Electricity utilization safety supervision method and system

Abstract

The invention relates to the technical field of power utilization monitoring, and discloses a power utilization safety supervision method and system, and the method comprises the steps: obtaining the multi-dimensional information of a plurality of data terminals and K data transfer terminals in a target region according to a fixed time interval in a first preset time period; dividing the plurality of data terminals into M wireless terminals and N wired terminals based on a connection mode, and collecting fine-grained transmission data of a data packet of each wireless terminal according to a fixed time interval; the analysis module is used for acquiring position coordinates of a plurality of base stations in a target area and analyzing each wireless terminal based on the position coordinates of the base stations to obtain a transmission path of each wireless terminal; the data processing module is used for processing transmission paths and fine-grained transmission data of M wireless terminals to obtain sample data and sample labels, and training to obtain a delay time prediction model; and based on the delay time prediction model, dynamically generating a data transmission scheme for the M wireless terminals in the target area to minimize the time window.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity consumption monitoring, and more specifically, to a method and system for monitoring electricity consumption safety. Background Art

[0002] In modern power monitoring systems, data collection usually uses wired or wireless transmission to obtain real-time monitoring data from smart meters, and then transmits it to the monitoring center after pre-processing in the data transfer area. Due to the differences in the arrival time of data transmitted from the monitoring point, in order to ensure that data with the same timestamp can be processed synchronously, the system usually uses a time window determined based on the earliest arrival time and the latest arrival time. However, the setting of the time window directly affects the data sampling frequency. When the sampling interval is less than the time window, data congestion will occur. If the sampling interval is greater than or equal to the time window, spectrum leakage may occur, resulting in the situation where the monitoring data appears normal but the actual data is abnormal. Therefore, in order to avoid the data acquisition delay of the system and reduce the possibility of spectrum leakage, it is necessary to reduce the size of the time window as much as possible. Summary of the invention

[0003] The present invention provides an electricity safety monitoring system to solve the technical problems raised in the background technology.

[0004] The present invention provides an electricity safety supervision system, comprising: The data acquisition module is used to obtain multi-dimensional information of a plurality of data terminals and K data transfer terminals in the target area at fixed time intervals within a first preset time period, including: connection mode and location coordinates; A data processing module, used to divide a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collect fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; A data analysis module is used to obtain the location coordinates of several base stations in the target area, and analyze each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; A model building module is used to process the transmission paths and fine-grained transmission data of M wireless terminals to obtain sample data and sample labels, and train to obtain a delay time prediction model; The time window optimization module is used to dynamically generate a data transmission plan for M wireless terminals in the target area within a second preset time period based on the delay time prediction model to minimize the time window.

[0005] Further, based on the connection mode, a number of data terminals are divided into M wireless terminals and N wired terminals, including: the connection mode includes: wired connection and wireless connection; based on the connection mode between the data terminal and the data transfer terminal, the data terminal is divided into M wireless terminals and N wired terminals.

[0006] Furthermore, each wireless terminal is analyzed based on the location coordinates of the base station, including: obtaining the data packet of each wireless terminal at the qth moment, and parsing the data packet to obtain the propagation path and transfer time of the corresponding wireless terminal's monitoring data; wherein the propagation path indicates that the monitoring data of the wireless terminal arrives at the base station via which the corresponding data transfer terminal transfers, and the transfer time indicates the time when the monitoring data of the wireless terminal arrives at each base station via which it transfers; wherein 1≤q≤T1 / t, T1 represents the first preset time period, and t represents a fixed time interval; the data packet includes: monitoring data, base stations via which it transfers, and the time at which it transfers through each base station.

[0007] Furthermore, the fine-grained transmission data is processed, including: within the qth moment in the first preset time period, according to the transmission time of the propagation path of the monitoring data of the mth wireless terminal, the delay time is calculated, including: obtaining the Euclidean distance between the wireless terminal and the base station, the Euclidean distance between the base stations, and the Euclidean distance between the base station and the data transfer terminal in the corresponding propagation path; based on the transmission speed of the electromagnetic wave, the standard transmission time is obtained; the difference time between the standard transmission time and the transmission time is used as the delay time; wherein, 1≤m≤M.

[0008] Furthermore, the sample data and sample labels include: normalizing the environmental data of each wireless terminal, base station and data transfer terminal in the propagation path of the monitoring data of the mth wireless terminal at the qth moment in the first preset time period to obtain the sample data; wherein the environmental data includes several types of weather, and each type of weather is respectively subjected to real number coding processing; and normalizing the delay time of the propagation path of the monitoring data of the mth wireless terminal to obtain the sample label.

[0009] Furthermore, the delay time prediction model is constructed based on a one-dimensional convolutional neural network and converges through the mean square error loss function.

[0010] Furthermore, a data transmission scheme is dynamically generated for the M wireless terminals in the target area, including: Step 71, retaining the pairing relationship between N wired terminals and the corresponding data transfer terminals, and performing formatting processing on the pairing relationship between M wireless terminals and the corresponding data transfer terminals; wherein the pairing relationship represents the data connection relationship between the wireless terminal and the data transfer terminal; Step 72, at the s-th moment in the second preset time period, obtain the environmental data of each wireless terminal, base station and data transfer terminal at the s-th moment; wherein 1≤s≤T2 / t, T2 represents the second preset time period; Step 73, generating R re-pairing schemes of the M wireless terminals and the K data transfer terminals that meet the constraint conditions, each re-pairing scheme represents generating a pairing relationship between the M wireless terminals and the K data transfer terminals; The constraint conditions include: the number of data terminals connected to each data transfer terminal, Lmin≤L≤Lmax; where Lmin represents the minimum number of data terminals connected to each data transfer terminal, and Lmax represents the maximum number of data terminals connected to each data transfer terminal; the Euclidean distance of the wireless terminal that has established a pairing relationship with the data transfer terminal is less than or equal to the preset distance threshold D; The fitness function includes: obtaining U predicted propagation paths for the monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal; wherein 1≤r≤R, 1≤n≤N; According to the environmental data of each wireless terminal, base station and data transfer terminal in each predicted propagation path, the predicted delay time of each predicted propagation path is calculated by the delay time prediction model; Based on the sum of the standard transmission time and the predicted delay time of each predicted propagation path, selecting the minimum sum time as the transmission time of the mth wireless terminal in the rth re-pairing scheme; Determine the maximum transmission time Q1 and the minimum transmission time W1 of the M wireless terminals in the rth re-pairing scheme, and the maximum transmission time Q2 and the minimum transmission time W2 of the N wired terminals; ;in, represents the time window of the jth re-pairing scheme, represents the maximum selection operation, Represents the minimum selection operation; Step 74: As the fitness function value of the rth re-pairing scheme, the R re-pairing schemes are sorted from small to large based on the fitness function value to obtain the feature sorting; Step 75, sorting the features from front to back to retain a preset number of re-pairing schemes, and performing crossover and mutation operations on the remaining re-pairing schemes based on a genetic algorithm to update the re-pairing schemes; Step 76, repeating steps 74 and 75 for a preset number of times to obtain a final feature ranking; using the re-pairing scheme ranked 1 in the feature ranking as the data transmission scheme at the sth moment in the second preset time period.

[0011] Further, obtaining U predicted propagation paths for the monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal includes: Establish a transmission topology, which includes topological nodes and topological edges; map each wireless terminal, data transfer terminal and base station to a topological node; calculate the Euclidean distance between any two topological nodes based on the location coordinates; if the Euclidean distance is less than or equal to a preset distance threshold D, construct a topological edge between the corresponding topological nodes to obtain a transmission topology; Based on the transmission topology, the topological node corresponding to the m-th wireless terminal is obtained, and U paths to the topological node corresponding to the n-th data transfer terminal are used as the predicted propagation paths.

[0012] In a second aspect, a method for monitoring power safety is applied to any one of the power safety monitoring systems described above, comprising: Step 81, within a first preset time period, obtaining multi-dimensional information of a plurality of data terminals and K data transfer terminals in a target area at fixed time intervals, including: connection modes and location coordinates; Step 82, dividing a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collecting fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; Step 83, obtaining the location coordinates of several base stations in the target area, and analyzing each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; Step 84, processing the transmission paths and fine-grained transmission data of the M wireless terminals to obtain sample data and sample labels, and training to obtain a delay time prediction model; Step 85: Dynamically generate a data transmission plan for the M wireless terminals in the target area within the second preset time period based on the delay time prediction model to minimize the time window.

[0013] The beneficial effect of the present invention is that by integrating fine data collection, delay prediction and dynamic transmission scheme optimization technology, wireless and wired terminal data are collected in real time, a delay prediction model is constructed using a one-dimensional convolutional neural network, and a genetic algorithm is combined to optimize data transfer pairing, thereby minimizing the transmission time window. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a module diagram of a power safety supervision system of the present invention; Figure 2 The present invention is a flow chart of a method for monitoring electricity safety. DETAILED DESCRIPTION

[0015] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0016] like Figure 1 As shown, a power safety supervision system includes: The data acquisition module is used to obtain multi-dimensional information of a plurality of data terminals and K data transfer terminals in the target area at fixed time intervals within a first preset time period, including: connection mode and location coordinates; A data processing module, used to divide a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collect fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; A data analysis module is used to obtain the location coordinates of several base stations in the target area, and analyze each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; A model building module is used to process the transmission paths and fine-grained transmission data of M wireless terminals to obtain sample data and sample labels, and train to obtain a delay time prediction model; The time window optimization module is used to dynamically generate a data transmission plan for M wireless terminals in the target area within a second preset time period based on the delay time prediction model to minimize the time window.

[0017] In one embodiment of the present invention, a number of data terminals are divided into M wireless terminals and N wired terminals based on the connection mode, including: the connection mode includes: wired connection and wireless connection; based on the connection mode between the data terminal and the data transfer terminal, the data terminal is divided into M wireless terminals and N wired terminals.

[0018] Specifically, the system divides the terminals into two categories based on the connection method between the data terminal and the data transfer terminal: wired terminals and wireless terminals. Specifically, the wired terminal and the data transfer terminal are connected through a physical connection, which is relatively stable and not easily affected by the external environment. At the same time, any change to this connection method requires a high cost; while wireless terminals rely on wireless connections, which are more flexible and the connection status can be adjusted as needed without the need for actual physical modification.

[0019] In one embodiment of the present invention, each wireless terminal is analyzed based on the location coordinates of the base station, including: obtaining the data packet of each wireless terminal at the qth moment, and parsing the data packet to obtain the propagation path and transfer time of the corresponding wireless terminal's monitoring data; wherein the propagation path indicates that the monitoring data of the wireless terminal arrives at the base station via which the corresponding data transfer terminal transfers, and the transfer time indicates the time when the monitoring data of the wireless terminal arrives at each base station via which it transfers; wherein 1≤q≤T1 / t, T1 represents a first preset time period, and t represents a fixed time interval; the data packet includes: monitoring data, base stations via which it transfers, and the time at which it transfers to each base station.

[0020] Specifically, the system analyzes the transmission path by parsing the data packets transmitted by the wireless terminal. Specifically, each data packet will automatically record the base stations it passes through and the time it arrives at each base station during the forwarding process. By parsing these data packets, the system can restore the complete path of the wireless terminal data from the source to the data transfer end, and obtain the transfer time of each transfer base station.

[0021] In one embodiment of the present invention, fine-grained transmission data is processed, including: within the qth moment in the first preset time period, according to the transmission time of the propagation path of the monitoring data of the mth wireless terminal, the delay time is calculated, including: obtaining the Euclidean distance between the wireless terminal and the base station, the Euclidean distance between the base stations, and the Euclidean distance between the base station and the data transfer terminal in the corresponding propagation path; based on the transmission speed of electromagnetic waves, the standard transmission time is obtained; the difference time between the standard transmission time and the transmission time is used as the delay time; wherein, 1≤m≤M.

[0022] Specifically, the system performs fine-grained processing on the data packets transmitted by the wireless terminal to quantify the delay in the actual transmission process. Specifically, first, based on the Euclidean distance between the wireless terminal and the base station, and between the base stations, and using the theory that electromagnetic waves propagate at the speed of light, a theoretically required standard transmission time is calculated (i.e., transmission time = distance / speed of light). However, the actual transmission time is often affected by factors such as environmental interference and signal attenuation, and there is a difference from the standard transmission time. By calculating the difference between the actual transmission time and the standard transmission time, a delay time can be obtained, which is used to quantify the negative impact of environmental factors on data transmission.

[0023] In one embodiment of the present invention, sample data and sample labels include: normalizing the environmental data of each wireless terminal, base station and data transfer terminal in the propagation path of the monitoring data of the mth wireless terminal at the qth moment in the first preset time period to obtain sample data; wherein the environmental data includes several types of weather, and real number coding is performed on each type of weather; and normalizing the delay time of the propagation path of the monitoring data of the mth wireless terminal to obtain a sample label.

[0024] Specifically, the system constructs sample data and sample labels for training the delay prediction model by normalizing the monitoring data of the wireless terminal at a specific moment (the qth moment) within the first preset time period. Specifically, the sample data comes from the environmental data of all nodes (wireless terminals, base stations, and data transfer terminals) in the propagation path of the monitoring data of the mth wireless terminal. These environmental data contain several types of weather information. By encoding each type of weather data with real numbers and then normalizing it, the data features are within a unified numerical range; and the sample label is the normalization of the delay time calculated in the propagation path, the purpose of which is to eliminate the influence of different scales on model training, so as to more effectively reflect the influence of environmental factors on data transmission delay.

[0025] For example, the order of a propagation path is , the corresponding environmental data is , where weather 2 corresponds to wireless terminal m, weather 3 corresponds to base station 1, weather 5 corresponds to base station 2, and weather 4 corresponds to the data transfer terminal. Then, After normalization, the sample data is obtained.

[0026] In one embodiment of the present invention, the delay time prediction model is constructed based on a one-dimensional convolutional neural network and converges through a mean square error loss function.

[0027] Specifically, the delay time prediction model is built based on a one-dimensional convolutional neural network, and its training process relies on a strictly divided data set and a scientific verification method. First, all the data collected by the wireless terminal are preprocessed and normalized to form the input features of the model, and the corresponding delay time is normalized as the target output. In order to ensure that the model has good generalization ability, the data set is usually divided into a training set, a validation set, and a test set, for example, according to the ratio of 70%, 15%, and 15%; at the same time, in order to further eliminate the randomness of data division and improve data utilization, the K-fold cross validation method can also be used, that is, the entire data set is divided into K subsets, each subset is used as a validation set in turn, and the rest is used as a training set, and finally the average is taken to evaluate the model performance. In terms of network structure, the one-dimensional convolutional neural network (1D-CNN) uses the convolution layer to extract the input time series features locally, and then reduces the dimension and denoises through the activation function and pooling layer, and finally the fully connected layer maps the extracted features to the delay prediction value. The training goal of the entire model is to minimize the mean square error (MSE) between the predicted value and the actual delay value, which is a common loss function for measuring prediction errors. The MSE loss function can effectively punish large deviations by calculating the square of the difference between the predicted value and the true value and averaging it, thereby guiding the model to correct errors faster. During the training process, the back propagation algorithm is combined with gradient descent or advanced optimization algorithms such as Adam to gradually update the model parameters so that the loss function value continues to decrease until convergence. With the help of K-fold cross-validation, not only can the stability of the model under different data partitions be verified, but also a reliable basis can be provided for adjusting hyperparameters (such as learning rate, convolution kernel size, and number of layers), thereby further improving the prediction accuracy and robustness of the model. Overall, this training strategy enables the delay time prediction model to make full use of the timing and environmental information in the data, accurately capture the complex factors that affect data transmission delay, and achieve accurate prediction of transmission delay.

[0028] In one embodiment of the present invention, dynamically generating a data transmission scheme for M wireless terminals in a target area includes: Step 71, retaining the pairing relationship between N wired terminals and the corresponding data transfer terminals, and performing formatting processing on the pairing relationship between M wireless terminals and the corresponding data transfer terminals; wherein the pairing relationship represents the data connection relationship between the wireless terminal and the data transfer terminal; Step 72, at the s-th moment in the second preset time period, obtain the environmental data of each wireless terminal, base station and data transfer terminal at the s-th moment; wherein 1≤s≤T2 / t, T2 represents the second preset time period; Step 73, generating R re-pairing schemes of the M wireless terminals and the K data transfer terminals that meet the constraint conditions, each re-pairing scheme represents generating a pairing relationship between the M wireless terminals and the K data transfer terminals; The constraint conditions include: the number of data terminals connected to each data transfer terminal, Lmin≤L≤Lmax; where Lmin represents the minimum number of data terminals connected to each data transfer terminal, and Lmax represents the maximum number of data terminals connected to each data transfer terminal; the Euclidean distance of the wireless terminal that has established a pairing relationship with the data transfer terminal is less than or equal to the preset distance threshold D; The fitness function includes: obtaining U predicted propagation paths for the monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal; wherein 1≤r≤R, 1≤n≤N; According to the environmental data of each wireless terminal, base station and data transfer terminal in each predicted propagation path, the predicted delay time of each predicted propagation path is calculated by the delay time prediction model; Based on the sum of the standard transmission time and the predicted delay time of each predicted propagation path, selecting the minimum sum time as the transmission time of the mth wireless terminal in the rth re-pairing scheme; Determine the maximum transmission time Q1 and the minimum transmission time W1 of the M wireless terminals in the rth re-pairing scheme, and the maximum transmission time Q2 and the minimum transmission time W2 of the N wired terminals; ;in, represents the time window of the jth re-pairing scheme, represents the maximum selection operation, Represents the minimum selection operation; Step 74: As the fitness function value of the rth re-pairing scheme, the R re-pairing schemes are sorted from small to large based on the fitness function value to obtain the feature sorting; Step 75, sorting the features from front to back to retain a preset number of re-pairing schemes, and performing crossover and mutation operations on the remaining re-pairing schemes based on a genetic algorithm to update the re-pairing schemes; Step 76, repeating steps 74 and 75 for a preset number of times to obtain a final feature ranking; using the re-pairing scheme ranked 1 in the feature ranking as the data transmission scheme at the sth moment in the second preset time period.

[0029] Specifically, the system retains a stable and fixed pairing relationship between N wired terminals and the data transfer terminal, while the connection between M wireless terminals and the data transfer terminal needs to be reformatted (step 71), which lays a data foundation for the subsequent dynamic re-pairing. Next, at a certain time s in the second preset time period (step 72), the system collects the environmental data of each wireless terminal, base station and data transfer terminal at that time to provide real-time information for dynamically adjusting the transmission plan. In step 73, the system generates R re-pairing schemes that meet the preset constraints based on these real-time data. The constraints include: the number of wireless terminals connected to each data transfer terminal must be between Lmin and Lmax, and at the same time, the Euclidean distance between the wireless terminal and the data transfer terminal must be less than or equal to a preset distance threshold D. For each candidate solution, the system further obtains U predicted propagation paths from each wireless terminal to the specified data transfer terminal, and uses the previously trained delay prediction model to calculate the predicted delay time of each path. By summing the standard transmission time of each propagation path (calculated based on the Euclidean distance and the speed of light) and its predicted delay time, the smallest sum is selected as the transmission time of the wireless terminal in the scheme, and the maximum and minimum transmission times of the wireless terminal and the wired terminal in the entire scheme are calculated, thereby obtaining the corresponding transmission time window. In step 74, the system sorts the R rematching schemes according to the fitness function value (i.e., the time window index) calculated above, and selects the best candidate scheme. Then in step 75, the system not only retains some of the top-ranked schemes, but also updates the remaining schemes using the crossover and mutation operations in the genetic algorithm to introduce more diversity and optimization potential. After multiple iterations (step 76), the rematching scheme ranked first is finally selected as the best data transmission scheme at that moment. This method not only takes into account the physical connection constraints and distance restrictions between the wireless terminal and the data transfer terminal, but also combines the real-time environmental data and the output of the delay prediction model, and continuously optimizes through the genetic algorithm, thereby effectively reducing the transmission time window.

[0030] In one embodiment of the present invention, obtaining U predicted propagation paths for monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal includes: Establish a transmission topology, which includes topological nodes and topological edges; map each wireless terminal, data transfer terminal and base station to a topological node; calculate the Euclidean distance between any two topological nodes based on the location coordinates; if the Euclidean distance is less than or equal to a preset distance threshold D, construct a topological edge between the corresponding topological nodes to obtain a transmission topology; Based on the transmission topology, the topological node corresponding to the m-th wireless terminal is obtained, and U paths to the topological node corresponding to the n-th data transfer terminal are used as the predicted propagation paths.

[0031] Specifically, in order to predict the propagation path of the data of the mth wireless terminal from the mth wireless terminal to the nth data transfer terminal, the system first constructs a transmission topology structure. All data entities involved (wireless terminals, data transfer terminals, and base stations) are mapped to topological nodes. Based on the location information of each node, the Euclidean distance between any two topological nodes is calculated; if the Euclidean distance between the two nodes is less than or equal to the preset distance threshold D, a topological edge is established between them, thereby forming a transmission network that reflects the possibility of actual physical connection. Using the constructed transmission topology, the system starts from the topological node corresponding to the mth wireless terminal and searches for all possible paths to the topological node corresponding to the nth data transfer terminal; U of them are selected as predicted propagation paths to evaluate the delay and reliability of data transmission from the wireless terminal to the data transfer terminal.

[0032] A method for monitoring power safety, applied to any one of the power safety monitoring systems, comprising: Step 81, within a first preset time period, obtaining multi-dimensional information of a plurality of data terminals and K data transfer terminals in a target area at fixed time intervals, including: connection modes and location coordinates; Step 82, dividing a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collecting fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; Step 83, obtaining the location coordinates of several base stations in the target area, and analyzing each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; Step 84, processing the transmission paths and fine-grained transmission data of the M wireless terminals to obtain sample data and sample labels, and training to obtain a delay time prediction model; Step 85: Dynamically generate a data transmission plan for the M wireless terminals in the target area within the second preset time period based on the delay time prediction model to minimize the time window.

[0033] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A power safety supervision system, characterized in that: include: The data acquisition module is used to obtain multi-dimensional information of a plurality of data terminals and K data transfer terminals in the target area at fixed time intervals within a first preset time period, including: connection mode and location coordinates; A data processing module, used to divide a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collect fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; A data analysis module is used to obtain the location coordinates of several base stations in the target area, and analyze each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; A model building module is used to process the transmission paths and fine-grained transmission data of M wireless terminals to obtain sample data and sample labels, and train to obtain a delay time prediction model; The time window optimization module is used to dynamically generate a data transmission plan for M wireless terminals in the target area within a second preset time period based on the delay time prediction model to minimize the time window.

2. The power safety supervision system according to claim 1, characterized in that: Based on the connection mode, a number of data terminals are divided into M wireless terminals and N wired terminals, including: the connection mode includes: wired connection and wireless connection; based on the connection mode between the data terminal and the data transfer terminal, the data terminal is divided into M wireless terminals and N wired terminals.

3. The power safety supervision system according to claim 2, characterized in that: Each wireless terminal is analyzed based on the location coordinates of the base station, including: obtaining the data packet of each wireless terminal at the qth moment, and parsing the data packet to obtain the propagation path and transfer time of the corresponding wireless terminal's monitoring data; wherein the propagation path represents the base station via which the monitoring data of the wireless terminal arrives at the corresponding data transfer terminal, and the transfer time represents the time when the monitoring data of the wireless terminal arrives at each base station via which it is transferred; wherein 1≤q≤T1 / t, T1 represents a first preset time period, and t represents a fixed time interval; the data packet includes: monitoring data, base stations via which it is transferred, and the time at which it is transferred to each base station.

4. The power safety supervision system according to claim 3, characterized in that: Processing fine-grained transmission data includes: within the qth moment of the first preset time period, calculating the delay time according to the transmission time of the propagation path of the monitoring data of the mth wireless terminal, including: obtaining the Euclidean distance between the wireless terminal and the base station, the Euclidean distance between the base stations, and the Euclidean distance between the base station and the data transfer terminal in the corresponding propagation path; obtaining the standard transmission time based on the transmission speed of the electromagnetic wave; and taking the difference time between the standard transmission time and the transmission time as the delay time; wherein 1≤m≤M.

5. The power safety supervision system according to claim 4, characterized in that: Sample data and sample labels include: normalizing the environmental data of each wireless terminal, base station and data transfer terminal in the propagation path of the monitoring data of the mth wireless terminal at the qth moment in the first preset time period to obtain sample data; wherein the environmental data includes several types of weather, and real number coding is performed on each type of weather; normalizing the delay time of the propagation path of the monitoring data of the mth wireless terminal to obtain a sample label.

6. The power safety supervision system according to claim 5, characterized in that: The delay time prediction model is built based on a one-dimensional convolutional neural network and converges through the mean square error loss function.

7. The power safety supervision system according to claim 6, characterized in that: Dynamically generate a data transmission plan for M wireless terminals in the target area, including: Step 71, retaining the pairing relationship between N wired terminals and the corresponding data transfer terminals, and performing formatting processing on the pairing relationship between M wireless terminals and the corresponding data transfer terminals; wherein the pairing relationship represents the data connection relationship between the wireless terminal and the data transfer terminal; Step 72, at the s-th moment in the second preset time period, obtain the environmental data of each wireless terminal, base station and data transfer terminal at the s-th moment; wherein 1≤s≤T2 / t, T2 represents the second preset time period; Step 73, generating R re-pairing schemes of the M wireless terminals and the K data transfer terminals that meet the constraint conditions, each re-pairing scheme represents generating a pairing relationship between the M wireless terminals and the K data transfer terminals; The constraint conditions include: the number of data terminals connected to each data transfer terminal, Lmin≤L≤Lmax; where Lmin represents the minimum number of data terminals connected to each data transfer terminal, and Lmax represents the maximum number of data terminals connected to each data transfer terminal; the Euclidean distance of the wireless terminal that has established a pairing relationship with the data transfer terminal is less than or equal to the preset distance threshold D; The fitness function includes: obtaining U predicted propagation paths for the monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal; wherein 1≤r≤R, 1≤n≤N; According to the environmental data of each wireless terminal, base station and data transfer terminal in each predicted propagation path, the predicted delay time of each predicted propagation path is calculated by the delay time prediction model; Based on the sum of the standard transmission time and the predicted delay time of each predicted propagation path, selecting the minimum sum time as the transmission time of the mth wireless terminal in the rth re-pairing scheme; Determine the maximum transmission time Q1 and the minimum transmission time W1 of the M wireless terminals in the rth re-pairing scheme, and the maximum transmission time Q2 and the minimum transmission time W2 of the N wired terminals; ;in, represents the time window of the jth re-pairing scheme, represents the maximum selection operation, Represents the minimum selection operation; Step 74: As the fitness function value of the rth re-pairing scheme, the R re-pairing schemes are sorted from small to large based on the fitness function value to obtain the feature sorting; Step 75, sorting the features from front to back to retain a preset number of re-pairing schemes, and performing crossover and mutation operations on the remaining re-pairing schemes based on a genetic algorithm to update the re-pairing schemes; Step 76, repeating steps 74 and 75 for a preset number of times to obtain a final feature ranking; using the re-pairing scheme ranked 1 in the feature ranking as the data transmission scheme at the sth moment in the second preset time period.

8. The power safety supervision system according to claim 7, characterized in that: Obtaining U predicted propagation paths for the monitoring data of the mth wireless terminal in the rth re-pairing scheme to reach the nth data transfer terminal, including: Establish a transmission topology, which includes topological nodes and topological edges; map each wireless terminal, data transfer terminal and base station to a topological node; calculate the Euclidean distance between any two topological nodes based on the location coordinates; if the Euclidean distance is less than or equal to a preset distance threshold D, construct a topological edge between the corresponding topological nodes to obtain a transmission topology; Based on the transmission topology, the topological node corresponding to the m-th wireless terminal is obtained, and U paths to the topological node corresponding to the n-th data transfer terminal are used as the predicted propagation paths.

9. A method for monitoring electricity safety, characterized in that: An electricity safety supervision system applied to any one of claims 1 to 8, comprising: Step 81, within a first preset time period, obtaining multi-dimensional information of a plurality of data terminals and K data transfer terminals in a target area at fixed time intervals, including: connection modes and location coordinates; Step 82, dividing a number of data terminals into M wireless terminals and N wired terminals based on the connection mode, and collecting fine-grained transmission data of the data packets of each wireless terminal at fixed time intervals, including: transmission time and environment data; Step 83, obtaining the location coordinates of several base stations in the target area, and analyzing each wireless terminal based on the location coordinates of the base stations to obtain the transmission path of each wireless terminal; Step 84, processing the transmission paths and fine-grained transmission data of the M wireless terminals to obtain sample data and sample labels, and training to obtain a delay time prediction model; Step 85: Dynamically generate a data transmission plan for the M wireless terminals in the target area within the second preset time period based on the delay time prediction model to minimize the time window.

Images