Risk handling methods, devices, computer equipment, and storage media based on theoretical line loss calculation for low-voltage distribution areas.

Abstract

This application relates to a risk processing method, apparatus, computer equipment, and storage medium based on theoretical line loss calculation for low-voltage distribution areas. The method includes: inputting meter data from meters in the low-voltage distribution area to be analyzed into a trained meter data analysis model to obtain the initial meter error of the meters and the initial line loss rate of the low-voltage distribution area; adjusting the meter data based on the initial meter error to obtain adjusted meter data; iteratively updating the initial line loss rate based on the adjusted meter data to obtain the current line loss rate of the low-voltage distribution area; inputting the current line loss rate into a trained distribution area risk level prediction model to obtain the current risk level of the low-voltage distribution area; and performing corresponding risk processing on the low-voltage distribution area according to the current risk level. Using this method can improve the accuracy of risk processing for low-voltage distribution areas.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a risk management method, apparatus, computer equipment, computer-readable storage medium, and computer program product based on theoretical line loss calculation for low-voltage distribution areas. Background Technology

[0002] Currently, in order to ensure the power supply safety of low-voltage distribution areas, it is crucial to accurately handle the risks in low-voltage distribution areas.

[0003] In traditional technology, when handling risks in low-voltage distribution areas, the line loss rate of the low-voltage distribution area is generally calculated by using the readings of the sub-meters in the low-voltage distribution area. However, most of the readings of the sub-meters in the low-voltage distribution area have errors (such as the error of fault meters reaching 10%), which can easily lead to a low accuracy rate in determining the line loss results, and thus a low accuracy in risk handling of the low-voltage distribution area. Summary of the Invention

[0004] Therefore, it is necessary to provide a risk handling method, apparatus, computer equipment, computer-readable storage medium, and computer program product based on theoretical line loss calculation of low-voltage distribution areas, which can improve the accuracy of risk handling in low-voltage distribution areas.

[0005] Firstly, this application provides a risk management method based on theoretical line loss calculation for low-voltage distribution areas, including:

[0006] In response to a risk handling request for the low-voltage distribution area to be analyzed, the meter data of the meter to be analyzed in the low-voltage distribution area to be analyzed is obtained;

[0007] The meter data is input into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0008] Based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meter to be analyzed.

[0009] Based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed;

[0010] The current line loss rate is input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0011] Based on the current risk level of the distribution area, a risk handling instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk handling is performed on the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0012] In one embodiment, the step of iteratively updating the initial line loss rate based on the adjusted meter data to obtain the current line loss rate of the low-voltage distribution area to be analyzed includes:

[0013] The adjusted meter data is input into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed.

[0014] The line loss rate difference of the low-voltage distribution area to be analyzed is determined based on the initial line loss rate and the adjusted line loss rate.

[0015] Based on the adjusted meter error, the adjusted meter data of the meter to be analyzed is adjusted again to obtain the processed meter data of the meter to be analyzed.

[0016] The adjusted line loss rate is used as the new initial line loss rate, and the processed meter data of the meter to be analyzed is used as the new adjusted meter data. Then, the process jumps to the step of inputting the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed. This process continues until the difference between the adjusted meter error and the line loss rate both meet the preset conditions. Finally, the last adjusted line loss rate is used as the current line loss rate of the low-voltage distribution area to be analyzed.

[0017] In one embodiment, the step of readjusting the adjusted meter data of the meter to be analyzed based on the adjusted meter error to obtain the processed meter data of the meter to be analyzed includes:

[0018] The maximum meter error among the adjusted meter errors is determined, and the target meter corresponding to the maximum meter error is determined from the meter to be analyzed;

[0019] Based on the maximum meter error, the adjusted meter data of the target meter is adjusted again to obtain the processed meter data of the target meter.

[0020] Based on the processed meter data of the target meter, the processed meter data of the meter to be analyzed is obtained.

[0021] In one embodiment, the step of inputting the current line loss rate into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed includes:

[0022] Obtain the historical line loss rate corresponding to the current line loss rate;

[0023] Determine the first time interval corresponding to the current line loss rate and the second time interval corresponding to the historical line loss rate;

[0024] Obtain the first power supply of the low-voltage distribution area to be analyzed within the first time interval, and the second power supply of the low-voltage distribution area to be analyzed within the second time interval;

[0025] Based on the current line loss rate, the historical line loss rate, the first power supply, and the second power supply, the target line loss rate corresponding to the low-voltage distribution area to be analyzed is determined;

[0026] The target line loss rate, the current line loss rate, the first power supply, the historical line loss rate, and the second power supply are input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0027] In one embodiment, the step of inputting the target line loss rate, the current line loss rate, the first power supply, the historical line loss rate, and the second power supply into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed includes:

[0028] The current line loss rate and the first power supply are fused to obtain the first line loss amount of the low-voltage distribution area to be analyzed, and the historical line loss rate and the second power supply are fused to obtain the second line loss amount of the low-voltage distribution area to be analyzed.

[0029] The first line loss power and the second line loss power are fused to obtain the target line loss power of the low-voltage distribution area to be analyzed.

[0030] Using the target line loss rate as the primary data and the target line loss power as the auxiliary data, the data are input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0031] In one embodiment, the trained meter data analysis model is obtained by training in the following manner:

[0032] Obtain sample meter data from the sample low-voltage distribution area;

[0033] The sample meter data is input into the meter data analysis model to be trained to obtain the predicted meter error of the sample meter and the predicted line loss rate of the sample low-voltage distribution area.

[0034] Obtain the actual metering error of the sample electricity meters and the actual line loss rate of the sample low-voltage distribution area;

[0035] A first loss value is obtained based on the difference between the predicted meter error and the actual meter error, and a second loss value is obtained based on the difference between the predicted line loss rate and the actual line loss rate.

[0036] The first loss value and the second loss value are fused together to obtain the target loss value;

[0037] Based on the target loss value, the meter data analysis model to be trained is iteratively trained to obtain the trained meter data analysis model.

[0038] Secondly, this application also provides a risk management device based on theoretical line loss calculation for low-voltage distribution areas, comprising:

[0039] The data acquisition module is used to acquire the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed in response to a risk handling request for the low-voltage distribution area to be analyzed.

[0040] The data analysis module is used to input the meter data into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0041] The data adjustment module is used to adjust the meter data according to the initial meter error to obtain the adjusted meter data of the meter to be analyzed.

[0042] The data update module is used to iteratively update the initial line loss rate based on the adjusted meter data to obtain the current line loss rate of the low-voltage distribution area to be analyzed.

[0043] The risk level prediction module is used to input the current line loss rate into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0044] The risk processing module is used to generate a risk processing instruction corresponding to the low-voltage distribution area to be analyzed based on the current risk level of the distribution area, and to perform corresponding risk processing on the low-voltage distribution area to be analyzed according to the risk processing instruction.

[0045] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0046] In response to a risk handling request for the low-voltage distribution area to be analyzed, the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed is obtained;

[0047] The meter data is input into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0048] Based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meter to be analyzed.

[0049] Based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed;

[0050] The current line loss rate is input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0051] Based on the current risk level of the distribution area, a risk handling instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk handling is performed on the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0052] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0053] In response to a risk handling request for the low-voltage distribution area to be analyzed, the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed is obtained;

[0054] The meter data is input into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0055] Based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meter to be analyzed.

[0056] Based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed;

[0057] The current line loss rate is input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0058] Based on the current risk level of the distribution area, a risk handling instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk handling is performed on the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0059] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0060] In response to a risk handling request for the low-voltage distribution area to be analyzed, the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed is obtained;

[0061] The meter data is input into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0062] Based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meter to be analyzed.

[0063] Based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed;

[0064] The current line loss rate is input into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0065] Based on the current risk level of the distribution area, a risk handling instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk handling is performed on the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0066] The aforementioned risk processing method, apparatus, computer equipment, storage medium, and computer program product based on theoretical line loss calculation for low-voltage distribution areas first respond to a risk processing request for the low-voltage distribution area to be analyzed by acquiring the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed, and inputting the meter data into a trained meter data analysis model to obtain the initial meter error of the meters to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed. Then, based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meters to be analyzed. Next, based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed. Then, the current line loss rate is input into a trained distribution area risk level prediction model to obtain the current distribution area risk level of the low-voltage distribution area to be analyzed. Finally, based on the current distribution area risk level, a risk processing instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk processing is performed on the low-voltage distribution area to be analyzed according to the risk processing instruction. In this way, when handling risks in low-voltage distribution areas, the initial meter error and initial line loss rate are accurately extracted from the meter data using a meter data analysis model, avoiding the deviations of traditional manual calculations. Then, the meter data is iteratively adjusted and the line loss rate is updated based on the error, gradually eliminating the interference of meter error on core data. This ensures that the current line loss rate matches the actual operating conditions of the distribution area, which helps improve the accuracy of determining the line loss result. Combined with the distribution area risk level prediction model, the current risk level of the low-voltage distribution area can be predicted more accurately. Based on the current risk level, risk handling of the low-voltage distribution area can be carried out more accurately, further improving the accuracy of risk handling in low-voltage distribution areas. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart illustrating a risk handling method based on theoretical line loss calculation for low-voltage distribution areas in one embodiment.

[0069] Figure 2 This is a flowchart illustrating the steps for obtaining the current line loss rate of the low-voltage distribution area to be analyzed in one embodiment.

[0070] Figure 3 This is a flowchart illustrating a risk handling method based on theoretical line loss calculation for low-voltage distribution areas, as described in another embodiment.

[0071] Figure 4This is a structural block diagram of a risk handling device based on theoretical line loss calculation for low-voltage distribution areas in one embodiment.

[0072] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0075] In one exemplary embodiment, such as Figure 1 As shown, a risk management method based on theoretical line loss calculation for low-voltage distribution areas is provided. This embodiment illustrates the application of this method to a server; it is understood that this method can also be applied to terminals, and can also be applied to systems including terminals and servers, and implemented through interaction between the terminals and servers. The terminals can be, but are not limited to, various personal computers, laptops, smartphones, and tablets; the servers can be independent servers or server clusters composed of multiple servers. In this embodiment, the method includes the following steps:

[0076] Step S101: In response to a risk handling request for the low-voltage distribution area to be analyzed, obtain the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed.

[0077] Among them, the low-voltage distribution area to be analyzed refers to the low-voltage power supply area that needs to be risk-handled. It is usually centered around a distribution transformer and covers all users within its power supply range. It is the smallest power supply unit in the power system that directly faces low-voltage power users such as residents and micro and small enterprises (such as the low-voltage power supply area of ​​a community or a street).

[0078] Among them, the risk processing request refers to the request to process the risk of the low-voltage distribution area to be analyzed.

[0079] The meters to be analyzed refer to all meters within the low-voltage distribution area to be analyzed, including the main meter (also called the master meter) at the transformer outlet of the low-voltage distribution area and the individual meters (also called individual meters) at each user end. The master meter is used to record the total power supply of the entire low-voltage distribution area to be analyzed, while the individual meters are used to record the power consumption of individual users within the low-voltage distribution area to be analyzed.

[0080] Among them, the electricity meter data refers to the cumulative electricity consumption (such as the meter value, also known as the reading) of the electricity meter to be analyzed at the start and end times within the current time interval (such as "June 2025").

[0081] For example, the server receives a risk handling request for the low-voltage distribution area to be analyzed from the terminal via the network path of the terminal corresponding to the low-voltage distribution area to be analyzed, and performs a reasonableness verification on the risk handling request to obtain the verification result of the risk handling request; if the verification result indicates that the risk handling request has passed the verification, the server responds to the risk handling request by determining the main electricity meter at the transformer outlet and the sub-meters at each user terminal in the low-voltage distribution area to be analyzed based on the distribution area identifier (such as the distribution area code) carried in the risk handling request, and uses them as the electricity meters to be analyzed in the low-voltage distribution area to be analyzed; then, the server remotely obtains the electricity meter data of the electricity meters to be analyzed within a preset time interval (such as "June 2025") through the power user electricity consumption information collection system (such as a concentrator or collection terminal).

[0082] Step S102: Input the meter data into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0083] Among them, the meter data analysis model refers to a network model that can use the meter data of the meters to be analyzed in the low-voltage distribution area to obtain the initial meter error of the meters to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed, such as a recurrent neural network model.

[0084] The initial meter error refers to the meter error of the meter to be analyzed output by the model after the meter data is input into the trained meter data analysis model.

[0085] Meter error is used to represent the deviation between the measured value and the actual value of the meter being analyzed.

[0086] The initial line loss rate refers to the line loss rate of the low-voltage distribution area to be analyzed, which is output by the model after the meter data is input into the trained meter data analysis model.

[0087] Among them, the line loss rate is used to represent the ratio of the difference between the power supply (i.e. the total electricity consumption recorded by the main meter) and the electricity sales (i.e. the total electricity consumption recorded by all sub-meters) of the low-voltage distribution area to the power supply (i.e., the power loss).

[0088] For example, the server performs format conversion processing on the meter data (e.g., converting it into a structured format recognizable by the model, such as a two-dimensional array or feature matrix) to obtain processed meter data that meets the preset data format; then, the server inputs the processed meter data into a feature extraction model for feature extraction processing to obtain the feature vector of the processed meter data; then, the server inputs the feature vector of the processed meter data into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0089] Step S103: Adjust the meter data according to the initial meter error to obtain the adjusted meter data of the meter to be analyzed.

[0090] The adjusted meter data refers to the meter data that has been adjusted based on the initial meter error.

[0091] For example, the server determines the adjustment coefficient of the meter data (adjustment coefficient = 1 ÷ (1 + initial meter error)) based on the initial meter error and the meter metering principle, and adjusts the meter data according to the adjustment coefficient to obtain the adjusted meter data of the meter to be analyzed.

[0092] Step S104: Based on the adjusted meter data, iteratively update the initial line loss rate to obtain the current line loss rate of the low-voltage distribution area to be analyzed.

[0093] The current line loss rate refers to the line loss rate of the low-voltage distribution area to be analyzed within a preset time interval (e.g., "June 2025").

[0094] For example, the server updates the initial line loss rate based on the adjusted meter data to obtain the updated initial line loss rate; then, the server updates the updated initial line loss rate again until the updated initial line loss rate meets the preset update conditions, and then the last updated initial line loss rate is used as the current line loss rate of the low-voltage distribution area to be analyzed.

[0095] Step S105: Input the current line loss rate into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0096] Among them, the transformer area risk level prediction model refers to a network model that can obtain the current risk level of the low-voltage transformer area to be analyzed by using the current line loss rate of the low-voltage transformer area to be analyzed, such as a convolutional neural network model.

[0097] The current risk level of the low-voltage distribution area refers to the risk level of the low-voltage distribution area to be analyzed within the current time period (e.g., "June 2025"), including high risk, medium risk, and low risk.

[0098] For example, the server obtains the sample line loss rate of the sample low-voltage transformer area and inputs it into the transformer area risk level prediction model to be trained, thereby obtaining the predicted transformer area risk level of the sample low-voltage transformer area; then, the server obtains the actual transformer area risk level of the sample low-voltage transformer area and obtains the loss value based on the difference between the predicted and actual transformer area risk levels; then, the server adjusts the model parameters of the transformer area risk level prediction model to be trained based on the loss value, thereby obtaining the transformer area risk level prediction model with adjusted model parameters; finally, the server adjusts the transformer area risk level prediction model with adjusted model parameters. The risk level prediction model is retrained until the loss value obtained by the trained transformer area risk level prediction model is less than the loss value threshold. Then, the training stops, and the trained transformer area risk level prediction model is used as the completed transformer area risk level prediction model. Then, the server inputs the current line loss rate into the completed transformer area risk level prediction model to obtain the predicted probability of the low-voltage transformer area to be analyzed under each preset transformer area risk level. Next, the server selects the preset transformer area risk level with the highest predicted probability from each preset transformer area risk level and uses it as the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0099] Step S106: Based on the current risk level of the distribution area, generate the corresponding risk handling instruction for the low-voltage distribution area to be analyzed, and perform the corresponding risk handling for the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0100] Among them, the risk handling instruction refers to the instruction to handle the risk of the low-voltage distribution area to be analyzed, including information such as the risk handling priority, risk handling measures, and risk handling entity of the low-voltage distribution area to be analyzed.

[0101] For example, the server obtains the historical risk level of the low-voltage transformer area to be analyzed; then, based on the current risk level and the historical risk level, the server determines the trend of risk level change of the low-voltage transformer area to be analyzed (e.g., continuous increase, fluctuating decrease, sudden jump, etc.); then, the server uses the current risk level as the primary data and the trend of risk level change as the auxiliary data, and inputs it into the trained risk handling instruction prediction model to obtain the risk handling instruction corresponding to the low-voltage transformer area to be analyzed; then, the server performs the corresponding risk handling for the low-voltage transformer area to be analyzed according to the risk handling instruction.

[0102] In the aforementioned risk handling method based on theoretical line loss calculation for low-voltage distribution areas, the process first responds to a risk handling request for the low-voltage distribution area to be analyzed by acquiring the meter data of the meters to be analyzed in the low-voltage distribution area and inputting the meter data into a trained meter data analysis model to obtain the initial meter error of the meters to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed. Then, based on the initial meter error, the meter data is adjusted to obtain the adjusted meter data of the meters to be analyzed. Next, based on the adjusted meter data, the initial line loss rate is iteratively updated to obtain the current line loss rate of the low-voltage distribution area to be analyzed. Then, the current line loss rate is input into a trained distribution area risk level prediction model to obtain the current risk level of the low-voltage distribution area to be analyzed. Finally, based on the current risk level of the distribution area, a risk handling instruction corresponding to the low-voltage distribution area to be analyzed is generated, and the corresponding risk handling is performed on the low-voltage distribution area to be analyzed according to the risk handling instruction. In this way, when handling risks in low-voltage distribution areas, the initial meter error and initial line loss rate are accurately extracted from the meter data using a meter data analysis model, avoiding the deviations of traditional manual calculations. Then, the meter data is iteratively adjusted and the line loss rate is updated based on the error, gradually eliminating the interference of meter error on core data. This ensures that the current line loss rate matches the actual operating conditions of the distribution area, which helps improve the accuracy of determining the line loss result. Combined with the distribution area risk level prediction model, the current risk level of the low-voltage distribution area can be predicted more accurately. Based on the current risk level, risk handling of the low-voltage distribution area can be carried out more accurately, further improving the accuracy of risk handling in low-voltage distribution areas.

[0103] In one exemplary embodiment, such as Figure 2 As shown, step S104 above iteratively updates the initial line loss rate based on the adjusted meter data to obtain the current line loss rate of the low-voltage distribution area to be analyzed. This specifically includes the following steps:

[0104] Step S201: Input the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed.

[0105] Step S202: Determine the difference in line loss rate between the low-voltage distribution area to be analyzed based on the initial line loss rate and the adjusted line loss rate.

[0106] Step S203: Based on the adjusted meter error, the adjusted meter data of the meter to be analyzed is adjusted again to obtain the processed meter data of the meter to be analyzed.

[0107] Step S204: The adjusted line loss rate is used as the new initial line loss rate, and the processed meter data of the meter to be analyzed is used as the new adjusted meter data. Then, the process jumps to inputting the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed. This process continues until the difference between the adjusted meter error and the line loss rate meets the preset conditions. Finally, the last adjusted line loss rate is used as the current line loss rate of the low-voltage distribution area to be analyzed.

[0108] Among them, the adjusted meter error refers to the meter error of the meter to be analyzed output by the model after the adjusted meter data is input into the trained meter data analysis model.

[0109] The adjusted line loss rate refers to the line loss rate of the low-voltage distribution area to be analyzed, which is output by the model after the adjusted meter data is input into the trained meter data analysis model.

[0110] The line loss rate difference represents the absolute difference between the initial line loss rate and the adjusted line loss rate. It measures the magnitude of change in the line loss rate after data adjustment and reflects the effectiveness of iterative updates. For example, a difference of 3% indicates that the initial adjustment has a significant impact on reducing the line loss rate; if the difference drops to 0.2%, it indicates that the line loss rate has stabilized.

[0111] Among them, the processed meter data refers to the adjusted meter data after further adjustment based on the adjusted meter error.

[0112] Among them, the preset conditions refer to the threshold standards for determining whether the iterative update process should be terminated, including preset meter error and preset line loss rate difference.

[0113] For example, the server inputs the adjusted meter data into a feature extraction model for feature extraction processing to obtain the feature vector of the adjusted meter data. Then, the server inputs the feature vector of the adjusted meter data into a trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed. Next, the server subtracts the initial line loss rate from the adjusted line loss rate to obtain the line loss rate difference value of the low-voltage distribution area to be analyzed. Then, the server judges the difference between the adjusted meter error and the line loss rate. If the maximum meter error in the adjusted meter error is greater than the preset meter error, or if the maximum meter error in the adjusted meter error is less than or equal to the preset meter error, and the line loss rate difference is greater than the preset line loss rate difference value, then the server will determine the line loss rate difference value. In the case of line loss rate difference, the server adjusts the adjusted meter data of the meter to be analyzed again based on the adjusted meter error, and obtains the processed meter data of the meter to be analyzed. Then, the server uses the adjusted line loss rate as the new initial line loss rate, uses the processed meter data of the meter to be analyzed as the new adjusted meter data, and jumps to the step of inputting the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed. This process continues until the maximum meter error in the adjusted meter error is less than or equal to the preset meter error, and the line loss rate difference is less than or equal to the preset line loss rate difference. Then, the last adjusted line loss rate obtained is used as the current line loss rate of the low-voltage distribution area to be analyzed.

[0114] In this embodiment, by gradually eliminating meter reading deviations to ensure that the data closely reflects the actual electricity consumption scenario, and by using the stability of the line loss rate difference as a basis for judging optimization convergence, the final obtained current line loss rate has both accuracy and stability, effectively avoiding the possible deviations and omissions that may occur in a single correction, and greatly improving the reliability of low-voltage distribution area line loss assessment.

[0115] In an exemplary embodiment, step S203 above, which involves further adjusting the adjusted meter data of the meter to be analyzed based on the adjusted meter error, to obtain the processed meter data of the meter to be analyzed, specifically includes the following: determining the maximum meter error among the adjusted meter errors, and determining the target meter corresponding to the maximum meter error from the meter to be analyzed; further adjusting the adjusted meter data of the target meter based on the maximum meter error to obtain the processed meter data of the target meter; and obtaining the processed meter data of the meter to be analyzed based on the processed meter data of the target meter.

[0116] The maximum meter error refers to the error value with the largest absolute value among the adjusted meter errors. For example, if the adjusted meter errors for a certain distribution area are -3%, +1%, -7%, and +2%, then the maximum meter error is -7% (absolute value 7%), indicating that the meter corresponding to this error has the most prominent current metering deviation.

[0117] The target meter refers to the meter with the largest meter error among the meters to be analyzed.

[0118] For example, the server determines the error value with the largest absolute value among the adjusted meter errors as the maximum meter error, and identifies the target meter corresponding to the maximum meter error from the meters to be analyzed. Then, based on the maximum meter error, the server determines the adjustment coefficient corresponding to the adjusted meter data of the target meter, and adjusts the adjusted meter data of the target meter again according to the adjustment coefficient, to obtain the processed meter data of the target meter. Then, the server uses the processed meter data of the target meter and the adjusted meter data of the other meters in the meters to be analyzed as the processed meter data of the meter to be analyzed.

[0119] In this embodiment, by focusing on the largest meter error and the corresponding target meter among the adjusted meter errors, the meter data with the most significant deviation is corrected first. This avoids the waste of resources from repeatedly adjusting all meters and can quickly reduce the interference of the overall metering error on line loss calculation by solving the core deviation, which is conducive to improving the efficiency of iterative updates.

[0120] In an exemplary embodiment, step S105, which involves inputting the current line loss rate into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed, specifically includes the following: obtaining the historical line loss rate corresponding to the current line loss rate; determining the first time interval corresponding to the current line loss rate and the second time interval corresponding to the historical line loss rate; obtaining the first power supply of the low-voltage transformer area to be analyzed within the first time interval and the second power supply of the low-voltage transformer area to be analyzed within the second time interval; determining the target line loss rate corresponding to the low-voltage transformer area to be analyzed based on the current line loss rate, the historical line loss rate, the first power supply, and the second power supply; and inputting the target line loss rate, the current line loss rate, the first power supply, the historical line loss rate, and the second power supply into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0121] The historical line loss rate refers to the line loss rate of the low-voltage transformer area to be analyzed within a historical time interval (a past time interval of the same length as the time interval corresponding to the current line loss rate, such as "May 2024").

[0122] The first time interval is used to represent the statistical time range corresponding to the current line loss rate (e.g., "June 2025 (within 1 month)").

[0123] The second time interval is used to represent the statistical time range corresponding to the historical line loss rate (e.g., "May 2025 (within 1 month)").

[0124] The first power supply refers to the total power supply of the low-voltage distribution area to be analyzed within the first time interval.

[0125] The second power supply refers to the total power supply of the low-voltage distribution area to be analyzed during the second time interval.

[0126] The target line loss rate refers to the comprehensive line loss rate corresponding to the low-voltage distribution area to be analyzed.

[0127] For example, the server extracts the historical line loss rate corresponding to the current line loss rate from the line loss rate change curve of the low-voltage distribution area to be analyzed; then, the server determines the first time interval corresponding to the current line loss rate and the second time interval corresponding to the historical line loss rate; then, the server retrieves the first power supply of the low-voltage distribution area to be analyzed within the first time interval and the second power supply of the low-voltage distribution area to be analyzed within the second time interval from the database; then, the server determines the first weight corresponding to the current line loss rate and the second weight corresponding to the historical line loss rate based on the first power supply and the second power supply; for example, the server calculates the sum between the first power supply and the second power supply to obtain the total power supply, and then calculates the ratio between the first power supply and the total power supply. The server calculates the first weight corresponding to the current line loss rate by taking the value of the first weight and the second weight corresponding to the historical line loss rate by taking the ratio of the second power supply to the total power supply. Then, the server sums the current line loss rate and the historical line loss rate according to the first weight and the second weight to obtain the target line loss rate for the low-voltage distribution area to be analyzed. Next, the server inputs the target line loss rate, the current line loss rate, the first power supply, the historical line loss rate and the second power supply into the trained distribution area risk level prediction model to obtain the predicted probability of the low-voltage distribution area to be analyzed under each preset distribution area risk level. Then, the server selects the preset distribution area risk level with the highest predicted probability from each preset distribution area risk level as the current distribution area risk level of the low-voltage distribution area to be analyzed.

[0128] In this embodiment, the target line loss rate is determined by integrating the current line loss rate and the corresponding power supply in comparable historical periods. This not only balances the possible short-term fluctuations in the current line loss rate with historical data, but also eliminates the interference of differences in power supply scale in different periods on line loss assessment by using power supply quantity as a correlation weight, making the target line loss rate more in line with the long-term real loss level of the transformer area.

[0129] In an exemplary embodiment, the target line loss rate, current line loss rate, first power supply, historical line loss rate, and second power supply are input into a trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed. Specifically, this includes: fusing the current line loss rate and the first power supply to obtain the first line loss amount of the low-voltage transformer area to be analyzed; fusing the historical line loss rate and the second power supply to obtain the second line loss amount of the low-voltage transformer area to be analyzed; fusing the first line loss amount and the second line loss amount to obtain the target line loss amount of the low-voltage transformer area to be analyzed; and inputting the target line loss rate as the primary data and the target line loss amount as the auxiliary data into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0130] The first line loss power is used to represent the actual power loss of the low-voltage distribution area to be analyzed within the first time interval.

[0131] The second line loss power is used to represent the actual power loss of the low-voltage distribution area to be analyzed during the second time interval.

[0132] The target line loss power refers to the comprehensive loss power obtained after merging the first line loss power and the second line loss power.

[0133] For example, the server multiplies the current line loss rate and the first power supply to obtain the first line loss amount of the low-voltage distribution area to be analyzed, and multiplies the historical line loss rate and the second power supply to obtain the second line loss amount of the low-voltage distribution area to be analyzed. Then, the server sums the first line loss amount and the second line loss amount to obtain the target line loss amount of the low-voltage distribution area to be analyzed. Then, the server uses the target line loss rate as the main data and the target line loss amount as the auxiliary data, and inputs them into the trained distribution area risk level prediction model to obtain the predicted probability of the low-voltage distribution area to be analyzed under each preset distribution area risk level. Then, the server selects the preset distribution area risk level with the highest predicted probability from each preset distribution area risk level as the current distribution area risk level of the low-voltage distribution area to be analyzed.

[0134] In this embodiment, the line loss rate and power supply are combined to obtain the line loss power. The target line loss rate reflects the proportion of losses in the transformer area, and the target line loss power reflects the actual scale of losses. This makes up for the shortcomings of the single line loss rate, which may cover up special risk scenarios. It allows the model to capture the risk characteristics of the transformer area more comprehensively and significantly improves the accuracy of risk level determination.

[0135] In an exemplary embodiment, the risk processing method based on theoretical line loss calculation for low-voltage distribution areas provided in this application further includes a training process for a trained meter data analysis model, specifically including the following: acquiring sample meter data of sample meters in a sample low-voltage distribution area; inputting the sample meter data into the meter data analysis model to be trained to obtain the predicted meter error of the sample meters and the predicted line loss rate of the sample low-voltage distribution area; acquiring the actual meter error of the sample meters and the actual line loss rate of the sample low-voltage distribution area; obtaining a first loss value based on the difference between the predicted meter error and the actual meter error, and obtaining a second loss value based on the difference between the predicted line loss rate and the actual line loss rate; fusing the first loss value and the second loss value to obtain a target loss value; and iteratively training the meter data analysis model to be trained based on the target loss value to obtain a trained meter data analysis model.

[0136] Among them, the sample low-voltage area refers to the low-voltage area used for iterative training of the electricity meter data analysis model to be trained.

[0137] Among them, the sample meters refer to all the meters in the sample low-voltage distribution area.

[0138] Among them, sample meter data refers to the meter data of the sample meters.

[0139] Among them, the predicted meter error refers to the predicted value corresponding to the meter error of the sample electricity meters.

[0140] The predicted line loss rate refers to the predicted value corresponding to the line loss rate of the sample low-voltage transformer area.

[0141] The actual meter error refers to the true value corresponding to the meter error of the sample meter.

[0142] The first loss value refers to the loss value obtained based on the difference between the predicted meter error and the actual meter error.

[0143] The actual line loss rate refers to the predicted value corresponding to the line loss rate of the sample low-voltage distribution area.

[0144] The second loss value refers to the loss value obtained based on the difference between the predicted line loss rate and the actual line loss rate.

[0145] The target loss value refers to the loss value obtained by fusing the first loss value and the second loss value.

[0146] For example, in response to a model training instruction for a meter data analysis model to be trained, the server retrieves sample meter data from a sample low-voltage distribution area in a database; then, the server inputs the sample meter data into the meter data analysis model to be trained to obtain the predicted meter error of the sample meters and the predicted line loss rate of the sample low-voltage distribution area; then, the server retrieves the actual meter error of the sample meters and the actual line loss rate of the sample low-voltage distribution area from the database; then, the server obtains a first loss value based on the difference between the predicted meter error and the actual meter error, and a second loss value based on the difference between the predicted line loss rate and the actual line loss rate; then, the server obtains... The first loss value corresponds to the first fusion weight, and the second loss value corresponds to the second fusion weight. The first loss value and the second loss value are summed according to the first fusion weight and the second fusion weight to obtain the target loss value. Then, based on the target loss value, the model parameters of the meter data analysis model to be trained are adjusted to obtain the meter data analysis model with adjusted model parameters. Next, the server retrains the meter data analysis model with adjusted model parameters until the target loss value obtained by the trained meter data analysis model is less than the preset loss value. Then, training stops, and the trained meter data analysis model is taken as the completed meter data analysis model.

[0147] In this embodiment, by simultaneously training the meter data analysis model, the dual-dimensional losses of meter error and line loss rate are incorporated and fused to form a target loss value for iterative optimization. This ensures that the model can accurately predict the metering deviation of a single meter and also guarantees the accuracy of its calculation of the overall line loss rate of the transformer area. This provides more reliable algorithmic support for the determination of meter error and line loss rate in practical applications.

[0148] In one exemplary embodiment, such as Figure 3 As shown, another risk management method based on theoretical line loss calculation for low-voltage distribution areas is provided. Taking the application of this method to servers as an example, the specific steps include:

[0149] Step S301: In response to a risk handling request for the low-voltage distribution area to be analyzed, obtain the meter data of the meters to be analyzed in the low-voltage distribution area to be analyzed.

[0150] Step S302: Input the meter data into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0151] Step S303: Adjust the meter data according to the initial meter error to obtain the adjusted meter data of the meter to be analyzed.

[0152] Step S304: Input the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed.

[0153] Step S305: Determine the difference in line loss rate between the low-voltage distribution area to be analyzed based on the initial line loss rate and the adjusted line loss rate.

[0154] Step S306: Determine the maximum meter error in the adjusted meter errors, and determine the target meter corresponding to the maximum meter error from the meters to be analyzed.

[0155] Step S307: Based on the maximum meter error, the adjusted meter data of the target meter is adjusted again to obtain the processed meter data of the target meter.

[0156] Step S308: Based on the processed meter data of the target meter, obtain the processed meter data of the meter to be analyzed.

[0157] Step S309: The adjusted line loss rate is used as the new initial line loss rate, and the processed meter data of the meter to be analyzed is used as the new adjusted meter data. Then, the process jumps to the step of inputting the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed. This process continues until the difference between the adjusted meter error and the line loss rate meets the preset conditions. Then, the last adjusted line loss rate is used as the current line loss rate of the low-voltage distribution area to be analyzed.

[0158] Step S310: Obtain the historical line loss rate corresponding to the current line loss rate; determine the first time interval corresponding to the current line loss rate and the second time interval corresponding to the historical line loss rate.

[0159] Step S311: Obtain the first power supply of the low-voltage distribution area to be analyzed in the first time interval and the second power supply of the low-voltage distribution area to be analyzed in the second time interval; determine the target line loss rate corresponding to the low-voltage distribution area to be analyzed based on the current line loss rate, historical line loss rate, first power supply and second power supply.

[0160] Step S312: The current line loss rate and the first power supply are fused to obtain the first line loss power of the low-voltage distribution area to be analyzed, and the historical line loss rate and the second power supply are fused to obtain the second line loss power of the low-voltage distribution area to be analyzed.

[0161] Step S313: The first line loss power and the second line loss power are fused to obtain the target line loss power of the low-voltage distribution area to be analyzed; the target line loss rate is used as the main data and the target line loss power is used as the auxiliary data, and the data is input into the trained distribution area risk level prediction model to obtain the current distribution area risk level of the low-voltage distribution area to be analyzed.

[0162] Step S314: Based on the current risk level of the distribution area, generate the corresponding risk handling instruction for the low-voltage distribution area to be analyzed, and perform the corresponding risk handling for the low-voltage distribution area to be analyzed in accordance with the risk handling instruction.

[0163] In the aforementioned risk management method based on theoretical line loss calculation for low-voltage distribution areas, when managing the risks of low-voltage distribution areas, the initial meter error and initial line loss rate are accurately extracted from the meter data using a meter data analysis model. This avoids the biases inherent in traditional manual calculations. Then, the meter data is iteratively adjusted and the line loss rate is updated based on the error, gradually eliminating the interference of meter errors on the core data. This ensures that the current line loss rate closely matches the actual operating conditions of the distribution area, improving the accuracy of line loss determination. Combined with a distribution area risk level prediction model, the current risk level of the low-voltage distribution area can be predicted more accurately. Based on this current risk level, risk management of the low-voltage distribution area can be more precise, further improving the accuracy of risk management for low-voltage distribution areas.

[0164] In one exemplary embodiment, to more clearly illustrate the risk handling method based on the theoretical line loss calculation of low-voltage distribution areas provided by the embodiments of this application, the following specific embodiment will be used to describe the risk handling method based on the theoretical line loss calculation of low-voltage distribution areas. In one embodiment, this application also provides another risk handling method based on the theoretical line loss calculation of low-voltage distribution areas. Specifically, it includes the following:

[0165] (1) Collect basic metering data of the low-voltage distribution area in the current time interval (e.g., "June 2025"), including the readings of the main meter (main meter) and the user sub-meter (sub-meter) at the distribution transformer outlet of the low-voltage distribution area.

[0166] (2) Based on the basic metering data, calculate the initial metering error of each sub-meter and the initial line loss rate of the low-voltage area. Based on the initial metering error of each sub-meter, adjust the readings of each sub-meter in the basic metering data (e.g., when the error is -10%, the correction value = reading ÷ 0.9) to obtain the adjusted basic metering data.

[0167] (3) Based on the adjusted basic metering data, the adjusted metering error of each sub-meter and the adjusted line loss rate of the low-voltage area are calculated again, and the maximum metering error in the adjusted metering error of each sub-meter and the range of line loss rate of the low-voltage area are determined.

[0168] (4) When the maximum meter error and the line loss rate range both meet the preset conditions (i.e., the maximum meter error is less than or equal to the preset meter error, and the line loss rate range is less than or equal to the preset line loss rate range, which can be set according to the actual scenario of the transformer area), the last adjusted line loss rate is used as the line loss rate of the low-voltage transformer area in the current time interval; otherwise, the reading of the sub-meter corresponding to the maximum meter error is adjusted according to the maximum meter error to obtain the basic metering data after adjustment, and the calculation steps of the maximum meter error and the line loss rate range are performed again.

[0169] (5) Obtain the line loss rate of the low-voltage distribution area in the historical time interval (e.g., from spring to summer of the same year), and combine the power supply of the two time intervals to merge the two line loss rates to obtain the final calculated line loss rate of the low-voltage distribution area.

[0170] (6) Input the final calculated line loss rate (which can be combined with the historical trend of the maximum meter error and other features as auxiliary data) into the neural network model to predict the risk level of the low-voltage distribution area, and carry out corresponding risk treatment for the low-voltage distribution area according to the risk level (e.g., high risk → on-site inspection + meter verification within 72 hours, medium risk → weekly tracking of line loss fluctuations).

[0171] In the above embodiments, when handling risks in low-voltage distribution areas, the initial meter error and initial line loss rate are first accurately extracted from the meter data using a meter data analysis model, avoiding the deviations of traditional manual calculations. Then, based on the error, the meter data is iteratively adjusted and the line loss rate is updated, gradually eliminating the interference of meter errors on core data. This ensures that the current line loss rate closely matches the actual operating conditions of the distribution area, improving the accuracy of line loss determination. Combined with a distribution area risk level prediction model, the current risk level of the low-voltage distribution area can be predicted more accurately. Based on this current risk level, risk handling of the low-voltage distribution area can be more precise, further improving the accuracy of risk handling. Simultaneously, multi-dimensional optimization through data processing, error correction, result fusion, and risk modeling improves the accuracy of line loss calculation, thereby enhancing the accuracy of risk handling for low-voltage distribution areas.

[0172] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0173] Based on the same inventive concept, this application also provides a risk processing device for implementing the risk processing method based on the theoretical line loss calculation of low-voltage distribution areas as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the risk processing device based on the theoretical line loss calculation of low-voltage distribution areas provided below can be found in the limitations of the risk processing method based on the theoretical line loss calculation of low-voltage distribution areas described above, and will not be repeated here.

[0174] In one exemplary embodiment, such as Figure 4 As shown, a risk processing device based on theoretical line loss calculation for low-voltage distribution areas is provided, comprising: a data acquisition module 401, a data analysis module 402, a data adjustment module 403, a data update module 404, a risk level prediction module 405, and a risk processing module 406, wherein:

[0175] The data acquisition module 401 is used to acquire the meter data of the meters to be analyzed in the low-voltage distribution area in response to the risk handling request for the low-voltage distribution area to be analyzed.

[0176] The data analysis module 402 is used to input the meter data into the trained meter data analysis model to obtain the initial meter error of the meter to be analyzed and the initial line loss rate of the low-voltage distribution area to be analyzed.

[0177] The data adjustment module 403 is used to adjust the meter data according to the initial meter error to obtain the adjusted meter data of the meter to be analyzed.

[0178] The data update module 404 is used to iteratively update the initial line loss rate based on the adjusted meter data to obtain the current line loss rate of the low-voltage distribution area to be analyzed.

[0179] The risk level prediction module 405 is used to input the current line loss rate into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the low-voltage transformer area to be analyzed.

[0180] The risk processing module 406 is used to generate risk processing instructions for the low-voltage distribution area to be analyzed based on the current risk level of the distribution area, and to perform corresponding risk processing on the low-voltage distribution area to be analyzed according to the risk processing instructions.

[0181] In an exemplary embodiment, the data update module 404 is further configured to input the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed; determine the line loss rate difference of the low-voltage distribution area to be analyzed based on the initial line loss rate and the adjusted line loss rate; adjust the adjusted meter data of the meter to be analyzed again based on the adjusted meter error to obtain the processed meter data of the meter to be analyzed; use the adjusted line loss rate as the new initial line loss rate, use the processed meter data of the meter to be analyzed as the new adjusted meter data, and jump to the step of inputting the adjusted meter data into the trained meter data analysis model to obtain the adjusted meter error of the meter to be analyzed and the adjusted line loss rate of the low-voltage distribution area to be analyzed, until the adjusted meter error and the line loss rate difference both meet the preset conditions, then use the last obtained adjusted line loss rate as the current line loss rate of the low-voltage distribution area to be analyzed.

[0182] In an exemplary embodiment, the data update module 404 is further configured to determine the maximum meter error in the adjusted meter errors, and to determine the target meter corresponding to the maximum meter error from the meters to be analyzed; based on the maximum meter error, to perform further adjustment processing on the adjusted meter data of the target meter to obtain the processed meter data of the target meter; and based on the processed meter data of the target meter, to obtain the processed meter data of the meters to be analyzed.

[0183] In an exemplary embodiment, the risk level prediction module 405 is further configured to: obtain the historical line loss rate corresponding to the current line loss rate; determine the first time interval corresponding to the current line loss rate and the second time interval corresponding to the historical line loss rate; obtain the first power supply of the low-voltage distribution area to be analyzed within the first time interval and the second power supply of the low-voltage distribution area to be analyzed within the second time interval; determine the target line loss rate corresponding to the low-voltage distribution area to be analyzed based on the current line loss rate, the historical line loss rate, the first power supply, and the second power supply; and input the target line loss rate, the current line loss rate, the first power supply, the historical line loss rate, and the second power supply into the trained distribution area risk level prediction model to obtain the current distribution area risk level of the low-voltage distribution area to be analyzed.

[0184] In an exemplary embodiment, the risk level prediction module 405 is further configured to fuse the current line loss rate and the first power supply to obtain the first line loss amount of the low-voltage distribution area to be analyzed, and to fuse the historical line loss rate and the second power supply to obtain the second line loss amount of the low-voltage distribution area to be analyzed; to fuse the first line loss amount and the second line loss amount to obtain the target line loss amount of the low-voltage distribution area to be analyzed; and to input the target line loss rate as the main data and the target line loss amount as the auxiliary data into the trained distribution area risk level prediction model to obtain the current distribution area risk level of the low-voltage distribution area to be analyzed.

[0185] In an exemplary embodiment, the risk processing device based on theoretical line loss calculation for low-voltage distribution areas further includes a model training module, comprising: acquiring sample meter data of sample meters in sample low-voltage distribution areas; inputting the sample meter data into a meter data analysis model to be trained to obtain the predicted meter error of the sample meters and the predicted line loss rate of the sample low-voltage distribution areas; acquiring the actual meter error of the sample meters and the actual line loss rate of the sample low-voltage distribution areas; obtaining a first loss value based on the difference between the predicted meter error and the actual meter error, and obtaining a second loss value based on the difference between the predicted line loss rate and the actual line loss rate; fusing the first loss value and the second loss value to obtain a target loss value; and iteratively training the meter data analysis model to be trained based on the target loss value to obtain a trained meter data analysis model.

[0186] The various modules in the risk management device based on theoretical line loss calculation for low-voltage distribution areas can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0187] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as meter readings and current line loss rates. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a risk management method based on theoretical line loss calculations for low-voltage distribution areas.

[0188] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0189] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0190] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0191] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0192] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0193] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A risk processing method based on low-voltage area theoretical line loss calculation, characterized in that, The method comprises: in response to a risk processing request for a low-voltage area to be analyzed, obtaining meter data of meters to be analyzed in the low-voltage area to be analyzed; inputting the meter data into a trained meter data analysis model to obtain an initial meter error of the meters to be analyzed and an initial line loss rate of the low-voltage area to be analyzed; adjusting the meter data according to the initial meter error to obtain adjusted meter data of the meters to be analyzed; inputting the adjusted meter data into the trained meter data analysis model to obtain an adjusted meter error of the meters to be analyzed and an adjusted line loss rate of the low-voltage area to be analyzed; determining a line loss rate difference of the low-voltage area to be analyzed according to the initial line loss rate and the adjusted line loss rate; re-adjusting the adjusted meter data of the meters to be analyzed according to the adjusted meter error to obtain processed meter data of the meters to be analyzed; taking the adjusted line loss rate as a new initial line loss rate, taking the processed meter data of the meters to be analyzed as new adjusted meter data, and jumping to the step of inputting the adjusted meter data into the trained meter data analysis model to obtain an adjusted meter error of the meters to be analyzed and an adjusted line loss rate of the low-voltage area to be analyzed until the maximum meter error in the adjusted meter error is less than or equal to a preset meter error and the line loss rate difference is less than or equal to a preset line loss rate difference, and then taking the last obtained adjusted line loss rate as a current line loss rate of the low-voltage area to be analyzed; inputting the current line loss rate into a trained area risk level prediction model to obtain a current area risk level of the low-voltage area to be analyzed; generating a risk processing instruction corresponding to the low-voltage area to be analyzed according to the current area risk level, and performing corresponding risk processing on the low-voltage area to be analyzed according to the risk processing instruction.

2. The method of claim 1, wherein, The re-adjusting the adjusted meter data of the meters to be analyzed according to the adjusted meter error to obtain the processed meter data of the meters to be analyzed comprises: determining a maximum meter error in the adjusted meter error and a target meter corresponding to the maximum meter error from the meters to be analyzed; re-adjusting the adjusted meter data of the target meter according to the maximum meter error to obtain processed meter data of the target meter; obtaining the processed meter data of the meters to be analyzed based on the processed meter data of the target meter.

3. The method of claim 1, wherein, The inputting the current line loss rate into the trained area risk level prediction model to obtain the current area risk level of the low-voltage area to be analyzed comprises: obtaining a historical line loss rate corresponding to the current line loss rate; the historical line loss rate is used to represent a line loss rate of a previous time interval of a first time interval corresponding to the current line loss rate; determining the first time interval corresponding to the current line loss rate and a second time interval corresponding to the historical line loss rate; acquire a first power supply amount of the to-be-analyzed low-voltage transformer area in the first time interval and a second power supply amount of the to-be-analyzed low-voltage transformer area in the second time interval; determine a target line loss rate corresponding to the to-be-analyzed low-voltage transformer area according to the current line loss rate, the historical line loss rate, the first power supply amount and the second power supply amount; input the target line loss rate, the current line loss rate, the first power supply amount, the historical line loss rate and the second power supply amount into the trained transformer area risk level prediction model to obtain a current transformer area risk level of the to-be-analyzed low-voltage transformer area.

4. The method of claim 3, wherein, The inputting the target line loss rate, the current line loss rate, the first power supply amount, the historical line loss rate and the second power supply amount into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the to-be-analyzed low-voltage transformer area comprises: performing fusion processing on the current line loss rate and the first power supply amount to obtain a first line loss power of the to-be-analyzed low-voltage transformer area, and performing fusion processing on the historical line loss rate and the second power supply amount to obtain a second line loss power of the to-be-analyzed low-voltage transformer area; performing fusion processing on the first line loss power and the second line loss power to obtain a target line loss power of the to-be-analyzed low-voltage transformer area; inputting the target line loss rate as main data and the target line loss power as auxiliary data into the trained transformer area risk level prediction model to obtain the current transformer area risk level of the to-be-analyzed low-voltage transformer area.

5. The method according to any one of claims 1 to 4, characterized in that, The trained meter data analysis model is obtained through the following manner: acquiring sample meter data of a sample meter in a sample low-voltage transformer area; inputting the sample meter data into a to-be-trained meter data analysis model to obtain a predicted meter error of the sample meter and a predicted line loss rate of the sample low-voltage transformer area; acquiring an actual meter error of the sample meter and an actual line loss rate of the sample low-voltage transformer area; obtaining a first loss value according to a difference between the predicted meter error and the actual meter error, and obtaining a second loss value according to a difference between the predicted line loss rate and the actual line loss rate; performing fusion processing on the first loss value and the second loss value to obtain a target loss value; iteratively training the to-be-trained meter data analysis model according to the target loss value to obtain the trained meter data analysis model.

6. A risk processing device based on low-voltage area theoretical line loss calculation, characterized in that, The apparatus comprises: a data acquisition module configured to acquire meter data of a to-be-analyzed meter in a to-be-analyzed low-voltage transformer area in response to a risk processing request for the to-be-analyzed low-voltage transformer area; a data analysis module configured to input the meter data into a trained meter data analysis model to obtain an initial meter error of the to-be-analyzed meter and an initial line loss rate of the to-be-analyzed low-voltage transformer area; a data adjustment module configured to adjust the meter data according to the initial meter error to obtain adjusted meter data of the to-be-analyzed meter; The data updating module is configured to input the adjusted electric meter data into the trained electric meter data analysis model to obtain an adjusted meter error of the electric meter to be analyzed and an adjusted line loss rate of the low-voltage transformer area to be analyzed; determine a line loss rate difference of the low-voltage transformer area to be analyzed according to the initial line loss rate and the adjusted line loss rate; perform re-adjustment processing on the adjusted electric meter data of the electric meter to be analyzed according to the adjusted meter error to obtain processed electric meter data of the electric meter to be analyzed; take the adjusted line loss rate as a new initial line loss rate, take the processed electric meter data of the electric meter to be analyzed as new adjusted electric meter data, and jump to the step of inputting the adjusted electric meter data into the trained electric meter data analysis model to obtain the adjusted meter error of the electric meter to be analyzed and the adjusted line loss rate of the low-voltage transformer area to be analyzed until a maximum meter error in the adjusted meter error is less than or equal to a preset meter error and the line loss rate difference is less than or equal to a preset line loss rate difference, and then take the last obtained adjusted line loss rate as a current line loss rate of the low-voltage transformer area to be analyzed; The grade prediction module is configured to input the current line loss rate into a trained transformer area risk grade prediction model to obtain a current transformer area risk grade of the low-voltage transformer area to be analyzed. The risk processing module is configured to generate a risk processing instruction corresponding to the low-voltage transformer area to be analyzed according to the current transformer area risk grade, and perform corresponding risk processing on the low-voltage transformer area to be analyzed according to the risk processing instruction.

7. The apparatus of claim 6, wherein, The data updating module is further configured to determine a maximum meter error in the adjusted meter error and determine a target electric meter corresponding to the maximum meter error from the electric meters to be analyzed, perform re-adjustment processing on the adjusted electric meter data of the target electric meter according to the maximum meter error to obtain processed electric meter data of the target electric meter, and obtain the processed electric meter data of the electric meters to be analyzed based on the processed electric meter data of the target electric meter.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 5.

Images