Unmanned ship directional operation system cooperating with unmanned aerial vehicle to cruise

Abstract

The invention relates to the technical field of unmanned systems, and discloses an unmanned ship directional operation system cooperating with unmanned aerial vehicle cruise, and the system comprises an unmanned aerial vehicle module which carries a high-precision sensor and is used for scanning a water area environment in real time and generating a high-precision map; the unmanned ship module is provided with a navigation system and an operation device and is used for executing water surface orientation operation according to the high-precision map; the cloud platform module is connected with the unmanned aerial vehicle module and the unmanned ship module through a wireless communication network and is used for data interaction, task scheduling and remote control; and the intelligent charging shore base is used for providing automatic charging support for the unmanned ship module. A high-precision water area map is generated in real time through the unmanned aerial vehicle module and is transmitted to the unmanned ship module, so that the unmanned ship can optimize the route and the obstacle avoidance operation according to the dynamically updated environment data, and the task execution precision is remarkably improved; therefore, the effectiveness of patrolling, cleaning and monitoring tasks is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned systems, and particularly to an unmanned ship directional operation system for collaborative UAV cruising. Background Art

[0002] Water area management and governance are important fields of environmental protection and resource utilization. In the prior art, unmanned devices have been widely used for related operations. For example, UAVs are often used for patrol tasks over water areas, collecting shoreline images and environmental data by carrying cameras and sensors; unmanned ships are used for surface operations, such as floating garbage cleaning, water quality parameter monitoring, and fishing condition detection. These devices are usually equipped with navigation systems (such as GPS) and basic obstacle avoidance functions, and can perform tasks according to preset routes. In addition, some systems transmit the collected data to a remote control center through wireless communication technology to support water pollution monitoring and ecological management.

[0003] However, the prior art has certain limitations. First, UAVs and unmanned ships usually operate independently, lacking an effective collaboration mechanism, resulting in the failure to fully combine the aerial vision and surface operation capabilities, and the task execution efficiency is low. For example, the map data generated by UAVs often cannot be transmitted to unmanned ships in real time, restricting the path optimization ability of unmanned ships in complex water areas. Second, the autonomous operation level of existing unmanned ships is low, mostly relying on manual remote control or simple preset routes. Under changing water currents, dense obstacles, or adverse weather conditions, the positioning accuracy and obstacle avoidance effect are insufficient, and yaw or collision is likely to occur. In addition, the integration degree of existing multi-functional devices is not high, and inspection, cleaning, and monitoring tasks need to be completed using different devices respectively, increasing the operation complexity and resource investment. These problems reduce the overall efficiency of water area management and are difficult to meet the operation requirements in large areas and complex environments. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an unmanned ship directional operation system for collaborative UAV cruising, which solves the problem that in the prior art, UAVs and unmanned ships usually operate independently, lacking an effective collaboration mechanism, resulting in the failure to fully combine the aerial vision and surface operation capabilities, and the task execution efficiency is low.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An unmanned ship directional operation system for collaborative UAV cruising, comprising: A UAV module, equipped with high-precision sensors, for real-time scanning of the water area environment and generating a high-precision map; An unmanned ship module, equipped with a navigation system and working devices, for performing surface directional operations according to the high-precision map; A cloud platform module, connected to the UAV module and the unmanned ship module through a wireless communication network, for data interaction, task scheduling, and remote control; An intelligent charging shore base, used to provide automatic charging support for the unmanned ship module; Among them, the drone module transmits the generated high-precision map to the unmanned boat module through the cloud platform, and the unmanned boat module performs path planning and obstacle avoidance operations according to the high-precision map to carry out patrol, cleaning and monitoring tasks in the water area.

[0006] Preferably, the high-precision sensors carried by the drone module include a lidar and a visual camera. The drone module uses a SLAM algorithm combined with sensor data fusion technology to generate the high-precision map, and uses a multi-spectral imager to identify water surface features under low light or foggy conditions.

[0007] Preferably, the navigation system of the unmanned ship module includes an intelligent assisted directional cruise unit and an adaptive path planning unit. The intelligent assisted directional cruise unit is used to lock a preset route and adjust the hull state through a posture control module to keep the navigation trajectory stable. The adaptive path planning unit dynamically optimizes the route according to water flow speed, wind direction and obstacle position based on a deep learning algorithm.

[0008] Preferably, the unmanned ship module also includes an obstacle avoidance system, which includes an ultrasonic sensor and a millimeter-wave radar for 360-degree all-round obstacle detection, and the obstacle avoidance distance is greater than or equal to 10 meters.

[0009] Preferably, the drone module also includes an intelligent patrol unit, which identifies illegal shoreline behaviors and records evidence through intelligent image capture technology. The cloud platform module integrates an image recognition algorithm to classify and mark the illegal behaviors, and supports remote shouting and warning and expulsion through a voice module, with the shouting distance being greater than or equal to 200 meters.

[0010] Preferably, the unmanned boat module has L4 level fully autonomous operation capability, including a high-precision inertial navigation system and a differential GPS module, with a positioning error of less than 0.5 meters, and can autonomously plan paths and avoid obstacles in blind spots under bridges or in severe weather conditions.

[0011] Preferably, the unmanned boat module also includes a water quality monitoring unit, which is equipped with a pH sensor, a temperature sensor, a turbidity sensor, a dissolved oxygen sensor and a conductivity sensor, and is used to collect water quality parameters in real time along the predetermined route and perform online analysis and storage through the cloud platform module.

[0012] Preferably, the smart charging shore base adopts wireless charging technology, and the charging efficiency is greater than or equal to 90%. The unmanned ship module is equipped with an intelligent power monitoring system, and automatically returns to the smart charging shore base for charging when the power is less than 20%. The single full charging time is less than or equal to 2 hours.

[0013] Preferably, the unmanned boat module further includes a fishing situation monitoring unit which is equipped with sonar equipment with a detection range of greater than or equal to 50 meters. It is used to monitor the position and density of fish schools in real time and regulate the distribution of fish schools through acoustic fish driving technology, and the acoustic wave frequency range is 20 - 200 kHz.

[0014] Preferably, the cloud platform module supports the multi - boat scheduling function, allocates the charging priorities of the intelligent charging shore bases according to task requirements, and generates a water quality distribution heat map and a fish school distribution map through big data analysis technology, providing data support for water area governance and ecological restoration.

[0015] The present invention provides an unmanned boat directional operation system for collaborative drone cruising, having the following beneficial effects: 1. The present invention generates a high - precision water area map in real time through the drone module and transmits it to the unmanned boat module, enabling the unmanned boat to optimize the route and obstacle avoidance operations according to the dynamically updated environmental data. Compared with the traditional single - device operation, this collaborative mechanism makes full use of the aerial vision of the drone and the water surface operation ability of the unmanned boat, significantly improving the accuracy of task execution, and thus enhancing the effectiveness of inspection, cleaning, and monitoring tasks.

[0016] 2. The present invention realizes the L4 - level full - autonomous operation ability through the high - precision inertial navigation system, differential GPS, and adaptive algorithm integrated in the unmanned boat module, and can complete path planning and obstacle avoidance without manual intervention. Compared with the existing technologies that rely on manual remote control or simple automation, this system can still maintain a positioning error of less than 0.5 meters under the conditions of blind areas under bridges, a water flow speed of up to 2 m / s, or a wave height of 0.8 meters. This adaptability reduces the workload of operators and improves the stability and safety of operations in harsh environments.

[0017] 3. The present invention integrates multiple functions such as inspection, cleaning, water quality monitoring, and fishing situation management. Through the collaborative operation of the drone and the unmanned boat, it realizes a one - stop water area management solution. Compared with the traditional governance methods of separate devices and separate tasks, this system can simultaneously complete water quality parameter collection (such as pH, dissolved oxygen), garbage cleaning (about 50 kilograms per hour), and illegal behavior monitoring in a single task, reducing equipment investment and task switching time, and the comprehensive efficiency is increased by about 2 - 3 times, providing more comprehensive technical support for water area management.

[0018] 4. The obstacle avoidance system of the unmanned boat module of the present invention consists of ultrasonic sensors and millimeter-wave radars, combined with a path planning algorithm based on deep learning, which can detect and avoid obstacles in real time, and the obstacle avoidance distance can reach more than 10 meters. Compared with traditional unmanned boats with fixed routes or no obstacle avoidance function, this system can automatically adjust the course when encountering floating objects or bridge piers, and the calculation time of the detour path is less than 0.5 seconds, avoiding the risk of collision. The realization of this function reduces the equipment damage rate and ensures the safety of the operation process.

[0019] 5. The water quality monitoring unit and sonar equipment carried by the unmanned boat module of the present invention can collect parameters such as pH value and dissolved oxygen, as well as fish population distribution data in real time, and analyze and store them through the cloud platform. Compared with traditional manual sampling or fixed monitoring stations, this system has a wider coverage range (the single voyage can reach 10 kilometers), the sampling frequency is adjustable (the default is 5 minutes / time), and the data upload delay is less than 50 milliseconds. This real-time and comprehensiveness provides a reliable basis for pollution source location and fish population density regulation, and helps the scientific management of the water ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall system of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 2 It is a working schematic diagram of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 3 It is a front three-dimensional schematic view of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 4 It is a side three-dimensional schematic view of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 5 It is a bottom three-dimensional schematic view of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 6 It is a front three-dimensional schematic view of the drone module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 7 It is a side three-dimensional schematic view of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 8 It is a bottom three-dimensional schematic view of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 9 It is a system framework diagram of the unmanned boat module of an unmanned boat directional operation system for collaborative drone cruise according to the present invention; Figure 10Schematic diagram of the mapping process of the UAV module of an unmanned ship directional operation system for collaborative UAV cruising according to the present invention.

[0021] Among them, 1. UAV module; 2. Unmanned ship module; 3. Intelligent charging shore base. Specific embodiments

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to the attached Figure 1 - attached Figure 2 , the embodiment of the present invention provides an unmanned ship directional operation system for collaborative UAV cruising, including: UAV module 1, equipped with a high-precision sensor, for real-time scanning of the water area environment and generating a high-precision map; Unmanned ship module 2, equipped with a navigation system and an operation device, for performing water surface directional operations according to the high-precision map; Cloud platform module, connected to the UAV module 1 and the unmanned ship module 2 through a wireless communication network, for data interaction, task scheduling and remote control; Intelligent charging shore base 3, for providing automatic charging support for the unmanned ship module 2; Among them, the UAV module 1 transmits the generated high-precision map to the unmanned ship module 2 through the cloud platform, and the unmanned ship module 2 performs path planning and obstacle avoidance operations according to the high-precision map to perform water area inspection, cleaning and monitoring tasks.

[0024] Specifically, in practical applications, the drone module 1 adopts a quadcopter or hexacopter structure, with the body weight controlled between 5 - 10 kilograms and the flight altitude ranging from 10 - 100 meters to adapt to the inspection requirements of different waters. The high-precision sensor has a working frequency of 10Hz and a scanning range covering a circular area with a diameter of 500 meters to ensure the comprehensiveness of map data. The unmanned boat module 2 selects a catamaran design, 3 meters long and 1.5 meters wide, with a maximum speed of 5 m / s. The operating devices carried include a robotic arm and a sewage suction pump. The robotic arm has an extended length of 1.2 meters, and the sewage suction pump has a power of 500W, which is used to clean floating debris on the water surface. The cloud platform module is deployed on a remote server, equipped with an 8-core CPU and 32GB of memory, supporting the data interaction of 10 unmanned boats and 5 drones simultaneously, with a communication delay of less than 50 milliseconds. The intelligent charging shore base 3 adopts a fixed or floating design and is installed at the edge of the water area. A single station can charge 3 unmanned boats simultaneously. The system workflow is as follows: After taking off, the drone module 1 flies along the water area boundary. After completing the map construction, it uploads the data to the cloud platform through the 5G network, and the data packet size is approximately 200MB. After receiving the map, the unmanned boat module 2 uses the built-in processor (main frequency 2.5GHz) to parse the data and generate a route, with the route error controlled within 0.3 meters. Subsequently, it executes the inspection task. If a polluted area is found, the robotic arm is activated for cleaning, and 50 kilograms of floating garbage can be processed per hour.

[0025] Please refer to the attached Figure 6 - attached Figure 8 and attached Figure 10 , the high-precision sensors carried by the drone module 1 include lidar and visual cameras. The drone module 1 uses the SLAM algorithm combined with the sensor data fusion technology to generate a high-precision map and identify water surface features under low-light or foggy conditions through a multispectral imager.

[0026] Specifically, the lidar of the UAV module 1 selects a 16-line or 32-line model, with a vertical field of view of 30°, a horizontal scanning frequency of 20 Hz, and a ranging accuracy of ±2 cm, capable of detecting floating objects on the water surface and shoreline vegetation. The resolution of the visual camera is 4K, with 12 million pixels, supporting video acquisition at 30 frames per second, a lens focal length of 24 mm, and having the functions of waterproof and anti-fog. The SLAM algorithm runs on the embedded computing unit (GPU computing power of 50 TOPS) built into the UAV. Through point cloud matching and visual feature extraction, 500,000 three-dimensional coordinate points are generated per second, and the map resolution reaches 5 cm. The multispectral imager covers the visible and near-infrared bands (400 - 1000 nm). Under foggy conditions where the light intensity is less than 10 lux or the visibility is less than 50 m, it can still identify oil pollution on the water surface (reflectivity difference greater than 20%) and illegal buildings on the shoreline (such as temporary buildings with a height greater than 1 m). In the embodiment, the UAV module 1 flies over a certain river, takes 15 minutes to complete the map construction of a 5-square-kilometer water area, and the generated data is transmitted to the cloud platform through an encryption protocol (AES-256) to ensure data security.

[0027] Please refer to the attached Figure 3 - attached Figure 5 and attached Figure 9 , the navigation system of the unmanned boat module 2 includes an intelligent auxiliary orientation and cruise unit and an adaptive path planning unit. The intelligent auxiliary orientation and cruise unit is used to lock the preset route and adjust the hull state through the attitude control module to maintain the stability of the navigation trajectory. The adaptive path planning unit dynamically optimizes the route based on the deep learning algorithm according to the water flow velocity, wind direction, and obstacle position.

[0028] Specifically, the navigation system of the unmanned boat module 2 uses an embedded industrial computer (8GB of memory, 256GB of storage) to run the intelligent auxiliary orientation and cruise unit. The preset route is input in GPX format through the cloud platform, including at least 50 waypoints, and the route length can reach 10 km. The attitude control module consists of 6 servo motors and a gyroscope. The motor power is 100W, the rotation speed range is 0 - 3000 revolutions per minute, the gyroscope sampling frequency is 200Hz, and it can adjust the hull inclination angle (range ±15°) in real time, and keep the course deviation less than 5° under the countercurrent condition where the water flow velocity reaches 2 m / s. The adaptive path planning unit is trained based on the convolutional neural network (CNN) and the reinforcement learning model. The training data set contains 1000 hours of water area navigation records. The input parameters include water flow velocity (0 - 3 m / s), wind speed (0 - 15 m / s), and obstacle coordinates, and the minimum radius of curvature of the adjusted route output is 2 m. In practical applications, the unmanned boat module 2 cruises along a circular route (circumference 8 km) in a certain lake, automatically detours when encountering floating wooden stakes, and the extended detour path does not exceed 50 m. The entire task takes 2 hours, and the track deviation from the preset route does not exceed 0.2 m.

[0029] Please refer to the appendix Figure 9 . The unmanned boat module 2 further includes an obstacle avoidance system, which includes ultrasonic sensors and millimeter-wave radars for 360-degree all-round obstacle detection, and the obstacle avoidance distance is greater than or equal to 10 meters.

[0030] Specifically, the obstacle avoidance system of the unmanned boat module 2 includes 4 ultrasonic sensors and 2 millimeter-wave radars. The ultrasonic sensors are installed around the hull, with a detection frequency of 40 kHz, a detection angle of 60°, and an effective distance of 0.2 - 15 meters, suitable for detecting floating objects at close range; the millimeter-wave radars are located at the bow and stern of the boat, with a working frequency band of 77 GHz, a detection distance of 5 - 50 meters, and a horizontal field of view angle of 120°, capable of identifying high-speed moving objects (such as small boats with a speed of 10 m / s). The data of the two types of sensors are fused through the Kalman filtering algorithm, and the position of obstacles is updated 20 times per second, and the system response time is less than 0.1 second. The obstacle avoidance operation is divided into two modes: deceleration and detour. When the distance to the obstacle is less than 5 meters, the boat speed is reduced to 1 m / s; when the distance is greater than 5 meters and less than 10 meters, a detour path is generated, and the path calculation takes less than 0.5 second. In the embodiment, when the unmanned boat module 2 is operating in a certain river channel, it detects a floating plastic bag (diameter 0.5 meters) ahead, automatically adjusts the heading to deflect 30°, and resumes the original course after detouring, and the whole process takes 15 seconds without collision.

[0031] Please refer to the appendix Figure 10 . The unmanned aerial vehicle module 1 further includes an intelligent inspection unit. The intelligent inspection unit uses intelligent image capture technology to identify illegal behaviors on the shoreline and record evidence. The cloud platform module integrates image recognition algorithms to classify and mark illegal behaviors, and supports remote shouting and warning to drive away through the voice module, and the shouting distance is greater than or equal to 200 meters.

[0032] Specifically, the intelligent inspection unit of the unmanned aerial vehicle module 1 includes a high-definition camera and an infrared thermal imager. The camera has a field of view angle of 90° and supports 1080p video recording. The resolution of the thermal imager is 640×480, and the temperature measurement range is -20°C to 150°C, capable of identifying the thermal signals of human activities on the shoreline (temperature difference greater than 5°C). The intelligent image capture technology is based on the object detection algorithm (YOLOv5). The training data set contains 5000 images of water area illegal behaviors, and the recognition accuracy rate reaches 95%. The capture interval is 1 second, and the single storage capacity is 1000 images. The power of the voice module is 50W, with 10 pre-recorded warning audio (such as "Please immediately stop illegal fishing") built-in, and the volume can be adjusted to 120 decibels, and the shouting content is remotely updated through the cloud platform. In practical applications, when the unmanned aerial vehicle module 1 conducts inspections on the shoreline of a certain lake, it discovers an illegally built shed (area about 10 square meters), captures 5 photos and uploads them to the cloud platform. The system automatically marks it as "illegal building" and issues a warning at the same time. The target person evacuates within 30 seconds, and the law enforcement efficiency is increased by 80%.

[0033] Please refer to the appendix Figure 9 , the unmanned boat module 2 has the ability of L4-level full autonomous operation, including a high-precision inertial navigation system and a differential GPS module, with a positioning error of less than 0.5 meters, and can autonomously plan paths and avoid obstacles in blind areas under bridges or in bad weather conditions.

[0034] Specifically, the L4-level autonomous operation ability of the unmanned boat module 2 relies on a high-precision inertial navigation system (INS) and a differential GPS module. The INS includes a three-axis accelerometer and a gyroscope, with a sampling frequency of 400 Hz and a drift rate of less than 0.01° / hour; the differential GPS supports RTK positioning, receives dual Beidou and GPS signals, and has an update frequency of 10 Hz. It can still maintain positioning through inertial navigation in the blind area under the bridge (with a satellite signal occlusion rate of up to 90%). The autonomous planning algorithm is based on the A* algorithm and the dynamic window method, with a calculation period of 0.2 seconds and a path point interval of 0.5 meters, and can cope with harsh conditions of a wind speed of 15 m / s and a wave height of 0.8 meters. In the embodiment, the unmanned boat module 2 operates under a certain bridge, with a bridge height of 5 meters and a width of 50 meters, and a signal interruption time of 2 minutes. The system relies on the INS to maintain straight navigation, with a positioning deviation of only 0.4 meters, successfully avoiding the bridge piers (with a diameter of 2 meters), and automatically returning after the task is completed, with a total voyage of 5 kilometers.

[0035] Please refer to the appendix Figure 9 , the unmanned boat module 2 also includes a water quality monitoring unit, which is equipped with a pH sensor, a temperature sensor, a turbidity sensor, a dissolved oxygen sensor, and a conductivity sensor, and is used to collect water quality parameters in real time along a predetermined route and perform online analysis and storage through the cloud platform module.

[0036] Specifically, the water quality monitoring unit of the unmanned boat module 2 is installed at the bottom of the boat, and the sensors are fixed on a liftable bracket with a telescopic range of 0 - 0.5 meters and adjustable sampling depth. The pH sensor has a measurement range of 0 - 14 and an accuracy of ±0.1; the temperature sensor has a range of -5°C to 50°C and an accuracy of ±0.5°C; the turbidity sensor has a measurement range of 0 - 1000 NTU and an accuracy of ±5%; the dissolved oxygen sensor has a measurement range of 0 - 20 mg / L and an accuracy of ±0.1 mg / L; the conductivity sensor has a measurement range of 0 - 2000 μS / cm and an accuracy of ±2%. The data collection frequency is default 5 minutes / time, and can be adjusted to 1 minute / time through the cloud platform. The single data packet size is 50 KB. The cloud platform uses a time series database to store data, supports querying 1 million records within 1 year, and the analysis module generates water quality trend charts and anomaly alarms (such as when the pH is below 6). In the embodiment, when the unmanned boat samples in a certain river and finds that the turbidity in a certain section reaches 800 NTU, the system automatically marks the location of the pollution source and notifies the management center. The sampling task covers a 10-kilometer water area and takes 3 hours.

[0037] Please refer to the appendixFigure 1 , the intelligent charging shore base 3 adopts wireless charging technology with a charging efficiency of greater than or equal to 90%. The unmanned boat module 2 is equipped with an intelligent power monitoring system. When the power is lower than 20%, it automatically returns to the intelligent charging shore base 3 for charging, and the single full charge time is less than or equal to 2 hours.

[0038] Specifically, the intelligent charging shore base 3 adopts a floating platform with a length of 5 meters and a width of 3 meters. The top is paved with a wireless charging coil (diameter 1 meter), and the output power is 2kW. During the charging process, the alignment error between the unmanned boat module 2 and the shore base is less than 10 centimeters, and automatic docking is achieved by relying on magnetic positioning technology. The built-in lithium battery of the unmanned boat has a capacity of 100Ah and a rated voltage of 48V. The power monitoring system detects the remaining power in real time through a current sensor (accuracy ±1%). When it is lower than 20% (about 20Ah), the navigation system calculates the distance to the nearest shore base (error ±1 meter), and the return speed is 3 m / s. The charging efficiency reaches 92% under the conditions of a temperature of 25°C and a humidity of 60%, and it takes 1.8 hours to fully charge. In the embodiment, after the unmanned boat module 2 operates on a certain lake for 6 hours, the power drops to 18%, and it automatically returns to the shore base 3 (2 kilometers away). After docking, it is charged to 100% without manual intervention. The shore base simultaneously schedules another unmanned boat for charging, and the waiting time is less than 10 minutes.

[0039] Please refer to the appendix Figure 9 , the unmanned boat module 2 further includes a fishing condition monitoring unit. The fishing condition monitoring unit is equipped with a sonar device with a detection range of greater than or equal to 50 meters, which is used to monitor the position and density of fish schools in real time, and regulate the distribution of fish schools through acoustic fish driving technology. The acoustic frequency range is 20 - 200kHz.

[0040] Specifically, the fishing condition monitoring unit of the unmanned boat module 2 is installed at the bow of the boat. The sonar device is a single-beam model with a working frequency of 200kHz, a beam angle of 15°, a detection depth range of 0.5 - 50 meters, a resolution of 0.1 meter, and can identify individuals of fish schools with a length greater than 5 centimeters. The acoustic fish driving technology generates 20 - 200kHz pulsed waves through an adjustable-frequency transmitter (power 100W), and the driving range is a diameter of 20 meters. The moving speed of the fish school is increased to 0.5 m / s. The monitoring data is updated every minute, generating a fish school density distribution map (resolution 1 meter × 1 meter), and performing correlation analysis with water quality data. In the embodiment, the unmanned boat patrols a certain reservoir, discovers a fish school gathering area (density up to 50 fish per cubic meter), activates the fish driving function, and disperses the fish school to a density lower than 20 fish per cubic meter within 10 minutes. The system records the data and uploads it to assist in the management of the water area ecological balance, with a total voyage of 15 kilometers.

[0041] Please refer to the appendix Figure 9 - appendix Figure 10, the cloud platform module supports the multi-vessel scheduling function, allocates the charging priorities of the intelligent charging shore base 3 according to task requirements, and generates a water quality distribution heat map and a fish population distribution map through big data analysis technology to provide data support for water area governance and ecological restoration.

[0042] Specifically, the multi-vessel scheduling function of the cloud platform module is based on a priority algorithm. The input parameters include the battery power of the unmanned vessel (0 - 100%), the task urgency level (1 - 5), and the distance from the shore base 3 (0 - 5 km). The scheduling instructions are updated every minute, and the instruction transmission delay is less than 20 milliseconds. The intelligent charging shore base 3 supports 3 charging positions and can complete 20 charging tasks per day. The big data analysis technology uses the Hadoop framework to process water quality and fishery data, with a storage capacity of 1 TB. The water quality distribution heat map is displayed in color coding (red, yellow, and green represent severe pollution, medium pollution, and normal respectively), with a resolution of 0.1 km × 0.1 km; the fish population distribution map is displayed in a dot matrix form, with each point representing 10 fish. In the embodiment, the system schedules 5 unmanned vessels to operate in a certain lake. 2 low-battery vessels are charged first, and the remaining 3 continue to patrol. The cloud platform generates a heat map showing that the dissolved oxygen in a certain area is lower than 5 mg / L and the fish population density is 30 fish per cubic meter. The management center adjusts the governance plan accordingly, and the task takes 8 hours.

[0043] The following introduces the specific embodiments of the structure: Embodiment 1: Lake floating garbage cleaning and water area patrol In this embodiment, the system is deployed in a lake with an area of 10 square kilometers to clean the floating garbage on the water surface and patrol the shoreline conditions. The drone module 1 adopts a six-rotor design and is equipped with a 32-line lidar (ranging accuracy ±2 cm) and a 4K visual camera (field of view angle 90°). The flight altitude is set at 50 meters, covering the entire lake area. It takes 20 minutes to generate a high-precision 3D map with a resolution of 5 cm and a data size of approximately 300 MB. The map is transmitted to the cloud platform through a 5G network, and the transmission time is 15 seconds. The unmanned vessel module 2 is of a catamaran type, 3.5 meters long, equipped with a robotic arm (gripping force 50 N) and a sewage suction pump (flow rate 30 L / min). After receiving the map, it automatically plans a circular route (total length 12 km) with a speed of 3 m / s. During the patrol, the system detects a floating plastic bag (area approximately 1 square meter). The robotic arm grabs it and stores it in the onboard trash bin (capacity 100 L). Approximately 60 kg of garbage is cleaned per hour. The intelligent charging shore base 3 is located on the east bank of the lake, adopts a floating design, and is equipped with 2 charging positions. When the battery power of the unmanned vessel drops to 18%, it automatically returns to the shore for charging. It is 1.5 km away from the shore base, and the return trip takes 8 minutes. It takes 1.5 hours to charge to 100%. The cloud platform records the cleaning progress in real time, generates a garbage distribution map, and shows that the pollution concentration area is located in the northwest corner of the lake, assisting the management department to optimize the subsequent governance plan. The entire task takes 4 hours, and the cleaning efficiency is 3 times higher than that of manual work.

[0044] Example 2: Monitoring of Illegal Fishing in Rivers and Law Enforcement Support In this example, the system is applied to a 15-kilometer-long river with the goal of monitoring illegal fishing and providing law enforcement support. The drone module 1 is a quadcopter type equipped with a 16-line lidar and an infrared thermal imager (resolution 640×480). It flies at an altitude of 30 meters along the riverbank and takes 25 minutes to complete the construction of the river map, identifying shoreline vegetation and human thermal signals (temperature difference greater than 5°C). After the map is transmitted to the cloud platform, the unmanned boat module 2 (3 meters long, speed 4 m / s) plans a patrol route according to the map, and the route includes 20 key points. During the drone patrol, 3 illegal fishermen are found in the middle reaches of the river section. The intelligent image capture technology (based on the YOLOv5 algorithm) captures 5 high-definition photos with an identification accuracy of 96%, and issues a warning through the voice module (power 50W): "Please immediately stop illegal fishing and leave within 30 seconds", and the shouting distance reaches 250 meters. The unmanned boat then approaches the target area, and the on-board camera records the evacuation process. The video is uploaded to the cloud platform to generate a law enforcement report. The cloud platform marks the illegal location and notifies the management center, and the law enforcement officers arrive at the scene for handling within 5 minutes. The intelligent charging shore base 3 is located downstream of the river, with a fixed design. Single charging supports the unmanned boat to operate continuously for 6 hours. This task takes 2 hours, successfully drives away all illegal personnel, and improves the law enforcement efficiency by 80%.

[0045] Example 3: Reservoir Water Quality Monitoring and Fish Population Management In this example, the system is deployed in a reservoir (area 8 square kilometers, average water depth 10 meters) for water quality monitoring and fish population density regulation. The drone module 1 is equipped with a lidar and a high-definition camera, flying at an altitude of 40 meters, and completes the construction of the reservoir map within 15 minutes, marking the water surface boundary and the positions of the inlet and outlet. The unmanned boat module 2 is 3.2 meters long, equipped with a water quality monitoring unit (pH, temperature, turbidity, dissolved oxygen, conductivity sensors) and a sonar device (detection depth 50 meters), and travels along the central route of the reservoir (10 kilometers long) at a speed of 2.5 m / s. Water quality data is collected every 5 minutes. It is found that the pH value in a certain area is 5.8 and the dissolved oxygen is 4.5 mg / L, which is lower than the normal standard. After the data is uploaded to the cloud platform, a heat map is generated, indicating that the pollution source may be located at the upstream sewage outlet. Sonar monitoring shows that the fish population density reaches 60 fish per cubic meter, which is too dense. The system activates the acoustic fish repelling function (frequency 150 kHz, range 20 meters), and reduces the density to 25 fish per cubic meter within 10 minutes, restoring the ecological balance. The intelligent charging shore base 3 is located on the south bank of the reservoir, using wireless charging technology. When the battery level is lower than 20%, the unmanned boat returns to the shore for charging, at a distance of 1 kilometer, and it takes 1.8 hours to fully charge. After the cloud platform analyzes the data, it generates a fish population distribution map and a water quality report, providing a basis for reservoir management. The total task takes 5 hours.

[0046] Example 4: Obstacle Avoidance and Emergency Rescue in Complex Coastal Waters This embodiment tests the obstacle avoidance and emergency rescue capabilities of the system for a coastal area (an area of 20 square kilometers, including reefs and bridge piers). UAV module 1 is a six-rotor heavy-duty model (load capacity 15 kg), equipped with a 32-line laser radar and a multispectral imager, with a flight altitude of 60 meters. It takes 30 minutes to generate a map and identify the locations of reefs (height 0.5-2 meters) and bridge piers (diameter 3 meters). Unmanned boat module 2 is equipped with an obstacle avoidance system (ultrasonic sensor detection distance 15 meters, millimeter wave radar 50 meters) and rescue equipment (lifebuoy dispenser, range 30 meters), and plans a complex route (including 50 turning points, total length 15 kilometers) after receiving the map. When encountering floating wooden piles (2 meters long) during navigation, the obstacle avoidance system automatically decelerates to 1 meter / second and detours, and the detour path is extended by 20 meters, which takes 10 seconds. In the emergency scenario, the system receives a distress signal from the drowning person (coordinate error ±1 meter), the drone shoots the location and guides the unmanned boat to approach, releases the lifebuoy, and the rescue takes 5 minutes. Smart charging shore base 3 is floating, located at the coastal dock, supports fast charging (power 2.5kW), and the unmanned boat returns when the power drops to 15%, with a distance of 2.5 kilometers, and it takes 1 hour to charge to 80%. The cloud platform records obstacle avoidance and rescue data, optimizes subsequent paths, and the total mission takes 6 hours, successfully coping with complex environments.

[0047] Example 5: Multi-vessel collaborative operation and large-area water management This embodiment tests the multi-ship coordination capability in a lake with an area of 50 square kilometers, with the goal of large-scale water management. The system deploys 3 drone modules 1 and 5 unmanned boat modules 2. The drones fly in a ladder formation, with altitudes of 30 meters, 50 meters, and 70 meters, covering different perspectives, completing map construction within 40 minutes, with a resolution of 10 cm and a total data size of 1GB. The unmanned boat module 2 is divided into a cleaning group (2 ships, equipped with sewage pumps) and a monitoring group (3 ships, equipped with water quality and sonar equipment). The cloud platform assigns tasks according to the map: the cleaning group cleans floating garbage (50 kg per hour per ship), and the monitoring group collects water quality data (once per minute) and fish information (density distribution map). The cleaning group route is grid-shaped (1 km × 1 km per grid), and the monitoring group is circular (30 km in circumference). The smart charging shore base 3 has 3 stations, each station supports 2 ships to charge, and the multi-ship scheduling algorithm gives priority to unmanned ships with less than 20% of power to return, with a charging efficiency of 90% and an average waiting time of 5 minutes. The cloud platform generated a governance report, showing that the turbidity in the southeast of the lake exceeded the standard (1000 NTU) and fish gathered (80 fish / m3), and recommended measures to drive fish away and discharge sewage. The task took 10 hours, with a coverage rate of 95%, and governance efficiency increased by 5 times.

[0048] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An unmanned ship directional operation system for collaborative UAV cruising, characterized in that, Including: A drone module (1), equipped with high-precision sensors, for real-time scanning of the water area environment and generating a high-precision map; An unmanned boat module (2), equipped with a navigation system and working devices, for performing surface orientation operations according to the high-precision map; A cloud platform module, connected to the drone module (1) and the unmanned boat module (2) through a wireless communication network, for data interaction, task scheduling, and remote control; An intelligent charging shore base (3), for providing automatic charging support for the unmanned boat module (2); Wherein, the drone module (1) transmits the generated high-precision map to the unmanned boat module (2) through the cloud platform, and the unmanned boat module (2) performs path planning and obstacle avoidance operations according to the high-precision map to carry out inspection, cleaning, and monitoring tasks of the water area.

2. The unmanned ship directional operation system for collaborative UAV cruise according to claim 1, wherein The high-precision sensors carried by the drone module (1) include lidar and visual cameras. The drone module (1) uses the SLAM algorithm combined with sensor data fusion technology to generate the high-precision map, and identifies water surface features under low light or foggy conditions through a multispectral imager.

3. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, wherein The navigation system of the unmanned boat module (2) includes an intelligent assisted orientation and cruising unit and an adaptive path planning unit. The intelligent assisted orientation and cruising unit is used to lock the preset route and adjust the hull state through the attitude control module to maintain a stable navigation trajectory. The adaptive path planning unit dynamically optimizes the route based on a deep learning algorithm according to the water flow velocity, wind direction, and obstacle position.

4. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, characterized in that, The unmanned boat module (2) further includes an obstacle avoidance system, and the obstacle avoidance system includes ultrasonic sensors and millimeter wave radars, for performing 360-degree omnidirectional obstacle detection, and the obstacle avoidance distance is greater than or equal to 10 meters.

5. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, wherein, The drone module (1) further includes an intelligent inspection unit. The intelligent inspection unit identifies illegal behaviors on the shoreline through intelligent image capture technology and records evidence. The cloud platform module integrates an image recognition algorithm to classify and mark the illegal behaviors, and supports remote shouting and warning and driving away through the voice module, and the shouting distance is greater than or equal to 200 meters.

6. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, characterized in that, The unmanned boat module (2) has L4-level full autonomous operation ability, including a high-precision inertial navigation system and a differential GPS module, with a positioning error less than 0.5 meters, and can autonomously plan paths and avoid obstacles in blind areas under bridges or in bad weather conditions.

7. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, characterized in that, The unmanned boat module (2) further includes a water quality monitoring unit. The water quality monitoring unit is equipped with a pH sensor, a temperature sensor, a turbidity sensor, a dissolved oxygen sensor, and a conductivity sensor, for real-time collecting water quality parameters along a predetermined route and performing online analysis and storage through the cloud platform module.

8. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, characterized in that, The intelligent charging shore base (3) adopts wireless charging technology, and the charging efficiency is greater than or equal to 90%. The unmanned boat module (2) is equipped with an intelligent power monitoring system. When the power is lower than 20%, it automatically returns to the intelligent charging shore base (3) for charging, and the single charging time is less than or equal to 2 hours.

9. The unmanned ship directional operation system for collaborative UAV cruise according to claim 1, wherein, The unmanned boat module (2) further includes a fishing condition monitoring unit, which is equipped with sonar equipment with a detection range of greater than or equal to 50 meters. It is used to monitor the position and density of fish schools in real time, and regulate the distribution of fish schools through acoustic fish driving technology. The acoustic wave frequency range is 20 - 200 kHz.

10. The unmanned ship directional operation system for collaborative UAV cruising according to claim 1, wherein, The cloud platform module supports multi-boat scheduling functions, allocates the charging priorities of the intelligent charging shore base (3) according to task requirements, and generates a water quality distribution heat map and a fish school distribution map through big data analysis technology, providing data support for water area governance and ecological restoration.

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