Electrical switch cabinet intelligent detection method based on multi-sensor fusion

Abstract

The invention relates to the technical field of electrical switch cabinets, in particular to an electrical switch cabinet intelligent detection method based on multi-sensor fusion, and the method comprises the steps: deploying sensing equipment in an electrical switch cabinet, and sensing the operation state parameters and operation environment state parameters of the electrical switch cabinet based on the sensing equipment; feature parameter picking and recording are carried out on the sensing parameters; the method comprises the following steps: recording the operation state parameters of the electrical switch cabinet, setting a characteristic parameter calling logic, calling the characteristic parameters from the recorded characteristic parameters based on the characteristic parameter calling logic, and evaluating the safety of the operation state of the electrical switch cabinet according to the called characteristic parameters. And in the parameter acquisition process, the acquired parameters are more representative and characteristic relative to the electrical switch cabinet by using specific acquisition logic, so that the operation safety of the electrical switch cabinet is comprehensively evaluated, and the daily operation safety of the electrical switch cabinet is comprehensively and effectively maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical switch cabinets, and in particular to an intelligent detection method for electrical switch cabinets based on multi-sensor fusion. Background Art

[0002] Electrical switchgear is an important equipment in the power system. It integrates various electrical components such as circuit breakers and contactors into a metal cabinet. Its function is to distribute power, control circuits and provide safety protection. Through precise operation, it ensures stable power transmission and protects the safety of equipment and personnel. It is widely used in factories, buildings and other places.

[0003] The invention patent with application number 202010719186.1 discloses a multi-source information fusion detection and early warning system for partial discharge faults in switch cabinets based on wireless transmission technology, which is characterized by: including a detection terminal, a power supply module, a wireless communication module and a system background module; the detection terminal is connected to the system background module through a wireless communication module; the detection terminal includes a transient ground voltage detection sensor, a UHF sensor, and an ultrasonic sensor; the system background module includes a data processing module, a central processing unit, a storage module and a display module.

[0004] The application aims to solve the following problems: "The transient ground voltage detection method has low sensitivity for most defects; UHF partial discharge detection is easily affected by UHF electromagnetic interference in the environment; external sensors cannot detect fully metal-enclosed power equipment; ultrasonic technology has obvious advantages in defect identification, and the amplitude increases with the severity of the defect, but under on-site detection conditions, some equipment has severe vibration and sound, and ultrasonic signals are easily drowned out by background noise; some equipment has good sealing, and ultrasonic signals are transmitted from the discharge source through the gap in the switch cabinet, which will have a large attenuation, which to a certain extent leads to a reduction in the defect detection rate."

[0005] However, currently, in the daily operation and maintenance management of electrical switchgear, most of them monitor one or several operating parameters of the electrical switch, and perform operation safety checks on the electrical switchgear according to these monitoring parameters.

[0006] To this end, we proposed an intelligent detection method for electrical switchgear based on multi-sensor fusion. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an intelligent detection method for an electrical switch cabinet based on multi-sensor fusion, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An intelligent detection method for an electrical switch cabinet based on multi-sensor fusion, comprising:

[0010] A sensing device is deployed inside the electrical switch cabinet, and based on the sensing device, the operating status parameters and operating environment status parameters of the electrical switch cabinet are sensed, and characteristic parameters of the sensing parameters are picked up and recorded; the characteristic parameter retrieval logic is set, and the characteristic parameters are retrieved from the recorded characteristic parameters based on the characteristic parameter retrieval logic, and the operating status safety of the electrical switch cabinet is evaluated according to the retrieved characteristic parameters, and the absolute safety judgment threshold of the electrical switch cabinet is set. The main switch of the electrical switch cabinet responds to the operation in real time based on the comparison result between the absolute safety judgment threshold of the electrical switch cabinet and the evaluation result of the operating status safety of the electrical switch cabinet; the evaluation result of the operating status safety of the electrical switch cabinet is continuously obtained, and the evaluation result is stored. When the stored evaluation results are not less than five groups, the prediction operation of the operating failure risk of the electrical switch cabinet is continuously performed; the output stage of the operation record of the main switch of the electrical switch cabinet and the prediction result of the operating failure risk of the electrical switch cabinet is output.

[0011] Furthermore, the sensing devices deployed inside the electrical switch cabinet include: acoustic sensors, UHF sensors, temperature and humidity sensors, dust sensors, capacitive touch sensors, and the sensing devices deployed inside the electrical switch cabinet operate to sense the operating state parameters of the electrical switch cabinet, including: operating vibration audio, partial discharge, and capacitance value, and the sensing devices deployed inside the electrical switch cabinet operate to sense the operating environment state parameters of the electrical switch cabinet, including: temperature, humidity, and dust content in the air;

[0012] Among them, the sensing device runs at least once per second, and the sensing device continuously senses the operating status parameters and operating environment status parameters of the electrical switch cabinet. Each set of parameters sensed by the sensing device is marked with a sensing timestamp.

[0013] Furthermore, when performing feature parameter picking in perception parameters, it follows that:

[0014] Logic1: Set the parameter picking start time and parameter picking frequency;

[0015] Logic2: Pick up parameters from the perception parameters based on the parameter picking time and picking frequency, and record them as feature parameters;

[0016] After the characteristic parameters are picked up, the picked characteristic parameters are used as recording targets to record the characteristic parameters.

[0017] Furthermore, when the characteristic parameters are recorded, the computer device or the cloud database is used as a recording medium for the characteristic parameters, and the characteristic parameters are transmitted to the computer device or the cloud database and recorded;

[0018] The parameter picking start time is manually set by the user end. The parameter picking start time is initially set by default as the timestamp of the corresponding mark of the first group of perceived electrical switch cabinet operating state parameters and operating environment state parameters. The parameter picking frequency complies with:

[0019]

[0020] Where: T is the interval time of parameter picking operation; m is the collection of power equipment in the electrical switch cabinet; g j is the number of other power equipment in the jth group of power equipment series set m; n is the total number of electrical switch cabinet failures; t i is the duration of the fault when the electrical switch cabinet fails for the i-th time;

[0021] in, Table pair The average of the parameter picking operation interval T is rounded down by truncation, f(·) is the constraint function, When the value of ≥3, When the value of is less than 3, T=3.

[0022] Furthermore, the operation of evaluating the operating status safety of the electrical switch cabinet is continuous. Each time the operation of evaluating the operating status safety of the electrical switch cabinet is performed, the characteristic parameters applied are the characteristic parameters of the latest record. After each operation of evaluating the operating status safety of the electrical switch cabinet is completed, the operation of evaluating the operating status safety of the electrical switch cabinet is performed again.

[0023] The characteristic parameter calling logic is:

[0024] The number of characteristic parameters to be retrieved is not less than three groups. The shorter the time from the last fault of the electrical switch cabinet, the higher the frequency of characteristic parameter retrieval. Conversely, the lower the frequency of characteristic parameter retrieval.

[0025] When the time from the last fault of the electrical switch cabinet reaches three days, the electrical switch cabinet is judged as a safe electrical switch cabinet;

[0026] The longer the safety electrical switch cabinet does not report a fault, the higher the frequency of characteristic parameter retrieval, and vice versa, the lower the frequency of characteristic parameter retrieval;

[0027] Among them, when the electrical switch cabinet is judged to be unsafe, the main switch of the electrical switch cabinet responds in real time to cut off the power supply of the electrical switch cabinet. When the main switch of the electrical switch cabinet is judged to be safe, the electrical switch cabinet does not operate and maintains the original operating state.

[0028] Furthermore, the operation of the electrical switch cabinet operating state safety assessment is expressed as:

[0029] A=kself ×ω1+k env ×ω2;

[0030] Where: A is the safety performance value of the electrical switchgear operating status; k self k is the safety performance value of the electrical switchgear based on its own operating status parameters; env is the safety performance value of the electrical switchgear based on the operating environment status parameter level; ω1 and ω2 are weights;

[0031] Among them, the sum of the weights ω1 and ω2 is 1, and ω1≥ω2, and the weight ω1 obeys: during the safety assessment stage of the operating status of the electrical switchgear, the higher the frequency of characteristic parameter retrieval, the larger the value of the weight ω1, and vice versa, the smaller the value of the weight ω1.

[0032] Furthermore, the k self and k env The formula is as follows:

[0033]

[0034] Where: u is the total number of sensing device operations; sim(q v ,q v+1 ) is the frequency spectrum similarity of the vibration audio of the electrical switch cabinet sensed by the sensing device during the vth and v+1th operations; L MAX is the maximum partial discharge value sensed by the sensing device during each operation; ci is the capacitance value sensed by the sensing device during the vth operation; S C S is the duration of the operating environment temperature of the sensing device within the safe temperature range in the time threshold corresponding to the total number of times the sensing device operates; RH The duration of the humidity of the operating environment of the sensing device within the safe temperature range in the time threshold corresponding to the total number of times the sensing device is running; ρ max It is the maximum dust content in the air sensed by the sensing device during each operation.

[0035] Furthermore, based on k self and k env The larger the safety performance value A of the operating status of the electrical switch cabinet is, the safer the electrical switch cabinet is. Conversely, the higher the operating risk of the electrical switch cabinet is.

[0036] Furthermore, the prediction logic of the electrical switch cabinet operation failure risk is expressed as:

[0037]

[0038] Where: A1, A2, A3, A4, A5 are the latest obtained safety assessment results of the operating status of the first, second, third, third, and fifth groups of electrical switchgear;

[0039] Among them, A1, A2, A3, A4, and A5 are all evaluation results that do not meet the absolute safety judgment threshold of the electrical switch cabinet. In the above formulas, if any one of them is true, it means that the electrical switch cabinet has a fault. If none of the above formulas is true, it means that the electrical switch cabinet has no fault.

[0040] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0041] The present invention provides an intelligent detection method for an electrical switch cabinet based on multi-sensor fusion. During the execution of the method, the operating status parameters and environmental status parameters of the electrical switch cabinet are comprehensively collected in real time through multiple sensors. In the process of collecting parameters, a specific collection logic is used to make the collected parameters more representative and characteristic with respect to the electrical switch cabinet, thereby bringing a comprehensive evaluation of the operating safety of the electrical switch cabinet and comprehensively and effectively maintaining the daily operating safety of the electrical switch cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 The figure is a flow chart of an intelligent detection method for electrical switch cabinets based on multi-sensor fusion. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0045] The present invention will be further described below in conjunction with the embodiments.

[0046] Embodiment 1:

[0047] An intelligent detection method for electrical switch cabinet based on multi-sensor fusion in this embodiment is as follows: Figure 1 As shown, including:

[0048] A sensing device is deployed inside the electrical switch cabinet, and based on the sensing device, the operating state parameters and operating environment state parameters of the electrical switch cabinet are sensed, and characteristic parameters of the sensed parameters are picked up and recorded;

[0049] The sensing devices deployed inside the electrical switch cabinet include: acoustic sensors, UHF sensors, temperature and humidity sensors, dust sensors, capacitive touch sensors. The sensing devices deployed inside the electrical switch cabinet operate to sense the operating status parameters of the electrical switch cabinet, including: operating vibration audio, partial discharge, capacitance value. The sensing devices deployed inside the electrical switch cabinet operate to sense the operating environment status parameters of the electrical switch cabinet, including: temperature, humidity, and dust content in the air.

[0050] The sensing device operates at least once per second, and the sensing device continuously senses the operating state parameters and operating environment state parameters of the electrical switch cabinet. Each set of parameters sensed by the sensing device is marked with a sensing timestamp.

[0051] When performing feature parameter picking in perception parameters, obey:

[0052] Logic1: Set the parameter picking start time and parameter picking frequency;

[0053] Logic2: Pick up parameters from the perception parameters based on the parameter picking time and picking frequency, and record them as feature parameters;

[0054] After the characteristic parameters are picked up, the picked characteristic parameters are used as recording targets to record the characteristic parameters;

[0055] Setting characteristic parameter retrieval logic, retrieving characteristic parameters from recorded characteristic parameters based on the characteristic parameter retrieval logic, evaluating the safety of the operating state of the electrical switch cabinet according to the retrieved characteristic parameters, setting the absolute safety judgment threshold of the electrical switch cabinet, and the main switch of the electrical switch cabinet responds to the operation in real time based on the comparison result between the absolute safety judgment threshold of the electrical switch cabinet and the safety evaluation result of the operating state of the electrical switch cabinet;

[0056] Continuously obtain the safety assessment results of the operating status of the electrical switch cabinet, store the assessment results, and continuously perform the prediction operation of the operating failure risk of the electrical switch cabinet when the stored assessment results are not less than five groups;

[0057] The prediction logic of the operational failure risk of electrical switchgear is expressed as:

[0058]

[0059] Where: A1, A2, A3, A4, A5 are the latest obtained safety assessment results of the operating status of the first, second, third, third, and fifth groups of electrical switchgear;

[0060] Among them, A1, A2, A3, A4, and A5 are all evaluation results that do not meet the absolute safety judgment threshold of the electrical switch cabinet. In the above formula, if any one of the above formulas is true, it means that the electrical switch cabinet has a fault, and if none of the above formulas is true, it means that the electrical switch cabinet has no fault;

[0061] The output stage of the main switch operation record of the electrical switch cabinet and the risk prediction results of the electrical switch cabinet operation failure.

[0062] In this embodiment, through the execution of the method in the above embodiment, the operating status parameters and operating environment status parameters of the electrical switch cabinet are jointly monitored by multiple sensors, which provides comprehensive and real-time operation safety detection for the daily operation of the electrical switch cabinet, ensuring that the electrical switch cabinet can stably perform power dispatching, management and maintenance work.

[0063] Embodiment 2:

[0064] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The intelligent detection method of an electrical switch cabinet based on multi-sensor fusion in Example 1 is further described in detail:

[0065] When the characteristic parameters are recorded, the computer device or cloud database is used as a recording medium for the characteristic parameters, and the characteristic parameters are transmitted to the computer device or cloud database and recorded;

[0066] The parameter picking start time is manually set by the user. The initial default setting of the parameter picking start time is the timestamp of the corresponding mark of the first group of perceived electrical switchgear operating status parameters and operating environment status parameters. The parameter picking frequency follows:

[0067]

[0068] Where: T is the interval time of parameter picking operation; m is the collection of power equipment in the electrical switch cabinet; g j is the number of other power equipment in the jth group of power equipment series set m; n is the total number of electrical switch cabinet failures; t i is the duration of the fault when the electrical switch cabinet fails for the i-th time;

[0069] in, Table pair The average of the parameter picking operation interval T is rounded down by truncation, f(·) is the constraint function, When the value of ≥3, When the value of is less than 3, T=3.

[0070] In this embodiment, through the above settings and formula limitations, a specified picking logic is provided when the feature parameter picking operation is executed.

[0071] Embodiment three:

[0072] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The intelligent detection method of an electrical switch cabinet based on multi-sensor fusion in Example 1 is further described in detail:

[0073] The evaluation operation of the operating status safety of the electrical switch cabinet is continuous. Each time the operating status safety evaluation operation of the electrical switch cabinet is performed, the characteristic parameters applied are the characteristic parameters of the latest record. After each execution of the operating status safety evaluation operation of the electrical switch cabinet is completed, the operating status safety evaluation operation of the electrical switch cabinet is performed again.

[0074] The feature parameter calling logic is:

[0075] The number of characteristic parameters to be retrieved is not less than three groups. The shorter the time from the last fault of the electrical switch cabinet, the higher the frequency of characteristic parameter retrieval. Conversely, the lower the frequency of characteristic parameter retrieval.

[0076] When the time from the last fault of the electrical switch cabinet reaches three days, the electrical switch cabinet is judged as a safe electrical switch cabinet;

[0077] The longer the safety electrical switch cabinet does not report a fault, the higher the frequency of characteristic parameter retrieval, and vice versa, the lower the frequency of characteristic parameter retrieval;

[0078] Among them, when the main switch of the electrical switch cabinet is determined to be unsafe, the main switch of the electrical switch cabinet responds in real time to cut off the power supply of the electrical switch cabinet. When the main switch of the electrical switch cabinet is determined to be safe, the electrical switch cabinet does not operate and maintains the original operating state;

[0079] The operation of the safety assessment of the operating status of the electrical switchgear is expressed as:

[0080] A=k self ×ω1+k env ×ω2;

[0081] Where: A is the safety performance value of the electrical switchgear operating status; k self k is the safety performance value of the electrical switchgear based on its own operating status parameters; env is the safety performance value of the electrical switchgear based on the operating environment status parameter level; ω1 and ω2 are weights;

[0082] Among them, the sum of weights ω1 and ω2 is 1, and ω1 ≥ ω2, and the weight ω1 obeys: in the stage of safety assessment of the operating status of the electrical switchgear, the higher the frequency of characteristic parameter retrieval, the larger the value of weight ω1, and vice versa, the smaller the value of weight ω1;

[0083] k self and k env The formula is as follows:

[0084]

[0085] Where: u is the total number of sensing device operations; sim(q v ,q v+1 ) is the frequency spectrum similarity of the vibration audio of the electrical switch cabinet sensed by the sensing device during the vth and v+1th operations; L MAX is the maximum partial discharge amount sensed by the sensing device during each operation; c i S is the capacitance value sensed by the sensing device during the vth operation; C S is the duration of the operating environment temperature of the sensing device within the safe temperature range in the time threshold corresponding to the total number of times the sensing device operates; RH The duration of the humidity of the operating environment of the sensing device within the safe temperature range in the time threshold corresponding to the total number of times the sensing device is running; ρ max It is the maximum dust content in the air sensed by the sensing device during each operation;

[0086] Based on k self and k env The larger the safety performance value A of the operating status of the electrical switch cabinet is, the safer the electrical switch cabinet is. Conversely, the higher the operating risk of the electrical switch cabinet is.

[0087] In this embodiment, through the above-mentioned settings, the operation of the safety assessment of the operating status of the electrical switch cabinet is further limited to ensure that when the method in Example 1 is executed, the operating safety of the electrical switch cabinet is continuously and stably judged, thereby bringing reliable, comprehensive and effective detection and management effects to the electrical switch cabinet.

[0088] In summary, during the execution of the method in the above embodiment, comprehensive real-time collection of the operating status parameters and environmental status parameters of the electrical switch cabinet is performed through multiple sensors, and in the process of collecting parameters, a specific collection logic is used to make the collected parameters more representative and characteristic relative to the electrical switch cabinet, thereby providing a comprehensive evaluation of the operating safety of the electrical switch cabinet and comprehensively and effectively maintaining the daily operating safety of the electrical switch cabinet.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent detection method for electrical switch cabinet based on multi-sensor fusion, characterized in that: include: A sensing device is deployed inside the electrical switch cabinet, and based on the sensing device, the operating state parameters and operating environment state parameters of the electrical switch cabinet are sensed, and characteristic parameters of the sensed parameters are picked up and recorded; Setting characteristic parameter retrieval logic, retrieving characteristic parameters from recorded characteristic parameters based on the characteristic parameter retrieval logic, evaluating the safety of the operating state of the electrical switch cabinet according to the retrieved characteristic parameters, setting the absolute safety judgment threshold of the electrical switch cabinet, and the main switch of the electrical switch cabinet responds to the operation in real time based on the comparison result between the absolute safety judgment threshold of the electrical switch cabinet and the safety evaluation result of the operating state of the electrical switch cabinet; Continuously obtain the safety assessment results of the operating status of the electrical switch cabinet, store the assessment results, and continuously perform the prediction operation of the operating failure risk of the electrical switch cabinet when the stored assessment results are not less than five groups; The output stage of the main switch operation record of the electrical switch cabinet and the risk prediction results of the electrical switch cabinet operation failure.

2. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 1 is characterized in that: The sensing devices deployed inside the electrical switch cabinet include: acoustic sensors, UHF sensors, temperature and humidity sensors, dust sensors, and capacitive touch sensors. The sensing devices deployed inside the electrical switch cabinet operate to sense the operating state parameters of the electrical switch cabinet, including: operating vibration audio, partial discharge, and capacitance value. The sensing devices deployed inside the electrical switch cabinet operate to sense the operating environment state parameters of the electrical switch cabinet, including: temperature, humidity, and dust content in the air. Among them, the sensing device runs at least once per second, and the sensing device continuously senses the operating status parameters and operating environment status parameters of the electrical switch cabinet. Each set of parameters sensed by the sensing device is marked with a sensing timestamp.

3. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 2 is characterized in that: When performing feature parameter picking in perception parameters, obey: Logic1: Set the parameter picking start time and parameter picking frequency; Logic2: Pick up parameters from the perception parameters based on the parameter picking time and picking frequency, and record them as feature parameters; After the characteristic parameters are picked up, the picked characteristic parameters are used as recording targets to record the characteristic parameters.

4. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 3 is characterized in that: When the characteristic parameters are recorded, the computer device or the cloud database is used as the recording medium of the characteristic parameters, and the characteristic parameters are transmitted to the computer device or the cloud database and recorded; The parameter picking start time is manually set by the user end. The parameter picking start time is initially set by default as the timestamp of the corresponding mark of the first group of perceived electrical switch cabinet operating state parameters and operating environment state parameters. The parameter picking frequency complies with: Where: T is the interval time of parameter picking operation; m is the collection of power equipment in the electrical switch cabinet; g j is the number of other power equipment in the jth group of power equipment series set m; n is the total number of electrical switch cabinet failures; t i is the duration of the fault when the electrical switch cabinet fails for the i-th time; in, Table pair The average of the parameter picking operation interval T is rounded down by truncation, f(·) is the constraint function, When the value of ≥3, When the value of is less than 3, T=3.

5. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 1 is characterized in that: The operation of assessing the operating status safety of the electrical switch cabinet is continuous. Each time the operation of assessing the operating status safety of the electrical switch cabinet is performed, the characteristic parameters applied are the characteristic parameters of the latest record. After each operation of assessing the operating status safety of the electrical switch cabinet is completed, the operation of assessing the operating status safety of the electrical switch cabinet is performed again. The characteristic parameter calling logic is: The number of characteristic parameters to be retrieved is not less than three groups. The shorter the time from the last fault of the electrical switch cabinet, the higher the frequency of characteristic parameter retrieval. Conversely, the lower the frequency of characteristic parameter retrieval. When the time from the last fault of the electrical switch cabinet reaches three days, the electrical switch cabinet is judged as a safe electrical switch cabinet; The longer the safety electrical switch cabinet does not report a fault, the higher the frequency of characteristic parameter retrieval, and vice versa, the lower the frequency of characteristic parameter retrieval; Among them, when the electrical switch cabinet is judged to be unsafe, the main switch of the electrical switch cabinet responds in real time to cut off the power supply of the electrical switch cabinet. When the main switch of the electrical switch cabinet is judged to be safe, the electrical switch cabinet does not operate and maintains the original operating state.

6. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 1 or 5, characterized in that: The operation of the electrical switchgear operating status safety assessment is expressed as follows: A=k self ×ω1+k env ×ω2; Where: A is the safety performance value of the electrical switchgear operating status; k self k is the safety performance value of the electrical switchgear based on its own operating status parameters; env is the safety performance value of the electrical switchgear based on the operating environment status parameter level; ω1 and ω2 are weights; Among them, the sum of the weights ω1 and ω2 is 1, and ω1≥ω2, and the weight ω1 obeys: during the safety assessment stage of the operating status of the electrical switchgear, the higher the frequency of characteristic parameter retrieval, the larger the value of the weight ω1, and vice versa, the smaller the value of the weight ω1.

7. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 6 is characterized in that: The k self and k env The formula is as follows: Where: u is the total number of sensing device operations; sim(q v ,q v+1 ) is the frequency spectrum similarity of the vibration audio of the electrical switch cabinet sensed by the sensing device during the vth operation and the v+1th operation; L MAX is the maximum partial discharge amount sensed by the sensing device during each operation; c i S is the capacitance value sensed by the sensing device during the vth operation; C The duration of the operating environment temperature of the sensing device being within the safe temperature range in the time threshold corresponding to the total number of times the sensing device is operated; S RH The duration of the humidity of the operating environment of the sensing device being within the safe temperature range in the time threshold corresponding to the total number of times the sensing device is operated; ρ max It is the maximum dust content in the air sensed by the sensing device during each operation.

8. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 7 is characterized in that: Based on k self and k env The larger the safety performance value A of the operating status of the electrical switch cabinet is, the safer the electrical switch cabinet is. Conversely, the higher the operating risk of the electrical switch cabinet is.

9. The intelligent detection method for electrical switch cabinet based on multi-sensor fusion according to claim 1 is characterized in that: The prediction logic of the electrical switch cabinet operation failure risk is expressed as: Where: A1, A2, A3, A4, A5 are the latest obtained safety assessment results of the operating status of the first, second, third, third, and fifth groups of electrical switchgear; Among them, A1, A2, A3, A4, and A5 are all evaluation results that do not meet the absolute safety judgment threshold of the electrical switch cabinet. In the above formulas, if any one of them is true, it means that the electrical switch cabinet has a fault. If none of the above formulas is true, it means that the electrical switch cabinet has no fault.

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