An intelligent control method and system for transmission lines based on distribution data feedback

Abstract

The present invention discloses an intelligent control method and system for transmission lines based on distribution data feedback, which relates to the technical field of transmission line control and management, and includes: by integrating multi-source data, constructing a historical environment - electrical coupling feature matrix, using a long short-term memory network to extract temporal features, combining with a convolutional neural network to capture spatial distribution rules, establishing a dynamic line loss prediction model to simulate the minimum line loss value under different environmental scenarios. Detecting abnormal periods through the isolation forest algorithm, screening key loss-causing factors by combining Pearson correlation analysis and SHAP value analysis, dynamically generating loss reduction strategy priority events and executing them, and optimizing model parameters using error backpropagation and reinforcement learning. Finally, generating a loss reduction case library for typical environmental scenarios based on the optimized model, and simulating the effectiveness of strategies under extreme conditions through digital twin technology to form a reusable decision rule set, thereby improving the operation efficiency of transmission lines and power supply reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line control, and specifically relates to an intelligent control method and system for transmission lines based on distribution data feedback. Background Art

[0002] With the continuous expansion of the scale of the power system, the operating environment of transmission lines has become increasingly complex. Traditional control methods are difficult to meet the requirements. The intelligent control method and system for transmission lines based on distribution data feedback can accurately grasp the state of transmission lines by means of real-time feedback of distribution data, timely discover potential faults and risks, optimize the operation and maintenance strategies of transmission lines, improve power supply reliability, reduce operation and maintenance costs, meet the development requirements of smart grids, and ensure stable power supply.

[0003] A Chinese patent application with the publication number CN118551177B discloses a line loss control method, device and electronic equipment for distribution lines. First, based on the operating parameters of the target line, line loss-related analysis can be performed on at least one distribution line to be analyzed to determine whether each distribution line to be analyzed belongs to a high-loss distribution line; secondly, when there is a distribution line to be analyzed that belongs to a high-loss distribution line among at least one distribution line to be analyzed, for each distribution line to be analyzed that belongs to a high-loss distribution line, determine the loss reduction potential and high-loss causes of the distribution line to be analyzed, and obtain the corresponding loss reduction potential parameters and high-loss cause information; then, based on the corresponding loss reduction potential parameters and high-loss cause information, perform line loss control operations. Based on the above content, the problem of relatively low reliability of line loss control in existing technologies can be improved.

[0004] In the above prior art, based on the operating parameters of the target line, line loss-related analysis is performed on at least one distribution line to be analyzed to determine whether each distribution line to be analyzed belongs to a high-loss distribution line. Then, by determining the loss reduction potential and high-loss causes of the distribution line to be analyzed, the corresponding loss reduction potential parameters and high-loss cause information are obtained. Finally, based on the corresponding loss reduction potential parameters and high-loss cause information, line loss control operations are performed. However, when the above prior art controls transmission lines, it lacks the analysis of the influence of environmental factors on the loss reduction potential and high-loss causes of the lines, and cannot ensure the accuracy of transmission line fault detection, thus unable to ensure the accuracy of transmission line control.

[0005] Therefore, the present invention provides an intelligent control method and system for transmission lines based on distribution data feedback to solve the above-mentioned problems. Summary of the Invention

[0006] To solve the above technical problems, a smart control method and system for transmission lines based on distribution data feedback are provided. This technical solution solves the problem in the above background technology that when the existing technology controls transmission lines, it lacks the analysis of the influence of environmental factors on the line loss reduction potential and the causes of high line losses, and cannot ensure the accuracy of transmission line fault detection, thus unable to ensure the accuracy of transmission line control.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A smart control method for transmission lines based on distribution data feedback, characterized by comprising:

[0009] S1. Obtain the historical operation parameters of each transmission line to be analyzed. Then, obtain the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, and perform spatio-temporal alignment with the historical operation parameters of the line to construct a historical environment - electrical coupling feature matrix;

[0010] S2. Use a long short-term memory network to extract the historical environmental time series features in the historical environment - electrical coupling feature matrix, combine a convolutional neural network to capture the spatial distribution law, establish a dynamic line loss prediction model, and obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model;

[0011] S3. Obtain the historical actual line loss values and compare the differences with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model. According to the difference comparison results, determine whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters, and then screen the key loss-causing factors through Pearson correlation analysis;

[0012] S4. Obtain environmental prediction data and real-time operation parameters of the line, and combine the key loss-causing factors and the dynamic line loss prediction model to dynamically generate loss reduction strategy priority events;

[0013] S5. Execute the loss reduction strategy priority events, obtain the actual line loss data after execution, synchronously perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and use the reinforcement learning algorithm to optimize the dynamic line loss prediction model;

[0014] S6. Based on the optimized dynamic line loss prediction model, generate a loss reduction case library for typical environmental scenarios, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set.

[0015] In an alternative embodiment, step S1 specifically includes:

[0016] S1.1. Collect the historical operation parameters of each transmission line to be analyzed through the SCADA system. The historical operation parameters of the line include historical current, historical voltage, historical load rate, and historical fault records.

[0017] S1.2. Collect historical temperature, historical humidity, historical wind speed, historical rainfall, and historical ice coating degree through the weather stations in the areas where each transmission line to be analyzed is located to obtain historical meteorological data.

[0018] S1.3. Collect historical terrain slope, historical vegetation coverage rate, historical soil resistivity, and historical altitude through the weather stations in the areas where each transmission line to be analyzed is located.

[0019] S1.4. Collect historical industrial pollution concentration, historical traffic noise decibel value, and historical electromagnetic radiation intensity of wireless base stations through the weather stations in the areas where each transmission line to be analyzed is located to obtain historical humanistic data.

[0020] S1.5. Apply moving average filtering to the historical meteorological data, historical geographical data, and historical line operation parameters to eliminate high-frequency noise, and apply median filtering to the historical humanistic data to suppress mutation interference.

[0021] S1.6. Use the KNN interpolation method to fill in the missing values in the historical meteorological data, and at the same time use linear regression to fill in the missing values in the geographical data.

[0022] S1.7. Eliminate the abnormal data points beyond the normal range based on the 3σ principle, and project all the data processed in steps 1.4 to 1.6 onto the UTM coordinate system. Taking the center point of the transmission line as the origin, establish a local coordinate grid.

[0023] S1.8. Based on the GPS timing system, align the historical meteorological data, historical geographical data, historical humanistic data, and historical line operation parameters projected onto the UTM coordinate system according to the minute-level time stamps.

[0024] S1.9. Use principal component analysis for dimensionality reduction, retain the principal components with a cumulative variance contribution rate greater than or equal to 95%, and then generate a historical environment - electrical coupling feature matrix with a dimension less than or equal to 50.

[0025] In an alternative embodiment, step S2 specifically includes:

[0026] S2.1. Design a double-layer long short-term memory network, set the input layer dimension to 50, the number of hidden layer units to 128, the activation function to tanh, and the output layer predicts the line loss sequence for the next 24 hours.

[0027] S2.2. Divide the historical data in the historical environment - electrical coupling feature matrix into training samples according to a 24-hour sliding window, with a step size of 1 hour, to generate N time series segments;

[0028] S2.3. Adopt a 3-layer convolutional neural network, set the convolutional kernel size to 3×3, the step size to 1, and the padding method to SAME, to extract the spatial distribution features of the historical geographical data in the historical environment - electrical coupling feature matrix;

[0029] S2.4. Concatenate the N time series segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network, input them into the fully connected layer, and the output layer generates the dynamic line loss prediction value, thus completing the construction of the dynamic line loss prediction model;

[0030] S2.5. According to the N time series segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network, obtain the historical environment sensitivity coefficient matrix, and use the historical environment sensitivity coefficient matrix to correct the historical line operation parameters in the historical environment - electrical coupling feature matrix;

[0031] S2.6. Set the loss function and use the Adam algorithm to dynamically adjust the learning rate, and then input the N time series segments generated by the corrected double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network into the dynamic line loss prediction model to train the model;

[0032] S2.7. Simulate and generate the environment - electrical coupling feature simulation matrix under different environmental scenarios, and input the simulated meteorological data, simulated geographical data, and simulated human data in the environment - electrical coupling feature simulation matrix into the trained dynamic line loss prediction model to obtain the minimum line loss values under different environmental scenarios output by the trained dynamic line loss prediction model.

[0033] In an alternative embodiment, step S3 specifically includes:

[0034] Obtain the difference between the historical actual line loss values corresponding to the N time series segments and the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model;

[0035] If the difference between the historical actual line loss value and the minimum line loss value under different environmental scenarios output by the dynamic line loss prediction model is greater than or equal to 0.15 times the minimum line loss value, trigger the isolation forest algorithm to obtain the anomaly score of each data point;

[0036] Filter out the anomaly scores higher than T thresholdThe simulated meteorological data, simulated geographical data, and simulated human data in the environment-electrical coupling characteristic simulation matrix corresponding to the data points of = μ(s) + 3σ(s) are obtained to obtain the first abnormal data, where μ(s) is the global mean of the minimum line loss values corresponding to all data points, σ(s) is the global standard deviation of the minimum line loss values corresponding to all data points, and T threshold is the abnormal data screening threshold;

[0037] Based on the first abnormal data and its corresponding minimum line loss value, the first correlation coefficient is obtained, and at the same time, the first abnormal data with the first correlation coefficient greater than or equal to 0.8 is obtained;

[0038] Through the SHAP value analysis model, the contribution degree of each factor in the first abnormal data to the line loss is quantified, and the factor with the largest contribution degree is marked as the key loss-causing factor;

[0039] Among them, triggering the isolation forest algorithm, the calculation formula for the abnormal score of each data point is:

[0040]

[0041] In the formula, is the abnormal score in the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the historical actual line loss value at the i-th time series segment of the transmission circuit corresponding to the t-th environmental scenario, is the mean value in the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the standard deviation of the minimum line loss value in the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment.

[0042] In an alternative embodiment, step S4 specifically includes:

[0043] Obtain the meteorological data, geographical data, and human data for the next 24 hours from the meteorological bureau to obtain the environmental prediction data, and simultaneously obtain the real-time operation parameters of each transmission line to be analyzed;

[0044] Input the environmental prediction data and the real-time operation parameters of the line into the dynamic line loss prediction model to obtain the minimum line loss value corresponding to the environmental prediction data and the real-time operation parameters of the line;

[0045] Compare the environmental prediction data and the real-time operation parameters of the line with the key loss-causing factors to dynamically generate the dynamic loss-causing value;

[0046] Perform a weighted sum of the minimum line loss value and the dynamic loss-causing value to obtain the reference value for the loss reduction strategy;

[0047] Based on the reference value for the loss reduction strategy, dynamically generate the loss reduction strategy priority event.

[0048] In an alternative embodiment, step S5 specifically includes:

[0049] After executing the loss reduction strategy priority event, obtain the actual line loss value of the transmission line corresponding to the loss reduction strategy priority event during the abnormal period to obtain the actual line loss data after execution;

[0050] Synchronously obtain the actual environment-electrical coupling characteristic matrix of the transmission line corresponding to the loss reduction strategy priority event;

[0051] Import the data in the actual environment-electrical coupling characteristic matrix into the dynamic line loss prediction model to obtain the predicted line loss data output by the dynamic line loss prediction model;

[0052] Compare the predicted line loss value included in the predicted line loss data output by the dynamic line loss prediction model with the actual line loss value to obtain the difference between the two for error backpropagation, and synchronously use the Adam optimizer to update the parameters in the dynamic line loss prediction model to realize the optimization of the dynamic line loss prediction model using the reinforcement learning algorithm.

[0053] In an alternative embodiment, step S6 specifically includes:

[0054] Based on the optimized dynamic line loss prediction model, obtain the minimum line loss values under different environmental scenarios;

[0055] Classify the meteorological data, geographical data, humanistic data, and line operation parameters corresponding to the minimum line loss values under different environmental scenarios into typical scenarios, and combine them with the loss reduction strategy priority event to generate a typical environmental scenario loss reduction case library;

[0056] Build a three-dimensional line model based on the Unreal Engine, and inject extreme environmental parameters and extreme line operation parameters into the three-dimensional line model using physical simulation to simulate the effectiveness of the strategy under extreme conditions;

[0057] According to the effectiveness of the strategy under extreme conditions, obtain the loss reduction effective events under extreme conditions, and use the loss reduction effective events under extreme conditions to supplement the typical environmental scenario loss reduction case library to form a reusable decision rule set.

[0058] Furthermore, a transmission line intelligent control system based on distribution data feedback is proposed for implementing the control method as described in any one of the above, including:

[0059] An acquisition module, the acquisition module is used to obtain the historical line operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, for obtaining environmental prediction data and line real-time operation parameters;

[0060] A data processing module, which is used to perform spatio-temporal alignment on historical meteorological data, historical geographical data, historical humanistic data, and line historical operation parameters in the area where each transmission line to be analyzed is located, and construct a historical environment - electrical coupling feature matrix, which is used to extract historical environment time series features from the historical environment - electrical coupling feature matrix by using a long short-term memory network, capture the spatial distribution law in combination with a convolutional neural network, and establish a dynamic line loss prediction model;

[0061] A main control module, which is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, obtain historical actual line loss values and compare the difference with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and judge whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters according to the difference comparison result, then screen key loss-causing factors through Pearson correlation analysis, obtain environmental prediction data and line real-time operation parameters, and combine key loss-causing factors and the dynamic line loss prediction model to dynamically generate loss reduction strategy priority events, execute loss reduction strategy priority events, and obtain the actual line loss data after execution, which is used to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, optimize the dynamic line loss prediction model by using a reinforcement learning algorithm, generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set;

[0062] A display module, which is used to display the data and information collected by the collection module, display the data processing process and results of the data processing module, and display the data analysis process and results of the main control module.

[0063] In an optional embodiment, the collection module includes:

[0064] A first collection unit, which is used to obtain the line historical operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data, and historical humanistic data in the area where each transmission line to be analyzed is located;

[0065] A second collection unit, which is used to obtain environmental prediction data and line real-time operation parameters.

[0066] In an optional embodiment, the main control module includes:

[0067] A line loss evaluation unit, which is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model;

[0068] A loss-causing factor judgment unit, which is used to obtain historical actual line loss values, compare the differences with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and judge whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters according to the difference comparison results, and then screen key loss-causing factors through Pearson correlation analysis;

[0069] A loss reduction execution unit, which is used to obtain environmental prediction data and real-time line operation parameters, and combine key loss-causing factors and the dynamic line loss prediction model to dynamically generate loss reduction strategy priority events, execute the loss reduction strategy priority events, and obtain the actual line loss data after execution;

[0070] A model optimization unit, which is used to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and optimize the dynamic line loss prediction model using the reinforcement learning algorithm;

[0071] A decision rule generation unit, which is used to generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of strategies under extreme conditions through digital twin technology to form a reusable decision rule set.

[0072] Compared with the prior art, the beneficial effects of the present invention are:

[0073] An intelligent control method for transmission lines based on distribution data feedback proposed by this solution, by integrating multi-source data (line operation parameters, meteorology, geography and human data) and constructing a historical environment-electrical coupling feature matrix, combining LSTM and CNN to construct a dynamic line loss prediction model, realizing the prediction of the minimum line loss value under different environmental scenarios. Detect abnormal periods through the isolation forest algorithm, combine Pearson correlation and SHAP value analysis to screen key loss-causing factors, dynamically generate and execute loss reduction strategy priority events, and use error backpropagation and reinforcement learning to optimize model parameters. Solve the technical problems of inaccurate fault detection and lagging strategy optimization caused by traditional methods ignoring environmental factors, have the advantages of improving detection accuracy, enhancing dynamic adaptability, reducing line loss rate, and improving power supply reliability, and simulate extreme conditions through digital twin technology to form a reusable decision rule set, promoting the refined control of smart grids. Description of the Drawings

[0074] Figure 1 It is a flowchart of an intelligent control method for transmission lines based on distribution data feedback proposed by the present invention;

[0075] Figure 2 It is a flowchart for obtaining the historical environment-electrical coupling feature matrix in the present invention;

[0076] Figure 3 Flow chart for obtaining the minimum line loss value in the present invention;

[0077] Figure 4 System framework diagram of an intelligent control system for transmission lines based on distribution data feedback proposed in the present invention. Detailed implementation manners

[0078] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and other obvious variations can be thought of by those skilled in the art.

[0079] Refer to Figure 1 - Figure 4 As shown, an intelligent control method for transmission lines based on distribution data feedback, characterized by including:

[0080] S1. Obtain the historical operation parameters of each transmission line to be analyzed, and then obtain the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, and perform spatio-temporal alignment with the historical operation parameters of the line to construct a historical environment - electrical coupling feature matrix.

[0081] Further, step S1 specifically includes:

[0082] S1.1. Collect the historical operation parameters of each transmission line to be analyzed through the SCADA system, and the historical operation parameters of the line include historical current, historical voltage, historical load rate, and historical fault records;

[0083] S1.2. Collect historical temperature, historical humidity, historical wind speed, historical rainfall, and historical icing degree through the weather stations in the area where each transmission line to be analyzed is located to obtain historical meteorological data;

[0084] S1.3. Collect historical terrain slope, historical vegetation coverage rate, historical soil resistivity, and historical altitude through the weather stations in the area where each transmission line to be analyzed is located;

[0085] S1.4. Collect historical industrial pollution concentration, historical traffic noise decibel value, and historical electromagnetic radiation intensity of wireless base stations through the weather stations in the area where each transmission line to be analyzed is located to obtain historical humanistic data, and the data collection is historical data in the past 5 - 10 years;

[0086] S1.5. Use moving average filtering (window size is 5) for historical meteorological data, historical geographical data, and historical line operation parameters to eliminate high-frequency noise, and use median filtering for historical humanistic data to suppress mutation interference;

[0087] S1.6. Fill in the missing values in the historical meteorological data using the KNN interpolation method (with k = 5), and at the same time use linear regression to fill in the missing values in the geographical data;

[0088] S1.7. Eliminate the abnormal data points beyond the normal range based on the 3σ principle, and project all the data processed in steps 1.4 to 1.6 onto the UTM coordinate system. Taking the center point of the transmission line as the origin, establish a local coordinate grid (with a resolution less than or equal to 10m×10m);

[0089] S1.8. Based on the GPS timing system, align the historical meteorological data, historical geographical data, historical humanistic data, and historical line operation parameters projected onto the UTM coordinate system according to the minute-level time stamps, allowing the maximum time deviation to be controlled within ±1 minute;

[0090] S1.9. Use principal component analysis for dimensionality reduction, retain the principal components with a cumulative variance contribution rate greater than or equal to 95%, and then generate a historical environment - electrical coupling feature matrix with a dimension less than or equal to 50.

[0091] S2. Use a long short-term memory network to extract the historical environment time series features in the historical environment - electrical coupling feature matrix, combine a convolutional neural network to capture the spatial distribution law, establish a dynamic line loss prediction model, and obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model.

[0092] Specifically, eliminate the abnormal data points beyond the normal range based on the 3σ principle, such as abnormal records with a temperature greater than 50°C or a humidity greater than 100%.

[0093] Further, step S2 specifically includes:

[0094] S2.1. Design a two-layer long short-term memory network, set the input layer dimension to 50 (the number of features after PCA dimensionality reduction), set the number of hidden layer units to 128, set the activation function to tanh, and the output layer predicts the line loss sequence for the next 24 hours;

[0095] S2.2. Divide the historical data in the historical environment - electrical coupling feature matrix into training samples according to a 24-hour sliding window, set the step size to 1 hour, and generate N time series segments;

[0096] S2.3. Use a three-layer convolutional neural network, set the convolutional kernel size to 3×3, set the step size to 1, and set the padding method to SAME, and extract the spatial distribution features of the historical geographical data in the historical environment - electrical coupling feature matrix (such as terrain slope gradient, vegetation coverage hot spots);

[0097] S2.4. Concatenate the N temporal segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the three-layer convolutional neural network, input them into the fully connected layer (with 256 neurons), and the output layer generates the dynamic line loss prediction value, thus completing the construction of the dynamic line loss prediction model;

[0098] S2.5. Obtain the historical environmental sensitivity coefficient matrix based on the N temporal segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the three-layer convolutional neural network, and use the historical environmental sensitivity coefficient matrix to correct the historical line operation parameters in the historical environment-electrical coupling feature matrix;

[0099] S2.6. Set the loss function and use the Adam algorithm to dynamically adjust the learning rate. Then, input the N temporal segments generated by the corrected double-layer long short-term memory network and the spatial distribution features obtained by the three-layer convolutional neural network into the dynamic line loss prediction model to train the model;

[0100] S2.7. Simulate and generate the environmental-electrical coupling feature simulation matrix under different environmental scenarios, and input the simulated meteorological data, simulated geographical data, and simulated human data in the environmental-electrical coupling feature simulation matrix into the trained dynamic line loss prediction model to obtain the minimum line loss values under different environmental scenarios output by the trained dynamic line loss prediction model;

[0101] Among them, the loss function is:

[0102]

[0103] In the formula, , is the model validation set, are the model parameters;

[0104] The mathematical definition of MSE is:

[0105]

[0106] In the formula, is the historical actual line loss value corresponding to the i-th temporal segment, is the predicted line loss value corresponding to the i-th temporal segment output by the model.

[0107] It can be understood that the steps for obtaining the historical environmental sensitivity coefficient matrix are:

[0108] S2.5.1. Input data:

[0109] Temporal features : Historical environmental temporal features extracted by the double-layer LSTM, T is the number of time steps, d LSTM is the output dimension of the LSTM.

[0110] Spatial feature h CNN ∈R H×W×C :

[0111] Geospatial distribution features extracted by a three - layer CNN, where H×W is the spatial resolution and C is the number of channels.

[0112] S2.5.2. Concatenate the temporal feature h LSTM and the spatial feature h CNN into a comprehensive feature vector:

[0113] h fusion =Concat(h LSTM， h CNN ), where h fusion ∈R dfusion , d fusion =d LSTM +C·H·W;

[0114] S2.5.3. Map the fused feature to the environmental sensitivity coefficient matrix through a fully - connected layer:

[0115] K env =σ(W·h fusion +b);

[0116] where: W∈R n env ×d fusion is the weight matrix of the fully - connected layer, b∈R n env is the bias term, n env is the dimension of the environmental sensitivity coefficient matrix (e.g., n env =n env_params ×n elec_params ), and σ(·) is the activation function (e.g., ReLU);

[0117] S2.5.4. Reconstruct the output K env of the fully - connected layer into the target shape (n env_params , n elec_params ), i.e.:

[0118] K env ∈R n env_params ×n elec_params ;

[0119] where, n env_params is the number of types of environmental data, geographical data, and human data (e.g., temperature, humidity, wind speed, etc.), and n elec_params is the number of types of line operation parameters (e.g., current, etc.).

[0120] Example: Assume that the output dimension d of the LSTM LSTM = 128, the CNN outputs spatial features h CNN ∈ R32 (after global average pooling), and the weights W of the fully connected layer ∈ R 6×160 (mapping 160-dimensional features to 6 environmental parameters × 3 line operation parameters).

[0121] Then the environmental sensitivity coefficient matrix is:

[0122] K env = ReLU(W · [h LSTM ; h CNN )

[0123] The output shape is 6 × 3. For example:

[0124] .

[0125] It can be understood that the matrix element k i，j represents the sensitivity of the i-th environmental data, geographical data, and human data to the j-th line operation parameter. For example, K temp,R = 0.002 means that when the temperature increases by 1 °C, the resistance increases by 0.2%.

[0126] It can also be understood that the historical line operation parameters in the historical environment - electrical coupling feature matrix are corrected using the historical environmental sensitivity coefficient matrix. Taking the AC resistance of the transmission line as an example:

[0127] R env-adjusted = R0 · (1 + αΔT + βΔH + γΔU wind )

[0128] R0 is the reference resistance (the resistance value under normal temperature and dry conditions);

[0129] ΔT, ΔH, ΔU wind are the change amounts of temperature, humidity, and wind speed respectively;

[0130] α, β, γ are the historical environmental sensitivity coefficients (obtained by looking up the historical environmental sensitivity coefficient matrix).

[0131] S3. Obtain the historical actual line loss value and compare the difference with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model. Then, based on the difference comparison result, determine whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters, and then screen the key damage-causing factors through Pearson correlation analysis.

[0132] Furthermore, step S3 specifically includes:

[0133] Obtain the difference between the historical actual line loss value corresponding to N time series segments and the minimum line loss value under different environmental scenarios output by the dynamic line loss prediction model;

[0134] If the difference between the historical actual line loss value and the minimum line loss value under different environmental scenarios output by the dynamic line loss prediction model is greater than or equal to 0.15 times the minimum line loss value, trigger the Isolation Forest algorithm to obtain the anomaly score of each data point;

[0135] Screen the simulated meteorological data, simulated geographical data, and simulated humanistic data in the environmental-electrical coupling feature simulation matrix corresponding to the data points with anomaly scores higher than T threshold =μ(s)+3σ(s) to obtain the first abnormal data, where μ(s) is the global mean of the minimum line loss values corresponding to all data points, σ(s) is the global standard deviation of the minimum line loss values corresponding to all data points, and T threshold is the abnormal data screening threshold;

[0136] Based on the first abnormal data and its corresponding minimum line loss value, obtain the first correlation coefficient, and at the same time obtain the first abnormal data with the first correlation coefficient greater than or equal to 0.8;

[0137] Through the SHAP value analysis model, quantify the contribution degree of each factor in the first abnormal data to the line loss, and mark the factor with the largest contribution degree as the key loss-causing factor;

[0138] Among them, the calculation formula for triggering the Isolation Forest algorithm to obtain the anomaly score of each data point is:

[0139]

[0140] In the formula, is the anomaly score under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the historical actual line loss value at the i-th time series segment corresponding to the transmission circuit under the t-th environmental scenario, is the mean value under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the standard deviation of the minimum line loss value under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment;

[0141] The calculation process for obtaining the first correlation coefficient based on the first abnormal data and its corresponding minimum line loss value is:

[0142] S3.1.1. Let the first abnormal data be and its corresponding minimum line loss value be ;

[0143] S3.1.2. Through the Pearson correlation coefficient calculation formula , obtain the first correlation coefficient r, where is the value of the th abnormal data point, is the line loss value corresponding to the th abnormal data point, is the mean value corresponding to the abnormal data point, is the mean value of the line loss values corresponding to the abnormal data point.

[0144] It can be understood that through the SHAP value analysis model, the contribution degree of each factor in the first abnormal data to the line loss is quantified, and the specific steps of marking the factor with the largest contribution degree as the key damage-causing factor are

[0145] S3.2.1. Conduct SHAP value analysis on the selected first abnormal data (such as humidity, pollution level), and quantify its contribution degree to the line loss: , where represents the contribution degree of each factor in the first abnormal data to the line loss (the marginal contribution of this feature to the model output);

[0146] S3.2.2. If the C of a certain factor j > 0.3·∑C j , then it is marked as the key damage-causing factor.

[0147] S4. Obtain the environmental prediction data and the real-time operation parameters of the line, and combine the key damage-causing factors and the dynamic line loss prediction model to dynamically generate the priority events of the loss reduction strategy.

[0148] Furthermore, step S4 specifically includes:

[0149] Obtain the meteorological data, geographical data and human data for the next 24 hours from the meteorological bureau to get the environmental prediction data, and synchronously obtain the real-time operation parameters of each transmission line to be analyzed;

[0150] Input the environmental prediction data and the real-time operation parameters of the line into the dynamic line loss prediction model to obtain the minimum line loss value corresponding to the environmental prediction data and the real-time operation parameters of the line;

[0151] Compare the environmental prediction data and the real-time operation parameters of the line with the key damage-causing factors to dynamically generate the dynamic damage-causing value. If the environmental prediction data and the real-time operation parameters of the line meet one of the key damage-causing factors, the dynamically generated dynamic damage-causing value +1, and normalize the dynamically generated dynamic damage-causing value to obtain the dynamic damage-causing value within the range of 0-1. For example, assume that the key damage-causing factors are temperature (K = 1), humidity (K = 2), wind speed (K = 3), and the total number of factors K = 3:

[0152] Environmental prediction data: T env= 32 °C, H env = 75%, W env = 8 m / s

[0153] Real-time operating parameters: T real = 31 °C, H real = 82%, W real = 9 m / s

[0154] Trigger judgment:

[0155] Factor 1 (temperature): T real = 31 °C > 30 °C → Trigger (I(1) = 1).

[0156] Factor 2 (humidity): H real = 82% > 80% → Trigger (I(2) = 1).

[0157] Factor 3 (wind speed): W env = 8 m / s < 10 m / s, W real = 9 m / s < 10 m / s → No trigger (I(3) = 0).

[0158] Dynamic damage value calculation:

[0159] D raw = 1 + 1 + 0 = 2, Dnorm = 32 ≈ 0.67;

[0160] Perform weighted summation of the minimum line loss value and the dynamic damage value to obtain a reference value for the loss reduction strategy;

[0161] Based on the reference value of the loss reduction strategy, dynamically generate loss reduction strategy priority events.

[0162] S5. Execute the loss reduction strategy priority event, obtain the actual line loss data after execution, synchronously perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and optimize the dynamic line loss prediction model using the reinforcement learning algorithm.

[0163] Furthermore, step S5 specifically includes:

[0164] After executing the loss reduction strategy priority event, obtain the actual line loss value of the transmission line corresponding to the loss reduction strategy priority event during the abnormal period to obtain the actual line loss data after execution;

[0165] Synchronously obtain the actual environment - electrical coupling feature matrix of the transmission line corresponding to the loss reduction strategy priority event;

[0166] Import the data in the actual environment - electrical coupling feature matrix into the dynamic line loss prediction model to obtain the predicted line loss data output by the dynamic line loss prediction model;

[0167] Compare the predicted line loss value included in the predicted line loss data output by the dynamic line loss prediction model with the actual line loss value to obtain the difference between the two, which is used for error backpropagation. At the same time, use the Adam optimizer to update the parameters in the dynamic line loss prediction model, so as to optimize the dynamic line loss prediction model by using the reinforcement learning algorithm.

[0168] S6. Based on the optimized dynamic line loss prediction model, generate a loss reduction case library for typical environmental scenarios, and simulate the effectiveness of strategies under extreme conditions through digital twin technology to form a reusable decision rule set.

[0169] Furthermore, step S6 specifically includes:

[0170] Based on the optimized dynamic line loss prediction model, obtain the minimum line loss value under different environmental scenarios;

[0171] Classify the meteorological data, geographical data, humanistic data, and line operation parameters corresponding to the minimum line loss value under different environmental scenarios into typical scenarios, and combine the loss reduction strategy priority events to generate a loss reduction case library for typical environmental scenarios;

[0172] Build a three-dimensional line model based on Unreal Engine, and inject extreme environmental parameters and extreme line operation parameters into the three-dimensional line model by using physical simulation to simulate the effectiveness of strategies under extreme conditions;

[0173] According to the effectiveness of the strategy under extreme conditions, obtain the loss reduction effective events under extreme conditions, and use the loss reduction effective events under extreme conditions to supplement the loss reduction case library for typical environmental scenarios to form a reusable decision rule set, and use the reusable decision rule set to conduct intelligent control of the transmission line.

[0174] Furthermore, a transmission line intelligent control system based on distribution data feedback is proposed, which is used to implement the control method as described in any one of the above, including:

[0175] A collection module, which is used to obtain the line historical operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, and is used to obtain environmental prediction data and line real-time operation parameters;

[0176] A data processing module, which is used to perform spatio-temporal alignment on the historical meteorological data, historical geographical data, historical humanistic data, and line historical operation parameters of the area where each transmission line to be analyzed is located, and construct a historical environment-electrical coupling feature matrix, which is used to extract the historical environment time series features in the historical environment-electrical coupling feature matrix by using a long short-term memory network, and capture the spatial distribution law in combination with a convolutional neural network to establish a dynamic line loss prediction model;

[0177] The main control module is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, obtain the historical actual line loss values, compare the difference with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and based on the difference comparison result, judge whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters. Then, screen key loss-causing factors through Pearson correlation analysis, obtain environmental prediction data and real-time line operation parameters, combine the key loss-causing factors and the dynamic line loss prediction model, dynamically generate loss reduction strategy priority events, execute the loss reduction strategy priority events, and obtain the actual line loss data after execution. It is used to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, optimize the dynamic line loss prediction model using the reinforcement learning algorithm, generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set;

[0178] The display module is used to display the data and information collected by the collection module, display the data processing process and results of the data processing module, and display the data analysis process and results of the main control module.

[0179] Furthermore, the collection module includes:

[0180] The first collection unit is used to obtain the historical line operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located;

[0181] The second collection unit is used to obtain environmental prediction data and real-time line operation parameters.

[0182] Furthermore, the main control module includes:

[0183] The line loss evaluation unit is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model;

[0184] The loss-causing factor judgment unit is used to obtain the historical actual line loss values, compare the difference with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and based on the difference comparison result, judge whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters. Then, screen key loss-causing factors through Pearson correlation analysis;

[0185] A loss reduction execution unit, which is used to obtain environmental prediction data and real-time line operation parameters, and combine key loss-causing factors and a dynamic line loss prediction model to dynamically generate loss reduction strategy priority events, execute the loss reduction strategy priority events, and obtain the actual line loss data after execution;

[0186] A model optimization unit, which is used to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and optimize the dynamic line loss prediction model using a reinforcement learning algorithm;

[0187] A decision rule generation unit, which is used to generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set.

[0188] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for transmission lines based on distribution data feedback, characterized in that Including: S1. Obtain the historical operation parameters of each transmission line to be analyzed. Then, obtain the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, and perform spatio-temporal alignment with the historical operation parameters of the line to construct a historical environment - electrical coupling feature matrix; S2. Use a long short-term memory network to extract the historical environment time series features in the historical environment - electrical coupling feature matrix, combine a convolutional neural network to capture the spatial distribution law, establish a dynamic line loss prediction model, and obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model; S3. Obtain the historical actual line loss value and compare the difference with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model. According to the difference comparison result, determine whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters, and then screen the key loss-causing factors through Pearson correlation analysis; S4. Obtain the environmental prediction data and the real-time operation parameters of the line, and dynamically generate the priority events of the loss reduction strategy in combination with the key loss-causing factors and the dynamic line loss prediction model; S5. Execute the priority events of the loss reduction strategy, obtain the actual line loss data after execution, synchronously perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and use the reinforcement learning algorithm to optimize the dynamic line loss prediction model; S6. Based on the optimized dynamic line loss prediction model, generate a loss reduction case library for typical environmental scenarios, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set.

2. The intelligent control method for transmission lines based on distribution data feedback according to claim 1, wherein, Step S1 specifically includes: S1.

1. Collect the historical operation parameters of each transmission line to be analyzed through the SCADA system. The historical operation parameters of the line include historical current, historical voltage, historical load rate, and historical fault records; S1.

2. Collect historical temperature, historical humidity, historical wind speed, historical rainfall, and historical ice coating degree through the weather stations in the area where each transmission line to be analyzed is located to obtain historical meteorological data; S1.

3. Collect historical terrain slope, historical vegetation coverage rate, historical soil resistivity, and historical altitude through the weather stations in the area where each transmission line to be analyzed is located; S1.

4. Collect historical industrial pollution concentration, historical traffic noise decibel value, and historical electromagnetic radiation intensity of wireless base stations through the weather stations in the area where each transmission line to be analyzed is located to obtain historical humanistic data; S1.

5. Use moving average filtering for historical meteorological data, historical geographical data, and historical line operation parameters to eliminate high-frequency noise, and use median filtering for historical humanistic data to suppress mutation interference; S1.

6. Use the KNN interpolation method to fill in the missing values in the historical meteorological data, and use linear regression to fill in the missing values in the geographical data at the same time; S1.

7. Eliminate abnormal data points outside the normal range based on the 3σ principle, and project all the data processed in steps 1.4 to 1.6 to the UTM coordinate system. Taking the center point of the transmission line as the origin, establish a local coordinate grid; S1.

8. Align the historical meteorological data, historical geographical data, historical humanistic data, and historical line operation parameters projected onto the UTM coordinate system according to the minute-level timestamps. S1.

9. Use principal component analysis for dimensionality reduction, retain the principal components with a cumulative variance contribution rate greater than or equal to 95%, and then generate a historical environment - electrical coupling feature matrix with a dimension less than or equal to 50.

3. The intelligent control method for transmission lines based on distribution data feedback according to claim 1 is characterized in that, Step S2 specifically includes: S2.

1. Design a double-layer long short-term memory network, set the input layer dimension to 50, the number of hidden layer units to 128, the activation function to tanh, and the output layer to predict the line loss sequence for the next 24 hours. S2.

2. Divide the historical data in the historical environment - electrical coupling feature matrix into training samples according to a 24-hour sliding window, set the step size to 1 hour, and generate N time series segments. S2.

3. Use a 3-layer convolutional neural network, set the convolutional kernel size to 3×3, the step size to 1, and the padding method to SAME, to extract the spatial distribution features of the historical geographical data in the historical environment - electrical coupling feature matrix. S2.

4. Concatenate the N time series segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network, input them into the fully connected layer, and the output layer generates the dynamic line loss prediction value, thus completing the construction of the dynamic line loss prediction model. S2.

5. According to the N time series segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network, obtain the historical environment sensitivity coefficient matrix, and use the historical environment sensitivity coefficient matrix to correct the historical line operation parameters in the historical environment - electrical coupling feature matrix. S2.

6. Set the loss function, and use the Adam algorithm to dynamically adjust the learning rate, and then input the corrected N time series segments generated by the double-layer long short-term memory network and the spatial distribution features obtained by the 3-layer convolutional neural network into the dynamic line loss prediction model to train the model. S2.

7. Simulate and generate an environment - electrical coupling feature simulation matrix under different environmental scenarios, and input the simulated meteorological data, simulated geographical data, and simulated humanistic data in the environment - electrical coupling feature simulation matrix into the trained dynamic line loss prediction model to obtain the minimum line loss values under different environmental scenarios output by the trained dynamic line loss prediction model.

4. The intelligent control method for transmission lines based on distribution data feedback according to claim 1, characterized in that, Step S3 specifically includes: Obtain the difference between the historical actual line loss values corresponding to the N time series segments and the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model. If the difference between the historical actual line loss values and the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model is greater than or equal to 0.15 times the minimum line loss value, trigger the isolation forest algorithm to obtain the anomaly score for each data point. Screen for data points with abnormal scores higher than T threshold = the simulated meteorological data, simulated geographical data, and simulated human data in the environmental-electrical coupling characteristic simulation matrix corresponding to the data points where μ(s)+3σ(s), where μ(s) is the global mean of the minimum line loss values corresponding to all data points, σ(s) is the global standard deviation of the minimum line loss values corresponding to all data points, and T threshold is the abnormal data screening threshold; Based on the first abnormal data and its corresponding minimum line loss value, obtain the first correlation coefficient, and at the same time obtain the first abnormal data with the first correlation coefficient greater than or equal to 0.

8. Through the SHAP value analysis model, quantify the contribution degree of each factor in the first abnormal data to the line loss, and mark the factor with the largest contribution degree as the key damage-causing factor. Among them, the calculation formula for triggering the isolation forest algorithm to obtain the anomaly score for each data point is: In the formula, is the anomaly score under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the historical actual line loss value at the i-th time series segment corresponding to the transmission circuit under the t-th environmental scenario, is the mean value under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment, is the standard deviation of the minimum line loss value under the t-th environmental scenario output by the dynamic line loss prediction model corresponding to the i-th time series segment.

5. The intelligent control method for transmission lines based on distribution data feedback according to claim 1, wherein Step S4 specifically includes: Obtain meteorological data, geographical data, and humanistic data for the next 24 hours from the meteorological bureau to obtain environmental prediction data, and simultaneously obtain the real-time operation parameters of each transmission line to be analyzed; Input the environmental prediction data and the real-time operation parameters of the line into the dynamic line loss prediction model to obtain the minimum line loss value corresponding to the environmental prediction data and the real-time operation parameters of the line; Compare the environmental prediction data and the real-time operation parameters of the line with the key damage-causing factors to dynamically generate a dynamic damage-causing value; Perform weighted summation on the minimum line loss value and the dynamic damage-causing value to obtain a reference value for the loss reduction strategy; Based on the reference value for the loss reduction strategy, dynamically generate loss reduction strategy priority events.

6. The intelligent control method for transmission lines based on distribution data feedback according to claim 1 is characterized in that, Step S5 specifically includes: After executing the loss reduction strategy priority event, obtain the actual line loss value of the transmission line corresponding to the loss reduction strategy priority event during the abnormal period to obtain the actual line loss data after execution; Simultaneously obtain the actual environment-electrical coupling characteristic matrix of the transmission line corresponding to the loss reduction strategy priority event; Import the data in the actual environment-electrical coupling characteristic matrix into the dynamic line loss prediction model to obtain the predicted line loss data output by the dynamic line loss prediction model; Compare the predicted line loss value included in the predicted line loss data output by the dynamic line loss prediction model with the actual line loss value to obtain the difference between the two for error backpropagation, and simultaneously use the Adam optimizer to update the parameters in the dynamic line loss prediction model to optimize the dynamic line loss prediction model using the reinforcement learning algorithm.

7. The intelligent control method for transmission lines based on distribution data feedback according to claim 1, characterized in that, Step S6 specifically includes: Based on the optimized dynamic line loss prediction model, obtain the minimum line loss values under different environmental scenarios; Perform typical scenario division on the meteorological data, geographical data, humanistic data, and line operation parameters corresponding to the minimum line loss values under different environmental scenarios, and combine with the loss reduction strategy priority events to generate a typical environmental scenario loss reduction case library; Construct a three-dimensional line model based on the Unreal Engine, and inject extreme environmental parameters and extreme line operation parameters into the three-dimensional line model using physical simulation to simulate the effectiveness of the strategy under extreme conditions; According to the effectiveness of the strategy under extreme conditions, obtain the loss reduction effective events under extreme conditions, and use the loss reduction effective events under extreme conditions to supplement the typical environmental scenario loss reduction case library to form a reusable decision rule set.

8. An intelligent control system for transmission lines based on distribution data feedback, which is used to implement the control method described in any one of claims 1-7, characterized in that, It includes: A collection module, which is used to obtain the historical operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data, and historical humanistic data of the area where each transmission line to be analyzed is located, and is used to obtain environmental prediction data and real-time operation parameters of the line; A data processing module, which is used to perform spatio-temporal alignment on the historical meteorological data, historical geographical data, historical humanistic data, and historical operation parameters of the area where each transmission line to be analyzed is located, and construct a historical environment-electrical coupling characteristic matrix, and is used to extract the historical environmental time series characteristics in the historical environment-electrical coupling characteristic matrix using a long short-term memory network, and capture the spatial distribution law in combination with a convolutional neural network to establish a dynamic line loss prediction model; A main control module, which is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, to obtain the historical actual line loss values and compare the differences with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and based on the difference comparison results, to determine whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters, and then screen key loss-causing factors through Pearson correlation analysis, to obtain environmental prediction data and real-time line operation parameters, and combine the key loss-causing factors and the dynamic line loss prediction model to dynamically generate loss reduction strategy priority events, execute the loss reduction strategy priority events, and obtain the actual line loss data after execution, to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, use the reinforcement learning algorithm to optimize the dynamic line loss prediction model, to generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set; A display module, which is used to display the data and information collected by the collection module, display the data processing process and results of the data processing module, and display the data analysis process and results of the main control module.

9. The intelligent control system for transmission lines based on distribution data feedback according to claim 8, characterized in that, The collection module includes: A first collection unit, which is used to obtain the historical operation parameters of each transmission line to be analyzed and the historical meteorological data, historical geographical data and historical human data of the area where each transmission line to be analyzed is located; A second collection unit, which is used to obtain environmental prediction data and real-time line operation parameters.

10. An intelligent control system for transmission lines based on distribution data feedback according to claim 8, characterized in that, The main control module includes: A line loss evaluation unit, which is used to obtain the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model; A loss-causing factor judgment unit, which is used to obtain the historical actual line loss values and compare the differences with the minimum line loss values under different environmental scenarios output by the dynamic line loss prediction model, and based on the difference comparison results, to determine whether to trigger the isolation forest algorithm to detect abnormal periods of environmental parameters, and then screen key loss-causing factors through Pearson correlation analysis; A loss reduction execution unit, which is used to obtain environmental prediction data and real-time line operation parameters, and combine the key loss-causing factors and the dynamic line loss prediction model to dynamically generate loss reduction strategy priority events, execute the loss reduction strategy priority events, and obtain the actual line loss data after execution; A model optimization unit, which is used to perform error backpropagation on the actual line loss data and the predicted line loss data output by the dynamic line loss prediction model, and use the reinforcement learning algorithm to optimize the dynamic line loss prediction model; A decision rule generation unit, which is used to generate a loss reduction case library for typical environmental scenarios based on the optimized dynamic line loss prediction model, and simulate the effectiveness of the strategy under extreme conditions through digital twin technology to form a reusable decision rule set.

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