A photovoltaic power station equipment detection method, system, device and medium

Abstract

The present application relates to the technical field of photovoltaic equipment detection, and particularly relates to a photovoltaic power station equipment detection method, system, device and medium, obtaining monitoring video files of each electronic equipment of a photovoltaic power station, frame dividing the monitoring video files in time sequence to obtain a plurality of electronic equipment key component images; performing pixel decomposition on the electronic equipment key component images according to time coordinates; performing key component vibration image analysis according to pixel points of the decomposed images and electronic equipment key component regions; and detecting the health status of the key components of the electronic equipment according to the vibration image analysis result. The method can accurately detect and reduce operation and maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic equipment testing technology, specifically to a method, system, equipment, and medium for testing photovoltaic power station equipment. Background Technology

[0002] With the rapid development of the photovoltaic industry, the scale of photovoltaic power plants is constantly expanding. The tightness of the components and the stability of the support structure in photovoltaic power plants are crucial for their normal operation. However, traditional testing technologies face many challenges in meeting this demand. In the field of vibration measurement, contact measurement (such as accelerometers) is a commonly used method, and it has a certain application foundation in the vibration measurement of mechanical structures. In addition, non-contact measurement methods such as laser interferometry and digital speckle method are also used in some mechanical structure inspection scenarios. These technologies have their specific measurement principles and technical means in their respective application scenarios. Meanwhile, with the development of video technology, video data acquisition equipment is widely deployed in photovoltaic power plants, providing a hardware foundation for video-based measurement technologies.

[0003] Photovoltaic power plants have a large number of modules and supports that are widely distributed, making it difficult for traditional inspection and monitoring methods to cover them comprehensively, resulting in blind spots in the detection of module tightness and support deformation.

[0004] For detecting mechanical structural issues such as component tightness and support deformation, appropriate measurement methods are required. Traditional vibration measurement methods have limitations. Contact measurements (such as accelerometers) are limited in terms of measurement location and number, while non-contact measurements such as laser interferometry and digital speckle methods increase testing complexity due to the need for additional equipment. These methods are difficult to apply to photovoltaic power plants due to cost and complexity factors. Summary of the Invention The technical problem to be solved by the present invention is to provide a method, system, equipment and medium for testing photovoltaic power station equipment, which addresses the shortcomings of the prior art and solves the technical problems of limitations and inaccuracies in the testing of photovoltaic power stations.

[0005] The objective of this invention is achieved through the following technical solutions: In a first aspect, the present invention provides a method for testing photovoltaic power station equipment, comprising: Acquire monitoring video files of various electronic devices in a photovoltaic power station, divide the monitoring video files into frames according to time sequence, and obtain images of several key components of electronic devices; The images of the key components of the electronic device are decomposed into pixels according to the time coordinate; Analysis of vibration images of key components is performed based on the pixels of the decomposed images and the key component regions of electronic devices. The health status of key components of electronic devices can be detected based on the results of vibration image analysis. As a further improvement of the present invention, the step of acquiring monitoring video files of various electronic devices in a photovoltaic power station, and dividing the monitoring video files into frames according to time sequence to obtain images of several key components of the electronic devices, specifically includes: Establish a video monitoring system connected to various electronic devices in the photovoltaic power station to acquire monitoring video files of these devices. Determine the frame rate of the video file, where the frame rate represents the number of frames per second; Starting from the first frame of the video file, the video file is divided into individual image frames at fixed time intervals in chronological order; and each frame is numbered in chronological order during the frame division process. Image recognition is performed on the numbered image frames to obtain the key electronic components contained in each image frame; The portion containing key components is segmented from the whole frame image to obtain several images of key components of electronic devices.

[0006] As a further improvement of the present invention, the step of acquiring monitoring video files of various electronic devices in a photovoltaic power station, and dividing the monitoring video files into frames according to time sequence to obtain images of several key components of the electronic devices, specifically includes: Establish a video monitoring system connected to various electronic devices in the photovoltaic power station to acquire monitoring video files of these devices. Determine the frame rate of the video file, where the frame rate represents the number of frames per second; Starting from the first frame of the video file, the video file is divided into individual image frames at fixed time intervals in chronological order; and each frame is numbered in chronological order during the frame division process. Image recognition is performed on the numbered image frames to obtain the key electronic components contained in each image frame; The portion containing key components is segmented from the whole frame image to obtain several images of key components of electronic devices.

[0007] As a further improvement of the present invention, the image of the key components of the electronic device is decomposed into pixels according to the time coordinate, specifically including: Spatial filtering is performed on images containing key components of electronic devices to obtain images at several different resolution scales. The images at different resolution scales are subjected to time-domain bandpass filtering to obtain image frames with a set frequency band. Pixel-level bandpass filtering is performed on the three-dimensional pixel units of the image frame with a set frequency band to enhance the signal at the frequency of interest; Differential approximation and linear amplification are performed on the signals in each frequency band of the image frame to obtain the motion information of the image frame. The motion information of the image frame is then combined with the corresponding image frame to form new image data.

[0008] As a further improvement of the present invention, the spatial filtering processing of the image containing key components of the electronic device specifically includes: Pyramid multi-resolution decomposition is performed on a series of consecutive image frames of key components of the electronic device to obtain image frames at different resolution levels; Laplacian pyramid processing is performed on the image frames after multi-resolution pyramid decomposition to obtain multi-size edge and shape description information of the image frames.

[0009] As a further improvement of the present invention, the Laplacian pyramid processing of the image frames after pyramid multi-resolution decomposition specifically includes: Starting from each frame of the original video, using the original image as the bottom layer of the pyramid, the upper layers of the pyramid are constructed using the Gaussian kernel function; A difference image is obtained by subtracting the image of the previous layer (after upsampling and convolution) from the image of a certain layer in a Gaussian pyramid. The difference image contains information about the changes in edge and shape descriptions of the current layer relative to the image of the previous layer.

[0010] As a further improvement of the present invention, based on the pixels of the decomposed image and the key component regions of the electronic device, vibration image analysis of the key components is performed, specifically including: The motion characteristics during the vibration process are analyzed, and vibration characteristics are analyzed based on these motion characteristics: statistical analysis is performed on the displacement changes of pixels in each image frame to determine the vibration amplitude of key components; the degree of change in shape complexity of key component regions is analyzed based on the edge and shape description information of key component regions of electronic devices. Spectral analysis is performed on the images corresponding to key components: vibration signals are extracted from the image frames corresponding to key components; the spectrum of the extracted vibration signals is calculated using Fourier transform; a spectrum diagram is obtained based on the spectrum calculation results, and the spectral characteristics are analyzed using the spectrum diagram. Calculate the phase difference of the vibration signal between key components and analyze the transformation law of the phase difference.

[0011] As a further improvement of the present invention, detecting the health status of key components of electronic devices based on vibration image analysis results specifically includes: The vibration frequency information of key components is extracted based on the vibration image analysis results; the vibration frequency information of the component is compared with the standard vibration frequency range; if the vibration frequency information is not within the standard vibration frequency range, the electronic device corresponding to the key component is abnormal. Based on the vibration image analysis results, the amplitude values ​​of key components are determined to be within the standard amplitude range. If they are within the standard amplitude range, the electronic equipment corresponding to the key component is normal; otherwise, the electronic equipment corresponding to the key component is abnormal. The phase difference between key components is extracted from the vibration image analysis results. If the phase difference is abnormal, it indicates that there is a problem with the connection between the key components or a fault in the internal transmission mechanism.

[0012] Secondly, the present invention also provides a photovoltaic power station equipment testing system for implementing the above-mentioned photovoltaic power station equipment testing method, comprising: The data acquisition module is used to acquire monitoring video files of various electronic devices in the photovoltaic power station, and divide the monitoring video files into frames according to time sequence to obtain images of several key components of electronic devices. The data decomposition module is used to decompose the images of key components of the electronic device into pixels according to the time coordinate. The data analysis module is used to analyze the vibration images of key components based on the pixels of the decomposed images and the key component areas of electronic devices. The equipment detection module is used to detect the health status of key components of electronic equipment based on vibration image analysis results.

[0013] Thirdly, the present invention also provides a computer-readable storage medium for storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform the above-described photovoltaic power station equipment detection method.

[0014] Fourthly, the present invention also provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for performing the above-described photovoltaic power plant equipment testing method.

[0015] The beneficial effects of this invention are as follows: The photovoltaic power station equipment detection method of this invention acquires monitoring video files of various electronic devices through the video monitoring system of the photovoltaic power station. These video files should contain clear images of key components of the electronic devices and cover a sufficiently long time period for subsequent analysis. Multi-layer pixel decomposition is performed on the framed images of key components of the electronic devices. Decomposing the image into smaller pixel units allows for more precise analysis of the vibration of key components. Pixel decomposition can be performed according to time coordinates and image coordinates to obtain more comprehensive pixel information. Through pixel decomposition and vibration image analysis, the health status of key components can be more accurately determined, which helps improve the accuracy and reliability of equipment fault diagnosis. Based on the vibration image analysis results, more reasonable equipment maintenance strategies can be formulated. Through real-time monitoring and early warning, preventive maintenance can be carried out before equipment failure occurs. This helps reduce equipment maintenance costs and improve the overall economic benefits of the photovoltaic power station. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the photovoltaic power station equipment testing method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the photovoltaic power station equipment testing system in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0019] The present invention provides a method, system, equipment, and medium for testing photovoltaic power plant equipment. The method mainly includes: Acquire monitoring video files of various electronic devices in the photovoltaic power station, divide the monitoring video files into frames according to time sequence to obtain images of several key components of the electronic devices; these video files should contain clear images of the key components of the electronic devices and cover a sufficiently long time period for subsequent analysis.

[0020] Images of key components of electronic devices are decomposed into multiple pixel units based on time and image coordinates. This decomposition allows for more precise analysis of the vibration of key components. Pixel decomposition is performed using both time and image coordinates to obtain more comprehensive pixel information.

[0021] Based on the pixels of the decomposed image and the key component regions of the electronic device, the vibration image of the key component is analyzed; the vibration of the key component is detected by comparing the pixel changes at different time points; the vibration features of the key component are extracted using image processing algorithms (such as edge detection, contour extraction, etc.) and then quantitatively analyzed.

[0022] Vibration image analysis is used to detect the health status of critical components in electronic devices. Vibration characteristics are used to determine if any abnormalities or malfunctions exist in these critical components.

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] Example 1 like Figure 1 As shown in the figure, the specific implementation method of the photovoltaic power station equipment testing method provided by the present invention is as follows.

[0025] The monitoring video files of each electronic device in the photovoltaic power station are acquired, and the monitoring video files are divided into frames according to time sequence to obtain images of several key components of the electronic devices.

[0026] First, a video monitoring system is established, connected to all electronic devices in the photovoltaic power station, to obtain monitoring video files of each device. This video monitoring system should have the capability to comprehensively cover all electronic devices, ensuring complete recording of their operating status. After obtaining the monitoring video files, to facilitate subsequent detailed analysis of key components of the electronic devices, this embodiment processes the monitoring video files frame by frame in chronological order.

[0027] Specifically, a comprehensive video monitoring system is deployed within the photovoltaic power station. This system comprises multiple high-definition cameras, carefully positioned to ensure complete coverage of all electronic equipment within the station, including but not limited to photovoltaic modules, inverters, combiner boxes, and distribution boxes. Each camera is connected to data acquisition equipment, enabling real-time capture of the electronic equipment's operation and conversion of this footage into digital video signals. These video signals are stored in dedicated storage devices, forming monitoring video files. To ensure the integrity and accuracy of the video files, high frame rates and high resolutions are employed during acquisition, and real-time verification and error correction are performed on the video signals to prevent data loss or errors.

[0028] During the framing process, it is crucial to ensure the accuracy and continuity of the framing, so that each video frame accurately reflects the state of the electronic equipment at the corresponding moment. Through precise algorithms and programs, the video file is sequentially broken down into individual image frames according to a strict chronological order. These image frames encompass the state information of the photovoltaic power station's electronic equipment at different times. After this framing operation, images of several key components of the electronic equipment are obtained. These images contain visual information about key components of the electronic equipment, such as photovoltaic modules, inverters, and combiner boxes.

[0029] After reading the monitoring video file from the storage device, professional video processing software or algorithms are used to perform frame segmentation. First, the frame rate of the video file is determined; the frame rate represents the number of frames per second, which is a crucial basis for frame segmentation. Following chronological order, starting from the first frame of the video file, the video is divided into individual image frames at fixed time intervals (determined by the frame rate). During frame segmentation, each frame needs to be numbered to clarify its temporal order within the video. For each obtained frame, further image recognition and segmentation operations are performed to identify key electronic components, such as solar cells in photovoltaic modules, power modules in inverters, and fuses in combiner boxes. Then, these portions containing key components are segmented from the full frame image, resulting in several images specifically targeting these key electronic components, preparing for subsequent analysis.

[0030] Secondly, the images of key components of electronic devices are decomposed into multiple pixel layers according to time coordinates and image coordinates. Specifically, this includes: Spatial filtering is performed on images containing key components of electronic devices to obtain images at several different resolution scales. Pyramid multi-resolution decomposition is performed on a series of consecutive image frames of key components of the electronic device to obtain image frames at different resolution levels. First, the video sequence consists of a series of consecutive image frames. Pyramid multi-resolution decomposition is a method for constructing multi-scale representations of images. We start with each frame of the original video, using the original image as the bottom layer of the pyramid (i.e., the highest resolution layer).

[0031] Then, the upper layers of the pyramid are gradually constructed using specific algorithms (e.g., convolution and downsampling operations using a Gaussian kernel). With each layer upwards, the image resolution decreases, resulting in image representations at different resolution levels. This process is analogous to building a pyramid, with the finest image at the bottom and becoming increasingly coarser as you move upwards, but still containing information at different scales. This multi-resolution decomposition provides comprehensive image structural information for subsequent analysis, capturing features from local details to overall contours at different scales.

[0032] Each frame of the video is processed using a Laplacian pyramid to obtain multi-scale edge and shape descriptions. For each frame, Laplacian pyramid processing is performed based on the already obtained multi-resolution pyramid decomposition. The Laplacian pyramid is obtained by performing specific operations on the images of two adjacent layers in a Gaussian pyramid. Specifically, it involves subtracting the image of the previous layer (which has been upsampled and convolved) from the image of a certain layer in the Gaussian pyramid. This difference image contains information about the changes in edges and shapes of the current layer relative to the previous layer. By performing this operation on each frame of the video, edge and shape descriptions of each frame at different scales can be obtained. These descriptions highlight the structural information such as edges and contours in the image, which is helpful for subsequent analysis of changes in the shape and structure of objects, because in vibration analysis, changes in the edges and shapes of objects are often related to the vibration state.

[0033] Temporal bandpass filtering is performed on several images at different resolution scales to obtain image frames with a defined frequency band; specifically including: Temporal bandpass filtering is applied to the image at each scale to obtain several frequency bands of interest. After spatial filtering, we obtain images with rich edge and shape information at different scales. For each scale image, we treat it as a time-varying signal sequence (because video consists of a series of frames arranged in chronological order).

[0034] Time-domain bandpass filtering is a method of filtering signals in the time domain. It allows signals within a specific frequency range to pass through while blocking signals of other frequencies. We set the parameters of the bandpass filter as needed, such as the center frequency and bandwidth. By applying time-domain bandpass filtering to an image at each scale, we can extract several frequency bands of interest from the temporal variations of the image. These frequency bands may be related to the vibration frequencies of objects, and different vibration frequencies may correspond to different vibration modes or states of the object.

[0035] Pixel-level bandpass filtering is performed on the three-dimensional pixel units of the image frame with a set frequency band to enhance the signal at the frequency of interest; Specifically, this involves performing pixel-level temporal bandpass filtering on each three-dimensional pixel unit (which can be simply considered as a pixel in a two-dimensional image) at each scale, enhancing the signal at frequencies of interest and filtering out noise at frequencies of no interest. Here, the three-dimensional pixel unit is the basic building block of the image. In the image at each scale, pixel-level temporal bandpass filtering is performed on each three-dimensional pixel unit. This means that for each pixel, its time-varying signal is filtered. This further enhances the signal intensity at frequencies of interest, which are related to the vibration characteristics of the object being detected. Simultaneously, the filtering operation effectively filters out noise signals at frequencies of no interest, improving signal quality and analyzability. This pixel-level filtering process allows for more detailed processing of each element in the image, uncovering more precise vibration-related information.

[0036] Differential approximation and linear amplification are performed on the signals in each frequency band of the image frame to obtain the motion information of the image frame. The motion information of the image frame is then combined with the corresponding image frame to form new image data.

[0037] Specifically, after time-domain filtering, we obtained signals in different frequency bands. For each frequency band, we used Taylor series for differential approximation. Taylor series is a method of representing a function as an infinite power series.

[0038] The principle of Taylor series is used to approximate the changes in frequency band signals. The Taylor series expansion of the signal at a certain point is calculated, and a finite number of terms are taken to approximate the actual value of the signal. Then, the differential approximation result is linearly amplified. For example, if we want to amplify by a factor of two, we multiply the approximation result by 2. This enhances the signal amplitude, making the originally weak vibration-related signal changes more obvious, facilitating subsequent analysis and observation.

[0039] After performing Taylor series differential approximation and linear amplification on the frequency band signal, the motion information of the image frame is obtained. The motion information of the image frame is related to the motion of the object (i.e., vibration-related motion in vibration analysis).

[0040] Next, these motion-related signals after filtering undergo motion amplification. This motion amplification is based on the previously obtained amplified signal features, and a specific algorithm is used to further amplify the motion components in the signal. Then, the amplified motion signal is superimposed back onto the original three-dimensional pixel units. The purpose of this is to reintegrate the amplified motion information into the original image features, making the motion (vibration) of objects in the image more visually significant, so as to better observe and analyze the vibration of objects.

[0041] After completing the previous amplification and filtering operations, we obtained processed image elements (including spatial filtering, temporal filtering, and amplification filtering), which contain amplified and optimized information related to object vibration.

[0042] The process of image synthesis involves recombining these processed elements into a complete image according to certain rules. These rules ensure that the position and relationship of each element in the image correspond to the original image, while also guaranteeing that the magnified vibration information is correctly reflected in the synthesized image. Through image synthesis, we obtain an image that more clearly reflects the vibration of the object, providing more intuitive and effective image data for subsequent spectral and phase difference analysis of key components.

[0043] Based on the pixels of the decomposed image and the key component areas of the electronic device, vibration images of the key components are analyzed; the health status of the key components of the electronic device is detected based on the vibration image analysis results.

[0044] Specifically, the motion characteristics during the vibration process are analyzed, and vibration characteristics are analyzed based on the motion characteristics. This includes: statistical analysis of the displacement changes of pixels in each image frame to determine the vibration amplitude of key components; and analysis of the degree of change in shape complexity of key component regions based on edge and shape description information of key component regions of electronic devices.

[0045] This significant improvement is reflected in our ability to more clearly observe the minute displacements, shape changes, and other motion characteristics of objects during vibration. For example, minute vibrations at the edges of objects that were previously difficult to detect in the original video become easier to observe in images processed with image magnification technology, providing a better visual basis for further in-depth analysis of the vibration characteristics of objects.

[0046] The vibration frequency information of key components is extracted based on the vibration image analysis results; the vibration frequency information of the component is compared with the standard vibration frequency range; if the vibration frequency information is not within the standard vibration frequency range, the electronic device corresponding to the key component is abnormal. Spectral analysis is performed on the images corresponding to key components: vibration signals are extracted from the image frames corresponding to key components; the spectrum of the extracted vibration signals is calculated using Fourier transform; a spectrum diagram is obtained based on the spectrum calculation results, and the spectral characteristics are analyzed using the spectrum diagram.

[0047] Based on the vibration image analysis results, the amplitude values ​​of key components are determined to be within the standard amplitude range. If they are within the standard amplitude range, the electronic equipment corresponding to the key component is normal; otherwise, the electronic equipment corresponding to the key component is abnormal. This embodiment also calculates the phase difference of vibration signals between key components and analyzes the transformation law of the phase difference. The phase difference reflects the relative relationship between different signals in time. In vibration analysis, the phase difference between key components can provide information about the vibration coordination of an object, structural connection relationships, etc. By calculating the phase difference of vibration signals between key components and analyzing the variation law of the phase difference, we can determine the interrelationship between key components and the overall health of the structure. For example, if the phase difference between two key components suddenly changes, it may mean that there is a problem with the connection structure between them or that there are other factors affecting vibration coordination.

[0048] The phase difference between key components is extracted from the vibration image analysis results. If the phase difference is abnormal, it indicates that there is a problem with the connection between the key components or a fault in the internal transmission mechanism. Example 2 like Figure 2 As shown, this embodiment provides a photovoltaic power station equipment testing system for implementing the photovoltaic power station equipment testing method in Embodiment 1. The system includes: The data acquisition module is used to acquire monitoring video files of various electronic devices in the photovoltaic power station, and divide the monitoring video files into frames according to time sequence to obtain images of several key components of electronic devices. The data decomposition module is used to decompose the images of key components of the electronic device into pixels according to the time coordinate. The data analysis module is used to analyze the vibration images of key components based on the pixels of the decomposed images and the key component areas of electronic devices. The equipment detection module is used to detect the health status of key components of electronic equipment based on vibration image analysis results.

[0049] Example 3 In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0050] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0051] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0052] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the photovoltaic power station equipment testing method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Acquire monitoring video files of various electronic devices in a photovoltaic power station, divide the monitoring video files into frames according to time sequence, and obtain images of several key components of electronic devices; The images of the key components of the electronic device are decomposed into pixels according to the time coordinate; Analysis of vibration images of key components is performed based on the pixels of the decomposed images and the key component regions of electronic devices. The health status of key components of electronic devices can be detected based on the results of vibration image analysis.

[0053] Example 4 Please see Figure 3 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fluid composition calculation method in the reservoir stimulation wellbore of this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the computing system constituted in this embodiment. To avoid repetition, these details are not elaborated here.

[0054] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 3 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0055] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0056] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the computer device 60.

[0057] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0058] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0059] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

Claims

1. A method for testing photovoltaic power station equipment, characterized in that, include: Acquire monitoring video files of various electronic devices in a photovoltaic power station, divide the monitoring video files into frames according to time sequence, and obtain images of several key components of electronic devices; The images of key components of the electronic device are decomposed into multiple pixels according to time coordinates and image coordinates; Based on the pixels of the decomposed image and the key component areas of the electronic device, the vibration images of the key components are analyzed. The health status of key components of electronic devices can be detected based on the results of vibration image analysis.

2. The photovoltaic power station equipment testing method according to claim 1, characterized in that, The process of acquiring monitoring video files of various electronic devices in a photovoltaic power station, and then dividing the monitoring video files into frames according to time sequence to obtain images of several key components of the electronic devices, specifically includes: Establish a video monitoring system connected to various electronic devices in the photovoltaic power station to acquire monitoring video files of these devices. Determine the frame rate of the video file, where the frame rate represents the number of frames per second; Starting from the first frame of the video file, the video file is divided into individual image frames at fixed time intervals in chronological order; and each frame is numbered in chronological order during the frame division process. Image recognition is performed on the numbered image frames to obtain the key electronic components contained in each image frame; The portion containing key components is segmented from the whole frame image to obtain several images of key components of electronic devices.

3. The photovoltaic power station equipment testing method according to claim 1, characterized in that, The images of key components of the electronic device are decomposed into pixels according to the time coordinate, specifically including: Spatial filtering is performed on images containing key components of electronic devices to obtain images at several different resolution scales. The images at different resolution scales are subjected to time-domain bandpass filtering to obtain image frames with a set frequency band. Pixel-level bandpass filtering is performed on the three-dimensional pixel units of the image frame with a set frequency band to enhance the signal at the frequency of interest; Differential approximation and linear amplification are performed on the signals in each frequency band of the image frame to obtain the motion information of the image frame. The motion information of the image frame is then combined with the corresponding image frame to form new image data.

4. The photovoltaic power station equipment testing method according to claim 3, characterized in that, The spatial filtering process for images containing key components of electronic devices specifically includes: Pyramid multi-resolution decomposition is performed on a series of consecutive image frames of key components of the electronic device to obtain image frames at different resolution levels; Laplacian pyramid processing is performed on the image frames after multi-resolution pyramid decomposition to obtain multi-size edge and shape description information of the image frames.

5. The photovoltaic power station equipment testing method according to claim 4, characterized in that, The Laplacian pyramid processing of the image frames after multi-resolution pyramid decomposition specifically includes: Starting from each frame of the original video, using the original image as the bottom layer of the pyramid, the upper layers of the pyramid are constructed using the Gaussian kernel function; A difference image is obtained by subtracting the image of the previous layer (after upsampling and convolution) from the image of a certain layer in a Gaussian pyramid. The difference image contains information about the changes in edge and shape descriptions of the current layer relative to the image of the previous layer.

6. The photovoltaic power station equipment testing method according to claim 1, characterized in that, Based on the pixels of the decomposed image and the key component regions of the electronic device, the vibration images of the key components are analyzed, specifically including: The motion characteristics during the vibration process are analyzed, and vibration characteristics are analyzed based on these motion characteristics: statistical analysis is performed on the displacement changes of pixels in each image frame to determine the vibration amplitude of key components; the degree of change in shape complexity of key component regions is analyzed based on the edge and shape description information of key component regions of electronic devices. Spectral analysis is performed on the images corresponding to key components: vibration signals are extracted from the image frames corresponding to key components; the spectrum of the extracted vibration signals is calculated using Fourier transform; a spectrum diagram is obtained based on the spectrum calculation results, and the spectral characteristics are analyzed using the spectrum diagram. Calculate the phase difference of the vibration signal between key components and analyze the transformation law of the phase difference.

7. The photovoltaic power station equipment testing method according to claim 1, characterized in that, The health status of key components of electronic equipment is detected based on vibration image analysis results, specifically including: The vibration frequency information of key components is extracted based on the vibration image analysis results; the vibration frequency information of the component is compared with the standard vibration frequency range; if the vibration frequency information is not within the standard vibration frequency range, the electronic device corresponding to the key component is abnormal. Based on the vibration image analysis results, the amplitude values ​​of key components are determined to be within the standard amplitude range. If they are within the standard amplitude range, the electronic equipment corresponding to the key component is normal; otherwise, the electronic equipment corresponding to the key component is abnormal. The phase difference between key components is extracted from the vibration image analysis results. If the phase difference is abnormal, it indicates that there is a problem with the connection between the key components or a fault in the internal transmission mechanism.

8. A photovoltaic power station equipment testing system, used to implement the photovoltaic power station equipment testing method according to any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire monitoring video files of various electronic devices in the photovoltaic power station, and divide the monitoring video files into frames according to time sequence to obtain images of several key components of electronic devices. The data decomposition module is used to decompose the images of key components of the electronic device into pixels according to the time coordinate. The data analysis module is used to analyze the vibration images of key components based on the pixels of the decomposed images and the key component areas of electronic devices. The equipment detection module is used to detect the health status of key components of electronic equipment based on vibration image analysis results.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the photovoltaic power plant equipment testing method according to any one of claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the photovoltaic power plant equipment testing method of any one of claims 1 to 7.

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