Remote control method, system, device and medium of switch cabinet based on CAN bus communication

Abstract

This application relates to a method, system, device, and medium for remote control of switchgear based on CAN bus communication, belonging to the technical field of switchgear control. The switchgear's data acquisition device, panel controller, and expansion interface circuit are all connected to a microprocessor via a CAN bus interface circuit. The microprocessor communicates with the electronic device via the CAN bus interface circuit. The method is executed by the electronic device and includes: acquiring monitoring data sent by the data acquisition device, including distribution circuit voltage, distribution circuit current, busbar joint temperature, cabinet ambient temperature and humidity, and switch status; analyzing the switchgear's operating status based on the monitoring data to determine abnormal values; when an abnormal value reaches a pre-defined abnormal value, applying a machine learning-based intelligent analysis model to analyze the monitoring data, determining a switchgear adjustment strategy, and causing the switchgear to execute the adjustment strategy and display the abnormal monitoring data. This application improves the efficiency and accuracy of resolving switchgear faults.

Description

Technical Field

[0001] The present application relates to the technical field of switch cabinet control, and in particular to a remote control method, system, device and medium for a switch cabinet based on CAN bus communication. Background Art

[0002] Switchgear is the key equipment in the power system, and its operating status directly affects the stability and safety of the entire power system. With the development trend of intelligence and networking, the traditional power control system has gradually shifted to distributed intelligent control based on communication technology. Among them, CAN bus has been widely used in power automation control with its high reliability and flexibility, improving the real-time response capability and maintenance efficiency of the system. At present, there is a switchgear remote control system based on CAN bus applied to switchgear, which has strong scalability and is convenient for staff to remotely monitor and perform fault diagnosis and troubleshooting.

[0003] However, when remotely monitoring, staff need to rely more on experience to determine the best troubleshooting solution based on various monitoring data of the switch cabinet, which makes it difficult to meet the needs of high efficiency and high accuracy in troubleshooting. Summary of the invention

[0004] In order to improve the efficiency and accuracy of solving switch cabinet failures, the present application provides a switch cabinet remote control method, system, device and medium based on CAN bus communication.

[0005] In the first aspect, the present application provides a remote control method for a switch cabinet based on CAN bus communication, which adopts the following technical solution: Applied to a switch cabinet based on CAN bus communication, the switch cabinet based on CAN bus communication includes a data acquisition device, a microprocessor, a CAN bus interface circuit, a panel controller and an expansion interface circuit, the data acquisition device, the panel controller and the expansion interface circuit are all connected to the microprocessor through the CAN bus interface circuit, the microprocessor communicates with the electronic device through the CAN bus interface circuit, and the method is executed by the electronic device, including: Acquire monitoring data sent by the data acquisition device, wherein the monitoring data includes power distribution circuit voltage, power distribution circuit current, busbar connector temperature, cabinet ambient temperature and humidity, and switch status; Analyzing the operating status of the switch cabinet according to the monitoring data to determine abnormal values; When the abnormal value is lower than the abnormal predicted value, the abnormal monitoring data is displayed; When the abnormal value reaches the abnormal pre-value, an intelligent analysis model based on machine learning is applied to analyze the monitoring data, determine the switch cabinet adjustment strategy, and make the switch cabinet execute the adjustment strategy to display the abnormal monitoring data.

[0006] By adopting the above technical solution, monitoring data is obtained in real time through data acquisition equipment, providing comprehensive data support for the operation status analysis of the switch cabinet. According to the monitoring data, the system can analyze the operation status of the switch cabinet, which is helpful to timely discover potential faults or safety hazards. When the abnormal value is lower than but close to the abnormal preset value, the system can display abnormal monitoring data to remind the operator to pay attention and take corresponding preventive measures. When the abnormal value reaches the abnormal preset value, the intelligent analysis model based on machine learning is used to conduct in-depth analysis of the monitoring data to determine the adjustment strategy of the switch cabinet, which can quickly respond to faults or abnormal situations, reduce downtime and maintenance costs, and more accurately predict and respond to various problems that may arise in the switch cabinet. Operators can perform targeted maintenance operations based on the abnormal data and adjustment strategies provided by the system, thereby improving maintenance efficiency and quality.

[0007] Further, analyzing the operating status of the switch cabinet according to the monitoring data to determine abnormal values ​​includes: Compare each monitoring data with the corresponding standard data to determine the deviation value corresponding to each monitoring data; According to the comparison between the deviation value and the preset table, the initial abnormal value corresponding to the deviation value is determined; Acquire historical feedback information of the deviation value, determine a correction value of the initial abnormal value according to the historical feedback information, add the initial abnormal value and the correction value, and determine the abnormal value corresponding to each data item; The abnormal value of the switch cabinet is calculated based on the abnormal value of each monitoring data and the weight value corresponding to each data.

[0008] By adopting the above technical solution, comprehensive, accurate and real-time monitoring of the switchgear operating status is achieved through precise data comparison and analysis, rapid determination of initial abnormal values, correction and optimization based on historical feedback, and comprehensive consideration of the weights of various monitoring data.

[0009] Further, determining the correction value of the initial abnormal value according to the historical feedback information includes: If the historical feedback information is an adjustment value of the user for the initial abnormal value under the same deviation value, then the average of the plurality of adjustment values ​​is determined as the correction value; If the historical feedback information is a user's feedback statement regarding the initial abnormal value under the same deviation value, the feedback statement is analyzed using a semantic analysis model, and the average of the adjustment values ​​corresponding to the multiple feedback statements is determined as the correction value; If the historical feedback information includes the user's adjustment value for the initial abnormal value and the user's feedback statement for the initial abnormal value under the same deviation value, then the first correction value corresponding to each of the historical feedback information is determined respectively, and the average of each of the first correction values ​​is calculated to obtain the correction value.

[0010] By adopting the above technical solution and introducing data-driven correction value determination method and semantic analysis technology, the system can more accurately determine the correction value based on historical feedback information, thereby improving the correction accuracy of the initial abnormal value. By calculating the average of multiple adjustment values ​​or adjustment values ​​corresponding to feedback statements to determine the correction value, the system can reduce the impact of a single data point and improve the stability and reliability of the correction value.

[0011] Furthermore, the application analyzes the monitoring data based on a machine learning intelligent analysis model, determines a switch cabinet adjustment strategy, and enables the switch cabinet to execute the adjustment strategy, including: Inputting the monitoring data into an intelligent analysis model based on machine learning for classification to determine the cause of the switch cabinet abnormality; Acquire at least one adjustment strategy for solving the cause of the abnormality through a database; If the adjustment strategies include multiple ones, sorting the adjustment strategies according to the order of priority of accuracy of the adjustment strategies to obtain a first sequence; The switch cabinet is caused to execute the adjustment strategy according to the order of the first sequence until the abnormal value is lower than the abnormal preset value.

[0012] By adopting the above technical solution and classifying the monitoring data through the intelligent analysis model, the system can more accurately determine the abnormal cause of the switch cabinet, thereby reducing the possibility of misdiagnosis and missed diagnosis. Through database support and strategy sorting, the switch cabinet can efficiently obtain and execute the optimal adjustment strategy, improving the efficiency and success rate of problem solving. The automated execution of adjustment strategies reduces the need for manual intervention and improves the automation level and reliability of the switch cabinet.

[0013] Furthermore, before sorting the adjustment strategies according to the order of priority of the adjustment strategies, the method further includes: Get the number of calls and resolution success rates for each adjustment strategy; Arrange the adjustment strategies in descending order according to the number of calls to determine a second sequence; Arrange the adjustment strategies in descending order according to the success rate of the solution to determine a third sequence; Determine the first sequence number of the adjustment strategy in the second sequence and the second sequence number in the third sequence, calculate the first sequence number and the second sequence number according to a preset ratio to obtain a third sequence number, generate an ascending fourth sequence according to the third sequence number, and determine the order of the adjustment strategy in the fourth sequence as the priority.

[0014] By adopting the above technical solution, by comprehensively considering the number of times the adjustment strategy is called and the success rate of the solution, and calculating the comprehensive serial number according to the preset ratio to generate the priority order, it provides strong support for the abnormal handling and status adjustment of the switch cabinet, which not only improves the intelligence and automation of the system, but also optimizes the selection and execution process of the adjustment strategy, which helps to improve the stability and reliability of the power system.

[0015] Furthermore, the method further comprises: Get the number of abnormalities for each monitoring data within the preset time period; Compare the number of abnormalities of each monitoring data with the corresponding calibration number within a preset time period to determine the difference; Calculate the ratio of the differences corresponding to each monitoring data, and determine the abnormal monitoring parameter with the largest ratio; Generates prompt information including abnormal monitoring parameters.

[0016] By adopting the above technical solution, by counting the number of abnormalities of each monitoring data within a preset time period, the abnormal frequency of each monitoring parameter can be intuitively understood, providing data support for subsequent abnormal analysis, and comparing the number of abnormalities of each monitoring data with the corresponding calibration number within the preset time period, and calculating the difference, it is helpful to identify which monitoring parameters have abnormal frequencies exceeding the normal range, thereby preliminarily screening out potential abnormal parameters, and calculating the ratio of the corresponding differences of each monitoring data, the abnormal item of the monitoring parameter with the largest ratio can be determined, and through quantitative analysis, the most important abnormal parameters can be further accurately identified, which helps to quickly locate the source of the fault and improve the efficiency of troubleshooting.

[0017] In the second aspect, the present application provides a remote control device for a switch cabinet based on CAN bus communication, which adopts the following technical solution: A monitoring data acquisition module is used to acquire the monitoring data sent by the data acquisition device, wherein the monitoring data includes the distribution circuit voltage, the distribution circuit current, the busbar joint temperature, the cabinet environment temperature and humidity, and the switch status; An abnormal value determination module, used to analyze the operating status of the switch cabinet according to the monitoring data and determine the abnormal value; An abnormal display module is used to display abnormal monitoring data when the abnormal value is lower than the abnormal pre-value; The adjustment strategy determination module is used to analyze the monitoring data using an intelligent analysis model based on machine learning when the abnormal value reaches the abnormal preset value, determine the switch cabinet adjustment strategy, enable the switch cabinet to execute the adjustment strategy, and display the abnormal monitoring data.

[0018] By adopting the above technical solution, the monitoring data acquisition module obtains monitoring data in real time through the data acquisition equipment, providing comprehensive data support for the operation status analysis of the switch cabinet. The abnormal value determination module can analyze the operation status of the switch cabinet based on the monitoring data, which is helpful to timely discover potential faults or safety hazards. When the abnormal value is lower than but close to the abnormal preset value, the abnormal display module can display abnormal monitoring data to remind the operator to pay attention and take corresponding preventive measures. When the abnormal value reaches the abnormal preset value, the adjustment strategy determination module uses the intelligent analysis model based on machine learning to conduct an in-depth analysis of the monitoring data to determine the adjustment strategy of the switch cabinet, which can quickly respond to faults or abnormal situations, reduce downtime and maintenance costs, and more accurately predict and respond to various problems that may occur in the switch cabinet. Operators can perform targeted maintenance operations based on the abnormal data and adjustment strategies provided by the system, thereby improving maintenance efficiency and quality.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: execute the method as described in any one of the first aspects.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and executes the method as described in any one of the first aspects.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. In switch cabinets based on CAN bus communication, the intelligence level, safety and reliability of switch cabinets can be significantly improved, and maintenance costs can be reduced; 2. The operating status of the switchgear can be analyzed based on the monitoring data, which helps to timely discover potential faults or safety hazards. When the abnormal value reaches the abnormal prediction value, the intelligent analysis model based on machine learning is used to conduct in-depth analysis of the monitoring data to determine the adjustment strategy of the switchgear, which can quickly respond to faults or abnormal situations, reduce downtime and maintenance costs, and more accurately predict and respond to various problems that may occur in the switchgear. Operators can perform targeted maintenance operations based on the abnormal data and adjustment strategies provided by the system, thereby improving maintenance efficiency and quality; 3. By classifying the monitoring data through intelligent analysis models, the system can more accurately determine the cause of the switch cabinet abnormality, thereby reducing the possibility of misdiagnosis and missed diagnosis. Through database support and strategy sorting, the switch cabinet can efficiently obtain and execute the optimal adjustment strategy, improving the efficiency and success rate of problem solving. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural schematic diagram of a CAN bus communication switch cabinet in an embodiment of the present application.

[0023] Figure 2 It is a flow chart of a remote control method for a switch cabinet based on CAN bus communication in an embodiment of the present application.

[0024] Figure 3 It is a structural block diagram of a remote control device for a switch cabinet based on CAN bus communication in an embodiment of the present application.

[0025] Figure 4 It is a structural block diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The present application embodiment discloses a CAN bus communication switch cabinet, referring to Figure 1, including a data acquisition device, a microprocessor, a CAN bus interface circuit, a panel controller and an expansion interface circuit. The data acquisition device, the panel controller and the expansion interface circuit are all connected to the microprocessor through the CAN bus interface circuit, and the microprocessor communicates with the electronic equipment through the CAN bus interface circuit.

[0029] Among them, the data acquisition equipment collects various monitoring data in the switch cabinet in real time, including the ammeter, voltmeter, busbar joint temperature sensor, cabinet temperature sensor and cabinet humidity sensor in the distribution circuit. Therefore, the data acquisition equipment collects the distribution circuit voltage, distribution circuit current, busbar structure temperature, cabinet environment temperature and humidity of the switch cabinet.

[0030] Microprocessors usually use high-performance embedded processors, such as ARM Cortex-A series, DSP, etc.

[0031] The CAN bus interface circuit is usually composed of a CAN controller and a CAN transceiver. The CAN controller is responsible for processing the communication protocol and data frame format of the CAN bus, while the CAN transceiver is responsible for converting digital signals into differential signals on the CAN bus and performing electrical isolation and anti-interference processing. Commonly used CAN controllers include SJA1000, MCP2515, etc., while CAN transceivers are often selected as TJA1050, PCA82C250, etc.

[0032] The panel controller can display the data collected by the data acquisition equipment, the operating status of the switch cabinet, etc., so that users can operate and monitor it easily.

[0033] The expansion interface circuit is used to connect other external devices or sensors, expand the functions and performance of the switch cabinet, and facilitate the remote transmission and sharing of data.

[0034] Therefore, the microprocessor can send monitoring data to the electronic equipment, the electronic equipment analyzes the status of the switch cabinet, and sends instructions or adjustment strategies to the microprocessor when adjustment is required. The microprocessor can display the monitoring data, instructions or adjustment strategies on the panel controller, execute instructions or prompt the staff to execute the adjustment strategy.

[0035] There are various electrical devices in the switch cabinet, and the microprocessor is connected to each electrical device, which can remotely adjust the switch cabinet as needed. The electrical devices include circuit breakers, conventional relays, isolating switches, capacitor cabinets, etc. For example, the circuit breaker can be disconnected or connected according to the control of the microprocessor, and the conventional relay can be controlled to act accordingly when the current, voltage and other parameters in the circuit change abnormally.

[0036] The present application embodiment discloses a remote control method for a switch cabinet based on CAN bus communication. Figure 2, applied to a CAN bus communication switch cabinet, executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto. It includes (steps S101 to S104): Step S101: Acquire monitoring data sent by a data acquisition device, the monitoring data including power distribution circuit voltage, power distribution circuit current, busbar connector temperature, cabinet environment temperature and humidity, and switch status.

[0037] Specifically, the electronic device communicates with the microprocessor in the switch cabinet, and then obtains the monitoring data sent by the data acquisition device.

[0038] Step S102: Analyze the operating status of the switch cabinet according to the monitoring data and determine abnormal values.

[0039] Specifically, the abnormal value is obtained by the electronic device according to the monitoring data analysis, including (step S1021 to step S1024): Step S1021: Compare each monitoring data item with the corresponding standard data to determine the deviation value corresponding to each monitoring data item.

[0040] Specifically, the electronic device presets standard data corresponding to each item of monitoring data, and calculates a deviation value by subtracting the monitoring data from the standard data.

[0041] For example, the monitoring data is the distribution circuit voltage, distribution circuit current, busbar structure temperature, cabinet environment temperature and humidity, etc., and the deviation value can be obtained by subtraction. If the monitoring data is the switch state, when the monitoring data is the same as the standard data, the deviation value is 0, and when it is different, the deviation value is 1.

[0042] Step S1022: Determine the initial abnormal value corresponding to the deviation value by comparing the deviation value with a preset table.

[0043] Specifically, the electronic device presets a table corresponding to the deviation value, and stores the abnormal value corresponding to multiple deviation values ​​of each monitoring data in the table. Generally, the larger the deviation value, the larger the corresponding abnormal value. The electronic device determines the abnormal value determined by comparison as the initial abnormal value.

[0044] Step S1023: Obtain historical feedback information on the deviation value, determine a correction value for the initial abnormal value based on the historical feedback information, add the initial abnormal value and the correction value, and determine the abnormal value corresponding to each monitoring data.

[0045] Specifically, the electronic device obtains historical feedback information, which may be the user's adjustment value for the initial abnormal value under the same deviation value, or the user's feedback statement on the initial abnormal value, for example: the initial abnormal value is too high; on the other hand, the historical feedback information may also be the last adjustment strategy executed in the historical data.

[0046] Step S1023 includes (steps Sa to Sc) when determining the correction value of the initial abnormal value according to the historical feedback information: Step Sa: If the historical feedback information is the user's adjustment value for the initial abnormal value under the same deviation value, the average of the multiple adjustment values ​​is determined as the correction value.

[0047] Specifically, if the user has different opinions on the initial abnormal value, the user may input an adjustment value to the electronic device, and the electronic device may obtain multiple adjustment values, and then determine the average of the multiple adjustment values ​​as the correction value.

[0048] Step Sb: If the historical feedback information is a user's feedback statement regarding the initial abnormal value under the same deviation value, the feedback statement is analyzed using a semantic analysis model, and the average of the adjustment values ​​corresponding to the multiple feedback statements is determined as the correction value.

[0049] Specifically, if the user inputs a feedback sentence, the feedback sentence is input into the semantic analysis model to analyze the words in the feedback sentence and determine whether the semantics is appropriate, too high or too low. If the semantic information contains a correction value, the correction value is directly determined. For example, if it is too high by 0.3, the correction value is -0.3.

[0050] If the semantic information does not contain a correction value, a value is automatically assigned based on the severity of the semantics. If it is slightly too high, the corresponding correction value is -0.1, and if it is too high, the corresponding correction value is -0.2.

[0051] When there are multiple feedback sentences, the average of each feedback sentence is calculated to obtain the correction value.

[0052] Step Sc: If the historical feedback information includes the user's adjustment value for the initial abnormal value and the user's feedback statement for the initial abnormal value under the same deviation value, then determine the first correction value corresponding to each historical feedback information respectively, calculate the average of each first correction value, and obtain the correction value.

[0053] Specifically, since the historical feedback information may include more than one, if the historical feedback information includes both the user's adjustment value and the feedback statement, the corresponding first correction values ​​are determined respectively, and the average value is calculated to obtain the correction value.

[0054] Step S1024: Calculate the abnormal value of the switch cabinet according to the abnormal value of each monitoring data and the weight value corresponding to each monitoring data.

[0055] Specifically, each monitoring data has a corresponding weight, and the sum of the weights of each data is 1. For example, the frequency of abnormalities in the distribution circuit voltage, distribution circuit current, busbar joint temperature, and cabinet ambient temperature and humidity is low, and the danger when abnormalities occur is relatively low, and it is preventable. The abnormal switch state is the most dangerous and requires emergency treatment. Therefore, the weight value corresponding to the switch state is the largest, and the weights corresponding to other monitoring data can be equal or unequal, but are all smaller than the weight value corresponding to the switch state.

[0056] Therefore, the electronic device multiplies the abnormal value of each monitoring data by the corresponding weight value to calculate the score, and adds the scores to calculate the abnormal value. The abnormal value integrates the status of each monitoring data and comprehensively considers the danger of each monitoring data to obtain an objective abnormal value.

[0057] Step S103: When the abnormal value is lower than the abnormal pre-value, the abnormal monitoring data is displayed.

[0058] Step S104: When the abnormal value reaches the abnormal pre-value, the intelligent analysis model based on machine learning is applied to analyze the monitoring data, determine the switch cabinet adjustment strategy, make the switch cabinet execute the adjustment strategy, and display the abnormal monitoring data.

[0059] Specifically, the electronic equipment presets an abnormal preset value. When the abnormal value is lower than the abnormal preset value, it may be a normal operation change in the switch cabinet, such as the distribution circuit voltage, distribution circuit current and bus joint temperature are slightly higher or lower than the normal value, or the temperature and humidity in the cabinet environment change caused by the change of the external environment, but it will not cause a fault that affects safety and operation. In this case, the panel controller will display abnormal monitoring data to facilitate the staff to determine whether remote intervention is needed.

[0060] If the abnormal value reaches the abnormal preset value, it may be that the distribution circuit voltage, distribution circuit current, busbar joint temperature and ambient temperature and humidity in the cabinet have changed much beyond the normal value, or the switch state is abnormal. The possibility of a safety accident is very high and timely intervention is required.

[0061] Therefore, when the abnormal value reaches the abnormal pre-value, the electronic device uses an intelligent analysis model based on machine learning to analyze the monitoring data and determine the switch cabinet adjustment strategy, including (steps S1041 to S1044): Step S1041: input the monitoring data into the intelligent analysis model based on machine learning for classification to determine the cause of the switch cabinet abnormality.

[0062] Specifically, when training an intelligent analysis model based on machine learning, the electronic device uses a large amount of monitoring data and the corresponding abnormal causes as samples for training. Therefore, the intelligent analysis model based on machine learning can determine the cause of the abnormality based on the monitoring data.

[0063] Step S1042: Obtain at least one adjustment strategy for resolving the cause of the abnormality through a database.

[0064] Specifically, a plurality of adjustment strategies corresponding to solving abnormal causes are pre-stored in the database, and the electronic device determines the adjustment strategy according to the abnormal cause index, and then selects a suitable adjustment strategy therefrom.

[0065] Step S1043: If there are multiple adjustment strategies, sort the adjustment strategies according to the order of their accuracy priority to obtain a first sequence.

[0066] Specifically, before the electronic device sorts the adjustment strategies, the method further includes (steps S11 to S14): Step S11: Obtain the number of calls and the resolution success rate corresponding to each adjustment strategy.

[0067] Specifically, the number of calls is the number of times the current exception is solved within a preset time period. The more times, the more effective it is. The success rate of solving can also directly reflect the effectiveness of the adjustment strategy.

[0068] Step S12: Arrange the adjustment strategies in descending order according to the number of calls to determine a second sequence.

[0069] Step S13: Arrange the adjustment strategies in descending order according to the success rate of resolution to determine a third sequence.

[0070] Specifically, the smaller the sequence number in the second sequence is, the greater the number of calls is; and the smaller the sequence number in the third sequence is, the higher the success rate of resolution is.

[0071] Step S14: Determine the first sequence number of the adjustment strategy in the second sequence and the second sequence number in the third sequence, calculate the third sequence number by the first sequence number and the second sequence number according to a preset ratio, generate an ascending fourth sequence according to the third sequence number, and determine the order of the adjustment strategy in the fourth sequence as the priority.

[0072] Specifically, the sum of the preset ratios corresponding to the success rate of resolution and the number of calls is 1. The preset ratio can be set according to actual needs. If the success rate of resolution is more persuasive than the number of calls, the ratio corresponding to the success rate of resolution is higher than the ratio corresponding to the number of calls.

[0073] The electronic device calculates a fourth sequence based on the second sequence number, the third sequence number and the corresponding preset ratio. In the fourth sequence, the smaller the sequence number is, the higher the corresponding number of calls and the success rate of resolution is, and therefore the accuracy priority is higher.

[0074] Therefore, the electronic device sorts the adjustment strategies according to the order of the fourth sequence, and the adjustment strategies with smaller sequence numbers in the first sequence have higher accuracy in solving the anomaly.

[0075] Step S1044: the switch cabinet executes the adjustment strategy according to the order of the first sequence until the abnormal value is lower than the abnormal preset value.

[0076] Specifically, the electronic device enables the switch cabinet to execute the adjustment strategy according to the order of the first sequence, and repeatedly obtains the abnormal value, compares the abnormal value with the abnormal preset value, and continues to call the next adjustment strategy if the abnormal value still does not drop below the abnormal preset value.

[0077] In another possible implementation, the method further includes (steps S21 to S24): Step S21: Obtain the number of abnormalities of each monitoring data within a preset time period.

[0078] Step S22: Compare the number of abnormalities of each monitoring data item with the corresponding calibration number within a preset time period to determine the difference.

[0079] Step S23: Calculate the ratio of the differences corresponding to each monitoring data, and determine the abnormal monitoring parameter item with the largest ratio.

[0080] Step S24: Generate prompt information including abnormal items of monitoring parameters.

[0081] Specifically, when the switch cabinet is in normal operation, there may be several normal abnormal fluctuations within a preset time period, that is, a preset calibration number of times. The electronic device calculates the difference corresponding to each monitoring data, and then calculates the ratio according to each difference. For example, the difference of the monitoring data is 2, 2, 2, 0, 4, respectively, then the corresponding ratio is 1:1:1:0:2, and the monitoring parameter abnormal item with the largest ratio is the switch state corresponding to the ratio of 2. Therefore, the switch state is used as a parameter abnormal item, and a prompt message is generated to prompt the staff to focus on the switch state.

[0082] In order to better implement the above method, the present application embodiment also provides a remote control device for a switch cabinet based on CAN bus communication, referring to Figure 3 , the switch cabinet remote control device 200 based on CAN bus communication includes: The monitoring data acquisition module 201 is used to acquire the monitoring data sent by the data acquisition device, and the monitoring data includes the distribution circuit voltage, distribution circuit current, busbar joint temperature, cabinet environment temperature and humidity, and switch status; An abnormal value determination module 202 is used to analyze the operating status of the switch cabinet according to the monitoring data and determine the abnormal value; The abnormal display module 203 is used to display abnormal monitoring data when the abnormal value is lower than the abnormal pre-value; The adjustment strategy determination module 204 is used to analyze the monitoring data using an intelligent analysis model based on machine learning when the abnormal value reaches the abnormal pre-value, determine the switch cabinet adjustment strategy, enable the switch cabinet to execute the adjustment strategy, and display the abnormal monitoring data.

[0083] Furthermore, the outlier determination module 202 is specifically configured to: Compare each monitoring data with the corresponding standard data to determine the deviation value corresponding to each monitoring data; According to the comparison between the deviation value and the preset table, the initial abnormal value corresponding to the deviation value is determined; Obtain historical feedback information on the deviation value, determine a correction value for the initial abnormal value based on the historical feedback information, add the initial abnormal value and the correction value, and determine the abnormal value corresponding to each data item; The abnormal value of the switch cabinet is calculated based on the abnormal value of each monitoring data and the weight value corresponding to each data.

[0084] Furthermore, when the abnormal value determination module 202 determines the correction value of the initial abnormal value according to the historical feedback information, it is specifically used to: If the historical feedback information is the user's adjustment value for the initial abnormal value under the same deviation value, the average of the multiple adjustment values ​​is determined as the correction value; If the historical feedback information is a user's feedback statement regarding the initial abnormal value under the same deviation value, the feedback statement is analyzed using a semantic analysis model, and the average of the adjustment values ​​corresponding to the multiple feedback statements is determined as the correction value; If the historical feedback information includes the user's adjustment value for the initial abnormal value and the user's feedback statement for the initial abnormal value under the same deviation value, the first correction value corresponding to each historical feedback information is determined respectively, and the average of each first correction value is calculated to obtain the correction value.

[0085] Furthermore, the adjustment strategy determination module 204 is specifically configured to: Input monitoring data into the intelligent analysis model based on machine learning for classification and determine the cause of switch cabinet abnormality; Obtaining at least one adjustment strategy for resolving the cause of the abnormality through a database; If there are multiple adjustment strategies, sort the adjustment strategies according to the order of their accuracy priority to obtain a first sequence; The switch cabinet executes the adjustment strategy according to the order of the first sequence until the abnormal value is lower than the abnormal preset value.

[0086] Furthermore, the switch cabinet remote control device 200 based on CAN bus communication also includes: The call count and success rate acquisition module is used to obtain the call count and resolution success rate corresponding to each adjustment strategy; A second sequence determination module, used to sort each adjustment strategy in descending order according to the number of calls to determine a second sequence; A third sequence determination module, used to sort the adjustment strategies in descending order according to the success rate of resolution, and determine the third sequence; The fourth sequence determination module is used to determine the first sequence number of the adjustment strategy in the second sequence and the second sequence number in the third sequence, calculate the third sequence number according to a preset ratio of the first sequence number and the second sequence number, generate an ascending fourth sequence according to the third sequence number, and determine the order of the adjustment strategy in the fourth sequence as the priority.

[0087] Furthermore, the switch cabinet remote control device 200 based on CAN bus communication also includes: The abnormality number acquisition module is used to obtain the abnormality number of each monitoring data within a preset time period; A difference determination module is used to compare the number of abnormalities of each monitoring data with the corresponding calibration number within a preset time period to determine the difference; An abnormal item determination module is used to calculate the ratio of the difference values ​​corresponding to each monitoring data and determine the abnormal item of the monitoring parameter with the largest ratio; The prompt information generation module is used to generate prompt information including abnormal items of monitoring parameters.

[0088] The various variations and specific examples of the methods in the aforementioned embodiments are also applicable to the remote control device of the switch cabinet based on CAN bus communication in the present embodiment. Through the aforementioned detailed description of the remote control method of the switch cabinet based on CAN bus communication, those skilled in the art can clearly know the implementation method of the remote control device of the switch cabinet based on CAN bus communication in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0089] In order to better implement the above method, the present application embodiment provides an electronic device, referring to Figure 4 , the electronic device 300 includes: a processor 301, a memory 303 and a display screen 305. Among them, the memory 303 and the display screen 305 are connected to the processor 301, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0090] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0091] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 may be divided into an address bus, a data bus, a control bus, etc.

[0092] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0093] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0094] Figure 4 The electronic device 300 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0095] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the program is executed by the processor, it implements the remote control method of the switch cabinet based on CAN bus communication provided in the above embodiment, and obtains monitoring data in real time through the data acquisition equipment, so as to provide comprehensive data support for the operation status analysis of the switch cabinet. According to the monitoring data, the system can analyze the operation status of the switch cabinet, which is helpful to timely discover potential faults or safety hazards. When the abnormal value is lower than but close to the abnormal preset value, the system can display abnormal monitoring data to remind the operator to pay attention and take corresponding preventive measures. When the abnormal value reaches the abnormal preset value, the monitoring data is deeply analyzed by the intelligent analysis model based on machine learning to determine the adjustment strategy of the switch cabinet, which can quickly respond to faults or abnormal situations, reduce downtime and maintenance costs, and more accurately predict and respond to various problems that may occur in the switch cabinet. The operator can perform targeted maintenance operations based on the abnormal data and adjustment strategies provided by the system, thereby improving maintenance efficiency and quality.

[0096] In this embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0097] The computer program in this embodiment includes program codes for executing all the aforementioned methods, and the program codes may include instructions corresponding to the steps of the methods provided in the above embodiments. The computer program can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network (such as the Internet, a local area network, a wide area network, and / or a wireless network). The computer program can be executed completely on the user's computer or as an independent software package.

[0098] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

[0099] In addition, it is to be understood that relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprises," "comprising," or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or device.

Claims

1. A remote control method for switch cabinet based on CAN bus communication, characterized in that: Applied to a switch cabinet based on CAN bus communication, the switch cabinet based on CAN bus communication includes a data acquisition device, a microprocessor, a CAN bus interface circuit, a panel controller and an expansion interface circuit, the data acquisition device, the panel controller and the expansion interface circuit are all connected to the microprocessor through the CAN bus interface circuit, the microprocessor communicates with the electronic device through the CAN bus interface circuit, and the method is executed by the electronic device, including: Acquire monitoring data sent by the data acquisition device, wherein the monitoring data includes power distribution circuit voltage, power distribution circuit current, busbar connector temperature, cabinet ambient temperature and humidity, and switch status; Analyzing the operating status of the switch cabinet according to the monitoring data to determine abnormal values; When the abnormal value is lower than the abnormal predicted value, the abnormal monitoring data is displayed; When the abnormal value reaches the abnormal pre-value, an intelligent analysis model based on machine learning is applied to analyze the monitoring data, determine the switch cabinet adjustment strategy, and make the switch cabinet execute the adjustment strategy to display the abnormal monitoring data.

2. The method according to claim 1, characterized in that Analyzing the operating state of the switch cabinet according to the monitoring data to determine abnormal values ​​includes: Compare each monitoring data with the corresponding standard data to determine the deviation value corresponding to each monitoring data; According to the comparison between the deviation value and the preset table, the initial abnormal value corresponding to the deviation value is determined; Acquire historical feedback information of the deviation value, determine a correction value of the initial abnormal value according to the historical feedback information, add the initial abnormal value and the correction value, and determine the abnormal value corresponding to each data item; The abnormal value of the switch cabinet is calculated based on the abnormal value of each monitoring data and the weight value corresponding to each data.

3. The method according to claim 2, characterized in that The determining of a correction value for the initial abnormal value according to the historical feedback information includes: If the historical feedback information is an adjustment value of the user for the initial abnormal value under the same deviation value, then the average of the plurality of adjustment values ​​is determined as the correction value; If the historical feedback information is a user's feedback statement regarding the initial abnormal value under the same deviation value, the feedback statement is analyzed using a semantic analysis model, and the average of the adjustment values ​​corresponding to the multiple feedback statements is determined as the correction value; If the historical feedback information includes the user's adjustment value for the initial abnormal value and the user's feedback statement for the initial abnormal value under the same deviation value, then the first correction value corresponding to each of the historical feedback information is determined respectively, and the average of each of the first correction values ​​is calculated to obtain the correction value.

4. The method according to claim 1, characterized in that: The application analyzes the monitoring data based on a machine learning intelligent analysis model, determines a switch cabinet adjustment strategy, and enables the switch cabinet to execute the adjustment strategy, including: Inputting the monitoring data into an intelligent analysis model based on machine learning for classification to determine the cause of the switch cabinet abnormality; Acquire at least one adjustment strategy for solving the cause of the abnormality through a database; If the adjustment strategies include multiple ones, sorting the adjustment strategies according to the order of priority of accuracy of the adjustment strategies to obtain a first sequence; The switch cabinet is caused to execute the adjustment strategy according to the order of the first sequence until the abnormal value is lower than the abnormal preset value.

5. The method according to claim 4, characterized in that Before sorting the adjustment strategies according to the order of priority of the adjustment strategies, the method further includes: Get the number of calls and resolution success rates for each adjustment strategy; Arrange the adjustment strategies in descending order according to the number of calls to determine a second sequence; Arrange the adjustment strategies in descending order according to the success rate of the solution to determine a third sequence; Determine the first sequence number of the adjustment strategy in the second sequence and the second sequence number in the third sequence, calculate the first sequence number and the second sequence number according to a preset ratio to obtain a third sequence number, generate an ascending fourth sequence according to the third sequence number, and determine the order of the adjustment strategy in the fourth sequence as the priority.

6. The method according to claim 4, characterized in that The method further comprises: Get the number of abnormalities for each monitoring data within the preset time period; Compare the number of abnormalities of each monitoring data with the corresponding calibration number within a preset time period to determine the difference; Calculate the ratio of the differences corresponding to each monitoring data, and determine the abnormal monitoring parameter with the largest ratio; Generates prompt information including abnormal monitoring parameters.

7. A remote control device for switch cabinet based on CAN bus communication, characterized in that: include: A monitoring data acquisition module is used to acquire the monitoring data sent by the data acquisition device, wherein the monitoring data includes the distribution circuit voltage, the distribution circuit current, the busbar joint temperature, the cabinet environment temperature and humidity, and the switch status; An abnormal value determination module, used to analyze the operating status of the switch cabinet according to the monitoring data and determine the abnormal value; An abnormal display module is used to display abnormal monitoring data when the abnormal value is lower than the abnormal pre-value; The adjustment strategy determination module is used to analyze the monitoring data using an intelligent analysis model based on machine learning when the abnormal value reaches the abnormal preset value, determine the switch cabinet adjustment strategy, enable the switch cabinet to execute the adjustment strategy, and display the abnormal monitoring data.

8. An electronic device, characterized in that: include: at least one processor; Memory; At least one computer program, wherein the at least one computer program is stored in the memory and configured to be executed by the at least one processor, the at least one computer program being configured to: execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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