A humidity monitoring method and system for moisture-proof distribution box

Abstract

The present application provides a humidity monitoring method and system for a moisture-proof distribution box, which collects a set of moisture images of the outer shell of the moisture-proof distribution box; then extracts the moisture feature image of the outer shell of the moisture-proof distribution box, and divides the moisture feature image into an edge dry area and an edge wet area; obtains the temperature-humidity data inside the moisture-proof distribution box, and then determines the humidity correlation factor of each edge wet block in the edge wet area, and can map all humidity correlation factors in the edge wet area to the humidity field outside the moisture-proof distribution box and determine the humidity fault points and temperature diffusion coefficients of different monitoring points inside the moisture-proof distribution box, and then obtain the humidity diffusion threshold inside the moisture-proof distribution box; further determine the humidity monitoring amount of the moisture-proof distribution box, and monitor the moisture-proof distribution box in real time through the humidity monitoring amount. The scheme of the present application can reduce the influence of the humidity gradient phenomenon on the overall humidity monitoring of the moisture-proof distribution box, thereby improving the accuracy of the humidity monitoring of the moisture-proof distribution box.

Description

Technical Field

[0001] The present application relates to the technical field of humidity monitoring, and more specifically, to a humidity monitoring method and system for a moisture-proof distribution box. Background Art

[0002] Humidity monitoring is used to measure the moisture content in the air or objects. It plays an important role in various fields, including meteorology, industrial production, agriculture and healthcare. There are various methods for humidity monitoring, the most common of which include resistive, capacitive and optical humidity sensors. Resistive humidity sensors use the property that the resistivity of hygroscopic materials changes with humidity to determine the humidity level by measuring the resistance value. Capacitive humidity sensors use the property that the dielectric constant changes with humidity to estimate humidity by measuring the capacitance value, while optical humidity sensors indirectly measure humidity by measuring the changes in the absorption and scattering of light by water molecules in the medium. The development of humidity monitoring technology enables people to better understand and control environmental humidity, thereby improving production efficiency, improving product quality, and providing better treatment and monitoring in the field of healthcare.

[0003] Humidity monitoring of moisture-proof distribution boxes is a key technology to ensure that the humidity of distribution boxes is within a safe range. Humidity sensors or humidity monitoring devices are used. These devices measure the humidity level in the environment. Once the humidity exceeds the safe range, the system will sound an alarm. However, there will be a humidity gradient phenomenon inside the moisture-proof distribution box. The humidity gradient phenomenon refers to the phenomenon that there are differences in humidity levels at different locations in the same space of the moisture-proof distribution box. The humidity gradient will cause the humidity in some areas inside the moisture-proof distribution box to be too high or too low, making the equipment in an unfavorable environment. Excessive humidity may cause circuit board corrosion, aging of insulation materials or short circuit of electronic components, while too low humidity may cause electrostatic discharge or material drying and cracking, thereby increasing the risk of damage and failure of the equipment. Therefore, how to reduce the impact of the humidity gradient phenomenon on the monitoring of the overall humidity of the moisture-proof distribution box, thereby improving the accuracy of humidity monitoring of the moisture-proof distribution box has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a humidity monitoring method and system for a moisture-proof distribution box, which can reduce the impact of the humidity gradient phenomenon on monitoring the overall humidity of the moisture-proof distribution box, thereby improving the accuracy of humidity monitoring of the moisture-proof distribution box.

[0005] In a first aspect, the present application provides a humidity monitoring method for a moisture-proof distribution box, comprising the following steps:

[0006] Collect the moisture image of the target moisture-proof distribution box shell to obtain a moisture image set;

[0007] Extracting a moisture feature image of a target moisture-proof distribution box housing from the moisture image set, and dividing an image area of ​​the moisture feature image into an edge dry area and an edge wet area based on edge distribution characteristics of each moisture image in the moisture image set;

[0008] Acquire temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box;

[0009] Based on the temperature-humidity data, the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined, and humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box;

[0010] The humidity monitoring amount of the target moisture-proof distribution box is determined according to the humidity field and the humidity diffusion threshold, and the target moisture-proof distribution box is monitored in real time through the humidity monitoring amount.

[0011] In some embodiments, extracting the moisture feature image of the target moisture-proof distribution box housing from the moisture image set specifically includes:

[0012] Preprocessing the wet image set to obtain a preprocessed wet image set;

[0013] Performing pixel-level difference operation on the preprocessed wet image set and the wet image set to obtain a difference wet image set;

[0014] The moisture characteristic image of the target moisture-proof distribution box casing is determined according to the difference moisture image set.

[0015] In some embodiments, dividing the image area of ​​the wet feature image into a dry edge area and a wet edge area based on the edge distribution characteristics of each wet image in the wet image set specifically includes:

[0016] Determining edge distribution characteristics of each wet image in the wet image set;

[0017] Extracting a wet contour area in the wet feature image according to the edge distribution characteristics of each wet image in the wet image set;

[0018] Determining the local contrast of each pixel in the wet contour area;

[0019] Determine the edge dry area by using a preset segmentation threshold and the local contrast of each pixel point in the wet contour area;

[0020] Determine a plurality of edge wet area blocks according to a preset segmentation threshold and a local contrast of each pixel point in the wet contour area;

[0021] The image area consisting of all edge wet blocks is regarded as the edge wet area.

[0022] In some embodiments, determining the humidity correlation factor between each edge wet area block in the edge wet region and the ambient humidity according to the temperature-humidity data specifically includes:

[0023] determining the amount of moisture in the edge wet area based on the temperature-humidity data;

[0024] Obtaining the values ​​of each pixel point in each edge wet area block in the edge wet area on three color channels of red, green and blue;

[0025] Performing Gaussian fitting on the values ​​corresponding to all pixel points in each color channel in each edge wet area block of the edge wet region to obtain Gaussian fitting coefficients, Gaussian expected values, and Gaussian variance values ​​of each color channel;

[0026] A humidity correlation factor between each edge wet area in the edge wet region and the ambient humidity is determined according to the moisture amount, all Gaussian fitting coefficients, all Gaussian expected values, and all Gaussian variance values.

[0027] In some embodiments, mapping all humidity-related factors in the edge wet area to a humidity field outside the target moisture-proof distribution box specifically includes:

[0028] Determine a humidity mapping value of each edge dry pixel in the edge dry area;

[0029] All humidity correlation factors in the edge wet area are mapped according to all humidity mapping values ​​to obtain the humidity field outside the target moisture-proof distribution box.

[0030] In some embodiments, determining the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data specifically includes:

[0031] Extracting temperature features from all temperature data in the temperature-humidity data to obtain temperature diffusion features inside the target moisture-proof distribution box;

[0032] Determining the temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box according to the temperature diffusion characteristics;

[0033] Determine the humidity gradient of different monitoring points inside the target moisture-proof distribution box through all humidity data in the temperature-humidity data;

[0034] The humidity fault points at different monitoring points inside the target moisture-proof distribution box are determined based on all humidity gradients and all temperature diffusion coefficients.

[0035] In some embodiments, humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box, specifically including:

[0036] Perform linear fitting on all temperature diffusion coefficients to obtain the temperature diffusion curve;

[0037] The humidity diffusion difference amount of each humidity fault point corresponding to each temperature diffusion coefficient is determined by the temperature diffusion curve.

[0038] In a second aspect, the present application provides a humidity monitoring system for a moisture-proof distribution box, comprising:

[0039] An acquisition module is used to collect moisture images of the target moisture-proof distribution box casing to obtain a moisture image set;

[0040] A processing module, used to extract a moisture feature image of a target moisture-proof distribution box housing from the moisture image set, and divide an image area of ​​the moisture feature image into an edge dry area and an edge wet area based on edge distribution characteristics of each moisture image in the moisture image set;

[0041] The processing module is further used to obtain temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box;

[0042] The processing module is further used to determine the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data, and perform humidity diffusion analysis on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box;

[0043] An execution module is used to determine the humidity monitoring value of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold, and to perform real-time monitoring of the target moisture-proof distribution box through the humidity monitoring value.

[0044] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned humidity monitoring method for a moisture-proof distribution box.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the humidity monitoring method of the moisture-proof distribution box is implemented.

[0046] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0047] In the humidity monitoring method and system for a moisture-proof distribution box provided in the present application, a moisture image of the outer shell of a target moisture-proof distribution box is collected to obtain a moisture image set; a moisture feature image of the outer shell of the target moisture-proof distribution box is extracted from the moisture image set, and the image area of ​​the moisture feature image is divided into an edge dry area and an edge wet area based on the edge distribution characteristics of each moisture image in the moisture image set; temperature-humidity data inside the target moisture-proof distribution box is obtained, and a humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity is determined according to the temperature-humidity data, and all humidity correlation factors in the edge wet area are mapped to a humidity field outside the target moisture-proof distribution box; humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined based on the temperature-humidity data, and humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain a humidity diffusion threshold inside the target moisture-proof distribution box; a humidity monitoring amount of the target moisture-proof distribution box is determined according to the humidity field and the humidity diffusion threshold, and the target moisture-proof distribution box is monitored in real time through the humidity monitoring amount.

[0048] It can be seen that the humidity monitoring quantity of the target moisture-proof distribution box is determined according to the humidity field and the humidity diffusion threshold, and the target moisture-proof distribution box is monitored in real time through the humidity monitoring quantity; first, all humidity mapping values ​​are used to map the humidity correlation factors of all pixel points in the edge wet area to obtain a humidity field for describing the distribution changes of humidity information at different positions outside the moisture-proof distribution box; secondly, the humidity diffusion threshold for measuring the critical point of humidity change in the internal environment of the moisture-proof distribution box is extracted from each humidity fault point through all temperature diffusion coefficients; then, the humidity monitoring quantity describing the overall humidity distribution of the moisture-proof distribution box is obtained according to the humidity field and the humidity diffusion threshold, and the overall humidity change of the moisture-proof distribution box can be grasped through the humidity monitoring quantity; finally, the target moisture-proof distribution box is monitored in real time through the humidity monitoring quantity; in summary, the scheme of the present application can reduce the influence of the humidity gradient phenomenon on the overall humidity monitoring of the moisture-proof distribution box, thereby improving the accuracy of humidity monitoring of the moisture-proof distribution box. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is an exemplary flow chart of a humidity monitoring method for a moisture-proof distribution box according to some embodiments of the present application;

[0050] Figure 2 is a schematic diagram of a process for determining a moisture characteristic image in some embodiments of the present application;

[0051] Figure 3 is a schematic diagram of a process for determining a humidity fault point and a temperature diffusion coefficient in some embodiments of the present application;

[0052] Figure 4 is a structural block diagram of a humidity monitoring system for a moisture-proof distribution box in some embodiments of the present application;

[0053] Figure 5 It is a structural schematic diagram of a computer device for implementing a humidity monitoring method for a moisture-proof distribution box according to some embodiments of the present application. DETAILED DESCRIPTION

[0054] The core of this application is to collect moisture images of the target moisture-proof distribution box shell to obtain a moisture image set; extract the moisture feature image of the target moisture-proof distribution box shell from the moisture image set, and divide the image area of ​​the moisture feature image into an edge dry area and an edge wet area based on the edge distribution characteristics of each moisture image in the moisture image set; obtain the temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box; determine the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data, perform humidity diffusion analysis on each humidity fault point according to all temperature diffusion coefficients, and obtain the humidity diffusion threshold inside the target moisture-proof distribution box; determine the humidity monitoring amount of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold, and monitor the target moisture-proof distribution box in real time through the humidity monitoring amount. The solution of the present application can reduce the impact of the humidity gradient phenomenon on the overall humidity monitoring of the moisture-proof distribution box, thereby improving the accuracy of humidity monitoring of the moisture-proof distribution box.

[0055] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a humidity monitoring method for a moisture-proof distribution box according to some embodiments of the present application. The humidity monitoring method 100 for a moisture-proof distribution box mainly includes the following steps:

[0056] In step 101, a moisture image of a target moisture-proof distribution box casing is collected to obtain a moisture image set.

[0057] In the specific implementation, an industrial camera is used to collect a set of wet images of the target moisture-proof distribution box shell. It should be noted that the wet image set described in the present application represents a set of wet images of the target moisture-proof distribution box shell collected continuously at intervals of one hour in the past month, and the wet images in the wet images represent images collected of the target moisture-proof distribution box shell in a humid environment; it should be noted that the industrial camera in the present application may be: a charge-coupled device camera, a line scan camera, and in other implementations, other types of addition may be used for collection, which will not be repeated here.

[0058] In step 102, a moisture feature image of the target moisture-proof distribution box housing is extracted from the moisture image set, and the image area of ​​the moisture feature image is divided into an edge dry area and an edge wet area based on the edge distribution characteristics of each moisture image in the moisture image set.

[0059] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining the moisture characteristic image in some embodiments of the present application. In this embodiment, the moisture characteristic image of the target moisture-proof distribution box housing is extracted from the moisture image set, which can be achieved by the following steps:

[0060] First, in step 1021, the wet image set is preprocessed to obtain a preprocessed wet image set;

[0061] Next, in step 1022, pixel-level difference operation is performed between the pre-processed wet image set and the wet image set to obtain a difference wet image set;

[0062] Then, in step 1023, the moisture characteristic image of the target moisture-proof distribution box casing is determined according to the difference moisture image set.

[0063] In specific implementation, the wet image set is preprocessed to obtain the preprocessed wet image set, which can be implemented in the following manner, namely: first, the wet image set is subjected to image denoising, and image denoising can be performed using Gaussian filtering, and then the denoised wet image set is subjected to image enhancement, and image enhancement can use a multi-scale Retinex algorithm, and a set of all images obtained after image enhancement is used as a preprocessed wet image set, and each image in the preprocessed wet image set is used as a preprocessed wet image. In other embodiments, other methods can also be used for implementation, which are not limited here.

[0064] It should be noted that each wet image in the wet image set described in the present application corresponds to a pre-processed wet image.

[0065] In specific implementation, the preprocessed wet image set and the wet image set are subjected to pixel-level difference operation to obtain the difference wet image set, which can be implemented in the following manner, namely: first, a preprocessed wet image is selected from the preprocessed wet image set, and then a wet image corresponding to the preprocessed wet image is selected from the wet image set, and the gradient amplitude is calculated for each pixel in the wet image corresponding to the preprocessed wet image using the Sobel operator; secondly, the pixel value of each pixel in the preprocessed wet image is subtracted from the gradient amplitude of the corresponding pixel, so that the image obtained after subtracting the gradient amplitude is used as the difference wet image of the preprocessed wet image; then, the above steps are repeated to obtain the difference wet images of the remaining wet images in the wet image set; finally, the set consisting of all the difference wet images is used as the difference wet image set. It should be noted that the pixel-level difference operation in the present application can highlight the edge difference features in the wet image, and other methods can also be used in other embodiments, which are not limited here.

[0066] It should be noted that the moisture feature image described in the present application is a specific image extracted based on the moisture difference information in the difference moisture image set, which can ensure that the difference in moisture features is more obvious and help to improve the representativeness of the image. As a preferred embodiment, the moisture feature image of the target moisture-proof distribution box shell is determined based on the difference moisture image set. The following method can be used, namely: the variance of the pixel values ​​of each difference moisture image in the difference moisture image set is calculated, and the difference moisture image with the largest variance is used as the moisture feature image of the target moisture-proof distribution box shell.

[0067] In some embodiments, dividing the image area of ​​the wet feature image into a dry edge area and a wet edge area based on the edge distribution characteristics of each wet image in the wet image set can be achieved by using the following steps:

[0068] Determining edge distribution characteristics of each wet image in the wet image set;

[0069] Extracting a wet contour area in the wet feature image according to the edge distribution characteristics of each wet image in the wet image set;

[0070] Determining the local contrast of each pixel in the wet contour area;

[0071] Determine the edge dry area by using a preset segmentation threshold and the local contrast of each pixel point in the wet contour area;

[0072] Determine a plurality of edge wet area blocks according to a preset segmentation threshold and a local contrast of each pixel point in the wet contour area;

[0073] The image area consisting of all edge wet blocks is regarded as the edge wet area.

[0074] It should be noted that the edge distribution characteristics described in the present application represent the distribution area where the pixel values ​​in the wet image change rapidly. As a preferred embodiment, determining the edge distribution characteristics of each wet image in the wet image set can be achieved in the following manner. The edge features of each wet image can be extracted using an edge detection algorithm, such as Sobel, Prewitt, and Canny edge detection algorithms. In addition, it should be noted that the segmentation threshold described in the present application represents the threshold for judging the local contrast of pixel points in the wet contour area, which can be preset through a large number of experiments.

[0075] In specific implementation, the following method can be used to extract the wet contour area of ​​the image area in the wet feature image according to the edge distribution characteristics of each wet image in the wet image set, namely: first, use a contour extraction algorithm (such as the findContours function in the OpenCV library, etc.) to extract the contour information of the edge distribution characteristics of each wet image. It should be noted that the contour information in the present application represents the description of the contour boundary of the humidity of the target moisture-proof distribution box in the wet image. Secondly, obtain the pixel points at the contour information of the edge distribution characteristics of all wet images, and then calculate the mean of the pixel values ​​of all the obtained pixel points, and use the obtained mean as the wet boundary threshold, and mark the pixel points in the wet feature image that are greater than the wet boundary threshold as wet contour pixel points, and then use the area composed of all wet contour pixels as the wet contour area. Other methods can also be used in other embodiments, which will not be repeated here.

[0076] It should be noted that the wet contour area described in the present application represents the boundary area of ​​the wet part in the image of the target moisture-proof distribution box casing.

[0077] It should be noted that the local contrast represents the brightness difference between a pixel in an image and its neighboring pixels. The local contrast can increase the brightness difference in the area with high local contrast in the image, making the image details clearer, and thus can be used for the detection of wet areas. As a preferred embodiment, the local contrast of each pixel in the wet contour area can be determined in the following manner, namely: a pixel is selected from the wet contour area, and the contrast analysis method in image processing technology is used to calculate the contrast between the pixel and its neighboring pixels, the mean of all the contrasts obtained is calculated, and the obtained mean is used as the local contrast of the pixel, and the above steps are repeated to obtain the local contrast of the remaining pixels in the wet contour area. Other methods can also be used in other embodiments, which are not limited here.

[0078] In specific implementation, the edge dry area can be determined by a preset segmentation threshold and the local contrast of each pixel point in the wet contour area in the following manner, namely: a pixel point is selected from the wet contour area, and if the local contrast of the pixel point is greater than the preset segmentation threshold, the pixel point is used as the edge dry pixel point, and the above steps are repeated to obtain the remaining edge dry pixels, and the image area composed of all edge dry pixels is used as the edge dry area.

[0079] It should be noted that the dry edge area described in the present application refers to an image area composed of multiple pixels that can reflect the characteristics of the dry side of the wet edge of the moisture-proof distribution box casing.

[0080] In a specific implementation, the following steps can be used to determine multiple edge wet blocks based on a preset segmentation threshold and the local contrast of each pixel in the wet contour area, namely: select a pixel from the wet contour area, and if the local contrast of the pixel is less than or equal to the preset segmentation threshold, then take the area composed of the pixels adjacent to the pixel as the edge wet block, and take the edge wet block as the edge wet block of the pixel, repeat the above steps to obtain the edge wet blocks of the remaining pixels in the wet contour area, thereby obtaining multiple edge wet blocks.

[0081] It should be noted that the edge wet area block described in the present application represents an area composed of a pixel point and its adjacent pixel points. The edge wet area block can reflect the local area of ​​the humidity distribution characteristics on the wet side of the wet edge of the moisture-proof distribution box casing.

[0082] In step 103, the temperature-humidity data inside the target moisture-proof distribution box is obtained, and the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity is determined based on the temperature-humidity data, and all humidity correlation factors in the edge wet area are mapped to the humidity field outside the target moisture-proof distribution box.

[0083] In the specific implementation, a sensor array is arranged inside the target moisture-proof distribution box, and the temperature-humidity data inside the target moisture-proof distribution box is obtained through the sensor array. It should be noted that the sensor array described in the present application includes multiple temperature and humidity sensors, which can be connected to a data acquisition device or controller in a centralized or distributed manner, and monitor the temperature and humidity of multiple locations at the same time; in addition, it should be noted that the temperature-humidity data described in the present application is composed of temperature data and humidity data of different monitoring points inside the target moisture-proof distribution box collected continuously at intervals of one hour in the past month.

[0084] In some embodiments, determining the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data may be implemented by the following steps:

[0085] determining the amount of moisture in the edge wet area based on the temperature-humidity data;

[0086] Obtaining the values ​​of each pixel point in each edge wet area block in the edge wet area on three color channels of red, green and blue;

[0087] Performing Gaussian fitting on the values ​​corresponding to all pixel points in each color channel in each edge wet area block of the edge wet region to obtain Gaussian fitting coefficients, Gaussian expected values, and Gaussian variance values ​​of each color channel;

[0088] A humidity correlation factor between each edge wet area in the edge wet region and the ambient humidity is determined according to the moisture amount, all Gaussian fitting coefficients, all Gaussian expected values, and all Gaussian variance values.

[0089] In specific implementation, the moisture content of the edge wet area can be determined based on the temperature-humidity data in the following manner, namely: performing differential processing on the humidity data in the temperature-humidity data to obtain a humidity differential sequence, and then selecting two humidity values ​​corresponding to the maximum humidity differential value from the humidity differential sequence, and then taking the average of the two humidity values ​​as the moisture content of the edge wet area. Other methods may also be used in other embodiments, which are not limited here.

[0090] It should be noted that the moisture content described in the present application refers to the humidity inside the target moisture-proof distribution box mapped to the humidity content of the edge wet area in the moisture-proof distribution box shell image.

[0091] In specific implementation, Gaussian fitting is performed on the values ​​corresponding to all pixel points under each color channel in each edge wet block of the edge wet area, and the Gaussian fitting coefficient, Gaussian expected value and Gaussian variance value of each color channel are obtained. This can be achieved in the following manner, namely: first, for each color channel, the values ​​corresponding to all pixel points in each edge wet block of the edge wet area are obtained, and these values ​​are normalized, and then, Gaussian fitting is performed on the normalized values ​​to obtain the Gaussian fitting coefficient, Gaussian expected value and Gaussian variance value of each color channel. Other methods can also be used in other embodiments, which will not be repeated here.

[0092] In specific implementation, the humidity correlation factor between each edge wet area block in the edge wet area and the ambient humidity is determined according to the moisture amount, all Gaussian fitting coefficients, all Gaussian expected values ​​and all Gaussian variance values. The following method can be used to achieve this, namely: first, the Gaussian expected value of the red channel is added to the Gaussian expected value of the blue channel, and the summed value is divided by the Gaussian expected value of the green channel, and the divided value is used as the color channel expected value; secondly, the Gaussian variance of the red channel is added to the Gaussian variance of the blue channel, and the summed value is divided by the Gaussian variance of the green channel, and the divided value is multiplied by 2 and then squared, and the squared value is used as the color channel variance value; thirdly, the Gaussian fitting coefficient of the red channel, the Gaussian fitting coefficient of the green channel and the Gaussian fitting coefficient of the blue channel are calculated. The mean is taken as the color channel fitting coefficient, then the color channel expected value is negative and divided by the color channel variance value, the value obtained by division is subjected to an exponential function with the natural logarithm as the base, and then the value obtained after taking the exponential function with the natural logarithm as the base is multiplied by the color channel fitting coefficient, and the multiplied value is used as the color channel information value. Finally, an edge wet block is selected from the edge wet area, the pixel values ​​of all pixels in the edge wet block are multiplied by the moisture amount, and then all the multiplied values ​​are summed, and then the summed value is divided by the color channel value, and the divided value is used as the humidity correlation factor between the edge wet block and the surface ambient humidity of the moisture-proof distribution box, and the above steps are repeated to obtain the humidity correlation factors between the remaining edge wet blocks in the edge wet area and the surface ambient humidity of the moisture-proof distribution box.

[0093] It should be noted that the humidity correlation factor described in the present application represents the degree of correlation between the edge wet area extracted from the edge wet area in the humidity feature image and the environmental humidity distribution on the surface of the moisture-proof distribution box shell. The larger the humidity correlation factor, the higher the degree of correlation between the edge wet area extracted from the edge wet area in the humidity feature image and the environmental humidity distribution on the surface of the moisture-proof distribution box shell, and vice versa.

[0094] In some embodiments, mapping all humidity-related factors in the edge wet area to the humidity field outside the target moisture-proof distribution box can be achieved by using the following steps:

[0095] Determine the humidity mapping value of each edge dry pixel in the edge dry area;

[0096] All humidity correlation factors in the edge wet area are mapped according to all humidity mapping values ​​to obtain the humidity field outside the target moisture-proof distribution box.

[0097] In specific implementation, the humidity mapping value of each edge dry pixel in the edge dry area can be determined in the following manner, namely: first, an edge dry pixel in the edge dry area is selected, and the 8 pixel points around the edge dry pixel point can be used as comparison pixel points, the pixel values ​​of all comparison pixel points are subtracted from the pixel value of the edge dry pixel point and the average is taken, the value obtained after taking the average is divided by the pixel value of the edge dry pixel point, and the value obtained by division is used as the humidity mapping value of the edge dry pixel point, and the above steps are repeated to obtain the humidity mapping values ​​of the remaining edge dry pixels in the edge dry area. Other methods can also be used in other embodiments, which are not limited here.

[0098] It should be noted that the humidity mapping value described in the present application reflects the degree of mapping of the edge dry pixel points to humidity. The larger the humidity mapping value, the more obvious the mapping degree of the edge dry pixel points to humidity. The smaller the humidity mapping value, the less obvious the mapping degree of the edge dry pixel points to humidity.

[0099] In specific implementation, all humidity correlation factors in the edge wet area are mapped according to all humidity mapping values, and the humidity field outside the target moisture-proof distribution box can be obtained in the following manner, namely: an edge wet pixel point is selected in the edge wet area, and then the edge dry pixel point closest to the edge wet pixel point is selected from the edge dry area, and the humidity correlation factor of the edge wet pixel point is multiplied by the humidity mapping value of the nearest edge dry pixel point, and the multiplied value is used as the humidity information amount of the edge wet pixel point, and the above steps are repeated to obtain the humidity information amount of the remaining edge wet pixels in the edge wet area, and the spatial distribution of the humidity information amounts of all edge wet pixels is used as the humidity field outside the target moisture-proof distribution box. Other methods can also be used in other embodiments, which are not limited here.

[0100] It should be noted that the humidity field described in the present application represents a description of the changes in the distribution of humidity information at different positions outside the target moisture-proof distribution box, which can reflect the changes in humidity in the edge wet area of ​​the moisture feature image corresponding to the outside of the target moisture-proof distribution box. In addition, the humidity information amount represents the quantitative description of the edge wet pixel points for the distribution of humidity information in the external space of the moisture-proof distribution box.

[0101] In step 104, the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined based on the temperature-humidity data, and humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box.

[0102] In some embodiments, reference Figure 3As shown, this figure is a schematic diagram of the process of determining the humidity fault point and the temperature diffusion coefficient in some embodiments of the present application. In this embodiment, the humidity fault point and the temperature diffusion coefficient of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data can be determined by the following steps:

[0103] First, in step 1041, temperature features are extracted from all temperature data in the temperature-humidity data to obtain temperature diffusion features inside the target moisture-proof distribution box;

[0104] Secondly, in step 1042, the temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined according to the temperature diffusion characteristics;

[0105] Then, in step 1043, the humidity gradients of different monitoring points inside the target moisture-proof distribution box are determined through all the humidity data in the temperature-humidity data;

[0106] Finally, in step 1044, the humidity fault points of different monitoring points inside the target moisture-proof distribution box are determined according to all humidity gradients and all temperature diffusion coefficients.

[0107] In specific implementation, temperature feature extraction is performed on all temperature data in the temperature-humidity data to obtain the temperature diffusion feature inside the target moisture-proof distribution box. This can be achieved in the following manner, namely: first, differential processing is performed on all temperature data in the temperature-humidity data, and the data obtained after the differential processing are all used as temperature differential data. Then, the maximum value is selected from all the temperature differential data, and the obtained maximum value is used as the temperature diffusion feature inside the target moisture-proof distribution box. Other methods may also be used in other embodiments, which are not limited here.

[0108] It should be noted that the temperature diffusion characteristics described in the present application represent the maximum temperature change per unit time, and the temperature diffusion characteristics can reflect the degree of fluctuation of the internal temperature change of the target moisture-proof distribution box.

[0109] In specific implementation, the temperature diffusion coefficient of different monitoring points inside the target moisture-proof distribution box can be determined according to the temperature diffusion characteristics. The following method is adopted, namely: first, the standard deviation and variance of the temperature data of different monitoring points inside the target moisture-proof distribution box are calculated; secondly, the temperature diffusion characteristics are multiplied by the standard deviation and variance of the temperature data of each monitoring point, and the obtained standard deviation is used as the temperature standard deviation of the corresponding monitoring point, and the values ​​obtained after multiplying by the variance are used as the diffusion variance values ​​of the corresponding monitoring points; then, the temperature standard deviation of each monitoring point is divided by the arithmetic square root of the diffusion variance value of the corresponding monitoring point, and the value obtained by the division is used as the temperature diffusion coefficient of the corresponding monitoring point. Other methods can also be used in other embodiments, which are not limited here.

[0110] It should be noted that the temperature diffusion coefficient described in this application reflects the temperature diffusion rate of the monitoring point inside the target distribution box. The larger the temperature diffusion coefficient, the faster the temperature diffusion rate of the monitoring point inside the target distribution box. The smaller the temperature diffusion coefficient, the slower the temperature diffusion rate of the monitoring point inside the target distribution box.

[0111] In specific implementation, the humidity gradients of different monitoring points inside the target moisture-proof distribution box can be determined by all the humidity data in the temperature-humidity data in the following manner, namely: first, select a monitoring point inside the target moisture-proof distribution box, and then select a monitoring point farthest from the monitoring point, subtract the humidity data of the two monitoring points, take the average of all the differences obtained, and divide the obtained average by the distance between the two monitoring points, and use the value obtained by division as the humidity gradient of the monitoring point, repeat the above steps to obtain the humidity gradients of the remaining monitoring points. Other methods can also be used in other embodiments, which will not be repeated here.

[0112] In specific implementation, the humidity fault points of different monitoring points inside the target moisture-proof distribution box can be determined according to all humidity gradients and all temperature diffusion coefficients. The following method can be used, namely: first, the humidity gradient of each monitoring point is divided by the temperature diffusion coefficient of the corresponding monitoring point, and the value obtained by the division is used as the humidity fault value of the corresponding monitoring point. Secondly, a monitoring point inside the target moisture-proof distribution box is selected, and then the monitoring point closest to the monitoring point and with the largest temperature fault value is selected, and the selected monitoring point is used as the temperature fault point of the monitoring point. Then, the above steps are repeated to obtain the temperature fault points of the remaining monitoring points inside the target moisture-proof distribution box.

[0113] It should be noted that the temperature fault point described in this application represents the monitoring point where the humidity changes significantly between different monitoring points in the internal space of the target moisture-proof distribution box. The temperature fault point can be used to identify the boundary of humidity changes between different areas inside the target moisture-proof distribution box.

[0114] In some embodiments, humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients, and the humidity diffusion threshold inside the target moisture-proof distribution box can be obtained by the following steps:

[0115] Perform linear fitting on all temperature diffusion coefficients to obtain the temperature diffusion curve;

[0116] Determine the humidity diffusion difference of each temperature diffusion coefficient corresponding to the humidity fault point through the temperature diffusion curve;

[0117] The humidity diffusion threshold inside the target moisture-proof distribution box is determined according to all humidity diffusion differences.

[0118] In specific implementation, linear fitting is performed on all temperature diffusion coefficients to obtain a temperature diffusion curve, which can be implemented in the following manner, namely: sorting all temperature diffusion coefficients in ascending order, and using the sorted sequence as the temperature diffusion sequence. The temperature diffusion sequence can be fitted using a linear fitting algorithm, for example: least squares method, polynomial fitting, least squares support vector machine and other fitting algorithms, which are not limited here, and the fitted curve is used as the temperature diffusion curve; it should be noted that the humidity diffusion difference described in the present application represents the difference between the medium humidity propagation rate and the temperature diffusion coefficient change inside the target moisture-proof distribution box. As a preferred embodiment, the humidity diffusion difference of each temperature diffusion coefficient corresponding to the humidity fault point is determined by the temperature diffusion curve. The following method can be used to implement it, namely: taking the derivative of the temperature diffusion coefficient corresponding to the temperature diffusion curve, and using the obtained derivatives as the humidity diffusion difference of the humidity fault point corresponding to each temperature diffusion coefficient. The humidity diffusion difference can be used to identify areas with large humidity gradients. Other methods can also be used in other embodiments, which are not limited here.

[0119] It should be noted that the humidity diffusion threshold described in the present application represents the critical point of humidity change in the internal environment of the target moisture-proof distribution box. The humidity diffusion threshold can be used to judge the abnormal humidity situation in the internal environment of the distribution box. As a preferred embodiment, the humidity diffusion threshold inside the target moisture-proof distribution box is determined based on all humidity diffusion differences. The following method can be used, namely: taking the average of all humidity diffusion differences, multiplying the average by the temperature diffusion characteristic, and using the multiplied value as the humidity diffusion threshold inside the target moisture-proof distribution box.

[0120] In step 105, the humidity monitoring value of the target moisture-proof distribution box is determined according to the humidity field and the humidity diffusion threshold, and the target moisture-proof distribution box is monitored in real time by using the humidity monitoring value.

[0121] In some embodiments, determining the humidity monitoring amount of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold can be achieved by using the following steps:

[0122] Performing scale screening on all humidity information in the humidity field to obtain multiple humidity scales;

[0123] The humidity monitoring amount of the target moisture-proof distribution box is determined according to all humidity scales and the humidity diffusion threshold.

[0124] In specific implementation, all humidity information quantities in the humidity field are scaled and screened to obtain multiple humidity scales, which can be implemented in the following manner, namely: first, all humidity quantities are sorted in ascending order, and the sequence obtained by sorting is used as a humidity quantity sequence; second, a sequence is selected, the total number of all humidity quantities is taken as the arithmetic square root and then rounded down, and the value obtained after rounding down is used as the length of the sequence, and the sequence is used as a sliding window; third, a position of the sliding window is made to correspond one-to-one with the first position of the humidity quantity sequence, the mean of all humidity quantities in the sliding window is calculated, and the obtained mean is used as the first humidity scale; the sliding window is moved, the first position of the sliding window is made to correspond one-to-one with the second position of the humidity quantity sequence, the mean of all humidity quantities in the sliding window is calculated, and the obtained mean is used as the second humidity scale; then, the above steps are repeated, and the sliding window is moved in sequence until the first position of the sliding window corresponds one-to-one with the last position of the humidity quantity sequence, thereby obtaining multiple humidity scales. In other embodiments, other methods can also be used for implementation, which is not limited here.

[0125] It should be noted that the humidity scale described in the present application represents a measure of the humidity change trend between different surface areas of the target moisture-proof distribution box casing. The humidity scale can be used to extract the law of humidity gradient changes inside the target moisture-proof distribution box.

[0126] In addition, it should be noted that the humidity monitoring quantity described in the present application represents the parameter quantity of the humidity distribution of the target moisture-proof distribution box as a whole. The humidity monitoring quantity can be used to grasp the humidity changes of the target moisture-proof distribution box as a whole, so as to take dehumidification, ventilation and other measures in time to effectively prevent moisture from causing damage to electrical equipment and lines. As a preferred embodiment, the humidity monitoring quantity of the target moisture-proof distribution box is determined according to all humidity scales and the humidity diffusion threshold. It can be achieved in the following way, namely: taking the average of all humidity scales, multiplying the value obtained by taking the average by the humidity diffusion threshold, and using the multiplied value as the humidity monitoring quantity of the target moisture-proof distribution box.

[0127] In specific implementation, real-time monitoring of the target moisture-proof distribution box through the humidity monitoring value can be achieved in the following manner, namely: obtaining the real-time humidity inside the target moisture-proof distribution box; if the real-time humidity is greater than or equal to the humidity monitoring value, issuing a warning to the early warning center; if the real-time humidity is less than the humidity monitoring value, continuing to monitor the real-time humidity.

[0128] In addition, in another aspect of the present application, in some embodiments, the present application provides a humidity monitoring system for a moisture-proof distribution box, referring to Figure 4, which is a schematic diagram of exemplary hardware and / or software of a humidity monitoring system for a moisture-proof distribution box according to some embodiments of the present application, the humidity monitoring system 400 for a moisture-proof distribution box includes: an acquisition module 401, a processing module 402 and an execution module 403, which are described as follows:

[0129] Acquisition module 401, in this application, acquisition module 401 is mainly used to collect the moisture image of the target moisture-proof distribution box shell to obtain a moisture image set;

[0130] Processing module 402, in the present application, the processing module 402 is used to extract a moisture feature image of the target moisture-proof distribution box housing from the moisture image set, and divide the image area of ​​the moisture feature image into an edge dry area and an edge wet area based on the edge distribution characteristics of each moisture image in the moisture image set;

[0131] It should be noted that the processing module 402 in the present application is also used to obtain the temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all the humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box;

[0132] In addition, it should be noted that the processing module 402 in the present application is also used to determine the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data, and perform humidity diffusion analysis on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box;

[0133] Execution module 403, in the present application, the execution module 403 is mainly used to determine the humidity monitoring value of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold, and to perform real-time monitoring of the target moisture-proof distribution box through the humidity monitoring value.

[0134] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned humidity monitoring method for the moisture-proof distribution box.

[0135] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device using a humidity monitoring method for a moisture-proof distribution box according to some embodiments of the present application. The humidity monitoring method for a moisture-proof distribution box in the above embodiment can be performed by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0136] The processor 501 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more components for controlling the execution of the humidity monitoring method of the moisture-proof distribution box in the present application.

[0137] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0138] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0139] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method described in the above method embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0140] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0141] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0142] The above-mentioned computer device can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0143] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the humidity monitoring method of the moisture-proof distribution box is implemented.

[0144] In summary, in the humidity monitoring method and system for a moisture-proof distribution box disclosed in the embodiment of the present application, a moisture image of the outer shell of the target moisture-proof distribution box is collected to obtain a moisture image set; a moisture feature image of the outer shell of the target moisture-proof distribution box is extracted from the moisture image set, and the image area of ​​the moisture feature image is divided into an edge dry area and an edge wet area based on the edge distribution characteristics of each moisture image in the moisture image set; the temperature-humidity data inside the target moisture-proof distribution box is obtained, and the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity is determined according to the temperature-humidity data, and all humidity correlation factors in the edge wet area are mapped to the humidity field outside the target moisture-proof distribution box; based on the temperature-humidity data, the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined, and humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain a humidity diffusion threshold inside the target moisture-proof distribution box; the humidity monitoring amount of the target moisture-proof distribution box is determined according to the humidity field and the humidity diffusion threshold, and the target moisture-proof distribution box is monitored in real time through the humidity monitoring amount. The solution of the present application can reduce the impact of the humidity gradient phenomenon on the overall humidity monitoring of the moisture-proof distribution box, thereby improving the accuracy of humidity monitoring of the moisture-proof distribution box.

[0145] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A humidity monitoring method for a moisture-proof distribution box, characterized in that: The steps include: Collect the moisture image of the target moisture-proof distribution box shell to obtain a moisture image set; Extracting a moisture feature image of a target moisture-proof distribution box housing from the moisture image set, and dividing an image area of ​​the moisture feature image into an edge dry area and an edge wet area based on edge distribution characteristics of each moisture image in the moisture image set; Acquire temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box; Based on the temperature-humidity data, the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box are determined, and humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box; Determine the humidity monitoring amount of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold, and perform real-time monitoring of the target moisture-proof distribution box through the humidity monitoring amount; Wherein, determining the humidity correlation factor between each edge wet area block in the edge wet area and the ambient humidity according to the temperature-humidity data specifically includes: determining the amount of moisture in the edge wet area based on the temperature-humidity data; Obtaining the values ​​of each pixel point in each edge wet area block in the edge wet area on three color channels of red, green and blue; Performing Gaussian fitting on the values ​​corresponding to all pixel points in each color channel in each edge wet area block of the edge wet region to obtain Gaussian fitting coefficients, Gaussian expected values, and Gaussian variance values ​​of each color channel; Determine a humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the humidity amount, all Gaussian fitting coefficients, all Gaussian expected values ​​and all Gaussian variance values, wherein the humidity correlation factor represents the degree of correlation between the edge wet block in the edge wet area extracted from the humidity feature image and the ambient humidity distribution on the surface of the moisture-proof distribution box housing; The step of mapping all humidity-related factors in the edge wet area to the humidity field outside the target moisture-proof distribution box specifically includes: Determine a humidity mapping value of each edge dry pixel in the edge dry area, wherein the humidity mapping value reflects a mapping degree of the edge dry pixel to humidity; Map all humidity correlation factors in the edge wet area according to all humidity mapping values ​​to obtain a humidity field outside the target moisture-proof distribution box, wherein the humidity field represents a change in humidity information distribution at different positions outside the target moisture-proof distribution box; Wherein, determining the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data specifically includes: Extracting temperature features from all temperature data in the temperature-humidity data to obtain temperature diffusion features inside the target moisture-proof distribution box; Determine the temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box according to the temperature diffusion characteristics, wherein the temperature diffusion coefficients reflect the temperature diffusion speed of the monitoring points inside the target distribution box; Determine the humidity gradient of different monitoring points inside the target moisture-proof distribution box through all humidity data in the temperature-humidity data; Determine humidity fault points at different monitoring points inside the target moisture-proof distribution box according to all humidity gradients and all temperature diffusion coefficients, wherein the humidity fault points represent monitoring points where humidity changes significantly between different monitoring points in the internal space of the target moisture-proof distribution box; Among them, humidity diffusion analysis is performed on each humidity fault point according to all temperature diffusion coefficients, and the humidity diffusion threshold inside the target moisture-proof distribution box is obtained, which specifically includes: Perform linear fitting on all temperature diffusion coefficients to obtain the temperature diffusion curve; Determine the humidity diffusion difference of each temperature diffusion coefficient corresponding to the humidity fault point through the temperature diffusion curve; Determine a humidity diffusion threshold inside the target moisture-proof distribution box according to all humidity diffusion differences, wherein the humidity diffusion threshold represents a critical point of humidity change in the internal environment of the target moisture-proof distribution box; Wherein, determining the humidity monitoring amount of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold specifically includes: Performing scale screening on all humidity information in the humidity field to obtain multiple humidity scales, where the humidity scale represents a measure of humidity change trends between different surface areas of the target moisture-proof distribution box housing; The humidity monitoring amount of the target moisture-proof distribution box is determined according to all humidity scales and the humidity diffusion threshold, and the humidity monitoring amount represents a parameter amount of the overall humidity distribution of the target moisture-proof distribution box.

2. The method according to claim 1, characterized in that Extracting the moisture feature image of the target moisture-proof distribution box casing from the moisture image set specifically includes: Preprocessing the wet image set to obtain a preprocessed wet image set; Performing pixel-level difference operation on the preprocessed wet image set and the wet image set to obtain a difference wet image set; The moisture characteristic image of the target moisture-proof distribution box casing is determined according to the difference moisture image set.

3. The method according to claim 1, characterized in that Dividing the image area of ​​the wet feature image into an edge dry area and an edge wet area based on the edge distribution characteristics of each wet image in the wet image set specifically includes: Determining edge distribution characteristics of each wet image in the wet image set; Extracting a wet contour area in the wet feature image according to the edge distribution characteristics of each wet image in the wet image set; Determine the local contrast of each pixel in the wet contour area; Determine the edge dry area by using a preset segmentation threshold and the local contrast of each pixel point in the wet contour area; Determine a plurality of edge wet area blocks according to a preset segmentation threshold and a local contrast of each pixel point in the wet contour area; The image area consisting of all edge wet blocks is regarded as the edge wet area.

4. A humidity monitoring system for a moisture-proof distribution box, which uses the method described in any one of claims 1 to 3 to perform humidity monitoring, characterized in that: The humidity monitoring system of the moisture-proof distribution box includes: An acquisition module is used to collect moisture images of the target moisture-proof distribution box casing to obtain a moisture image set; A processing module, used to extract a moisture feature image of a target moisture-proof distribution box housing from the moisture image set, and divide an image area of ​​the moisture feature image into an edge dry area and an edge wet area based on edge distribution characteristics of each moisture image in the moisture image set; The processing module is further used to obtain temperature-humidity data inside the target moisture-proof distribution box, determine the humidity correlation factor between each edge wet block in the edge wet area and the ambient humidity according to the temperature-humidity data, and map all humidity correlation factors in the edge wet area to the humidity field outside the target moisture-proof distribution box; The processing module is further used to determine the humidity fault points and temperature diffusion coefficients of different monitoring points inside the target moisture-proof distribution box based on the temperature-humidity data, and perform humidity diffusion analysis on each humidity fault point according to all temperature diffusion coefficients to obtain the humidity diffusion threshold inside the target moisture-proof distribution box; An execution module is used to determine the humidity monitoring value of the target moisture-proof distribution box according to the humidity field and the humidity diffusion threshold, and to perform real-time monitoring of the target moisture-proof distribution box through the humidity monitoring value.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the humidity monitoring method for a moisture-proof distribution box according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the humidity monitoring method for a moisture-proof distribution box according to any one of claims 1 to 3 are implemented.

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