Water conservancy inspection system and method

Abstract

The invention belongs to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering inspection system and method, and the method comprises the steps: a positioning monitoring deployment module reasonably distributes the number and positions of positioning monitoring assemblies; the route planning module plans an optimal unmanned aerial vehicle flight route; planning an advancing route, a water entry position and a water surface leaving position of the underwater robot; the cruise monitoring module carries out all-directional inspection on a water conservancy area; the image analysis module identifies abnormal conditions; the infrared data analysis module recognizes abnormal conditions in time; the radar data analysis module identifies abnormal conditions; the comprehensive analysis module comprehensively evaluates the water conservancy condition; the risk prediction module predicts potential problems and risks. According to the invention, the positioning monitoring assembly and the cruise monitoring module are combined to realize omnibearing monitoring of a water conservancy area; omnidirectional three-dimensional inspection is realized, multi-modal data collaborative analysis is realized, abnormal conditions are identified, potential problems and risks are predicted, and possible faults or disaster hidden dangers of water conservancy facilities can be found in advance.

Description

Technical Field

[0001] This application relates to the field of water conservancy project monitoring technology, and more specifically, to a water conservancy inspection system and method. Background Technology

[0002] Water conservancy inspection systems play a crucial role in the operation and management of water conservancy projects, primarily in the following aspects: Ensuring safe operation of projects by real-time monitoring of key locations of water conservancy facilities, such as water level, water quality, displacement, and settlement, and conducting comprehensive inspections of the water conservancy area. This enables the timely detection of potential safety hazards, such as dam cracks, seepage, and equipment malfunctions. Improving water resource management efficiency; enabling rapid fault location and handling; allowing managers to formulate reasonable scheduling plans, maintenance plans, and emergency response plans based on real-time monitoring data and risk prediction information, improving the scientific accuracy of decision-making, and optimizing the operation and management of water conservancy projects. Helping to understand the operational status and performance trends of facilities, providing data support for long-term maintenance, upgrading, and renovation of facilities, extending the service life of water conservancy facilities, and fully realizing the long-term benefits of water conservancy projects.

[0003] The prior art publication CN119026428A provides a method and system for monitoring water conservancy projects, relating to the field of water conservancy project monitoring technology. The method involves analyzing water conservancy project design data to construct a three-dimensional model; performing stress finite element analysis on the three-dimensional model to obtain a finite element analysis model; constructing a water contact simulation surface for the water body contact surface of the finite element analysis model and adjusting the water body feature mapping; inputting the adjusted water contact simulation surface into the finite element analysis model to determine the stress parameters of different important blocks; collecting real-time water conservancy characteristic data to construct a real-time water contact simulation surface and performing comparative analysis to determine the stress parameters of the water conservancy project per unit time, as well as the inspection parameters of important blocks; and determining inspection strategies for different blocks of the water conservancy project based on the inspection parameters. The above technical solution of this invention optimizes the inspection strategy and avoids aggravated damage to water conservancy projects due to missed inspections.

[0004] While the existing technical solutions described above can achieve the relevant beneficial effects through their structure, they still have the following drawbacks: 1. Traditional water conservancy inspection technologies rely heavily on manual inspections or fixed-point monitoring with single equipment, making it difficult to achieve comprehensive coverage of water conservancy areas. Manual inspections are limited by terrain, environment, and manpower, making it impossible to reach some remote and dangerous areas; while when fixed-point monitoring equipment is not rationally deployed, monitoring gaps are likely to occur, making it impossible to promptly detect potential problems at non-monitoring points and to comprehensively grasp information on water conservancy facilities and the environment. 2. Existing inspection technologies often employ a single monitoring method, such as relying solely on surface monitoring equipment or conducting only aerial inspections. This single mode cannot perform three-dimensional, multi-dimensional detection of water conservancy areas, making it difficult to cover key locations above, below, and on land, easily overlooking hidden problems, and unable to promptly detect various potential risks like a comprehensive three-dimensional monitoring mode. 3. Traditional water conservancy inspection technologies focus on detecting the current situation and lack the ability to predict potential future risks.

[0005] In view of this, we propose a water conservancy inspection system and method. Summary of the Invention

[0006] 1. Technical problems to be solved

[0007] The purpose of this application is to provide a water conservancy inspection system and method, which solves the technical problems mentioned in the background art above, and realizes all-round monitoring of water conservancy areas by combining positioning monitoring components and cruise monitoring modules; it can realize all-round three-dimensional inspection, realize multi-modal data collaborative analysis, identify abnormal situations, and the risk prediction module integrates the results of multiple modules, historical data and meteorological data, and predicts potential problems and risks through data analysis and model calculation, which helps to discover potential failures or disaster hazards in water conservancy facilities in advance, buy time for the formulation of preventive measures, and reduce the probability of accidents.

[0008] 2. Technical Solution

[0009] This application provides a water conservancy inspection system, including: a drone, a clamping component, an underwater robot, a motor, a synchronous transmission mechanism, a positioning and monitoring component, and a monitoring mechanism;

[0010] The drone has four rotatable clamping components located beneath it;

[0011] The drone is equipped with a fixed motor and a synchronous transmission mechanism, which engages with four gripping components. This synchronous transmission mechanism drives the four gripping components to rotate synchronously. The synchronous transmission mechanism is also connected to the output of the motor. The underwater robot can inspect water conservancy areas and equipment underwater. It can be gripped by the four gripping components, and then moved and repositioned by the drone. The drone is also equipped with a fixed monitoring mechanism for comprehensive inspection of the water area. Positioning monitoring components are installed at multiple key water conservancy locations for targeted monitoring of critical areas.

[0012] As an optional embodiment of the present invention, the monitoring agency includes:

[0013] Data collection module: Collects geological, equipment, and hydrological data (including historical data) of the water conservancy area; labels the data as reference samples;

[0014] Location monitoring deployment module: Based on the address data and monitoring needs of the water conservancy area, rationally deploy the number and location of location monitoring components; location monitoring components include water level gauges, water quality monitors, etc.

[0015] Route planning module: Based on the geological data and monitoring requirements of the water conservancy area, the module plans the optimal flight route for the drone; it also plans the underwater robot's route, entry point, and exit point.

[0016] Patrol and monitoring module: Uses drones equipped with high-definition cameras, lidar, and infrared thermal imagers to conduct comprehensive inspections and monitoring of water conservancy areas;

[0017] Meteorological data acquisition module: Acquires meteorological data in real time;

[0018] Image analysis module: preprocesses, extracts, and analyzes the acquired images to promptly identify anomalies in the water conservancy area;

[0019] Infrared data analysis module: Analyzes infrared data to promptly identify anomalies in water conservancy areas;

[0020] Radar data analysis module: Analyzes radar data to promptly identify anomalies in water conservancy areas;

[0021] Comprehensive Analysis Module: Combines the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation;

[0022] Risk prediction module: It integrates the monitoring results of the positioning monitoring component, the image analysis module, the infrared data analysis module and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks;

[0023] Alarm module: includes an alarm that promptly issues an alert when abnormal situations or potential risks are detected;

[0024] Control Center: Network connected to the data collection module, positioning and monitoring deployment module, route planning module, cruise monitoring module, risk prediction module, underwater robot, motor, multi-stage electric telescopic pole, alarm module, image analysis module, meteorological data acquisition module, infrared data analysis module, and comprehensive analysis module.

[0025] This invention provides a water conservancy inspection method, comprising the following steps:

[0026] S1. The data collection module collects address data, equipment data, and hydrological data (including historical data) of the water conservancy area; it also labels the data as a reference sample.

[0027] S2. The positioning and monitoring deployment module rationally deploys the number and location of positioning and monitoring components based on the address data and monitoring needs of the water conservancy area; the positioning and monitoring components include water level gauges, water quality monitors, etc.

[0028] S3, the route planning module plans the optimal flight route for the drone based on the address data and monitoring requirements of the water conservancy area; it also plans the underwater robot's travel route, entry point into the water, and exit point from the water surface.

[0029] S4, the patrol monitoring module uses a drone equipped with a high-definition camera, lidar, and infrared thermal imager to conduct comprehensive inspection and monitoring of the water conservancy area; the meteorological data acquisition module acquires meteorological data in real time;

[0030] S41. The drone carrying the underwater robot takes off and flies to the target waters along a preset route.

[0031] S42. After reaching the designated location, the drone hovers, and the motor drives the synchronous transmission mechanism to release the underwater robot to the water surface using the clamping assembly.

[0032] S44. The underwater robot autonomously dives into the water to begin its inspection mission, inspecting the water conservancy area and equipment underwater. The drone flies above the water surface, utilizing monitoring agencies to conduct aerial inspections of the water area and obtain real-time information on water conservancy facilities and the environment.

[0033] S45, the positioning and monitoring component monitors the status changes of key water conservancy locations in real time.

[0034] S46. After the inspection task is completed, the drone flies above the underwater robot, retrieves it using the gripping component 2, and then returns to the designated location to land.

[0035] S5, the image analysis module preprocesses, extracts features, and analyzes and identifies the acquired images, and promptly identifies abnormal situations in the water conservancy area;

[0036] S6, the infrared data analysis module analyzes infrared data to promptly identify abnormal situations in the water conservancy area;

[0037] S7, the radar data analysis module analyzes radar data and promptly identifies abnormal situations in the water conservancy area;

[0038] S8. The comprehensive analysis module combines the monitoring results of the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation.

[0039] S9. The risk prediction module integrates the monitoring results of the positioning monitoring component, the results of the image analysis module, the infrared data analysis module, and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks.

[0040] S10. When an abnormal situation or potential risk is detected, the alarm module will issue an alarm in a timely manner.

[0041] 3. Beneficial effects

[0042] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0043] 1. This invention achieves comprehensive monitoring of water conservancy areas by combining positioning monitoring components and cruise monitoring modules;

[0044] 2. Enables comprehensive, three-dimensional inspection: The patrol monitoring module utilizes drones equipped with various devices for aerial inspections, underwater robots perform underwater detection, and positioning monitoring components monitor key locations in real time. This comprehensive, three-dimensional monitoring mode, encompassing air, water, and land, can cover every corner of the water conservancy area, acquiring information on water conservancy facilities and the environment without blind spots, and promptly identifying various potential problems.

[0045] 3. Multimodal Data Collaborative Analysis: Image, infrared, and radar data analysis modules process their respective data to identify anomalies, and the comprehensive analysis module then fuses and evaluates the results from multiple data sources. Multimodal data collaboration can reflect the status of water conservancy areas from different perspectives, complementing and verifying each other, avoiding the limitations of a single data source, and improving the accuracy of anomaly identification and assessment.

[0046] 4. Foresight in Risk Prediction: The risk prediction module integrates results from multiple modules, historical data, and meteorological data. Through data analysis and model calculations, it predicts potential problems and risks. This helps to identify potential malfunctions or disaster hazards in water conservancy facilities in advance, allowing time to develop preventative measures and reducing the probability of accidents. Attached Figure Description

[0047] Figure 1 This is an overall schematic diagram of a water conservancy inspection system disclosed in a preferred embodiment of this application;

[0048] Figure 2 This is a schematic diagram of the support component structure of a water conservancy inspection system disclosed in a preferred embodiment of this application;

[0049] Figure 3 This is a schematic diagram of the sub-structure of a water conservancy inspection system disclosed in a preferred embodiment of this application.

[0050] Reference numerals: 1. Unmanned aerial vehicle (UAV); 2. Clamping assembly; 3. Underwater robot; 4. Motor; 5. Synchronous transmission mechanism; 6. High-definition camera; 7. LiDAR; 8. Camera; 9. Infrared thermal imager; 11. Side guard; 12. Protective plate; 21. Multi-stage electric telescopic rod; 22. L-shaped support plate; 23. Rotary seat; 51. Gear disc; 52. Spur gear; 53. Bevel gear A; 54. Bevel gear B; 55. Shaft. Detailed Implementation

[0051] The present application will be further described in detail below with reference to the accompanying drawings.

[0052] Reference Figure 1 and Figure 2 This application provides a water conservancy inspection system, including: a drone 1, a clamping component 2, an underwater robot 3, a motor 4, a synchronous transmission mechanism 5, a positioning and monitoring component, and a monitoring mechanism;

[0053] The drone 1 has four rotatable clamping components 2 located at its base. The drone 1 uses a carbon fiber composite material fuselage, reducing weight and improving endurance while maintaining structural strength. A foldable propeller is fitted to the top of the fuselage for easy storage and transportation. Standardized interfaces are provided at the bottom for installing monitoring mechanisms and other expansion equipment; these interfaces feature a quick-plug design for easy equipment replacement and maintenance. It is equipped with a high-energy-density lithium battery pack, coupled with an intelligent power management system that monitors battery level, voltage, and temperature in real time, optimizing battery charging and discharging strategies to extend battery life.

[0054] A motor 4 is fixedly installed on the drone 1, and a synchronous transmission mechanism 5 is installed on the drone 1. The synchronous transmission mechanism 5 is engaged and connected with four clamping components 2; the synchronous transmission mechanism 5 can drive the four clamping components 2 to rotate synchronously.

[0055] Synchronous transmission mechanism 5 is connected to the output end of motor 4;

[0056] The underwater robot 3 can inspect water conservancy areas and equipment underwater; it can be held by four clamping components 2, and then moved and changed positions by the drone 1.

[0057] The underwater robot 3 is equipped with a variety of detection devices, including a high-definition camera, a sonar system, a water quality sensor, a corrosion detector, and a temperature and humidity sensor. The high-definition camera captures high-definition underwater video and images for inspecting the appearance of hydraulic facilities; the sonar system models the underwater environment by emitting sound waves to detect obstacles and facility structures; the water quality sensor monitors real-time indicators such as pH, dissolved oxygen, and chemical oxygen demand; the corrosion detector detects the degree of corrosion on metal structures; and the temperature and humidity sensor monitors changes in underwater temperature and humidity. Employing a multi-thruster layout, including horizontal and vertical thrusters, it can achieve various movement postures such as forward, backward, left turn, right turn, surfacing, and sinking, demonstrating excellent maneuverability and flexibility.

[0058] The drone 1 is equipped with a monitoring device that conducts comprehensive inspections of the water area.

[0059] Positioning and monitoring components were installed at multiple key water conservancy locations; fixed-point monitoring was carried out in key areas.

[0060] In this technical solution, UAV 1 carries underwater robot 3 and takes off, flying to the target water area along a preset route. Upon reaching the designated location, UAV 1 hovers, and motor 4 drives synchronous transmission mechanism 5, causing gripping component 2 to release underwater robot 3 to the water surface. Underwater robot 3 then autonomously submerges to begin its inspection mission. UAV 1 flies above the water surface, using monitoring mechanisms to conduct aerial inspections of the water area, acquiring real-time information on water conservancy facilities and the environment. Underwater robot 3 inspects the water conservancy area and equipment, transmitting the inspection data back to UAV 1 via an underwater acoustic communication module. Simultaneously, the positioning and monitoring component monitors the status changes of key water conservancy locations in real time. After the inspection mission is completed, UAV 1 flies above underwater robot 3, retrieves it using gripping component 2, and then returns to the designated location to land.

[0061] Furthermore, side guards 11 are fixedly installed around the lower perimeter of the drone 1; foam pads are fixedly installed on the inner side of the side guards 11; the underwater robot 3 can be protected by the four side guards 11; the height of the side guards 11 is less than the length of the outriggers of the drone 1.

[0062] A protective plate 12 is fixedly mounted on the underside of the drone 1 via multiple support rods, and a foam pad is fixedly mounted on the underside of the protective plate 12. A camera 8 is mounted on the protective plate 12.

[0063] The front of the drone 1 is fixedly equipped with a high-definition camera 6, a lidar 7, and an infrared thermal imager 9.

[0064] In this technical solution, when the drone carries the underwater robot, the four side guards form a three-dimensional protective frame to prevent rigid collisions with external obstacles. This is especially beneficial when traversing complex environments such as bridges and trees, reducing the risk of collision damage. The high-definition camera 6 is equipped with a 120° ultra-wide-angle lens and supports 8K video recording for detailed inspection of the appearance of water conservancy facilities. The lidar 7 uses solid-state MEMS technology with a scanning frequency of up to 200Hz, enabling millimeter-level 3D modeling of structures such as dams and bridges. The infrared thermal imager 9 has both thermal imaging and visible light modes, allowing for rapid location of abnormal heat sources such as overheating and leakage in equipment.

[0065] Reference Figure 2 The synchronous transmission mechanism 5 includes a gear disc 51, a spur gear 52, a bevel gear A 53, a bevel gear B 54, and a shaft 55;

[0066] The gear disk 51 is rotatably mounted on the bottom of the UAV 1 via a bearing, and the gear disk 51 is coaxially and fixedly connected to the output end of the motor 4;

[0067] The bottom of the UAV 1 is evenly distributed with four rotatable spur gears 52; each spur gear 52 is meshed with a gear disk 51 for transmission.

[0068] A bevel gear A53 is coaxially fixed on the spur gear 52; a bevel gear B54 is meshed and connected to the bevel gear A53; a shaft 55 is coaxially fixed on the bevel gear B54; the shaft 55 is detachably fixedly connected to the clamping assembly 2.

[0069] In this technical solution, the starter motor 4 drives the gear disk 51 to rotate, the gear disk 51 drives the four spur gears 52 to rotate, and the four spur gears 52 drive the bevel gear B54 and the shaft 55 to rotate synchronously through the bevel gear A53, thereby adjusting the angle of the clamping assembly 2.

[0070] Reference Figure 3 The clamping assembly 2 includes a multi-stage electric telescopic rod 21, an L-shaped support plate 22, and a rotating base 23;

[0071] Four rotatable mounts 23 are evenly distributed on the underside of the drone 1;

[0072] Each rotating base 23 is fixedly equipped with a multi-stage electric telescopic rod 21; the rotating base 23 is detachably and fixedly connected to the corresponding shaft 55; an L-shaped support plate 22 is fixedly installed on the last stage movable rod of the multi-stage electric telescopic rod 21; a rubber pad is fixedly installed on the inner side of the L-shaped support plate 22.

[0073] An angle sensor is fixedly installed on the multi-stage electric telescopic pole 21.

[0074] In this technical solution, four multi-stage electric telescopic rods 21 drive an L-shaped support plate 22 to extend below the water surface, clamping the underwater robot 3; then, the underwater robot 3 is lifted out of the water. The rubber pads on the inner side of the L-shaped support plate 22 effectively buffer the impact force generated during the gripping and lifting process, protecting the outer shell and internal equipment of the underwater robot 3 from damage. Pressure and displacement sensors are integrated on the L-shaped support plate 22 to monitor the clamping force and the extension position of the telescopic rods in real time. Through feedback control, the extension length and clamping force of the multi-stage electric telescopic rods 21 can be precisely adjusted to prevent the underwater robot 3 from falling or being damaged due to excessive or insufficient clamping force.

[0075] Furthermore, the monitoring agencies include:

[0076] Data collection module: Collects geological, equipment, and hydrological data (including historical data) of the water conservancy area; labels the data as reference samples;

[0077] Location monitoring deployment module: Based on the address data and monitoring needs of the water conservancy area, rationally deploy the number and location of location monitoring components; location monitoring components include water level gauges, water quality monitors, etc.

[0078] Route planning module: Based on the address data and monitoring requirements of the water conservancy area, the optimal flight route of UAV 1 is planned; the underwater robot 3's travel route, entry point and exit point are also planned.

[0079] Patrol and monitoring module: The drone 1 carries a high-definition camera 6, a lidar 7 and an infrared thermal imager 9 to conduct comprehensive patrol and monitoring of the water conservancy area;

[0080] Meteorological data acquisition module: Acquires meteorological data in real time;

[0081] Image analysis module: preprocesses, extracts, and analyzes the acquired images to promptly identify anomalies in the water conservancy area;

[0082] Infrared data analysis module: Analyzes infrared data to promptly identify anomalies in water conservancy areas;

[0083] Radar data analysis module: Analyzes radar data to promptly identify anomalies in water conservancy areas;

[0084] Comprehensive Analysis Module: Combines the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation;

[0085] Risk prediction module: It integrates the monitoring results of the positioning monitoring component, the image analysis module, the infrared data analysis module and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks;

[0086] Alarm module: includes an alarm that promptly issues an alert when abnormal situations or potential risks are detected;

[0087] Control Center: Network connected to the data collection module, positioning and monitoring deployment module, route planning module, cruise monitoring module, risk prediction module, underwater robot 3, motor 4, multi-stage electric telescopic pole 21, alarm module, image analysis module, meteorological data acquisition module, infrared data analysis module, and comprehensive analysis module.

[0088] Furthermore, the positioning and monitoring deployment module rationally deploys the number and location of positioning and monitoring components based on the address data and monitoring needs of the water conservancy area; including the following steps:

[0089] 1. Data Collection and Processing: Collect geographic data for the water conservancy area, obtaining detailed geographic information from data sources such as Geographic Information Systems (GIS), topographic maps, and satellite imagery, including topography, water area, and surrounding environment. Collect data on water conservancy facilities, processing information on various facilities within the water conservancy area. Collect hydrological data, obtaining historical hydrological data to determine key parameters and areas requiring monitoring. Clarify monitoring needs, communicating with water conservancy management departments, relevant experts, or users to clarify the purpose and focus of monitoring.

[0090] 2. Regional Division: Based on geographical features, distribution of water conservancy facilities, and monitoring needs, the water conservancy region is divided into different sub-regions. Each sub-region has relatively independent hydrological characteristics and monitoring priorities. Each sub-region is assessed to determine its monitoring priority. Factors considered include the region's importance, potential risks (such as floods, water pollution, geological disasters, etc.), and the criticality of water conservancy facilities. Based on these priorities, the parameters and indicators requiring focused monitoring in each region are determined.

[0091] 3. Select Location Monitoring Components: Based on monitoring needs and regional characteristics, determine the specific parameters that need to be monitored in each sub-region. For example, in a reservoir area, parameters such as water level, water quality, water temperature, and dam displacement need to be monitored; in a river area, parameters such as flow rate, flow velocity, and water quality may need to be monitored. Based on the determined monitoring parameters, select the appropriate location monitoring components. Monitoring components include water level gauges, water quality monitors, flow meters, weather stations, and displacement sensors.

[0092] 4. Preliminary location planning:

[0093] 4.1 Determination of Key Monitoring Locations: For each sub-area, key monitoring locations should be determined based on the structure and function of the water conservancy facilities. On the dam, displacement sensors and piezometers should be installed at the dam crest, abutments, and foundation; near the sluice gates, water level gauges and flow meters should be installed to monitor the operational status of the sluice gates.

[0094] 4.2 Considering Water Flow and Quality Distribution: For water bodies such as rivers and reservoirs, the distribution of water flow and quality should be considered. Water quality monitoring instruments and flow velocity meters should be installed at locations with significant water flow changes, at sources where water quality may be affected, and in areas sensitive to water quality changes to accurately grasp the dynamic changes in water flow and quality.

[0095] 4.3 Principle of Uniform Monitoring Point Distribution: In large bodies of water or areas, a uniform monitoring point distribution method is adopted to comprehensively understand the hydrological and water quality conditions. The spacing between monitoring points is rationally determined based on the size and shape of the area to ensure that the monitoring data reflects the characteristics of the entire area.

[0096] 5. Simulation and Evaluation: Utilizing tools such as Geographic Information Systems (GIS), hydrodynamic models, and water quality models, a mathematical model of the water conservancy area is established. Geographic data, hydrological data, and monitoring requirements are input into the model to simulate the impact of different locations and quantities of monitoring components on monitoring effectiveness. The monitoring effectiveness under different monitoring schemes is evaluated through model simulation. Evaluation indicators include the accuracy, representativeness, coverage, and timeliness of monitoring data. The capabilities of each scheme in monitoring key parameters, detecting anomalies, and predicting disasters are analyzed. Based on the evaluation results, the preliminary location planning scheme is optimized.

[0097] 6. On-site Survey and Adjustment: Conduct an on-site survey. Check whether the planned monitoring locations are reasonable and whether there are any construction difficulties or safety hazards. Observe the actual environment on site, such as topography, surrounding obstacles, and communication conditions, and make further adjustments to the plan. Assess the installation feasibility of each monitoring location, including the installation space, fixing method, power supply, and communication connection of the equipment.

[0098] 7. Final Scheme Determination and Implementation: Based on the results of the on-site survey and adjustments, the final deployment scheme for the positioning and monitoring components will be determined. The specific location, model, quantity, installation method, and technical requirements of each monitoring component will be clearly defined.

[0099] Furthermore, the route planning module, based on the geological data and monitoring requirements of the water conservancy area, plans the optimal flight route for UAV 1; and plans the travel route, entry point, and exit point for underwater robot 3; including the following steps:

[0100] 1. Data Collection and Analysis: Collect geographic data for the water conservancy area, obtaining high-precision topographic and geomorphological data from a Geographic Information System (GIS), such as the distribution of mountains, rivers, and lakes, as well as ground elevation. Collect detailed location and extent information for water conservancy facilities. Collect meteorological data, including wind speed, wind direction, temperature, and precipitation probability. Determine specific monitoring targets and requirements. Identify potential obstacles within the water conservancy area, such as high-voltage lines, buildings, and trees. Obtain information on the location, height, and extent of these obstacles to facilitate route planning and avoidance.

[0101] 2. Delineate Monitoring Areas: Based on monitoring needs and the characteristics of the water conservancy area, determine the areas requiring key monitoring. For dams, key monitoring areas may include the dam surface, dam shoulders, and dam foundation; for rivers, key monitoring areas may be near pollution sources or areas with significant changes in water flow. Divide the entire water conservancy area into several sub-regions, each with relatively independent geographical characteristics and monitoring needs. When delineating these areas, consider factors such as topography, distribution of water conservancy facilities, and monitoring targets to ensure that the monitoring tasks in each sub-region are reasonable and feasible.

[0102] 3. Drone flight route planning:

[0103] 3.1 Determine Flight Altitude and Speed: Based on monitoring requirements, terrain, and weather conditions, determine the UAV's flight altitude and speed. The flight speed should be set appropriately based on the UAV's performance and the sampling frequency of the monitoring equipment to ensure the acquisition of clear and accurate monitoring data.

[0104] 3.2 Initial Route Planning: Using Dijkstra's algorithm, combined with geographical data and obstacle information of the water conservancy area, the initial flight route of the UAV is planned. The initial route should cover as much of the monitoring area as possible while avoiding collisions with obstacles. During the planning process, the UAV's endurance should be considered, and intermediate stopping points or turnaround points should be reasonably set.

[0105] 3.3 Adjusting the route considering meteorological factors: Adjust the initial route based on real-time meteorological data.

[0106] 3.4 Simulated Flight Verification: The planned flight route is verified using flight simulation software. During the simulation, the route's rationality, its ability to meet monitoring requirements, and any potential safety hazards are checked. Based on the simulation results, the route is further optimized and adjusted.

[0107] 4. Planning of underwater robot's travel route and entry / exit points:

[0108] 4.1 Determine the operating depth and speed of the underwater robot: Based on monitoring requirements and underwater environmental conditions, determine the operating depth and speed of the underwater robot. The operating depth should consider the pressure resistance of the underwater robot and the effective working range of the monitoring equipment; the operating speed should be reasonably set according to the complexity of the underwater environment and the requirements for collecting monitoring data.

[0109] 4.2 Planning the Underwater Route: Based on the underwater topography, water flow conditions, and monitoring requirements of the water conservancy area, plan the underwater robot's route. The route should cover as much of the underwater monitoring area as possible, while avoiding collisions with underwater obstacles (such as reefs, shipwrecks, etc.). During the planning process, the underwater robot's endurance and communication range should be considered, and intermediate stopping points or turnaround points should be reasonably set.

[0110] 4.3 Choosing the Entry and Exit Points: The following factors should be considered when choosing the entry and exit points:

[0111] Terrain conditions: Select an area with flat terrain, suitable water depth, and no obstacles to facilitate the deployment and retrieval of underwater robots.

[0112] Water flow conditions: Avoid areas with rapid currents and many whirlpools to ensure the safe entry and exit of the underwater robot.

[0113] Coordination with drones: The entry and exit points from the water surface should be coordinated with the flight path of the drone to facilitate the deployment and retrieval of the underwater robot by the drone.

[0114] Monitoring requirements: Select locations as close as possible to key monitoring areas to reduce the underwater robot's travel time and energy consumption.

[0115] 4.4. Simulated Underwater Operation Verification: Underwater robot simulation software is used to simulate and verify the planned route, entry points, and exit points. During the simulation, the rationality of the route, its ability to meet monitoring requirements, and the existence of potential safety hazards are checked. Based on the simulation results, the route and location are further optimized and adjusted.

[0116] 5. Collaborative Planning and Optimization:

[0117] 5.1 Consider the collaborative operation of UAVs and underwater robots: During route planning, the collaborative operation needs of UAVs and underwater robots should be fully considered. For example, UAVs can provide positioning and navigation support when the underwater robot enters and leaves the water, or monitor the water surface conditions during the underwater robot's operation. The flight path of the UAV and the movement path of the underwater robot should be coordinated to avoid conflicts and interference.

[0118] 5.2 Comprehensive Route Optimization: The flight path of the UAV, the movement path of the underwater robot, and the entry and exit points from the water surface are comprehensively considered, and a multi-objective optimization algorithm is used for overall optimization. Optimization objectives include covering all monitoring areas, reducing flight and travel time, reducing energy consumption, and improving monitoring data quality. Through comprehensive optimization, the optimal collaborative operation route scheme is obtained.

[0119] 6. On-site Survey and Adjustment: Conduct an on-site survey. Check whether the planned route and location match the actual situation, and whether there are any unforeseen obstacles or other problems. At the same time, observe the on-site meteorological and hydrological conditions, and make further adjustments and optimizations to the route.

[0120] 7. Final Plan Determination and Implementation: Based on the results of on-site surveys and adjustments, determine the final flight path of the UAV, the underwater robot's travel path, and the entry and exit points from the water. The plan should record detailed information such as the specific coordinates of the route, flight and travel parameters, and the time and method of entry and exit from the water.

[0121] 8. Evaluation and Feedback: After the operation is completed, the effectiveness of the route planning is evaluated. Evaluation indicators include the coverage of the monitoring area, the quality of monitoring data, operation time, and energy consumption. Through evaluation, the advantages and disadvantages of the route planning scheme are analyzed. Based on the evaluation results, lessons learned are summarized, and feedback and improvements are made to the route planning module.

[0122] The image analysis module preprocesses, extracts features, and analyzes the acquired images to promptly identify anomalies in the water conservancy area; this includes the following steps:

[0123] 1. Image Acquisition: Receives image data of the water conservancy area collected by devices such as high-definition cameras mounted on drones and cameras on protective plates, and imports the images into the image analysis module's processing system. Collects extensive address, architectural, and historical data of water conservancy facilities to establish a target model library for these facilities.

[0124] 2. Image preprocessing: The acquired images are preprocessed, including grayscale conversion, noise reduction and enhancement. The enhancement process uses histogram equalization, contrast stretching and other methods to enhance the contrast and brightness of the image, highlight key details in the image, and make the features of water conservancy facilities, water surfaces and other areas in the image more obvious.

[0125] 3. Feature extraction: Feature extraction is performed on the preprocessed image. The extracted features include color, texture, and shape.

[0126] Shape feature extraction: Edge detection algorithms, such as the Canny operator and the Sobel operator, are used to detect the edges of objects in the image and obtain the contour information of the water conservancy facilities, providing a foundation for subsequent shape analysis and recognition.

[0127] Texture feature extraction: Extract texture features from images, such as gray-level co-occurrence matrix and local binary pattern, and analyze the texture differences of different objects (such as water surface, vegetation and concrete facility surface) in water conservancy areas to distinguish different types of objects and identify abnormal texture changes.

[0128] Color feature extraction: Extracting color features from an image, such as RGB values ​​and HSV color space component values, to identify specific objects or abnormal areas, such as judging water pollution or floating objects on the water surface by color.

[0129] 4. Analysis and Identification: The extracted features are compared with a pre-established database of water conservancy facility target models. Target detection algorithms, such as YOLO and Faster R-CNN based on deep learning, are used to detect the presence of water conservancy facilities and their components in the image, determining whether their location, size, and shape are normal. The current image is compared with historical normal images. By calculating the differences between the images, changes in the water conservancy area are detected, such as changes in water surface area, building structure, and vegetation cover, to promptly identify potential anomalies. Based on the analysis and identification results, and according to set rules and thresholds, anomalies in the water conservancy area are determined. If anomalies are detected, the abnormal areas are marked on the image, such as by framing them with red boxes and indicating the anomaly type and level, to facilitate rapid identification and handling by subsequent staff.

[0130] 5. Results Output and Storage: Output the analysis and identification results (including information such as whether there are anomalies, the location of the anomalies, and the type of anomalies) in a visual interface to the operator, and store the images and analysis results in the database for easy subsequent query, statistics and analysis.

[0131] Furthermore, the infrared data analysis module analyzes the infrared data to promptly identify anomalies in the water conservancy area; this includes the following steps:

[0132] 1. Data Reception: The system receives infrared data of the water conservancy area collected by the infrared thermal imager 9 in real time. The infrared thermal imager 9, mounted on the UAV 1, performs a comprehensive scan of water conservancy facilities and water areas along a predetermined cruise route, acquiring temperature distribution image data formed by thermal radiation from the object's surface, and transmitting this data to the module in a timely manner.

[0133] 2. Data Preprocessing: This includes format conversion and noise reduction. The received raw infrared data is converted into a standard data format to ensure that the data can be effectively recognized and processed by the analysis algorithms within the module. Filtering algorithms are used to remove noise from the data caused by environmental interference, equipment errors, etc.

[0134] 3. Establish a temperature distribution model: Based on different objects in the water conservancy area, such as dams, sluices, water surfaces, and surrounding vegetation, the infrared data are classified and organized. For each type of object, a corresponding temperature distribution model is constructed based on historical normal temperature data and current real-time data to clarify the temperature range, variation patterns, and other characteristics of each type of object under normal conditions.

[0135] 4. Anomaly Identification: The real-time acquired infrared data is compared with the normal temperature thresholds in the established temperature distribution model. When the temperature in a certain area exceeds the upper limit of the normal range or falls below the lower limit, an anomaly is initially determined in that area. For areas with relatively concentrated temperature data, clustering analysis algorithms are used to analyze the distribution characteristics of the temperature data. If clusters that differ significantly from the normal temperature distribution pattern appear, it is further confirmed that there may be anomalies in that area, such as localized overheating of equipment or temperature changes caused by abnormal water flow. 5. Anomaly Marking: Once an anomaly is identified, the abnormal area is clearly marked on the infrared thermal imaging image, indicating the anomaly type (e.g., overheating, undercooling) and approximate severity for easy review later. Relevant information about the anomaly, including its location, type, and severity, is promptly reported to the comprehensive analysis module and alarm module, providing a basis for comprehensive assessment and rapid response to water conservancy conditions.

[0136] Furthermore, the radar data analysis module analyzes the radar data to promptly identify anomalies in the water conservancy area; this includes the following steps:

[0137] 1. Data Acquisition and Reception: The module receives real-time radar data from the water conservancy area collected by the lidar. Following a planned UAV flight path, the lidar performs a comprehensive scan of the water conservancy facilities and surrounding terrain, collecting the three-dimensional spatial location information of the objects and transmitting it to this module in the form of point cloud data.

[0138] 2. Data preprocessing: Preprocessing the collected data, including data cleaning, format conversion, and data filtering;

[0139] 3. Point cloud data processing: including point cloud segmentation and point cloud registration;

[0140] Point cloud segmentation: Based on the characteristics of different objects in the water conservancy area, the point cloud data is segmented into different subsets, such as the point cloud data corresponding to dams, sluices, rivers, vegetation, etc., to facilitate targeted analysis.

[0141] Point cloud registration: For data collected from multiple periods or from different angles, a registration algorithm is used to align them to the same coordinate system, ensuring data consistency and comparability. Point cloud registration is performed according to the following formula: E(R,T)=(1 / N)Σ N i=1 ||Rp i +Tq’ i || 2 In the formula, E(R,T) is the objective function, whose value reflects the degree of difference between two point cloud datasets (after rotation and translation transformations). Our goal is to achieve point cloud data registration by adjusting the rotation matrix R and the translation vector T to minimize the value of the objective function E(R,T). R is the rotation matrix, a 3×3 matrix (because point cloud data is typically three-dimensional). It is used to rotate the points in the original point cloud P to make them closer in direction to the other point cloud Q. T is the translation vector, a three-dimensional vector (x,y,z). It is used to translate the rotated point cloud P in space to better match the position of point cloud Q. N is the number of points in the point cloud data. It represents the total number of points considered when calculating the objective function. i Let p represent the i-th point in the point cloud P, where i = 1, 2, ..., N. Each p... i They are all three-dimensional coordinates (x) i ,y i ,z i ), representing the position of a point in three-dimensional space. q ’ i Represents the relationship between point p and point Q in the point cloud. i The nearest neighbor point. That is, finding the point in the point cloud Q that is closest to point p. i The nearest point is denoted as q. ’ i 。 ||٠|| 2 This represents the square of the Euclidean distance.

[0142] 4. Feature Extraction: Feature extraction is performed on the point cloud data, including geometric and structural feature extraction. Geometric parameters of the water conservancy facilities, such as length, width, height, and slope, are calculated. Features such as the cross-sectional shape and riverbed undulations of the river channel are extracted to form geometric feature descriptions of each object. Structural features such as the structural connections and component composition of the water conservancy facilities are analyzed to determine whether the facility structure is complete and free from deformation. 5. Anomaly Identification: The extracted features are compared with a pre-established normal model of the water conservancy facilities and historical data. The shape and size of the water conservancy facilities are compared to see if there are changes, and whether the river topography has changed significantly. If the difference exceeds a set threshold, an anomaly is initially identified. The changing trends of various objects in the water conservancy area are analyzed by combining historical data from multiple periods. For example, whether river siltation is continuously worsening, and whether the dams are showing a slow deformation trend, are observed. If abnormal trends are found, potential problems are confirmed.

[0143] 6. Anomaly Marking: In the processed point cloud data or 3D model, identified abnormal areas are clearly marked, indicating the anomaly type (e.g., dam cracks, river blockages) and severity for easy and intuitive viewing. Detailed information about the anomaly, including its location, type, severity, and analytical basis, is promptly transmitted to the comprehensive analysis module and alarm module, providing a basis for comprehensive assessment and emergency response to water conservancy conditions.

[0144] Furthermore, the comprehensive analysis module combines the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation; this includes the following steps:

[0145] 1. Data Collection: The positioning and monitoring component continuously monitors the location and displacement of water conservancy facilities, collecting relevant data such as dam settlement data and sluice gate location change data. The image analysis module analyzes images of the water conservancy area (such as images of water conservancy facilities and water areas taken by drones) to extract feature information, such as the shape of the river channel and the appearance of the water conservancy facilities. The infrared data analysis module analyzes infrared data of the water conservancy area to obtain information such as temperature distribution, and determines whether there are abnormal heat sources or abnormal temperature areas, such as temperature changes caused by possible seepage points inside the dam.

[0146] 2. Data Integration: The monitoring results from the positioning and monitoring components, the analysis results from the image analysis module, and the analysis results from the infrared data analysis module are integrated to form a comprehensive dataset.

[0147] 3. Establish an evaluation index system: For different parts of water conservancy facilities (such as dams, sluices, and waterways) and different aspects (such as structural safety and water flow conditions), determine corresponding evaluation indicators. Set reasonable threshold ranges for each evaluation indicator; these thresholds can be determined based on the design standards of water conservancy facilities, historical data, and industry specifications.

[0148] 4. Assessment and Calculation: Based on the integrated data, calculate the value of each assessment indicator. Compare the calculated assessment indicator value with the set threshold to determine if it exceeds the threshold range. If an assessment indicator value exceeds the threshold range, it indicates that there may be a problem with the corresponding part or aspect of the water conservancy facility.

[0149] 5. Comprehensive Assessment: A holistic assessment of the water conservancy situation is made by comprehensively considering all assessment indicators. This includes not only individual indicators but also the interrelationships between them. The correlation between results from different modules is analyzed. For example, the relationship between dam cracks detected by the image analysis module, displacement changes detected by the positioning monitoring component, and temperature anomalies detected by the infrared data analysis module. By comprehensively analyzing these results, the actual condition of the water conservancy facilities can be more accurately determined. The comprehensive assessment is conducted according to the following formula:

[0150] E=φ i w i {1+w st i [sign(δ i -δ j ) - +△δ i,t ]};

[0151] φ i ={(v i -θ i min ) / θ i min ,v i <θ i min ;0,θ i min ≤v i ≤θ i max ;(v i -θ i max ) / θ i max ,v i >θ i max};

[0152] △δ i,t =δ i,t -δ i,t-1 In the formula, w i This represents the initial weight of the i-th evaluation indicator. It reflects the static importance of this indicator in the overall evaluation. st i This is the comprehensive influence coefficient, used to comprehensively measure the degree of influence of spatial and temporal factors on weight correction. sign(δ) i -δ j ) - This represents the set N of the i-th index and its spatial neighborhood indexes. s i The average of the sign function of the deviation difference of each indicator. Where sign(٠) is the sign function, when (δ... i-δ j When )>0, sign(δ) i -δ j )=1; when (δ) i -δ j When )=0, sign(δ) i -δ j )=0; when (δ) i -δ j When )<0, sign(δ) i -δ j ) = -1. △δ i,t δ represents the change in deviation of the i-th indicator at the current time step t. i,t δ represents the deviation of index i at time step t. i,t-1 φ represents the deviation of index i from the previous time step t-1. i denoted as the threshold deviation of the i-th evaluation metric, used to measure the degree of deviation between the actual value of the metric and the set threshold. i θ is the actual measured value of the i-th indicator, obtained through monitoring methods such as positioning monitoring components, image analysis modules, and infrared data analysis modules. This data reflects the real-time status of the corresponding indicator during the actual operation of the water conservancy facilities. i min This is the lower limit of the safety threshold for the i-th indicator, a critical value determined based on design standards for water conservancy facilities, historical operational data, and industry regulations. When the actual measured value of the indicator v... i A value below this lower limit may indicate a potential risk or abnormality in the water conservancy facilities. i max This is the upper limit of the safety threshold for the i-th indicator, a critical value set based on the design standards, historical data, and industry specifications of water conservancy facilities. When the actual measured value v of the indicator... i Exceeding this upper limit indicates that the water conservancy facilities may face safety hazards and require timely measures to address them.

[0153] 6. Generate an assessment report: Based on the comprehensive assessment results, generate a detailed water conservancy situation assessment report. The report should include an overall description of the water conservancy facilities' condition, specific details of each assessment indicator, existing problems, and potential risks. The report can use charts and graphs to visually represent the water conservancy situation, such as plotting dam displacement over time or comparing river cross-sectional morphology, to make the assessment results easier to understand.

[0154] Furthermore, the risk prediction module integrates the monitoring results from the location monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module, combining historical data and meteorological data to predict potential problems and risks; this includes the following steps:

[0155] 1. Data Collection and Integration: Real-time displacement and settlement data of water conservancy facilities are acquired from the positioning and monitoring components; the image analysis module outputs information on changes in the appearance of water conservancy facilities and river morphology; the infrared data analysis module provides temperature anomaly distribution data; and the comprehensive analysis module summarizes existing assessment results. Simultaneously, historical monitoring data (covering various indicators across multiple years and seasons) and real-time and forecast data issued by meteorological departments (including precipitation, wind speed, temperature, flood warnings, etc.) are collected. Data cleaning and standardization are performed to remove noise, outliers, and duplicate data from each module, and data of different formats and units are uniformly converted to a standard format to ensure accuracy and consistency for subsequent analysis. A comprehensive database is constructed, integrating the cleaned data into a single database, establishing relationships between data, and forming a comprehensive dataset containing real-time, historical, and meteorological data.

[0156] 2. Feature Engineering: Analyze the data in the comprehensive dataset to extract key features related to potential risks. For example, extract displacement change rate and settlement acceleration from location monitoring data; extract crack propagation trends and river siltation levels from image data; and extract rainfall variation gradients and sudden wind speed changes from meteorological data. Study the correlations between features of different types of data, such as analyzing the relationship between precipitation and river water levels and dam seepage, to uncover potential patterns in the data and provide richer input information for risk prediction models.

[0157] 3. Risk Prediction Model Construction: Based on data characteristics and prediction objectives, a Convolutional Neural Network (CNN) model is selected. Historical data is divided into training and test sets. The selected model is trained using the training set, and the prediction accuracy and generalization ability are improved by adjusting model parameters and optimizing the algorithm. The trained model is evaluated using the test set to verify its reliability. If the performance is unsatisfactory, the model is readjusted.

[0158] 4. Potential Risk Prediction: The integrated real-time monitoring data and the latest meteorological data are input into a trained risk prediction model. Based on the input data, the model predicts potential problems and risks that may occur in water conservancy facilities in the near future, outputting assessment results such as the probability of risk occurrence, the scope of impact, and the severity of the risk. For example, it predicts the probability of cracks expanding in a dam within the next week, or the risk level of river flooding. The prediction model is as follows:

[0159] L = -(1 / M)Σ M-1 i=1 {u i [y i log(p i )+(1-y i log(1-p) i)]};u i =exp(a|pi-yi|); where L represents the improved cross-entropy loss function value, used to measure the difference between the model's predicted result and the true label; the smaller the value, the more accurate the model's prediction. M represents the total number of samples in the training set, i.e., the number of samples participating in training. i is the sample index, used to traverse each sample in the training set, with a value ranging from 1 to M. u i y is the weight of the i-th sample. i p is the true label of the i-th sample, which takes the value 0 or 1 in a binary classification problem. i is the probability that the model predicts the i-th sample as positive, and its value ranges from 0 to 1. 'a' is an adjustment parameter that assigns higher weights to difficult examples (samples with large prediction errors) to enhance the model's sensitivity to anomaly risks.

[0160] 5. Risk Warning: Based on risk prediction results, potential risks are classified according to preset risk level standards (e.g., low risk, medium risk, high risk). When a risk is predicted to reach or exceed a certain level, the warning mechanism is immediately triggered, sending warning information to relevant management personnel via SMS, email, system pop-ups, etc. Corresponding emergency plans are developed in advance for different types and levels of risks. For example, for high-risk flood warnings, the plan should include personnel evacuation routes, material allocation plans, and emergency rescue measures; for the risk of dam cracks, the plan should clearly define the specific procedures and division of responsibilities for repair and reinforcement.

[0161] 6. Model Updates and Optimization: During risk management, continuously collect actual data, compare the predicted results with the actual situation, and analyze the accuracy and shortcomings of the model's predictions. Based on feedback, regularly update and optimize the risk prediction model. Retrain the model using new historical data, adjust model parameters, or improve the model structure to continuously enhance its predictive power and adaptability, thereby better addressing future potential risks.

[0162] This invention provides a water conservancy inspection method, comprising the following steps:

[0163] S1. The data collection module collects address data, equipment data, and hydrological data (including historical data) of the water conservancy area; it also labels the data as a reference sample.

[0164] S2. The positioning and monitoring deployment module rationally deploys the number and location of positioning and monitoring components based on the address data and monitoring needs of the water conservancy area; the positioning and monitoring components include water level gauges, water quality monitors, etc.

[0165] S3, the route planning module plans the optimal flight route of UAV 1 based on the address data and monitoring requirements of the water conservancy area; it also plans the travel route, entry point and exit point of underwater robot 3.

[0166] S4, the patrol monitoring module uses a drone 1 carrying a high-definition camera 6, a lidar 7 and an infrared thermal imager 9 to conduct comprehensive inspection and monitoring of the water conservancy area; the meteorological data acquisition module acquires meteorological data in real time;

[0167] S41, UAV 1 carrying underwater robot 3 takes off and flies to the target waters according to the preset route.

[0168] S42. After reaching the designated location, the drone 1 hovers, and the motor 4 drives the synchronous transmission mechanism 5, so that the clamping assembly 2 releases the underwater robot 3 to the water surface.

[0169] S44. Underwater robot 3 autonomously submerges into the water to begin its inspection mission, conducting underwater inspections of the water conservancy area and equipment. Drone 1 flies above the water surface, utilizing monitoring equipment to conduct aerial inspections of the water area and acquire real-time information on water conservancy facilities and the environment.

[0170] S45, the positioning and monitoring component monitors the status changes of key water conservancy locations in real time.

[0171] S46. After the inspection task is completed, the drone 1 flies above the underwater robot 3, retrieves it using the gripping component 2, and then returns to the designated location to land.

[0172] S5, the image analysis module preprocesses, extracts features, and analyzes and identifies the acquired images, and promptly identifies abnormal situations in the water conservancy area;

[0173] S6, the infrared data analysis module analyzes infrared data to promptly identify abnormal situations in the water conservancy area;

[0174] S7, the radar data analysis module analyzes radar data and promptly identifies abnormal situations in the water conservancy area;

[0175] S8. The comprehensive analysis module combines the monitoring results of the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation.

[0176] S9. The risk prediction module integrates the monitoring results of the positioning monitoring component, the results of the image analysis module, the infrared data analysis module, and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks.

[0177] S10. When an abnormal situation or potential risk is detected, the alarm module will issue an alarm in a timely manner.

[0178] The working principle of the water conservancy inspection system of this invention is as follows: A data collection module collects address data, equipment data, and hydrological data of the water conservancy area; the data is labeled as reference samples; a positioning and monitoring deployment module rationally deploys the number and location of positioning and monitoring components based on the address data and monitoring needs of the water conservancy area; the positioning and monitoring components include water level gauges, water quality monitors, etc.; a route planning module plans the optimal flight route of the UAV 1 based on the address data and monitoring needs of the water conservancy area; it plans the travel route, entry point, and exit point of the underwater robot 3; a patrol monitoring module uses the UAV 1, equipped with a high-definition camera 6, a lidar 7, and an infrared thermal imager 9, to conduct comprehensive inspection and monitoring of the water conservancy area; a meteorological data acquisition module acquires meteorological data in real time; and an image analysis module... The acquired images undergo preprocessing, feature extraction, and analysis to promptly identify anomalies in the water conservancy area; the infrared data analysis module analyzes infrared data to promptly identify anomalies in the water conservancy area; the radar data analysis module analyzes radar data to promptly identify anomalies in the water conservancy area; the comprehensive analysis module combines the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation; the risk prediction module integrates the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks; when anomalies or potential risks are detected, the alarm module promptly issues an alert.

[0179] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A water conservancy inspection method, characterized in that, Includes the following steps: S1. The data collection module collects address data, equipment data, and hydrological data for the water conservancy area; S2. The positioning and monitoring deployment module should reasonably arrange the number and location of positioning and monitoring components; S3, the route planning module plans the optimal flight route for the drone; it also plans the underwater robot's travel route, entry point into the water, and exit point from the water surface. S4, the patrol monitoring module conducts comprehensive inspection and monitoring of the water conservancy area; The meteorological data acquisition module acquires meteorological data in real time; S5, the image analysis module preprocesses, extracts features, and analyzes and identifies the acquired images, and promptly identifies abnormal situations in the water conservancy area; S6, the infrared data analysis module analyzes infrared data to promptly identify abnormal situations in the water conservancy area; S7, the radar data analysis module analyzes radar data and promptly identifies abnormal situations in the water conservancy area; S8, the comprehensive analysis module integrates multi-source data to conduct a comprehensive assessment of the water conservancy situation; S9, the risk prediction module integrates multi-source data, combining historical data and meteorological data, to predict potential problems and risks; S10. When an abnormal situation or potential risk is detected, the alarm module will issue an alarm in a timely manner.

2. The water conservancy inspection method according to claim 1, characterized in that: Step S4 includes the following steps: S41. The drone carrying the underwater robot takes off and flies to the target waters along a preset route; S42. After reaching the designated location, the drone hovers, and the motor drives the synchronous transmission mechanism to release the underwater robot to the water surface using the clamping assembly. S44. The underwater robot autonomously dives into the water to begin its inspection mission, inspecting the water conservancy area and equipment underwater; the drone flies above the water surface, using monitoring agencies to conduct aerial inspections of the water area. S45. The positioning and monitoring component monitors the status changes of key water conservancy locations in real time. S46. After the inspection mission is completed, the drone flies above the underwater robot, retrieves it using the gripping component, and then returns to the designated location to land.

3. The water conservancy inspection method according to claim 1, characterized in that: Step S3 includes the following steps: S31. Data Collection and Analysis: Collect geographical data of the water conservancy area, and collect detailed location and scope of water conservancy facilities; collect meteorological data and identify potential obstacles within the water conservancy area; S32. Divide the monitoring area: Divide the entire water conservancy area into several sub-areas, each with relatively independent geographical characteristics and monitoring needs; S33. Drone Flight Route Planning: Determine the drone's flight altitude and speed; use Dijkstra's algorithm to plan the drone's initial flight route; adjust the initial route based on real-time weather data; use flight simulation software to simulate and verify the planned flight route. S34. Planning of underwater robot's travel route and entry / exit points: S35: Collaborative Planning and Optimization: Consider the collaborative operation of UAVs and underwater robots; comprehensively consider the flight path of UAVs, the travel path of underwater robots, and their entry and exit points from the water surface, and use multi-objective optimization algorithms for overall optimization; S36. On-site survey and adjustment: Conduct an on-site survey; make further adjustments and optimizations to the route; S37. Final Plan Determination and Implementation: Based on the results of on-site surveys and adjustments, the final flight path of the drone, the route of the underwater robot, and the entry and exit points from the water surface will be determined. S38: Evaluation and Feedback: Evaluate the effectiveness of route planning, and provide feedback and improvements to the route planning module based on the evaluation results.

4. The water conservancy inspection method according to claim 1, characterized in that: Step S8 includes the following steps: S81. Data Collection: Collect monitoring results from the positioning monitoring component, image analysis module, infrared data analysis module, and radar data analysis module; S82. Data Integration: Integrate the monitoring results of the positioning monitoring component, the analysis results of the radar data analysis module and the image analysis module, and the analysis results of the infrared data analysis module. S83. Establish an evaluation indicator system: Set reasonable threshold ranges for each evaluation indicator; S84. Evaluation Calculation: Based on the integrated data, calculate the value of each evaluation indicator; compare the calculated evaluation indicator value with the set threshold to determine whether it exceeds the threshold range; S85. Comprehensive assessment: Taking into account the situation of various assessment indicators, make an overall judgment on the water conservancy situation; S86. Generate Assessment Report: Based on the comprehensive judgment results, generate a detailed water conservancy situation assessment report.

5. The water conservancy inspection method according to claim 1, characterized in that: Step S9 includes the following steps: S91. Data Collection and Integration: Acquire real-time displacement and settlement data of water conservancy facilities; acquire information on changes in the appearance of water conservancy facilities and river morphology; acquire temperature anomaly distribution data provided by the infrared data analysis module; collect historical monitoring data and meteorological data, and integrate and process the data. S92. Feature Engineering: Analyze and integrate the data to extract key features related to potential risks; S93. Risk prediction model construction: Select the convolutional neural network (CNN) model; divide the historical data into training and test sets, use the training set to train the selected model, and use the test set to evaluate the trained model; S94. Potential Risk Prediction: Input the integrated real-time monitoring data and the latest meteorological data into the trained risk prediction model; predict potential problems and risks. S95. Risk Warning: Based on the risk prediction results, potential risks are classified; when a risk is predicted to reach or exceed a certain level, the warning mechanism is immediately triggered. S96. Model Update and Optimization: Continuously collect actual data, compare the differences between the prediction results and the actual situation, and update and optimize the risk prediction model based on the feedback results.

6. The water conservancy inspection method according to claim 2, characterized in that: The synchronous transmission mechanism includes a gear disc, a spur gear, a bevel gear A, a bevel gear B, and a shaft. The gear is rotatably mounted on the bottom of the drone, and the gear is coaxially and fixedly connected to the output end of the motor; The drone has four rotatable spur gears evenly distributed on its bottom; each spur gear meshes with a gear disk for transmission; a bevel gear A is coaxially fixed on each spur gear; bevel gear B is meshed with bevel gear A for transmission, and a shaft is coaxially fixed on bevel gear B; the shaft is detachably fixed to the clamping assembly.

7. The water conservancy inspection method according to claim 6, characterized in that: The clamping assembly 2 includes a multi-stage electric telescopic rod, an L-shaped support plate, and a rotating base; four rotating bases are evenly distributed and rotatably arranged below the UAV; a multi-stage electric telescopic rod is fixedly mounted on each rotating base; the rotating base is detachably and fixedly connected to the corresponding shaft; an L-shaped support plate is fixedly mounted on the final movable rod of the multi-stage electric telescopic rod.

8. The water conservancy inspection method according to claim 7, characterized in that: A rubber pad is fixedly installed on the inside of the L-shaped support plate; an angle sensor is fixedly installed on the multi-stage electric telescopic rod.

9. The water conservancy inspection method according to claim 1, characterized in that: Monitoring agencies include: Data collection module: Collects geological, equipment, and hydrological data for the water conservancy area; labels the data as reference samples; Location monitoring deployment module: Based on the address data and monitoring needs of the water conservancy area, rationally deploy the number and location of location monitoring components; location monitoring components include water level gauges and water quality monitors; Route planning module: Based on the geological data and monitoring requirements of the water conservancy area, the module plans the optimal flight route for the drone; it also plans the underwater robot's route, entry point, and exit point. Patrol and monitoring module: Conducts comprehensive inspection and monitoring of water conservancy areas; Meteorological data acquisition module: Acquires meteorological data in real time; Image analysis module: preprocesses, extracts, and analyzes the acquired images to promptly identify anomalies in the water conservancy area; Infrared data analysis module: Analyzes infrared data to promptly identify anomalies in water conservancy areas; Radar data analysis module: Analyzes radar data to promptly identify anomalies in water conservancy areas; Comprehensive Analysis Module: Combines the monitoring results from the positioning monitoring component, the image analysis module, the infrared data analysis module, and the comprehensive analysis module to conduct a comprehensive assessment of the water conservancy situation; Risk prediction module: It integrates the monitoring results of the positioning monitoring component, the image analysis module, the infrared data analysis module and the comprehensive analysis module, and combines historical data and meteorological data to predict potential problems and risks; Alarm module: Issues an alarm promptly when abnormal situations or potential risks are detected; Control Center: Network connected to the data collection module, positioning and monitoring deployment module, route planning module, cruise monitoring module, risk prediction module, underwater robot 3, motor 4, multi-stage electric telescopic pole 21, alarm module, image analysis module, meteorological data acquisition module, infrared data analysis module, and comprehensive analysis module.

10. A water conservancy inspection system, comprising: Unmanned aerial vehicles (UAVs), gripping components, underwater robots, motors, synchronous transmission mechanisms, positioning and monitoring components, and monitoring mechanisms; characterized in that: The drone has four rotatable clamping components located beneath it; The drone is equipped with a motor and a synchronous transmission mechanism, which is engaged with four gripping components. The synchronous transmission mechanism can drive the four gripping components to rotate synchronously. The synchronous transmission mechanism is also connected to the output of the motor. The underwater robot can inspect water conservancy areas and equipment underwater. The underwater robot is gripped by the four gripping components, and the drone moves and changes position. The drone is equipped with a monitoring mechanism. Positioning and monitoring components are set at multiple key water conservancy locations for fixed-point monitoring of key areas.

Images