Hydropower station unmanned aerial vehicle security construction method and system

Abstract

The invention discloses a hydropower station unmanned aerial vehicle security construction method and system, and relates to the technical field of unmanned aerial vehicle intelligent security, and the method comprises the steps: automatically setting an inspection path based on an unmanned aerial vehicle cloud computing platform; deploying ground camera monitoring equipment, and performing identification based on a first technology; and transmitting the monitoring data in real time through the first communication mode. According to the method, automatic planning and dynamic adjustment of a task path are realized through routing inspection path setting based on an unmanned aerial vehicle cloud computing platform, the capability of all-weather real-time monitoring is realized through deployment of multifunctional ground camera monitoring equipment, cooperative work of the unmanned aerial vehicle and a ground system is realized by adopting a linkage mechanism, and the unmanned aerial vehicle and the ground system are integrated. Through the first communication mode, the real-time data transmission between the unmanned aerial vehicle and the ground equipment is ensured, the signal coverage capability is enhanced by adopting the multi-point relay device, the problem of communication blind areas in a complex terrain environment is effectively solved, the signal intensity is monitored in real time, the transmission path is dynamically adjusted, and the communication stability is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) intelligent security, and in particular to a method and system for constructing UAV security for a hydropower station. Background Art

[0002] At present, the construction of safety monitoring systems for hydropower stations has been widely valued in the industry, especially during flood seasons and extreme weather conditions. Safety inspections of rivers, dams, reservoirs and other areas are directly related to the stability of power transmission and the safety of public electricity use. However, traditional safety inspection methods mainly rely on manual regional inspections, usually using handheld devices to record data or manual registration. This method is inefficient and labor-intensive, especially in complex terrain and dangerous areas. The coverage of personnel inspections is greatly limited. In addition, manual inspections cannot achieve all-weather continuous operations. When extreme weather or geological disasters occur, inspection efficiency and personnel safety are difficult to guarantee, posing a major safety hazard.

[0003] Secondly, traditional monitoring methods mainly rely on fixed cameras, which are usually deployed at fixed points such as along rivers and dams. Although they have solved the monitoring needs of some areas to a certain extent, their monitoring range is fixed and cannot cover inspection blind spots. In addition, these cameras mainly rely on manual real-time monitoring of video images and lack intelligent recognition and automatic alarm functions. Even if abnormal situations are detected, the system cannot quickly locate and link emergencies, resulting in inefficient security management.

[0004] Finally, the application of drones in existing technologies is gradually increasing, but most applications focus on simple image capture and information recording, and fail to fully combine AI technology for real-time anomaly identification and early warning. In addition, drone path planning usually adopts a preset route method, lacks dynamic adjustment capabilities, and is difficult to optimize the flight path in real time according to specific geographical features such as winding rivers and undulating terrain, resulting in low inspection accuracy. In terms of data transmission and management, existing technologies usually adopt traditional wireless communication methods, which are easily interfered in complex terrain, and signal blind spots and interruptions are more prominent. After communication is interrupted, data cannot be transmitted back or stored in real time, resulting in missing monitoring information, affecting the real-time and integrity of safety management. In addition, traditional systems lack the ability to centrally manage and analyze data, and decentralized storage methods make data traceability and comprehensive utilization efficiency low. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing method for constructing UAV security for hydropower stations has insufficient dynamic adaptability of path planning, low coordination efficiency between ground monitoring equipment and UAVs, poor reliability of data transmission and storage, and the problem of how to realize dynamic inspection path adjustment, multi-level linkage monitoring, and real-time storage and traceability of data.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for constructing UAV security for a hydropower station, comprising automatically setting an inspection route based on a UAV cloud computing platform; deploying ground video monitoring equipment and identifying it based on a first technology; and transmitting monitoring data in real time through a first communication method.

[0008] As a preferred solution of the method for constructing UAV security for a hydropower station described in the present invention, the UAV cloud computing platform includes detecting the power level and flight status of the UAV.

[0009] As a preferred solution of the method for constructing UAV security for a hydropower station described in the present invention, the automatic setting of the inspection route includes automatically adjusting the flight altitude and the flight speed according to the terrain of the hydropower station.

[0010] As a preferred solution of the method for constructing UAV security for a hydropower station described in the present invention, the deployment of ground video surveillance equipment includes establishing a multi-level linkage mechanism with the UAV cloud computing platform.

[0011] As a preferred solution of the method for constructing UAV security for a hydropower station described in the present invention, the first technology includes identifying illegal intrusions and environmental changes.

[0012] As a preferred solution of the method for constructing hydropower station UAV security according to the present invention, the first communication method includes setting up a multi-point relay device, optimizing the first communication signal coverage in the signal blind area, transmitting the monitoring data between the UAV and the ground station in real time, and detecting the signal strength of the communication between the UAV and the ground station in real time, and dynamically switching to the optimal signal path.

[0013] As a preferred solution of the hydropower station drone security construction method described in the present invention, the real-time transmission of monitoring data includes automatically activating a backup communication scheme when communication is interrupted or the signal is weakened to achieve uninterrupted data transmission; when the communication signal is completely interrupted and the mission cannot be continued, the drone returns to the nearest alternate landing point according to a preset path; and the monitoring data is stored in the drone cloud computing platform.

[0014] Another object of the present invention is to provide a hydropower station drone security construction system, which can transmit monitoring data between the drone and the ground station in real time through a first communication method, and store the monitoring data in the drone cloud computing platform, thereby solving the problem of poor reliability of current data transmission technology.

[0015] As a preferred solution of the hydropower station UAV security construction system described in the present invention, it includes: a UAV task management module, a ground monitoring and linkage module, and a communication and data management module; the UAV task management module is used to detect the UAV power and flight status, automatically set the inspection path, adjust the flight altitude and speed according to the hydropower station terrain, and dynamically adjust the path or return according to abnormal situations during task execution; the ground monitoring and linkage module is used to deploy ground video monitoring equipment to realize the identification of illegal intrusion and environmental changes, and establish a multi-level linkage mechanism with the UAV cloud computing platform to cooperate with the UAV to conduct key area inspections and abnormal alarms; the communication and data management module is used to transmit the monitoring data between the UAV and the ground station in real time through the first communication method, and store the monitoring data in the UAV cloud computing platform.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for constructing a hydropower station UAV security system.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for constructing a hydropower station UAV security system.

[0018] Beneficial effects of the present invention: The hydropower station drone security construction method provided by the present invention realizes automatic planning and dynamic adjustment of the task path through the inspection path setting based on the drone cloud computing platform. Through the setting of the automatic inspection path, the complexity of manual planning is reduced, and the automation level of drone task execution is improved. The dynamic adjustment path not only reduces the error, but also can cover the blind spots that are difficult to reach by traditional manual inspections. By deploying multifunctional ground camera monitoring equipment, the ability of all-weather real-time monitoring is realized. The linkage mechanism further realizes the coordinated work of the drone and the ground system, ensuring the rapid response of security incidents and the comprehensive coverage of key areas. Based on the recognition function of the first technology, illegal intrusion behaviors, environmental changes and potential threats can be effectively detected. Through the first communication mode, the real-time transmission of data between the drone and the ground equipment is ensured. The multi-point relay device is used to enhance the signal coverage capability, effectively solving the communication blind spot problem in complex terrain environments, real-time monitoring of signal strength and dynamic adjustment of the transmission path, further ensuring the stability of communication, and the backup communication scheme can be automatically connected when the signal is weakened or interrupted to ensure that the task data is not interrupted. The present invention achieves better results in terms of efficiency, adaptability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 An overall flow chart of a method for constructing UAV security for a hydropower station provided in the first embodiment of the present invention.

[0021] Figure 2 A schematic diagram of a module of a hydropower station drone security construction system provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0023] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for constructing a hydropower station UAV security system, comprising:

[0024] S1: Based on the UAV cloud computing platform, the inspection route is automatically set.

[0025] Furthermore, the drone cloud computing platform includes detecting the drone’s battery level and flight status, and setting inspection routes through a dynamic programming algorithm.

[0026] It should be noted that the automatic setting of the inspection route includes automatically adjusting the flight altitude and flight speed according to the complexity of the terrain of the hydropower station to avoid obstacles.

[0027] It should also be noted that after receiving the inspection mission, the UAV uses the Beidou navigation system and inertial navigation module to collaboratively determine the best take-off point, and dynamically adjusts the path or returns according to abnormal situations during the mission execution. The inspection path is updated in real time through an adaptive learning algorithm during the flight. When the communication between the UAV and the cloud computing platform is interrupted, the backup mode is enabled, and the inspection mission is completed or the take-off and landing point is safely returned based on inertial navigation. When the UAV detects an abnormal situation, it classifies and judges the recognition results based on the AI ​​model, including but not limited to illegal intrusion, geological disasters or equipment damage, and triggers a return or alarm signal.

[0028] S2: Deploy ground video surveillance equipment and perform identification based on the first technology.

[0029] Furthermore, the deployment of ground-based video surveillance equipment includes establishing a multi-level linkage mechanism with the drone cloud computing platform.

[0030] It should be noted that the deployment of ground video surveillance equipment also includes installing multifunctional AI cameras to cover key areas such as rivers, dams and power transmission lines, embedding climate adaptation modules in AI cameras to enable them to work normally in high temperature, low temperature and high humidity environments, adding ground loudspeaker modules to warn and drive away illegal intruders in real time, and using AI deep learning models to detect people, vehicles and floating objects in the monitoring screen in real time, combining with the geographic information system (GIS) to locate abnormal areas, and when illegal intrusion or environmental abnormalities are found, the drone is linked to conduct key inspections of the designated area and record event data and upload it to the cloud platform. The ground video equipment can not only cover key areas such as rivers, dams, and power transmission lines, but also enhance the overall collaboration of monitoring technology by establishing a multi-level linkage mechanism with the drone cloud computing platform. Based on the recognition function of the first technology, it can effectively detect illegal intrusions, environmental changes and potential threats, such as floating objects blocking the river, slope landslides or foreign vehicles staying.

[0031] It should also be noted that the first technology includes identifying illegal intrusions and environmental changes. The first technology can be a variety of methods, such as a target detection method based on AI deep learning or a real-time monitoring method based on edge computing.

[0032] As a preference, in this embodiment, the first technology adopts a target detection method based on AI deep learning, and its principle steps are as follows:

[0033] First, real-time images within the monitoring area are collected through a multifunctional AI camera.

[0034] Secondly, the images are analyzed in real time based on the deep learning algorithm model to identify illegal intruders, abnormal vehicles and floating objects.

[0035] Finally, the ground loudspeaker and drone are linked to warn illegal intruders and conduct precise inspections of abnormal areas, and finally upload the event data to the cloud computing platform.

[0036] Specifically, the multifunctional AI camera collects real-time images in the monitoring area at a rate of 30 frames per second, covering key areas such as rivers, dams and transmission lines, and transmits the images to the cloud computing platform at a high resolution (1920x1080 pixels); based on the YOLOv5 deep learning target detection algorithm, the model analyzes the images frame by frame, and classifies and identifies illegal intruders, abnormal vehicles and floating objects in the images. Specifically, illegal intruders are classified by body contours and dynamic behavior characteristics, and floating objects are distinguished by shape and motion trajectory models. When the floating objects exceed 5 square meters or the stay time exceeds 120 seconds, they are considered abnormal events; based on the classification results output by the algorithm, the system links the ground loudspeaker to issue a real-time voice warning, prompting illegal intruders to evacuate immediately. At the same time, after receiving the inspection instructions issued by the cloud platform, the drone adjusts the inspection path according to the coordinates of the target area, performs low-altitude flight to obtain detailed images of the abnormal area, and uploads the inspection data to the cloud computing platform in real time after completing the task for archiving and further analysis. The entire process realizes the seamless connection of real-time monitoring, abnormal classification and linkage response, and improves the response speed and processing efficiency of the monitoring system.

[0037] It should also be noted that in another feasible embodiment, the first technology adopts a real-time monitoring method based on edge computing, and its principle steps are:

[0038] First, edge computing cameras are deployed in the monitoring area, and image processing algorithms are used to screen out areas of abnormal changes.

[0039] Secondly, preliminary classification is completed on the device side to identify illegal intrusions or environmental anomalies.

[0040] Finally, the linkage mechanism is triggered, including calling ground equipment for early warning and uploading abnormal data to the cloud platform or mobilizing drones to perform further inspection tasks.

[0041] Specifically, edge computing cameras are installed along rivers, around dams, and near transmission lines. Each device is equipped with a real-time data processing unit and an image analysis module. It collects monitoring images at a frequency of 25 frames per second and performs preliminary processing locally. The image processing algorithm detects the dynamically changing areas in the picture through the frame difference method, and combines the background modeling technology to screen out possible abnormal areas, and uses the size of the detection area and the frequency of dynamic changes as the basis for preliminary classification. For example, if a vehicle is detected to have stayed for more than 90 seconds or the area of ​​floating objects exceeds 3 square meters, it is marked as a potential abnormal event. The edge computing unit completes the classification and preliminary analysis of the abnormal situation locally, and determines whether the path trajectory of the illegal intruder crosses the preset warning line or whether there is any dangerous behavior through built-in rules. , such as frequent approach to the dam area. After detecting an abnormality, the edge computing device triggers the linkage mechanism and sends a warning signal through the built-in voice playback unit. At the same time, the processed abnormal event data is uploaded to the cloud platform for storage and further analysis. After receiving the abnormal signal, the cloud platform immediately sends an inspection command to the drone. The drone performs low-altitude cruising according to the event location to further confirm the abnormal situation and obtain high-resolution images. After completing the inspection, the detailed data is synchronized to the cloud platform for subsequent evaluation and decision-making. By realizing real-time processing and screening on the local device side, this method effectively reduces the dependence on network bandwidth and improves the timeliness of data processing. It is especially suitable for application scenarios with poor communication network stability or high real-time requirements for task processing.

[0042] S3: Transmit monitoring data in real time through the first communication method.

[0043] Furthermore, the first communication method includes setting up a multi-point relay device, optimizing the first communication signal coverage in the signal blind area, transmitting the monitoring data between the UAV and the ground station in real time, and detecting the signal strength of the communication between the UAV and the ground station in real time, and dynamically switching to the optimal signal path.

[0044] It should be noted that real-time transmission of monitoring data includes automatically activating backup communication solutions to ensure uninterrupted data transmission when communications are interrupted or signals weaken; when communications signals are completely interrupted and the mission cannot continue, the drone returns to the nearest alternate landing point based on a preset path; and the monitoring data is stored in the drone cloud computing platform.

[0045] It should also be noted that the first communication method is used to realize real-time communication between the UAV and the ground monitoring equipment. The first communication method can be a variety of methods, such as microwave communication method or 5G communication method.

[0046] As a preference, in this embodiment, the first communication mode adopts a microwave communication mode, and its principle steps are as follows:

[0047] First, microwave communication modules are installed in the monitoring area, and multi-point relay devices are deployed to expand the signal coverage through relay stations to ensure signal stability in complex terrain environments.

[0048] Secondly, the drone establishes a real-time communication link with the ground station through wireless microwave signals to transmit high-definition image data and inspection mission status information.

[0049] Next, the communication link monitors the signal strength in real time, and the system dynamically adjusts the transmission path according to the signal strength to avoid signal blind spots and interference.

[0050] Finally, when communication is interrupted or the signal weakens, the backup path switching algorithm is triggered to ensure uninterrupted data transmission. If the signal is completely interrupted and the mission cannot be continued, the drone automatically returns to the nearest alternate landing point according to the preset path.

[0051] All real-time transmitted data is synchronously stored in the drone cloud computing platform for subsequent safety analysis and record tracing

[0052] Specifically, microwave communication modules are installed in the monitoring area, and end-to-end encryption protocol (AES-256) is used to ensure data transmission security. A multi-point relay device with a coverage range of 500 meters is set up. Each relay station maximizes the signal transmission coverage through a high-gain antenna, thereby providing stable communication capabilities in complex terrain environments. The drone establishes a real-time communication link with the ground station through the equipped microwave communication module, and continuously transmits high-definition image data and inspection task status information at a transmission rate of 10Mbps. The communication link is equipped with a signal strength monitoring module, which measures the signal strength with a sampling period of 1 second, and automatically switches to the backup path when the signal is lower than -70dBm through a dynamic path optimization algorithm. The system can avoid signal blind spots caused by mountain obstruction or equipment interference. When the communication signal is interrupted or weakened to the point where it cannot maintain transmission (below -90dBm), the system triggers the backup path switching algorithm and tries to enable the adjacent relay station for relay transmission. If all paths are unavailable, the drone returns to the nearest alternate landing point according to the preset emergency path, and stores the remaining mission status in the local storage module in low-power mode, which will be synchronized to the cloud computing platform after communication is restored. All real-time transmitted data is finally stored in batches in units of 5 seconds on the cloud computing platform for subsequent mission review, security analysis and event tracing. The microwave communication method achieves efficient and stable data transmission capabilities in complex terrain.

[0053] It should also be noted that in another feasible embodiment, the first communication mode adopts a 5G communication mode, and its principle steps are as follows:

[0054] First, deploy 5G base stations in the drone operation area or access the existing 5G network to achieve real-time communication between the drone and the ground station through 5G signal coverage.

[0055] Secondly, based on the 5G communication protocol, the drone and the ground station establish an ultra-low latency communication link to ensure real-time transmission of inspection images and mission data.

[0056] Next, multi-path data offload technology is used to dynamically allocate transmission tasks to multiple 5G channels, improving transmission stability and enhancing anti-interference capabilities.

[0057] Finally, when the 5G signal weakens or coverage is insufficient, the system automatically switches to a low-priority network (such as LoRa or backup Wi-Fi) to ensure uninterrupted data transmission.

[0058] All monitoring data transmitted by drones in real time are ultimately stored on the cloud computing platform to ensure data integrity and mission traceability.

[0059] Specifically, a 5G base station with a coverage radius of 1 km is deployed in the drone operation area, or the existing 5G network is connected. The low latency characteristics of the 5G network ensure that the communication delay between the drone and the ground station is maintained within 10 milliseconds to achieve real-time transmission of high-frequency inspection images and mission data. The drone establishes a stable uplink through the 5G communication protocol, and the transmission bandwidth is set to 20Mbps to meet the needs of synchronous transmission of high-definition video streams and multi-sensor data. During the data transmission process, the multi-path data diversion technology is used to dynamically allocate the transmission tasks to multiple 5G networks according to the real-time signal strength and available bandwidth of the path. G channel, and enhances anti-interference capability through repeated redundancy strategy to avoid the impact of single path interruption on transmission. When the 5G signal strength is lower than -85dBm or the base station load rate exceeds 80%, the communication system automatically switches to a low-priority network, such as LoRa technology with long-distance coverage, or relies on the backup Wi-Fi channel to maintain the transmission stability of basic data to ensure that data is not interrupted. When the signal is completely lost and the mission cannot be continued, the drone returns to the nearest alternate landing point according to the preset path, and caches the remaining mission data through the built-in storage module, and synchronizes the data to the cloud computing platform after the network is restored. The real-time transmitted monitoring data is stored in groups according to timestamps on the cloud computing platform, with an interval of 5 seconds between each group to ensure the integrity of the data, easy traceability and continuity of subsequent analysis. Through this efficient and dynamic communication mechanism, 5G technology can meet the stringent requirements of large data volume and ultra-low latency transmission scenarios.

[0060] Example 2 is an embodiment of the present invention, which provides a method for constructing UAV security for a hydropower station. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0061] In order to verify the feasibility and actual operation effect of a hydropower station drone security construction method of the present invention, including data transmission stability, AI recognition accuracy, linkage response efficiency and full-area blind-spot monitoring capability.

[0062] The experimental equipment includes a UAV platform, ground equipment, a communication system and a data platform. The experimental environment is a hydropower station with complex terrain. The UAV platform is equipped with a dual-light camera, a Beidou navigation module, an RTK positioning system and an automatic charging module to complete precise inspections and automatic return. The ground equipment includes a multi-function AI camera that supports climate adaptation functions to adapt to high temperature, low temperature and high humidity environments. It also includes a ground loudspeaker for real-time warnings, a microwave relay station to expand the signal coverage range, and a photovoltaic power supply module. The photovoltaic panel has a power of 300W and a battery capacity of 250Ah, which can provide energy guarantee for continuous operation of the equipment. The communication system consists of a wireless microwave communication module and a multi-point relay device, and is equipped with a backup 5G network access device to ensure signal stability and uninterrupted data transmission. The data platform uses cloud computing to realize the storage, processing and AI analysis of monitoring data, and supports multi-device linkage and real-time early warning. The experimental environment is a hydropower station, covering rivers, reservoirs, dams and transmission lines. The terrain features include valleys, steep slopes and wide rivers, with a variety of test conditions to verify the system's adaptability in complex terrain.

[0063] 1. UAV inspection mission experiment

[0064] The purpose of the UAV inspection mission experiment is to test the stability and accuracy of the UAV's automatic flight route. First, the 3D modeling technology carried by the UAV is used to generate environmental data and set the inspection route, including daily planned inspections and temporary task inspection modes. The experiment starts the one-key takeoff function to observe whether the UAV can complete the inspection autonomously according to the preset route. During the experiment, under simulated abnormal conditions, such as communication signal interruption or rainfall exceeding 10 mm, the UAV is tested to see whether it can safely return to the preset alternate landing point. The experimental evaluation indicators include flight accuracy maintaining a deviation of less than 2 meters, a return success rate of or exceeding 98%, and an abnormal response time of less than 5 seconds.

[0065] 2. Ground monitoring system test

[0066] The purpose of the ground monitoring system test is to verify the all-weather working capability of the camera equipment and the recognition effect based on the AI ​​algorithm. The experiment first sets the coverage of the camera equipment to ensure that the monitoring area includes rivers, dams and transmission lines, and simulates abnormal events such as illegal intrusion, vehicle detention and floating object blockage in the experimental scene. The AI ​​deep learning model is enabled to detect abnormal events in the picture, and the abnormal area is accurately located in combination with the geographic information system. At the same time, a warning signal is sent out through the linkage ground loudspeaker, and the drone is triggered to perform precise inspections of the target area. The experimental evaluation indicators include AI recognition accuracy higher than 95%, linkage response time less than 10 seconds, and loudspeaker coverage of more than 100 meters.

[0067] 3. Communication system test

[0068] The purpose of the communication system test is to verify the stability and adaptability of microwave communication technology and backup 5G networks in complex terrain. The experiment sets up microwave communication modules and multi-point relay devices in the coverage area to test the signal strength and transmission stability under different distances and obstruction conditions. In addition, the experiment simulates microwave signal blind spots to test whether the 5G network can automatically access and maintain uninterrupted data transmission. The experiment also compares the transmission delay and data packet loss rate of microwave communication and 5G communication methods in different mission scenarios. The experimental evaluation indicators include signal coverage exceeding 95%, data transmission delay less than 20 milliseconds in microwave communication mode and less than 10 milliseconds in 5G mode, and data integrity reaching more than 99.9%.

[0069] 4. Comprehensive linkage experiment

[0070] The purpose of the comprehensive linkage experiment is to verify the linkage response capability of the UAV and ground equipment and the data processing effect of the cloud computing platform. The experiment simulates a variety of abnormal scenarios, including dam collapse, vehicle intrusion and floating object blockage. By starting the ground camera equipment to observe whether the system can quickly identify abnormal situations and link the UAV to perform response tasks, it detects whether the event data can be uploaded to the cloud computing platform in real time, and verifies whether the abnormal analysis results displayed on the large screen are complete and timely. The experimental evaluation indicators include the linkage efficiency from ground equipment to UAV not exceeding 10 seconds, the data storage completeness rate reaching 100%, and the system alarm delay controlled within 3 seconds.

[0071] The experimental results show that the flight path deviation of the drone system is controlled within 1.5 meters, and the return success rate reaches 99%. In the simulation test of communication signal interruption, the average return time of the drone was shortened to 3 seconds. The AI ​​recognition accuracy of the ground monitoring system reached 96.8%, and the coverage range of the ground loudspeaker extended to 120 meters. The communication system test showed that microwave communication maintained stable transmission in 80% of the complex environment tests, and the 5G network provided an effective backup communication link under blind spot conditions, with a data packet loss rate of only 0.05%. The comprehensive linkage experiment verified the precise linkage capability of the ground camera and the drone system, with an average alarm delay of 2.8 seconds. The cloud computing platform successfully stored and displayed all abnormal data, and the completeness rate of the analysis results reached 100%. The experiment shows that the overall system has a high degree of stability, accuracy and real-time response capability in complex terrain, so the present invention is creative.

[0072] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a hydropower station UAV security construction system, including a UAV task management module, a ground monitoring and linkage module, and a communication and data management module.

[0073] The UAV task management module is used to detect the UAV's battery level and flight status, automatically set the inspection route, adjust the flight altitude and speed according to the terrain of the hydropower station, and dynamically adjust the route or return according to abnormal situations during task execution; the ground monitoring and linkage module is used to deploy ground camera monitoring equipment to identify illegal intrusions and environmental changes, and establish a multi-level linkage mechanism with the UAV cloud computing platform to cooperate with the UAV to conduct inspections of key areas and abnormal alarms; the communication and data management module is used to transmit the monitoring data between the UAV and the ground station in real time through the first communication method, and store the monitoring data in the UAV cloud computing platform.

[0074] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0075] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0076] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0077] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing UAV security for a hydropower station, characterized in that: include: Based on the drone cloud computing platform, the inspection route is automatically set; Deploy ground video surveillance equipment and identify based on the first technology; The monitoring data is transmitted in real time via the first communication method.

2. The method for constructing a hydropower station drone security system according to claim 1, characterized in that: The UAV cloud computing platform includes detecting the power and flight status of the UAV.

3. The method for constructing a hydropower station drone security system according to claim 2, characterized in that: The automatic setting of the inspection route includes automatically adjusting the flight altitude and the flight speed according to the topography of the hydropower station.

4. The method for constructing a hydropower station drone security system as claimed in claim 3, characterized in that: The deployment of ground video surveillance equipment includes establishing a multi-level linkage mechanism with the drone cloud computing platform.

5. The method for constructing a hydropower station drone security system as claimed in claim 4, characterized in that: The first technology includes identifying illegal intrusion and environmental changes.

6. The method for constructing a hydropower station drone security system according to claim 5, characterized in that: The first communication method includes setting up a multi-point relay device, optimizing the first communication signal coverage in the signal blind area, transmitting the monitoring data between the UAV and the ground station in real time, and detecting the signal strength of the communication between the UAV and the ground station in real time, and dynamically switching to the optimal signal path.

7. The method for constructing a hydropower station drone security system according to claim 6, characterized in that: The real-time transmission of monitoring data includes automatically activating a backup communication scheme when communication is interrupted or the signal is weakened to achieve uninterrupted data transmission; When the communication signal is completely interrupted and the mission cannot be continued, the drone returns to the nearest alternate landing point according to the preset path; The monitoring data will be stored in the drone cloud computing platform.

8. A system using the method for constructing hydropower station security by drones as claimed in any one of claims 1 to 7, characterized in that: Including UAV mission management module, ground monitoring and linkage module, communication and data management module; The UAV task management module is used to detect the UAV power and flight status, automatically set the inspection path, adjust the flight altitude and speed according to the hydropower station terrain, and dynamically adjust the path or return according to abnormal conditions during task execution; The ground monitoring and linkage module is used to deploy ground video surveillance equipment to identify illegal intrusions and environmental changes, and to establish a multi-level linkage mechanism with the drone cloud computing platform to cooperate with drones to conduct inspections of key areas and abnormal alarms; The communication and data management module is used to transmit the monitoring data between the UAV and the ground station in real time through the first communication method, and store the monitoring data in the UAV cloud computing platform.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hydropower station drone security construction method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hydropower station drone security construction method described in any one of claims 1 to 7 are implemented.

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