A method and system for detecting faults in distribution box switch cabinets

Abstract

The present invention relates to the technical field of power equipment monitoring, and discloses a fault detection method and system for a distribution box switch cabinet. The method includes: collecting the environmental and operating state data of the distribution box switch cabinet; generating an environmental feature matrix through dynamic weight calculation and weighted fusion; calculating the fault probability distribution based on the SVM fault classification model, determining the fault type, and generating a warning information report. Compared with the prior art, there are technical problems such as low detection accuracy, unclear determination of fault types, and especially the inability to achieve real-time and reliable data transmission and fault monitoring under low-bandwidth and high-interference network conditions. By adopting dynamic weight fusion, preset network transmission processing, and an SVM-based fault classification model, the present application realizes the comprehensive analysis of multiple fault parameters, thereby avoiding the misjudgment problem caused by the dependence on a single parameter in the traditional method and improving the accuracy and real-time performance of fault detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of power equipment monitoring, and particularly relates to a fault detection method and system for distribution box switch cabinets. Background Art

[0002] At present, as important control and protection equipment in the power system, distribution box switch cabinets have a complex operating environment and various fault forms. However, there are many deficiencies in the existing fault detection technologies for distribution box switch cabinets. For example, most existing detection methods only monitor based on a single parameter such as temperature or current, and cannot comprehensively analyze multi-dimensional data for fault analysis. At the same time, under low-bandwidth and high-interference network conditions, traditional methods are difficult to achieve stable data transmission, resulting in data loss or transmission delay. In addition, existing fault classification models often use fixed weights and fail to adjust model parameters according to the dynamic characteristics of sensors, resulting in low detection accuracy. The existing technologies cannot fully meet the requirements of distribution box switch cabinets in aspects such as multi-parameter comprehensive analysis, low-bandwidth reliable transmission, and dynamic fault determination. Therefore, there is an urgent need for a fault detection method for distribution box switch cabinets that can still achieve multi-dimensional data fusion, real-time transmission, and high-precision fault classification under complex operating environments and network conditions, so as to improve the operating reliability and safety of the power system. Summary of the Invention

[0003] Aiming at the above-mentioned technical deficiencies, the purpose of the present invention is to propose a fault detection method for distribution box switch cabinets, aiming to solve the technical problems of low detection accuracy and unclear determination of fault types in the existing technology, especially the inability to achieve real-time and reliable data transmission and fault monitoring under low-bandwidth and high-interference network conditions.

[0004] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a fault detection method for distribution box switch cabinets.

[0005] The fault detection method for distribution box switch cabinets includes:

[0006] Step S10: Collect environmental and equipment operating state data through various sensors installed in the distribution box switch cabinet, including:

[0007] A temperature sensor for collecting the operating temperature T of the distribution box switch cabinet;

[0008] A humidity sensor for collecting the humidity H of the distribution box switch cabinet;

[0009] A gas concentration sensor for collecting the sulfur hexafluoride gas concentration G;

[0010] A current sensor and a voltage sensor: respectively used for collecting the current I and the voltage V;

[0011] Normalize the above data to generate a normalized multi-parameter data set D = {T norn ,

[0012] H norn , G norn , I norn , V norn};

[0013] Step S20: By introducing dynamic weights, fuse the multi-parameter data set to generate an environmental feature matrix M. The steps include:

[0014] Obtain the historical data of the sensor within a preset time period, and calculate the dynamic weight based on the standard deviation of the historical data of the sensor. The formula is

[0015]

[0016] where i is the i-th sensor, σ i is the standard deviation of the i-th sensor, and ω i is the dynamic weight of the i-th sensor;

[0017] Construct the dynamically weighted fused environmental feature matrix M, M = {T norn ·ω T , H norn ·ω H , G norn ·ω G , I norn ·ω I , V norn ·ω V}, where ω T is the temperature dynamic weight, ω H is the humidity dynamic weight, ω G is the gas concentration dynamic weight, ω I is the current dynamic weight, and ω V is the voltage dynamic weight;

[0018] Step S30: Package the environmental feature matrix M generated in Step S20 and transmit it to the remote server in the form of a data packet through a preset network;

[0019] Step S40: After receiving the data packet, the remote server uses the cyclic redundancy check code in the data packet for data verification:

[0020] CRC packet = R(packet) mod G(X)

[0021] where R(packet) is the binary polynomial representation of the data packet, G(X) is a predefined fixed polynomial, and CRC packetis the calculated value of the cyclic redundancy check code, and mod is the remainder operation;

[0022] If the calculated value CRC of the cyclic redundancy check code packet is equal to the cyclic redundancy check code CRC, the check passes, and the data packet is marked as valid;

[0023] If the calculated value CRC of the cyclic redundancy check code packet is not equal to the cyclic redundancy check code CRC, the check fails, and the requesting device is requested to resend;

[0024] Step S50: Input the feature matrix M in the data packet marked as valid into the SVM fault classification model, and output the fault probability distribution P, where,

[0025] P = {p1, p2, p3, p4, p5, p6}

[0026] where, p1 is the probability of over-temperature fault, p2 is the probability of abnormal humidity fault, p3 is the probability of current overload fault, p4 is the probability of abnormal voltage fault, p5 is the probability of gas leakage fault, and p6 is the probability of grounding fault;

[0027] Arrange p1, p2, p3, p4, p5, p6 in ascending order, and set the maximum value among them as p max ;

[0028] Preset a probability threshold p t , when p max > p t , it is determined that there is a fault type corresponding to p max , when p max ≤ p t , it is determined to be in a normal state;

[0029] Step S60: Generate a warning information report based on the determination result of the fault type, trigger an alarm, and send the warning information report to the remote maintenance terminal.

[0030] Preferably, in step S30, the preset network includes a physical layer, a network layer, a transport layer, and an application layer. The physical layer and the network layer of the preset network use the NB-IoT protocol for data frame transmission, the transport layer of the preset network uses the UDP protocol for data frame transmission, and the application layer of the preset network uses the CoAP protocol.

[0031] Preferably, in step S30, where the data packet format is: Packet = [DeviceID, Timestamp, M, CRC], Packet is the data packet, DeviceID is the unique identifier of the distribution box switch cabinet, Timestamp is the data acquisition time, and CRC is the cyclic redundancy check code.

[0032] Preferably, in step S50, the SVM fault classification model is offline trained using historical fault data. During offline training, the input is the standardized feature vector, and the output is the true fault probability.

[0033] Preferably, in step S50, the SVM fault classification model calculates the fault probability through the following formula:

[0034]

[0035] where k is the k-th type of fault, ω k is the classification hyperplane parameter, b k is the offset, f k (x) is the score of the k-th type of fault, and T is the matrix transpose;

[0036] The Softmax function is used to convert the score of the k-th type to a probability.

[0037] Preferably, in step S60, the Softmax function is used to convert the score of the k-th type to a probability, and the formula is:

[0038]

[0039] where p k is the probability of the k-th type of fault, exp is the exponential mapping, and j represents the index.

[0040] Preferably, in step S10, the normalization process adopts the maximum-minimum normalization method.

[0041] The present invention also provides a fault detection system for a distribution box switch cabinet, including:

[0042] A data acquisition module, configured to collect environmental and equipment operation state data through a variety of sensors installed in the distribution box switch cabinet, including:

[0043] A temperature sensor, configured to collect the operating temperature T of the distribution box switch cabinet;

[0044] A humidity sensor, configured to collect the humidity H of the distribution box switch cabinet;

[0045] A gas concentration sensor, configured to collect the sulfur hexafluoride gas concentration G;

[0046] A current sensor and a voltage sensor: respectively configured to collect the current I and the voltage V;

[0047] The above data is normalized to generate a normalized multi-parameter data set D = {T norn , H norn , G norn , I norn , V norn};

[0048] An environmental feature construction module, which is used to generate an environmental feature matrix M by introducing dynamic weights and fusing multi-parameter data sets. The steps include:

[0049] Obtain the historical data of the sensor within a preset time period, and calculate the dynamic weight based on the standard deviation of the historical data of the sensor. The formula is

[0050]

[0051] where i is the i-th sensor, σ i is the standard deviation of the i-th sensor, and ω i is the dynamic weight of the i-th sensor;

[0052] Construct the environmental feature matrix M after dynamic weighted fusion. M = {T norn ·ω T , H norn ·ω H , G norn ·ω G , I norn ·ω I , V norn ·ω V}, where ω T is the temperature dynamic weight, ω H is the humidity dynamic weight, ω G is the gas concentration dynamic weight, ω I is the current dynamic weight, ω V is the voltage dynamic weight;

[0053] A data transmission module, which is used to pack the environmental feature matrix M generated in step S20 and transmit it to the remote server in the form of a data packet through a preset network;

[0054] A data verification module, which is used to perform data verification using the cyclic redundancy check code in the data packet after the remote server receives the data packet:

[0055] CRC packet = R(packet) mod G(X)

[0056] where R(packet) is the binary polynomial representation of the data packet, G(X) is a predefined fixed polynomial, CRC packet is the calculated value of the cyclic redundancy check code, and mod is the remainder operation;

[0057] If the calculated value CRC of the cyclic redundancy check code packet is equal to the cyclic redundancy check code CRC, the verification passes, and the data packet is marked as valid;

[0058] If the calculated value CRC of the cyclic redundancy check code packet is not equal to the cyclic redundancy check code CRC, the check fails, and the device side is requested to resend;

[0059] A fault classification module, which is used to input the feature matrix M in the marked valid data packet into the SVM fault classification model and output the fault probability distribution P, where

[0060] P = {p1, p2, p3, p4, p5, p6}

[0061] where p1 is the probability of over-temperature fault, p2 is the probability of abnormal humidity fault, p3 is the probability of current overload fault, p4 is the probability of abnormal voltage fault, p5 is the probability of gas leakage fault, and p6 is the probability of grounding fault;

[0062] Arrange p1, p2, p3, p4, p5, p6 in ascending order, and set the maximum value among them as p max ;

[0063] Preset a probability threshold p t , when p max > p t , it is determined that there is a fault type corresponding to p max , when p max ≤ p t , it is determined to be in a normal state;

[0064] An early warning generation module, which is used to generate an early warning information report based on the determination result of the fault type, trigger an alarm and send the early warning information report to the remote maintenance terminal.

[0065] The present invention also provides a distribution box switch cabinet fault detection device, including a memory, a processor, and a distribution box switch cabinet fault detection program stored on the memory and operable on the processor. When the distribution box switch cabinet fault detection program is executed by the processor, the distribution box switch cabinet fault detection method described above is implemented.

[0066] The present invention also provides a computer program product, including a distribution box switch cabinet fault detection program. When the distribution box switch cabinet fault detection program is executed by a processor, the distribution box switch cabinet fault detection method described above is implemented.

[0067] The beneficial effects of the present invention are as follows: Compared with the prior art, there are technical problems such as low detection accuracy, unclear determination of fault types, and especially the inability to achieve real-time and reliable data transmission and fault monitoring under low-bandwidth and high-interference network conditions. By adopting dynamic weight fusion, preset network transmission processing, and a fault classification model based on SVM, the present application realizes the comprehensive analysis of multiple fault parameters, thereby avoiding the misjudgment problem caused by relying on a single parameter in the traditional method and improving the accuracy and real-time performance of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0069] Figure 1 It is a schematic flowchart of the first embodiment of a method for detecting faults in a distribution box switch cabinet of the present invention.

[0070] Figure 2 It is a schematic diagram of the equipment for a method for detecting faults in a distribution box switch cabinet of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0072] Embodiment 1: As Figure 1 shown, it is a schematic flowchart of the first embodiment of a method for detecting faults in a distribution box switch cabinet of the present invention, and the first embodiment of a method for detecting faults in a distribution box switch cabinet of the present invention is proposed.

[0073] In the first embodiment, the method for detecting faults in a distribution box switch cabinet includes:

[0074] Step S10: Collect environmental and equipment operating state data through a variety of sensors installed in the distribution box switch cabinet, including:

[0075] A temperature sensor for collecting the operating temperature T of the distribution box switch cabinet;

[0076] A humidity sensor for collecting the humidity H of the distribution box switch cabinet;

[0077] A gas concentration sensor for collecting the concentration G of sulfur hexafluoride gas;

[0078] A current sensor and a voltage sensor, respectively used for collecting current I and voltage V;

[0079] Normalize the above data to generate a normalized multi-parameter data set D = {T norn , H norn , G norn , I norn , V norn};

[0080] It should be noted that the multi-dimensional data of the environment and equipment operating status, such as temperature, humidity, current, etc., are collected in real time, covering the important parameters affecting the safe operation of distribution box switchgear; early detection of insulating gas leakage can avoid a significant decline in electrical insulation performance caused by gas leakage; after data normalization, a unified standard data set is formed, ensuring a reasonable distribution of the relative weights between multi-parameters in subsequent analysis; with the comprehensive data support of temperature, humidity, gas concentration, current, and voltage, potential faults can be identified earlier and warnings can be issued, thereby improving the operating reliability of the equipment and the stability of the power system.

[0081] Step S20: By introducing dynamic weights, fuse the multi-parameter data set to generate an environmental feature matrix M. The steps include:

[0082] Obtain the historical data of the sensor within a preset time period, and calculate the dynamic weights based on the standard deviation of the historical data of the sensor. The formula is

[0083]

[0084] where i is the i-th sensor, σ i is the standard deviation of the i-th sensor, and ω i is the dynamic weight of the i-th sensor;

[0085] Construct the dynamically weighted fused environmental feature matrix M, M = {T norn ·ω T , H norn ·ω H , G norn ·ω G , I norn ·ω I , V norn ·ω V}, where ω T is the temperature dynamic weight, ω H is the humidity dynamic weight, ω G is the gas concentration dynamic weight, ω I is the current dynamic weight, ω Vis the voltage dynamic weight;

[0086] It should be noted that different sensors are affected by factors such as noise and interference to different degrees in the operating environment. The introduction of dynamic weights can improve the credibility of sensor data; the dynamic weights are updated in real time as the sensor performance or environmental conditions change, enhancing the adaptability and robustness of the system.

[0087] It can be understood that after weighting the features by weights, data with higher stability, such as sensors with smaller standard deviations, have higher weights in the analysis, ensuring more reliable results; the change of weights reflects the contribution degree of sensors to fault detection in different environments, thus optimizing the overall detection performance. Fixed weights usually cannot adapt to the measurement fluctuations caused by sensor aging, environmental changes, etc.; dynamic weights are automatically adjusted according to the historical standard deviation, which can better reflect the actual situation of the current environment, thereby improving the accuracy of detection.

[0088] Step S30: Pack the environmental feature matrix M generated in step S20 and transmit it to the remote server in the form of data packets through a preset network;

[0089] It should be noted that the preset network includes the physical layer, network layer, transport layer, and application layer. The physical layer and network layer of the preset network use the NB-IoT protocol for data frame transmission, the transport layer of the preset network uses the UDP protocol for data frame transmission, and the application layer of the preset network uses the CoAP protocol

[0090] It should be understood that the NB-IoT network can provide stable low-bandwidth communication in wide coverage and complex environments, which is suitable for real-time monitoring applications of distribution boxes and switch cabinets; the cyclic redundancy check code can quickly determine whether the data packet is damaged or lost during transmission;

[0091] Step S40: After receiving the data packet, the remote server uses the cyclic redundancy check code in the data packet for data verification:

[0092] CRC packet = R(packet) mod G(X)

[0093] where R(packet) is the binary polynomial representation of the data packet, G(X) is the predefined fixed polynomial, CRC packet is the calculated value of the cyclic redundancy check code, and mod is the remainder operation;

[0094] If the calculated value CRC of the cyclic redundancy check code packet is equal to the cyclic redundancy check code CRC, the verification passes, and the data packet is marked as valid;

[0095] If the calculated value CRC of the cyclic redundancy check code packetNot equal to the cyclic redundancy check code CRC, check failed, request the device side to resend;

[0096] It should be noted that CRC check is a fast and efficient error detection method, especially suitable for verifying the integrity of data transmission in network communication. It has a small computational amount and is suitable for scenarios such as embedded systems or low-bandwidth networks, such as the NB-IoT network;

[0097] It can be understood that during the data transmission process, the data may be affected by noise, signal interference, etc., resulting in errors in some bit positions. The CRC check detects these errors to avoid misjudgment of fault classification caused by packet errors.

[0098] Step S50: Input the feature matrix M in the valid packet into the SVM fault classification model, and output the fault probability distribution P, where,

[0099] P = {p1, p2, p3, p4, p5, p6}

[0100] Among them, p1 is the probability of over-temperature fault, p2 is the probability of abnormal humidity fault, p3 is the probability of current overload fault, p4 is the probability of abnormal voltage fault, p5 is the probability of gas leakage fault, and p6 is the probability of grounding fault;

[0101] Arrange p1, p2, p3, p4, p5, p6 in ascending order, and set the maximum value among them as p max ;

[0102] Preset the probability threshold p t , when p max > p t , then it is determined that there is a fault type corresponding to p max , when p max ≤ p t , then it is determined to be in a normal state;

[0103] It should be understood that the SVM model can effectively process high-dimensional input data, such as the environmental feature matrix M, and has strong generalization ability; compared with the traditional rule determination method, the SVM model can dynamically adjust the classification boundary according to historical data to improve the classification accuracy.

[0104] It should be understood that the maximum probability determination method significantly reduces the uncertainty of the classification result by selecting p max , even if the multi-class probabilities are relatively close, it can still make an optimal choice according to p max to reduce the risk of misjudgment.

[0105] Step S60: Generate a warning information report based on the determination result of the fault type, trigger an alarm and send the warning information report to the remote maintenance terminal.

[0106] It should be noted that the warning information report includes the status of the device and recommended maintenance actions. The specific contents are as follows:

[0107] Device identifier, used to uniquely locate the distribution box switchgear where the fault occurs;

[0108] Fault type, the determined fault type, such as over-temperature fault, humidity anomaly fault, etc.;

[0109] Fault probability, the maximum probability related to the fault type, reflecting the severity of the problem;

[0110] Trigger time, records the time when the fault event occurs, used for event tracing;

[0111] Recommended measures, providing specific maintenance suggestions for the fault type.

[0112] It can be understood that the system triggers an alarm according to the fault type and severity. The alarm forms include on-site buzzers, warning lights, and remote notifications such as text messages, APP push, and emails.

[0113] In addition, a distribution box switchgear fault detection system provided by the present invention adopts a distribution box switchgear fault detection method in the above-mentioned embodiment, and can solve the technical problem of distribution box switchgear fault detection. Compared with the prior art, the beneficial effects of the distribution box switchgear fault detection system provided by the present invention are the same as those of the distribution box switchgear fault detection method provided by the above-mentioned embodiment, and other technical features in the distribution box switchgear fault detection system are the same as the features disclosed in the above-mentioned embodiment method, and will not be elaborated here.

[0114] The present invention provides a distribution box switchgear fault detection device. Please refer to Figure 2, a power distribution box switch cabinet fault detection device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a power distribution box switch cabinet fault detection method in Embodiment 1 above. A power distribution box switch cabinet fault detection device in an embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A power distribution box switch cabinet fault detection device is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention. A power distribution box switch cabinet fault detection device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of a power distribution box switch cabinet fault detection device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow a power distribution box switch cabinet fault detection device to communicate with other devices wirelessly or wiredly to exchange data. Although a power distribution box switch cabinet fault detection device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0115] The present invention also provides a computer program product, including a computer program, which when executed by a processor, implements the steps of a method for detecting faults in a distribution box switchgear as described above. The computer program product provided by the present invention can solve the technical problem of detecting faults in a distribution box switchgear. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the method for detecting faults in a distribution box switchgear provided in the above embodiments, and will not be elaborated here.

[0116] In particular, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment disclosed by the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above functions defined in the methods of the embodiments disclosed by the present invention.

[0117] It should be understood that the various parts disclosed by the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0118] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting faults in a distribution box switch cabinet, characterized in that: Methods include: Step S10: Collecting environment and equipment operation status data through various sensors installed in the switch cabinet of the distribution box, including: Temperature sensor, used to collect the operating temperature T of the distribution box switch cabinet; Humidity sensor, used to collect humidity H of the switch cabinet of the distribution box; Gas concentration sensor, used to collect sulfur hexafluoride gas concentration G; Current sensor and voltage sensor: used to collect current I and voltage V respectively; The above data are normalized to generate a normalized multi-parameter data set D = {T norn , H norn , G norn , I norn , V norn }; Step S20: Generate an environmental feature matrix M by introducing dynamic weights and fusing multi-parameter data sets. The steps include: Get the historical data of each sensor within the preset time period, and calculate the dynamic weight based on the standard deviation of the historical data of each sensor. The formula is: Where i is the i-th sensor, σ i is the standard deviation of the ith sensor, ω i is the dynamic weight of the i-th sensor; Construct the environment feature matrix M after dynamic weighted fusion, M = {T norn ·ω T , H norn ·ω H , G norn ·ω G , I norn ·ω I , V norn ·ω V }, where ω T is the temperature dynamic weight, ω H is the humidity dynamic weight, ω G is the dynamic weight of gas concentration, ω I is the current dynamic weight, ω V is the voltage dynamic weight; Step S30: Packing the environmental feature matrix M generated in step S20 and transmitting it to the remote server via a preset network in the form of a data packet; Step S40: After receiving the data packet, the remote server uses the cyclic redundancy check code in the data packet to perform data verification: CRC packet =R(packet)mod G(X) Among them, R(packet) is the binary polynomial representation of the data packet, G(X) is a predefined fixed polynomial, CRC packet is the calculated value of the cyclic redundancy check code, and mod is the remainder operation; If the calculated value of the cyclic redundancy check code CRC packet Equal to the cyclic redundancy check code CRC, the check passes and the data packet is marked as valid; If the calculated value of the cyclic redundancy check code CRC packet Not equal to the cyclic redundancy check code CRC, the check fails, and the device is requested to resend; Step S50: Input the feature matrix M in the data packet marked as valid into the SVM fault classification model and output the fault probability distribution P, where: P={p1,p2,p3,p4,p5,p6} Among them, p1 is the probability of over-temperature fault, p2 is the probability of abnormal humidity fault, p3 is the probability of current overload fault, p4 is the probability of abnormal voltage fault, p5 is the probability of gas leakage fault, and p6 is the probability of grounding fault; Arrange p1, p2, p3, p4, p5, and p6 in ascending order, and set the largest value among them as p max ; Preset the probability threshold p t , when p max >p t , then determine whether there is max The corresponding fault type, when p max ≤p t , it is judged as normal state; Step S60: Generate a warning information report based on the determination result of the fault type, trigger an alarm and send the warning information report to the remote maintenance terminal.

2. A method for detecting faults in a distribution box switch cabinet according to claim 1, characterized in that: In step S30, the preset network includes a physical layer, a network layer, a transport layer and an application layer. The physical layer and the network layer of the preset network use the NB-IoT protocol to transmit data frames, the transport layer of the preset network uses the UDP protocol to transmit data frames, and the application layer of the preset network uses the CoAP protocol.

3. A method for detecting faults in a distribution box switch cabinet according to claim 1, characterized in that: In step S30, the data packet format is: Packet = [DeviceID, Timestamp, M, CRC], Packet is the data packet, DeviceID is the unique identifier of the distribution box switch cabinet, Timestamp is the data collection time, and CRC is the cyclic redundancy check code.

4. A method for detecting faults in a distribution box switch cabinet according to claim 1, characterized in that: In step S50, the SVM fault classification model is trained offline using historical fault data. During offline training, the input is a standardized feature vector and the output is a true fault probability.

5. A method for detecting faults in a distribution box switch cabinet according to claim 1, characterized in that: In step S50, the SVM fault classification model calculates the fault probability by the following formula: Where k is the kth type of fault, ω k is the classification hyperplane parameter, b k is the offset, f k (x) is the score of the k-th type of fault, T is the transpose of the matrix; Use the Softmax function to convert the k-th class score into probability.

6. A method for detecting faults in a distribution box switch cabinet as claimed in claim 5, characterized in that: In step S60, the Softmax function is used to convert the k-th class score into probability, and the formula is: Among them, p k is the probability of the kth type of failure, exp is the exponential mapping, and j represents the index.

7. A method for detecting faults in a distribution box switch cabinet according to claim 1, characterized in that: In step S10, the normalization process adopts the maximum and minimum normalization method.

8. A distribution box switch cabinet fault detection system, characterized in that: The distribution box switch cabinet fault detection system comprises: The data acquisition module is used to collect environmental and equipment operating status data through various sensors installed in the distribution box switch cabinet, including: Temperature sensor, used to collect the operating temperature T of the distribution box switch cabinet; Humidity sensor, used to collect humidity H of the switch cabinet of the distribution box; Gas concentration sensor, used to collect sulfur hexafluoride gas concentration G; Current sensor and voltage sensor: used to collect current I and voltage V respectively; The above data are normalized to generate a normalized multi-parameter data set D = {T norn , H norn , G norn , I norn , V norn }; The environment feature construction module is used to generate the environment feature matrix M by introducing dynamic weights and fusing multi-parameter data sets. The steps include: Get the historical data of the sensor within the preset time period, and calculate the dynamic weight based on the standard deviation of the historical data of the sensor. The formula is: Where i is the i-th sensor, σ i is the standard deviation of the ith sensor, ω i is the dynamic weight of the i-th sensor; Construct the environment feature matrix M after dynamic weighted fusion, M = {T norn ·ω T , H norn ·ω H , G norn ·ω G , I norn ·ω I , V norn ·ω V }, where ω T is the temperature dynamic weight, ω H is the humidity dynamic weight, ω G is the dynamic weight of gas concentration, ω I is the current dynamic weight, ω V is the voltage dynamic weight; A data transmission module, used to package the environmental feature matrix M generated in step S20 and transmit it to a remote server via a preset network in the form of a data packet; The data verification module is used to verify the data using the cyclic redundancy check code in the data packet after the remote server receives the data packet: CRC packet =R(packet)mod G(X) Among them, R(packet) is the binary polynomial representation of the data packet, G(X) is a predefined fixed polynomial, CRC packet is the calculated value of the cyclic redundancy check code, and mod is the remainder operation; If the calculated value of the cyclic redundancy check code CRC packet Equal to the cyclic redundancy check code CRC, the check passes and the data packet is marked as valid; If the calculated value of the cyclic redundancy check code CRC packet Not equal to the cyclic redundancy check code CRC, the check fails, and the device is requested to resend; The fault classification module is used to input the feature matrix M in the data packet marked as valid into the SVM fault classification model and output the fault probability distribution P, where P={p1,p2,p3,p4,p5,p6} Among them, p1 is the probability of over-temperature fault, p2 is the probability of abnormal humidity fault, p3 is the probability of current overload fault, p4 is the probability of abnormal voltage fault, p5 is the probability of gas leakage fault, and p6 is the probability of grounding fault; Arrange p1, p2, p3, p4, p5, and p6 in ascending order, and set the largest value among them as p max ; Preset the probability threshold p t , when p max >p t , then determine whether there is max The corresponding fault type, when p max ≤p t , it is judged as normal state; The warning generation module is used to generate a warning information report based on the determination result of the fault type, trigger an alarm and send the warning information report to the remote maintenance terminal.

9. A distribution box switch cabinet fault detection device, characterized in that: The distribution box switch cabinet fault detection device includes: a memory, a processor, and a distribution box switch cabinet fault detection program stored in the memory and executable on the processor. When the distribution box switch cabinet fault detection program is executed by the processor, the distribution box switch cabinet fault detection method described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a distribution box switch cabinet fault detection program, and when the distribution box switch cabinet fault detection program is executed by a processor, it implements the distribution box switch cabinet fault detection method described in any one of Claims 1 to 7.

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