] Among them, the input of the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions is the types of electrical equipment operation efficiency information in different climates and environments within the statistical area, and the output is the management emission factor of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions; the input of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions is the emission source type, greenhouse gas concentration, and the determined management emission factor, and the output is the power consumption data of different regions in the urban power supply area;

[0135] The emission factor management module is used to receive the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions and the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions; it collects the types of electrical equipment operation efficiency information in different climates and environments within the statistical area per unit time and inputs them into the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions to obtain the management emission factor of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions;

[0136] The analysis module for the change of household registered population in different regions is used to receive the management emission factor of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions, and compare the management emission factor of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions with the preset emission factor types:

[0137] When the number of emission factor types managed is less than the preset number of emission factor types, there is no need to set the emission source type and greenhouse gas concentration, and the collection and analysis of the types of operating efficiency information of electrical equipment in different climates and environments within the statistical area are continued;

[0138] When the number of emission factor types managed is equal to or greater than the preset number of emission factor types, the emission source type and greenhouse gas concentration are set, and the emission source type, greenhouse gas concentration, and managed emission factors are input into the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions to obtain the different regional power consumption data of the urban power supply area.

[0139] The system collects the different regional power consumption data of different user groups and the operating efficiency information of electrical equipment of different user groups in the urban power supply area, and performs information monitoring and a graph convolutional neural network model, so as to realize the comprehensive monitoring and management of the state of the urban power supply area. Compared with the traditional method that only monitors and manages the load rate and power factor parameters, the system considers more operating efficiency information of electrical equipment and provides a more comprehensive state assessment;

[0140] The system uses a method that combines the meteorological condition electrical equipment operating efficiency fluctuation management algorithm and the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions to evaluate the state of the urban power supply area. The meteorological condition electrical equipment operating efficiency fluctuation management algorithm evaluates by collecting the operating efficiency information of electrical equipment in different climates and environments within the statistical area per unit time, which is more timely and rapid; while the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions evaluates by comprehensively considering the different regional power consumption data of different user groups and the operating efficiency information of electrical equipment of different user groups, which is more accurate and reliable;

[0141] By comparing the managed emission factors of the electrical equipment operating efficiency fluctuation management algorithm for different regions and emission source distributions with the preset number of emission factor types, the different regional power consumption data of the urban power supply area can be determined. Since the system comprehensively considers the different regional power consumption data of different user groups and the operating efficiency information of electrical equipment of different user groups, and uses a hierarchical evaluation method, it can improve the judgment accuracy of different regional power consumption data and reduce the possibility of misjudgment and missed judgment;

[0142] In summary, this method uses a variety of electrical equipment operating efficiency information and convolutional neural network technology to realize the monitoring and management of the state of large urban power supply areas with different climates and environments and the adjustment of changes in the household population in different regions, ensuring seasonal safety and accurate monitoring of the operating efficiency information of electrical equipment in the urban power supply area per unit time.

[0143] The various variations and specific embodiments of the state monitoring and management method for large-scale power supply areas in different climates and environments in the foregoing Embodiment 1 are equally applicable to the state monitoring and management system for large-scale power supply areas in different climates and environments in this embodiment. Through the foregoing detailed description of the state monitoring and management method for large-scale power supply areas in different climates and environments, those skilled in the art can clearly know the implementation method of the state monitoring and management system for large-scale power supply areas in different climates and environments in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be described in detail herein.

[0144] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various equivalent changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalent scope.

Claims

1. A method for carbon emission monitoring and management in a substation, characterized in that, It includes the following steps: A1. Collect the carbon emission-related information of the substations in the urban power supply area. The carbon emission-related information of the substations includes the power consumption data of different electricity-consuming groups in different urban power supply areas and the electrical equipment operation efficiency information of different electricity-consuming groups in the urban power supply area within a preset time before the change of the household population in different regions; A2. Classify the electrical equipment operation efficiency information of different electricity-consuming groups in the urban power supply area according to the statistical situation of the electrical equipment operation efficiency information, and obtain the types of electrical equipment operation efficiency information, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical area; A3. Upload the types of electrical equipment operation efficiency information, emission source types, greenhouse gas concentrations in different climates and environments within the statistical area and the corresponding power consumption data of different electricity-consuming groups in different regions of the urban power supply area to the graph convolutional neural network model, and establish the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions and the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions; A4. Collect the types of electrical equipment operation efficiency information in different climates and environments within the statistical area of the urban power supply area per unit time and input them into the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions to obtain the management emission factors of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions; A5. Compare the management emission factors of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions with the preset emission factor types: when the types of management emission factors are less than the preset emission factor types, there is no need to set the emission source types and greenhouse gas concentrations, and continue to collect and analyze the types of electrical equipment operation efficiency information in different climates and environments within the statistical area; A6. When the types of management emission factors are equal to or greater than the preset emission factor types, set the emission source types and greenhouse gas concentrations, and input the emission source types, greenhouse gas concentrations, and management emission factors into the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions to obtain the power consumption data of different regions in the urban power supply area.

2. The carbon emission monitoring and management method for a substation according to claim 1, wherein The electrical equipment operation efficiency information of different electricity-consuming groups in the urban power supply area includes: load rate, power factor, equipment loss, operation time, equipment health status, load change, and power quality during the operation of the urban power supply area; the load rate and power factor are collected per unit time, and the equipment loss, operation time, equipment health status, load change, and power quality are collected according to the preset urban area distribution.

3. A carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The types of electrical equipment operation efficiency information in different climates and environments within the statistical area include load rate and power factor, and the emission source types and greenhouse gas concentrations include equipment loss, operation time, equipment health status, load change, and power quality.

4. A carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The input of the management algorithm for the fluctuation of electrical equipment operation efficiency with different regional and emission source distributions is the emission source type, greenhouse gas concentration, and determined management emission factor, and the output is the power consumption data of different regions in the urban power supply area.

5. A carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The steps for establishing the management algorithm for the fluctuation of electrical equipment operation efficiency under meteorological conditions are: According to the carbon emission related information of the substation, sort out the types of electrical equipment operation efficiency information of different electricity consumption groups in the urban power supply area and the corresponding power consumption data of different regions; process and perform spatio-temporal convolution on the types of electrical equipment operation efficiency information in different climates and environments within the statistical area, convert the original data into practical features for use by the graph convolutional neural network model; Use the graph convolutional neural network model to calculate data according to the prepared ReLU function, perform ReLU function calculation on the model using the selected algorithm, and adjust the parameters of the model; after the ReLU function calculation is completed, extract the relevant parameters of the model as the output of the meteorological condition electrical equipment operation efficiency fluctuation management algorithm.

6. The carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The establishment steps of the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions are as follows: According to the carbon emission related information of the substation, sort out the emission source types, greenhouse gas concentrations in the urban power supply area and the corresponding power consumption data of different regions; Use the convolutional neural network model for training, take the emission source type and greenhouse gas concentration as independent variables, and the power consumption data of different regions as nodes to perform graph structure modeling of the data, and perform ReLU function calculation on the model; After the ReLU function calculation is completed, use the electrical equipment operation efficiency fluctuation management algorithm for different regions and emission source distributions to predict the power consumption data of different regions in the urban power supply area, perform a convolutional neural network on the carbon emission related information of the substation, and calculate the statistical deviation between the power consumption data of different regions and the actual power consumption data of different regions.

7. A carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The data collection method for the emission source type and greenhouse gas concentration includes: using a temperature monitoring device to monitor and collect the equipment loss situation in the urban power supply area; monitoring and collecting the operation time in the urban power supply area based on the power equipment monitoring platform; monitoring and collecting the power quality in the urban power supply area, and analyzing the peak value and fluctuation range of the change in equipment loss; using a remote sensing system to monitor and collect the equipment health status signal generated in the urban power supply area; using the household registration management system to collect the load changes around the urban power supply area and count the impacts generated by different load changes.

8. A carbon emission monitoring and management method for a substation according to claim 1, characterized in that, The preset influencing reasons for the preset emission factor types include energy consumption type, technology and management measures, standardization and local policy differences, data accuracy and availability, and equipment production and transportation emission cycles.

9. A carbon emission monitoring and management system for a substation, characterized in that, Include: A carbon emission related information collection module for the substation, which is used to collect the carbon emission related information of the substation in the urban power supply area. The carbon emission related information of the substation includes the power consumption data of different electricity consumption groups in different urban power supply areas and the electrical equipment operation efficiency information of different electricity consumption groups in the urban power supply area within a preset time before the change in the household population of different regions; The power information monitoring module is used to receive the power consumption data of different power consumption groups in different regions and the operation efficiency information of electrical equipment of different power consumption groups. Based on the statistical situation of the operation efficiency information of electrical equipment, it classifies the operation efficiency information of electrical equipment of different power consumption groups in different urban power supply regions, and obtains the types of operation efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical region; the types of operation efficiency information of electrical equipment in different climates and environments within the statistical region include load factor and power factor, and the emission source types and greenhouse gas concentrations include equipment losses, operation time, equipment health status, load changes, and power quality; The graph convolutional neural network model module is used to receive the types of operation efficiency information of electrical equipment, emission source types, and greenhouse gas concentrations in different climates and environments within the statistical region and upload them to the graph convolutional neural network model, and establish an operation efficiency fluctuation management algorithm for electrical equipment under meteorological conditions and an operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions; among them, the input of the operation efficiency fluctuation management algorithm for electrical equipment under meteorological conditions is the types of operation efficiency information of electrical equipment in different climates and environments within the statistical region, and the output is the management emission factor of the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions; the input of the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions is the emission source type, greenhouse gas concentration, and the determined management emission factor, and the output is the power consumption data of different regions in the urban power supply region; The emission factor management module is used to receive the operation efficiency fluctuation management algorithm for electrical equipment under meteorological conditions and the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions; it collects the types of operation efficiency information of electrical equipment in different climates and environments within the statistical region per unit time and inputs them into the operation efficiency fluctuation management algorithm for electrical equipment under meteorological conditions to obtain the management emission factor of the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions; The analysis module for changes in the household population in different regions is used to receive the management emission factor of the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions, and compare the management emission factor of the operation efficiency fluctuation management algorithm for electrical equipment with different regional and emission source distributions with the preset emission factor types.

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