Real-time monitoring method and system for operation data of power distribution cabinet

Abstract

The invention relates to the field of power failure monitoring, in particular to a power distribution cabinet operation data real-time monitoring method and system. The method comprises the steps that a temperature sensor and a current sensor are used for collecting the environment temperature and actual current of the power distribution cabinet; calculating a first fault probability of the power distribution cabinet under the environment temperature by using the first long-short-term memory network model, and calculating a second fault probability of the power distribution cabinet under the actual current by using the second long-short-term memory network model; integrating the first fault probability, the second fault probability, the first weight of the temperature sensor and the second weight of the current sensor by using an evidence theory, calculating the comprehensive credibility of the power distribution cabinet fault, and when the comprehensive credibility is greater than a threshold value, giving a fault warning; when the environment temperature is out of the working temperature range of the current sensor, the static abnormality is introduced, and the dynamic abnormality represents the second weight. The problem that in the prior art, operation data of the power distribution cabinet cannot be monitored in real time and faults of the power distribution cabinet cannot be found timely and accurately is effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of power fault monitoring, and more specifically, to a method and system for real-time monitoring of operating data of a power distribution cabinet. Background Art

[0002] The distribution cabinet is divided into power distribution cabinet, lighting distribution cabinet and metering cabinet. It is the terminal equipment of the power distribution system. The distribution cabinet is a general term for the motor control center. The distribution cabinet is suitable for occasions with relatively dispersed loads and fewer circuits; the motor control center is used in occasions with concentrated loads and more circuits. They distribute the power of a circuit of the upper-level distribution equipment to the nearest load. In daily life, the distribution cabinet needs to be monitored during its use. Traditional monitoring methods may not be able to provide real-time fault information or early warning of potential problems. Through real-time collection, remote monitoring and intelligent analysis of the operation data of the distribution cabinet, the timeliness and accuracy of fault monitoring can be improved, maintenance costs can be reduced, the stable operation of the power system can be guaranteed, and ultimately the reliability and safety of power supply can be improved to avoid safety accidents.

[0003] At present, the patent application document with the publication number of "CN106291272A" and the name of "A high-voltage switch cabinet fault monitoring device" discloses a method of using an air pump to extract gas from each area of ​​the switch cabinet through a gas pipeline, and then using a gas detector to detect the oxygen in the gas. 3 、NO、N 2 O、NO 2 、NO 3 ,CO,N 2 O 5 The concentration changes can be used to monitor the switch cabinet.

[0004] Gas detection is easily affected by the internal structure and environment of the distribution cabinet, so indirect monitoring of distribution cabinet failures often cannot detect failures in a timely and accurate manner. Summary of the invention

[0005] In order to solve the problem in the prior art that a distribution cabinet fault cannot be detected in a timely and accurate manner, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for real-time monitoring of operation data of a power distribution cabinet, comprising: The ambient temperature and actual current of the distribution cabinet are collected by using a temperature sensor and a current sensor respectively; the first long short-term memory network model is used to calculate the first fault probability of the distribution cabinet under the ambient temperature, and the second long short-term memory network model is used to calculate the second fault probability of the distribution cabinet under the actual current; the first fault probability, the second fault probability, the first weight of the temperature sensor and the second weight of the current sensor are integrated by using the evidence theory to calculate the comprehensive trust of the distribution cabinet fault, and when the comprehensive trust of the distribution cabinet fault is greater than the threshold, a fault warning is issued; wherein, when the ambient temperature is within the operating temperature range of the current sensor, the first weight and the second weight in the evidence theory are the same; when the ambient temperature is outside the operating temperature range of the current sensor, the first weight is preset to calculate the second weight, and the second weight is positively correlated with the first weight and the credibility of the current sensor at the ambient temperature; the credibility of the current sensor at the ambient temperature is related to the static abnormality and dynamic abnormality of the current sensor; the static abnormality is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor; the dynamic abnormality is used to characterize the difference between the fluctuation of the actual current and the fluctuation of the current of the current sensor at the operating temperature.

[0007] The reliability of the current sensor in collecting current can be characterized from two aspects: the operating temperature and the fluctuation degree of the collected current. Therefore, static abnormality and dynamic abnormality are introduced to calculate the credibility of the current sensor at ambient temperature. Then, evidence theory is introduced, and the first fault probability and the second fault probability are used as evidence. The first weight of the temperature sensor is used as the weight of the first fault probability, and the second weight of the current sensor is used as the weight of the second fault probability. The second weight is positively correlated with the aforementioned credibility. At the same time, the reliability of the data collected by the temperature sensor and the current sensor is considered, and the probability of failure caused by the ambient temperature and the actual current is also considered. Moreover, the temperature sensor and the current sensor can directly and accurately reflect the circuit conditions in the distribution cabinet. Therefore, the invention can timely and accurately detect the failure of the distribution cabinet through real-time monitoring of the operation data of the distribution cabinet.

[0008] Preferably, after the current sensor collects the actual current of the power distribution cabinet, it also includes: performing difference completion processing on the actual current.

[0009] In some cases, the data collected by the current sensor may be missing, and difference completion can fill in these missing values ​​to make the calculation more accurate.

[0010] Preferably, the static abnormality of the current sensor includes: ,in, represents the static abnormality, Indicates the lower limit of the operating temperature of the current sensor, Indicates the upper limit of the operating temperature of the current sensor, Indicates the ambient temperature.

[0011] Current sensors generally have an operating temperature range. When the upper limit is exceeded, the upper limit temperature is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor. When the lower limit is below the lower limit, the lower limit temperature is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor. This can avoid the problem of extremely large static abnormality when the temperature difference between the temperature and the operating temperature range is not large.

[0012] Preferably, the dynamic abnormality of the current sensor includes: , Indicates the dynamic abnormality, Indicates the actual current fluctuation value of the current sensor at the ambient temperature. represents the current fluctuation value of the current sensor at the lower limit of the operating temperature, t represents the ambient temperature, Indicates the lower limit of the operating temperature of the current sensor; Indicates the current fluctuation value of the current sensor at the upper limit of the operating temperature. Indicates the upper limit of the operating temperature of the current sensor.

[0013] The current fluctuation value collected by the current sensor at the upper and lower limits of the operating temperature is generally also the upper and lower limits of the fluctuation. Therefore, the dynamic abnormality is divided into two cases to better characterize the difference between the actual current fluctuation and the current fluctuation of the current sensor at the operating temperature.

[0014] Preferably, the current fluctuation value is the standard deviation of the current collected at the corresponding temperature.

[0015] Preferably, the credibility of the current sensor at the ambient temperature is related to the static abnormality and dynamic abnormality of the current sensor, specifically: ,in, Indicates the reliability of the current sensor at this ambient temperature. represents a natural constant, Indicates the static abnormality of the current sensor. Indicates the dynamic abnormality of the current sensor, is an empirical constant.

[0016] The combined static abnormality and dynamic abnormality is equivalent to reflecting the credibility of the current sensor from the perspective of temperature and fluctuation, and enhances the accuracy of the credibility.

[0017] Preferably, when the ambient temperature is outside the operating temperature range, the weight of the temperature sensor is preset, and the weight of the current sensor is calculated according to the weight of the temperature sensor and the credibility of the current sensor at the ambient temperature, specifically: ,in, represents the second weight of the current sensor, Indicates the reliability of the current sensor at this ambient temperature. represents a first weight of the temperature sensor, and the weight of the temperature sensor is greater than 1 / 3.

[0018] Preferably, the first long short-term memory network model includes: collecting historical ambient temperature and corresponding distribution cabinet status labels as samples, the distribution cabinet status labels are divided into normal and faulty, constructing a long short-term memory network model, the input layer is used to input the historical ambient temperature, the hidden layer is used to learn the historical ambient temperature characteristics and its relationship with the distribution cabinet failure, the output layer uses the Sigmoid activation function to output the distribution cabinet failure probability, and uses the cross entropy loss function to measure the difference between the distribution cabinet failure probability and the distribution cabinet status label. The constructed long short-term memory network model is trained using the samples to obtain a trained first long short-term memory network model.

[0019] In a second aspect, the present invention further provides a system for real-time monitoring of operating data of a distribution cabinet, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the above-mentioned method for real-time monitoring of operating data of a distribution cabinet.

[0020] The beneficial effect of the present invention is that the reliability of the current sensor in collecting current can be characterized from two aspects: the working temperature and the fluctuation degree of the collected current. Therefore, static abnormality and dynamic abnormality are introduced to calculate the credibility of the current sensor at ambient temperature. Then, evidence theory is introduced to calculate the first fault probability and the second fault probability using the first long short-term memory network and the second long short-term memory network. The first fault probability and the second fault probability are used as evidence, the first weight of the temperature sensor is used as the weight of the first fault probability, and the second weight of the current sensor is used as the weight of the second fault probability. The second weight is positively correlated with the aforementioned credibility. At the same time, the reliability of the data collected by the temperature sensor and the current sensor is considered, and the probability of failure that may be caused by the ambient temperature and the actual current is also considered. Moreover, the temperature sensor and the current sensor can directly and accurately reflect the circuit conditions in the distribution cabinet. Therefore, the present invention can timely and accurately detect the failure of the distribution cabinet. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 This is a flow chart of a method for real-time monitoring of operation data of a power distribution cabinet provided by an embodiment of the present invention; Figure 2The present invention provides a block diagram of a real-time monitoring system for operation data of a power distribution cabinet. DETAILED DESCRIPTION

[0022] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 The present invention is a flowchart of a method for real-time monitoring of operation data of a power distribution cabinet according to an embodiment of the present invention, comprising the following steps: S101, using a temperature sensor and a current sensor to respectively collect the ambient temperature and actual current of the power distribution cabinet; In some embodiments, to avoid missing data collected by the current sensor, after the current sensor collects the actual current of the power distribution cabinet, the method further includes: performing difference completion processing on the actual current. Generally, interpolation methods such as linear interpolation and polynomial interpolation can be used.

[0025] S102, using the first long short-term memory network model to calculate the first failure probability of the power distribution cabinet at the ambient temperature, and using the second long short-term memory network model to calculate the second failure probability of the power distribution cabinet under the actual current; Generally, the first long short-term memory network model can be a long short-term memory network model trained according to historical ambient temperature and corresponding distribution cabinet status, and the second long short-term memory network model can be a long short-term memory network model trained according to historical current and corresponding distribution cabinet status.

[0026] In some embodiments, the first long short-term memory network model includes: collecting historical ambient temperature and corresponding distribution cabinet status labels as samples, the distribution cabinet status labels are divided into normal and faulty, constructing a long short-term memory network model, the input layer is used to input historical ambient temperature, the hidden layer is used to learn the historical ambient temperature characteristics and its relationship with the distribution cabinet failure, the output layer uses a Sigmoid activation function to output the distribution cabinet failure probability, and the cross entropy loss function is used to measure the difference between the distribution cabinet failure probability and the distribution cabinet status label. The constructed long short-term memory network model is trained using the samples to obtain a trained first long short-term memory network model. The specific process of training to obtain the second long short-term memory network model is basically the same as the process of training the first long short-term memory network model, and will not be repeated here.

[0027] S103. Utilize evidence theory to integrate the first fault probability, the second fault probability, the first weight of the temperature sensor, and the second weight of the current sensor to calculate the comprehensive trust of the distribution cabinet fault. When the comprehensive trust of the distribution cabinet fault is greater than a threshold, issue a fault warning.

[0028] Exemplarily, the specific process of calculating the comprehensive trust of the distribution cabinet failure according to the evidence theory includes the following steps: Step 1: Define the framework; In this embodiment, the state is defined : The power distribution cabinet fails; status : There is no fault in the power distribution cabinet.

[0029] Step 2: Construct a trust function; For temperature sensors, the supported status Trust , its support status Trust , where a is the first weight and A is the first fault probability; for the current sensor, its support state Trust , its support status Trust , where b is the second weight and B is the second failure probability.

[0030] Step 3: Calculate the comprehensive trust of the distribution cabinet failure; , K is the conflict coefficient, which indicates the state of the temperature sensor and the current sensor and status The trust conflict is as follows: ,therefore .

[0031] The comprehensive confidence level of the power distribution cabinet failure is calculated and compared with the preset threshold. For example, if the comprehensive confidence level of the power distribution cabinet failure is 90% and the preset threshold is 80%, it can be considered that the power distribution cabinet has failed and a fault warning is issued.

[0032] In some embodiments, when the ambient temperature is within the operating temperature range of the current sensor, the first weight and the second weight in the evidence theory are the same, and a=b=0.5 can generally be preset at this time.

[0033] In other embodiments, when the ambient temperature is outside the operating temperature range of the current sensor, a first weight is preset to calculate a second weight, and the second weight is positively correlated with the first weight and the credibility of the current sensor at the ambient temperature; the credibility of the current sensor at the ambient temperature is related to the static abnormality and dynamic abnormality of the current sensor; the static abnormality is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor; the dynamic abnormality is used to characterize the difference between the fluctuation of the actual current and the fluctuation of the current of the current sensor at the operating temperature.

[0034] The static abnormality is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor. Therefore, in some embodiments, the static abnormality of the current sensor is ,in, represents the static abnormality, Indicates the lower limit of the operating temperature of the current sensor, Indicates the upper limit of the operating temperature of the current sensor, Indicates the ambient temperature. For example, the operating temperature of the current sensor is a℃-b℃. If the current temperature is x℃ and x>b, then the static abnormality of the current sensor is = , if the current temperature is y℃ and y<a, then the static abnormality of the current sensor is = If the current temperature is within the operating temperature range of the current sensor, it means that the current sensor is very reliable and there is no static abnormality. 0.

[0035] The dynamic abnormality is used to characterize the difference between the actual current fluctuation and the current fluctuation of the current sensor at the working temperature. Therefore, in some embodiments, the dynamic abnormality of the current sensor is , Indicates the dynamic abnormality, Indicates the actual current fluctuation value of the current sensor at the ambient temperature. represents the current fluctuation value of the current sensor at the lower limit of the operating temperature, t represents the ambient temperature, Indicates the lower limit of the operating temperature of the current sensor; Indicates the current fluctuation value of the current sensor at the upper limit of the operating temperature. Indicates the upper limit of the operating temperature of the current sensor. If the ambient temperature is within the operating temperature range of the current sensor, the current fluctuation value can be considered normal fluctuation, that is, there is no abnormality, and the dynamic abnormality =0. Since the power demand is different at different times, even if the upper or lower limit of the operating temperature of the power distribution cabinet is the same, the current fluctuation value may be different. Therefore, in some embodiments, Refers to the current fluctuation value of the current sensor at the lower limit of the working temperature at the most recent time when the current ambient temperature is collected. It refers to the current fluctuation value of the current sensor at the upper limit of the working temperature that is closest to the moment when the current ambient temperature is collected. For example, the working temperature range of the current sensor is -10℃-100℃. There are several groups of data in chronological order, and the content in brackets is (time, ambient temperature, current fluctuation value): (17:48:30, 82℃, 10A), (17:48:40, 92℃, 12A), (17:48:45, 100℃, 12A), (17:48:55, 100℃, 15A), (17:49:05, 105℃, 16A). Obviously, the current ambient temperature is 105℃, which exceeds the upper limit of the working temperature of the current sensor. The current fluctuation value of the current sensor at the upper limit of the working temperature of 100℃ is 15A. Therefore, the dynamic abnormality of the current sensor at this time is .

[0036] In order to better and more accurately describe the current fluctuation, in some embodiments, the current fluctuation value is the standard deviation of the current collected at the corresponding temperature. For example, when the ambient temperature is 80°C, n current data I 1 ,I 2 ,I 3 ,……I n , calculate the average value of these currents , and then calculate the standard deviation of these currents , the standard deviation That is, it is the current fluctuation value of the actual current collected at the ambient temperature.

[0037] The credibility of the current sensor at the ambient temperature. After obtaining the static abnormality and dynamic abnormality of the current sensor, the credibility of the current sensor at the ambient temperature can be further calculated. The specific calculation formula is: ,in, Indicates the reliability of the current sensor at this ambient temperature. represents a natural constant, Indicates the static abnormality of the current sensor. Indicates the dynamic abnormality of the current sensor, is an empirical constant.

[0038] When the ambient temperature is outside the operating temperature range, the weight of the temperature sensor is preset, and the weight of the current sensor is calculated according to the weight of the temperature sensor and the credibility of the current sensor at the ambient temperature, specifically: ,in, represents the second weight of the current sensor, Indicates the reliability of the current sensor at this ambient temperature. represents a first weight of the temperature sensor, and the weight of the temperature sensor is greater than 1 / 3.

[0039] In other embodiments of the present invention, the ambient temperature of the distribution cabinet can be collected by using a temperature sensor, and the actual voltage of the distribution cabinet can be collected by a voltage sensor. Then, the first fault probability of the distribution cabinet under the ambient temperature and the third fault probability of the distribution cabinet under the actual voltage can be calculated by using a long short-term memory network model. The first fault probability, the third fault probability, the first weight of the temperature sensor and the third weight of the voltage sensor can be integrated by using evidence theory to calculate the comprehensive trust of the distribution cabinet failure. When the comprehensive trust of the distribution cabinet failure is greater than a threshold, a fault warning is issued. The method of calculating the third weight of the voltage sensor is similar to the method of calculating the second weight of the current sensor, and will not be repeated here.

[0040] In other embodiments of the present invention, in order to make fault monitoring more accurate, temperature, current and voltage can also be considered at the same time. That is, the ambient temperature of the distribution cabinet is collected by a temperature sensor, the actual current of the distribution cabinet is collected by a current sensor, and the actual voltage of the distribution cabinet is collected by a voltage sensor. Then, the first fault probability of the distribution cabinet under the ambient temperature, the second fault probability of the distribution cabinet under the actual current, and the third fault probability of the distribution cabinet under the actual voltage are calculated. The first fault probability, the second fault probability, the third fault probability, the first weight of the temperature sensor, the second weight of the current sensor, and the third weight of the voltage sensor are integrated by evidence theory to calculate the comprehensive trust of the distribution cabinet failure. When the comprehensive trust of the distribution cabinet failure is greater than the threshold, a fault warning is issued. It should be noted that in this embodiment, when the ambient temperature is within the operating temperature range of the current sensor and the voltage sensor, the first weight, the second weight, and the third weight in the evidence theory are all 1 / 3; when the ambient temperature is not within the operating temperature range of the current sensor and the voltage sensor, the method of calculating the second weight of the current sensor and the third weight of the voltage sensor is different, specifically: ,in, represents the second weight of the current sensor, represents the third weight of the current sensor, Indicates the reliability of the current sensor at this ambient temperature. Indicates the reliability of the voltage sensor at this ambient temperature. The calculation method is the same as above The calculation method is the same as represents the first weight of the temperature sensor; when the ambient temperature is not within the operating temperature range of the current sensor but within the operating temperature range of the voltage sensor, or when the ambient temperature is within the operating temperature range of the current sensor but not within the operating temperature range of the voltage sensor, the weight of the sensor not within the operating temperature range can be calculated preferentially, and the weight of the other sensor can be calculated by subtracting the first weight of the temperature sensor and the weight of the sensor not within the operating temperature range from 1. For example, when the ambient temperature is not within the operating temperature range of the current sensor but within the operating temperature range of the voltage sensor, .

[0041] The above-mentioned real-time monitoring method of distribution cabinet operation data provided by an embodiment of the present invention takes into account the reliability of data collected by temperature sensors and current sensors, and also takes into account the probability of failure that may be caused by ambient temperature and actual current. Moreover, the temperature sensor and current sensor can directly and accurately reflect the circuit conditions in the distribution cabinet. Therefore, the invention can timely and accurately detect distribution cabinet failures.

[0042] The present invention also provides a real-time monitoring system for the operation data of a power distribution cabinet. Figure 2 As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for real-time monitoring of operating data of a power distribution cabinet described in the present invention is implemented.

[0043] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0044] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0045] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for real-time monitoring of power distribution cabinet operation data, characterized in that: include: Use temperature sensors and current sensors to collect the ambient temperature and actual current of the power distribution cabinet respectively; The first long short-term memory network model is used to calculate the first failure probability of the power distribution cabinet under the ambient temperature, and the second long short-term memory network model is used to calculate the second failure probability of the power distribution cabinet under the actual current; The first fault probability, the second fault probability, the first weight of the temperature sensor and the second weight of the current sensor are integrated by using the evidence theory to calculate the comprehensive trust of the distribution cabinet fault. When the comprehensive trust of the distribution cabinet fault is greater than the threshold, a fault warning is issued. Wherein, when the ambient temperature is within the operating temperature range of the current sensor, the first weight and the second weight in the evidence theory are the same; when the ambient temperature is outside the operating temperature range of the current sensor, the first weight is preset to calculate the second weight, and the second weight is positively correlated with the first weight and the credibility of the current sensor at the ambient temperature; the credibility of the current sensor at the ambient temperature is related to the static abnormality and dynamic abnormality of the current sensor; The static abnormality is used to characterize the difference between the current ambient temperature and the operating temperature of the current sensor; the dynamic abnormality is used to characterize the difference between the fluctuation of the actual current and the fluctuation of the current of the current sensor at the operating temperature.

2. The method for real-time monitoring of power distribution cabinet operation data according to claim 1, characterized in that: After the current sensor collects the actual current of the power distribution cabinet, the method further includes: performing difference completion processing on the actual current.

3. The method for real-time monitoring of power distribution cabinet operation data according to claim 2 is characterized in that: The static abnormality of the current sensor includes: ,in, represents the static abnormality, Indicates the lower limit of the operating temperature of the current sensor, Indicates the upper limit of the operating temperature of the current sensor, Indicates the ambient temperature.

4. The method for real-time monitoring of power distribution cabinet operation data according to claim 2, characterized in that: Dynamic abnormality of the current sensor, including: , Indicates the dynamic abnormality, Indicates the actual current fluctuation value of the current sensor at the ambient temperature. represents the current fluctuation value of the current sensor at the lower limit of the operating temperature, t represents the ambient temperature, Indicates the lower limit of the operating temperature of the current sensor; Indicates the current fluctuation value of the current sensor at the upper limit of the operating temperature. Indicates the upper limit of the operating temperature of the current sensor.

5. The method for real-time monitoring of power distribution cabinet operation data according to claim 4 is characterized in that: The current fluctuation value is the standard deviation of the current collected at the corresponding temperature.

6. The method for real-time monitoring of power distribution cabinet operation data according to claim 4, characterized in that: The credibility of the current sensor at the ambient temperature is related to the static abnormality and dynamic abnormality of the current sensor, specifically: ,in, Indicates the reliability of the current sensor at this ambient temperature. represents a natural constant, Indicates the static abnormality of the current sensor. Indicates the dynamic abnormality of the current sensor, is an empirical constant.

7. The method for real-time monitoring of power distribution cabinet operation data according to claim 6, characterized in that: When the ambient temperature is outside the operating temperature range, the weight of the temperature sensor is preset, and the weight of the current sensor is calculated according to the weight of the temperature sensor and the credibility of the current sensor at the ambient temperature, specifically: ,in, represents the second weight of the current sensor, Indicates the reliability of the current sensor at this ambient temperature. represents a first weight of the temperature sensor, and the weight of the temperature sensor is greater than 1 / 3.

8. The method for real-time monitoring of power distribution cabinet operation data according to claim 1, characterized in that: The first long short-term memory network model comprises: Historical ambient temperatures and corresponding distribution cabinet status labels are collected as samples. The distribution cabinet status labels are divided into normal and faulty. A long short-term memory network model is constructed. The input layer is used to input historical ambient temperatures, and the hidden layer is used to learn the historical ambient temperature characteristics and their relationship with distribution cabinet failures. The output layer uses a Sigmoid activation function to output the distribution cabinet failure probability, and a cross entropy loss function is used to measure the difference between the distribution cabinet failure probability and the distribution cabinet status label. The constructed long short-term memory network model is trained using the samples to obtain a trained first long short-term memory network model.

9. A real-time monitoring system for distribution cabinet operation data, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for real-time monitoring of operating data of a distribution cabinet as described in any one of claims 1 to 8.

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