Image recognition and data analysis method for photovoltaic construction of mountain land with gulfweed landform

Abstract

The invention belongs to the related technical field of photovoltaic construction, and particularly relates to an image recognition and data analysis method for photovoltaic construction of a mountain land with a bamboo shoot landform, and the method comprises the following steps: S1, shooting a construction project in a photovoltaic construction region with the bamboo shoot landform through unmanned aerial vehicle shooting equipment, obtaining a real-time image in the construction process, and storing the real-time image in a database; and the real-time position information of the construction target is obtained, and the all-directional multi-angle shooting is performed on the key place in the construction project in the real-time image shooting process of the construction project, so that the all-directional real-time image of the key place in the project construction process is obtained. By preprocessing the collected real-time image of the project construction site, irrelevant information in the image can be eliminated, useful real information can be recovered, the detectability of related information can be enhanced, and data can be simplified to the greatest extent, so that the reliability of feature extraction, matching and recognition can be improved.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic construction related technology, specifically involving image recognition and data analysis methods for photovoltaic construction in mountainous areas with stalagmite landforms. Background Technology

[0002] With the adjustment of the global energy structure and the rapid development of renewable energy, photovoltaic power generation, as an important component of clean energy, is expanding its application scale. Mountain photovoltaic power stations, due to their advantages such as high land utilization and good power generation efficiency, are gradually becoming an important development direction for the photovoltaic industry. However, as a typical karst landform, the stalagmite landform has characteristics such as complex terrain, steep slopes, and exposed rocks, which bring huge challenges to the construction of photovoltaic power stations. In the process of constructing stalagmite landform mountain photovoltaic projects, traditional manual inspection is inefficient and costly. Under complex terrain, it is difficult to control the construction quality. Therefore, drone systems are used to take real-time pictures of the construction site and identify and analyze the pictures to control the construction behavior and construction quality during the project construction process.

[0003] Problems with existing technology: Existing image recognition and data analysis methods for photovoltaic construction in mountainous areas with stalagmite topography have significant shortcomings in image analysis and processing. Current image analysis methods mostly rely on manual post-processing of images collected by drones, lacking intelligent analysis tools. They cannot quickly and accurately identify defect information of construction targets. In addition, it is difficult to combine historical data for trend analysis, making it difficult to predict potential problems during construction and thus unable to take timely and effective countermeasures. Summary of the Invention

[0004] The purpose of this invention is to provide an image recognition and data analysis method for photovoltaic construction in mountainous areas with stalagmite landforms. This method addresses the significant shortcomings of existing image recognition and data analysis methods for photovoltaic construction in mountainous areas with stalagmite landforms in terms of image analysis and processing. Existing image analysis methods mostly rely on manual post-processing of images collected by drones, lacking intelligent analysis tools. They cannot quickly and accurately identify the defect information of construction targets. Furthermore, they are difficult to combine with historical data for trend analysis, making it difficult to predict potential problems during construction and resulting in the inability to take timely and effective countermeasures.

[0005] The specific technical solution adopted by this invention is as follows: A method for image recognition and data analysis of photovoltaic construction in stalagmite-shaped mountainous terrain includes the following steps: S1. Use drone photography equipment to photograph the construction project in the photovoltaic construction area of ​​the stalactite landform, obtain real-time images of the construction process, and obtain the real-time location information of the construction target. At the same time, during the real-time image shooting of the construction project, take all-round and multi-angle shots of key places in the construction project to obtain all-round real-time images of key places in the project construction process. S2. Preprocess the acquired real-time images, including correcting distortion, enhancing image contrast, and denoising the images. S3. Based on the set key feature information extraction set, identify and label the target information on the pre-processed image. The feature information extraction set includes the construction materials used in the project construction process, the completed building features in the project construction process, the construction equipment used in the project, the actions of the construction personnel in the construction process, and the safety protection equipment worn in the construction process. S4. Based on the progress of project construction, conduct violation analysis on the key features marked on the images in step S3 and determine whether any violations have occurred during the project construction process. At the same time, analyze the completion rate of project construction by performing feature matching on the collected images based on the project building information model. S5. Based on the analysis results given in step S4 and combined with historical data, predict the risks that exist in the project construction process and the time required to complete the project schedule. S6. Generate a report based on the prediction results in step S5 and rank the severity of the risks associated with the violations. At the same time, provide reasonable and feasible rectification suggestions based on the potential risks during the project construction process.

[0006] The steps for key aspects of the construction project in S1 include: the placement of construction materials and equipment during the construction process, the operational procedures of construction personnel during the construction process, the installation details of equipment during the construction process, and the hazardous areas that exist during the construction process.

[0007] The method for correcting image distortion in step S2 is to use the checkerboard calibration method to correct image distortion; the method for enhancing image contrast in step S2 is to use the CLAHE algorithm (contrast-limited adaptive histogram equalization) to adjust the image to make it clearer, and to address the shadow problem in mountainous areas, the Retinex algorithm is used to enhance dark details; the method for denoising the image in step S2 is to remove speckle noise through median filtering and to smooth the image through Gaussian filtering.

[0008] Violations during the project construction process in step S4 include construction personnel not wearing safety protective equipment properly, improper equipment operation, whether equipment and materials are placed in safe areas, and dangerous behaviors in hazardous areas during the project construction process.

[0009] The risks involved in the project construction process mentioned in step S5 include the risk of materials being overturned and damaged due to disorderly stacking during the project construction process, the risk of operators being easily injured during the construction process due to not wearing safety protective equipment properly, the risk of safety accidents caused by construction personnel entering dangerous areas during the project construction process, and the safety risks caused by equipment aging and damage during the project construction process.

[0010] The specific and feasible rectification suggestions proposed in step S6 include: promptly rearranging materials in a timely manner to ensure they are in a safe area, and reinforcing them appropriately as needed; immediately stopping work before major safety hazards are resolved or rectified; providing visual safety education on the behavioral norms of construction personnel during project construction; establishing a positive incentive mechanism; and conducting real-time monitoring of hazardous areas in the project and setting up clear signs in these areas.

[0011] The technical effects achieved by this invention are as follows: This invention preprocesses real-time images of the project construction site to eliminate irrelevant information, restore useful and accurate information, enhance the detectability of relevant information, and simplify data to the maximum extent. This improves the reliability of feature extraction, matching, and recognition, highlights key structural features, and helps in subsequent analysis and judgment of safety risks and construction quality issues during the project construction process. Then, by intelligently extracting and labeling key feature information from the real-time images, it accurately identifies problems existing in the project construction process. By combining these problems with timely data analysis, it accurately predicts potential risks during the project construction process and promptly proposes specific and implementable reasonable suggestions to ensure construction safety and quality. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0013] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0014] like Figure 1 As shown, the image recognition and data analysis method for photovoltaic construction in stalagmite-shaped mountainous terrain includes the following steps: S1. Use drones to photograph the construction project in the photovoltaic construction area of ​​the stalactite landform, obtain real-time images of the construction process, and obtain real-time location information of the construction target. At the same time, while taking real-time images of the construction project, take all-round and multi-angle photos of key areas of the construction project to obtain all-round real-time images of key areas during the construction process. S2. Preprocess the acquired real-time images, including correcting distortion, enhancing image contrast, and denoising the images. S3. Based on the set key feature information extraction set, the target information on the preprocessed image is identified and labeled. The feature information extraction set includes the construction materials used in the project construction process, the completed building features in the project construction process, the construction equipment used in the project, the actions of the construction personnel in the construction process, and the safety protection equipment worn in the construction process. By extracting key feature information on the image, the construction safety behavior and building construction status in the project construction process can be accurately and quickly identified, thereby improving the efficiency of image recognition and analysis. S4. Based on the progress of project construction, conduct violation analysis on the key features marked on the images in step S3 and determine whether any violations have occurred during the project construction process. At the same time, analyze the completion rate of project construction by performing feature matching on the collected images based on the project building information model. S5. Based on the analysis results given in step S4 and combined with historical data, predict the risks that exist in the project construction process and the time required to complete the project schedule. S6. Generate a report based on the prediction results in step S5 and rank the severity of the risks associated with the violations. At the same time, provide reasonable and feasible rectification suggestions based on the potential risks during the project construction process.

[0015] Furthermore, the steps in S1 include key areas of the construction project such as: the placement of construction materials and equipment during the construction process, the operational procedures of construction personnel, the installation details of equipment, and hazardous areas. By taking comprehensive, multi-angle photos of these key areas, the images can be accurately identified and analyzed. Combined with historical data, this allows for a more accurate prediction of the risks present during the construction process. This improves the accuracy of identifying and analyzing the construction site based on real-time images.

[0016] Furthermore, in step S2, the method for correcting image distortion is to use a checkerboard calibration method to correct image distortion; the method for enhancing image contrast in step S2 is to use the CLAHE algorithm (contrast-limited adaptive histogram equalization) to adjust the image to make it clearer, and to address the issue of shadows in mountainous areas, the Retinex algorithm is used to enhance dark details; the method for denoising the image in step S2 is to use median filtering to remove speckle noise and Gaussian filtering to smooth the image. Preprocessing the real-time images of the acquired project construction site can eliminate irrelevant information in the image, restore useful real information, enhance the detectability of relevant information, simplify the data to the maximum extent, and thus improve the reliability of feature extraction, matching, and recognition. In the images of the construction site, preprocessing can remove noise and irrelevant background, highlight key structural features, and help subsequent analysis and judgment of safety risks and construction quality issues during the project construction process.

[0017] Violations during the project construction process in step S4 include construction personnel not wearing safety protective equipment properly, improper operation of equipment, whether equipment and materials are placed in safe areas, and dangerous behaviors in hazardous areas during the project construction process.

[0018] The risks involved in the project construction process in step S5 include the risk of materials being overturned and damaged due to disorderly stacking during the project construction process; the risk of workers being easily injured during the construction process due to not wearing safety protective equipment properly; the risk of safety accidents caused by workers entering dangerous areas during the project construction process; and the safety risks caused by equipment aging and damage during the project construction process.

[0019] The specific and feasible rectification suggestions proposed in step S6 include: promptly rearranging materials to ensure they are in a safe area when they are scattered during project construction; reinforcing materials as needed during the placement process; immediately halting work until major safety hazards are resolved or rectified; providing visual safety education on the behavioral norms of construction personnel during project construction; establishing a positive incentive mechanism; and conducting real-time monitoring of hazardous areas in the project and setting up clear signs in these areas. By analyzing existing construction problems in conjunction with timely data, the potential risks during project construction can be accurately predicted, and specific and feasible reasonable suggestions can be made to ensure construction safety and quality during the project construction process.

[0020] The specific method of this invention is as follows: S1. Use drone photography equipment to photograph the construction project in the photovoltaic construction area of ​​the stalactite landform, obtain real-time images of the construction process, and obtain the real-time location information of the construction target. At the same time, during the real-time image shooting of the construction project, take all-round and multi-angle shots of key places in the construction project to obtain all-round real-time images of key places in the project construction process. S2. Preprocess the acquired real-time images, including correcting distortion, enhancing image contrast, and denoising the images. S3. Based on the set key feature information extraction set, identify and label the target information on the pre-processed image. The feature information extraction set includes the construction materials used in the project construction process, the completed building features in the project construction process, the construction equipment used in the project, the actions of the construction personnel in the construction process, and the safety protection equipment worn in the construction process. S4. Based on the progress of project construction, conduct violation analysis on the key features marked on the images in step S3 and determine whether any violations have occurred during the project construction process. At the same time, analyze the completion rate of project construction by performing feature matching on the collected images based on the project building information model. S5. Based on the analysis results given in step S4 and combined with historical data, predict the risks that exist in the project construction process and the time required to complete the project schedule. S6. Generate a report based on the prediction results in step S5 and rank the severity of the risks associated with the violations. At the same time, provide reasonable and feasible rectification suggestions based on the potential risks during the project construction process.

[0021] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for image recognition and data analysis of photovoltaic construction in mountainous areas with stalagmite landforms, characterized by: Includes the following steps: S1. Use drone photography equipment to photograph the construction project in the photovoltaic construction area of ​​the stalactite landform, obtain real-time images of the construction process, and obtain the real-time location information of the construction target. At the same time, during the real-time image shooting of the construction project, take all-round and multi-angle shots of key places in the construction project to obtain all-round real-time images of key places in the project construction process. S2. Preprocess the acquired real-time images, including correcting distortion, enhancing image contrast, and denoising the images. S3. Based on the set key feature information extraction set, identify and label the target information on the pre-processed image. The feature information extraction set includes the construction materials used in the project construction process, the completed building features in the project construction process, the construction equipment used in the project, the actions of the construction personnel in the construction process, and the safety protection equipment worn in the construction process. S4. Based on the progress of project construction, conduct violation analysis on the key features marked on the images in step S3 and determine whether any violations have occurred during the project construction process. At the same time, analyze the completion rate of project construction by performing feature matching on the collected images based on the project building information model. S5. Based on the analysis results given in step S4 and combined with historical data, predict the risks that exist in the project construction process and the time required to complete the project schedule. S6. Generate a report based on the prediction results in step S5 and rank the severity of the risks associated with the violations. At the same time, provide reasonable and feasible rectification suggestions based on the potential risks during the project construction process.

2. The method for image recognition and data analysis of photovoltaic construction in stalagmite landforms according to claim 1, characterized in that: The steps for key aspects of the construction project in S1 include: the placement of construction materials and equipment during the construction process, the operational procedures of construction personnel during the construction process, the installation details of equipment during the construction process, and the hazardous areas that exist during the construction process.

3. The method for image recognition and data analysis of photovoltaic construction in stalagmite-shaped mountainous terrain according to claim 1, characterized in that: The method for correcting image distortion in step S2 is to use the checkerboard calibration method to correct image distortion; the method for enhancing image contrast in step S2 is to use the CLAHE algorithm (contrast-limited adaptive histogram equalization) to adjust the image to make it clearer, and to address the shadow problem in mountainous areas, the Retinex algorithm is used to enhance dark details; the method for denoising the image in step S2 is to remove speckle noise through median filtering and to smooth the image through Gaussian filtering.

4. The method for image recognition and data analysis of photovoltaic construction in stalagmite-shaped mountainous terrain according to claim 1, characterized in that: Violations during the project construction process in step S4 include construction personnel not wearing safety protective equipment properly, improper equipment operation, whether equipment and materials are placed in safe areas, and dangerous behaviors in hazardous areas during the project construction process.

5. The method for image recognition and data analysis of photovoltaic construction in stalagmite landforms according to claim 1, characterized in that: The risks involved in the project construction process mentioned in step S5 include the risk of materials being overturned and damaged due to disorderly stacking during the project construction process, the risk of operators being easily injured during the construction process due to not wearing safety protective equipment properly, the risk of safety accidents caused by construction personnel entering dangerous areas during the project construction process, and the safety risks caused by equipment aging and damage during the project construction process.

6. The method for image recognition and data analysis of photovoltaic construction in stalagmite-shaped mountainous terrain according to claim 1, characterized in that: The specific and feasible rectification suggestions proposed in step S6 include: promptly rearranging materials in a timely manner to ensure they are in a safe area, and reinforcing them appropriately as needed; immediately stopping work before major safety hazards are resolved or rectified; providing visual safety education on the behavioral norms of construction personnel during project construction; establishing a positive incentive mechanism; and conducting real-time monitoring of hazardous areas in the project and setting up clear signs in these areas.

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