An intelligent diagnosis method, device and electronic equipment for distribution network switch faults

Abstract

An intelligent diagnosis method, device and electronic equipment for distribution network switch faults, which relate to the field of power systems. In this method, the operation state data of the distribution network switch is obtained; a multi-dimensional feature template of the distribution network switch in the normal state is constructed, and the multi-dimensional feature template is trained by historical operation state data; the similarity between the operation state data and each feature template is calculated to obtain a plurality of similarity values; the plurality of similarity values are fused to obtain the comprehensive health index of the distribution network switch; the comprehensive health index is compared with a preset fault threshold to determine whether the distribution network switch has a fault; if it is determined that the comprehensive health index is greater than or equal to the preset fault threshold, it is determined that the distribution network switch has no fault; if it is determined that the comprehensive health index is less than the preset fault threshold, it is determined that the distribution network switch has a fault. Implementing the technical solution provided by this application improves the efficiency of distribution network switch fault diagnosis.

Description

Technical Field

[0001] The present application relates to the field of power systems, and particularly to an intelligent diagnosis method, device and electronic equipment for distribution network switch faults. Background Art

[0002] With the rapid development of power systems and the continuous improvement of the degree of intelligence, the stable and safe operation of distribution networks has become increasingly important. As a key device in the power system, the health status of distribution network switches directly affects the reliability and power supply quality of the entire power grid.

[0003] Currently, in many power systems, especially in some relatively traditional distribution networks, fault diagnosis still relies on manual judgment methods. This method is mainly based on the experience of on-site technicians and regular equipment inspections, which takes a long time. A single inspection may take several hours or even several days, resulting in low efficiency of fault diagnosis.

[0004] Therefore, there is an urgent need for an intelligent diagnosis method, device and electronic equipment for distribution network switch faults. Summary of the Invention

[0005] The present application provides an intelligent diagnosis method, device and electronic equipment for distribution network switch faults, which improves the efficiency of distribution network switch fault diagnosis.

[0006] In the first aspect of the present application, an intelligent diagnosis method for distribution network switch faults is provided. The method includes: obtaining operation status data of the distribution network switch, where the operation status data includes current data, voltage data, temperature data and vibration data of the distribution network switch; constructing a multi-dimensional feature template of the distribution network switch in a normal state, the multi-dimensional feature template includes a plurality of feature templates, the feature templates include a current feature template, a voltage feature template, a temperature feature template and a vibration feature template, and the multi-dimensional feature template is trained by historical operation status data; calculating the similarity between the operation status data and each of the feature templates to obtain a plurality of similarity values; fusing the plurality of similarity values to obtain a comprehensive health index of the distribution network switch; comparing the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has a fault; if it is determined that the comprehensive health index is greater than or equal to the preset fault threshold, it is determined that the distribution network switch has no fault; if it is determined that the comprehensive health index is less than the preset fault threshold, it is determined that the distribution network switch has a fault.

[0007] By adopting the above technical solution, by obtaining the operation status data of the distribution network switch, constructing a multi-dimensional feature template in the normal state, calculating the similarity between the real-time operation status and the feature template, and fusing multiple similarity values to obtain a comprehensive health index, and finally comparing the comprehensive health index with a preset fault threshold, the intelligent diagnosis of the distribution network switch fault is realized. This method makes full use of the multi-source heterogeneous data in the operation process of the switch, depicts the health status of the switch from multiple angles of current, voltage, temperature, and vibration, and improves the comprehensiveness and accuracy of fault diagnosis. At the same time, by constructing a feature template in the normal state, the dependence on a large amount of fault data is avoided, and the difficulty and cost of model training are reduced. This method can monitor the operation status of the distribution network switch in real time, discover potential fault hazards in time, and provide a reliable guarantee for the safe and stable operation of the distribution network. At the same time, through the quantified comprehensive health index, an intuitive decision-making basis is provided for the operation and maintenance personnel, and the intelligent level and work efficiency of the distribution network operation and maintenance are improved.

[0008] Optionally, the construction of the multi-dimensional feature template of the distribution network switch in the normal state specifically includes: obtaining the historical operation status data of multiple normally operating distribution network switches to obtain a historical data set, where the historical data set includes historical current data, historical voltage data, historical temperature data, and historical vibration data; performing a preprocessing operation on the historical data set to obtain a target historical data set; the preprocessing operation includes outlier removal, data normalization, and data smoothing; using the sliding time window method, extracting the multi-domain features of the target historical data set on different time scales to obtain a feature sample set corresponding to each historical operation status data, where the multi-domain features include time domain features, frequency domain features, and time-frequency domain features; based on the feature sample set corresponding to each historical operation status data, training a support vector data description model respectively to obtain the current feature template, the voltage feature template, the temperature feature template, and the vibration feature template.

[0009] By adopting the above technical solutions, historical operation status data of multiple normally operating distribution network switches are collected. After preprocessing operations such as outlier removal, data normalization, and smoothing, a high-quality target historical data set is obtained. Then, using the sliding time window method, multi-domain features of the target historical data set are extracted at different time scales, including time-domain features, frequency-domain features, and time-frequency domain features, to obtain a comprehensive and rich feature sample set. Finally, based on the feature sample set, a support vector data description model is trained to obtain current feature templates, voltage feature templates, temperature feature templates, and vibration feature templates. This method fully exploits the health status information contained in historical data. Through multi-domain feature extraction and data preprocessing, the representation ability and generalization ability of the feature templates are effectively improved. The feature templates can accurately depict the normal operation mode of the distribution network switches, providing a reliable reference standard for subsequent fault diagnosis. At the same time, the support vector data description model has good anomaly detection ability, can effectively identify fault states deviating from the normal mode, and improve the sensitivity and accuracy of fault diagnosis.

[0010] Optionally, calculating the similarity between the operation status data and each feature template to obtain multiple similarity values specifically includes: determining the corresponding features of the operation status data relative to the feature template to obtain corresponding to-be-tested feature sequences, where the to-be-tested feature sequences include current feature sequences, voltage feature sequences, temperature feature sequences, and vibration feature sequences; calculating the optimal matching distance between the to-be-tested feature sequences and the feature template; within a preset time window, calculating the average value of the optimal matching distance to obtain an average matching distance, and performing an exponential transformation on the average matching distance to obtain a similarity value.

[0011] By adopting the above technical solutions, by determining the corresponding features of the operation status data relative to the feature template to obtain to-be-tested feature sequences, then calculating the optimal matching distance between the to-be-tested feature sequences and the feature template, calculating the average value of the optimal matching distance within a preset time window, and performing an exponential transformation on the average matching distance to obtain a similarity value. This method fully considers the time-series characteristics of the operation status of the distribution network switches. By measuring the difference between the to-be-tested feature sequences and the feature template through the optimal matching distance, it can accurately evaluate the health status of the switches. At the same time, by averaging the optimal matching distance within the time window, the fluctuations and noise interference of a single match are effectively reduced, and the stability and reliability of the similarity calculation are improved. The exponential transformation maps the matching distance to the similarity space, making the similarity value more intuitively represent the quality of the health status. This method can dynamically track the changes in the operation status of the distribution network switches, evaluate their health levels in real time, provide a quantitative basis for fault diagnosis and predictive maintenance, and improve the pertinence and effectiveness of the operation and maintenance of the distribution network.

[0012] Optionally, the calculation formula for the optimal matching distance between the to-be-tested feature sequence and the feature template is:

[0013] ;

[0014] where D(i, j) represents the cumulative distance between the first i elements of the to-be-tested feature sequence and the first j elements of the feature template, x i represents the i-th element of the to-be-tested feature sequence, and y j represents the j-th element of the feature template, (x i - y j ) 2 represents the Euclidean distance between the i-th element of the to-be-tested feature sequence and the j-th element of the feature template.

[0015] By adopting the above technical solution, the optimal matching distance between the to-be-tested feature sequence and the feature template is efficiently calculated through a recurrence formula. This formula is based on the idea of dynamic programming, makes full use of the optimal solutions of sub-problems that have been calculated, avoids repeated calculations, and greatly improves the efficiency of distance calculation. At the same time, by comparing the current element with the cumulative distances in three directions, the optimal matching path is obtained, which can effectively handle the stretching and offset of the sequence, and improve the fault tolerance and robustness of the matching. The square of the Euclidean distance is used as the local distance metric, which can sensitively reflect the subtle differences of feature values and improve the matching accuracy. This formula can quickly and accurately evaluate the similarity between the to-be-tested feature sequence and the feature template, providing a reliable quantitative index for fault diagnosis. At the same time, by optimizing the matching path, abnormal change patterns of the switch operating state can be identified, providing important clues for fault cause analysis and location, and improving the intelligent level of distribution network operation and maintenance.

[0016] Optionally, within the preset time window, the specific calculation formula for calculating the average value of the optimal matching distance to obtain the average matching distance is:

[0017] ;

[0018] where is the average matching distance, n is the number of the preset time windows, is the optimal matching distance within the k-th time window.

[0019] By adopting the above technical solution, the average matching distance is obtained by calculating the average value of the optimal matching distances within a preset time window. This method fully considers the continuity and time correlation of the operating states of the distribution network switches. By extracting the optimal matching distance sequence through a sliding time window, it can dynamically reflect the changing trend of the switch health state. Taking the average value of the optimal matching distance sequence can effectively smooth the fluctuations and noise interference of single matching, and improve the stability and reliability of the matching results. At the same time, by adjusting the size of the preset time window, the time scale of health assessment can be flexibly controlled, taking into account the short-term and long-term changes in operating states. In practical applications, the average matching distance can quantify the overall health level of the distribution network switches over a period of time, providing an important basis for fault trend prediction and health state tracking. By updating the average matching distance in real time, abnormal deviations in the switch operating states can be detected in a timely manner, providing reliable support for fault warning and maintenance decision-making, and improving the initiative and real-time performance of distribution network operation and maintenance.

[0020] Optionally, the specific calculation formula for obtaining the similarity value by performing an exponential transformation on the average matching distance is:

[0021] ;

[0022] where S is the similarity value, λ is the similarity adjustment factor, is the average matching distance.

[0023] By adopting the above technical solution, the similarity value is obtained by performing an exponential transformation on the average matching distance. The exponential transformation can map the average matching distance from the Euclidean space to the similarity space, making the similarity value fall between 0 and 1, and more intuitively characterizing the health state of the distribution network switches. Through the similarity adjustment factor, the non-linear degree of the exponential transformation can be flexibly controlled to balance between matching accuracy and robustness.

[0024] Optionally, the specific formula for fusing multiple similarity values to obtain the comprehensive health index of the distribution network switch is:

[0025] ;

[0026] where H is the comprehensive health index, N is the number of feature templates, w i is the weight of the i-th feature template, S i is the similarity value of the i-th feature template, and α i is the exponential coefficient for adjusting the i-th feature template.

[0027] By adopting the above technical solution, the similarity values of multiple feature dimensions are fused into a comprehensive health index through a weighted exponential average formula. This formula fully considers the differences in the influence of different feature dimensions on the health state of the distribution network switch. By introducing a weight factor, the importance of each feature dimension is quantified, enabling the comprehensive health index to more accurately reflect the overall health level of the switch. At the same time, the exponential transformation maps the similarity values from a linear space to an exponential space. By adjusting the exponential coefficient, the non-linear contributions of different features can be flexibly controlled, highlighting the influence of key features and suppressing the interference of secondary features. In practical applications, the comprehensive health index provides a comprehensive switch health assessment index, integrating the operating state information of multiple dimensions such as current, voltage, temperature, and vibration, greatly simplifying the judgment and decision-making process of maintenance personnel.

[0028] In the second aspect of the present application, there is provided an intelligent fault diagnosis device for a distribution network switch. The device includes an acquisition module and a processing module, where: the acquisition module is used to acquire the operating state data of the distribution network switch, and the operating state data includes the current data, voltage data, temperature data, and vibration data of the distribution network switch; the processing module is used to construct a multi-dimensional feature template of the distribution network switch in the normal state. The multi-dimensional feature template includes multiple feature templates, and the feature templates include a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. The multi-dimensional feature template is trained by historical operating state data; the processing module is further used to calculate the similarity between the operating state data and each of the feature templates to obtain multiple similarity values; the processing module is further used to fuse the multiple similarity values to obtain the comprehensive health index of the distribution network switch; the processing module is further used to compare the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has a fault; the processing module is further used to determine that the distribution network switch has no fault if it is determined that the comprehensive health index is greater than or equal to the preset fault threshold; the processing module is further used to determine that the distribution network switch has a fault if it is determined that the comprehensive health index is less than the preset fault threshold.

[0029] In the third aspect of the present application, there is provided an electronic device, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to enable the electronic device to execute the method described in any one of the above.

[0030] In the fourth aspect of the present application, there is provided a computer-readable storage medium storing instructions, which when executed, execute the method described in any one of the above.

[0031] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0032] 1. By obtaining the operation status data of the distribution network switch, constructing a multi-dimensional feature template under normal conditions, calculating the similarity between the real-time operation status and the feature template, and fusing multiple similarity values to obtain a comprehensive health index, and finally comparing the comprehensive health index with a preset fault threshold, intelligent diagnosis of distribution network switch faults is realized. This method makes full use of the multi-source heterogeneous data in the switch operation process, describes the health status of the switch from multiple angles of current, voltage, temperature, and vibration, and improves the comprehensiveness and accuracy of fault diagnosis. At the same time, by constructing a feature template under normal conditions, the dependence on a large amount of fault data is avoided, and the difficulty and cost of model training are reduced. This method can monitor the operation status of the distribution network switch in real time, timely discover potential fault hazards, and provide a reliable guarantee for the safe and stable operation of the distribution network. At the same time, through the quantitative comprehensive health index, an intuitive decision-making basis is provided for the operation and maintenance personnel, and the intelligent level and work efficiency of distribution network operation and maintenance are improved.

[0033] 2. By collecting the historical operation status data of multiple normally operating distribution network switches, through preprocessing operations such as outlier removal, data normalization, and smoothing, a high-quality target historical data set is obtained. Then, using the sliding time window method, multi-domain features of the target historical data set are extracted at different time scales, including time-domain features, frequency-domain features, and time-frequency domain features, to obtain a comprehensive and rich feature sample set. Finally, based on the feature sample set, a support vector data description model is trained to obtain a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. This method fully excavates the health status information contained in the historical data, and through multi-domain feature extraction and data preprocessing, effectively improves the representation ability and generalization ability of the feature template. The feature template can accurately describe the normal operation mode of the distribution network switch, providing a reliable reference standard for subsequent fault diagnosis. At the same time, the support vector data description model has good anomaly detection ability, can effectively identify fault states deviating from the normal mode, and improves the sensitivity and accuracy of fault diagnosis.

[0034] 3. By determining the corresponding features of the operating status data with respect to the feature template, the to-be-tested feature sequence is obtained. Then, the optimal matching distance between the to-be-tested feature sequence and the feature template is calculated. The average value of the optimal matching distance is calculated within a preset time window, and an exponential transformation is performed on the average matching distance to obtain a similarity value. This method fully considers the temporal characteristics of the operating status of the distribution network switch. By measuring the difference between the to-be-tested feature sequence and the feature template through the optimal matching distance, it can accurately evaluate the health status of the switch. At the same time, by averaging the optimal matching distance within the time window, the fluctuations and noise interference of a single match are effectively reduced, improving the stability and reliability of the similarity calculation. The exponential transformation maps the matching distance to the similarity space, making the similarity value more intuitively represent the quality of the health status. This method can dynamically track the changes in the operating status of the distribution network switch, real-time evaluate its health level, provide a quantitative basis for fault diagnosis and predictive maintenance, and improve the pertinence and effectiveness of the operation and maintenance of the distribution network. Description of the Drawings

[0035] Figure 1 is a schematic flowchart of a method for intelligent diagnosis of distribution network switch faults disclosed in an embodiment of the present application;

[0036] Figure 2 is a schematic block diagram of a device for intelligent diagnosis of distribution network switch faults disclosed in an embodiment of the present application;

[0037] Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0038] Description of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0039] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0040] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0041] In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0042] The present application provides an intelligent diagnosis method for distribution network switch faults. Refer to Figure 1 , Figure 1 which is a schematic flowchart of an intelligent diagnosis method for distribution network switch faults provided by an embodiment of the present application. This method is applied to a server. The server is a server that executes an intelligent diagnosis program for distribution network switch faults. The server can be a single server, a server cluster composed of multiple servers, or a cloud computing service center. This method includes steps S101 to S107, and the above steps are as follows:

[0043] Step S101: Obtain the operation status data of the distribution network switch. The operation status data includes current data, voltage data, temperature data, and vibration data of the distribution network switch.

[0044] In step S101, various sensors are installed on-site for the distribution network switch, such as current sensors, voltage sensors, temperature sensors, and vibration sensors. These sensors collect the operation status data of the distribution network switch in real time and transmit the data to the server through a wired or wireless communication network. The server receives the operation status data, including current data, voltage data, temperature data, and vibration data.

[0045] Step S102: Construct a multi-dimensional feature template for the distribution network switch in the normal state. The multi-dimensional feature template includes multiple feature templates. The feature templates include a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. The multi-dimensional feature template is obtained by training with historical operation status data.

[0046] In step S102, a multi-dimensional feature template of the distribution network switch in the normal state is constructed, specifically including: obtaining the historical operation state data of multiple normally operating distribution network switches to obtain a historical data set, where the historical data set includes historical current data, historical voltage data, historical temperature data, and historical vibration data; performing preprocessing operations on the historical data set to obtain a target historical data set; the preprocessing operations include outlier removal, data normalization, and data smoothing; using the sliding time window method, multi-domain features of the target historical data set are extracted at different time scales to obtain a feature sample set corresponding to each historical operation state data, and the multi-domain features include time-domain features, frequency-domain features, and time-frequency domain features; based on the feature sample set corresponding to each historical operation state data, a support vector data description model is trained respectively to obtain a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template.

[0047] Specifically, the server obtains the historical operation state data of multiple normally operating distribution network switches to form a historical data set. These data can come from the historical archives of the distribution network operation and maintenance department or can be accumulated over a long time through a real-time data acquisition system. The obtained historical data set often has quality problems, such as data missing, outliers, and noise. Therefore, the server performs preprocessing operations on the historical data set, and the preprocessing operations include: outlier removal, data normalization, and data smoothing; for outlier removal, the server uses statistical methods (such as the 3σ principle) or machine learning algorithms (such as isolation forest) to detect and remove outliers in the data; for data normalization, the server scales different-dimension data to the same scale (such as the 0-1 interval) to eliminate the influence of dimension differences on feature extraction. For data smoothing, the server uses filtering algorithms (such as moving average, Kalman filter) to smooth the data and remove high-frequency noise. The preprocessed historical data set is called the target historical data set and is used for subsequent feature extraction and template construction.

[0048] The server performs feature extraction on the target historical data set obtained after preprocessing. Feature extraction is a key step in constructing the feature template. The server uses the sliding time window method to extract multi-domain features of the target historical data set at different time scales. Specifically:

[0049] For time domain features, the server extracts statistical features of the data, such as mean, variance, peak-to-peak value, and root mean square, to reflect the overall distribution of the data. For frequency domain features, the server performs Fourier transform on the data to extract spectral features of the data, such as frequency, amplitude, and phase, to reflect the periodicity and frequency distribution of the data. For time-frequency domain features, the server performs wavelet transform or Hilbert-Huang transform on the data to extract the joint time-frequency features of the data, such as wavelet coefficients and instantaneous frequency, to reflect the non-stationary characteristics of the data. Through feature extraction, the server obtains the feature sample set corresponding to each historical operating status data (current, voltage, temperature, vibration).

[0050] Based on the feature sample set, the server trains the support vector data description (SVDD) model to obtain the corresponding feature template; for each feature sample set, the server trains an SVDD model to learn the optimal hypersphere of normal data so that it can tightly surround most normal data points. Finally, the server can obtain feature templates in four feature dimensions: current, voltage, temperature, and vibration, which together constitute the multi-dimensional feature template of the distribution network switch, including current feature template, voltage feature template, temperature feature template, and vibration feature template.

[0051] For example, suppose that the server collects operating status data from 10 normally operating distribution network switches for one month, with a data sampling frequency of once per minute. In the preprocessing stage, the server removes individual missing or abnormal data points and normalizes the maximum and minimum values of the data in each dimension. Then, the server sets the time window size to 1 hour, extracts 20 time domain features, 10 frequency domain features, and 5 time-frequency domain features in each time window, and finally obtains feature samples of each switch at different time points. Next, the server uses these feature samples to train the SVDD model of current, voltage, temperature, and vibration features, and optimizes the model hyperparameters through cross-validation and grid search to obtain a feature template that describes the normal operating status. The feature template can provide a reference for subsequent distribution network switch fault diagnosis.

[0052] Step S103: Calculate the similarity between the running status data and each feature template to obtain multiple similarity values.

[0053] In step S103, the similarity between the operating status data and each feature template is calculated to obtain multiple similarity values, specifically including: determining the corresponding features of the operating status data relative to the feature template to obtain the corresponding feature sequence to be measured, the feature sequence to be measured includes a current feature sequence, a voltage feature sequence, a temperature feature sequence and a vibration feature sequence; calculating the optimal matching distance between the feature sequence to be measured and the feature template; within a preset time window, calculating the average value of the optimal matching distance to obtain the average matching distance, and performing an exponential transformation on the average matching distance to obtain a similarity value.

[0054] Specifically, the server extracts the to-be-tested feature sequence corresponding to the feature template from the operation status data of the distribution network switches collected in real time. Specifically, for the four physical quantities of current, voltage, temperature, and vibration, their time-domain, frequency-domain, and time-frequency domain features are respectively extracted to form four to-be-tested feature sequences. This step is the same as the feature extraction method when constructing the feature template, ensuring the comparability between the to-be-tested data and the feature template.

[0055] For each to-be-tested feature sequence, the server calculates the optimal matching distance between it and the corresponding feature template. Through the optimal matching distance, the optimal non-linear alignment method between two time series can be found to measure their shape similarity. After obtaining the optimal matching distance, the server calculates the average value of the optimal matching distance within a preset time window (such as 1 minute) as the average matching distance between the switch operation status and the feature template within this time window. The smaller the average matching distance, the closer the switch operation status is to the normal state. To map the average matching distance to a bounded similarity value, the server performs an exponential transformation on the average matching distance to obtain the corresponding similarity value.

[0056] In a possible implementation manner, the calculation formula for calculating the optimal matching distance between the to-be-tested feature sequence and the feature template is:

[0057] ;

[0058] where D(i, j) represents the cumulative distance between the first i elements of the to-be-tested feature sequence and the first j elements of the feature template, x i represents the i-th element of the to-be-tested feature sequence, y j represents the j-th element of the feature template, (x i -y j ) 2 represents the Euclidean distance between the i-th element of the to-be-tested feature sequence and the j-th element of the feature template.

[0059] Specifically, the above formula finds the optimal matching path between two sequences (the to-be-tested feature sequence and the feature template) through stretching and offset on the time axis, making the cumulative distance between the matching point pairs the smallest. Let the to-be-tested feature sequence be X = (x1, x2,..., x n ), and the feature template sequence be Y = (y1, y2,..., y m) Define the distance matrix D, where D(i, j) represents the cumulative distance between the first i elements of X and the first j elements of Y, that is, the optimal matching distance. According to the idea of dynamic programming, when calculating D(i, j), the optimal solutions of the subproblems that have been calculated, namely D(i - 1, j), D(i - 1, j - 1), and D(i, j - 1), can be utilized, and then added with the distance between the current matching point pair (x i , y j ), so as to obtain the recurrence formula for D(i, j): D(i, j) = min{D(i - 1, j), D(i - 1, j - 1), D(i, j - 1)} + (x i - y j ) 2 ;

[0060] where, (x i - y j ) 2 represents the square of the Euclidean distance between x i and y j , that is, the local distance metric between the matching point pairs. The meaning of the above recurrence formula is: when calculating D(i, j), the optimal matching path can come from three directions:

[0061] (i - 1, j): It means that x i and y j do not match, and the optimal path comes from D(i - 1, j), that is, x i is skipped.

[0062] (i - 1, j - 1): It means that x i and y j match, and the optimal path comes from D(i - 1, j - 1), that is, x i and y j are matched.

[0063] (i, j - 1): It means that x i and y j do not match, and the optimal path comes from D(i, j - 1), that is, y j is skipped.

[0064] The server selects the one with the minimum cumulative distance among these three directions, and then adds the distance of the current matching point pair, thus obtaining the optimal solution of D(i, j). Among them, the boundary conditions are: D(0, 0) = 0, D(i, 0) = D(0, j) = ∞ (infinity), that is, the distance between an empty sequence and any sequence is 0, while the distance between any sequence and an empty sequence is ∞. By filling the distance matrix D through dynamic programming, finally D(n, m) is the optimal matching distance between the to-be-tested feature sequence X and the feature template sequence Y, that is, the similarity metric between the two sequences.

[0065] For example, assume that the feature sequence to be measured is X = (1, 2, 3, 4, 5) and the feature template sequence is Y = (2, 3, 4). Then the calculation process of the distance matrix D is as follows:

[0066] Initialize D(0, 0) = 0; D(i, 0) = D(0, j) = ∞, i = 1, 2,..., 5; j = 1, 2, 3; Calculate the first row D(1, 1) = min{∞, ∞, ∞} + (1 - 2) 2 = 1, D(1, 2) = min{∞, 1, ∞} + (1 - 3) 2 = 5, D(1, 3) = min{∞, 5, ∞} + (1 - 4) 2 = 14; Calculate the second row D(2, 1) = min{1, ∞, ∞} + (2 - 2) 2 = 1, D(2, 2) = min{5, 1, 1} + (2 - 3) 2 = 2, D(2, 3) = min{14, 2, 5} + (2 - 4) 2 = 6;

[0067] And so on until the entire distance matrix is filled:

[0068] ;

[0069] Finally, D(5, 3) = 6 is the optimal matching distance between the feature sequence X to be measured and the feature template sequence Y. By backtracking the distance matrix, the optimal matching path can be obtained as ((1, 2), (2, 3), (4, 3), (5, 3)), and the corresponding matching relationship is x1y2, x2y3, x4y3, x5y3.

[0070] In a possible implementation, within a preset time window, calculate the average value of the optimal matching distances. The specific calculation formula for the average matching distance is:

[0071]

[0072] Among them, is the average matching distance, n is the number of preset time windows, is the optimal matching distance within the k-th time window.

[0073] Specifically, in the above formula, the server calculates the average value of the optimal matching distances within the preset time window to obtain the average matching distance. In the formula, represents the average matching distance, n represents the number of preset time windows, The superscript (k) of represents the optimal matching distance within the k-th time window.

[0074] The meaning of this formula is as follows: within a preset time window, calculate the optimal matching distance for each time window, then sum up these optimal matching distances, and divide by the number of time windows to obtain the average matching distance.

[0075] For example, assume the preset time window is 5 minutes. Within these 5 minutes, calculate the optimal matching distance once per minute, and the obtained sequence of optimal matching distances is {2.3, 1.9, 2.5, 2.1, 2.4}. Then the calculation process of the average matching distance is as follows: =(2.3 + 1.9 + 2.5 + 2.1 + 2.4) / 5 = 2.24

[0076] In a possible implementation manner, perform an exponential transformation on the average matching distance, and the specific calculation formula for obtaining the similarity value is:

[0077]

[0078] where S is the similarity value, and λ is the similarity adjustment factor, is the average matching distance.

[0079] Specifically, in the above formula, the server performs an exponential transformation on the average matching distance to obtain the similarity value. In the formula, S represents the similarity value, and λ represents the similarity adjustment factor, represents the average matching distance. The meaning of this formula is: first, take the negative value of the average matching distance, then multiply it by a similarity adjustment factor λ, and finally take the exponential of the product with the natural constant e as the base to obtain the similarity value.

[0080] The purpose of the exponential transformation is to map the average matching distance into the interval (0, 1] to obtain a normalized similarity measure. Since the smaller the average matching distance, the more similar the two sequences are, it is necessary to take its negative value, and then through the exponential transformation, convert it into a similarity value that is closer to 1 the more similar, and closer to 0 the less similar. The role of the similarity adjustment factor λ is to control the rate and shape of the exponential transformation. The larger λ is, the faster the exponential transformation rate, and the similarity value will quickly approach 1 when the distance is small; the smaller λ is, the slower the exponential transformation rate, and the similarity value will also maintain a certain degree of discrimination when the distance is large. By adjusting the size of λ, a balance can be achieved between matching accuracy and robustness.

[0081] For example, assume the average matching distance = 2.24, and the similarity adjustment factor λ = 0.5. Then the calculation process of the similarity value is as follows: S = e^(-0.5 * 2.24) = e^(-1.12) ≈ 0.326

[0082] It can be seen that when the average matching distance is 2.24, the similarity value is 0.326, indicating that the similarity between the two sequences is relatively low. If λ = 1, the similarity value will become e^(-2.24) ≈ 0.106. It can be seen that the increase of λ will accelerate the attenuation rate of the similarity value. The value of λ can be designed according to the requirements of the actual application scenario, and this application does not make any limitations in this regard.

[0083] Step S104: Integrate multiple similarity values to obtain the comprehensive health index of the distribution network switch.

[0084] In step S104, the specific formula for integrating multiple similarity values to obtain the comprehensive health index of the distribution network switch is:

[0085]

[0086] where H is the comprehensive health index, N is the number of feature templates, w i is the weight of the i-th feature template, S i is the similarity value of the i-th feature template, and α i is the exponential coefficient for adjusting the i-th feature template.

[0087] Specifically, in the health status assessment of the distribution network switch, multiple feature dimensions are usually considered comprehensively, such as current, voltage, temperature, and vibration. Each dimension has a corresponding feature template. By calculating the similarity between the real-time operation data of the switch and each feature template, a set of similarity values {S1, S2,..., S n} can be obtained, where N is the number of feature templates. In order to integrate these similarity values into a comprehensive health index, considering the importance differences of different feature dimensions and the non-linear influence of similarity values, a weighted exponential averaging method is adopted. In the formula, H represents the comprehensive health index, w i represents the weight of the i-th feature template, S i represents the similarity value of the i-th feature template, and α i represents the exponential coefficient for adjusting the i-th feature template. The numerator part of the formula is to perform an exponential transformation on the similarity value of each feature template, then multiply it by the corresponding weight, and then sum. Among them, the role of the exponential transformation is to map the similarity value Si from the linear space to the exponential space. By adjusting the size of α i , the non-linear degree of the mapping can be controlled. The larger α i is, the stronger the non-linearity of the exponential transformation, and the subtle differences in similarity values will be amplified; the smaller α i is, the weaker the non-linearity of the exponential transformation, and the differences in similarity values will be compressed.

[0088] The role of the weight w i is to reflect the importance of the i-th feature template in the comprehensive evaluation. wi The larger it is, the greater the contribution of the i-th feature template to the comprehensive health index; w i The smaller it is, the smaller the influence of the i-th feature template. By reasonably setting the weights, key features can be highlighted, secondary features can be suppressed, and the pertinence of comprehensive evaluation can be improved. Some features (such as current and voltage) may be more critical to the health status of the device, so higher weights can be assigned. This application does not make any limitations in this regard. The denominator part of the formula sums up all the weights, which plays a role of normalization to ensure that the value range of the comprehensive health index H is between [0, 1].

[0089] Step S105: Compare the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has failed.

[0090] In step S105, the server compares the calculated comprehensive health index with the preset fault threshold to determine whether the switch has failed. The preset fault threshold is an empirical value that can be set according to historical data and expert knowledge, representing the demarcation point between the healthy state and the fault state. This application does not make any limitations on the specific value of the preset fault threshold.

[0091] Step S106: If it is determined that the comprehensive health index is greater than or equal to the preset fault threshold, it is determined that the distribution network switch has not failed.

[0092] In step S106, when the server determines that the comprehensive health index is greater than or equal to the preset fault threshold, it indicates that the operating state of the switch is generally normal and there are no significant fault symptoms.

[0093] Step S107: If it is determined that the comprehensive health index is less than the preset fault threshold, it is determined that the distribution network switch has failed.

[0094] In step S107, when the server determines that the comprehensive health index is less than the preset fault threshold, it indicates that the operating state of the distribution network switch deviates significantly from the normal operating conditions and a failure has occurred. After determining the failure, the server generates a fault report and triggers corresponding alarms and maintenance work orders to notify the operation and maintenance personnel to handle it as soon as possible to avoid the expansion of the failure.

[0095] Refer to Figure 2, this application also provides an intelligent diagnosis device for distribution network switch faults. The device is a server, which includes an acquisition module 201 and a processing module 202, where: The acquisition module 201 is used to acquire the operation status data of the distribution network switch, and the operation status data includes the current data, voltage data, temperature data, and vibration data of the distribution network switch; The processing module 202 is used to construct a multi-dimensional feature template of the distribution network switch in the normal state. The multi-dimensional feature template includes multiple feature templates, and the feature templates include a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. The multi-dimensional feature template is trained from historical operation status data; The processing module 202 is also used to calculate the similarity between the operation status data and each feature template to obtain multiple similarity values; The processing module 202 is also used to fuse multiple similarity values to obtain the comprehensive health index of the distribution network switch; The processing module 202 is also used to compare the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has a fault; The processing module 202 is also used to determine that the distribution network switch has no fault if it is determined that the comprehensive health index is greater than or equal to the preset fault threshold; The processing module 202 is also used to determine that the distribution network switch has a fault if it is determined that the comprehensive health index is less than the preset fault threshold.

[0096] In a possible implementation manner, the processing module 202 constructs a multi-dimensional feature template of the distribution network switch in the normal state, specifically including: The acquisition module 201 acquires the historical operation status data of multiple normally operating distribution network switches to obtain a historical data set, and the historical data set includes historical current data, historical voltage data, historical temperature data, and historical vibration data; The processing module 202 performs preprocessing operations on the historical data set to obtain a target historical data set; The preprocessing operations include outlier removal, data normalization, and data smoothing; The processing module 202 uses the sliding time window method to extract multi-domain features of the target historical data set at different time scales to obtain a feature sample set corresponding to each historical operation status data. The multi-domain features include time domain features, frequency domain features, and time-frequency domain features; The processing module 202 respectively trains a support vector data description model based on the feature sample set corresponding to each historical operation status data to obtain a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template.

[0097] In a possible implementation, the processing module 202 calculates the similarity between the operating state data and each feature template, obtaining multiple similarity values, specifically including: the processing module 202 determines the corresponding features of the operating state data relative to the feature template, obtaining a corresponding sequence of to-be-tested features, where the sequence of to-be-tested features includes a current feature sequence, a voltage feature sequence, a temperature feature sequence, and a vibration feature sequence; the processing module 202 calculates the optimal matching distance between the sequence of to-be-tested features and the feature template; the processing module 202 calculates the average value of the optimal matching distances within a preset time window, obtaining an average matching distance, and performs an exponential transformation on the average matching distance to obtain a similarity value.

[0098] In a possible implementation, the calculation formula for the processing module 202 to calculate the optimal matching distance between the sequence of to-be-tested features and the feature template is:

[0099] ;

[0100] where D(i, j) represents the cumulative distance between the first i elements of the sequence of to-be-tested features and the first j elements of the feature template, x i represents the i-th element of the sequence of to-be-tested features, y j represents the j-th element of the feature template, and (x i -y j ) 2 represents the Euclidean distance between the i-th element of the sequence of to-be-tested features and the j-th element of the feature template.

[0101] In a possible implementation, the specific calculation formula for the processing module 202 to calculate the average value of the optimal matching distances within a preset time window, obtaining an average matching distance, is:

[0102] ;

[0103] where, is the average matching distance, n is the number of preset time windows, is the optimal matching distance within the k-th time window.

[0104] In a possible implementation, the specific calculation formula for the processing module 202 to perform an exponential transformation on the average matching distance to obtain a similarity value is:

[0105] ;

[0106] where S is the similarity value, λ is the similarity adjustment factor, is the average matching distance.

[0107] In a possible implementation, the specific formula for the processing module 202 to fuse multiple similarity values to obtain the comprehensive health index of the distribution network switch is as follows:

[0108] ;

[0109] where H is the comprehensive health index, N is the number of feature templates, w i is the weight of the i-th feature template, S i is the similarity value of the i-th feature template, and α i is the exponential coefficient for adjusting the i-th feature template.

[0110] It should be noted that when the device provided in the above embodiment realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0111] This application also provides an electronic device. Referring to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0112] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0113] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0114] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0115] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, it performs various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0116] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , in the memory 305 as a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program of a method for intelligent diagnosis of distribution network switch faults.

[0117] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; while the processor 301 can be used to call the application program storing an intelligent diagnosis method for a distribution network switch fault in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the foregoing embodiments. It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0118] The present application also provides a computer-readable storage medium storing instructions. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the foregoing embodiments.

[0119] In the foregoing embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0121] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, in each embodiment of the present application, the functional units can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0123] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0124] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the disclosure of the practical truth, those skilled in the art will easily think of other implementation manners of the present disclosure.

[0125] This application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent diagnosis method for distribution network switch faults, characterized in that, The method includes: Obtaining the operation status data of the distribution network switch, where the operation status data includes the current data, voltage data, temperature data, and vibration data of the distribution network switch; Constructing a multi-dimensional feature template of the distribution network switch in the normal state, where the multi-dimensional feature template includes multiple feature templates, and the feature templates include a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. The multi-dimensional feature template is obtained by training with historical operation status data; Calculating the similarity between the operation status data and each of the feature templates to obtain multiple similarity values; Fusing the multiple similarity values to obtain the comprehensive health index of the distribution network switch; Comparing the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has a fault; If it is determined that the comprehensive health index is greater than or equal to the preset fault threshold, it is determined that the distribution network switch has no fault; If it is determined that the comprehensive health index is less than the preset fault threshold, it is determined that the distribution network switch has a fault; The calculating the similarity between the operation status data and each feature template to obtain multiple similarity values specifically includes: Determining the corresponding features of the operation status data relative to the feature template to obtain a corresponding sequence of features to be measured, where the sequence of features to be measured includes a current feature sequence, a voltage feature sequence, a temperature feature sequence, and a vibration feature sequence; Calculating the optimal matching distance between the sequence of features to be measured and the feature template; Calculating the average value of the optimal matching distance within a preset time window to obtain an average matching distance, and performing an exponential transformation on the average matching distance to obtain a similarity value; The calculation formula for the optimal matching distance between the sequence of features to be measured and the feature template is: ； Among them, D(i, j) represents the cumulative distance between the first i elements of the to-be-tested feature sequence and the first j elements of the feature template, x i represents the i-th element of the to-be-tested feature sequence, y j represents the j-th element of the feature template, (x i -y j ) 2 represents the Euclidean distance between the i-th element of the to-be-tested feature sequence and the j-th element of the feature template.

2. The method according to claim 1, wherein The constructing the multi-dimensional feature template of the distribution network switch in the normal state specifically includes: Obtaining the historical operation status data of multiple normally operating distribution network switches to obtain a historical data set, where the historical data set includes historical current data, historical voltage data, historical temperature data, and historical vibration data; Performing a preprocessing operation on the historical data set to obtain a target historical data set; the preprocessing operation includes outlier removal, data normalization, and data smoothing; Using the sliding time window method to extract multi-domain features of the target historical data set at different time scales to obtain a feature sample set corresponding to each historical operation status data, where the multi-domain features include time domain features, frequency domain features, and time-frequency domain features; Based on the feature sample set corresponding to each historical operation status data, training a support vector data description model respectively to obtain the current feature template, the voltage feature template, the temperature feature template, and the vibration feature template.

3. The method according to claim 1, wherein The specific calculation formula for calculating the average value of the optimal matching distance within a preset time window to obtain an average matching distance is: ； wherein, is the average matching distance, n is the number of the preset time windows, is the optimal matching distance within the k-th time window.

4. The method according to claim 1, wherein The specific calculation formula for performing an exponential transformation on the average matching distance to obtain a similarity value is: ； Wherein, S is the similarity value, and λ is the similarity adjustment factor, is the average matching distance.

5. The method according to claim 1, wherein The specific formula for fusing the multiple similarity values to obtain the comprehensive health index of the distribution network switch is as follows: ； Among them, H is the comprehensive health index, N is the number of the feature templates, w i is the weight of the i-th feature template, S i is the similarity value of the i-th feature template, α i is the exponential coefficient for adjusting the i-th feature template.

6. An intelligent diagnosis device for switch faults in a distribution network, characterized in that, The device is used to execute the method described in any one of claims 1-5. The device includes an acquisition module (201) and a processing module (202), where: The acquisition module (201) is used to acquire the operation status data of the distribution network switch, and the operation status data includes the current data, voltage data, temperature data, and vibration data of the distribution network switch; The processing module (202) is used to construct a multi-dimensional feature template for the distribution network switch in the normal state. The multi-dimensional feature template includes multiple feature templates, and the feature templates include a current feature template, a voltage feature template, a temperature feature template, and a vibration feature template. The multi-dimensional feature template is obtained by training with historical operation status data; The processing module (202) is further used to calculate the similarity between the operation status data and each of the feature templates to obtain multiple similarity values; The processing module (202) is further used to fuse the multiple similarity values to obtain the comprehensive health index of the distribution network switch; The processing module (202) is further used to compare the comprehensive health index with a preset fault threshold to determine whether the distribution network switch has a fault; The processing module (202) is further used to determine that the distribution network switch has no fault if it is determined that the comprehensive health index is greater than or equal to the preset fault threshold; The processing module (202) is further used to determine that the distribution network switch has a fault if it is determined that the comprehensive health index is less than the preset fault threshold.

7. An electronic device, characterized in that, It includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of claims 1-5 is executed.

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