Armored closed power equipment monitoring system

Abstract

The invention relates to the technical field of power equipment monitoring, and particularly discloses an armored closed power equipment monitoring system, which comprises an acquisition module used for acquiring infrared image data of power equipment, determining a temperature partition of the power equipment according to the infrared image data of the power equipment, and setting a target partition according to the temperature partition of the power equipment; the screening module is used for positioning the corresponding power equipment elements according to the target partition, and screening out abnormal elements according to the operation parameters of the power equipment elements; and the early warning module is used for acquiring the external environment temperature of the power equipment and performing early warning on the abnormal element according to the external environment temperature. The abnormal monitoring efficiency of the power equipment is improved, the reliability of state perception and defect early warning is improved, the working pressure of operation and maintenance personnel is reduced, and the safety of the power equipment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment monitoring, and more specifically, to an armored enclosed power equipment monitoring system. Background Art

[0002] Thermal defects of electrical equipment mainly refer to various thermal defects caused by poor connection of equipment that are exposed to the air and can be directly detected by certain means. According to the part where the defect exists, it can be divided into two categories: internal and external: one is the thermal defects caused by poor contact of exposed joints exposed to the atmosphere for a long time, or the thermal defects caused by the reduction of the conductive cross section due to surface contamination or conductor damage caused by mechanical force. The other is the thermal defects caused by equipment failure. Among the above two defects, the defect of poor conductor connection is the most common. The latter defect is more serious.

[0003] At present, the prevention and control of thermal defects in power equipment is mainly carried out through infrared thermal imaging, which monitors the temperature of each operating power equipment, detects thermal defects of equipment in time, and ensures the safe and stable operation of power equipment. Due to the factors of high pressure of operation and maintenance and single fault detection means, the operation of armored closed equipment is unable to intuitively, effectively and timely detect thermal defects of equipment, especially in hot climates, the possibility of equipment thermal defects is higher than that of ordinary sites; compared with the continuous monitoring of monitoring terminals, conventional inspection methods have obvious disadvantages. Although defects can be found, there is great uncertainty and irregularity in the occurrence of thermal defects. Conventional inspection methods cannot detect equipment thermal defects in time, and eventually develop into equipment accidents. Summary of the invention

[0004] The present invention provides an armored enclosed power equipment monitoring system, which is used to solve the problem of poor thermal defect fault monitoring effect of armored enclosed power equipment in the prior art, and comprises:

[0005] An acquisition module, used to acquire infrared image data of the electric equipment, determine the temperature partition of the electric equipment according to the infrared image data of the electric equipment, and set the target partition according to the temperature partition of the electric equipment;

[0006] A screening module, used to locate corresponding power equipment components according to target partitions, and screen out abnormal components according to operating parameters of the power equipment components;

[0007] The early warning module is used to obtain the external ambient temperature of the power equipment and issue early warnings to abnormal components based on the external ambient temperature.

[0008] Furthermore, the acquisition module determines the temperature zones of the power equipment according to the infrared image data of the power equipment, including:

[0009] Determine temperature data of the electric equipment according to the infrared image data of the electric equipment, and establish a device temperature data set according to the temperature data of the electric equipment;

[0010] Set the initial k value, determine the initial cluster center according to the initial k value, and calculate the Euclidean distance from the data points in the device temperature data set to the initial cluster center;

[0011] Divide each data point into the corresponding partition according to the Euclidean distance from the data point to the initial cluster center;

[0012] Update the cluster center according to the average value of the data points in each partition, and iterate the new cluster center repeatedly until the cluster center no longer changes;

[0013] Calculating the DBI index of the clustering result, and determining whether the DBI index is less than a first preset threshold;

[0014] If the DBI index is greater than or equal to the first preset threshold, the initial k value is increased by one, and the device temperature data set is clustered again until the DBI index is less than the first preset threshold. The clustering result is output to obtain the temperature partition of the power equipment.

[0015] Furthermore, the calculation of the DBI index of the clustering result includes:

[0016] The DBI index of the clustering result is calculated according to the DBI index calculation formula, and the DBI index calculation formula is specifically as follows:

[0017]

[0018] Among them, D DBI is the DBI index, s i ,s j is the average distance from all data in the i, j partition to the cluster center, d i,j is the distance from the i-th cluster center to the j-th cluster center.

[0019] Furthermore, the step of setting the target partition according to the temperature partition of the electric power equipment includes:

[0020] Determine the difference between the temperature data of the cluster center of each temperature partition and the preset standard temperature data according to the clustering result, and screen out abnormal temperature partitions whose difference between the temperature data of the cluster center of the temperature partition and the preset standard temperature data is greater than a second preset threshold;

[0021] Obtain the maximum temperature data of the abnormal temperature partition, and determine the temperature threshold according to the maximum temperature data;

[0022] The number of abnormal pixels whose temperature data is greater than the temperature threshold in the abnormal temperature partition is counted, and the target partition is set according to the number of abnormal pixels whose temperature data is greater than the temperature threshold.

[0023] Further, determining the temperature threshold according to the maximum temperature data includes:

[0024] Obtaining preset standard temperature data, calculating the difference between the maximum temperature data and the preset standard temperature data, and determining the maximum temperature deviation according to the difference between the maximum temperature data and the preset standard temperature data;

[0025] The maximum temperature deviation is normalized, and the normalized maximum temperature deviation is multiplied by the temperature data of the corresponding abnormal temperature partition cluster center to obtain the temperature threshold.

[0026] Furthermore, the screening module locates the corresponding power equipment components according to the target partition, and screens out abnormal components according to the operating parameters of the power equipment components, including:

[0027] Locating power equipment components at corresponding positions of the infrared image of the power equipment according to the target partition, obtaining operating parameters of the power equipment components, and predicting temperature data of corresponding positions according to the operating parameters of the power equipment components;

[0028] Draw a temperature prediction curve based on the temperature data of the power equipment components, obtain the temperature change curve of the current target partition, and calculate the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve;

[0029] Determine whether the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than the third preset threshold. If the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than the third preset threshold, determine that the corresponding power equipment component is an abnormal component.

[0030] Further, predicting the temperature data of the corresponding position according to the operating parameters of the power equipment element includes:

[0031] Acquire historical operating parameters of power equipment components and corresponding temperature change data, and establish a temperature change data set based on the historical operating parameters of power equipment components and the corresponding temperature change data;

[0032] An initial temperature prediction model is established according to the temperature change data set, and the initial temperature prediction model is trained to obtain a trained temperature prediction model;

[0033] The operating parameters of the current power equipment components are input into the trained temperature prediction model, and the corresponding predicted temperature data is output.

[0034] Furthermore, the early warning module warns of abnormal components according to the external environment temperature, including:

[0035] Obtaining the external ambient temperature of the power equipment, and calculating the difference between the external ambient temperature of the power equipment and the temperature data of the target partition cluster center;

[0036] The difference between the external environment temperature and the temperature data of the target partition cluster center is normalized to obtain the difference coefficient, and the warning threshold is determined according to the difference coefficient;

[0037] The warning level of the abnormal component is determined according to the warning threshold, and the abnormal component is warned according to the warning level.

[0038] Further, determining the warning threshold according to the difference coefficient includes:

[0039] The warning threshold is determined according to the difference coefficient calculation formula, and the difference coefficient calculation formula is specifically:

[0040]

[0041] Among them, T is the warning threshold, T α is the preset standard warning threshold, C is the difference coefficient, R is the preset range coefficient, and exp is the natural exponential function.

[0042] Further, determining the warning level of the abnormal component according to the warning threshold includes:

[0043] Calculate the difference between the temperature data of the current abnormal component and the warning threshold according to the warning threshold, and determine whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than a fourth preset threshold;

[0044] If the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fourth preset threshold, the preset first warning level is set as the warning level of the abnormal component;

[0045] If the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fourth preset threshold, then determine whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold;

[0046] If the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold, the preset second warning level is set as the warning level of the abnormal component;

[0047] If the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fifth preset threshold, the preset third warning level is set as the warning level of the abnormal component.

[0048] The beneficial effects of the present invention are:

[0049] By applying the above technical scheme, the present invention locates abnormal components of the power equipment through infrared images of the armored enclosed power equipment, and accurately evaluates the warning level of the abnormal components based on the temperature data of the abnormal components, thereby improving the abnormal monitoring efficiency of the power equipment, increasing the reliability of state perception and defect warning, reducing the work pressure of operation and maintenance personnel, and improving the safety of the power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A schematic structural diagram of an armored enclosed power equipment monitoring system proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] The present application embodiment provides an armored enclosed power equipment monitoring system, such as Figure 1 As shown, including:

[0054] An acquisition module is used to acquire infrared image data of the power equipment, determine the temperature partition of the power equipment based on the infrared image data of the power equipment, and set the target partition based on the temperature partition of the power equipment; a screening module is used to locate the corresponding power equipment components according to the target partition, and screen out abnormal components according to the operating parameters of the power equipment components; an early warning module is used to acquire the external ambient temperature of the power equipment and issue an early warning to the abnormal components based on the external ambient temperature.

[0055] In this embodiment, infrared image data of the power equipment is acquired by a fixed infrared thermal imaging camera, and the grayscale value of each pixel in the infrared image data reflects the temperature, thereby achieving real-time monitoring of the temperature of the power equipment.

[0056] In some embodiments of the present application, the acquisition module determines the temperature partition of the power equipment based on the infrared image data of the power equipment, including: determining the temperature data of the power equipment based on the infrared image data of the power equipment, and establishing a device temperature data set based on the temperature data of the power equipment; setting an initial k value, determining an initial clustering center based on the initial k value, and calculating the Euclidean distance from the data point in the device temperature data set to the initial clustering center; dividing each data point into a corresponding partition based on the Euclidean distance from the data point to the initial clustering center; updating the clustering center based on the average value of the data points in each partition, and repeatedly iterating the new clustering center until the clustering center no longer changes; calculating the DBI index of the clustering result, and determining whether the DBI index is less than a first preset threshold; if the DBI index is greater than or equal to the first preset threshold, adding one to the initial k value, re-clustering the device temperature data set until the DBI index is less than the first preset threshold, outputting the clustering result, and obtaining the temperature partition of the power equipment.

[0057] In some embodiments of the present application, the calculating the DBI index of the clustering result includes: calculating the DBI index of the clustering result according to a DBI index calculation formula, wherein the DBI index calculation formula is specifically:

[0058]

[0059] Among them, D DBI is the DBI index, s i ,s j is the average distance from all data in the i, j partition to the cluster center, d i,j is the distance from the i-th cluster center to the j-th cluster center.

[0060] In this embodiment, the initial k value is set to 2, the maximum k value is set to 5, and the DBI index is set, based on the compactness of data objects in the same cluster and the degree of dispersion between clusters, so that the objects in the same cluster are highly similar and the differences between clusters are obvious, so as to evaluate the clustering results and achieve better clustering effect. Effective partitioning of power equipment temperature data is achieved.

[0061] In some embodiments of the present application, the target partition is set according to the temperature partition of the power equipment, including: determining the difference between the temperature data of the cluster center of each temperature partition and the preset standard temperature data according to the clustering result, and screening out abnormal temperature partitions in which the difference between the temperature data of the cluster center of the temperature partition and the preset standard temperature data is greater than a second preset threshold; obtaining the maximum temperature data of the abnormal temperature partition, and determining the temperature threshold based on the maximum temperature data; counting the number of abnormal pixel points in the abnormal temperature partition whose temperature data is greater than the temperature threshold, and setting the target partition according to the number of abnormal pixel points whose temperature data is greater than the temperature threshold.

[0062] In this embodiment, the abnormal temperature partition with higher temperature data is determined by the difference between the temperature data of the temperature partition cluster center and the preset standard temperature data, and the partition with a large number of abnormal pixel points greater than the temperature threshold is selected as the target partition in the abnormal temperature partition, so as to accurately find the area where the power equipment has abnormalities.

[0063] In some embodiments of the present application, determining the temperature threshold based on the maximum temperature data includes: obtaining preset standard temperature data, calculating the difference between the maximum temperature data and the preset standard temperature data, and determining the maximum temperature deviation based on the difference between the maximum temperature data and the preset standard temperature data; normalizing the maximum temperature deviation, and multiplying the normalized maximum temperature deviation with the temperature data of the corresponding abnormal temperature partition cluster center to obtain the temperature threshold.

[0064] In this embodiment, the maximum temperature deviation is calculated based on the maximum temperature data in the partition, and the range of the maximum temperature deviation is limited to [1, 2] through normalization processing. The maximum temperature deviation after normalization is multiplied by the cluster center of the partition to obtain the temperature threshold corresponding to the partition. By setting the temperature threshold, accurate screening of abnormal pixels can be achieved.

[0065] In some embodiments of the present application, the screening module locates the corresponding power equipment components according to the target partition, and screens out abnormal components according to the operating parameters of the power equipment components, including: locating the power equipment components at the corresponding position of the infrared image of the power equipment according to the target partition, obtaining the operating parameters of the power equipment components, and predicting the temperature data of the corresponding position according to the operating parameters of the power equipment components; drawing a temperature prediction curve according to the temperature data of the power equipment components, obtaining the temperature change curve of the current target partition, and calculating the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve; judging whether the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than a third preset threshold value, if the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than the third preset threshold value, judging that the corresponding power equipment component is an abnormal component.

[0066] In this embodiment, the operating parameters of the power equipment components include power, frequency, vibration coefficient, etc. The corresponding temperature curve is predicted by calculating the operating parameters, and the abnormal components are located by calculating the correlation coefficient between the temperature change curve of the target partition and the temperature prediction curve. If the correlation coefficient is greater than the third preset threshold, it indicates that the abnormal temperature of the corresponding area is related to the operating parameters of the abnormal component, and the abnormal component can be located quickly and accurately.

[0067] In some embodiments of the present application, predicting the temperature data of the corresponding position according to the operating parameters of the power equipment elements includes: obtaining the historical operating parameters of the power equipment elements and the corresponding temperature change data, and establishing a temperature change data set according to the historical operating parameters of the power equipment elements and the corresponding temperature change data; establishing an initial temperature prediction model according to the temperature change data set and training the initial temperature prediction model to obtain a trained temperature prediction model; inputting the operating parameters of the current power equipment elements into the trained temperature prediction model, and outputting the corresponding predicted temperature data.

[0068] In this embodiment, an initial prediction temperature model is established based on a deep learning neural network algorithm, and the initial temperature prediction model is trained using historical operating parameters of power equipment components and corresponding temperature change data, thereby obtaining a trained temperature prediction model to achieve prediction of temperature data of power equipment components.

[0069] In some embodiments of the present application, the early warning module issues an early warning for abnormal components based on the external ambient temperature, including: obtaining the external ambient temperature of the power equipment, calculating the difference between the external ambient temperature of the power equipment and the temperature data of the target partition cluster center; normalizing the difference between the external ambient temperature and the temperature data of the target partition cluster center to obtain a difference coefficient, and determining an early warning threshold based on the difference coefficient; determining the early warning level of the abnormal component based on the early warning threshold, and issuing an early warning for the abnormal component based on the early warning level.

[0070] In some embodiments of the present application, determining the warning threshold according to the difference coefficient includes: determining the warning threshold according to a difference coefficient calculation formula, and the difference coefficient calculation formula is specifically:

[0071]

[0072] Among them, T is the warning threshold, T α is the preset standard warning threshold, C is the difference coefficient, R is the preset range coefficient, and exp is the natural exponential function.

[0073] In this embodiment, the difference between the external ambient temperature and the temperature data of the target partition cluster center is normalized so that the range of the difference is limited to [1, 2], thereby obtaining a difference coefficient. The larger the difference, the higher the difference coefficient and the corresponding warning threshold are, thereby preventing misjudgment of the warning level of the power equipment due to the ambient temperature.

[0074] In some embodiments of the present application, determining the warning level of the abnormal component according to the warning threshold includes: calculating the difference between the temperature data of the current abnormal component and the warning threshold according to the warning threshold, and judging whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than a fourth preset threshold; if the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fourth preset threshold, setting the preset first warning level as the warning level of the abnormal component; if the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fourth preset threshold, judging whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold; if the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold, setting the preset second warning level as the warning level of the abnormal component; if the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fifth preset threshold, setting the preset third warning level as the warning level of the abnormal component.

[0075] In this embodiment, the warning level is divided into the first warning level, the second warning level and the third warning level according to the severity. The corresponding warning level is set based on the difference between the temperature data of different abnormal components and the warning threshold, so as to achieve accurate assessment of the degree of fault.

[0076] By applying the above technical scheme, the present invention uses an acquisition module to acquire infrared image data of power equipment, determine the temperature partition of the power equipment according to the infrared image data of the power equipment, and set the target partition according to the temperature partition of the power equipment; a screening module is used to locate the corresponding power equipment components according to the target partition, and screen out abnormal components according to the operating parameters of the power equipment components; an early warning module is used to acquire the external ambient temperature of the power equipment, and warn the abnormal components according to the external ambient temperature.

[0077] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do 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 application.

Claims

1. An armored enclosed power equipment monitoring system, It is characterized in that include: An acquisition module, used to acquire infrared image data of the electric equipment, determine the temperature partition of the electric equipment according to the infrared image data of the electric equipment, and set the target partition according to the temperature partition of the electric equipment; A screening module, used to locate corresponding power equipment components according to target partitions, and screen out abnormal components according to operating parameters of the power equipment components; The early warning module is used to obtain the external ambient temperature of the power equipment and issue early warnings to abnormal components based on the external ambient temperature.

2. The armored enclosed power equipment monitoring system according to claim 1, It is characterized in that The acquisition module determines the temperature zones of the power equipment according to the infrared image data of the power equipment, including: Determine temperature data of the electric equipment according to the infrared image data of the electric equipment, and establish a device temperature data set according to the temperature data of the electric equipment; Set the initial k value, determine the initial cluster center according to the initial k value, and calculate the Euclidean distance from the data points in the device temperature data set to the initial cluster center; Divide each data point into the corresponding partition according to the Euclidean distance from the data point to the initial cluster center; Update the cluster center according to the average value of the data points in each partition, and iterate the new cluster center repeatedly until the cluster center no longer changes; Calculating the DBI index of the clustering result, and determining whether the DBI index is less than a first preset threshold; If the DBI index is greater than or equal to the first preset threshold, the initial k value is increased by one, and the device temperature data set is clustered again until the DBI index is less than the first preset threshold. The clustering result is output to obtain the temperature partition of the power equipment.

3. The armored enclosed power equipment monitoring system according to claim 2, It is characterized in that The DBI index of the clustering result is calculated, including: The DBI index of the clustering result is calculated according to the DBI index calculation formula, and the DBI index calculation formula is specifically as follows: Among them, D DBI is the DBI index, s i ,s j is the average distance from all data in the i, j partition to the cluster center, d i,j is the distance from the i-th cluster center to the j-th cluster center.

4. The armored enclosed power equipment monitoring system according to claim 2, It is characterized in that The step of setting the target partition according to the temperature partition of the electric power equipment comprises: Determine the difference between the temperature data of the cluster center of each temperature partition and the preset standard temperature data according to the clustering result, and screen out abnormal temperature partitions whose difference between the temperature data of the cluster center of the temperature partition and the preset standard temperature data is greater than a second preset threshold; Obtain the maximum temperature data of the abnormal temperature partition, and determine the temperature threshold according to the maximum temperature data; The number of abnormal pixels whose temperature data is greater than the temperature threshold in the abnormal temperature partition is counted, and the target partition is set according to the number of abnormal pixels whose temperature data is greater than the temperature threshold.

5. The armored enclosed power equipment monitoring system according to claim 4, It is characterized in that Determining the temperature threshold according to the maximum temperature data includes: Obtaining preset standard temperature data, calculating the difference between the maximum temperature data and the preset standard temperature data, and determining the maximum temperature deviation according to the difference between the maximum temperature data and the preset standard temperature data; The maximum temperature deviation is normalized, and the normalized maximum temperature deviation is multiplied by the temperature data of the corresponding abnormal temperature partition cluster center to obtain the temperature threshold.

6. The armored enclosed power equipment monitoring system according to claim 1, It is characterized in that The screening module locates the corresponding power equipment components according to the target partition, and screens out abnormal components according to the operating parameters of the power equipment components, including: Locating power equipment components at corresponding positions of the infrared image of the power equipment according to the target partition, obtaining operating parameters of the power equipment components, and predicting temperature data of corresponding positions according to the operating parameters of the power equipment components; Draw a temperature prediction curve based on the temperature data of the power equipment components, obtain the temperature change curve of the current target partition, and calculate the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve; Determine whether the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than the third preset threshold. If the correlation coefficient between the temperature change curve of the current target partition and the temperature prediction curve is greater than the third preset threshold, determine that the corresponding power equipment component is an abnormal component.

7. The armored enclosed power equipment monitoring system according to claim 6, It is characterized in that The predicting of temperature data at corresponding positions according to operating parameters of power equipment components includes: Acquire historical operating parameters of power equipment components and corresponding temperature change data, and establish a temperature change data set based on the historical operating parameters of power equipment components and the corresponding temperature change data; An initial temperature prediction model is established according to the temperature change data set, and the initial temperature prediction model is trained to obtain a trained temperature prediction model; The operating parameters of the current power equipment components are input into the trained temperature prediction model, and the corresponding predicted temperature data is output.

8. The armored enclosed power equipment monitoring system according to claim 1, It is characterized in that The early warning module warns of abnormal components according to the external environment temperature, including: Obtaining the external ambient temperature of the power equipment, and calculating the difference between the external ambient temperature of the power equipment and the temperature data of the target partition cluster center; The difference between the external environment temperature and the temperature data of the target partition cluster center is normalized to obtain the difference coefficient, and the warning threshold is determined according to the difference coefficient; The warning level of the abnormal component is determined according to the warning threshold, and the abnormal component is warned according to the warning level.

9. The armored enclosed power equipment monitoring system according to claim 8, It is characterized in that Determining the warning threshold according to the difference coefficient includes: The warning threshold is determined according to the difference coefficient calculation formula, and the difference coefficient calculation formula is specifically: Among them, T is the warning threshold, T α is the preset standard warning threshold, C is the difference coefficient, R is the preset range coefficient, and exp is the natural exponential function.

10. The armored enclosed power equipment monitoring system according to claim 8, It is characterized in that Determining the warning level of the abnormal component according to the warning threshold includes: Calculate the difference between the temperature data of the current abnormal component and the warning threshold according to the warning threshold, and determine whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than a fourth preset threshold; If the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fourth preset threshold, the preset first warning level is set as the warning level of the abnormal component; If the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fourth preset threshold, then determine whether the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold; If the difference between the temperature data of the current abnormal component and the warning threshold is greater than the fifth preset threshold, the preset second warning level is set as the warning level of the abnormal component; If the difference between the temperature data of the current abnormal component and the warning threshold is less than or equal to the fifth preset threshold, the preset third warning level is set as the warning level of the abnormal component.

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