A power grid state evaluation method and device, a terminal device and a storage medium

Abstract

The application discloses a kind of power grid state evaluation method, device, terminal equipment and storage medium, wherein, method includes: to each pixel point in the region to be detected of power grid equipment picture, by calculating slope degree and slope value, further calculate amplification data, then carry out convolution processing and binary processing, again calculate to obtain crack annotation graph data, finally according to the crack annotation graph data of all pixel points Statistics abnormal pixel number, further evaluate power grid state.The power grid state evaluation method provided by the application can automatically generate crack annotation graph data according to the power grid equipment picture, and the state of the crack can be determined according to the crack annotation graph data, to further evaluate the state of the power grid.The application is higher in precision compared with artificial visual inspection, saves manpower, improves power grid state evaluation accuracy and efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power transmission and transformation equipment fault identification, and in particular to a power grid state evaluation method and device based on graph data processing, a terminal device and a storage medium. BACKGROUND

[0002] With the continuous expansion of the power grid scale, the continuous development of smart grid technology, information technology and operation and maintenance technology, the state data of power transmission and transformation equipment gradually presents the characteristics of big data such as large volume and multiple types; accurately evaluating the operating state of power transmission and transformation equipment (such as power transformers, high-voltage circuit breakers, GIS, insulators, etc.) of the power grid and timely developing targeted maintenance and operation strategies are of great significance to ensure the safe, reliable and economic operation of the power system. For important power transmission and transformation equipment such as transformers and power boxes that have been exposed to the outdoors for a long time, there is a risk of cracks due to humidity, temperature, soil settlement or even abnormal salinity, which can change the operating state of the power transmission and transformation equipment, and even cause the power grid to operate in an emergency state. Therefore, the crack risk of power transmission and transformation equipment should be an important part of power grid state evaluation. The commonly used crack detection method for power transmission and transformation equipment is visual detection, which relies on the naked eye observation of inspection personnel. This method has low detection accuracy for small cracks and other subtle defects, and requires a large amount of manpower, resulting in low accuracy and efficiency of power grid state evaluation based on crack detection. SUMMARY

[0003] The present application provides a power grid state evaluation method, device, terminal device and storage medium to solve the technical problem of low detection accuracy and high labor cost caused by relying on human visual detection in the prior art.

[0004] To solve the above technical problems, the present application provides a power grid state evaluation method, comprising:

[0005] obtaining a power grid equipment picture; wherein the power grid equipment picture has circled a to-be-detected area where a crack may exist;

[0006] For each pixel point in the to-be-detected area, calculating the slope degree and slope value of the pixel point according to the coordinate information and pixel value of the pixel point; calculating the magnification data of the pixel point according to the slope degree and slope value; performing convolution processing on the magnification data to obtain a convolution result; performing binary processing on the convolution result to obtain binary data; and calculating crack annotation graph data of the pixel point according to the binary data;

[0007] According to the crack annotation map data of all pixel points in the to-be-detected region, the number of abnormal pixel points with a gray value less than a preset gray reference value is counted; if the number of abnormal pixel points is less than a preset first threshold, it is determined that the power grid state is a normal operation state; if the number of abnormal pixel points is not less than the first threshold and less than a preset second threshold, it is determined that the power grid state is an alert state; if the number of abnormal pixel points is not less than the second threshold, it is determined that the power grid state is an emergency state; wherein the first threshold is less than the second threshold.

[0008] As a preferred solution, the coordinate information includes a horizontal coordinate and a vertical coordinate.

[0009] The slope degree and the slope value of the pixel point are calculated according to the coordinate information and the pixel value of the pixel point, including:

[0010] The horizontal direction slope of the pixel point is calculated according to the horizontal coordinate and the pixel value of the pixel point.

[0011] The vertical direction slope of the pixel point is calculated according to the vertical coordinate and the pixel value of the pixel point.

[0012] The slope degree and the slope value of the pixel point are calculated according to the horizontal direction slope and the vertical direction slope.

[0013] The calculation formula of the slope degree is:

[0014]

[0015] In the formula, SL(p) represents the slope degree of the pth pixel point in the to-be-detected region; atan represents the inverse tangent function; ‖·‖ represents the norm function; and LF() represents the filtering function. represents the horizontal direction slope of the pth pixel point in the to-be-detected region. represents the vertical direction slope of the pth pixel point in the to-be-detected region.

[0016] The calculation formula of the slope value is:

[0017]

[0018] In the formula, VSL(p) represents the slope value of the pth pixel point in the to-be-detected region.

[0019] As a preferred solution, the filtering function is a linear filtering function, a mean filtering function, a median filtering function, or a Gaussian filtering function.

[0020] As a preferred solution, the calculation formula of the amplification data is:

[0021]

[0022] In the formula, AMP(p) represents the amplification data of the pth pixel point in the to-be-detected region; VSL(p) represents the slope value of the pth pixel point in the to-be-detected region; SL(p) represents the slope degree of the pth pixel point in the to-be-detected region; and K1 is a preset amplification adjustment coefficient.

[0023] As a preferred solution, the formula of the convolution processing is as follows:

[0024] FEE(p) = conv(AMP(p) NIE) ;

[0025]

[0026] In the formula, FEE(p) represents the convolution result of the pth pixel point in the to-be-detected region; conv() represents a convolution function; NIE represents a convolution kernel; and K2 is a preset convolution kernel adjustment coefficient.

[0027] As a preferred solution, the binary processing is as follows:

[0028] As a preferred solution, the formula for calculating the crack label map data is as follows:

[0029]

[0030] In the formula, LH(p) represents the crack label map data of the pth pixel point in the to-be-detected region; BFEE(p) represents the binary data of the pth pixel point in the to-be-detected region; and max() represents taking a maximum value.

[0031] On the basis of the above embodiment, another embodiment of the present application provides a power grid state evaluation device, comprising a data acquisition module, a data processing module and a state evaluation module.

[0032] The data acquisition module is configured to acquire a power grid equipment picture, wherein the power grid equipment picture has circled a to-be-detected region in which a crack may exist.

[0033] The data processing module is configured to, for each pixel point in the to-be-detected region, calculate a slope degree and a slope value of the pixel point according to coordinate information and a pixel value of the pixel point, calculate amplification data of the pixel point according to the slope degree and the slope value, perform convolution processing on the amplification data to obtain a convolution result, perform binary processing on the convolution result to obtain binary data, and calculate crack label map data of the pixel point according to the binary data.

[0034] The state evaluation module is configured to count the number of abnormal pixel points with a gray value less than a preset gray reference value according to the crack annotation map data of all pixel points in the to-be-detected region; if the number of abnormal pixel points is less than a preset first threshold, it is determined that the power grid state is a normal operation state; if the number of abnormal pixel points is not less than the first threshold and less than a preset second threshold, it is determined that the power grid state is an alert state; and if the number of abnormal pixel points is not less than the second threshold, it is determined that the power grid state is an emergency state; wherein the first threshold is less than the second threshold.

[0035] On the basis of the above-mentioned embodiments, a terminal device is provided in another embodiment of the application, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the power grid state evaluation method described in the above-mentioned embodiments of the application when executing the computer program.

[0036] On the basis of the above-mentioned embodiments, a storage medium is provided in another embodiment of the application, which comprises a stored computer program, wherein the storage medium controls a device where the storage medium is located to execute the power grid state evaluation method described in the above-mentioned embodiments of the application when the computer program is running.

[0037] Compared with the prior art, the embodiments of the application have the following beneficial effects:

[0038] The application calculates the slope degree and the slope value for each pixel point in the to-be-detected region of the power grid equipment picture, further calculates the magnification data, then performs convolution processing and binary processing, further calculates the crack annotation map data, and finally counts the number of abnormal pixel points according to the crack annotation map data of all pixel points, to further evaluate the power grid state. The power grid state evaluation method provided by the application can automatically generate the crack annotation map data according to the power grid equipment picture, determine the state of the crack according to the crack annotation map data, and further evaluate the state of the power grid. The application has higher accuracy than manual visual detection, saves manpower, and improves the accuracy and efficiency of power grid state evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of a power grid state evaluation method provided by an embodiment of the application;

[0040] Figure 2 is a to-be-detected region original picture provided by an embodiment of the application;

[0041] Figure 3 is a crack detection effect picture provided by an embodiment of the application;

[0042] Figure 4It is a structural schematic diagram of a power grid state evaluation device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort fall within the protection scope of the present application.

[0044] Embodiment one

[0045] Please refer to Figure 1 It is a flow schematic diagram of a power grid state evaluation method provided by an embodiment of the present application, comprising:

[0046] S1, obtaining a power grid equipment picture; wherein the power grid equipment picture has a region with possible cracks to be detected which is preliminarily circled by a human being.

[0047] It should be noted that the power grid equipment picture is a photo of power grid transmission and transformation equipment taken by a high-definition camera, and the region with possible cracks to be detected is preliminarily circled by a human being or preliminarily identified by a simple picture recognition method.

[0048] S2, for each pixel point in the region with possible cracks to be detected, calculating a slope degree and a slope value of the pixel point according to coordinate information and a pixel value of the pixel point; calculating magnification data of the pixel point according to the slope degree and the slope value; performing convolution processing on the magnification data to obtain a convolution result; performing binary processing on the convolution result to obtain binary data; and calculating crack annotation map data of the pixel point according to the binary data.

[0049] It should be noted that the crack annotation map data refers to data information used to show cracks, which is used to draw data of the accompanying drawings as shown in Figure 3 .

[0050] In a preferred embodiment, the coordinate information comprises a horizontal coordinate and a vertical coordinate.

[0051] The calculation of the slope degree and the slope value of the pixel point according to the coordinate information and the pixel value of the pixel point comprises:

[0052] calculating a horizontal direction slope of the pixel point according to the horizontal coordinate and the pixel value of the pixel point;

[0053] calculating a vertical direction slope of the pixel point according to the vertical coordinate and the pixel value of the pixel point.

[0054] According to the horizontal direction slope and the vertical direction slope, a slope degree and a slope value of the pixel point are calculated;

[0055] The calculation formula of the slope degree is:

[0056]

[0057] In the formula, SL(p) represents the slope degree of the pth pixel point in the to-be-detected region; atan represents an inverse tangent function; ‖·‖ represents a norm function; and LF() represents a filter function. represents the horizontal direction slope of the pth pixel point in the to-be-detected region; represents the vertical direction slope of the pth pixel point in the to-be-detected region.

[0058] The calculation formula of the slope value is:

[0059]

[0060] In the formula, VSL(p) represents the slope value of the pth pixel point in the to-be-detected region.

[0061] It should be noted that the horizontal direction slope is a ratio of a pixel value change amount of a pixel point relative to a pixel origin to a horizontal coordinate change amount; and the vertical direction slope is a ratio of the pixel value change amount of the pixel point relative to the pixel origin to a vertical coordinate change amount. The pixel origin refers to a pixel point with a coordinate of (0, 0) in a pixel coordinate system. For example, the horizontal direction slope of a pixel point (2, 4) is a pixel value of the pixel point (2, 4) minus a pixel value of the pixel origin (0, 0), and then divided by 2; and the vertical direction slope of the pixel point (2, 4) is the pixel value of the pixel (2, 4) minus the pixel value of the pixel origin (0, 0), and then divided by 4.

[0062] In a preferred embodiment, the filter function is a linear filter function, a mean filter function, a median filter function, or a Gaussian filter function.

[0063] In a preferred embodiment, the calculation formula of the amplification data is:

[0064]

[0065] In the formula, AMP(p) represents the amplification data of the pth pixel point in the to-be-detected region; VSL(p) represents the slope value of the pth pixel point in the to-be-detected region; SL(p) represents the slope degree of the pth pixel point in the to-be-detected region; and K1 is a preset amplification adjustment coefficient.

[0066] It should be noted that the amplification adjustment coefficient K1 is preferably 57.

[0067] In a preferred embodiment, the formula of the convolution processing is:

[0068] FEE(p) = conv(AMP(p) NIE);

[0069]

[0070] In the formula, FEE(p) represents the convolution result of the pth pixel point in the region to be detected; conv() represents a convolution function; NIE represents a convolution kernel; and K2 is a preset convolution kernel adjustment coefficient.

[0071] It should be noted that the convolution function conv() is a one-dimensional convolution function. The convolution kernel adjustment coefficient K2 takes a random number greater than 1 and less than 2.

[0072] The convolution processing on the amplified pixel point data can adjust the contrast of the crack edge, thereby enhancing the clarity and details of the crack, that is, achieving the sharpening and highlighting effects, so that the crack can be better recognized.

[0073] In a preferred embodiment, the binarization processing of the convolution result to obtain the binarization data comprises:

[0074] According to a preset convolution threshold, the convolution result is divided into foreground and background;

[0075] The foreground is taken as the binarization data.

[0076] It should be noted that the preset convolution threshold can be a threshold value at which the inter-class variance of the mean value of the foreground and background pixel points is maximum.

[0077] The determination process of the convolution threshold comprises: firstly, initializing a threshold value T and dividing the convolution result into foreground and background, and counting the number of foreground pixel points in the foreground and the number of background pixel points in the background; according to the number of foreground pixel points and the number of background pixel points, calculating the mean value of the pixel points and the inter-class variance of the mean value of the pixel points; constantly adjusting the threshold value and recalculating the inter-class variance of the mean value of the pixel points in the foreground region and the background region, finding the threshold value at which the inter-class variance of the mean value of the pixel points in the foreground region and the background region is maximum, and taking the threshold value as the convolution threshold.

[0078] In a preferred embodiment, the formula for calculating the crack annotation map data is:

[0079]

[0080] In the formula, LH(p) represents the crack annotation map data of the pth pixel point in the region to be detected; BFEE(p) represents the binarization data of the pth pixel point in the region to be detected; and max() represents the maximum value.

[0081] It should be noted that max(|BFEE(p)|) represents the maximum value of the absolute value of the binary data of all pixel points in the intercepted suspected crack area.

[0082] S3, according to the crack annotation map data of all pixel points in the to-be-detected area, the number of abnormal pixel points with a gray value less than a preset gray reference value is counted; if the number of abnormal pixel points is less than a first threshold, it is determined that the power grid state is a normal operation state; if the number of abnormal pixel points is not less than the first threshold and less than a second threshold, it is determined that the power grid state is an alert state; if the number of abnormal pixel points is not less than the second threshold, it is determined that the power grid state is an emergency state; wherein the first threshold is less than the second threshold.

[0083] It should be noted that the gray value is obtained by performing gray processing on the crack annotation map data, which can be directly realized by calling the function in matlab, and belongs to the existing image gray processing method.

[0084] In order to intuitively see the crack position, the crack annotation map data can be output as picture data and displayed. In combination with the Figure 2 and Figure 3 , Figure 2 The gray image of the original picture of the to-be-detected area of the power grid equipment picture is displayed, and there are fine defects, rust marks and tiny cracks that are difficult to be detected by naked eyes on the surface of the equipment. Figure 3 For the effect picture output by the crack annotation data of the application, it can be seen that there are about 10 black irregular lines with uneven thickness, and there are about 10 cracks on the surface of the detected power grid transmission and transformation equipment, and the detected crack position is located on the left side and the upper side of the middle of the picture, the black line is obvious and easy to be recognized by naked eyes.

[0085] When there are multiple small cracks or fine defects, Figure 3 The analysis result displayed may still not be directly recognized by naked eyes. Therefore, the application can evaluate the power grid state according to the crack annotation map data and the preset threshold, specifically, the number of gray values less than the gray reference value of the crack annotation map data is calculated, when the number less than the gray reference value is less than the first preset threshold, it can be determined that the crack degree of the power grid transmission and transformation equipment is low, and it is considered that the power grid state is a normal operation state; when the number less than the gray reference value is greater than or equal to the first preset threshold and less than the second preset threshold, it can be determined that the crack degree of the power grid transmission and transformation equipment is aggravated, and it is considered that the power grid state is an alert state; when the number less than the gray reference value is greater than or equal to the second preset threshold, it can be determined that the crack degree of the power grid transmission and transformation equipment is serious, and it is considered that the power grid state is an emergency state.

[0086] Since 0 value in the gray image represents black color and 255 value represents white color, the gray reference value should take a value close to 0, for example, can be 0.5 or 1. The first preset threshold value and the second preset threshold value can be flexibly set according to the inspection requirements.

[0087] Embodiment two

[0088] Please refer to Figure 4 A structural schematic diagram of a power grid state evaluation device provided by an embodiment of the application, comprising: a data acquisition module, a data processing module and a state evaluation module;

[0089] The data acquisition module is configured to acquire a power grid equipment picture; wherein the power grid equipment picture has circled a to-be-detected region in which a crack possibly exists;

[0090] The data processing module is configured to, for each pixel point in the to-be-detected region, calculate a slope degree and a slope value of the pixel point according to coordinate information and a pixel value of the pixel point; calculate magnification data of the pixel point according to the slope degree and the slope value; perform convolution processing on the magnification data to obtain a convolution result; perform binary processing on the convolution result to obtain binary data; and calculate crack annotation map data of the pixel point according to the binary data.

[0091] The state evaluation module is configured to, according to the crack annotation map data of all pixel points in the to-be-detected region, count a number of abnormal pixel points whose gray value is less than a preset gray reference value; if the number of abnormal pixel points is less than a preset first threshold value, determine that the power grid state is a normal operation state; if the number of abnormal pixel points is not less than the first threshold value and less than a preset second threshold value, determine that the power grid state is an alert state; and if the number of abnormal pixel points is not less than the second threshold value, determine that the power grid state is an emergency state; wherein the first threshold value is less than the second threshold value.

[0092] Embodiment three

[0093] Correspondingly, an embodiment of the application provides a terminal device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the power grid state evaluation method in the above-mentioned embodiments of the application when executing the computer program.

[0094] Embodiment four

[0095] Correspondingly, an embodiment of the application provides a storage medium, which comprises a stored computer program, wherein the computer program controls a device where the storage medium is located to execute the power grid state evaluation method in the above-mentioned embodiments of the application when running.

[0096] It should be noted that the apparatus embodiments described above are merely illustrative, and the units described as separate units can or can not be physically separate, and the units shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection between the modules in the apparatus embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0098] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The terminal device can include, but is not limited to, a processor and a memory.

[0099] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and connects various parts of the device through various interfaces and lines.

[0100] ​The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, and the like; and the data storage area can store data created according to the use of the mobile phone and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0101] The storage medium is a storage medium, and the computer program is stored in the storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be realized. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0102] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for assessing the state of a power grid, characterized in that, include: Obtain images of power grid equipment; wherein, the areas to be detected that may contain cracks are outlined in the images of the power grid equipment. For each pixel in the region to be detected, the slope and slope value of the pixel are calculated based on its coordinate information and pixel value; the magnified data of the pixel is calculated based on the slope and slope value; the magnified data is convolved to obtain a convolution result; the convolution result is binarized to obtain binarized data; and the crack annotation map data of the pixel is calculated based on the binarized data; wherein, the calculation formula for the crack annotation map data is: ； In the formula, Indicates the first in the region to be detected Crack annotation data per pixel; Indicates the first in the region to be detected Binarized data of each pixel; This indicates taking the maximum value; Based on the crack annotation data of all pixels in the area to be detected, the number of abnormal pixels whose grayscale values ​​are less than a preset grayscale reference value is counted. If the number of abnormal pixels is less than a preset first threshold, the power grid status is determined to be normal operation. If the number of abnormal pixels is not less than the first threshold and is less than a preset second threshold, the power grid status is determined to be alert. If the number of abnormal pixels is not less than the second threshold, the power grid status is determined to be emergency. Wherein, the first threshold is less than the second threshold.

2. The power grid condition assessment method as described in claim 1, characterized in that, The coordinate information includes: horizontal coordinates and vertical coordinates; The step of calculating the slope and slope value of the pixel based on the pixel's coordinate information and pixel value includes: Calculate the horizontal slope of the pixel based on its horizontal coordinates and pixel value; Calculate the vertical slope of the pixel based on its vertical coordinates and pixel value; Calculate the slope degree and slope value of the pixel based on the horizontal slope and the vertical slope; The formula for calculating the slope is as follows: ； In the formula, Indicates the first in the region to be detected The slope of each pixel; Represents the arctangent function; Represents the norm function; Represents the filter function; Indicates the first in the region to be detected The horizontal slope of each pixel; Indicates the first in the region to be detected The vertical slope of each pixel; The formula for calculating the slope value is: ； In the formula, Indicates the first in the region to be detected The slope value of each pixel.

3. The power grid condition assessment method as described in claim 2, characterized in that, The filtering function is a linear filtering function, a mean filtering function, a median filtering function, or a Gaussian filtering function.

4. The power grid condition assessment method as described in claim 1, characterized in that, The formula for calculating the amplified data is: ； In the formula, Indicates the first in the region to be detected Magnified data of each pixel; Indicates the first in the region to be detected The slope value of each pixel; Indicates the first in the region to be detected The slope of each pixel; This is the preset amplification adjustment coefficient.

5. The power grid condition assessment method as described in claim 1, characterized in that, The formula for the convolution process is: ； ； In the formula, Indicates the first in the region to be detected The convolution result of 1 pixel; Represents the convolution function; Represents the convolution kernel; The preset convolution kernel adjustment coefficient.

6. The power grid condition assessment method as described in claim 1, characterized in that, The step of performing binary processing on the convolution result to obtain binary data includes: Based on the preset convolution threshold, the convolution result is divided into foreground and background; The foreground is used as the binarized data.

7. A power grid condition assessment device, characterized in that, include: Data acquisition module, data processing module, and status assessment module; The data acquisition module is used to acquire images of power grid equipment; wherein, the images of power grid equipment have been marked with areas to be detected where cracks may exist; The data processing module is used to, for each pixel in the region to be detected, calculate the slope and slope value of the pixel based on the pixel's coordinate information and pixel value; calculate the magnified data of the pixel based on the slope and slope value; perform convolution processing on the magnified data to obtain a convolution result; perform binarization processing on the convolution result to obtain binarized data; and calculate the crack annotation map data of the pixel based on the binarized data; wherein, the calculation formula for the crack annotation map data is: ； In the formula, Indicates the first in the region to be detected Crack annotation data per pixel; Indicates the first in the region to be detected Binarized data of each pixel; This indicates taking the maximum value; The status assessment module is used to count the number of abnormal pixels whose grayscale values ​​are less than a preset grayscale reference value based on the crack annotation map data of all pixels in the area to be detected; if the number of abnormal pixels is less than a preset first threshold, the power grid status is determined to be a normal operating status; if the number of abnormal pixels is not less than the first threshold and is less than a preset second threshold, the power grid status is determined to be a warning status; if the number of abnormal pixels is not less than the second threshold, the power grid status is determined to be an emergency status; wherein, the first threshold is less than the second threshold.

8. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power grid condition assessment method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the power grid condition assessment method as described in any one of claims 1 to 6.

Images