Anti-misjudgment image analysis operation and maintenance system

Abstract

The invention discloses an anti-misjudgment image analysis operation and maintenance system, and relates to the technical field of image analysis, and the system comprises an image acquisition module, an image anti-misjudgment analysis module, an equipment operation and maintenance processing module and a local database. According to the method, the illumination intensity of the equipment in all directions is concerned, and color compensation is performed according to the illumination intensity, so that compensation can be performed according to colors of different indicating lamps, early warning signals of the indicating lamps can be determined more accurately and timely, whether the shell of the equipment is damaged can be determined better, and the accuracy of image operation and maintenance analysis is improved. By calculating the illumination threat coefficient of each device in each direction, the occurrence rate of the problem that the service life of the illumination sensor of the camera and the light sensing accuracy are reduced due to overexposure is reduced, so that the operation and maintenance cost is reduced, and the color accuracy of the image of the camera is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and in particular to an anti-misjudgment image analysis operation and maintenance system. Background Art

[0002] Traditional equipment operation and maintenance systems obtain the online, network, and operating status of equipment by accessing communications, or perform operation and maintenance analysis through image capture. However, due to the different external environments of the equipment, the captured images will be distorted, resulting in misjudgment of the equipment's operation and maintenance analysis. Therefore, it is necessary to perform image analysis to prevent misjudgment during equipment operation and maintenance.

[0003] Existing technologies such as the invention patent application with announcement number: CN118918526A discloses a rapid detection algorithm and system for relics based on a smart park computer room environment, and the method includes: obtaining a difference image; transforming the difference image to extract the contour of the difference part; selecting the outermost contour of the merged result as the final relic identification contour, and drawing it onto the detected image as the final detection result. This invention can reduce misjudgments caused by irregular objects and improve the accuracy of object recognition by optimizing algorithms and introducing clustering, merging and other technologies. It has the function of automatically adjusting parameters and optimizing algorithms, and can perform intelligent management according to the characteristics of different environments to meet the needs of different computer room environments.

[0004] Prior art, such as the invention patent application with announcement number: CN116972286A, discloses a computer room equipment status early warning identification device and method based on AI model comparison, the method comprising: an adjustment component is arranged inside the first shell, a flip component is arranged above the first shell, the flip component includes a second shell, and a camera protective shell is arranged on one side of the second shell; this invention, through camera shooting, identifies the panel status of equipment such as servers to determine whether the equipment has a fault, so that the operating status of the key equipment that changes first in the computer room can be controlled, and the height, direction and angle of the camera can be adjusted, and the height adjustment, direction adjustment and angle adjustment of the camera can be carried out simultaneously.

[0005] It can be seen from the above scheme that the current anti-misjudgment image analysis and operation and maintenance system lacks certain attention to reducing misjudgment by grayscale compensation of images through light intensity. When the camera shoots a device placed in a natural environment, it is very easy for the light intensity of the environment to be too high on the device, resulting in overexposure of the captured device image, causing deviation in the color characteristics of the original image, and due to the presence of the device indicator light, the overexposed color deviation will produce a greater deviation on the indicator light. If the color is not compensated at this time, it will cause distortion of the subsequent grayscale value analysis results, resulting in misjudgment and reducing the accuracy of image operation and maintenance analysis. At the same time, it lacks certain attention to determining the direction that needs to be blocked due to excessive light intensity. For the direction where the light intensity of the device is too high, if the light is not blocked in time, it is easy to reduce the life and photosensitivity accuracy of the camera light sensor due to overexposure, thereby increasing the operation and maintenance cost and reducing the color accuracy of the camera image. Summary of the invention

[0006] The purpose of the present invention is to provide an anti-misjudgment image analysis and operation and maintenance system, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an anti-misjudgment image analysis and operation and maintenance system, including: an image acquisition module, used to obtain the surface image of each device at each monitoring time point and the light intensity value in each direction within the target monitoring time period.

[0008] The image misjudgment prevention analysis module is used to screen the unobstructed monitoring time points of each device, analyze the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point, evaluate the damage coefficient of each device, and screen the damaged devices.

[0009] The equipment operation and maintenance processing module is used to analyze the blocked light directions of each device and send them and each damaged device to the operation and maintenance person in charge.

[0010] The beneficial effects of the present invention are as follows: (1) The image acquisition module of the present invention acquires data from each device, thereby facilitating subsequent analysis.

[0011] (2) The image misjudgment prevention analysis module of the present invention divides the device surface image into an indicator light image and a shell image, pays attention to the light intensity of the device in various directions, and performs color compensation accordingly. It can not only compensate according to the color of different indicator lights, thereby more accurately and timely determining the warning signal of the indicator light, but also better determine whether the shell of the device is damaged, thereby improving the accuracy of image operation and maintenance analysis and reducing the possibility of misjudgment.

[0012] (3) The device operation and maintenance processing module of the present invention calculates the light threat coefficient of each device in each direction and determines in which directions each device needs to be shielded, thereby reducing the occurrence rate of problems such as reducing the life of the camera light sensor and the photosensitivity accuracy due to overexposure, thereby reducing operation and maintenance costs and improving the color accuracy of the camera image. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Reference Figure 1 As shown, the present invention provides an anti-misjudgment image analysis and operation and maintenance system, including: an image acquisition module, an image anti-misjudgment analysis module, an equipment operation and maintenance processing module and a local database.

[0017] It should be noted that the image acquisition module is connected to the image misjudgment prevention analysis module, the image misjudgment prevention analysis module is connected to the equipment operation and maintenance processing module, and the local database is connected to the image misjudgment prevention analysis module and the equipment operation and maintenance processing module.

[0018] It should also be noted that the local database is used to store the initial appearance outline of each device, the appearance outline similarity threshold, the indicator lights of each device in each direction, the installation time of each indicator light of each device, the color of each indicator light of each device, the initial suitable light intensity upper limit corresponding to each color, the suitable light intensity drop value corresponding to each installation time, the lighting impact area of ​​each device in each direction, the coordinate range of each indicator light of each device, the grayscale value adjustment value corresponding to each color in each image lighting distortion coefficient interval, the warning grayscale value interval of each indicator light of each device, the indicator light warning fault coefficient adjustment parameter value of each indicator light of each device, the shell grayscale adjustment parameter value corresponding to each pixel point of the shell surface image of the lighting impact area of ​​each direction of each device in each image lighting distortion coefficient interval, the initial grayscale value of each pixel point of the shell surface image of the lighting impact area of ​​each device in each direction, the damage coefficient threshold, the lighting threat coefficient threshold, and the historical lighting hazard coefficient of each direction of each device in each historical monitoring time period.

[0019] The image acquisition module is used to acquire the surface image of each device at each monitoring time point and the light intensity value in each direction within the target monitoring time period.

[0020] In a specific embodiment, the surface image of each device at each monitoring time point and the light intensity value in each direction are obtained by a specific method: the surface image of each device at each monitoring time point is obtained by a camera, and the light intensity value in each direction of each device at each monitoring time point is obtained by a light sensor installed in each direction of each device.

[0021] The image acquisition module of the present invention acquires data from each device, thereby facilitating subsequent analysis.

[0022] The image misjudgment prevention analysis module is used to screen each unobstructed monitoring time point of each device, analyze the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point, evaluate the damage coefficient of each device, and screen each damaged device.

[0023] In a specific embodiment of the present invention, the specific screening method for screening each unobstructed monitoring time point of each device is: obtaining an initial appearance contour of each device from a local database.

[0024] Based on the surface image of each device at each monitoring time point, the image appearance contour of each device at each monitoring time point is extracted.

[0025] The initial appearance contour of each device is compared with the image appearance contour at each monitoring time point to obtain the appearance contour similarity of each device at each monitoring time point.

[0026] The appearance contour similarity threshold is obtained from the local database. If the appearance contour similarity of a device at a certain monitoring time point is greater than the appearance contour similarity threshold, the monitoring time point is marked as an unobstructed monitoring time point, thereby screening the unobstructed monitoring time points of each device.

[0027] In a specific embodiment, the appearance contour similarity of each device at each monitoring time point is obtained by: the existing contour similarity comparison method is relatively mature, and the appearance contour similarity of each device at each monitoring time point can be obtained by the existing contour similarity comparison method.

[0028] In a specific embodiment of the present invention, the image appearance contour of each device at each monitoring time point is extracted, and its specific extraction method is: by marking feature points on the contour edge of each device, thereby obtaining each feature point of each device, and numbering them to obtain the number of each feature point of each device, and based on the surface image of each device at each monitoring time point, obtaining the number of each feature point of the surface image of each device at each monitoring time point.

[0029] The feature points with adjacent numbers in the surface image are connected by line segments to obtain the image appearance contour of each device at each monitoring time point.

[0030] In a specific embodiment of the present invention, the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point is analyzed by a specific analysis method as follows: obtaining each indicator light of each device in each direction from a local database, and calculating the upper limit value of the appropriate illumination intensity of each device in each direction at each unobstructed monitoring time point , where x represents the number of each device, , y is a positive integer greater than 2, i represents the number of each monitoring time point, , j is a positive integer greater than 2, where n represents the number of each direction, , m is a positive integer greater than 2.

[0031] According to the light intensity values ​​of each device in each direction at each monitoring time point, extract the light intensity values ​​of each device in each direction at each unobstructed monitoring time point .

[0032] Calculate the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point , where e is represented by a natural constant.

[0033] In a specific embodiment of the present invention, the calculation of the upper limit value of the suitable light intensity in each direction of each device at each unobstructed monitoring time point is specifically calculated as follows: the installation time point of each indicator light of each device is obtained from a local database, and according to each unobstructed monitoring time point of each device, the earliest unobstructed monitoring time point of each device is selected as the target time point of the target monitoring time period, and the time difference between the target time point of the target monitoring time period of each device and the installation time point of a certain indicator light is used as the target monitoring time period and the installation duration of the indicator light, thereby obtaining the installation duration of each device and each indicator light in the target monitoring time period.

[0034] The color of each indicator light of each device is obtained from the local database, and the color of each indicator light of each device in each direction is mapped based on the indicator lights of each device in each direction.

[0035] The initial suitable light intensity upper limit value corresponding to each color and the suitable light intensity reduction value corresponding to each installation time are obtained from the local database, and the initial suitable light intensity upper limit value of each indicator light of each device in each direction is mapped, and the suitable light intensity reduction value of each indicator light of each device in each direction during the target monitoring time period is mapped, and the target light intensity value of each indicator light of each device in each direction during the target monitoring time period is calculated.

[0036] It should be noted that the longer the installation time, the greater the corresponding decrease in suitable light intensity. The longer the installation time, the filament structure inside the indicator light will volatilize, resulting in the weakening of the light intensity inside the indicator light, making it more susceptible to the influence of the external ambient light intensity.

[0037] The minimum target light intensity value among the target light intensity values ​​of each device in the target monitoring time period and each indicator light in each direction is used as the upper limit value of the appropriate light intensity of each device in each direction at each unobstructed monitoring time point.

[0038] In a specific embodiment, the target light intensity values ​​of each indicator light of each device in each target monitoring time period and in each direction are calculated by subtracting the appropriate light intensity drop value of each indicator light in the target monitoring time period and in the direction from the initial suitable light intensity upper limit value of each indicator light of each device in each direction. The target light intensity values ​​of each indicator light of each device in the target monitoring time period and in each direction are thus calculated.

[0039] In a specific embodiment of the present invention, the specific analysis method for evaluating the damage coefficient of each device is: obtaining the illumination influence area of ​​each device in each direction from a local database.

[0040] Based on the surface image of each device at each monitoring time point, the surface image of the lighting influence area of ​​each device in each direction at each monitoring time point is extracted, and the surface image of the lighting influence area of ​​each device in each direction at each unobstructed monitoring time point is extracted, and the surface images of the indicator lights and the surface images of the outer shell of the lighting influence area of ​​each device in each direction at each unobstructed monitoring time point are distinguished.

[0041] Calculate the warning failure coefficient of the indicator lights of each device in each direction at each unobstructed monitoring time point , and calculate the shell damage coefficient of each device in each direction at each unobstructed monitoring time point .

[0042] Calculate the damage factor of each device , where j represents the number of unobstructed monitoring time points and m represents the number of directions.

[0043] In a specific embodiment, the distinction is made into surface images of each indicator light and shell surface images of the lighting influence area of ​​each device in each direction at each unobstructed monitoring time point. The specific distinction method is: obtain the coordinate range of each indicator light of each device from the local database, and extract the surface images of each indicator light of the lighting influence area of ​​each device in each direction at each unobstructed monitoring time point based on this, and use the remaining images as the shell surface images of the lighting influence area of ​​each device in each direction at each unobstructed monitoring time point.

[0044] In a specific embodiment of the present invention, the calculation method of the warning fault coefficient of the indicator light of each device in each direction at each unobstructed monitoring time point is as follows: based on the surface image of each indicator light in the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point, the grayscale value of each pixel point of the surface image of each indicator light in the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point is extracted. , where p represents the number of each indicator light, , q is a positive integer greater than 2, r represents the number of each pixel, , w is a positive integer greater than 2.

[0045] Obtain the grayscale adjustment value corresponding to each color in each image illumination distortion coefficient interval from the local database, and map the grayscale adjustment value of each indicator light of each device in each direction at each unobstructed monitoring time point based on the color of each indicator light of each device in each direction and the image illumination distortion coefficient of each device at each unobstructed monitoring time point. , calculate the actual average grayscale value of each indicator light in each direction of each device at each unobstructed monitoring time point , where w represents the number of pixels.

[0046] It should be noted that, the greater the image illumination distortion coefficient, the greater the absolute value of the corresponding grayscale adjustment value, and the grayscale adjustment value may be positive or negative.

[0047] The warning grayscale value interval of each indicator light of each device is obtained from the local database, and the warning grayscale value interval of each indicator light of each device in each direction is mapped based on each indicator light of each device in each direction.

[0048] If the actual average grayscale value of a certain indicator light of a certain device in a certain direction at a certain unobstructed monitoring time point is included in the warning grayscale value interval, the indicator light is marked as a warning indicator light, thereby screening the warning indicators of each device in each direction at each unobstructed monitoring time point.

[0049] The indicator light warning failure coefficient adjustment parameter values ​​of each indicator light of each device are obtained from the local database, and the indicator light warning failure coefficient adjustment parameter values ​​of each warning indicator light of each device in each direction at each unobstructed monitoring time point are mapped to obtain the indicator light warning failure coefficients of each device in each direction at each unobstructed monitoring time point.

[0050] In a specific embodiment of the present invention, the shell damage coefficient of each device in each direction at each unobstructed monitoring time point is calculated by: obtaining the shell grayscale adjustment parameter value corresponding to each pixel point of the shell surface image of the illumination influence area in each direction of each device in each image illumination distortion coefficient interval from the local database, and mapping to obtain the shell grayscale adjustment parameter value of each pixel point of the shell surface image of the illumination influence area in each direction of each device at each unobstructed monitoring time point , where t represents the number of each pixel point of the shell surface image, , s is a positive integer greater than 2.

[0051] Obtain the initial grayscale value of each pixel of the shell surface image of the illumination influence area of ​​each device in each direction from the local database , based on the grayscale value of each pixel of the shell surface image of the illumination impact area of ​​each device in each direction at each unobstructed monitoring time point , calculate the shell damage coefficient of each device in each direction at each unobstructed monitoring time point , where s represents the number of pixels of the shell surface image.

[0052] In a specific embodiment, the screening of damaged devices includes a specific screening method of obtaining a damage coefficient threshold from a local database, and if a damage coefficient of a device is greater than the damage coefficient threshold, marking the device as a damaged device, thereby screening the damaged devices.

[0053] The image misjudgment prevention analysis module of the present invention divides the device surface image into an indicator light image and a shell image, pays attention to the light intensity of the device in all directions, and performs color compensation accordingly. It can not only compensate according to the colors of different indicator lights, thereby more accurately and timely determining the warning signals of the indicator lights, but also better determine whether the shell of the device is damaged, thereby improving the accuracy of image operation and maintenance analysis and reducing the possibility of misjudgment.

[0054] The equipment operation and maintenance processing module is used to analyze the blocked light directions of each device and send them and each damaged device to the operation and maintenance person in charge.

[0055] In a specific embodiment of the present invention, the specific analysis method for analyzing each blocked illumination direction of each device is as follows: based on the image illumination distortion coefficient of each device in each direction at each unblocked monitoring time point , calculate the excessive light hazard coefficient of each device in each direction , and analyze the historical comprehensive light hazard coefficient of each device in each direction .

[0056] Calculate the light threat factor of each device in each direction .

[0057] The light threat coefficient threshold is obtained from the local database. If the light threat coefficient of a certain direction of a device is greater than the light threat coefficient threshold, the direction is marked as a light blocking direction, thereby screening the light blocking directions of each device.

[0058] In a specific embodiment of the present invention, the historical comprehensive light hazard coefficients of each direction of each device are analyzed, and the specific analysis method is: obtaining the historical light hazard coefficients of each direction of each device in each historical monitoring time period from a local database, and adding up the historical comprehensive light hazard coefficients of each direction of each device.

[0059] The equipment operation and maintenance processing module of the present invention calculates the light threat coefficient of each device in each direction, determines in which directions each device needs to be shielded, reduces the occurrence rate of problems such as reducing the life of the camera light sensor and the photosensitivity accuracy due to overexposure, thereby reducing operation and maintenance costs and improving the color accuracy of the camera image.

[0060] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. Anti-misjudgment image analysis and operation and maintenance system, characterized in that: include: An image acquisition module is used to obtain the surface image of each device at each monitoring time point and the light intensity value in each direction within the target monitoring time period; The image misjudgment prevention analysis module is used to screen the unobstructed monitoring time points of each device, analyze the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point, evaluate the damage coefficient of each device, and screen the damaged devices; The equipment operation and maintenance processing module is used to analyze the blocked light directions of each device and send them and each damaged device to the operation and maintenance person in charge.

2. The anti-misjudgment image analysis and operation and maintenance system according to claim 1, characterized in that: The specific screening method for screening each unobstructed monitoring time point of each device is as follows: Obtaining an initial appearance profile of each device from a local database; According to the surface image of each device at each monitoring time point, extract the image appearance contour of each device at each monitoring time point; The initial appearance contour of each device is compared with the image appearance contour at each monitoring time point to obtain the appearance contour similarity of each device at each monitoring time point; The appearance contour similarity threshold is obtained from the local database. If the appearance contour similarity of a device at a certain monitoring time point is greater than the appearance contour similarity threshold, the monitoring time point is marked as an unobstructed monitoring time point, thereby screening the unobstructed monitoring time points of each device.

3. The anti-misjudgment image analysis and operation and maintenance system according to claim 2, characterized in that: The specific extraction method of extracting the image appearance contour of each device at each monitoring time point is as follows: By marking feature points on the contour edge of each device, each feature point of each device is obtained, and numbered to obtain the number of each feature point of each device, and according to the surface image of each device at each monitoring time point, the number of each feature point of the surface image of each device at each monitoring time point is obtained; The feature points with adjacent numbers in the surface image are connected by line segments to obtain the image appearance contour of each device at each monitoring time point.

4. The anti-misjudgment image analysis and operation and maintenance system according to claim 1, characterized in that: The specific analysis method for analyzing the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point is as follows: Obtain the indicator lights of each device in each direction from the local database, and calculate the upper limit of the appropriate light intensity of each device in each direction at each unobstructed monitoring time point , where x represents the number of each device, , y is a positive integer greater than 2, i represents the number of each monitoring time point, , j is a positive integer greater than 2, where n represents the number of each direction, , m is a positive integer greater than 2; According to the light intensity values ​​of each device in each direction at each monitoring time point, extract the light intensity values ​​of each device in each direction at each unobstructed monitoring time point ; Calculate the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point , where e is represented by a natural constant.

5. The anti-misjudgment image analysis and operation and maintenance system according to claim 4, characterized in that: The specific calculation method for calculating the upper limit of the appropriate light intensity of each device in each direction at each unobstructed monitoring time point is: The installation time point of each indicator light of each device is obtained from the local database, and according to each unobstructed monitoring time point of each device, the earliest unobstructed monitoring time point of each device is selected as the target time point of the target monitoring time period, and the time difference between the target time point of the target monitoring time period of each device and the installation time point of a certain indicator light is used as the target monitoring time period and the installation duration of the indicator light, thereby obtaining the installation duration of each device in the target monitoring time period and each indicator light; Obtain the color of each indicator light of each device from the local database, and map the color of each indicator light of each device in each direction according to the indicator lights of each device in each direction; Obtain the initial suitable light intensity upper limit value corresponding to each color and the suitable light intensity reduction value corresponding to each installation time from the local database, map to obtain the initial suitable light intensity upper limit value of each indicator light of each device in each direction, and map to obtain the suitable light intensity reduction value of each indicator light of each device in the target monitoring time period and in each direction, and calculate to obtain the target light intensity value of each indicator light of each device in the target monitoring time period and in each direction; The minimum target light intensity value among the target light intensity values ​​of each device in the target monitoring time period and each indicator light in each direction is used as the upper limit value of the appropriate light intensity of each device in each direction at each unobstructed monitoring time point.

6. The anti-misjudgment image analysis and operation and maintenance system according to claim 5, characterized in that: The specific analysis method for evaluating the damage coefficient of each device is as follows: Obtain the lighting influence area of ​​each device in each direction from the local database; According to the surface image of each device at each monitoring time point, the surface image of the illumination influence area of ​​each device in each direction at each monitoring time point is extracted, and the surface image of the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point is extracted, and the surface images of the indicator lights and the surface images of the shell of the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point are distinguished; Calculate the warning failure coefficient of the indicator lights of each device in each direction at each unobstructed monitoring time point , and calculate the shell damage coefficient of each device in each direction at each unobstructed monitoring time point ; Calculate the damage factor of each device , where j represents the number of unobstructed monitoring time points and m represents the number of directions.

7. The anti-misjudgment image analysis and operation and maintenance system according to claim 6, characterized in that: The specific calculation method for calculating the warning failure coefficient of the indicator light of each device in each direction at each unobstructed monitoring time point is: According to the surface images of each indicator light in the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point, the grayscale value of each pixel point of the surface image of each indicator light in the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point is extracted. , where p represents the number of each indicator light, , q is a positive integer greater than 2, r represents the number of each pixel, , w is a positive integer greater than 2; Obtain the grayscale adjustment value corresponding to each color in each image illumination distortion coefficient interval from the local database, and map the grayscale adjustment value of each indicator light of each device in each direction at each unobstructed monitoring time point based on the color of each indicator light of each device in each direction and the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point. , calculate the actual average grayscale value of each indicator light in each direction of each device at each unobstructed monitoring time point , where w represents the number of pixels; Obtain the warning grayscale value interval of each indicator light of each device from the local database, and map the warning grayscale value interval of each indicator light of each device in each direction according to each indicator light of each device in each direction; If the actual average grayscale value of a certain indicator light of a certain device in a certain direction at a certain unobstructed monitoring time point is included in the warning grayscale value interval, the indicator light is marked as a warning indicator light, thereby screening the warning indicators of each device in each direction at each unobstructed monitoring time point; The indicator light warning failure coefficient adjustment parameter values ​​of each indicator light of each device are obtained from the local database, and the indicator light warning failure coefficient adjustment parameter values ​​of each warning indicator light of each device in each direction at each unobstructed monitoring time point are mapped to obtain the indicator light warning failure coefficients of each device in each direction at each unobstructed monitoring time point.

8. The anti-misjudgment image analysis and operation and maintenance system according to claim 6, characterized in that: The specific calculation method for calculating the shell damage coefficient of each device in each direction at each unobstructed monitoring time point is: Obtain the shell grayscale adjustment parameter value corresponding to each pixel point of the shell surface image of the illumination influence area of ​​each device in each direction in each image illumination distortion coefficient interval from the local database, and map the shell grayscale adjustment parameter value of each pixel point of the shell surface image of the illumination influence area of ​​each device in each direction at each unobstructed monitoring time point , where t represents the number of each pixel point of the shell surface image, , s is a positive integer greater than 2; Obtain the initial grayscale value of each pixel of the shell surface image of the illumination-affected area of ​​each device in each direction from the local database , based on the grayscale value of each pixel of the shell surface image of the illumination impact area of ​​each device in each direction at each unobstructed monitoring time point , calculate the shell damage coefficient of each device in each direction at each unobstructed monitoring time point , where s represents the number of pixels of the shell surface image.

9. The anti-misjudgment image analysis and operation and maintenance system according to claim 4, characterized in that: The specific analysis method for analyzing each blocked illumination direction of each device is as follows: Based on the image illumination distortion coefficient of each device in each direction at each unobstructed monitoring time point , calculate the excessive light hazard coefficient of each device in each direction , and analyze the historical comprehensive light hazard coefficient of each device in each direction ; Calculate the light threat factor of each device in each direction ; The light threat coefficient threshold is obtained from the local database. If the light threat coefficient of a certain direction of a device is greater than the light threat coefficient threshold, the direction is marked as a light blocking direction, thereby screening the light blocking directions of each device.

10. The anti-misjudgment image analysis and operation and maintenance system according to claim 9, characterized in that: The specific analysis method of analyzing the historical comprehensive light hazard coefficient of each device in each direction is as follows: The historical light hazard coefficients of each direction of each device in each historical monitoring time period are obtained from the local database, and the historical comprehensive light hazard coefficients of each direction of each device are added together to calculate.

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