Method and device for evaluating surface stability in subsidence areas for building photovoltaic power stations

Abstract

The present application discloses a method and device for evaluating the surface stability of a subsidence area for the construction of a photovoltaic power station, and relates to the field of geological engineering image recognition technology. The method comprises: obtaining a monitoring image of the surface of the subsidence area, and identifying the monitoring image to obtain pixels corresponding to the crack position and the degree of crack; according to the pixels corresponding to the crack position and the degree of crack, establishing an energy function for characterizing the association between the pixels corresponding to the crack position and the degree of crack; according to the energy function, dividing a target element map from the monitoring image; according to the target element map, determining the surface stability result. The present invention solves the problem that the common image processing algorithm in the prior art can identify cracks, but it is difficult to give an accurate crack discrimination result according to the degree of cracking, thereby failing to provide an accurate basis for judging the surface stability according to the results of crack detection, and can improve the accuracy and automation level of crack detection.

Description

Technical Field

[0001] The present application relates to the technical field of geological engineering image recognition, and in particular to a method and device for evaluating the surface stability of a subsidence area for building a photovoltaic power station. Background Art

[0002] As an important renewable energy facility, the construction of photovoltaic power stations needs to ensure the stability of the ground foundation to ensure the long-term and effective operation of photovoltaic modules. The settlement and unevenness of the surface may cause the photovoltaic modules to tilt or be damaged, thus affecting the power generation efficiency and economy of the power station. Coal mining subsidence area refers to the area where the surface settles or collapses due to the mining of underground coal seams during the coal mining process. The formation of subsidence areas has a significant impact on the surface structure and environment, including problems such as uneven ground settlement, surface cracks and changes in soil strength. Traditional mining methods often lead to a serious decrease in the surface stability of the subsidence area, which poses a severe challenge to any facilities built on it, especially photovoltaic power stations.

[0003] Crack safety monitoring based on image analysis has become a widely used method. This technology uses computer vision and image processing algorithms to analyze images of construction sites and identify potential safety hazards or risks. However, in some cases, different degrees of cracking will not cause stability damage to the surface, and you only need to continue to pay attention to the cracks. Some degrees of cracks will affect the stability of the surface and need to be repaired or processed in time. Ordinary image processing algorithms can identify cracks, but it is difficult to give accurate crack identification results based on the degree of cracking, resulting in the problem that it is not possible to provide accurate surface stability judgment basis based on the crack detection results. Summary of the invention

[0004] The present application provides a method and device for evaluating the surface stability of a subsidence area for building a photovoltaic power station, which can improve the accuracy and automation level of crack detection.

[0005] The present application provides a method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, including:

[0006] Acquire monitoring images of the surface of the subsidence area, and identify the monitoring images to obtain pixels corresponding to crack positions and crack degrees;

[0007] According to the pixels corresponding to the crack positions and the crack degree, establishing an energy function for characterizing the association between the pixels corresponding to the crack positions and the crack degree;

[0008] According to the energy function, dividing a target element graph from the monitoring image;

[0009] Based on the target element map, a surface stability result is determined.

[0010] Further, in the above-mentioned method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, the crack degree includes target crack width information, target crack area information, target crack depth information and target crack strength information;

[0011] The step of establishing an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree comprises:

[0012] Determine the probability distribution of pixels in the monitoring image associated with the crack degree according to the pixels corresponding to the crack position, and obtain a probability model according to the probability distribution;

[0013] The energy function based on the probability model is established according to the proportion of the target crack width information, the target crack area information, the target crack depth information and the target crack strength information in the monitoring image.

[0014] Furthermore, the above-mentioned method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, wherein the probability distribution of pixels of the monitoring image associated with the degree of the crack is determined according to the pixels corresponding to the crack position to obtain a probability model, includes:

[0015] Determine the color value and grayscale value of the crack in the monitoring image according to the target crack strength information, and obtain a color statistical model and a grayscale statistical model according to the color value and the grayscale value;

[0016] A probability model is established according to the color statistical model and the grayscale statistical model.

[0017] Furthermore, in the above-mentioned method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, the probability model is:

[0018]

[0019] in, To monitor the image P i The probability that a pixel is related to the degree of crack, is the balance parameter, For the The color value of a pixel, To judge the events related to pixels and crack degree based on color statistical model, To determine the probability of pixels and crack levels based on the color statistical model, No. The gray value of a pixel, To judge the events related to pixels and crack degree based on grayscale statistical model, It is the probability of judging the relationship between pixels and crack degree based on the grayscale statistical model.

[0020] Furthermore, in the above-mentioned method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station, the energy function based on the probability model is established according to the proportion of the target crack width information, the target crack area information, the target crack depth information and the target crack intensity information in the monitoring image, including:

[0021] Obtaining an adjustment factor according to the proportion of the target crack width information, the target crack area information, the target crack depth information, and the target crack strength information in the monitoring image;

[0022] The energy function is established according to the number of adjustment factors.

[0023] Furthermore, the above-mentioned method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station further comprises:

[0024] The adjustment factor is calculated by a first formula, wherein the first formula is:

[0025] AF=αR width +βR area +γR depth +δR strength

[0026] Among them, AF represents the adjustment factor number, R width is the crack width ratio, R area is the crack area ratio, R depth is the crack depth ratio, R strength is the proportion of crack intensity, α, β, γ and δ are weight coefficients, which indicate the influence of each crack information on the adjustment factor number.

[0027] Furthermore, in the above-mentioned method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, the step of dividing a target element map from the monitoring image according to the energy function comprises:

[0028] Using the energy function as a loss function, training the deep learning model to obtain a trained deep learning model;

[0029] The monitoring image of the surface of the subsidence area is input into the trained deep learning model to obtain a target element map.

[0030] The present application also provides a device for evaluating the surface stability of a subsidence area for building a photovoltaic power station, comprising:

[0031] An acquisition and recognition module is used to acquire a monitoring image of the surface of the subsidence area, and recognize the monitoring image to obtain pixels corresponding to the crack position and the crack degree;

[0032] A first construction module is used to establish an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree;

[0033] A second construction module is used to divide a target element map from the monitoring image according to the energy function;

[0034] The result determination module is used to determine the surface stability result according to the target element map.

[0035] The present application also provides a computer-readable storage medium, in which a plurality of instructions are stored. The instructions are suitable for being loaded by a processor to execute any of the above-mentioned methods for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station.

[0036] The present application also provides an electronic device, including a processor and a memory, wherein the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used for the steps in any of the above-mentioned methods for evaluating the surface stability of a subsidence area for building a photovoltaic power station.

[0037] The present application provides a method and device for evaluating the surface stability of a subsidence area for building a photovoltaic power station. The present application first monitors the pixels where the cracks are located to determine whether the image contains cracks, thereby performing preliminary identification and screening, and eliminating invalid images, thereby improving the monitoring efficiency of the image. Subsequently, an energy function for characterizing the degree of association between pixels and crack degrees in the image is established based on the degree of cracks and the pixels where the cracks are located; the location and degree of cracks can be more accurately identified, and a target element map can be divided from the monitoring image based on the energy function and a deep learning algorithm. The cracking degree reflected by the target element map is used to determine whether it will affect the surface stability, thereby improving the accuracy and automation level of crack detection, and avoiding the problem that ordinary image processing algorithms can identify cracks, but it is difficult to give accurate crack discrimination results based on the cracking degree of the cracks, resulting in the problem that accurate surface stability judgment basis cannot be provided based on the results of crack detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The technical solution and other beneficial effects of the present application will be made apparent by describing in detail the specific implementation methods of the present application in conjunction with the accompanying drawings.

[0039] Figure 1 A flow chart of a method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station provided in an embodiment of the present application.

[0040] Figure 2 A schematic diagram of the structure of a device for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station provided in an embodiment of the present application.

[0041] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0043] The embodiment of the present application provides a method, device, storage medium and electronic device for evaluating the surface stability of a subsidence area for building a photovoltaic power station. The embodiment of the present application provides a device for evaluating the surface stability of a subsidence area for building a photovoltaic power station, which can be integrated in an electronic device, which can be a terminal, a server and other devices, wherein the terminal can include a tablet computer, a laptop computer, a personal computer (PC, Personal Computer), a micro processing box, or other devices.

[0044] See also Figure 1 , Figure 1 A flow chart of a method for evaluating the surface stability of a subsidence area for building a photovoltaic power station provided in an embodiment of the present application, which is applied to an electronic device, and the method for evaluating the surface stability of a subsidence area for building a photovoltaic power station comprises the following steps:

[0045] S1, obtaining a monitoring image of the surface of the subsidence area, and identifying the monitoring image to obtain pixels corresponding to the crack position and the crack degree.

[0046] The crack degree includes target crack width information, target crack area information, target crack depth information, target crack strength information, etc. The target crack width information is the width of the crack in the monitoring image, the target crack area information is the total area of ​​the crack, the target crack depth information is the depth of the crack, and the target crack strength information is the strength or severity of the crack (which may include the contrast or other quantitative characteristics of the crack).

[0047] S2, establishing an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree.

[0048] In one embodiment, step S2 comprises the following steps:

[0049] S21, determining the probability distribution of pixels in the monitoring image associated with the crack degree according to the pixels corresponding to the crack position, and obtaining a probability model according to the probability distribution.

[0050] Step S21 may include the following steps:

[0051] S211, determining the color value and grayscale value of the crack in the monitoring image according to the target crack strength information, and obtaining a color statistical model and a grayscale statistical model according to the color value and the grayscale value;

[0052] S212, establishing a probability model according to the color statistical model and the grayscale statistical model.

[0053] Specifically, the probability model is:

[0054]

[0055] in, To monitor the image P i The probability that a pixel is related to the degree of crack, is the balance parameter, For the The color value of a pixel, To judge the events related to pixels and crack degree based on color statistical model, To determine the probability of pixels and crack levels based on the color statistical model, No. The gray value of a pixel, To judge the events related to pixels and crack degree based on grayscale statistical model, It is the probability of judging the relationship between pixels and crack degree based on the grayscale statistical model.

[0056] In this embodiment, the probability model is based on the two dimensions of pixel color and grayscale to judge the degree of cracks. For example, when the target crack intensity information is greater, its pixel color and grayscale tend to have a greater contrast with the normal surface. Therefore, in this embodiment, the crack intensity can be characterized by the two dimensions of pixel color and grayscale, and these pixels are used as seed pixels to establish color statistical models and grayscale statistical models to quantify the degree of cracks. These models can be used to describe the distribution of color and grayscale features in monitoring images. Based on these statistical models, the probability of pixels being related to danger signs under the condition of color, or the probability of pixels being related to the degree of cracks under the condition of color, can be determined. According to the actual situation, the weights of the two factors can be adjusted by the balance parameter α.

[0057] S22, establishing an energy function based on a probability model according to the proportion of the target crack width information, the target crack area information, the target crack depth information and the target crack strength information in the monitoring image.

[0058] Step S22 may include the following steps:

[0059] S221, obtaining an adjustment factor according to the proportion of the target crack width information, the target crack area information, the target crack depth information and the target crack strength information in the monitoring image.

[0060] S222: Establish an energy function according to the number of adjustment factors.

[0061] The energy function generally includes a region term and a boundary term. The region term is used to characterize the penalty of paying pixel values ​​to the foreground and background, and the boundary term represents the boundary information of the segmented image. In this embodiment, the energy function is used to characterize the degree of association between pixels and crack degrees. The specific information of the crack is extracted from the image, and the proportion of this information in the image is calculated. Then, adjustment factors are calculated based on these proportions, and these adjustment factors are used to optimize the energy function. The optimized energy function will more accurately reflect the actual characteristics of the crack, thereby improving the accuracy of crack detection and evaluation.

[0062] Specifically, the crack width accounts for R width The average crack width can be compared with the total width of the image. area The total crack area can be compared with the total image area. depth The average crack depth can be compared with the image depth range. strength The average value of the crack intensity can be compared with the range of intensity values ​​in the image.

[0063] According to these proportion information, the adjustment factors are calculated. These adjustment factors will be used to adjust the energy function so that it better reflects the actual situation of the crack.

[0064] The adjustment factor is obtained by the following calculation formula:

[0065] AF=αR width +βR area +γR depth +δR strength

[0066] Among them, AF represents the adjustment factor number, R width is the crack width ratio, R area is the crack area ratio, R depth is the crack depth ratio, R strengthis the proportion of crack intensity, α, β, γ and δ are weight coefficients, which indicate the influence of each crack information on the adjustment factor number.

[0067] Among them, the target crack width information, target crack area information, target crack depth information and target crack strength information are crack parameters that characterize the cracks that just affect the surface stability. The target crack width information, target crack area information, target crack depth information and target crack strength information are used as the threshold information of the cracks. The actual crack information is compared with the target crack width information, target crack area information, target crack depth information and target crack strength information to determine whether the cracks are at a level that affects the surface stability.

[0068] The various characteristics of cracks (such as width, area, depth, and intensity) are combined with the probability distribution of image pixels, and an energy function based on the probability model is established and converted into a mathematical model, which makes crack detection and analysis systematic and improves the accuracy and efficiency of crack detection.

[0069] S3, divides the target feature map from the monitoring image according to the energy function.

[0070] In one embodiment, step S3 includes the following steps:

[0071] S31, using the energy function as the loss function, training the deep learning model to obtain a trained deep learning model;

[0072] S32, inputting the monitoring image of the surface of the subsidence area into the trained deep learning model to obtain the target feature map.

[0073] In this example, the trained deep learning model is applied to a new monitoring image. The model processes the input image and outputs a classification label or crack intensity value for each pixel. Based on these predicted values, a target feature map can be generated, showing the location and characteristics of the cracks in the image.

[0074] S4, determining the surface stability result according to the target element map.

[0075] The target feature map is the segmentation result of the image, usually represented as a binary map or a multi-channel map, each channel represents a different crack feature. Therefore, according to the location and characteristics of the cracks in the image, it is determined whether the cracks will affect the stability of the surface.

[0076] In an embodiment of the present application, a monitoring image of the surface of the subsidence area is first obtained, and the pixels and degree of cracks in the monitoring image are identified. The present invention first monitors the pixels where the cracks are located, determines whether the image contains cracks, and then performs preliminary identification and screening, and removes invalid images, thereby improving the monitoring efficiency of the image. Subsequently, according to the degree of cracks and the pixels where the cracks are located, an energy function for characterizing the degree of association between pixels and cracks in the image is established, which can more accurately identify the location and degree of cracks, and according to the energy function, a target element map is divided from the monitoring image based on a deep learning algorithm, and the cracking degree of the cracks reflected by the target element map is used to determine whether it will affect the stability of the surface, thereby improving the accuracy and automation level of crack detection, avoiding the problem that ordinary image processing algorithms can identify cracks, but it is difficult to give accurate crack discrimination results according to the degree of cracks, resulting in the problem that accurate ground stability judgment basis cannot be provided according to the results of crack detection.

[0077] Specifically, by establishing an energy function to quantify the relationship between the degree of cracks and pixels in the image, the specific location and severity of the cracks can be more accurately identified, and the information in the image can be converted into numerical data. By monitoring the degree of cracking, it is possible to understand which cracks must be maintained immediately and which are sustainable cracks and surface areas. It is possible to more reasonably allocate detection and maintenance resources, focus on the areas that need to be handled most, and improve resource utilization efficiency. And through the quantitative relationship, clear standards can be provided, so that subsequent deep learning models can be trained and optimized more effectively. And by establishing a quantitative relationship, the crack detection process can be more efficiently automated. The algorithm can automatically evaluate the degree of cracks based on this quantitative relationship without manual intervention, which improves processing speed and efficiency.

[0078] According to the method described in the above embodiment, this embodiment will be further described from the perspective of a surface stability evaluation device for a subsidence area for building a photovoltaic power station. The surface stability evaluation device for a subsidence area for building a photovoltaic power station can be implemented as an independent entity or integrated in an electronic device. The electronic device can be a terminal, a server or other device, wherein the terminal can include a tablet computer, a laptop computer, a personal computer (PC), a micro processing box, or other devices.

[0079] See also Figure 2 , Figure 2 The present invention specifically describes a device for evaluating the surface stability of a subsidence area for building a photovoltaic power station provided in an embodiment of the present invention, which is applied to an electronic device. The device for evaluating the surface stability of a subsidence area for building a photovoltaic power station may include:

[0080] The acquisition and recognition module is used to acquire the monitoring image of the surface of the subsidence area and recognize the monitoring image to obtain the pixels corresponding to the crack position and the crack degree;

[0081] The first building module is used to establish an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree;

[0082] The second building module is used to divide the target element map from the monitoring image according to the energy function;

[0083] The result determination module is used to determine the surface stability result according to the target element map.

[0084] During specific implementation, the above modules and / or units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above modules and / or units can refer to the previous method embodiments. The specific beneficial effects that can be achieved can also refer to the beneficial effects in the previous method embodiments, which will not be repeated here.

[0085] In addition, an embodiment of the present application also provides an electronic device, which may be a computer, a tablet computer, or other device.

[0086] The electronic device can implement the steps in any embodiment of the method for evaluating the surface stability of a subsidence area for building a photovoltaic power station provided in the embodiments of the present application. Therefore, it can achieve the beneficial effects that can be achieved by any method for evaluating the surface stability of a subsidence area for building a photovoltaic power station provided in the embodiments of the present application. For details, please see the previous embodiments and will not be repeated here.

[0087] Figure 3 The specific structural block diagram of the electronic device provided in the embodiment of the present invention is shown, and the electronic device can be used to implement the surface stability evaluation method for the subsidence area for building a photovoltaic power station provided in the above embodiment. The electronic device 500 can be a terminal, a server, etc., wherein the terminal can include a tablet computer, a laptop computer, a personal computer (PC, Personal Computer), a micro processing box, or other devices.

[0088] The RF circuit 510 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices. The RF circuit 510 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity module (SIM) cards, memories, etc. The RF circuit 510 can communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The above-mentioned wireless networks may use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE802.11g and / or IEEE802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, even those that have not yet been developed.

[0089] The memory 520 can be used to store software programs and modules, such as the corresponding program instructions / modules in the above embodiments. The processor 580 executes various functional applications and data processing by running the software programs and modules stored in the memory 520, that is, realizing the functions of taking pictures with the front camera, processing the captured images, and switching the display color of the display content on the display screen. The memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 520 may further include a memory remotely arranged relative to the processor 580, and these remote memories may be connected to the electronic device 500 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0090] The input unit 530 may be used to receive input digital or character information, and generate a keyboard and a mouse related to user settings and function control.

[0091] The display unit 540 may be used to display information input by the user or information provided to the user and various graphical user interfaces, which may be composed of graphics, text, icons, videos, and any combination thereof. The display unit 540 may include a display panel 541, which may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like.

[0092] The audio circuit 560, the speaker 561, and the microphone 562 can provide an audio interface between the user and the electronic device 500. The audio circuit 560 can transmit the electrical signal converted from the received audio data to the speaker 561, which is converted into a sound signal for output; on the other hand, the microphone 562 converts the collected sound signal into an electrical signal, which is received by the audio circuit 560 and converted into audio data, and then the audio data is output to the processor 580 for processing, and then sent to another terminal through the RF circuit 510, or the audio data is output to the memory 520 for further processing. The audio circuit 560 may also include an earplug jack to provide communication between an external headset and the electronic device 500.

[0093] The electronic device 500 can help the user receive requests, send information, etc. through the transmission module 570 (such as a Wi-Fi module), which provides the user with wireless broadband Internet access. Although the transmission module 570 is shown in the figure, it can be understood that it is not a necessary component of the electronic device 500 and can be omitted as needed without changing the essence of the invention.

[0094] The processor 580 is the control center of the electronic device 500. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 520, and calling data stored in the memory 520, it executes various functions of the electronic device 500 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 580 may include one or more processing cores; in some embodiments, the processor 580 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 580.

[0095] The electronic device 500 also includes a power supply 590 (such as a battery) for supplying power to various components. In some embodiments, the power supply can be logically connected to the processor 580 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 590 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0096] Although not shown, the electronic device 500 also includes a camera (such as a front camera, a rear camera), a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and are configured to be executed by one or more processors. One or more programs include instructions for performing the following operations:

[0097] Acquire monitoring images of the surface of the subsidence area, and identify the monitoring images to obtain pixels corresponding to crack positions and crack degrees;

[0098] According to the pixels corresponding to the crack positions and the crack degree, establishing an energy function for characterizing the association between the pixels corresponding to the crack positions and the crack degree;

[0099] According to the energy function, dividing a target element graph from the monitoring image;

[0100] Based on the target element map, a surface stability result is determined.

[0101] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above modules can be found in the previous method embodiments, which will not be repeated here.

[0102] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium, which stores a plurality of instructions, and the instructions can be loaded by a processor to execute the steps of any embodiment of the method for evaluating the surface stability of a subsidence area for building a photovoltaic power station provided in an embodiment of the present invention.

[0103] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0104] Since the instructions stored in the storage medium can execute the steps in any embodiment of the method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station provided in the embodiments of the present invention, the beneficial effects that can be achieved by any method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station provided in the embodiments of the present invention can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0105] The above is a detailed introduction to the surface stability evaluation method, device, storage medium and electronic device for the construction of a photovoltaic power station in a subsidence area provided in an embodiment of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for evaluating the surface stability of a subsidence area for building a photovoltaic power station, characterized in that: The following steps are involved: Acquire monitoring images of the surface of the subsidence area, and identify the monitoring images to obtain pixels corresponding to crack positions and crack degrees; According to the pixels corresponding to the crack positions and the crack degree, an energy function for characterizing the association between the pixels corresponding to the crack positions and the crack degree is established; wherein the crack degree includes target crack width information, target crack area information, target crack depth information and target crack strength information; The step of establishing an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree includes: determining the probability distribution of the pixels associated with the crack degree in the monitoring image according to the pixels corresponding to the crack position, and obtaining a probability model according to the probability distribution; and establishing the energy function based on the probability model according to the proportion of the target crack width information, the target crack area information, the target crack depth information, and the target crack strength information in the monitoring image; Determining the probability distribution of pixels in the monitoring image associated with the crack degree according to the pixels corresponding to the crack position, and obtaining a probability model according to the probability distribution, includes: determining the color value and grayscale value of the crack in the monitoring image according to the target crack intensity information, obtaining a color statistical model and a grayscale statistical model according to the color value and the grayscale value; and establishing a probability model according to the color statistical model and the grayscale statistical model; According to the energy function, dividing a target element graph from the monitoring image; Based on the target element map, a surface stability result is determined.

2. The method for evaluating the surface stability of a subsidence area for building a photovoltaic power station according to claim 1, characterized in that: The probability model is: in, To monitor the image P i The probability that a pixel is related to the degree of crack, is the balance parameter, For the The color value of a pixel, To judge the events related to pixels and crack degree based on color statistical model, To determine the probability of pixels and crack levels based on the color statistical model, No. The gray value of a pixel, To judge the events related to pixels and crack degree based on grayscale statistical model, It is the probability of judging the relationship between pixels and crack degree based on the grayscale statistical model.

3. The method for evaluating the surface stability of a subsidence area for building a photovoltaic power station according to claim 1, characterized in that: The energy function based on the probability model is established according to the proportion of the target crack width information, the target crack area information, the target crack depth information and the target crack strength information in the monitoring image, including: Obtaining an adjustment factor according to the proportion of the target crack width information, the target crack area information, the target crack depth information, and the target crack strength information in the monitoring image; According to the number of adjustment factors, the energy function based on the probability model is established.

4. The method for evaluating the surface stability of a subsidence area for building a photovoltaic power station according to claim 3 is characterized in that: The method further comprises: The adjustment factor is calculated by a first formula, wherein the first formula is: AF=αR width +βR area +γR depth +δR strength Among them, AF represents the adjustment factor number, R width is the crack width ratio, R area is the crack area ratio, R depth is the crack depth ratio, R strength is the proportion of crack intensity, α, β, γ and δ are weight coefficients, which indicate the influence of each crack information on the adjustment factor number.

5. The method for evaluating the surface stability of a subsidence area for building a photovoltaic power station according to claim 1, characterized in that: The step of dividing a target element graph from the monitoring image according to the energy function includes: Using the energy function as a loss function, training the deep learning model to obtain a trained deep learning model; The monitoring image of the surface of the subsidence area is input into the trained deep learning model to obtain a target element map.

6. A device for evaluating the surface stability of a subsidence area for building a photovoltaic power station, the device for evaluating the surface stability of a subsidence area for building a photovoltaic power station is used to implement the method for evaluating the surface stability of a subsidence area for building a photovoltaic power station according to claim 1, characterized in that: include: An acquisition and recognition module is used to acquire a monitoring image of the surface of the subsidence area, and recognize the monitoring image to obtain pixels corresponding to the crack position and the crack degree; A first construction module is used to establish an energy function for characterizing the association between the pixels corresponding to the crack position and the crack degree according to the pixels corresponding to the crack position and the crack degree; A second construction module is used to divide a target element map from the monitoring image according to the energy function; The result determination module is used to determine the surface stability result according to the target element map.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method for evaluating the surface stability of a subsidence area for constructing a photovoltaic power station as described in any one of claims 1 to 5.

8. An electronic device, characterized in that: It includes a processor and a memory, the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is used to execute the steps in the method for evaluating the surface stability of a subsidence area for building a photovoltaic power station as described in any one of claims 1 to 5.

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