Photovoltaic line damage detection method and system in rainy day environment

Abstract

The invention discloses a photovoltaic line damage detection method and system in a rainy day environment, and the method comprises the steps: receiving real-time current and real-time voltage, and obtaining the temperature and humidity; inputting the real-time current, the real-time voltage, the temperature and the humidity into a conductivity calculation function to obtain a plurality of conductivities; drawing a conductivity distribution curve graph according to the plurality of conductivities; determining a plurality of abnormal electric conductivities of which the electric conductivities are not in a preset interval in the electric conductivity distribution diagram; and marking a plurality of damage points corresponding to the plurality of abnormal electric conductivities in the electric conductivity distribution diagram to obtain a photovoltaic line detection diagram, and sending the photovoltaic line detection diagram to a detection terminal. According to the method, the accuracy and efficiency of photovoltaic line damage detection in a rainy day environment are improved.

Description

Technical Field

[0001] This application belongs to the field of data detection, and particularly relates to a method and system for detecting photovoltaic wire breakage in rainy weather. Background Art

[0002] With the intensification of the global energy crisis and the increasing demand for environmental protection, photovoltaic power generation, as a clean and renewable energy source, has received extensive attention and application. China's photovoltaic industry has achieved remarkable development results in the past few years, and the installed capacity of photovoltaic power generation has maintained rapid growth for many consecutive years. However, the stability and reliability of photovoltaic power generation systems are directly related to the development of the entire photovoltaic industry. Photovoltaic wire breakage detection, as a key link to ensure the normal operation of photovoltaic power generation systems, its current situation and future development have attracted much attention.

[0003] In related technologies, generally, the combination of an unmanned aerial vehicle (UAV) and an infrared thermal imager is used to detect photovoltaic wire breakage. The flexibility of the UAV and its property of being able to carry other devices can make the scope of photovoltaic wire breakage detection wider, and the UAV has good flexibility and can hover and fly arbitrarily. The UAV can quickly patrol a photovoltaic power station, find abnormal areas, and then use the infrared thermal imager for fine detection to find out the problems.

[0004] However, when using an infrared thermal imager to detect photovoltaic wires, the detected temperature data may be within a certain range. In normal weather, the temperature data at the break point is very obvious, but in rainy weather or other weather conditions that affect temperature detection, raindrops at the break will reduce its temperature. Also, because the infrared thermal imager relies on detecting temperature data, if the above situation occurs, the temperature data of a certain section may be inaccurate, and the temperature data of this section may appear very average, making it difficult to accurately find the specific location of the break point, and further manual investigation is required, which reduces the accuracy and efficiency of photovoltaic wire breakage detection. Summary of the Invention

[0005] This application provides a method and system for detecting photovoltaic wire breakage in rainy weather, which is used to improve the accuracy and efficiency of detecting photovoltaic wire breakage in rainy weather.

[0006] In a first aspect, this application provides a method for detecting photovoltaic wire breakage in rainy weather, which receives real-time current and real-time voltage, and obtains temperature and humidity. The real-time current is the current of a preset signal source, one end of the preset signal source is connected to the first end of the photovoltaic wire, the other end of the preset signal source is grounded, the real-time voltage is the voltage detected by a voltage detection device, the voltage detection device is connected to the second end of the photovoltaic wire, and the first end and the second end are the two ends of the photovoltaic wire; Input the real-time current, real-time voltage, temperature, and humidity into a conductivity calculation function to obtain several conductivities; Draw a conductivity distribution curve according to several conductivities; Determine several abnormal conductivities whose conductivities in the conductivity distribution map are not within the preset range; Mark several break points corresponding to the several abnormal conductivities in the conductivity distribution map to obtain a photovoltaic line detection map, and send the photovoltaic line detection map to the detection terminal.

[0007] By adopting the above technical solution, the system receives the real-time current and voltage data at both ends of the photovoltaic line, and synchronously obtains the ambient temperature and humidity information, comprehensively considering the influence of electrical parameters and meteorological factors on the conductivity of the photovoltaic line. By inputting multi-source heterogeneous data into the conductivity calculation function, the conductivity distribution at different positions of the photovoltaic line can be accurately calculated, objectively reflecting the spatial variation law of the conductivity performance. The drawing and analysis of the conductivity distribution curve can visually identify the areas with abnormal conductivity, accurately locate the positions of the break points, and provide a reliable basis for subsequent maintenance and replacement. The functions of accurate diagnosis, visual display and remote alarm of the photovoltaic line breakage fault are realized, and the accuracy and efficiency of the photovoltaic line breakage detection in rainy weather are greatly improved.

[0008] Combined with some embodiments of the first aspect, in some embodiments, before receiving the real-time current and real-time voltage and obtaining the temperature and humidity, the method further includes: Receive the real-time infrared image sent by the infrared imaging device, and the infrared imaging device is installed on the unmanned aerial vehicle; Determine the real-time temperature distribution according to the real-time infrared image; When it is determined that there is an abnormal temperature greater than the preset temperature value in the real-time temperature distribution, send the first instruction to the preset signal source and the voltage detection device respectively to make the preset signal source and the voltage detection device operate.

[0009] By adopting the above technical solution, using the infrared thermal imaging technology, the photovoltaic line can be scanned quickly and over a large area. By identifying the high-temperature areas in the infrared image, the positions of the suspected fault points can be initially judged, providing a key direction for subsequent refined detection. The automation of fault pre-screening is realized, and the breadth, depth, efficiency and accuracy of the breakage detection are improved.

[0010] Combined with some embodiments of the first aspect, in some embodiments, the conductivity calculation function is: In the formula, σ i is any one of several conductivities, I is the real-time current, V is the real-time voltage, A is the cross-sectional area of the photovoltaic line, x i is the position of the first end point, x i-1is the position of the second endpoint, x is the independent variable, α(x) is the temperature correction function at position x, β(x) is the temperature influence function at position x, T(x) is the real-time temperature at position x, T0 is the reference temperature, γ(x) is the humidity correction function at position x, H(x) is the real-time relative humidity at position x, H0 is the reference humidity, ζ(x) is the periodic influence function at position x, and L is the length of the photovoltaic line between the first endpoint and the second endpoint.

[0011] By adopting the above technical solution, the conductivity calculation function is used to quantitatively evaluate the conductivity performance of the photovoltaic line, with strong reliability and scientificity. Based on the measured current and voltage parameters, this function takes into account various physical quantities affecting conductivity, establishes a quantitative relationship between conductivity and electrical parameters, temperature, humidity, etc., and can objectively reflect the true conductivity state of the photovoltaic line under complex environments, providing a reliable data reference for subsequent positioning of the damage point.

[0012] Combined with some embodiments of the first aspect, in some embodiments, a conductivity distribution curve graph is drawn according to a number of conductivities, specifically including: Determine the detection length and position resolution between the first endpoint and the second endpoint; Establish a position coordinate system according to the detection length and position resolution; Mark a number of conductivities in the position coordinate system to obtain a corresponding number of data points; Connect a number of data points with a curve to obtain the conductivity distribution curve graph.

[0013] By adopting the above technical solution, the detection length determines the inspection coverage of the line, and the position resolution determines the spatial density of the sampling points. A high position resolution can carefully reflect the jumps and fluctuations of conductivity and improve the detection probability of the damage point; while an appropriate resolution is conducive to compressing the data volume and improving the calculation and transmission efficiency. Based on the geometric mapping of the position coordinate system, the discrete conductivity points are mapped to continuous spatial positions, establishing a corresponding relationship between conductivity and the actual position of the line. By drawing the conductivity scatter plot, the spatial aggregation trend of abnormal points can be displayed, the specific position of the damage point can be inferred, and the accuracy of detecting the damage of the photovoltaic line in rainy weather is improved.

[0014] Combined with some embodiments of the first aspect, in some embodiments, determining the detection length and the preset position resolution between the first endpoint and the second endpoint specifically includes: Determine the photovoltaic line labels corresponding to the first endpoint and the second endpoint; Match the photovoltaic line labels in the preset database to obtain the detection length between the first endpoint and the second endpoint; Determine the number of a number of conductivities and determine the position resolution according to the number of a number of conductivities.

[0015] By adopting the above technical solutions, the tags of each photovoltaic line are matched. The tags include line length, conductor model, laying path, etc. When conducting line detection, only the tag information at both ends of the line needs to be read, and the length parameters of the line can be quickly indexed and retrieved from the database without repeated measurement and calculation. At the same time, this method determines the detection position resolution according to the number of conductivity sampling points. The conductivity sampling points are the key factors affecting the resolution selection. The more the number of sampling points, the smaller the distance between adjacent sampling points, the higher the resolution, and the local changes of conductivity can be finely depicted; on the contrary, the fewer the number of sampling points, the lower the resolution, and only the overall trend of conductivity can be reflected. Appropriate position resolution can improve the accuracy of detecting breakage points.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after marking a number of breakage points corresponding to a number of abnormal conductivities in the conductivity distribution map to obtain a photovoltaic line detection map and sending the photovoltaic line detection map to the detection terminal, the method further includes: Determining whether there is a preset building within the preset danger range of the number of breakage points; If there is, an alarm message is sent to the detection terminal.

[0017] By adopting the above technical solutions, photovoltaic lines are prone to insulation aging, strand breakage and other breakage faults due to being exposed to harsh outdoor environments for a long time. On the one hand, these breakage points will reduce the power transmission efficiency and reliability and affect the power generation performance of the photovoltaic power station; on the other hand, electrical sparks, arc discharges, etc. caused by the decline of insulation performance at the breakage points may affect the buildings around the line, causing secondary disasters such as fires and equipment damage, threatening the life and property safety of the people nearby. If there is a preset building within the preset danger range of the number of breakage points, then an alarm message is sent to the detection terminal to prompt the detection terminal to take corresponding measures to avoid dangerous events.

[0018] Combined with some embodiments of the first aspect, in some embodiments, determining whether there is a preset building within the preset danger range of the number of breakage points specifically includes: Obtaining the map information within the preset danger range of the number of breakage points; Determining whether there is a preset building according to the map information.

[0019] By adopting the above technical solutions, obtaining the map information around the breakage points, analyzing the composition and distribution of map elements, and judging whether there are important buildings, providing more refined and objective data support for the risk judgment of breakage points, and further improving the pertinence and feasibility of early warning information.

[0020] Second aspect, an embodiment of the present application provides a photovoltaic wire breakage detection system in a rainy environment. The system includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code. The computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] Third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the system, enabling the above system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on the system, enabling the electronic device to execute the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for detecting breakage of photovoltaic wires in a rainy environment. The system receives real-time current and voltage data at both ends of the photovoltaic wire, and synchronously obtains environmental temperature and humidity information, comprehensively considering the influence of electrical parameters and meteorological factors on the electrical conductivity of the photovoltaic wire. By inputting multi-source heterogeneous data into the conductivity calculation function, the conductivity distribution at different positions of the photovoltaic wire can be accurately calculated, objectively reflecting the spatial variation law of the electrical conductivity. Through the drawing and analysis of the conductivity distribution curve graph, areas with abnormal conductivity can be visually identified, and the position of the breakage point can be accurately located, providing a reliable basis for subsequent repair and replacement. The functions of precise diagnosis, visual display, and remote alarm of photovoltaic line breakage faults are realized, greatly improving the accuracy and efficiency of detecting breakage of photovoltaic wires in a rainy environment.

[0024] 2. The detection length of the method for detecting breakage of photovoltaic wires in a rainy environment provided by the present application determines the inspection coverage of the line, and the position resolution determines the spatial density of the sampling points. High position resolution can reflect the jump and fluctuation of conductivity in detail, improving the detection probability of breakage points; while appropriate resolution is beneficial to compressing the data volume and improving the calculation and transmission efficiency. Based on the geometric mapping of the position coordinate system, the discrete conductivity points are mapped to continuous spatial positions, establishing the corresponding relationship between conductivity and the actual position of the line. By drawing the conductivity scatter plot, the spatial aggregation trend of abnormal points can be displayed, inferring the specific position of the breakage point, and improving the accuracy of detecting breakage of photovoltaic wires in a rainy environment.

[0025] 3. This application provides a method for detecting damage to photovoltaic wires in rainy weather. It matches the labels of each photovoltaic wire, which include line length, wire type, laying path, etc. When conducting line detection, only the label information at both ends of the line needs to be read, and the length parameters of the line can be quickly indexed and retrieved from the database, eliminating the need for repeated measurement and calculation. At the same time, this method determines the detection position resolution based on the number of conductivity sampling points. The conductivity sampling points are the key factors affecting the resolution selection. The more sampling points there are, the smaller the spacing between adjacent sampling points, the higher the resolution, and the local changes in conductivity can be finely depicted; conversely, the fewer sampling points there are, the lower the resolution, and only the overall trend of conductivity can be reflected. An appropriate position resolution can improve the accuracy of detecting damage points. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic flow chart of a method for detecting damage to photovoltaic wires in rainy weather according to an embodiment of this application.

[0027] Figure 2 is another schematic flow chart of a method for detecting damage to photovoltaic wires in rainy weather according to an embodiment of this application.

[0028] Figure 3 is a schematic structural diagram of an entity device of a system for detecting damage to photovoltaic wires in rainy weather provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and appended claims of this application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless clearly indicated otherwise in the context. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0031] As a clean and renewable energy source, photovoltaic power generation has received extensive attention and application against the backdrop of the increasingly severe global energy crisis and environmental protection requirements. China's photovoltaic industry has achieved remarkable development results in the past few years, with the installed capacity of photovoltaic power generation maintaining rapid growth for several consecutive years. However, the stability and reliability of the photovoltaic power generation system are directly related to the healthy development of the entire photovoltaic industry, and the damage problem of photovoltaic lines is one of the key factors affecting the normal operation of the photovoltaic power generation system.

[0032] Currently, the detection of photovoltaic line damage mainly uses the method of equipping an unmanned aerial vehicle (UAV) with an infrared thermal imager. Due to its flexibility and the ability to carry a variety of devices, the UAV can conduct large-scale and all-round inspections of photovoltaic lines, quickly detecting abnormal areas; then, the infrared thermal imager is used to conduct fine detection of the abnormal areas to find the specific location of the damage point. This method has improved the efficiency and accuracy of photovoltaic line damage detection to a certain extent, but there are still some deficiencies.

[0033] The infrared thermal imager mainly relies on detecting temperature anomalies around the damage point to judge the damage situation. Under normal weather conditions, due to the increased resistance at the damage point, the generated heat will make its temperature significantly higher than the surrounding area, making it easy to identify and locate. However, under adverse weather conditions such as rain, the cooling effect of rain may cover up the temperature anomaly at the damage point, resulting in errors in the temperature data detected by the infrared thermal imager and making it difficult to accurately determine the specific location of the damage point. In this case, manual further investigation and verification are often required, increasing the time cost and labor cost of detection and reducing the accuracy and efficiency of detection.

[0034] To address the above problems, a new method that can overcome the influence of adverse weather and improve the accuracy and efficiency of photovoltaic line damage detection is needed. This method should be able to accurately and quickly identify and locate the damage point under special weather conditions such as rain, reduce the need for manual investigation, and improve the operation and maintenance efficiency and reliability of photovoltaic lines. To solve the above technical problems, this application provides a method for detecting photovoltaic line damage in rainy weather, which is used to improve the accuracy and efficiency of detecting photovoltaic line damage in rainy weather.

[0035] The following combines Figure 1 , and describes a method for detecting photovoltaic line damage in rainy weather in an embodiment of this application: Please refer to Figure 1 , which is a schematic flowchart of a method for detecting photovoltaic line damage in rainy weather in an embodiment of this application.

[0036] S101. Receive the real-time infrared image sent by the infrared imaging device; When detecting damage to a photovoltaic circuit in a rainy environment, the system first receives real-time infrared image data sent by an infrared imaging device on a drone. The infrared imaging device uses uncooled focal plane array technology and can continuously and stably image the target area under adverse weather conditions to obtain high-quality infrared thermal images.

[0037] For the flight route of the drone, it can be a flight route preset by the detection terminal. The drone conducts inspections on the photovoltaic lines according to the preset flight route. For example, the infrared imaging device collects infrared images of the photovoltaic lines at a speed of 30 frames per second and sends the image data to the system of the ground monitoring center through a wireless transmission module. The infrared images received by the system are 640×480 pixels, the temperature resolution of each pixel is 0.1℃, and the temperature measurement range is -20℃ to 150℃.

[0038] The system receives a frame of infrared image with a timestamp of 2023-05-12, 14:30:25.000, and the image file name is "IR_20230512_143025000.jpg". This image covers an area of 500m×500m in the photovoltaic power station and contains 20 photovoltaic lines, and the length of each line is about 400m. Pixel points at different positions in the image show different brightness and colors, representing different temperature values.

[0039] By receiving real-time infrared images, the system can continuously monitor the temperature distribution of the photovoltaic lines, providing a data basis for subsequent identification and location of damage points. Infrared imaging technology overcomes the problems of poor imaging quality and low contrast of visible light imaging under adverse weather conditions and can obtain thermal characteristic information of the photovoltaic lines all-weather and all-day.

[0040] S102. Determine the real-time temperature distribution according to the real-time infrared image; After receiving the real-time infrared image of the photovoltaic line, the system processes and analyzes the infrared image, extracts effective information reflecting the temperature distribution characteristics of the photovoltaic line, and prepares for the determination of the damage point.

[0041] For example, the system can first preprocess the infrared image, including operations such as image denoising, enhancement, and correction, to improve the image quality and temperature measurement accuracy. Then, the system uses a thermal map stitching algorithm to perform spatial registration and fusion on infrared images taken at different times and from different perspectives to generate a large-scale thermal image covering the entire photovoltaic power station.

[0042] Next, the system performs temperature calibration on the spliced thermal image, establishing a correspondence between the image pixel values and the actual temperature values to obtain the actual temperature data for each pixel point. The system adopts an adaptive thermal map segmentation algorithm. According to the spatial distribution characteristics of temperature, the thermal image is divided into several temperature regions, and the temperature statistical parameters of each region are extracted, such as the highest temperature, the lowest temperature, the average temperature, the temperature difference, etc.

[0043] Finally, the system generates a real-time temperature distribution map of the photovoltaic circuit, visually showing the temperature levels and change trends at different positions. The temperature distribution map maps different temperature ranges to different colors through color coding. For example, blue represents the low-temperature area and red represents the high-temperature area, which is convenient for quickly locating abnormally high-temperature points. At the same time, the system can also generate various visualization forms such as contour maps and three-dimensional surface maps of the temperature distribution to meet the analysis needs of different users.

[0044] Suppose the system processes the infrared image received in the previous step to obtain a temperature distribution map of 800×800 pixels. The areas of different colors in the map represent different temperature ranges, with the temperature range from 25°C to 85°C, and each interval is 10°C. By observing the temperature distribution map, it can be found that there is an obvious high-temperature area on a certain photovoltaic circuit, and the highest temperature reaches 95°C, which is much higher than other areas, and there is likely a damaged point.

[0045] By generating a real-time temperature distribution map, the system can intuitively and comprehensively grasp the temperature distribution state of the photovoltaic circuit, providing a basis for judging the identification of damaged points. Based on infrared imaging and thermal map analysis technologies, the system can monitor the temperature anomalies of the photovoltaic circuit in real time under harsh weather conditions, timely discover and locate suspicious high-temperature areas, provide guidance for subsequent electrical parameter tests, and improve the pertinence and efficiency of damage detection.

[0046] S103. When it is determined that there are abnormal temperatures greater than the preset temperature value in the real-time temperature distribution, send a first instruction to the preset signal source and the voltage detection device respectively to make the preset signal source and the voltage detection device operate; After obtaining the real-time temperature distribution of the photovoltaic circuit through infrared image analysis, the system needs to judge whether there are abnormally high-temperature areas, that is, suspicious damaged points with temperatures significantly higher than other areas. Here, the concept of the preset temperature value is introduced as the threshold standard for judging abnormally high temperatures.

[0047] The system scans the temperature distribution map point by point, extracts the temperature value of each position, and compares it with the preset temperature value. If the temperature value of a certain position is greater than the preset temperature value, it is marked as an abnormally high-temperature point, and its spatial coordinates and temperature parameters are recorded. The system can set multiple preset temperature values corresponding to different abnormal levels and processing methods.

[0048] When an abnormally high temperature area is detected, the system automatically sends a control instruction (i.e., the first instruction) to a preset signal source and a voltage detection device to trigger them to start working. The preset signal source can be a signal generator used to inject an electrical signal with a specific frequency and amplitude on the photovoltaic line as a detection signal for break point location. The voltage detection device can be a digital oscilloscope or a data acquisition instrument used to collect voltage signals at different positions on the photovoltaic line and measure parameters such as the amplitude and phase of the voltage.

[0049] For example, the system finds an abnormally high temperature point with a temperature of 95°C and a spatial coordinate of (300, 500) on the temperature distribution map, which is significantly higher than the preset temperature value of 85°C. Then, the system sends a start instruction to the signal generator to inject a 1 kHz, 5 V sine detection signal on the photovoltaic line near the abnormal point. At the same time, the system controls the digital oscilloscope to collect the voltage signals of the line at intervals of 10 m on a 200 m line segment centered on the abnormal point, with 1000 data points collected at each position and a sampling frequency of 10 kHz.

[0050] By sending control instructions to the preset signal source and the voltage detection device, the system can perform further electrical parameter tests in the temperature abnormal area to obtain characteristic parameters such as voltage and current of the photovoltaic line corresponding to the break point. Compared with simple temperature detection, electrical parameters can provide a more accurate and reliable basis for judging the break point.

[0051] S104. Receive the real-time current and real-time voltage, and obtain the temperature and humidity; After the preset signal source and the voltage detection device are started, the system begins to receive and record the real-time electrical parameters and environmental parameters of the photovoltaic line, providing raw data for subsequent data analysis and break point location. The real-time current is the current of the preset signal source. One end of the preset signal source is connected to the first end point of the photovoltaic line, and the other end of the preset signal source is grounded. The real-time voltage is the voltage detected by the voltage detection device. The voltage detection device is connected to the second end point of the photovoltaic line. The first end point and the second end point are the two ends of the photovoltaic line.

[0052] After the preset signal source injects a detection signal into the photovoltaic line, the signal will be transmitted in the line and reflected and attenuated at the break point. The voltage detection device collects the voltage signals at different positions on the line and transmits the data to the system in real time. The voltage data received by the system is a time-domain waveform, which needs to be subjected to feature extraction and parameter calculation to obtain key indicators reflecting the line state, such as the effective voltage value, the number of zero-crossing points, the peak value, etc.

[0053] Meanwhile, the system also needs to obtain the temperature and humidity data of the current environment to consider the influence of environmental factors on the electrical parameters of the line. The acquisition path can be a temperature and humidity sensor, which can be deployed at multiple monitoring points in the photovoltaic power station to collect the environmental temperature and relative humidity in real time and upload the data to the system. After receiving the temperature and humidity data, the system filters, smooths, and curve-fits them to obtain a continuous temperature and humidity change curve for correcting the electrical parameter model. The acquisition path for temperature and humidity can be other paths, which are not limited here.

[0054] For example, the system receives a set of voltage data collected by a voltage detection device at a distance of 50 m from an abnormally high-temperature point. The data length is 1000 points, and the voltage amplitude range is 2 - 8 V. The system calculates that the effective voltage value at this position is 5.2 V, the number of zero-crossing points is 18, and the peak-to-peak value is 6.5 V. At the same time, the current sensor measures the real-time current of the line as 3.5 A, the environmental temperature as 28 °C, and the relative humidity as 60%. The system combines these data points with the data at other positions to construct a multi-dimensional parameter surface of the photovoltaic line, reflecting the spatial distribution characteristics of voltage, current, temperature, and humidity at different positions of the line.

[0055] By receiving and fusing multi-source parameter data, the system can comprehensively and dynamically grasp the real-time working state and environmental conditions of the photovoltaic line, laying a data foundation for intelligent fault diagnosis.

[0056] S105: Input the real-time current, real-time voltage, temperature, and humidity into the conductivity calculation function to obtain several conductivities; After obtaining the real-time current, real-time voltage, temperature, and humidity, the system inputs these data into the conductivity calculation function to obtain several conductivities. The system uses a conductivity calculation function based on a physical model, taking multi-source parameters as inputs to calculate the conductivity values at different positions of the line. This function comprehensively considers the influence of factors such as voltage, current, temperature, and humidity on conductivity and can accurately reflect the true conductivity state of the line. The conductivity calculation function is as follows: In the formula, σ i is any one of several conductivities, I is the real-time current, V is the real-time voltage, A is the cross-sectional area of the photovoltaic line, x i is the position of the first endpoint, x i-1 is the position of the second endpoint, x is the independent variable, α(x) is the temperature correction function at position x, β(x) is the temperature influence function at position x, T(x) is the real-time temperature at position x, T0 is the reference temperature, γ(x) is the humidity correction function at position x, H(x) is the real-time relative humidity at position x, H0 is the reference humidity, ζ(x) is the periodic influence function at position x, and L is the length of the photovoltaic line between the first endpoint and the second endpoint.

[0057] For example, for a photovoltaic line with a length of 1000 m, the system divides it into 100 small intervals, and the length of each interval Δx = 10 m. Given that the cross-sectional area A of the photovoltaic line is 70 mm 2 , the real-time current I = 5.6 A, the real-time voltage V = 220 V, the reference temperature T_0 = 25 °C, and the reference humidity H_0 = 60%. The real-time temperature collected by the system at the 50th small interval (at the position coordinate x = 500 m) is T(500) = 35 °C, and the real-time humidity is H(500) = 70%. Assume that the temperature correction function at this position is α(500) = 0.0026, the temperature influence function is β(500) = 0.0035, the humidity correction function is γ(500) = 0.0012, and the periodic influence function is ζ(500) = 0.02.

[0058] Substitute the above parameters into the conductivity calculation function and apply the composite trapezoidal formula, and the conductivity of the 50th small interval can be obtained as: 1.97×10^6 S / m Repeat the above calculation steps, and the conductivity values at different positions of the entire photovoltaic line can be obtained, forming a conductivity distribution curve. In the area where the conductivity value on the curve is significantly low, there is likely to be a break point or a serious defect, which needs to be focused on and repaired.

[0059] The above data is only applicable to this step, and the specific conductivity should be calculated according to the actual situation, which is not limited here.

[0060] S106. Draw a conductivity distribution curve according to a number of conductivities, and determine a number of abnormal conductivities in the conductivity distribution map that are not within the preset interval; After calculating the conductivity values at different positions of the photovoltaic line, the system needs to connect these discrete data points into a continuous curve to generate an intuitive conductivity distribution map for intuitive analysis and interpretation. At the same time, the system also needs to judge whether there are abnormal conductivity points on the curve according to the preset normal conductivity interval, providing a judgment basis for the positioning of the break point.

[0061] First, the system uses data visualization technology to map the conductivity data into a two-dimensional coordinate system to generate a scatter plot. The abscissa of the scatter plot represents the position coordinate of the photovoltaic line, and the ordinate represents the conductivity value. By observing the scatter plot, the distribution trend and dispersion degree of the conductivity along the line direction can be intuitively understood.

[0062] Next, the system uses a curve fitting algorithm to connect the discrete points on the scatter plot into a smooth curve, obtaining a conductivity distribution curve graph. Commonly used curve fitting algorithms include the least squares method, spline interpolation method, locally weighted regression, etc. Selecting an appropriate algorithm can, while ensuring the smoothness of the curve, maximize the retention of the characteristics of the original data. The fitted curve can clearly reflect the change trend and local anomalies of the conductivity, providing an intuitive basis for anomaly detection.

[0063] Then, the system performs anomaly detection on the conductivity distribution curve according to the preset normal conductivity range. The upper and lower limits of the normal range are determined comprehensively based on factors such as the material properties, operating conditions, and environmental conditions of the photovoltaic line, and can be obtained through statistical analysis of a large amount of historical data. Generally speaking, the range of the normal range is relatively stable, and the fluctuation of the conductivity curve within this range is a normal phenomenon.

[0064] The system scans the conductivity curve point by point, extracts the conductivity value of each data point, and compares it with the upper and lower limits of the normal range. If the conductivity value of a certain point exceeds the normal range, it is marked as an anomaly point, and its position coordinates and deviation degree are recorded. The system can divide the anomaly level according to the deviation degree of the anomaly point, such as mild anomaly, moderate anomaly, severe anomaly, etc., providing a reference for subsequent maintenance decisions.

[0065] For example, by analyzing the historical operation data of a large number of photovoltaic lines, the system determines that the normal range of conductivity is [1.8×10^6, 2.2×10^6] S / m. In the conductivity distribution curve generated in the previous step, the system finds that the conductivity value at 550 m is 1.5×10^6 S / m, which is significantly lower than the normal lower limit. Therefore, the system marks this point as a severe anomaly point, with its position coordinates x = 550 m and the deviation degree of (1.8 - 1.5) / 1.8×100% = 16.7%.

[0066] By drawing the conductivity distribution curve graph and conducting anomaly detection, the spatial distribution law of the conductivity performance of the photovoltaic line can be visually presented, and regular anomalies can be quickly identified, providing reliable clues for fault diagnosis and defect location. Conductivity anomalies often mean that there are damage defects such as insulation aging and joint damage in the line. The greater the deviation degree of the conductivity, the higher the risk of damage. Based on the trend extrapolation and similarity analysis of the conductivity curve, the aging trend of the line in the future period can also be predicted.

[0067] S107. Mark a number of damaged points corresponding to abnormal conductivities in the conductivity distribution graph to obtain a photovoltaic line detection graph, and send the photovoltaic line detection graph to the detection terminal.

[0068] After identifying the abnormal points on the conductivity distribution curve, the system needs to further determine the specific positions of these abnormal points on the photovoltaic line, generate an intuitive detection report, and send the detection results to relevant personnel to guide subsequent maintenance and disposal work.

[0069] First, the system maps the conductivity distribution curve to the physical topology of the photovoltaic line to determine the actual positions of each abnormal point on the line. Since the abscissa of the conductivity curve represents the position coordinates of the line, the coordinate values of the abnormal points can be directly corresponding to the actual length of the line. For example, if the coordinate of the abnormal point is x = 550m, it means that the abnormal point is located 550m from the starting point of the line towards the end point.

[0070] Next, the system marks the positions of the abnormal points on the CAD drawing or GIS electronic map of the photovoltaic line to generate a photovoltaic line detection map. In the detection map, the abnormal points are represented by eye-catching markers (such as red dots), and attribute information such as conductivity values and deviation degrees is attached. For areas where multiple abnormal points gather continuously, the system will delimit an abnormal interval, indicating that there are continuous damages or serious defects in the line within this interval. When necessary, the system will also make a qualitative assessment of the damage degree based on the positions and densities of the abnormal points, such as mild damage, moderate damage, severe damage, etc.

[0071] After generating the detection map, the system packages it into a detection report in a standard format and sends it to the detection terminal through a wireless communication network. The detection terminal can be a handheld PDA, a tablet computer, or a fixed industrial control computer, a monitoring large screen, etc., which is not limited here.

[0072] The above embodiments have the following beneficial effects: The system receives the real-time current and voltage data at both ends of the photovoltaic line and synchronously obtains the environmental temperature and humidity information, comprehensively considering the influence of electrical parameters and meteorological factors on the conductivity performance of the photovoltaic line. By inputting multi-source heterogeneous data into the conductivity calculation function, the conductivity distribution at different positions of the photovoltaic line can be accurately calculated, objectively reflecting the spatial variation law of the conductivity performance. The drawing and analysis of the conductivity distribution curve graph can visually identify the areas with abnormal conductivity, accurately locate the positions of the damaged points, and provide a reliable basis for subsequent maintenance and replacement. It realizes functions such as accurate diagnosis, visual display, and remote alarm of the photovoltaic line breakage fault, greatly improving the accuracy and efficiency of the damage detection of the photovoltaic line in rainy weather.

[0073] Using infrared thermal imaging technology, the photovoltaic line can be scanned quickly and over a large area. By identifying the high-temperature areas in the infrared image, the positions of suspected fault points can be preliminarily judged, providing a key direction for subsequent refined detection. It realizes the automation of fault pre-screening, improving the breadth, depth, efficiency, and accuracy of the damage detection.

[0074] The conductivity calculation function is used to quantitatively evaluate the conductivity status of the photovoltaic line, with strong reliability and scientificity. Based on the measured current and voltage parameters, this function takes into account various physical quantities that affect conductivity, establishes a quantitative relationship between conductivity and electrical parameters, temperature, humidity, etc., and can objectively reflect the true conductivity state of the photovoltaic line in a complex environment, providing a reliable data reference for subsequent positioning of the break point.

[0075] In step S106 of the above embodiment, the system draws a conductivity distribution curve based on several conductivities. Regarding how the conductivity distribution curve is drawn and what criteria are used for drawing, the following will be combined with Figure 2 , to describe how to implement this step: Please refer to Figure 2 , which is another process schematic diagram of a method for detecting breakage of a photovoltaic line in a rainy environment in the embodiment of the present application.

[0076] S201. Determine the detection length and position resolution between the first endpoint and the second endpoint; The system determines the detection length and position resolution between the first endpoint and the second endpoint. Specifically: Determine the photovoltaic line labels corresponding to the first endpoint and the second endpoint; Match the photovoltaic line labels in the preset database to obtain the detection length between the first endpoint and the second endpoint; Determine the number of several conductivities, and determine the position resolution according to the number of several conductivities.

[0077] Before drawing the conductivity distribution curve, the system needs to determine the detection length and position resolution between the two endpoints of the photovoltaic line, which is the basis for generating an accurate and reliable curve. The detection length determines the spatial scale and coverage range of the curve, and the position resolution determines the sampling density and detail description ability of the curve.

[0078] First, the system determines the positions of the first endpoint and the second endpoint by identifying the label information at both ends of the photovoltaic line. Each endpoint of the photovoltaic line has a unique identification code, such as a barcode, RFID tag, etc., which records key information such as the geographical coordinates, the affiliated line, and the installation time of the endpoint. By scanning or reading the endpoint label, the system can quickly obtain the spatial position and line attributes of the endpoint, providing basic data for subsequent detection length calculation.

[0079] Then, the system queries and obtains the actual distance between the first endpoint and the second endpoint, i.e., the detection length, in the preset database using the endpoint label as the index. The preset database is a comprehensive information repository that stores data such as the laying schemes, physical parameters, and operation records of all lines in the photovoltaic power station. By matching the endpoint labels, the system can retrieve the length parameter of this photovoltaic line from the database without on-site measurement, improving the detection efficiency and accuracy.

[0080] Finally, the system dynamically determines the sampling position resolution based on the number of conductivity data points. The conductivity data points are collected by the on-line monitoring system of the photovoltaic line, and the number may vary with factors such as the length of the line, operating conditions, and fault frequencies. The more data points there are, the smaller the distance between adjacent points and the higher the position resolution. To accurately depict the spatial distribution characteristics of conductivity on the curve graph, the system will dynamically adjust the position resolution according to the number of data points to ensure that the distance between adjacent sampling points does not exceed a certain threshold, such as 1m, 5m, etc.

[0081] For example, the system identifies that the first endpoint label of a certain photovoltaic line is "A-001" and the second endpoint label is "A-002". By querying the photovoltaic line database, the system learns that the actual distance between these two endpoints is 1500m, i.e., the detection length is 1500m. At the same time, the system receives a total of 1200 data points uploaded by the on-line conductivity monitoring system of this line. To ensure the smoothness and sampling density of the curve graph, the system sets the minimum value of the position resolution to 1m, i.e., the distance between adjacent sampling points is not greater than 1m. Therefore, the system calculates the position resolution as 1500m / 1200 = 1.25m, meeting the requirement of the minimum resolution.

[0082] S202. Establish a position coordinate system according to the detection length and the position resolution; After determining the detection length and the position resolution of the photovoltaic line, the system needs to establish a position coordinate system to provide a reference framework for the spatial mapping and curve drawing of the conductivity data points. The position coordinate system is the basis for describing and analyzing the spatial distribution characteristics of conductivity. A reasonable coordinate system design can simplify the calculation process, improve the algorithm efficiency, and is also conducive to intuitively understanding and analyzing the variation law of conductivity.

[0083] The system can use a one-dimensional rectangular coordinate system to represent the spatial position of the photovoltaic line. Taking the first endpoint as the origin and the direction of the line connecting the endpoints as the positive direction, a position coordinate axis is established. Each coordinate point on the coordinate axis represents a position on the photovoltaic line, and the coordinate value represents the distance of this position from the first endpoint. According to the detection length and the position resolution, the system determines the range and scale of the coordinate axis.

[0084] The range of the coordinate axis is [0, L], where L is the detection length, that is, the distance between the first endpoint and the second endpoint, with the unit of meter. This range covers the spatial scale of the entire photovoltaic line and is the spatial boundary for conducting conductivity distribution analysis.

[0085] The scale of the coordinate axis is the position resolution, that is, the distance between two adjacent coordinate points, with the unit of meter. The selection of the position resolution needs to balance the smoothness of the curve and the calculation efficiency. The higher the resolution, the denser the coordinate points, the smoother the curve, but the greater the calculation amount. The lower the resolution, the sparser the coordinate points, the rougher the curve, but the faster the calculation speed.

[0086] For example, for the above-mentioned photovoltaic line with a detection length of 1500m and a position resolution of 1.25m, the system establishes a position coordinate axis with a range of [0, 1500] and a scale of 1.25. Each coordinate point on the coordinate axis represents a specific position on the photovoltaic line. For example, the coordinate point x = 0 represents the position of the first endpoint, the coordinate point x = 1500 represents the position of the second endpoint, the coordinate point x = 750 represents the midpoint position, and the coordinate point x = 625 represents the 1 / 4 position between the first endpoint and the midpoint.

[0087] When the system generates the position coordinate axis, it will also calculate the coordinate values corresponding to each conductivity data point. By methods such as linear interpolation or geometric projection, the relative position of the conductivity data points is converted into the absolute position on the coordinate axis, establishing a mapping relationship between the data points and the positions. This mapping relationship is an important basis for drawing the conductivity distribution curve graph and lays a foundation for subsequent data visualization processing.

[0088] For example, for the 1200 conductivity data points received by the system, according to their relative positions on the photovoltaic line, their corresponding coordinate values on the coordinate axis are calculated in sequence to form a coordinate value array, such as [0, 1.25, 2.50,..., 1498.75, 1500]. Each coordinate value uniquely corresponds to a conductivity data point and records the spatial position information of the data point on the photovoltaic line.

[0089] S203. Mark several conductivities in the position coordinate system to obtain corresponding several data points; After establishing the position coordinate system, the system needs to map the conductivity data into the coordinate system to generate an intuitive data visualization graph to prepare for drawing the conductivity distribution curve. By associating the quantitative conductivity values with the qualitative coordinate positions, the spatial distribution law of the conductivity along the line direction can be represented, abnormal fluctuations and mutation regions can be identified, and an intuitive basis can be provided for fault location and cause analysis.

[0090] The system visualizes the conductivity data points in the position coordinate system in the form of a scatter plot. A scatter plot is a commonly used data visualization chart for showing the correlation and distribution characteristics between two variables. In this application scenario, the horizontal axis of the scatter plot represents the position coordinates of the photovoltaic circuit, and the vertical axis represents the conductivity value. The position of each data point in the graph is determined jointly by its position coordinates and conductivity value.

[0091] The system traverses all the conductivity data points and calculates their pixel coordinates in the scatter plot based on the position coordinates and conductivity values of each data point. Then, the system draws a marker symbol, such as a dot, triangle, cross, etc., at the corresponding pixel position to represent the data point. The color, size, and shape of the marker symbol can encode different attribute information, such as the numerical range of conductivity, anomaly level, etc.

[0092] The system can also draw some auxiliary elements in the background of the scatter plot, such as grid lines, reference lines, labels, etc. Grid lines can help readers quickly estimate and compare the coordinate values of data points. Reference lines can mark the normal range or threshold of conductivity, and labels can display the text information of key positions or values. These auxiliary elements can improve the readability and information transmission efficiency of the scatter plot.

[0093] For example, the system generated a total of 1200 data points on the position coordinate axis. The position coordinates and conductivity values of each data point are (0, 2.85E6), (1.25, 2.82E6), (2.50, 2.79E6),..., (1498.75, 1.08E6), (1500, 1.03E6). Based on these data points, the system drew a scatter plot of 1200×800 pixels. The horizontal axis range of the scatter plot is [0, 1500], the vertical axis range is [0, 5E6], the background is white, and the grid lines are light gray. Each data point is represented by a dot with a radius of 5 pixels. The filling color of the dot is mapped on a continuous color band of red - yellow - green according to the conductivity value. Red indicates low conductivity (possibly faulty), and green indicates high conductivity (good condition). The system also drew a black dashed line above the scatter plot to represent the normal lower limit of conductivity. Data points with conductivity below this line need to be focused on.

[0094] S204. Connect several data points with a curve to obtain a conductivity distribution curve graph.

[0095] Based on the conductivity scatter plot, the system needs to further draw a smooth and continuous conductivity distribution curve graph to more intuitively and accurately depict the change trend and regularity of conductivity along the circuit direction. Compared with the discrete scatter plot, the continuous curve graph can reveal the transition characteristics and correlations of conductivity between adjacent positions, capture more subtle change patterns, and provide richer information for fault location and trend prediction.

[0096] The system can adopt a curve fitting algorithm to connect the discrete data points on the scatter plot into a smooth curve. Curve fitting is a commonly used data analysis method that constructs a mathematical model to approximate and describe the distribution trend of discrete data points, making the curve as close as possible to each data point while having good smoothness and continuity.

[0097] The above embodiments have the following beneficial effects: The detection length determines the inspection coverage of the line, and the position resolution determines the spatial density of the sampling points. High position resolution can finely reflect the jumps and fluctuations of conductivity, improving the detection probability of breakage points; while moderate resolution is conducive to compressing the data volume and improving the calculation and transmission efficiency. Based on the geometric mapping of the position coordinate system, the discrete conductivity points are mapped to continuous spatial positions, establishing the corresponding relationship between conductivity and the actual position of the line. By plotting the conductivity scatter plot, the spatial aggregation trend of abnormal points can be displayed, the specific position of the breakage point can be inferred, and the accuracy of photovoltaic line breakage detection in rainy weather is improved.

[0098] Match the labels of each photovoltaic line, which include line length, wire model, laying path, etc. When conducting line detection, only need to read the label information at both ends of the line, and the length parameters of the line can be quickly indexed and retrieved from the database without repeated measurement and calculation. At the same time, this method determines the detection position resolution according to the number of conductivity sampling points. The conductivity sampling points are the key factors affecting the resolution selection. The more the number of sampling points, the smaller the spacing between adjacent sampling points, the higher the resolution, and the local changes of conductivity can be finely characterized; on the contrary, the fewer the number of sampling points, the lower the resolution, and only the overall trend of conductivity can be reflected. Appropriate position resolution can improve the accuracy of breakage point detection.

[0099] It should also be noted that for a photovoltaic line with a breakage point, there may be potential safety hazards within a certain range. If there are residents living near the breakage point, it may pose a threat to life and property safety. Then it is necessary to conduct a survey. Specifically: determine whether there are preset buildings within the preset dangerous range of several breakage points. Specifically: obtain the map information within the preset dangerous range of several breakage points; Determine whether there are preset buildings according to the map information; If there are, send an alarm message to the detection terminal.

[0100] In this way, relevant staff can be made aware of the danger signal and take timely countermeasures.

[0101] The above embodiments have the following beneficial effects: Due to long-term exposure to harsh outdoor environments, photovoltaic lines are prone to insulation aging, broken strands and other damage faults. On the one hand, these damaged points will reduce the power transmission efficiency and reliability, affecting the power generation performance of photovoltaic power stations; on the other hand, electrical sparks, arc discharges, etc. caused by the decline in insulation performance at the damaged points may affect the buildings around the line, triggering secondary disasters such as fires and equipment damage, threatening the lives and property safety of nearby people. If there is a preset building within the preset dangerous range of several damaged points, an alarm message will be sent to the detection terminal to prompt the detection terminal to take corresponding measures to avoid dangerous events.

[0102] Obtain the map information around the damaged point, analyze the composition and distribution of map elements, and judge whether there are important buildings, providing more refined and objective data support for the risk judgment of the damaged point, and further improving the pertinence and feasibility of early warning information.

[0103] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0104] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0105] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0106] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by a computer program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media that can store program codes such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A method for detecting damage to a photovoltaic wire in a rainy environment, characterized in that, Including: Receiving real-time current and real-time voltage, and obtaining temperature and humidity. The real-time current is the current of a preset signal source, one end of the preset signal source is connected to the first end point of the photovoltaic wire, the other end of the preset signal source is grounded, the real-time voltage is the voltage detected by a voltage detection device, the voltage detection device is connected to the second end point of the photovoltaic wire, and the first end point and the second end point are the two ends of the photovoltaic wire; Inputting the real-time current, the real-time voltage, the temperature and the humidity into a conductivity calculation function to obtain a plurality of conductivities; Drawing a conductivity distribution curve graph according to the plurality of conductivities; Determining a plurality of abnormal conductivities whose conductivities in the conductivity distribution graph are not within a preset interval; Marking a plurality of break points corresponding to the plurality of abnormal conductivities in the conductivity distribution graph to obtain a photovoltaic wire detection graph, and sending the photovoltaic wire detection graph to a detection terminal.

2. The method according to claim 1, characterized in that, Before receiving the real-time current and real-time voltage and obtaining the temperature and humidity, the method further includes: Receiving a real-time infrared image sent by an infrared imaging device, and the infrared imaging device is installed on a drone; Determining a real-time temperature distribution according to the real-time infrared image; When it is determined that there is an abnormal temperature greater than a preset temperature value in the real-time temperature distribution, sending a first instruction to the preset signal source and the voltage detection device respectively to make the preset signal source and the voltage detection device operate.

3. The method according to claim 1, characterized in that, The conductivity calculation function is: In the formula, the σ i is any one of the several conductivities, the I is the real-time current, the V is the real-time voltage, the A is the cross-sectional area of the photovoltaic wire, the x i is the position of the first end point, the x i-1 is the position of the second end point, the x is the independent variable, the α(x) is the temperature correction function at the position x, the β(x) is the temperature influence function at the position x, the T(x) is the real-time temperature at the position x, the T0 is the reference temperature, the γ(x) is the humidity correction function at the position x, the H(x) is the real-time relative humidity at the position x, the H0 is the reference humidity, the ζ(x) is the periodic influence function at the position x, and the L is the length of the photovoltaic wire between the first end point and the second end point.

4. The method according to claim 1, wherein The drawing the conductivity distribution curve graph according to the plurality of conductivities specifically includes: Determining the detection length and position resolution between the first end point and the second end point; Establishing a position coordinate system according to the detection length and the position resolution; Marking the plurality of conductivities in the position coordinate system to obtain corresponding data points; Using a curve to connect the plurality of data points to obtain the conductivity distribution curve graph.

5. The method according to claim 4, characterized in that The determining the detection length and preset position resolution between the first end point and the second end point specifically includes: Determining the photovoltaic wire labels corresponding to the first end point and the second end point; Matching the photovoltaic wire labels in a preset database to obtain the detection length between the first end point and the second end point; Determining the number of the plurality of conductivities, and determining the position resolution according to the number of the plurality of conductivities.

6. The method according to claim 1, wherein After marking the plurality of break points corresponding to the plurality of abnormal conductivities in the conductivity distribution graph to obtain a photovoltaic wire detection graph and sending the photovoltaic wire detection graph to a detection terminal, the method further includes: Determining whether there is a preset building within a preset dangerous range of the plurality of break points; If so, sending an alarm message to the detection terminal.

7. The method according to claim 6, wherein The determining whether there is a preset building within a preset dangerous range of the plurality of break points specifically includes: Obtaining map information within a preset dangerous range of the plurality of break points; Determining whether there is the preset building according to the map information.

8. A photovoltaic wire breakage detection system in a rainy environment, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions are run on the system, cause the system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, cause the system to execute the method according to any one of claims 1-7.

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