Switch cabinet thermal fault detection method and system, terminal and storage medium

Abstract

The invention provides a switch cabinet thermal fault detection method and system, a terminal and a storage medium, and relates to the technical field of switch cabinet fault detection, and the method comprises the steps: collecting the current temperature of a switch cabinet observation window; inputting the acquired current temperature of the observation window of the switch cabinet into the trained fuzzy prediction model, and calculating to obtain the occurrence condition of a thermal fault in the switch cabinet; the trained fuzzy prediction model is a model capable of detecting the occurrence condition of the thermal fault in the switch cabinet based on the input temperature of the observation window of the switch cabinet; the thermal fault occurrence condition in the switch cabinet is the thermal fault occurrence condition of an isolation knife switch contact contact part, a switch trolley contact contact part, a conducting bar lap joint part and a cable head connection part in the switch cabinet. According to the invention, the internal thermal fault of the switch cabinet can be detected by collecting the temperature of the observation window of the switch cabinet, the limitation of insufficient detection of the internal deep thermal fault by an infrared nondestructive detection technology is overcome, and the detection efficiency of the thermal fault of the switch cabinet is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of switch cabinet fault detection, and in particular to a switch cabinet thermal fault detection method, system, terminal and storage medium. Background Art

[0002] In modern power systems, substations are the hubs of power distribution and transmission, and their operational safety and reliability are directly related to the power supply quality and power consumption experience of the majority of users. As one of the core equipment of substations, 10kV switchgear undertakes the important task of power distribution and control. However, during long-term operation, various contact parts in the switchgear, such as isolation knife contacts, switch trolley contacts, conductive bar overlap parts, and cable head connection parts, are prone to heating problems due to environmental factors and operating conditions, which may cause short-circuit faults and cause large-scale power outages, posing a serious threat to the stable operation of the power system.

[0003] At present, infrared non-destructive testing technology is mainly used to detect and diagnose thermal faults inside switch cabinets. By detecting the infrared radiation intensity on the surface of the object, the temperature distribution and thermal fault location inside the switch cabinet can be inferred. Due to the limited penetration depth of infrared thermal imagers, they can usually only detect the temperature distribution of a few millimeters to a few centimeters deep on the surface of the material. For thermal faults deep inside the switch cabinet, the infrared non-destructive testing technology cannot accurately detect the location of the thermal fault inside the switch cabinet. This is the shortcoming of the existing technology. Summary of the invention

[0004] In order to solve the deficiencies of the prior art, the present invention provides a switch cabinet thermal fault detection method, system, terminal and storage medium. By constructing a fuzzy prediction model, it is only necessary to collect the temperature of the switch cabinet observation window to detect the occurrence of thermal faults inside the switch cabinet, thereby overcoming the limitation of infrared non-destructive testing technology in insufficient detection of thermal faults deep inside the cabinet, greatly saving manpower and material resources, and improving the detection efficiency of thermal faults in the switch cabinet.

[0005] In a first aspect, the present invention provides a switch cabinet thermal fault detection method, the method comprising: Collect the current switch cabinet observation window temperature; The collected current switch cabinet observation window temperature is input into the trained fuzzy prediction model, and the occurrence of thermal faults in the switch cabinet is calculated; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

[0006] Furthermore, the training method of the fuzzy prediction model includes: The training method of the fuzzy prediction model includes: Construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal fault conditions in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

[0007] Furthermore, the training method of the fuzzy prediction model also includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model as the model performance based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

[0008] Furthermore, the switch cabinet information is obtained and a switch cabinet simulation model is established, including: Obtaining the structure and material parameters of the switch cabinet, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the overlap part of the conductive bar and the connection part of the cable head; Establishing a three-dimensional simulation model of the switch cabinet according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including the temperature of the environment where the switch cabinet is located and the atmospheric pressure of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

[0009] In a second aspect, the present invention further provides a switch cabinet thermal fault detection system, the system comprising: Data acquisition module, used to collect the current switch cabinet observation window temperature; The fault judgment module is used to input the collected current switch cabinet observation window temperature into the trained fuzzy prediction model, and calculate the occurrence of thermal faults in the switch cabinet; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

[0010] Furthermore, in the fault judgment module, the training method of the fuzzy prediction model includes: Construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal fault conditions in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

[0011] Furthermore, the training method of the fuzzy prediction model also includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model as the model performance based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

[0012] Furthermore, the switch cabinet information is obtained and a switch cabinet simulation model is established, including: Obtaining the structure and material parameters of the switch cabinet, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the overlap part of the conductive bar and the connection part of the cable head; Establishing a three-dimensional simulation model of the switch cabinet according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including the temperature of the environment where the switch cabinet is located and the atmospheric pressure of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

[0013] In a third aspect, the present invention provides a terminal, the terminal comprising a memory and a processor; A memory, used for storing a switch cabinet thermal fault detection program; A processor is used to implement the switch cabinet thermal fault detection method described in any one of the first aspects when executing the switch cabinet thermal fault detection program.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein a switch cabinet thermal fault detection program is stored on the readable storage medium, and when the switch cabinet thermal fault detection program is executed by a processor, the switch cabinet thermal fault detection method described in any one of the first aspects is implemented.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: The present invention uses a fuzzy prediction model to detect and diagnose the occurrence of thermal faults inside the switch cabinet according to the collected current switch cabinet observation window temperature, overcoming the limitation of infrared non-destructive testing technology that is insufficient for detecting thermal faults deep inside. Only by collecting the switch cabinet observation window temperature, it can more directly and in real time reflect the thermal fault conditions of the isolation knife switch contact contact part, the switch trolley contact contact part, the conductive strip connection part and the cable head connection inside the switch cabinet, thereby greatly saving manpower and material resources, helping to timely discover potential switch cabinet thermal faults, and improving the detection efficiency of switch cabinet thermal faults.

[0016] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 A schematic flow chart of an embodiment of the switch cabinet thermal fault detection method of the present invention; Figure 2 A schematic block diagram of an embodiment of a switch cabinet thermal fault detection system according to the present invention; Figure 3 A schematic diagram of the structure of an embodiment of the terminal of the present invention; Figure 4 A schematic diagram of the structure of an embodiment of the readable storage medium of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0021] The switch cabinet thermal fault detection system provided by the embodiment of the present invention is executed by a computer device, and accordingly, the switch cabinet thermal fault detection method runs in the computer device.

[0022] like Figure 1 As shown, the present invention provides a switch cabinet thermal fault detection method, the method comprising: S1. Collect the current switch cabinet observation window temperature; S2. Input the collected current switch cabinet observation window temperature into the trained fuzzy prediction model, and calculate the occurrence of thermal faults in the switch cabinet; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

[0023] To facilitate understanding of the present invention, the switch cabinet thermal fault detection method provided by the present invention is further described below based on the principle of the switch cabinet thermal fault detection method of the present invention and in combination with the process of the switch cabinet thermal fault detection method in the embodiment.

[0024] Specifically, the switch cabinet thermal fault detection method includes: Step 110: Collect the current switch cabinet observation window temperature.

[0025] Specifically, in this embodiment, the infrared radiation on the surface of the observation window can be captured by an infrared thermal imager to collect the temperature of the observation window of the switch cabinet.

[0026] Step 120, input the collected current switch cabinet observation window temperature into the trained fuzzy prediction model, and calculate the occurrence of thermal faults in the switch cabinet; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

[0027] It should be noted that the occurrence of the thermal fault in the switch cabinet obtained by the fuzzy prediction model is the detected thermal fault of the switch cabinet.

[0028] Specifically, the occurrence of thermal faults in the switch cabinet includes the following 16 situations: There is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is no fault in the connection part of the cable head; there is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is no fault in the connection part of the cable head; there is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is no fault in the connection part of the cable head; there is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head There is no fault in the contact part of the isolation knife switch, there is no fault in the contact part of the switch trolley, there is no fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is a fault in the contact part of the isolation knife switch, there is a fault in the contact part of the switch trolley, there is no fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is a fault in the contact part of the isolation knife switch, there is a fault in the contact part of the switch trolley, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is a fault in the contact part of the isolation knife switch, there is a fault in the contact part of the switch trolley, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head Fault in the connection part; There is no fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is no fault in the connection part of the cable head; There is no fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; There is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; There is a fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head There is no fault in any part; there is a fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is a fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is no fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head; there is a fault in the contact part of the isolation knife switch contact, there is a fault in the contact part of the switch trolley contact, there is a fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head.

[0029] Specifically, construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal fault conditions in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

[0030] Specifically, each type of thermal fault in the switch cabinet is marked separately, including: There is no fault in the contact part of the isolation knife switch contact 0, there is no fault in the contact part of the switch trolley contact 0, there is no fault in the overlapping part of the conductive bar 0, and there is no fault in the connection part of the cable head 0; there is a fault in the contact part of the isolation knife switch contact 1, there is a fault in the contact part of the switch trolley contact 0, there is no fault in the overlapping part of the conductive bar 0, and there is no fault in the connection part of the cable head 0; there is no fault in the contact part of the isolation knife switch contact 0, there is a fault in the contact part of the switch trolley contact 1, there is no fault in the overlapping part of the conductive bar 0, and there is no fault in the connection part of the cable head 0; there is no fault in the contact part of the isolation knife switch contact 0, there is no fault in the contact part of the switch trolley contact 0, there is a fault in the overlapping part of the conductive bar 1, and there is a fault in the connection part of the cable head No fault 0; No fault in the contact part of the isolation knife switch contact 0, No fault in the contact part of the switch trolley contact 0, No fault in the overlapping part of the conductive bar 0, No fault in the connection part of the cable head 1; No fault in the contact part of the isolation knife switch contact 1, No fault in the contact part of the switch trolley contact 1, No fault in the overlapping part of the conductive bar 0, No fault in the connection part of the cable head 0; No fault in the contact part of the isolation knife switch contact 1, No fault in the contact part of the switch trolley contact 0, No fault in the overlapping part of the conductive bar 1, No fault in the connection part of the cable head 0; No fault in the contact part of the isolation knife switch contact 1, No fault in the contact part of the switch trolley contact 0, No fault in the overlapping part of the conductive bar 0, No fault in the connection part of the cable head Partial fault 1; No fault in the contact part of the isolation knife switch contact 0, 1 fault in the contact part of the switch trolley contact, 1 fault in the overlapping part of the conductive bar, and no fault in the cable head connection 0; No fault in the contact part of the isolation knife switch contact 0, 1 fault in the contact part of the switch trolley contact, 0 no fault in the overlapping part of the conductive bar, and 1 fault in the cable head connection; No fault in the contact part of the isolation knife switch contact 0, 0 no fault in the contact part of the switch trolley contact, 1 fault in the overlapping part of the conductive bar, and 1 fault in the cable head connection; No fault in the contact part of the isolation knife switch contact 1, 1 fault in the contact part of the switch trolley contact, 1 fault in the overlapping part of the conductive bar, and 1 fault in the cable head connection No fault 0; contact part fault of the isolation knife switch contact 1, contact part fault of the switch trolley contact 1, no fault in the conductive bus overlap part 0, cable head connection part fault 1; contact part fault of the isolation knife switch contact 1, no fault in the switch trolley contact 0, conductive bus overlap part fault 1, cable head connection part fault 1; contact part of the isolation knife switch contact 0, contact part of the switch trolley contact 1, conductive bus overlap part fault 1, cable head connection part fault 1; contact part of the isolation knife switch contact 1, contact part of the switch trolley contact 1, conductive bus overlap part fault 1, cable head connection part fault 1.

[0031] Specifically, the switch cabinet information is obtained and the switch cabinet simulation model is established, including: Obtaining the structure and material parameters of the switch cabinet, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the overlap part of the conductive bar and the connection part of the cable head; A three-dimensional simulation model of the switch cabinet is established according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model.

[0032] The structures in the switch cabinet mainly include switch cabinet structures related to the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the conductive bar overlap part and the cable head connection part.

[0033] Specifically, according to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including temperature parameters of the environment where the switch cabinet is located and atmospheric pressure parameters of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

[0034] Specifically, the training method of the fuzzy prediction model also includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model as the model performance based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

[0035] It should be noted that, according to each type of thermal fault occurrence in the switch cabinet and its corresponding switch cabinet observation window temperature, the temperature fuzzy label of each type of thermal fault occurrence in the switch cabinet and the temperature of the switch cabinet observation window temperature are matched one by one, and then the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault occurrence in the switch cabinet of the fuzzy output set is used as a conclusion to obtain a fuzzy rule. The fuzzy rule is designed based on the experience of experts in this technical field. For example: if the temperature fuzzy label of the switch cabinet observation window temperature is "normal temperature", then the thermal fault occurrence in the switch cabinet is "no fault in the contact part of the isolation knife switch contact, no fault in the contact part of the switch trolley contact, no fault in the overlap part of the conductive bar, and no fault in the connection part of the cable head"; if the temperature fuzzy label of the switch cabinet observation window temperature is "normal temperature is higher than normal ... If the temperature fuzzy label of the switch cabinet observation window temperature is "slightly medium temperature, the thermal fault in the switch cabinet is "no fault in the contact part of the isolation knife switch, no fault in the contact part of the switch trolley, no fault in the overlap part of the conductive bar, and no fault in the connection part of the cable head"; if the temperature fuzzy label of the switch cabinet observation window temperature is "slightly medium temperature, the thermal fault in the switch cabinet is "no fault in the contact part of the isolation knife switch, no fault in the contact part of the switch trolley, no fault in the overlap part of the conductive bar, and no fault in the connection part of the cable head"; if the temperature fuzzy label of the switch cabinet observation window temperature is "slightly medium temperature, the thermal fault in the switch cabinet is "There is no fault in the contact part of the isolation knife switch contact, there is no fault in the contact part of the switch trolley contact, there is no fault in the overlapping part of the conductive bar, and there is a fault in the connection part of the cable head"; if the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "medium temperature is low", the thermal fault in the switch cabinet is "fault in the contact part of the isolation knife switch contact, fault in the contact part of the switch trolley contact, no fault in the overlapping part of the conductive bar, and no fault in the connection part of the cable head"; if the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "medium temperature", the thermal fault in the switch cabinet is "fault in the contact part of the isolation knife switch contact, no fault in the contact part of the switch trolley contact If the temperature fuzzy label of the switch cabinet observation window temperature is "medium temperature is high", the thermal fault in the switch cabinet is "fault in the contact part of the isolation knife switch contact, no fault in the contact part of the switch trolley, no fault in the conductive bar overlap part, and fault in the cable head connection part"; if the temperature fuzzy label of the switch cabinet observation window temperature is "slightly high temperature is low", the thermal fault in the switch cabinet is "no fault in the contact part of the isolation knife switch contact, fault in the contact part of the switch trolley contact, fault in the conductive bar overlap part, and no fault in the cable head connection part";If the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "slightly high temperature", the thermal fault situation in the switch cabinet is "no fault in the contact part of the isolation knife switch contact, fault in the contact part of the switch trolley contact, no fault in the overlapping part of the conductive bar, and fault in the connection part of the cable head"; if the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "slightly high temperature", the thermal fault situation in the switch cabinet is "no fault in the contact part of the isolation knife switch contact, no fault in the contact part of the switch trolley contact, fault in the overlapping part of the conductive bar, and fault in the connection part of the cable head"; if the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "high temperature and low temperature", the thermal fault situation in the switch cabinet is "fault in the contact part of the isolation knife switch contact, fault in the contact part of the switch trolley contact, fault in the overlapping part of the conductive bar, and no fault in the connection part of the cable head"; if the temperature fuzzy label of the temperature in the observation window of the switch cabinet is "high temperature", the thermal fault situation in the switch cabinet is "fault in the contact part of the isolation knife switch contact, fault in the contact part of the switch trolley contact, fault in the overlapping part of the conductive bar, and no fault in the connection part of the cable head" "The contact part of the switch contact is faulty, the contact part of the switch trolley is faulty, the overlapping part of the conductive bar is faulty, and the connection part of the cable head is faulty"; if the temperature fuzzy label of the temperature in the switch cabinet observation window is "high temperature and high", the thermal fault in the switch cabinet is "the contact part of the isolation knife switch is faulty, the contact part of the switch trolley is faulty, the overlapping part of the conductive bar is faulty, and the connection part of the cable head is faulty"; if the temperature fuzzy label of the temperature in the switch cabinet observation window is "extremely high temperature and low", the thermal fault in the switch cabinet is "the contact part of the isolation knife switch is faulty, the contact part of the switch trolley is faulty, the overlapping part of the conductive bar is faulty, and the connection part of the cable head is faulty"; if the temperature fuzzy label of the temperature in the switch cabinet observation window is "extremely high temperature", the thermal fault in the switch cabinet is "the contact part of the isolation knife switch is faulty, the contact part of the switch trolley is faulty, the overlapping part of the conductive bar is faulty, and the connection part of the cable head is faulty".;

[0036] When using the trained fuzzy prediction model, the current switch cabinet observation window temperature is collected and input into the trained fuzzy prediction model. The input switch cabinet observation window temperature needs to be converted into the corresponding temperature fuzzy label of the switch cabinet observation window temperature according to the defined fuzzy input set. Fuzzy reasoning is performed according to the converted temperature fuzzy label through the defined fuzzy rules to obtain the occurrence of thermal faults in the switch cabinet.

[0037] As an embodiment of the present invention, the present invention further provides a switch cabinet thermal fault detection system. The system 200 can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules may include: a data acquisition module 210 and a fault judgment module 22. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0038] The data acquisition module 210 is used to collect the current switch cabinet observation window temperature; The fault judgment module 220 is used to input the collected current switch cabinet observation window temperature into the trained fuzzy prediction model, and calculate the occurrence of thermal faults in the switch cabinet; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

[0039] As an embodiment of the present invention, in the fault judgment module 220, the training method of the fuzzy prediction model includes: Construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal fault conditions in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

[0040] As an embodiment of the present invention, in the fault judgment module 220, the training method of the fuzzy prediction model further includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model as the model performance based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

[0041] As an embodiment of the present invention, the structure and material parameters of the switch cabinet are obtained, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the conductive bar overlap part and the cable head connection part; Establishing a three-dimensional simulation model of the switch cabinet according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including the temperature of the environment where the switch cabinet is located and the atmospheric pressure of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

[0042] Figure 3 The present invention provides a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the switch cabinet thermal fault detection method provided in an embodiment of the present invention.

[0043] The terminal 300 may include: a processor 310, a memory 320 and a communication module 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0044] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0045] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In an embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0046] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0047] like Figure 4 As shown, the present invention further provides a computer storage medium 400, wherein the computer storage medium may store a program 401, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0048] The present invention uses a fuzzy prediction model to detect and diagnose the occurrence of thermal faults inside the switch cabinet according to the collected current switch cabinet observation window temperature, overcoming the limitation of infrared non-destructive testing technology that is insufficient for detecting thermal faults deep inside. Only by collecting the switch cabinet observation window temperature, it can more directly and in real time reflect the thermal fault conditions of the isolation knife switch contact contact part, the switch trolley contact contact part, the conductive strip connection part and the cable head connection inside the switch cabinet, thereby greatly saving manpower and material resources, helping to timely discover potential switch cabinet thermal faults, and improving the efficiency of switch cabinet thermal fault detection and diagnosis.

[0049] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect.

[0050] The technical effects that can be achieved by this embodiment can be found in the above description and will not be repeated here.

[0051] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0052] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0053] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and methods can be implemented in other ways. For example, the method embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another method, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, methods or modules, which can be electrical, mechanical or other forms.

[0054] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0056] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of these shall be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A switch cabinet thermal fault detection method, characterized in that: The method comprises: Collect the current switch cabinet observation window temperature; The collected current switch cabinet observation window temperature is input into the trained fuzzy prediction model, and the occurrence of thermal faults in the switch cabinet is calculated; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

2. The switch cabinet thermal fault detection method according to claim 1, characterized in that: The training method of the fuzzy prediction model includes: Construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal faults in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

3. The switch cabinet thermal fault detection method according to claim 2, characterized in that: The training method of the fuzzy prediction model also includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set as the model performance for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

4. The switch cabinet thermal fault detection method according to claim 2, characterized in that: Obtain switchgear information and establish a switchgear simulation model, including: Obtaining the structure and material parameters of the switch cabinet, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the overlap part of the conductive bar and the connection part of the cable head; Establishing a three-dimensional simulation model of the switch cabinet according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including the temperature of the environment where the switch cabinet is located and the atmospheric pressure of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

5. A switch cabinet thermal fault detection system, characterized in that: The system comprises: Data acquisition module, used to collect the current switch cabinet observation window temperature; The fault judgment module is used to input the collected current switch cabinet observation window temperature into the trained fuzzy prediction model, and calculate the occurrence of thermal faults in the switch cabinet; the trained fuzzy prediction model is a model that can detect the occurrence of thermal faults in the switch cabinet based on the input switch cabinet observation window temperature; the occurrence of thermal faults in the switch cabinet is the occurrence of thermal faults in the contact parts of the isolation knife switch contacts, the contact parts of the switch trolley contacts, the conductive bar overlap parts and the cable head connection parts in the switch cabinet.

6. The switch cabinet thermal fault detection system according to claim 5, characterized in that: In the fault judgment module, the training method of the fuzzy prediction model includes: Construct a sample training set: Obtain switchgear information and establish a switchgear simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated to obtain different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperature; Each of the thermal faults in the switch cabinet is marked respectively, and the marking method is to mark the location where the thermal fault occurs with a fuzzy label "1", and mark the location where the thermal fault does not occur with a fuzzy label "0"; the locations include the contact location of the isolation knife switch contact, the contact location of the switch trolley contact, the conductive bar overlap location and the cable head connection location; According to the preset labeling rules, the corresponding temperature fuzzy labels are used to label the switch cabinet observation window temperature corresponding to each type of thermal fault in the switch cabinet. The temperature fuzzy labels include normal temperature, normal temperature higher, slightly medium temperature lower, slightly medium temperature, slightly medium temperature higher, medium temperature lower, medium temperature, medium temperature higher, slightly high temperature lower, slightly high temperature, slightly high temperature higher, high temperature lower, high temperature, high temperature higher, extremely high temperature lower and extremely high temperature; Take each marked thermal fault situation in the switch cabinet and its corresponding marked observation window temperature of the switch cabinet as a sample, and collect each obtained sample to obtain a sample training set; Among them, normal temperature <normal temperature is slightly higher <slightly lower medium temperature <slightly medium temperature <slightly higher medium temperature <medium temperature is slightly lower <medium temperature is slightly lower <medium temperature is slightly higher <medium temperature is slightly lower <slightly higher temperature <slightly lower high temperature <high ​​temperature <high ​​temperature <extremely lower high temperature <extremely high temperature.

7. The switch cabinet thermal fault detection system according to claim 6, characterized in that: The training method of the fuzzy prediction model also includes: Determine the fuzzy set: define the fuzzy input set, including the temperature fuzzy label of the switch cabinet observation window temperature, which is {normal temperature, normal temperature is high, slightly medium temperature is low, slightly medium temperature, slightly medium temperature is high, medium temperature is low, medium temperature, medium temperature is high, slightly high temperature is low, slightly high temperature, slightly high temperature is high, high temperature is low, high temperature, high temperature is high, extremely high temperature is low, extremely high temperature}; define the fuzzy output set, including the occurrence of thermal faults in the switch cabinet; Define fuzzy rules: According to each type of thermal fault in the switch cabinet and its corresponding switch cabinet observation window temperature, each type of thermal fault in the switch cabinet and the temperature fuzzy label of the switch cabinet observation window temperature are matched one by one. Then, the temperature fuzzy label of the fuzzy input set is used as a condition, and the thermal fault in the switch cabinet of the fuzzy output set is used as a conclusion to obtain the fuzzy rules. Initialize the model: define the corresponding membership function for each temperature fuzzy label in the fuzzy input set; Model training: import the constructed sample training set into the constructed fuzzy prediction model, convert the observation window temperature of the switch cabinet in the sample training set into the temperature fuzzy label in the corresponding fuzzy input set, perform fuzzy reasoning on the fuzzy input in the sample training set through the defined fuzzy rules to obtain the fuzzy output, defuzzify the obtained fuzzy output using the centroid method, and then calculate the accuracy of the model based on the defuzzified fuzzy output and the occurrence of thermal faults in the switch cabinet in the sample training set as the model performance for evaluation. If the accuracy of the model is lower than the preset accuracy threshold, adjust the fuzzy rules, and then perform fuzzy reasoning and model evaluation on the sample training set again through the redefined fuzzy rules until the model accuracy is higher than the preset accuracy threshold, then a trained fuzzy prediction model is obtained; if the model accuracy is greater than or equal to the preset accuracy threshold, then a trained fuzzy prediction model is obtained.

8. The switch cabinet thermal fault detection system according to claim 6, characterized in that: Obtain switchgear information and establish a switchgear simulation model, including: Obtaining the structure and material parameters of the switch cabinet, including the geometric dimensions, geometric shapes and main heat source parameters of each structure in the switch cabinet; the main heat source parameters include the temperature parameters of the contact part of the isolation knife switch contact, the contact part of the switch trolley contact, the overlap part of the conductive bar and the connection part of the cable head; Establishing a three-dimensional simulation model of the switch cabinet according to the structure and material parameters of the switch cabinet to obtain the switch cabinet simulation model; According to the switch cabinet simulation model, by setting different situations of thermal faults in the switch cabinet, the switch cabinet observation window temperature under different situations of thermal faults in the switch cabinet is simulated, and different situations of thermal faults in the switch cabinet and the corresponding switch cabinet observation window temperatures are obtained, including: The structure and material parameters of the switch cabinet are set as the boundary conditions of the 3D simulation model of the switch cabinet; Set environmental parameters, including the temperature of the environment where the switch cabinet is located and the atmospheric pressure of the environment where the switch cabinet is located; The boundary conditions and environmental parameters are coupled to the three-dimensional simulation model of the switch cabinet. Then, the three-dimensional simulation model of the switch cabinet is used to simulate the temperature of the switch cabinet observation window under different thermal fault conditions in the switch cabinet, and the occurrence of thermal faults in different switch cabinets and the corresponding switch cabinet observation window temperatures are obtained.

9. A terminal, characterized in that: The terminal includes a memory and a processor; A memory, used for storing a switch cabinet thermal fault detection program; A processor is used to implement the switch cabinet thermal fault detection method according to any one of claims 1 to 4 when executing the switch cabinet thermal fault detection program.

10. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a switch cabinet thermal fault detection program, and when the switch cabinet thermal fault detection program is executed by the processor, the switch cabinet thermal fault detection method according to any one of claims 1 to 4 is implemented.

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