Unmanned aerial vehicle intelligent inspection system and method for wind and light station

Abstract

The invention discloses an unmanned aerial vehicle intelligent inspection system and method for a wind and light station, and relates to the technical field of unmanned aerial vehicle inspection, the system comprises an unmanned aerial vehicle platform module, a ground control system module, a data transmission and processing module and an intelligent analysis module, the unmanned aerial vehicle platform module comprises an unmanned aerial vehicle landing platform and an inspection unmanned aerial vehicle, the ground control system module comprises an automatic task planning module and an automatic task execution module, the automatic task planning module can command the inspection unmanned aerial vehicle to autonomously execute an inspection task according to a preset inspection plan and a target area and a route planned by the automatic task planning module. According to the invention, the intelligent analysis module comprises an intelligent fault diagnosis and early warning module, the AI image recognition technology is utilized to intelligently analyze the collected images and videos, and the defects and faults of the wind power equipment and the photovoltaic module are automatically recognized, so that the faults do not need to be manually watched and checked, and the accuracy is improved while the manpower is saved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle inspection technology, and in particular to an unmanned aerial vehicle intelligent inspection system and method for wind and solar power stations. Background Art

[0002] A wind and solar power station is a new type of practical power station built using wind and solar energy. It combines two renewable energy sources, wind and solar energy, and can provide stable power supply under different weather conditions, improving the reliability and stability of the power system. A wind and solar power station is usually composed of wind turbines, solar photovoltaic panels, energy storage systems, inverters and other equipment; wind turbines and solar photovoltaic panels use wind and solar energy to generate electricity respectively, and the generated electricity is stored and adjusted by the energy storage system, and finally the DC power is converted into AC power through the inverter for users. The construction of a wind and solar power station needs to consider many factors, such as geographical location, climate conditions, resource distribution, power demand, etc.; during the construction process, detailed preliminary research and design are required to ensure the feasibility and economy of the power station; at the same time, the operation and maintenance of the power station also need to be considered to ensure the long-term stable operation and benefits of the power station. The traditional manual inspection method is inefficient, high-intensity and costly, especially for high-voltage lines, which are easy to threaten personal safety, so the drone inspection method has come into people's sight. The following problems exist in the existing technology:

[0003] Because the existing drone inspection method can only be used for inspection and shooting through high-definition lenses, the backstage staff rely on the shooting images to conduct inspection work, but this method has low accuracy and is too labor-intensive, and is prone to omissions. At the same time, the existing drone inspection method is difficult to make reasonable avoidance measures when facing complex environments, resulting in it being difficult to dodge in time when encountering objects such as flying birds in the air during the inspection, which makes it easy for collisions and damage to occur during the inspection. Summary of the invention

[0004] The present invention provides a UAV intelligent inspection system and method for wind and solar power stations to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A system and method for intelligent inspection of wind and solar power stations by unmanned aerial vehicles, comprising an unmanned aerial vehicle platform module, a ground control system module, a data transmission and processing module, and an intelligent analysis module. The unmanned aerial vehicle platform module comprises an unmanned aerial vehicle landing platform and an inspection unmanned aerial vehicle. The ground control system module comprises an automatic task planning module and an automatic task execution module. The automatic task planning module can automatically generate an optimal inspection path according to a preset inspection plan and a target area. The automatic task execution module can command the inspection unmanned aerial vehicle to autonomously perform the inspection task according to the route planned by the automatic task planning module, and support collaborative operation of multiple unmanned aerial vehicles. The data transmission and processing module comprises a real-time data acquisition module and a real-time data transmission module. The real-time data acquisition module can collect image data, video data, temperature data, and humidity data of the wind and solar power station in real time. The real-time data transmission module can transmit the data collected by the real-time data acquisition module to the ground control system module or a cloud server in real time through a high-speed data transmission link.

[0007] A further improvement of the technical solution of the present invention is that the real-time data acquisition module includes a high-definition camera, an infrared thermal imager, and a multi-spectral sensor, and the high-definition camera, infrared thermal imager, and multi-spectral sensor are all carried on a patrol drone.

[0008] A further improvement of the technical solution of the present invention is that the intelligent analysis module includes an intelligent fault diagnosis and early warning module and a data visualization analysis module. The intelligent fault diagnosis and early warning module uses AI image recognition technology to perform intelligent analysis on the collected images and videos, and automatically identify defects and faults of wind power equipment and photovoltaic components. The data visualization analysis module provides a data visualization display platform, presents the inspection results to users in the form of charts and reports, and uses big data analysis technology to deeply mine and analyze the inspection data.

[0009] A further improvement of the technical solution of the present invention is that the inspection UAV includes an autonomous flight module and an intelligent obstacle avoidance module. The autonomous flight module enables the inspection UAV to have fully autonomous flight capabilities, supports one-key take-off, automatic landing and autonomous charging functions, and the intelligent obstacle avoidance module adopts visual obstacle avoidance technology, which allows the inspection UAV to achieve automatic obstacle avoidance and precise positioning in complex environments.

[0010] A method for intelligent inspection of wind and solar power stations by drones, comprising the following steps:

[0011] S1: Take-off and navigation: the inspection drone takes off from the drone landing platform and navigates autonomously according to the preset route;

[0012] S2: Data collection: the inspection drone uses the high-definition camera, infrared thermal imager, and multi-spectral sensor to photograph and scan the wind turbines and photovoltaic panels;

[0013] S3: Intelligent identification and analysis. Inspection data is transmitted to the cloud or ground station in real time. Through AI image recognition technology, it can automatically detect equipment anomalies and failures, analyze the operating status and performance of the equipment, and evaluate power generation efficiency and safety.

[0014] S4: Generate reports and disposal. According to the data analysis results, the inspection report is automatically generated, including equipment status, abnormality description, and processing suggestions. The report is transmitted to relevant personnel so that timely measures can be taken for fault repair or preventive maintenance.

[0015] A further improvement of the technical solution of the present invention is that the GPS navigation and visual obstacle avoidance technology adopted by the intelligent obstacle avoidance module in S1 is used to ensure the safety and stability of the drone during the inspection process.

[0016] A further improvement of the technical solution of the present invention is that the visual obstacle avoidance technology captures images of the surrounding environment through a high-definition camera installed on the inspection drone, processes and analyzes the images using computer vision technology, and extracts information about obstacles. By identifying and classifying obstacles, the drone can determine the nature and degree of danger of the obstacles, and thus choose an avoidance strategy.

[0017] A further improvement of the technical solution of the present invention is that the inspection drone in S2 can fly over the towers and blades of wind turbines in the wind farm, detect defects and faults of the towers and blades through high-definition cameras and infrared thermal imagers, and can quickly traverse various areas of the photovoltaic power station, detect cracks, obstruction defects and faults of photovoltaic modules through multi-spectral sensors, and collect images and data.

[0018] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0019] 1. The present invention provides a UAV intelligent inspection system and method for wind and solar power stations. The intelligent analysis module includes an intelligent fault diagnosis and early warning module, which uses AI image recognition technology to perform intelligent analysis on the collected images and videos, automatically identify defects and faults of wind power equipment and photovoltaic components, thereby eliminating the need for manual inspection of faults, saving manpower and improving accuracy.

[0020] 2. The present invention provides a system and method for intelligent inspection of wind and solar power stations by drones. Through the visual avoidance technology adopted by the intelligent obstacle avoidance module, computer vision technology is used to process and analyze images, and obstacle information is extracted. The nature and degree of danger of the obstacle are determined by identifying and classifying the obstacle. According to the nature and degree of danger of the obstacle, a suitable avoidance strategy is selected to perfectly avoid obstacles that need to be avoided and reduce the risk of damage to the drone.

[0021] 3. The present invention provides a UAV intelligent inspection system and method for wind and solar power stations. Through the mutual cooperation among the UAV platform module, the ground control system module, the data transmission and processing module and the intelligent analysis module, the inspection UAV of the intelligent inspection system can realize functions such as automatic take-off, autonomous cruising, automatic data collection, real-time data transmission and processing, intelligent fault diagnosis and early warning, etc., which greatly improves the efficiency and accuracy of the inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a system schematic diagram of the present invention;

[0023] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described in detail below in conjunction with embodiments:

[0025] Example 1

[0026] like Figure 1 , Figure 2 As shown, the present invention provides a kind of UAV intelligent inspection system for wind and solar power stations, including a UAV platform module, a ground control system module, a data transmission and processing module and an intelligent analysis module. The UAV platform module includes a UAV landing platform and an inspection UAV. The ground control system module includes an automatic task planning module and an automatic task execution module. The automatic task planning module can automatically generate an optimal inspection path according to a preset inspection plan and a target area. The automatic task execution module can command the inspection UAV to autonomously perform the inspection task according to the route planned by the automatic task planning module, and support collaborative operation of multiple UAVs to improve the inspection coverage and efficiency. The data transmission and processing module includes a real-time data acquisition module and a real-time data transmission module. The real-time data acquisition module can collect image data, video data, temperature data and humidity data of the wind and solar power stations in real time. The real-time data transmission module can transmit the data collected by the real-time data acquisition module to the ground control system module or the cloud server in real time through a high-speed data transmission link.

[0027] In this embodiment, the inspection drone can quickly cover a large inspection area of ​​the wind farm station, improve inspection efficiency, realize non-stop inspection, reduce power generation loss and downtime, reduce the risk of high-altitude operations in manual inspections, ensure the safety of personnel, and can conduct inspections in severe weather or complex environments to reduce the risk of personal injury. At the same time, it can reduce inspection costs and labor costs, detect faults in a timely manner and take measures to deal with them, avoiding greater economic losses and safety accidents.

[0028] Example 2

[0029] like Figure 1, Figure 2 As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, the real-time data acquisition module includes a high-definition camera, an infrared thermal imager, and a multispectral sensor, and the high-definition camera, the infrared thermal imager, and the multispectral sensor are all mounted on the inspection drone, the intelligent analysis module includes an intelligent fault diagnosis and early warning module and a data visualization analysis module, the intelligent fault diagnosis and early warning module uses AI image recognition technology to perform intelligent analysis on the collected images and videos, and automatically identify defects and faults of wind power equipment and photovoltaic components, the data visualization analysis module provides a data visualization display platform, presents the inspection results to users in the form of charts and reports, and uses big data analysis technology to deeply mine and analyze the inspection data, the inspection drone includes an autonomous flight module and an intelligent obstacle avoidance module, the autonomous flight module can enable the inspection drone to have fully autonomous flight capabilities, support one-key takeoff, automatic landing and autonomous charging functions, reduce manual intervention and operation difficulty, and the intelligent obstacle avoidance module uses visual obstacle avoidance technology, which can enable the inspection drone to achieve automatic obstacle avoidance and precise positioning in complex environments.

[0030] In this embodiment, AI image recognition technology is based on a deep learning algorithm, which enables computers to autonomously learn and recognize features in images by imitating the working mode of the human brain neural network; its core principles include the following steps: data acquisition: collecting a large amount of image data collected by inspection drones, which comes from high-definition cameras, infrared thermal imagers, multi-spectral sensors and other equipment; feature extraction: processing image data layer by layer through neural networks, and gradually extracting edge, texture, and shape features; model training: optimizing the connection weights in the neural network through a large amount of training data to improve the recognition effect; recognition results: comparing the extracted images and data features of key components such as blades, gearboxes, generators, photovoltaic panels, etc. with the features in the database to identify objects in the image.

[0031] Example 3

[0032] like Figure 1 , Figure 2 As shown, the present invention provides a method for intelligent inspection of wind and solar power stations by unmanned aerial vehicles: preferably, the method comprises the following steps:

[0033] S1: Take-off and navigation: the inspection drone takes off from the drone landing platform and navigates autonomously according to the preset route;

[0034] S2: Data collection: the inspection drone uses the high-definition camera, infrared thermal imager, and multi-spectral sensor to photograph and scan the wind turbines and photovoltaic panels;

[0035] S3: Intelligent identification and analysis. Inspection data is transmitted to the cloud or ground station in real time. Through AI image recognition technology, it can automatically detect equipment anomalies and failures, analyze the operating status and performance of the equipment, and evaluate power generation efficiency and safety.

[0036] S4: Generate reports and disposal. According to the data analysis results, the inspection report is automatically generated, including equipment status, abnormality description, and processing suggestions. The report is transmitted to relevant personnel so that timely measures can be taken for fault repair or preventive maintenance.

[0037] Example 4

[0038] like Figure 1 , Figure 2 As shown, on the basis of Example 3, the present invention provides a technical solution: preferably, the GPS navigation and visual obstacle avoidance technology adopted by the intelligent obstacle avoidance module in S1 are used to ensure the safety and stability of the UAV during the inspection process. The visual obstacle avoidance technology captures the image of the surrounding environment through a high-definition camera installed on the inspection UAV, and uses computer vision technology to process and analyze the image to extract the information of the obstacle. By identifying and classifying the obstacles, the UAV can determine the nature and degree of danger of the obstacle, so as to select an avoidance strategy. In S2, the inspection UAV can fly over the tower and blades of the wind turbine in the wind farm, detect defects and faults of the tower and blades through high-definition cameras and infrared thermal imagers, and quickly traverse various areas of the photovoltaic power station. The cracks, obstruction defects and faults of the photovoltaic modules are detected through multi-spectral sensors, images and data are collected, and key indicators such as the working status and power generation of the photovoltaic modules are monitored to ensure the stable operation of the photovoltaic power station.

[0039] In this embodiment, the working principle of the visual obstacle avoidance technology mainly includes the following steps:

[0040] Image capture: The high-definition camera on the inspection drone captures images of the surrounding environment.

[0041] Image processing: Use computer vision technology to process and analyze images and extract information about obstacles.

[0042] Obstacle identification: By identifying and classifying obstacles, the nature and degree of danger of the obstacles can be determined.

[0043] Obstacle avoidance strategy selection: Select an appropriate avoidance strategy based on the nature and degree of danger of the obstacle.

[0044] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A UAV intelligent inspection system for wind and solar power stations, characterized by: It includes a UAV platform module, a ground control system module, a data transmission and processing module, and an intelligent analysis module. The UAV platform module includes a UAV landing platform and an inspection UAV. The ground control system module includes an automatic task planning module and an automatic task execution module. The automatic task planning module can automatically generate an optimal inspection path according to a preset inspection plan and target area. The automatic task execution module can command the inspection UAV to autonomously perform inspection tasks according to the route planned by the automatic task planning module, and support collaborative operation of multiple UAVs. The data transmission and processing module includes a real-time data acquisition module and a real-time data transmission module. The real-time data acquisition module can collect image data, video data, temperature data, and humidity data of wind and solar power stations in real time. The real-time data transmission module can transmit the data collected by the real-time data acquisition module to the ground control system module or the cloud server in real time through a high-speed data transmission link.

2. The intelligent inspection system for wind and solar power stations using unmanned aerial vehicles according to claim 1 is characterized by: The real-time data acquisition module includes a high-definition camera, an infrared thermal imager, and a multi-spectral sensor, and the high-definition camera, infrared thermal imager, and multi-spectral sensor are all carried on a patrol drone.

3. The intelligent inspection system for wind and solar power stations using unmanned aerial vehicles according to claim 1 is characterized by: The intelligent analysis module includes an intelligent fault diagnosis and early warning module and a data visualization analysis module. The intelligent fault diagnosis and early warning module uses AI image recognition technology to perform intelligent analysis on the collected images and videos, and automatically identify the defects and faults of wind power equipment and photovoltaic components. The data visualization analysis module provides a data visualization display platform, presents the inspection results to users in the form of charts and reports, and uses big data analysis technology to conduct in-depth mining and analysis of inspection data.

4. The intelligent inspection system for wind and solar power stations using unmanned aerial vehicles according to claim 1 is characterized in that: The inspection drone includes an autonomous flight module and an intelligent obstacle avoidance module. The autonomous flight module enables the inspection drone to have fully autonomous flight capabilities, supporting one-key take-off, automatic landing and autonomous charging functions. The intelligent obstacle avoidance module uses visual obstacle avoidance technology, which allows the inspection drone to achieve automatic obstacle avoidance and precise positioning in complex environments.

5. A method for intelligent inspection of wind and solar power stations by drones, characterized in that: The following steps are involved: S1: Take-off and navigation: the inspection drone takes off from the drone landing platform and navigates autonomously according to the preset route; S2: Data collection: the inspection drone uses the high-definition camera, infrared thermal imager, and multi-spectral sensor to photograph and scan the wind turbines and photovoltaic panels; S3: Intelligent identification and analysis. Inspection data is transmitted to the cloud or ground station in real time. Through AI image recognition technology, it can automatically detect equipment anomalies and failures, analyze the operating status and performance of the equipment, and evaluate power generation efficiency and safety. S4: Generate reports and disposal. According to the data analysis results, the inspection report is automatically generated, including equipment status, abnormality description, and processing suggestions. The report is transmitted to relevant personnel so that timely measures can be taken for fault repair or preventive maintenance.

6. The method for intelligent inspection of wind and solar power stations by drones according to claim 5 is characterized in that: The S1 utilizes the GPS navigation and visual obstacle avoidance technology adopted by the intelligent obstacle avoidance module to ensure the safety and stability of the drone during the inspection process.

7. The method for intelligent inspection of wind and solar power stations by drones according to claim 6 is characterized in that: The visual obstacle avoidance technology captures images of the surrounding environment through a high-definition camera installed on the inspection drone, processes and analyzes the images using computer vision technology, and extracts information about obstacles. By identifying and classifying obstacles, the drone can determine the nature and degree of danger of the obstacles and thus choose an avoidance strategy.

8. The method for intelligent inspection of wind and solar power stations by drones according to claim 5 is characterized in that: The inspection drone in S2 can fly over the towers and blades of wind turbines in wind farms, detect defects and faults of the towers and blades through high-definition cameras and infrared thermal imagers, and can quickly traverse various areas of photovoltaic power stations, detect cracks, obstruction defects and faults of photovoltaic modules through multi-spectral sensors, and collect images and data.

Images