A wind power generation device management system and method for unmanned aerial vehicle data collection

Abstract

The application provides a wind power generation device management system and method for unmanned aerial vehicle data collection, the system comprises a plurality of master unmanned aerial vehicles, a central processing calculation control center module and a cloud data processing module in communication with the plurality of master unmanned aerial vehicles, a plurality of sub-unmanned aerial vehicles, each of which is provided with a data collection module, each master unmanned aerial vehicle and the plurality of sub-unmanned aerial vehicles form a distributed edge processing local area network, the master unmanned aerial vehicle in the distributed edge processing local area network serves as an edge controller, and the plurality of sub-unmanned aerial vehicles serve as edge servers; the master unmanned aerial vehicle and the plurality of sub-unmanned aerial vehicles are connected in communication through a wireless communication module. The application uses the master unmanned aerial vehicle as an edge computing processing node, communicates with the cloud data processing module, thereby dispersing the data information processing pressure of the cloud data processing module, complementing the cloud computing of the cloud data processing module, and solving the problem that cannot be solved by only transmitting all data to the cloud data processing module for processing and calculation without edge computing.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation management technology, specifically relating to a wind power generation device management system and method for unmanned aerial vehicle (UAV) data acquisition. Background Technology

[0002] The natural environment contains a wide variety of energy sources, with wind, solar, vibrational, thermal, and electromagnetic energy being the most abundant. Wind energy, as a rich source of energy in the natural environment, is not only renewable and sustainable but also clean and environmentally friendly, making it ubiquitous. Therefore, wind energy has always been highly favored; it is not only one of the first energy sources extracted from the environment by humankind but also a key area of ​​research in clean energy self-sufficiency. Wind energy is a common natural phenomenon, especially in economically underdeveloped mountainous regions rich in wind resources. Increasing the number of local wind turbines can not only increase my country's economic value but also provide local energy supply.

[0003] Given the importance of wind energy to the current social structure, how to improve the real-time dynamic detectability of wind power generation equipment operation and allow for timely detection of problems throughout the entire operation of the equipment, so as to make the entire wind power generation equipment operation more stable and safe, is an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned shortcomings, this invention provides a management system and method for wind power generation devices that uses a second network protocol interface to directly connect the main UAV control device (acting as an edge controller) to a central processing computing and control center module, and a first network protocol interface to directly connect the main UAV control device (acting as an edge controller) to a cloud data processing center module. This enables cloud-edge collaborative development and separates the calculation of flight path control and energy consumption control for multiple sub-UAVs in a distributed edge processing local area network. This effectively improves accuracy and reduces the data computation load and energy consumption of the data processing center.

[0005] This invention provides the following technical solution: a wind power generation device management system for unmanned aerial vehicle (UAV) data acquisition, the system comprising several master UAVs, each master UAV forming a distributed edge processing local area network (DELAN) with multiple sub-UAVs, characterized in that the master UAVs in the DELAN serve as edge controllers, and the multiple sub-UAVs in the DELAN serve as edge servers; the master UAVs and the multiple sub-UAVs in the DELAN communicate with each other via wireless communication modules; the system further includes a central processing computing control center module and a cloud data processing module that communicate with the master UAVs, and each of the multiple sub-UAVs is equipped with a data acquisition module;

[0006] Each main UAV includes a first network protocol interface and a second network protocol interface. The first network protocol interface is used to communicate with multiple sub-UAVs and the cloud data processing module through a wireless communication system. The second network protocol interface is used to communicate with each main UAV and the central processing computing control center module.

[0007] Each of the main drones is equipped with a drone control module for local data processing, serving as an edge computing processing node of the edge controller. This enables multiple sub-drones, acting as edge servers, to ultimately communicate with the cloud data processing module through the edge controller.

[0008] Furthermore, the wireless communication system includes a PLC communication module, an LED display module, and a network protocol communication configuration module; the network protocol communication configuration module is used to configure specific communication parameters to enable communication between the UAV control module (which acts as an edge computing processing node) and the cloud data processing module; the LED display module is used to display real-time data monitoring information, historical data table information, and real-time data curve information formed from historical data.

[0009] Furthermore, the PLC communication module enables communication between multiple sub-UAVs and the main UAV within a distributed edge processing local area network based on the Socket protocol.

[0010] Furthermore, the network protocol configured in the network protocol communication configuration module is one of the following: Message Queuing Telemetry Transport Protocol, DDS Protocol, AMQP Protocol, or JMS Protocol.

[0011] Furthermore, the UAV control module is used to perform edge computing on the data collected by the data acquisition module, perform edge computing and storage on the status of the wind power generation device in the distributed edge processing local area network, and further track, adjust and control the energy consumption, cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time.

[0012] Furthermore, the central processing computing control center module is used to monitor the status and collected data of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time, control the area occupied by the distributed edge processing local area network formed by the main UAV and multiple sub-UAVs in the entire monitored and managed area, coordinate the real-time monitoring area of ​​multiple distributed edge processing local area networks, and ensure that the entire monitored and managed area is monitored and managed.

[0013] Furthermore, the cloud data processing module is used for cloud-based fusion computing of several master drones and multiple sub-drones corresponding to each master drone to optimize energy consumption.

[0014] Furthermore, the second network protocol interface uses the Modbus RTU communication protocol.

[0015] Furthermore, the data acquisition module includes a wind turbine rotor damage image acquisition module for the wind power generation device, a wind speed sensor, a wind direction sensor, a terrain and landform image data acquisition module for the area where the distributed edge processing local area network of the multiple sub-UAVs is located, a GPRS real-time positioning module, and a UAV energy consumption monitoring module.

[0016] This invention also provides a method for managing wind power generation devices using unmanned aerial vehicle (UAV) data acquisition, comprising the following steps:

[0017] S101. A distributed edge processing local area network of several master drones forms the entire area monitored by the wind power generation device management system. Each master drone transmits control commands for the drone control module to multiple sub-drones in real time to track and control the flight routes of multiple drones within the area where its distributed edge processing local area network is located through the first network protocol interface and the wireless communication system.

[0018] S102. The data acquisition modules on the multiple drones acquire image information of the wind turbine rotor damage of the wind power generation device in their respective areas, wind speed data information, wind direction data information, and terrain image data of the area where the distributed edge processing local area network of the multiple sub-drones is located, and transmit them to the drone control module of the main drone corresponding to the multiple sub-drones through the wireless communication system and the first network protocol interface.

[0019] S103. The UAV control module performs edge computing and stores the status of the wind power generation device within the distributed edge processing local area network, thereby further tracking, adjusting, and controlling the energy consumption, cruising range, and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time; the network protocol communication configuration module in the wireless communication system configures specific communication parameters, and transmits the energy consumption of the main UAV and multiple sub-UAVs obtained by the UAV control module of the main UAV (which is an edge computing processing node) through the first network protocol interface to the cloud data processing module; the cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network obtained by the UAV control module through the edge computing are transmitted to the central processing computing control module through the second network protocol interface;

[0020] S104. The central processing computing control center module receives the results of edge computing performed by the drone control modules of multiple master drones and the cruising range and path of the master drones and multiple sub-drones in the distributed edge processing local area network. It also monitors the status and collected data of the master drones and multiple sub-drones in the distributed edge processing local area network in real time, controls the area occupied by the distributed edge processing local area network formed by the master drones and multiple sub-drones in the entire monitored and managed area, coordinates the real-time monitoring area of ​​multiple distributed edge processing local networks, and ensures that the entire monitored and managed area is monitored and managed.

[0021] S105. The cloud data processing module receives the energy consumption of the main drone and multiple sub-drones in the distributed edge processing local area network calculated in step S103, and performs cloud-based fusion calculation to optimize the energy consumption of several main drones and multiple sub-drones corresponding to each main drone in multiple distributed edge processing local area networks.

[0022] The beneficial effects of this invention are as follows:

[0023] 1. This invention sets up several master drones as edge controllers and multiple sub-drones that wirelessly communicate with each master drone as edge servers. The drone control module of the master drone acts as an edge computing processing node, communicating with the cloud data processing module. This disperses the data processing pressure of the cloud data processing module, complementing the cloud computing of the cloud data processing module. The two work together to overcome the problems that cannot be solved by simply transmitting all data to the cloud data processing module for processing and calculation without edge computing. This breaks through the characteristics of cloud computing in the past centralized data processing and can share the original data and calculation results before and after edge computing with the cloud data processing module.

[0024] 2. This invention employs a distributed deployment design with several drone control modules as edge computing processing nodes. This effectively migrates some or all of the data to the edge of the Internet of Things (IoT) cloud computing, bringing it closer to the drone terminal devices and sensors in the ground. This enables distributed data processing, analysis, and storage, shortening the distance to the data source and effectively improving the data transmission efficiency between the underlying devices and the data processing center (i.e., the drone control modules of several drones). This not only reduces the computing pressure on the cloud service center but also effectively improves the real-time performance and privacy of network services and data processing. It eliminates the need to upload all the data collected by the terminal drones to the cloud where the cloud data processing module is located. Instead, the data is directly calculated and stored on the drone control modules of several drones, effectively reducing the risk of data leakage during network transmission and improving the security of wind power generation device data management. Attached Figure Description

[0025] The invention will now be described in more detail with reference to embodiments and the accompanying drawings.

[0026] Figure 1 A schematic diagram of an overall management system for wind power generation device data acquisition by unmanned aerial vehicles provided by the present invention;

[0027] Figure 2 A schematic diagram of the wireless communication system of the management system provided by the present invention;

[0028] Figure 3 This is a schematic diagram of the data acquisition module of the management system provided by the present invention, which is set on multiple sub-UAVs. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Example 1

[0031] like Figure 1 As shown in this embodiment, a wind power generation device management system for drone data acquisition is provided. The system includes several master drones, each master drone and multiple sub-drones forming a distributed edge processing local area network. The master drones in the distributed edge processing local area network act as edge controllers, and the multiple sub-drones in the distributed edge processing local area network act as edge servers. The master drones and multiple sub-drones in the distributed edge processing local area network are connected to each other through a wireless communication module. The system also includes a central processing computing control center module and a cloud data processing module that communicate with the master drones. Each of the multiple sub-drones is equipped with a data acquisition module.

[0032] like Figure 3 As shown, the data acquisition module includes a wind turbine rotor damage image acquisition module (first camera), a wind speed sensor, a wind direction sensor, a terrain and topographic image data acquisition module (second camera) for the area where the distributed edge processing local area network (DEL) of multiple sub-drones is located, a GPRS real-time positioning module, and a drone energy consumption monitoring module. The GPRS real-time positioning module is used to monitor the geographical location information of the main drone and multiple sub-drones within the area where their distributed edge processing local area network is located in real time. The drone energy consumption monitoring module is used to monitor the remaining energy of the multiple sub-drones during flight within the area where their distributed edge processing local area network is located in real time.

[0033] Each main UAV includes a first network protocol interface and a second network protocol interface. The first network protocol interface is used to communicate with multiple sub-UAVs and the cloud data processing module through a wireless communication system; the second network protocol interface is used to communicate with each main UAV and the central processing computing control center module.

[0034] Each of the main drones is equipped with a drone control module for local data processing. This module serves as an edge computing node of the edge controller, enabling multiple sub-drones, which act as edge servers, to communicate with the cloud data processing module via the edge controller.

[0035] like Figure 2 As shown, the wireless communication system includes a PLC communication module, an LED display module, and a network protocol communication configuration module. The network protocol communication configuration module is used to configure specific communication parameters to enable communication between the UAV control module (which acts as an edge computing processing node) and the cloud data processing module. The LED display module is used to display real-time data monitoring information, historical data tables, and real-time data curves formed from historical data.

[0036] The PLC communication module enables communication between multiple sub-UAVs and the main UAV within a distributed edge processing local area network based on the Socket protocol. The network protocol configured in the network protocol communication configuration module is one of the following: Message Queuing Telemetry Transport Protocol (MQTT), DDS, AMQP, or JMS.

[0037] The UAV control module is used to perform edge computing on the data collected by the data acquisition module, perform edge computing and storage on the status of wind power generation devices in the distributed edge processing local area network, and further track, adjust and control the energy consumption, cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time.

[0038] The central processing and computing control center module is used to monitor the status and collected data of the master drone and multiple sub-drones in the distributed edge processing local area network in real time, control the area occupied by the distributed edge processing local area network formed by the master drone and multiple sub-drones in the entire monitored and managed area, coordinate the real-time monitoring area of ​​multiple distributed edge processing local networks, and ensure that the entire monitored and managed area is monitored and managed.

[0039] The cloud data processing module is used for cloud-based fusion computing to optimize the energy consumption of several master drones and multiple sub-drones corresponding to each master drone across multiple distributed edge processing local area networks.

[0040] Furthermore, the second network protocol interface uses the Modbus RTU communication protocol.

[0041] Furthermore, the second network protocol interface can use the Modbus TCP communication protocol. To add an MBAP header to the Modbus RTU communication protocol, since TCP is a connection-based service, the Modbus TCP communication protocol no longer requires the CRC checksum found in the Modbus RTU communication protocol. Therefore, there is no CRC checksum in the Modbus TCP protocol, simplifying the communication process.

[0042] Example 2

[0043] This embodiment employs a wind power generation device management method based on UAV data acquisition using the system provided in Embodiment 1, including the following steps:

[0044] S101. A distributed edge processing local area network of several master drones forms the entire area monitored by the wind power generation device management system. Each master drone transmits control commands to multiple sub-drones through the first network protocol interface and the PLC communication module of the wireless communication system to track and control the flight routes of multiple drones in the area where the distributed edge processing local area network is located.

[0045] S102. Data acquisition modules on multiple drones collect image information on the damage status of wind turbine rotors of wind power generation devices in their respective areas, wind speed data, wind direction data, and terrain image data of the area where the distributed edge processing local area network of multiple sub-drones is located. The data is then transmitted to the drone control module of the main drone corresponding to the multiple sub-drones through the PLC communication module and the first network protocol interface of the wireless communication system. The LED display module in the wireless communication system displays data monitoring information, historical data table information, and real-time data curve information formed by historical data in real time.

[0046] S103, the UAV control module performs edge computing and stores the status of wind power generation devices within the distributed edge processing local area network, and further tracks, adjusts and controls the energy consumption, cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time; the network protocol communication configuration module in the wireless communication system configures specific communication parameters, and transmits the energy consumption of the main UAV and multiple sub-UAVs obtained by the UAV control module of the main UAV as an edge computing processing node to the cloud data processing module through the first network protocol interface; the cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network obtained by the UAV control module through the edge computing are transmitted to the central processing computing control module through the second network protocol interface;

[0047] S104. The central processing computing and control center module receives the results of edge computing from the drone control modules of multiple master drones and the cruising range and path of the master drones and multiple sub-drones in the distributed edge processing local area network. It also monitors the status and collected data of the master drones and multiple sub-drones in the distributed edge processing local area network in real time, controls the area occupied by the distributed edge processing local area network formed by the master drones and multiple sub-drones in the entire monitored and managed area, coordinates the real-time monitoring area of ​​multiple distributed edge processing local areas, and ensures that the entire monitored and managed area is monitored and managed.

[0048] S105. The cloud data processing module receives the energy consumption of the main drone and multiple sub-drones in the distributed edge processing local area network calculated in step S103, and performs cloud-based fusion calculation to optimize the energy consumption of several main drones and multiple sub-drones corresponding to each main drone in multiple distributed edge processing local area networks.

[0049] This invention addresses the need for real-time control strategies and command issuance in the distributed edge processing local area network formed by multiple sub-drones and a master drone. However, data generated during the system's operation is time-sensitive and may become outdated by the time it reaches the cloud data processing center. By configuring several master drones with edge computing capabilities as control modules, effective edge data processing can be achieved, thereby reducing response time, bandwidth and connection costs, and storage requirements.

[0050] By employing a second network protocol interface, the main UAV control device, acting as an edge controller, is directly connected to the central processing and computing control center module. Simultaneously, through the first network protocol interface, the main UAV control device, also acting as an edge controller, is directly connected to the cloud data processing center module. This collaborative cloud-edge development separates the calculation of flight path control and energy consumption control for multiple sub-UAVs within the distributed edge processing local area network. This effectively improves accuracy and reduces the data processing load and energy consumption of the data processing center, promoting the integration of UAVs with internet applications and monitoring operational and environmental data from wind power generation devices. By integrating the internet, cloud technology, and edge services, it realizes the comprehensive interconnection of people, machines, and things within the industrial internet.

[0051] Although the invention has been described with reference to preferred embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, the technical features mentioned in the various embodiments can be combined in any manner as long as there is no structural conflict. The invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A wind power plant management system for unmanned aerial data collection, the system comprising a plurality of master unmanned aerial vehicles, each master unmanned aerial vehicle forming a distributed edge processing local area network with a plurality of slave unmanned aerial vehicles, characterized in that, The master drone in the distributed edge processing local area network (DEP) acts as an edge controller, and multiple sub-drones in the DEP act as edge servers. The master drone and multiple sub-drones in the DEP communicate with each other via a wireless communication module. The system also includes a central processing computing control center module and a cloud data processing module that communicate with several master drones. Each of the multiple sub-drones is equipped with a data acquisition module. Each main UAV includes a first network protocol interface and a second network protocol interface. The first network protocol interface is used to communicate with multiple sub-UAVs and the cloud data processing module through a wireless communication system. The second network protocol interface is used to communicate with each main UAV and the central processing computing control center module. Each of the main drones is equipped with a drone control module for local data processing, serving as an edge computing processing node of the edge controller. This enables multiple sub-drones, which act as edge servers, to ultimately communicate with the cloud data processing module through the edge controller. The system implements the following wind power generation device management method, including the following steps: S101. A distributed edge processing local area network of several master drones forms the entire area monitored by the wind power generation device management system. Each master drone transmits control commands for the drone control module to multiple sub-drones in real time to track and control the flight routes of multiple drones within the area where its distributed edge processing local area network is located through the first network protocol interface and the wireless communication system. S102. The data acquisition modules on the multiple drones acquire image information of the wind turbine rotor damage of the wind power generation device in their respective areas, wind speed data information, wind direction data information, and terrain image data of the area where the distributed edge processing local area network of the multiple sub-drones is located, and transmit them to the drone control module of the main drone corresponding to the multiple sub-drones through the wireless communication system and the first network protocol interface. S103. The UAV control module performs edge computing and stores the status of the wind power generation device within the distributed edge processing local area network, thereby further tracking, adjusting, and controlling the energy consumption, cruising range, and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time; the network protocol communication configuration module in the wireless communication system configures specific communication parameters, and transmits the energy consumption of the main UAV and multiple sub-UAVs obtained by the UAV control module of the main UAV (which is the edge computing processing node) through the first network protocol interface to the cloud data processing module; the cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network obtained by the UAV control module through the edge computing are transmitted to the central processing computing control module through the second network protocol interface; S104. The central processing computing control center module receives the results of edge computing performed by the drone control modules of multiple master drones and the cruising range and path of the master drones and multiple sub-drones in the distributed edge processing local area network. It also monitors the status and collected data of the master drones and multiple sub-drones in the distributed edge processing local area network in real time, controls the area occupied by the distributed edge processing local area network formed by the master drones and multiple sub-drones in the entire monitored and managed area, coordinates the real-time monitoring area of ​​multiple distributed edge processing local networks, and ensures that the entire monitored and managed area is monitored and managed. S105. The cloud data processing module receives the energy consumption of the main drone and multiple sub-drones in the distributed edge processing local area network calculated in step S103, and performs cloud-based fusion calculation to optimize the energy consumption of several main drones and multiple sub-drones corresponding to each main drone in multiple distributed edge processing local area networks.

2. The wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The wireless communication system includes a PLC communication module, an LED display module, and a network protocol communication configuration module. The network protocol communication configuration module is used to configure specific communication parameters to enable communication between the drone control module (which acts as an edge computing processing node) and the cloud data processing module. The LED display module is used to display real-time data monitoring information, historical data tables, and real-time data curves formed from historical data.

3. The wind power generation device management system for UAV data acquisition according to claim 2, characterized in that, The PLC communication module is based on the Socket protocol to enable communication between multiple sub-UAVs and the main UAV within a distributed edge processing local area network.

4. A wind power generation device management system for UAV data acquisition according to claim 2, characterized in that, The network protocol configured in the network protocol communication configuration module is one of the following: Message Queuing Telemetry Transport Protocol, DDS Protocol, AMQP Protocol, or JMS Protocol.

5. A wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The UAV control module is used to perform edge computing on the data collected by the data acquisition module, perform edge computing and storage on the status of wind power generation devices in the distributed edge processing local area network, and further track, adjust and control the energy consumption, cruising range and path of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time.

6. A wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The central processing and computing control center module is used to monitor the status and collected data of the main UAV and multiple sub-UAVs in the distributed edge processing local area network in real time, control the area occupied by the distributed edge processing local area network formed by the main UAV and multiple sub-UAVs in the entire monitored and managed area, coordinate the real-time monitoring area of ​​multiple distributed edge processing local networks, and ensure that the entire monitored and managed area is monitored and managed.

7. A wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The cloud data processing module is used for cloud-based fusion computing to optimize the energy consumption of several master drones and multiple sub-drones corresponding to each master drone in multiple distributed edge processing local area networks.

8. A wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The second network protocol interface uses the Modbus RTU communication protocol.

9. A wind power generation device management system for UAV data acquisition according to claim 1, characterized in that, The data acquisition module includes a wind turbine rotor damage image acquisition module for wind power generation devices, a wind speed sensor, a wind direction sensor, a terrain and landform image data acquisition module for the area where the distributed edge processing local area network of the multiple sub-UAVs is located, a GPRS real-time positioning module, and a UAV energy consumption monitoring module.

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