Method and platform for detecting hidden construction danger of solar power station

Abstract

The invention relates to a construction hidden danger detection method and platform for a solar power station, and relates to the field of photovoltaic technology, and the method comprises the steps: collecting vegetation data and dust data in a plurality of solar module regions in a solar power station construction region, obtaining vegetation data distribution and dust data distribution, and collecting preset maintenance parameters of solar modules, the preset maintenance parameters comprise preset maintenance frequency and preset maintenance cost; processing to obtain bird droppings data distribution, and performing shielding analysis to obtain vegetation shielding frequency distribution, dust shielding frequency distribution and bird droppings shielding frequency distribution; and according to preset maintenance parameters, carrying out construction maintenance hidden danger analysis to obtain hidden danger information distribution, carrying out local hidden danger specificity analysis to obtain local hidden danger specificity, carrying out classification to obtain maintenance difficulty, and combining the hidden danger information distribution as a hidden danger detection result. The technical problem that in the prior art, in the construction process of a solar power station, potential shielding operation and maintenance hidden dangers cannot be detected is solved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic technology, specifically to a method and platform for detecting potential construction hazards in solar power plants. Background Technology

[0002] Currently, the site selection and construction planning of solar power plants are primarily based on the distribution of solar resources and power generation efficiency. However, in actual operation, a significant problem facing solar power plants is the impact of shading by vegetation, bird droppings, and dust. These obstructions accumulate during operation, leading to insufficient sunlight on the photovoltaic panel surface, reducing power generation efficiency. Furthermore, the varying degrees of shading across different solar modules make unified maintenance management difficult, increasing maintenance complexity. Existing technologies neglect how to analyze potential maintenance challenges after construction, leaving these challenges undetected and increasing maintenance frequency and costs. This is particularly pronounced for large-scale photovoltaic power plants, further increasing operation and maintenance costs. Without a proper analysis of potential maintenance challenges, the long-term operational efficiency and economic benefits of solar power plants may be adversely affected. Summary of the Invention

[0003] This invention addresses the technical problem in the prior art where potential shading hazards during the construction of solar power plants cannot be detected, thus affecting the long-term operating efficiency and economic benefits of solar power plants.

[0004] In a first aspect, the present invention provides a method for detecting potential construction hazards in solar power plants, including: collecting vegetation data and dust data in multiple solar module areas within the construction area of ​​the solar power plant to obtain the distribution of vegetation data and dust data, and collecting preset maintenance parameters of the solar modules, wherein the preset maintenance parameters include preset maintenance frequency and preset maintenance cost. Bird droppings data distribution is obtained by processing the vegetation data distribution; Using the aforementioned vegetation data distribution, dust data distribution, and bird droppings data distribution, occlusion analysis was performed to obtain the vegetation occlusion frequency distribution, dust occlusion frequency distribution, and bird droppings occlusion frequency distribution. Based on the preset maintenance parameters, and combined with the frequency distributions of vegetation obstruction, dust obstruction, and bird droppings obstruction, a construction and maintenance hazard analysis is performed to obtain the hazard information distribution. Furthermore, a local hazard peculiarity analysis is conducted to obtain the local hazard peculiarity, and the maintenance difficulty is classified accordingly. This, combined with the hazard information distribution, serves as the hazard detection result.

[0005] Secondly, this invention provides a platform for detecting potential construction hazards in solar power plants, the platform comprising: The power station data acquisition module is used to collect vegetation data and dust data in multiple solar module areas within the construction area of ​​the solar power station, obtain the distribution of vegetation data and dust data, and collect the preset maintenance parameters of the solar modules, including the preset maintenance frequency and preset maintenance cost. The bird droppings data acquisition module is used to obtain bird droppings data distribution based on the vegetation data distribution processing. The shading frequency analysis module is used to perform shading analysis using the vegetation data distribution, dust data distribution, and bird droppings data distribution to obtain the vegetation shading frequency distribution, dust shading frequency distribution, and bird droppings shading frequency distribution. The hazard detection module is used to perform construction and maintenance hazard analysis based on the preset maintenance parameters, combined with the frequency distribution of vegetation obstruction, sandstorm obstruction, and bird droppings obstruction, to obtain the hazard information distribution, and to perform local hazard peculiarity analysis to obtain the local hazard peculiarity, classify and obtain the maintenance difficulty, and combine the hazard information distribution as the hazard detection result.

[0006] Thirdly, the present invention provides an electronic device comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the method for detecting potential construction hazards in solar power plants as described in the first aspect.

[0007] Fourthly, the present invention provides a computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the method for detecting potential construction hazards in a solar power plant according to the first aspect.

[0008] This invention, through one or more of the aforementioned technical solutions and employing the method for detecting potential hazards in solar power plant construction, enables a comprehensive analysis of shading factors such as vegetation, dust, and bird droppings within the construction area of ​​a solar power plant, thereby detecting potential construction and maintenance hazards. This method, by collecting vegetation data, dust data, and their distribution, and combining this with preset maintenance frequency and cost parameters, can effectively predict the shading frequency and maintenance needs of different areas. Through precise analysis of the frequencies of vegetation shading, dust shading, and bird droppings shading, a hazard information distribution map based on shading frequency and maintenance cost is generated. The detection results not only help operators identify areas with high maintenance costs in advance but also optimize long-term maintenance plans and even adjust construction plans, reducing efficiency losses and maintenance difficulties during power plant operation. Simultaneously, through the analysis of the specificity of local hazards, the degree of difference in maintenance difficulty between different areas can be classified, providing a reference for comprehensive and unified maintenance, effectively improving the accuracy and value of hazard detection. This method provides a more comprehensive and accurate mechanism for detecting shading maintenance hazards in solar power plants, significantly improving their operational efficiency and economic benefits. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the method for detecting potential construction hazards in a solar power plant according to an embodiment of the present invention. Figure 2 A schematic diagram of the structure of a solar power plant construction hazard detection platform provided in an embodiment of the present invention; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of the present invention; Figure 4 A schematic diagram of the computer-readable storage medium provided by the present invention.

[0010] The attached diagram lists the components represented by each number as follows: The system includes a power station data acquisition module 11, a bird droppings data acquisition module 12, an obstruction frequency analysis module 13, a hidden danger detection module 14, an electronic device 100, a memory 110, a processor 120, a computer program 111, and a computer-readable storage medium 200. Detailed Implementation

[0011] Example 1 like Figure 1 As shown in the figure, this embodiment of the invention provides a method for detecting potential construction hazards in solar power plants, including the following steps.

[0012] S10: Collect vegetation data and dust data in multiple solar module areas within the solar power plant construction area to obtain vegetation data distribution and dust data distribution, and collect preset maintenance parameters for solar modules, including preset maintenance frequency and preset maintenance cost. In this embodiment of the application, during the operation and maintenance of the solar power station after its construction, vegetation growth and dust cover can obstruct the surface of the solar modules, affecting power generation. However, the degree of vegetation and dust obstruction varies in different areas of the solar modules. Therefore, it is necessary to collect vegetation data and dust data. For example, the solar power station is a solar power generation station, which includes multiple solar modules, each of which generates electricity based on solar energy.

[0013] The solar power plant construction area refers to the area where a solar power plant is planned to be built. It includes areas where multiple solar modules are installed and can be represented by geographical coordinates.

[0014] First, vegetation and dust data are collected for each region. Vegetation data is typically collected using remote sensing technology or drone monitoring, which can accurately determine the vegetation coverage area in each region and construct a vegetation data distribution.

[0015] Furthermore, by integrating environmental sensor networks or meteorological data, dust concentration information for each solar panel area can be collected, thereby obtaining dust data distribution.

[0016] Furthermore, after collecting vegetation and dust data, it is also necessary to collect preset maintenance parameters for the solar panels. These preset maintenance parameters are designed in advance for shading maintenance during the construction of the solar power plant. These parameters include: preset maintenance frequency and preset maintenance cost. The preset maintenance frequency refers to the frequency of routine cleaning and maintenance of the solar panels within a specific time period, such as once every three months. The preset maintenance cost includes the cost of cleaning and maintaining each solar panel, for example, 1000 yuan per year.

[0017] This application analyzes multiple solar panels being blocked by vegetation, dust, etc., and combines preset maintenance frequency and preset maintenance cost to analyze whether the preset maintenance parameters can meet the conditions of multiple solar panels being blocked. For example, whether the maintenance frequency meets the shading frequency, and whether the maintenance cost meets the shading conditions under the shading frequency, so as to detect potential shading hazards.

[0018] By collecting data on vegetation distribution and dust distribution, along with preset maintenance parameters, we can establish a data foundation for subsequent analysis to detect potential maintenance hazards, and ultimately optimize the operating efficiency and economic benefits of the solar power plant.

[0019] Step S10 in the method provided in this application embodiment includes: The coverage area ratio of vegetation types that can block solar modules in multiple solar module areas within the solar power plant construction area was collected to obtain multiple vegetation data. The average dust concentration within the areas of the multiple solar panels was collected to obtain multiple dust data. Based on the coordinates of the multiple solar panel regions, and combined with the multiple vegetation data and multiple dust data, a vegetation data distribution and a dust data distribution are constructed. The preset maintenance frequency and preset maintenance cost within a preset time period of the solar modules are collected as preset maintenance parameters.

[0020] In this embodiment of the application, when conducting detailed data collection and analysis of multiple solar panel areas within a solar power plant construction area, it is first necessary to measure the coverage area of ​​vegetation types that may obstruct the solar panels. Specifically, for each solar panel area (the geographical area occupied by each solar panel), the vegetation coverage area of ​​vegetation types that may obstruct the solar panels after growth is measured. Examples of vegetation types that may obstruct the solar panels after growth include trees and shrubs such as locust shrubs. By using remote sensing technology, drone aerial photography, or ground sensors, vegetation data within each solar panel area can be efficiently collected. Then, the coverage area of ​​vegetation types that may obstruct the solar panels is identified, and the ratio of the coverage area of ​​potentially obstructing vegetation types to the area area is calculated to obtain the coverage area ratio, for example, 30%. In this way, the coverage area ratios of vegetation types that may obstruct the solar panels within multiple solar panel areas are collected as multiple vegetation data.

[0021] Furthermore, by using environmental monitoring equipment or combining it with local meteorological data, the average dust concentration in the area of ​​each solar panel is collected, such as the average dust concentration over a month, like 100 µg / m³. 3 In this way, multiple dust data points were obtained.

[0022] Geographic coordinates, such as latitude and longitude coordinates, of multiple solar panel areas were collected. These were then combined with multiple vegetation and dust data sets to construct vegetation and dust data distributions. The vegetation data distribution includes mappings between the geographic coordinates of multiple solar panel areas and multiple vegetation data sets. The dust data distribution also includes mappings between the geographic coordinates of multiple solar panel areas and multiple dust data sets.

[0023] Furthermore, the system collects the preset maintenance frequency (e.g., once every 3 months) for each solar module designed during the construction of the solar power plant, as well as the preset maintenance cost for each solar module within a preset time period (e.g., annual maintenance cost, for example, 1000 yuan), as preset maintenance parameters. The preset maintenance frequency and preset maintenance cost are used as preset maintenance parameters. Multiple solar modules are designed with the same preset maintenance frequency and preset maintenance cost to achieve unified maintenance of multiple solar modules, thereby improving maintenance efficiency.

[0024] S20: Obtain bird droppings data distribution based on the vegetation data distribution; In this embodiment, vegetation attracts birds, which in turn cause bird droppings to fall on the solar panels, resulting in pollution and shading. Based on multiple vegetation data points within this vegetation data distribution, the bird droppings data corresponding to different vegetation data points, such as the frequency of bird droppings, are analyzed to obtain bird droppings data for multiple solar panels and construct a bird droppings data distribution.

[0025] Bird droppings data distribution can clearly identify which areas of solar panels are at higher risk of being blocked by bird droppings. Combined with vegetation and dust data distribution, the frequency of potential shading of multiple solar panels can be further analyzed. This allows for analysis of whether preset maintenance frequencies and costs meet requirements, and the detection of potential maintenance hazards. This data-driven analysis significantly improves the operational efficiency and maintenance accuracy of photovoltaic power plants.

[0026] Step S20 in the method provided in this application embodiment includes: Based on the monitoring data of bird droppings obstruction, a sample vegetation data set was collected, and the frequency of bird droppings under different sample vegetation data was collected to obtain a sample bird droppings data set. Construct a mapping table between the sample vegetation dataset and the sample bird droppings dataset to obtain a bird droppings data classifier; Multiple vegetation data within the vegetation data distribution are input into the bird droppings data classifier to obtain multiple bird droppings data. Based on the coordinates of the multiple solar panel regions, a bird droppings data distribution is constructed.

[0027] In this embodiment, based on historical data from previous solar power plant construction and maintenance, monitoring data records of bird droppings obstruction are obtained, including monitoring records of vegetation and bird droppings data for multiple previous sample solar modules. Sample vegetation data from these previous sample solar modules is collected, and a sample vegetation data set is obtained by calculating the coverage area ratio of vegetation types that may obstruct the solar modules within the sample solar module area.

[0028] Furthermore, the frequency of bird droppings falling in the sample solar panel area under different sample vegetation data is collected as the bird droppings frequency. For example, the number of bird droppings falling on the ground and solar panels in the sample solar panel area over a period of time (such as 1 day) is collected and divided by the length of that period to obtain the bird droppings frequency. Then, the average bird droppings frequency over multiple periods can be calculated as the sample bird droppings frequency, for example, 27 times / day, to obtain the sample bird droppings data set.

[0029] Furthermore, a mapping relationship is constructed between the sample vegetation dataset and the sample bird droppings dataset to obtain a mapping table, in which the sample vegetation data and sample bird droppings data of each sample solar panel are mapped to each other. This mapping table is used as a bird droppings data classifier.

[0030] Multiple vegetation data points from various solar panel regions within the current vegetation data distribution are input into this bird droppings data classifier for mapping and classification, resulting in multiple bird droppings data points for each solar panel region. Specifically, when multiple vegetation data points are input into the bird droppings data classifier, it is determined whether identical sample vegetation data exists. If so, the corresponding sample bird droppings data is output, thus obtaining the bird droppings data. If not, the sample vegetation data with the smallest and closest difference to the existing vegetation data is obtained, and the corresponding sample bird droppings data is output, thus obtaining the bird droppings data.

[0031] By combining multiple bird droppings data with the coordinates of multiple solar panel regions, a bird droppings data distribution is constructed. Thus, the bird droppings data distribution can be obtained by processing vegetation data distribution, and can then serve as the data basis for analyzing the frequency of bird droppings shading photovoltaic panels.

[0032] S30: Using the aforementioned vegetation data distribution, dust data distribution, and bird droppings data distribution, perform occlusion analysis to obtain the vegetation occlusion frequency distribution, dust occlusion frequency distribution, and bird droppings occlusion frequency distribution.

[0033] In this embodiment of the application, further, shading analysis is performed based on the distribution of vegetation, dust, and bird droppings to obtain the frequency of solar modules being shaded by vegetation, dust, or bird droppings.

[0034] Step S30 in the method provided in this application embodiment includes: Based on the historical maintenance data of solar modules, a set of sample vegetation data was collected, as well as the frequency of solar modules being blocked by vegetation under different sample vegetation data, which was used as a set of sample vegetation blocking frequencies. Construct a mapping table between the sample vegetation data set and the sample vegetation occlusion frequency set to obtain a vegetation occlusion classifier; The vegetation data distribution is input into the vegetation shading classifier to obtain the vegetation shading frequency distribution. Construct a dust obstruction classifier and a bird droppings obstruction classifier, and input the dust obstruction data distribution and the bird droppings data distribution to obtain the dust obstruction frequency distribution and the bird droppings obstruction frequency distribution.

[0035] In this embodiment of the application, based on the historical maintenance data of solar modules recorded by other solar power plants during module maintenance, the vegetation coverage area that may block the solar modules in the area of ​​other solar modules is collected as sample vegetation data to obtain a sample vegetation data set.

[0036] Furthermore, the frequency of solar panels being shaded by vegetation under different sample vegetation data was collected, such as the frequency of shading occurring every few days, for example, once every 15 days, or twice every 30 days. This frequency was labeled as the sample vegetation shading frequency, thus obtaining a set of sample vegetation shading frequencies. The larger the sample vegetation data, the higher the sample vegetation shading frequency.

[0037] Furthermore, a mapping table is constructed between the sample vegetation data set and the sample vegetation occlusion frequency set, that is, a data table with one-to-one correspondence between each sample vegetation data and the sample vegetation occlusion frequency, and this mapping table is used as a vegetation occlusion classifier.

[0038] Furthermore, multiple vegetation data points within the current vegetation data distribution are input into the vegetation shading classifier. If identical sample vegetation data exists, the corresponding sample vegetation shading frequency is mapped and output. If no identical sample vegetation data exists, the absolute difference between the input vegetation data and all sample vegetation data in the vegetation shading classifier is calculated, and the sample vegetation shading frequency corresponding to the closest sample vegetation data with the smallest absolute difference is mapped and output. In this way, multiple vegetation shading frequencies corresponding to multiple vegetation data points are mapped and classified, and combined with the coordinates of multiple solar panel regions, a vegetation shading frequency distribution is constructed.

[0039] In this embodiment, based on the historical maintenance data of solar modules recorded by other solar power plants during module maintenance, sample dust data sets and sample bird droppings data sets of solar modules in other solar power plants are collected. The frequency of dust obstructing solar modules under different sample dust data is collected to obtain a sample dust obstruction frequency set. The frequency of bird droppings obstructing solar modules under different bird droppings data is collected to obtain a sample bird droppings obstruction frequency set.

[0040] A mapping table is constructed between the sample dust storm dataset and the sample dust storm obstruction frequency set to obtain a dust storm obstruction frequency classifier. Similarly, a mapping table is constructed between the sample bird droppings dataset and the sample bird droppings obstruction frequency set to obtain a bird droppings obstruction classifier.

[0041] Multiple dust and bird droppings data points from the current distribution of dust and bird droppings data in multiple solar panel areas are input into the dust shading classifier and bird droppings shading classifier, respectively. The mapping and classification outputs yield multiple dust shading frequencies and multiple bird droppings shading frequencies. Combined with the coordinates of multiple solar panel areas, the dust shading frequency distribution and bird droppings shading frequency distribution are obtained.

[0042] This application embodiment classifies the shading frequency based on historical maintenance data of solar power plants, according to vegetation data, dust data, and bird droppings data, and obtains the vegetation shading frequency distribution, dust shading frequency distribution, and bird droppings shading frequency distribution. This can quantify the frequency of solar modules being shaded under different influences, and thus serve as a data basis for analyzing potential construction and maintenance hazards.

[0043] S40: Based on the preset maintenance parameters, combined with the distribution of vegetation obstruction frequency, dust obstruction frequency, and bird droppings obstruction frequency, conduct a construction and maintenance hazard analysis to obtain the hazard information distribution, and conduct a local hazard peculiarity analysis to obtain the local hazard peculiarity. Classify and obtain the maintenance difficulty, and combine the hazard information distribution as the hazard detection result.

[0044] In this embodiment of the application, based on the preset maintenance parameters mentioned above, combined with the distribution of vegetation shading frequency, sandstorm shading frequency, and bird droppings shading frequency, a construction and maintenance hazard analysis is conducted. Specifically, the maintenance quality of the preset maintenance parameters under the shading frequency is analyzed, and then the construction and maintenance hazards of the solar power station in the current construction area are analyzed to obtain the distribution of hazard information.

[0045] Step S40 in the method provided in this application embodiment includes: Based on the frequency distributions of vegetation obstruction, dust obstruction, and bird droppings obstruction, the maintenance cost distribution is obtained. The shading frequency distribution is calculated based on the vegetation shading frequency distribution, sandstorm shading frequency distribution, and bird droppings shading frequency distribution. Based on the preset maintenance cost and maintenance cost distribution, multiple cost deviation ratios are calculated to construct a cost hazard information distribution. Based on the preset maintenance frequency and the obstruction frequency distribution, multiple frequency deviation ratios are calculated to construct a frequency hazard information distribution. The distribution of hazard information is obtained by summing the distribution of cost hazard information and the distribution of frequency hazard information.

[0046] In this embodiment of the application, the maintenance cost required to maintain and clean the solar modules is first obtained by processing the distribution of vegetation shading frequency, dust shading frequency, and bird droppings shading frequency according to the distribution of vegetation shading frequency, dust shading frequency, and bird droppings shading frequency, thus obtaining the maintenance cost distribution.

[0047] The step of "processing to obtain the maintenance cost distribution based on the vegetation shading frequency distribution, sandstorm shading frequency distribution, and bird droppings shading frequency distribution" in the method provided in this application embodiment includes: By combining the maintenance costs of a single vegetation, dust, and bird droppings shielding maintenance, the vegetation maintenance cost, dust maintenance cost, and bird droppings maintenance cost within a preset time period are calculated, and the vegetation maintenance cost distribution, dust maintenance cost distribution, and bird droppings maintenance cost distribution are constructed. The maintenance cost distribution is calculated based on the distribution of vegetation maintenance costs, dust maintenance costs, and bird droppings maintenance costs.

[0048] In this embodiment of the application, the maintenance cost of a single instance of vegetation, dust, and bird droppings covering is first obtained, for example, the time cost or economic cost, such as 3 hours, 0.5 hours, and 1 hour.

[0049] Furthermore, based on the distribution of vegetation shading frequency, dust shading frequency, and bird droppings shading frequency, the cost of maintaining each solar panel area according to the corresponding vegetation shading frequency, dust shading frequency, and bird droppings shading frequency under the preset time period is calculated and used as the vegetation maintenance cost, dust shading maintenance cost, and bird droppings maintenance cost.

[0050] For example, if the preset time period is one month, the number of times each solar panel needs vegetation maintenance, dust suppression, and bird droppings maintenance within the preset time period can be calculated by dividing the preset time period by the frequency of vegetation shading, dust suppression, and bird droppings shading. Then, multiplying this by the maintenance cost per instance of vegetation, dust suppression, or bird droppings shading maintenance yields the vegetation maintenance cost, dust suppression cost, and bird droppings maintenance cost. For instance, if the vegetation shading frequency is 15 days / time, then the number of vegetation shading maintenance times within the preset time period is 30 / 15 = 2 times. If the cost of each instance of vegetation shading maintenance is 3 hours, then the vegetation maintenance cost is 2 × 3 = 6 hours.

[0051] In this way, the distribution of vegetation maintenance costs, dust maintenance costs, and bird droppings maintenance costs for multiple solar modules can be obtained.

[0052] Furthermore, based on the distribution of vegetation maintenance costs, dust storm maintenance costs, and bird droppings maintenance costs, a maintenance cost distribution is calculated. Specifically, the sum of the vegetation maintenance cost, dust storm maintenance cost, and bird droppings maintenance cost for each solar panel is calculated to obtain the maintenance cost. In this way, the maintenance costs of multiple solar panels are calculated, resulting in the maintenance cost distribution.

[0053] This application's embodiments calculate the maintenance costs of multiple solar modules under three types of shading conditions, serving as a data basis for analyzing potential maintenance risks in the construction and maintenance costs of solar power plants. This improves the accuracy and reliability of analyzing potential maintenance risks in the construction and maintenance of solar power plants.

[0054] In this embodiment of the application, after analyzing and obtaining the distribution of maintenance costs, it is also necessary to calculate a comprehensive shading frequency distribution. Specifically, the vegetation shading frequency, sandstorm shading frequency and bird droppings shading frequency corresponding to each solar module area will be summed to obtain the total shading frequency of the area.

[0055] For example, if the vegetation shading frequency in a certain area is 0.1 times / month, the dust shading frequency is 2 times / month, and the bird droppings shading frequency is 0.5 times / month, then the overall shading frequency for that area is 2.6 times / month. By performing this calculation on all solar panel areas, a shading frequency distribution that can intuitively reflect the theoretical cleanliness requirements of different locations throughout the entire power station is ultimately formed.

[0056] Furthermore, the adequacy of the maintenance budget under the current maintenance cost distribution is analyzed based on the preset maintenance cost. Specifically, the predicted total maintenance cost for each solar module area under the three types of shading within the maintenance cost distribution calculated in the above steps is compared with the preset maintenance cost to determine whether the preset maintenance cost deviates and the magnitude of the deviation. This allows for the analysis of potential problems in maintenance effectiveness caused by the impact of maintenance costs.

[0057] For example, for each region, its cost deviation ratio is calculated using the formula: (Predicted maintenance cost - Preset maintenance cost) / Preset maintenance cost. For instance, for a solar panel region, if the preset maintenance cost is 1000 yuan, but the predicted maintenance cost is as high as 1500 yuan, then the cost deviation ratio for that region is 0.5 (i.e., 50%). This indicates that there is a potential cost overrun in that solar panel region, which could lead to poor maintenance performance and other problems. The cost deviation ratios for all solar panel regions constitute the cost risk information distribution, where regions with larger positive values ​​have higher cost risks.

[0058] Furthermore, the frequency hazard information distribution is used to assess whether the preset maintenance plan can keep up with the actual rate of shading. Its construction process involves comparing the predicted total shading frequency for each solar module area within the shading frequency distribution with the preset maintenance frequency. For each solar module area, its frequency deviation ratio is calculated using the formula: (Predicted shading frequency - Preset maintenance frequency) / Preset maintenance frequency. For example, if the power plant is preset to be maintained once a month, but a certain area experiences a predicted shading frequency as high as three times a month due to heavy dust, then the frequency deviation ratio for that area is 2 (i.e., 200%). This indicates that within the preset maintenance interval, this area will experience three shading events but will only be maintained once, posing a serious risk of untimely maintenance. The frequency deviation ratios of all solar module areas constitute the frequency hazard information distribution, where areas with larger positive values ​​have a higher risk of power generation efficiency loss.

[0059] Furthermore, in order to comprehensively assess the potential risks in the construction of solar power plants, the distribution of the aforementioned cost risk information and frequency risk information will be synthesized.

[0060] For example, the cost deviation ratio of each solar module area within the cost hazard information distribution is summed with the frequency deviation ratio of each solar module area within the frequency hazard information distribution to obtain the final hazard information distribution. For instance, if a solar module area has a cost deviation ratio of 0.5 and a frequency deviation ratio of 2.0, its overall hazard information value is 2.5. This hazard information distribution provides a global risk perspective, clearly identifying high-risk hotspots within the power plant that require priority attention and handling, providing direct data support for subsequent differentiated allocation of maintenance resources or optimization of construction plans.

[0061] For example, the cost deviation ratio of each solar module area within the cost hazard information distribution and the frequency deviation ratio of each solar module area within the frequency hazard information distribution can be weighted and calculated. For example, the weights are 0.6 and 0.4 respectively. If the cost deviation ratio of a certain solar module area is 0.5 and the frequency deviation ratio is 2.0, then its comprehensive hazard information value is 0.6×0.5+0.4×2.0=1.1.

[0062] In this way, the potential hazard information of all multiple solar panel areas can be calculated, and the distribution of potential hazard information can be obtained.

[0063] In this embodiment, based on the distribution of hidden danger information obtained through analysis and calculation, the degree of specialization of local hidden dangers is analyzed to obtain the degree of specialization of local hidden dangers. In practice, the maintenance of multiple solar modules needs to be as consistent as possible to improve maintenance efficiency and quality. If the hidden dangers and maintenance of some solar modules are more special, non-standardized operations are required, leading to a decrease in maintenance efficiency and quality. Therefore, it is necessary to analyze the degree of specialization of local hidden dangers.

[0064] Step S40 in the method provided in this application embodiment includes: Based on the distribution of the hazard information, the average hazard information is calculated. Calculate the deviation between each hazard information and the average hazard information to obtain multiple local specificities, calculate the mean value, and obtain the local hazard specificity. Based on the specificity of the local hazards, the maintenance difficulty is classified and obtained, wherein the specificity of the local hazards is entered into the maintenance difficulty classification table to obtain the maintenance difficulty; The distribution of the aforementioned potential hazards and the difficulty of maintenance are used as the results of hazard detection for solar power plants.

[0065] In this embodiment of the application, based on the distribution of hidden danger information obtained through analysis and calculation, the mean value of multiple hidden danger information is first calculated as the average hidden danger information, reflecting the average level of hidden danger information in multiple solar module areas. For example, the average hidden danger information is 1.

[0066] Furthermore, the deviation between each hazard information and the average hazard information is calculated to obtain multiple local specificities. Specifically, the absolute value of the difference between each hazard information and the average hazard information is calculated, and then the ratio of this absolute value of the difference to the average hazard information is calculated as the local specificity. For example, if a hazard information has a specificity of 0.9 and the average hazard information has a specificity of 1, then the local specificity is |0.9-1| / 1=0.1. In this way, multiple local specificities are calculated, and the mean is further calculated to obtain the local hazard specificity.

[0067] Furthermore, based on the specificity of the local hazards, the maintenance difficulty is classified and obtained. Specifically, the specificity of the local hazards is input into a maintenance difficulty classification table to determine the maintenance difficulty. The maintenance difficulty classification table includes a pre-constructed mapping relationship between a set of sample local hazard specificities and a set of sample maintenance difficulties. These sets are obtained through maintenance data collection from other solar power plants.

[0068] For example, based on the steps described above, the environmental parameters of other solar power plant constructions can be analyzed to obtain a set of sample local hazard peculiarities. Then, the ratio of maintenance time to average maintenance time for solar power plants under different sample local hazard peculiarities can be obtained and labeled as sample maintenance difficulty, thus obtaining a sample maintenance difficulty set. For example, a sample maintenance difficulty of 1.5 or 0.5 represents high and low maintenance difficulty, respectively. Further, a mapping relationship between the sample local hazard peculiarity set and the sample maintenance difficulty set can be constructed to obtain a maintenance difficulty classification table.

[0069] Input the current local hazard peculiarity into the maintenance difficulty classification table. If there are samples with the same local hazard peculiarity in the maintenance difficulty classification table, obtain the corresponding mapped sample maintenance difficulty as the sample maintenance difficulty obtained from the classification. If there are no samples with the same local hazard peculiarity in the maintenance difficulty classification table, obtain the sample maintenance difficulty mapped to the sample with the smallest absolute difference from the current local hazard peculiarity as the sample maintenance difficulty obtained from the classification.

[0070] The distribution and maintenance difficulty of the hidden danger information obtained from the final analysis will be used as the hidden danger detection results of the solar power plant to guide the construction and maintenance of the solar power plant, such as adjusting the construction location or adjusting the maintenance strategy.

[0071] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects: This application embodiment, through the aforementioned scheme and method for detecting potential hazards in solar power plant construction, enables a comprehensive analysis of shading factors such as vegetation, dust, and bird droppings within the construction area of ​​a solar power plant, thereby detecting potential construction and maintenance hazards. This method, by collecting vegetation data, dust data, and their distribution, and combining this with preset maintenance frequency and cost parameters, can effectively predict the shading frequency and maintenance needs of different areas. Through precise analysis of the frequencies of vegetation shading, dust shading, and bird droppings shading, a hazard information distribution map based on shading frequency and maintenance cost is generated. The detection results of maintenance hazards not only help operators identify areas with high maintenance costs in advance but also optimize long-term maintenance plans for the power plant and even adjust construction plans, reducing efficiency losses and maintenance difficulties during power plant operation. Simultaneously, through the analysis of the specificity of local hazards, the degree of difference in maintenance difficulty between different areas can be classified, providing a reference for comprehensive and unified maintenance, effectively improving the accuracy and value of maintenance hazard detection. This method provides a more comprehensive and accurate mechanism for detecting shading operation and maintenance hazards in solar power plants, significantly improving the operating efficiency and economic benefits of solar power plants.

[0072] Example 2 like Figure 2 As shown, based on the same inventive concept as the method for detecting construction hazards in solar power plants provided in Embodiment 1, this embodiment of the invention provides a platform for detecting construction hazards in solar power plants, the platform comprising: The power station data acquisition module 11 is used to collect vegetation data and dust data in multiple solar module areas within the construction area of ​​the solar power station, obtain the distribution of vegetation data and dust data, and collect the preset maintenance parameters of the solar modules, including the preset maintenance frequency and preset maintenance cost. Bird droppings data acquisition module 12 is used to obtain bird droppings data distribution based on the vegetation data distribution processing; The shading frequency analysis module 13 is used to perform shading analysis using the vegetation data distribution, dust data distribution and bird droppings data distribution respectively, and obtain the vegetation shading frequency distribution, dust shading frequency distribution and bird droppings shading frequency distribution. The hazard detection module 14 is used to perform construction and maintenance hazard analysis based on the preset maintenance parameters and the distribution of vegetation obstruction frequency, sandstorm obstruction frequency, and bird droppings obstruction frequency, to obtain the hazard information distribution, and to perform local hazard peculiarity analysis to obtain the local hazard peculiarity, classify and obtain the maintenance difficulty, and combine the hazard information distribution as the hazard detection result.

[0073] Furthermore, the power plant data acquisition module 11 is also used for: The coverage area ratio of vegetation types that can block solar modules in multiple solar module areas within the solar power plant construction area was collected to obtain multiple vegetation data. The average dust concentration within the areas of the multiple solar panels was collected to obtain multiple dust data. Based on the coordinates of the multiple solar panel regions, and combined with the multiple vegetation data and multiple dust data, a vegetation data distribution and a dust data distribution are constructed. The preset maintenance frequency and preset maintenance cost within a preset time period of the solar modules are collected as preset maintenance parameters.

[0074] Furthermore, the bird droppings data acquisition module 12 is also used for: Based on the historical maintenance data of solar modules, a set of sample vegetation data was collected, as well as the frequency of solar modules being blocked by vegetation under different sample vegetation data, which was used as a set of sample vegetation blocking frequencies. Construct a mapping table between the sample vegetation data set and the sample vegetation occlusion frequency set to obtain a vegetation occlusion classifier; The vegetation data distribution is input into the vegetation shading classifier to obtain the vegetation shading frequency distribution. Construct a dust obstruction classifier and a bird droppings obstruction classifier, and input the dust obstruction data distribution and the bird droppings data distribution to obtain the dust obstruction frequency distribution and the bird droppings obstruction frequency distribution.

[0075] Furthermore, the occlusion frequency analysis module 13 is also used for: Based on the historical maintenance data of solar modules, a set of sample vegetation data was collected, as well as the frequency of solar modules being blocked by vegetation under different sample vegetation data, which was used as a set of sample vegetation blocking frequencies. Construct a mapping table between the sample vegetation data set and the sample vegetation occlusion frequency set to obtain a vegetation occlusion classifier; The vegetation data distribution is input into the vegetation shading classifier to obtain the vegetation shading frequency distribution. Construct a dust obstruction classifier and a bird droppings obstruction classifier, and input the dust obstruction data distribution and the bird droppings data distribution to obtain the dust obstruction frequency distribution and the bird droppings obstruction frequency distribution.

[0076] Furthermore, the hazard detection module 14 is also used for: Based on the frequency distributions of vegetation obstruction, dust obstruction, and bird droppings obstruction, the maintenance cost distribution is obtained. The shading frequency distribution is calculated based on the vegetation shading frequency distribution, sandstorm shading frequency distribution, and bird droppings shading frequency distribution. Based on the preset maintenance cost and maintenance cost distribution, multiple cost deviation ratios are calculated to construct a cost hazard information distribution. Based on the preset maintenance frequency and the obstruction frequency distribution, multiple frequency deviation ratios are calculated to construct a frequency hazard information distribution. The distribution of hazard information is obtained by summing the distribution of cost hazard information and the distribution of frequency hazard information.

[0077] The maintenance cost distribution is obtained by processing the frequency distributions of vegetation obstruction, dust obstruction, and bird droppings obstruction, including: By combining the maintenance costs of a single vegetation, dust, and bird droppings shielding maintenance, the vegetation maintenance cost, dust maintenance cost, and bird droppings maintenance cost within a preset time period are calculated, and the vegetation maintenance cost distribution, dust maintenance cost distribution, and bird droppings maintenance cost distribution are constructed. The maintenance cost distribution is calculated based on the distribution of vegetation maintenance costs, dust maintenance costs, and bird droppings maintenance costs.

[0078] This includes conducting a local hazard specificity analysis to obtain the specificity of local hazards, including: Based on the distribution of the hazard information, the average hazard information is calculated. Calculate the deviation between each hazard information and the average hazard information to obtain multiple local specificities, calculate the mean value, and obtain the local hazard specificity. Based on the specificity of the local hazards, the maintenance difficulty is classified and obtained, wherein the specificity of the local hazards is entered into the maintenance difficulty classification table to obtain the maintenance difficulty; The distribution of the aforementioned potential hazards and the difficulty of maintenance are used as the results of hazard detection for solar power plants.

[0079] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3As shown, an embodiment of the present invention provides an electronic device 100, including a memory 110, a processor 120, and a computer program 111 stored in the memory 110 and executable on the processor 120. When the processor 120 executes the computer program 111, it implements the steps of the method in embodiment 1.

[0080] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4 As shown, this embodiment provides a computer-readable storage medium 200 on which a computer program 111 is stored. When the computer program 111 is executed by a processor, it implements the steps of the method in embodiment 1.

[0081] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

Claims

1. A method for detecting a construction hazard of a solar power plant, characterized by, The method comprises: Collecting vegetation data and sand data in multiple solar component regions in a solar power station construction area, obtaining vegetation data distribution and sand data distribution, and collecting preset maintenance parameters of the solar components, the preset maintenance parameters including preset maintenance frequency and preset maintenance cost; Obtaining bird droppings data distribution according to the vegetation data distribution processing; Using the vegetation data distribution, sand data distribution and bird droppings data distribution, respectively performing shielding analysis to obtain vegetation shielding frequency distribution, sand shielding frequency distribution and bird droppings shielding frequency distribution; According to the preset maintenance parameters, combining the vegetation shielding frequency distribution, sand shielding frequency distribution and bird droppings shielding frequency distribution, performing construction and maintenance hazard analysis to obtain hazard information distribution, and performing local hazard speciality analysis to obtain local hazard speciality, classifying to obtain maintenance difficulty, and combining the hazard information distribution as a hazard detection result.

2. The method according to claim 1, wherein Collecting vegetation data and sand data in multiple solar component regions in a solar power station construction area, obtaining vegetation data distribution and sand data distribution, and collecting preset maintenance parameters of the solar components, including: Collecting coverage area proportion of vegetation categories of shieldable solar components in multiple solar component regions in a solar power station construction area to obtain multiple vegetation data; Collecting average sand concentration in the multiple solar component regions to obtain multiple sand data; According to the coordinates of the multiple solar component regions, combining the multiple vegetation data and multiple sand data, constructing vegetation data distribution and sand data distribution; Collecting preset maintenance frequency and preset maintenance cost in a preset time period of the solar components as preset maintenance parameters.

3. The method according to claim 1, wherein Obtaining bird droppings data distribution according to the vegetation data distribution processing, including: According to bird droppings shielding monitoring data records, collecting a sample vegetation data set, and collecting bird droppings frequency under different sample vegetation data to obtain a sample bird droppings data set; Constructing a mapping table of the sample vegetation data set and the sample bird droppings data set to obtain a bird droppings data classifier; Inputting multiple vegetation data in the vegetation data distribution into the bird droppings data classifier to classify and obtain multiple bird droppings data, and constructing bird droppings data distribution according to the coordinates of the multiple solar component regions.

4. The method according to claim 1, wherein Using the vegetation data distribution, sand data distribution and bird droppings data distribution, respectively performing shielding analysis, including: According to solar component maintenance historical data, collecting a sample vegetation data set, and frequency of solar components being shielded by vegetation under different sample vegetation data as a sample vegetation shielding frequency set; Constructing a mapping table of the sample vegetation data set and the sample vegetation shielding frequency set to obtain a vegetation shielding classifier; Inputting the vegetation data distribution into the vegetation shielding classifier to classify and obtain vegetation shielding frequency distribution; Constructing a sand shielding classifier and a bird droppings shielding classifier, inputting the sand data distribution and the bird droppings data distribution to obtain sand shielding frequency distribution and bird droppings shielding frequency distribution.

5. The method for detecting construction hazards of a solar power plant according to claim 1, characterized by, According to the preset maintenance parameter, the construction maintenance hidden danger analysis is performed in combination with the vegetation shielding frequency distribution, the sand and dust shielding frequency distribution and the bird droppings shielding frequency distribution, and hidden danger information distribution is obtained, including: According to the vegetation shielding frequency distribution, the sand and dust shielding frequency distribution, the bird droppings shielding frequency distribution, a maintenance cost distribution is processed and obtained; According to the vegetation shielding frequency distribution, the sand and dust shielding frequency distribution and the bird droppings shielding frequency distribution, a shielding frequency distribution is calculated and obtained; According to the preset maintenance cost and the maintenance cost distribution, a plurality of cost deviation ratios are calculated and constructed as a cost hidden danger information distribution; According to the preset maintenance frequency and the shielding frequency distribution, a plurality of frequency deviation ratios are calculated and constructed as a frequency hidden danger information distribution; The cost hidden danger information distribution and the frequency hidden danger information distribution are added and calculated to obtain the hidden danger information distribution.

6. The method according to claim 5, wherein According to the vegetation shielding frequency distribution, the sand and dust shielding frequency distribution, the bird droppings shielding frequency distribution, a maintenance cost distribution is processed and obtained, including: In combination with the maintenance cost of single vegetation, sand and dust, and bird droppings shielding maintenance, the vegetation maintenance cost, the sand and dust maintenance cost and the bird droppings maintenance cost in a preset time period are calculated and obtained to construct a vegetation maintenance cost distribution, a sand and dust maintenance cost distribution and a bird droppings maintenance cost distribution; According to the vegetation maintenance cost distribution, the sand and dust maintenance cost distribution and the bird droppings maintenance cost distribution, a maintenance cost distribution is calculated and obtained.

7. The method according to claim 1, wherein Local hidden danger speciality analysis is performed to obtain a local hidden danger speciality, including: According to the hidden danger information distribution, an average hidden danger information is calculated and obtained; The deviation of each hidden danger information and the average hidden danger information is calculated to obtain a plurality of local specialities, the average value is calculated to obtain the local hidden danger speciality; According to the local hidden danger speciality, a maintenance difficulty is classified and obtained, wherein the local hidden danger speciality is input into a maintenance difficulty classification table to classify and obtain the maintenance difficulty; In combination with the hidden danger information distribution and the maintenance difficulty, a hidden danger detection result of the solar power station is obtained.

8. A construction hazard detection platform for a solar power plant, characterized by, The platform includes: A power station data acquisition module is configured to acquire vegetation data and sand and dust data in a plurality of solar component regions in a solar power station construction area to obtain vegetation data distribution and sand and dust data distribution, and to acquire preset maintenance parameters of the solar component, the preset maintenance parameters including a preset maintenance frequency and a preset maintenance cost; A bird droppings data acquisition module is configured to acquire bird droppings data distribution according to the vegetation data distribution; A shielding frequency analysis module is configured to perform shielding analysis on the vegetation data distribution, the sand and dust data distribution and the bird droppings data distribution respectively to obtain a vegetation shielding frequency distribution, a sand and dust shielding frequency distribution and a bird droppings shielding frequency distribution; A hidden danger detection module is configured to perform construction maintenance hidden danger analysis according to the preset maintenance parameter in combination with the vegetation shielding frequency distribution, the sand and dust shielding frequency distribution and the bird droppings shielding frequency distribution to obtain hidden danger information distribution, to perform local hidden danger speciality analysis to obtain a local hidden danger speciality, to classify and obtain a maintenance difficulty, and to obtain a hidden danger detection result in combination with the hidden danger information distribution.

9. An electronic device, comprising: It includes: A memory is configured to store a computer software program; A processor is configured to read and execute the computer software program, thereby implementing the method for detecting construction hazards of a solar power station according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer software program, and the computer software program is executed by the processor to implement the method for detecting construction hazards of a solar power station according to any one of claims 1-7.

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