Intelligent unmanned rescue boat, rescue control method and rescue control equipment

Abstract

The invention discloses an intelligent unmanned rescue boat, a rescue control method and rescue control equipment. The rescue boat comprises a boat body, a power system, a multi-sensor system, a communication system, a processor and a memory. The multi-sensor system comprises a visual sensor, a thermal imaging sensor, a sonar sensor, a positioning sensor and a meteorological sensor. The communication system is used for exchanging data with external equipment; the processor is connected with the multi-sensor system and the communication system; the memory is connected with the processor and stores a computer program; and the processor executes the program in the memory, controls the multi-sensor system to scan a surrounding water area, identifies and positions at least one drowning person, determines an obstacle avoidance demand, generates an optimal rescue path, and controls the power system to move the ship body according to the optimal path to implement rescue. According to the invention, rescue tasks can be efficiently and accurately executed under complex sea conditions, and the success rate and efficiency of sea rescue are improved.

Description

Technical Field

[0001] The present invention relates to the field of water area or marine rescue technology, and in particular to an intelligent unmanned rescue boat, a rescue control method and a rescue control device. Background Art

[0002] Maritime rescue is a very challenging and time-sensitive task, with extremely high requirements for equipment and personnel. Traditional maritime rescue methods mainly rely on rescue ships and helicopters, but these methods often face many limitations. The first is human resource limitations. Professional maritime rescue personnel need to undergo long-term training, and the number is limited, which makes it difficult to meet the needs of large-scale rescue. The second is weather conditions. Bad weather such as strong winds, big waves, heavy rain or dense fog often affects the departure of rescue ships or the take-off of helicopters, thereby delaying the rescue time. In addition, the ability to cope with complex sea conditions is also a major challenge. In areas with dense reefs or ice areas, the maneuverability and safety of traditional rescue ships are seriously challenged. Night rescue is even more difficult. At night or in low visibility, the efficiency of traditional visual search is extremely low, which seriously affects the rescue effect. Insufficient continuous operation capability is also a problem. Long-term search and rescue operations can easily lead to fatigue of rescue personnel and affect the accuracy of judgment and operation. Finally, the ability to obtain and process information is limited. Traditional rescue methods are difficult to quickly and comprehensively obtain and process a large amount of environmental information, affecting the timeliness and accuracy of decision-making. These limitations make maritime rescue missions more complex and dangerous.

[0003] Therefore, developing an intelligent unmanned rescue boat with advanced perception, intelligent decision-making and efficient execution capabilities has important practical significance and application value. This can not only improve the success rate and efficiency of maritime rescue, but also provide strong support for traditional rescue forces in harsh environments, and maximize the safety of life of personnel at sea. Summary of the invention

[0004] In view of this, the embodiments of the present invention provide an intelligent unmanned rescue boat, a rescue control method and a rescue control device, so as to realize the rapid and accurate identification and positioning of the drowning person in a complex and changeable water or marine environment, and autonomously plan the optimal rescue path while considering various factors, thereby improving the efficiency and success rate of marine rescue.

[0005] To achieve the above objectives, in a first aspect, an intelligent unmanned rescue boat is provided, comprising:

[0006] hull;

[0007] A power system, used for driving the hull to move on the water surface;

[0008] Multi-sensor system including:

[0009] Vision sensors for identifying people and obstacles on the water surface in daylight conditions;

[0010] Thermal imaging sensors, used to identify body temperature characteristics of drowning people in night conditions;

[0011] Sonar sensors to detect people who have sunk into the water and underwater obstacles;

[0012] A positioning sensor, used to determine the position data of the intelligent unmanned rescue boat;

[0013] Meteorological sensors, used to detect the meteorological conditions of the surrounding environment;

[0014] Communication system for exchanging data with external devices;

[0015] a processor connected to the multi-sensor system and the communication system;

[0016] A memory, connected to the processor, storing a computer program;

[0017] The processor is used to execute the computer program to implement the following steps:

[0018] Controlling the multi-sensor system to scan surrounding waters;

[0019] Identify and locate one or more people in the water based on the data scanned and acquired by the multi-sensor system, and determine obstacle avoidance requirements;

[0020] Generate the optimal rescue path based on the location information of the drowning person, the number of drowning people, weather conditions and obstacle avoidance requirements;

[0021] The power system is controlled to move the hull along the optimal rescue path to approach the one or more persons who fall into the water, and rescue the one or more persons who fall into the water.

[0022] In some possible implementations, the hull includes a U-shaped hull; the identifying and locating multiple drowning persons based on the data scanned and acquired by the multi-sensor system specifically includes:

[0023] Controlling the visual sensor and the thermal imaging sensor to identify a person who falls into the water on the water surface;

[0024] Controlling the sonar sensor to detect a person who falls into the water;

[0025] Combined with the position data of the positioning sensor, the position and absolute geographic coordinates of each identified drowning person relative to the intelligent unmanned rescue boat are calculated, thereby locating multiple drowning people and obtaining their position information;

[0026] The determining of obstacle avoidance requirements based on the data scanned and acquired by the multi-sensor system specifically includes:

[0027] Controlling the visual sensor to identify obstacles on the water surface;

[0028] Controlling the sonar sensor to detect underwater obstacles;

[0029] Combined with the position data of the positioning sensor, the position and size of the identified obstacle relative to the intelligent unmanned rescue boat are calculated.

[0030] In some possible implementations, the processor is further configured to execute the computer program to implement the following steps:

[0031] Determine the rescue priority based on the number of people identified, their location information, their vital signs and the meteorological conditions of the surrounding environment;

[0032] The vital signs are obtained by detecting body temperature with the thermal imaging sensor and / or observing breathing movements with the visual sensor.

[0033] In some possible implementations, the processor is further configured to execute the computer program to implement the following steps:

[0034] The communication system sends location information of drowning persons and a rescue progress report to an external device, wherein the rescue progress report includes the number of drowning persons identified, the number of drowning persons approaching, the number of drowning persons successfully rescued, the current location of the unmanned rescue boat and the estimated time to complete the rescue.

[0035] In some possible implementations, the processor is further configured to execute the computer program to implement the following steps:

[0036] The accuracy of identifying and locating people who have fallen into the water is optimized using a machine learning algorithm, wherein the machine learning algorithm includes a deep learning model for image recognition and a reinforcement learning algorithm for optimizing the rescue path.

[0037] In some possible implementations, the intelligent unmanned rescue boat further includes a connection interface, which is disposed on the hull and is used for detachably electrically connecting an externally mounted rescue device, and the connection interface is electrically connected to the processor.

[0038] In some possible implementations, the rescue equipment includes at least one of the following:

[0039] Searchlight, used for nighttime rescue lighting;

[0040] A loudspeaker for voice communication with the drowning person;

[0041] A lifeline launcher, used to launch a rescue rope to a person who falls into the water, wherein the end of the rescue rope has a ring for grasping;

[0042] Medical equipment boxes for storing tourniquets, first aid medicines and nutritional foods;

[0043] Lifebuoy launcher, used to launch a lifebuoy to a person who falls into the water;

[0044] An inflatable life raft used to provide a temporary safe floating platform for multiple people who fall into the water.

[0045] In a second aspect, a rescue control method for any one of the intelligent unmanned rescue boats described in the first aspect is provided, comprising the following steps:

[0046] receiving rescue mission information from an external device;

[0047] Navigate to a designated search and rescue area according to the rescue mission information;

[0048] Controlling the multi-sensor system to scan the surrounding waters within the designated search and rescue area;

[0049] Identifying and locating one or more persons in the water based on data from the multi-sensor system, and determining obstacle avoidance requirements;

[0050] Generate the optimal rescue path based on the location information of the drowning person, the number of drowning people, weather conditions and obstacle avoidance requirements;

[0051] Controlling the power system to move the hull according to the optimal rescue path to approach the one or more drowning persons, and rescuing the one or more drowning persons;

[0052] The communication system is controlled to report the rescue progress to the external device.

[0053] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to the second aspect is implemented.

[0054] In a third aspect, a rescue control device for an intelligent unmanned rescue boat is provided, comprising:

[0055] one or more processors;

[0056] A storage device for storing one or more programs;

[0057] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the second aspect.

[0058] In a fourth aspect, a rescue control device for an intelligent unmanned rescue boat is provided, comprising:

[0059] one or more processors;

[0060] a storage device for storing one or more programs,

[0061] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the second aspect.

[0062] According to a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described in the second aspect is implemented.

[0063] According to a sixth aspect, a computer program product is provided, comprising a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed, the method described in the second aspect is performed.

[0064] The above technical solution has the following beneficial technical effects:

[0065] The present invention integrates visual sensors, thermal imaging sensors and sonar sensors to form an all-round, multi-dimensional environmental perception capability. This design can effectively identify and locate drowning people in various complex environments (such as daytime, nighttime, and underwater), greatly improving the accuracy and comprehensiveness of search and rescue.

[0066] The GPS / Beidou dual-mode positioning system ensures the accurate positioning of the rescue boat itself. Combined with multi-sensor data, it can accurately calculate the relative position and absolute geographic coordinates of the drowning person, providing accurate location information for rescue operations.

[0067] The computer program executed by the processor can autonomously plan the optimal rescue path by comprehensively considering multiple factors such as the location of the drowning person, the number of people, and weather conditions. This intelligent path planning greatly improves the rescue efficiency and reduces unnecessary time waste.

[0068] The meteorological sensors equipped can monitor environmental parameters such as wind speed, temperature, and humidity in real time, enabling the rescue boat to adapt to environmental changes in a timely manner, adjust rescue strategies, and ensure the safety and effectiveness of the rescue operation.

[0069] Through intelligent algorithms executed by the processor, the rescue boat can make rescue decisions autonomously without continuous human intervention. This feature allows rescue operations to continue in complex and dangerous environments without being affected by personnel fatigue or communication interruptions.

[0070] As an unmanned boat, it is designed to be lighter and more flexible, and can enter areas that are difficult for traditional rescue boats to reach, such as shallow waters or areas with dense obstacles, thus expanding the scope of rescue. It is not limited by personnel fatigue and can work continuously for 24 hours, making it particularly suitable for long-term and large-scale search and rescue missions.

[0071] By using unmanned boats for initial search and rescue, the risks faced by rescuers can be effectively reduced, especially in extreme weather or dangerous environments.

[0072] This technical solution provides a good foundation for further functional expansion and performance optimization, such as integrating more types of sensors, optimizing algorithms, or achieving multi-boat collaboration.

[0073] In the long run, intelligent unmanned rescue boats can reduce the manpower cost of rescue operations, improve rescue efficiency, and have good economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0075] Figure 1 This is a functional block diagram of an intelligent rescue unmanned boat according to an embodiment of the present invention;

[0076] Figure 2 is a functional block diagram of another intelligent rescue unmanned boat according to an embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram of the external structure of a U-shaped intelligent rescue unmanned boat product according to an embodiment of the present invention;

[0078] Figure 4 is a flowchart of the steps of a computer program executed by a processor according to an embodiment of the present invention;

[0079] Figure 5 is a specific flow chart of the processor generating the optimal rescue path according to an embodiment of the present invention;

[0080] Figure 6 It is a flow chart of a rescue control method of an intelligent rescue unmanned boat according to an embodiment of the present invention;

[0081] Figure 7 is a functional block diagram of a computer-readable storage medium according to an embodiment of the present invention;

[0082] Figure 8 It is a structural schematic diagram of a rescue control device of an intelligent unmanned rescue boat according to an embodiment of the present invention.

[0083] Description of Figure Numbers:

[0084] 100. Intelligent unmanned rescue boat;

[0085] 110. hull; 111. U-shaped shell; 112. handle; 113. indicator light;

[0086] 120. Power system;

[0087] 130. Multi-sensor system; 131. Visual sensor; 132. Thermal imaging sensor; 133. Sonar sensor; 134. Positioning sensor; 135. Meteorological sensor;

[0088] 140. Communication system; 150. Processor; 160. Memory; 170. Connection interface; 180. Rescue equipment; 190. Power module. DETAILED DESCRIPTION

[0089] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0090] Embodiment 1

[0091] Figure 1 FIG. 1 is a functional block diagram of an intelligent unmanned rescue boat 100 provided by an embodiment of the present invention. Figure 1 As shown, the structure and functions of the intelligent unmanned rescue boat 100 are as follows:

[0092] The intelligent unmanned rescue boat 100 in this embodiment adopts a hull 110 made of high-strength composite material, which has good impact resistance and corrosion resistance. The hull 110 adopts a streamlined design or a U-shaped design.

[0093] The power system 120 includes two electric propellers, which are respectively installed on the left and right sides of the rear of the hull 110, or installed on the two ends of the U-shaped hull 110, and the U-shaped hull 110 is provided with a plurality of handles 112 for the drowning person to grasp. The propeller adopts vector propulsion technology, which can realize 360-degree omnidirectional steering, greatly improving the maneuverability of the rescue boat.

[0094] In the multi-sensor system 130, the visual sensor 131 is equipped with, for example, four high-definition cameras, which are respectively located in the front, back, left, and right directions of the hull 110. Each camera has a 180-degree wide-angle lens and a 30-fold optical zoom capability. Under daylight conditions, based on the application of high-magnification optical zoom lenses and high-resolution sensors, drowning people and obstacles within a range of 1,000-2,000 meters can be clearly identified.

[0095] The thermal imaging sensor 132 is equipped with two high-sensitivity thermal imaging cameras, which are respectively installed at the front and rear of the hull 110. At night or under low visibility conditions, heat sources within a range of 1,000 meters can be detected, effectively identifying the body temperature characteristics of the person who falls into the water.

[0096] The sonar sensor 133 uses an advanced multi-beam sonar system installed on the bottom of the ship. It can detect underwater conditions within a depth of 50 meters and is used to identify people who have fallen into the water and underwater obstacles.

[0097] The positioning sensor 134 is equipped with a high-precision GPS (Global Positioning System) / Beidou dual-mode positioning system with a positioning accuracy of up to centimeters. It also integrates an inertial navigation system (INS) to maintain accurate positioning even when the GPS signal is limited.

[0098] The meteorological sensor 135 includes an anemometer, a thermometer, a hygrometer, a barometer, etc., which are used to monitor the meteorological conditions of the surrounding environment in real time.

[0099] The communication system 140 adopts a multi-mode design, including: a 4G / 5G mobile communication module for maintaining real-time communication with the land command center in the near-shore area; a satellite communication module for transmitting data in the offshore area; a shortwave communication system 140 as a backup communication method; and an underwater acoustic communication system 140 for communicating with other underwater equipment or rescue vessels.

[0100] The processor 150 is built into the hull 110 and uses a high-performance embedded processor 150 with a main frequency of 3.0 GHz, 8 cores and 16 threads, and is equipped with 32 GB RAM and a 1 TB solid state drive. The processor 150 runs specially developed rescue mission management software, which can process multi-sensor data in real time and execute complex path planning algorithms. The memory 160 is connected to the processor 150 and stores computer programs.

[0101] like Figure 4 As shown, the processor 150 is used to execute the computer program to implement the rescue process of the intelligent unmanned rescue boat 100, which includes the following steps:

[0102] S1. Control the multi-sensor system 130 to scan the surrounding waters:

[0103] The visual sensor 131 and the thermal imaging sensor 132 are controlled to perform a 360-degree scan at a frequency of once per second, the sonar sensor 133 is controlled to perform an all-round scan every 5 seconds, the positioning sensor 134 is controlled to update the position information once per second, and the meteorological sensor 135 is controlled to update the environmental data once per minute.

[0104] S2. Identify and locate one or more people who have fallen into the water based on the data scanned and acquired by the multi-sensor system 130:

[0105] The visual recognition algorithm processes the camera image to identify the human body outline on the water surface. The thermal imaging data is used to identify the heat source target of 36℃-37℃. The sonar data analysis identifies the possible human targets underwater. Combined with the GPS positioning data, the direction and distance of each drowning person relative to the intelligent unmanned rescue boat 100 are calculated.

[0106] S3. Determine obstacle avoidance requirements based on data scanned and acquired by the multi-sensor system 130:

[0107] Identify obstacles such as floating objects and reefs on the water surface through visual recognition algorithms. Identify underwater obstacles through sonar data analysis. Calculate the position, size and movement speed of all obstacles (if they are moving obstacles).

[0108] S4. Generate optimal rescue path:

[0109] Based on the A* algorithm and dynamic programming, the optimal rescue path is generated by comprehensively considering the location information of the drowning person, the number of people, weather conditions and the distribution of obstacles. Path planning considerations include: shortest total distance, avoiding dangerous areas, considering the influence of wind direction and ocean currents, and reserving a safe distance. The A* algorithm is a heuristic search algorithm used for path planning. It combines the heuristics of the best-first search and the rigor of the Dijkstra algorithm. In this system, the A* algorithm is used to find the optimal path from the starting point to the target in the known environmental information, taking into account the distance cost and estimated cost.

[0110] In some cases, the following alternative algorithms can be used to replace or supplement the A* algorithm:

[0111] Dijkstra's algorithm, which is a classic shortest path algorithm, is suitable for finding the shortest path in an unweighted graph or a positive weighted graph. Although the A* algorithm is usually more efficient than the Dijkstra algorithm, in some cases, especially when the heuristic function is difficult to define, the Dijkstra algorithm is a simpler and more reliable choice.

[0112] Rapidly-exploring Random Tree (RRT), the RRT algorithm is particularly suitable for path planning in high-dimensional space. It quickly explores the space through random sampling and builds a tree structure connecting the starting point and the end point. In complex three-dimensional environments, such as when considering factors such as sea conditions and ocean currents, the RRT algorithm may have more advantages than the A* algorithm.

[0113] D*Lite algorithm, which is an improved version of the D* algorithm, is particularly suitable for path planning in dynamic environments. Considering the constant changes in the marine rescue environment (such as obstacles moving, new drowning people appearing, etc.), D*Lite can handle such dynamic situations more effectively.

[0114] Genetic Algorithm is an optimization method based on evolutionary theory that can be used for complex multi-objective path planning problems. It can consider multiple factors at the same time, such as path length, safety, fuel consumption, etc., to find a comprehensive optimal solution.

[0115] Ant Colony Optimization is an optimization algorithm based on the foraging behavior of ants, which is particularly suitable for dealing with dynamically changing environments. In the maritime rescue scenario, it can simulate the collaborative work of multiple rescue units to find the overall optimal rescue path.

[0116] Artificial Potential Field: This method sets the target point as the attraction field and the obstacle as the repulsion field, and determines the direction of movement by calculating the resultant force. It performs well in real-time obstacle avoidance and dynamic path planning, and is particularly suitable for handling complex marine environments.

[0117] The alternative implementation algorithm may be any one of the above, or a combination thereof. For example, step S4 may include:

[0118] Based on the hybrid method of the improved D*Lite algorithm and the genetic algorithm, the optimal rescue path is generated by comprehensively considering the location information of the drowning person, the number of people, meteorological conditions and the distribution of obstacles. The D*Lite algorithm is used to deal with dynamically changing environments, while the genetic algorithm is used for multi-objective optimization, taking into account factors such as: shortest total distance, avoiding dangerous areas, considering the influence of wind direction and ocean currents, reserving safe distance, fuel consumption, etc. The hybrid algorithm can quickly generate and adjust the rescue path in a complex and changeable marine environment while balancing multiple rescue goals. This hybrid method combines the advantages of the D*Lite algorithm in dynamic environments and the powerful ability of the genetic algorithm in multi-objective optimization, and is more suitable for complex marine rescue scenarios.

[0119] like Figure 5 As shown, specifically, according to the position information of the drowning person, the number of drowning persons, weather conditions and obstacle avoidance requirements, the specific implementation process of generating the optimal rescue path may include the following steps:

[0120] Step S41: Perform data collection and preprocessing. First, collect the location information of all identified drowning persons, including their orientation and distance relative to the intelligent unmanned rescue boat 100. These data can be obtained through various positioning devices. At the same time, obtain the current meteorological conditions, especially factors such as wind direction, wind speed, wave height and visibility, because these conditions will directly affect the difficulty and strategy of the rescue operation. In addition, sort out the obstacle avoidance requirements and determine the location and size of all identified surface and underwater obstacles. This information will be used for subsequent path planning to ensure the safety of the rescue operation. After the data collection is completed, data preprocessing can be performed, including correction and standardization of the location information of the drowning person, analysis and conversion of meteorological data, and integration and mapping of obstacle information. These preprocessing steps ensure the consistency and accuracy of the data so that it can be used for subsequent path planning to ensure the safety and effectiveness of the rescue operation.

[0121] Step S42: Based on the number of drowning people, their location distribution and current weather conditions, each drowning person is evaluated for rescue priority. Factors considered include the distance from the rescue boat, the concentration of drowning people and the estimated survival time (based on weather conditions). Through a comprehensive evaluation of these factors, the priority of each drowning person is determined, thereby determining the order of rescue. This step is intended to ensure that the drowning person who needs help the most is rescued in the shortest time.

[0122] Step S43: Generate an initial rescue path using the improved A* algorithm. Set the current position of the intelligent unmanned rescue boat 100 as the starting point, and set the position of the drowning person as the waypoint in order of priority. The algorithm will consider the position of obstacles during the search process and automatically bypass unsafe areas to generate a preliminary and safe rescue path. This step is to quickly formulate a reasonable rescue plan so that rescue operations can be carried out quickly.

[0123] Step S44: Apply the dynamic programming algorithm to optimize the initial path. Considering the impact of wind direction and ocean current on the drift of the rescue boat and the drowning person, the rescue path is dynamically adjusted. The optimization objectives include minimizing the total travel distance, maximizing the rescue success rate, and minimizing fuel consumption. Through this optimization, it is ensured that the rescue path is not only fast and effective, but also economical. The purpose of this step is to improve the efficiency and effectiveness of the rescue.

[0124] Step S45: Conduct a safety assessment on the optimized rescue path to ensure that a safe distance is maintained from all obstacles during the entire rescue process. Consider the risks that may be caused by meteorological conditions, such as areas with large waves, and make necessary path adjustments. The purpose of this step is to ensure the safety of the rescue boat and the drowning person while ensuring the rescue efficiency, and to avoid secondary accidents caused by unexpected situations.

[0125] Step S46: Generate multiple optional rescue path plans and use a comprehensive scoring mechanism to evaluate each plan. Evaluation factors include total rescue time, fuel consumption, safety factor, and rescue success rate. Through the comprehensive scoring of these factors, the plan with the highest comprehensive score is selected as the final optimal rescue path. This step aims to select the most advantageous path from multiple alternative plans to ensure the best implementation of the rescue mission.

[0126] Step S47: Set up a real-time adjustment mechanism for the rescue path to cope with new situations that may arise during the rescue process. If a new drowning person or obstacle is found, the system will quickly re-execute steps S41-S46 to generate an updated optimal rescue path. This mechanism ensures that the rescue operation is highly flexible and adaptable, and can quickly respond to any emergency, thereby improving the overall rescue success rate.

[0127] Through this detailed process, the intelligent unmanned rescue boat 100 is able to comprehensively consider various factors and generate a safe and efficient rescue path to maximize the success rate of rescue. This process makes full use of advanced algorithms and real-time data processing capabilities, reflecting the intelligence and adaptability of the system.

[0128] S5. Execute rescue:

[0129] The power system 120 is controlled to move along the planned optimal rescue path to approach the drowning person. The course and speed are adjusted in real time to ensure safe approach. After reaching the drowning person, the rescue equipment 180 (such as a lifebuoy transmitter or a life rope transmitter) is activated for rescue, or the drowning person is prompted to hold the handle 112 on the intelligent rescue unmanned boat through a loudspeaker or a speaker.

[0130] For example, on a clear day, the intelligent unmanned rescue boat 100 receives a distress signal from a drowning person. Through the visual sensor 131 scanning, three drowning people are identified within a range of 1,000 meters. The system quickly calculates the optimal rescue path, first approaching the two closest drowning people, and then heading to the location of the third drowning person. The entire rescue process takes 15 minutes, and all the drowning people are successfully rescued.

[0131] For example, in stormy weather conditions at night, the intelligent unmanned rescue boat 100 receives a signal of a shipwreck. Through the thermal imaging sensor 132, five heat sources of suspected drowning people are identified within a range of 800 meters. At the same time, the sonar detects two targets that may be drowning people 20 meters underwater. The system comprehensively considers meteorological factors such as wind direction and wave height to plan a rescue path that avoids dangerous areas. The intelligent unmanned rescue boat 100 first rescues the drowning people on the water surface, and then uses sonar guidance to accurately locate underwater targets, successfully rescuing all seven drowning people. The whole process takes 40 minutes.

[0132] The beneficial technical effects of the above technical solution of the embodiment of the present invention are:

[0133] Through the collaborative work of the multi-sensor system 130, the intelligent unmanned rescue boat 100 can effectively identify and locate people who fall into the water under various light and weather conditions, greatly improving the rescue success rate and realizing all-weather rescue capabilities.

[0134] Based on advanced algorithms and multi-source data fusion, it can quickly generate the optimal rescue path, reduce rescue time and improve rescue efficiency.

[0135] Through real-time obstacle avoidance and path planning, safety during the rescue process is ensured and the risk of collision is reduced.

[0136] It can automatically adjust rescue strategies according to different rescue scenarios and environmental conditions to adapt to complex and changeable maritime rescue situations.

[0137] Through the advanced communication system 140, information can be shared with the command center and other rescue units in real time to facilitate coordinated rescue.

[0138] Through machine learning algorithms, the system can continuously learn and optimize rescue strategies to improve the success rate of future rescue missions.

[0139] The design and functions of this intelligent unmanned rescue boat 100 greatly improve the efficiency and success rate of water rescue, especially the rescue capabilities in severe weather and night conditions.

[0140] Embodiment 2

[0141] This embodiment provides an intelligent unmanned rescue boat 100, which is optimized in terms of the structural design of the hull 110, the coordinated operation of multiple sensors, and obstacle recognition.

[0142] like Figure 3 As shown, the intelligent unmanned rescue boat 100 in this embodiment adopts a U-shaped shell 111 design. The U-shaped shell 111 is made of high-strength composite materials and is U-shaped as a whole. The two sides of the hull 110 are recessed inward to form an open central area. The U-shaped shell 111 is provided with a handle 112 for the drowning person to hold tightly and an indicator light 113. When the intelligent unmanned rescue boat 100 is launched, the indicator light 113 automatically lights up. The hull 110 can be 6 meters long, with a maximum width of 2.5 meters, a height of 1.8 meters, a U-shaped opening width of 1.5 meters, and a depth of 1 meter. This design enhances the stability of the hull 110, especially the anti-capsulation ability under large wave conditions. The U-shaped opening area is convenient for accommodating the drowning person and improves the rescue efficiency. At the same time, this design also improves the hydrodynamic performance of the hull 110 and reduces the sailing resistance. In another design, the intelligent unmanned rescue boat 100 adopts a U-shaped shell 111 with a length of 1004mm and a width of 794mm.

[0143] In terms of identifying people who fall into the water, this embodiment is equipped with a visual sensor 131 and a thermal imaging sensor 132 for identifying people who fall into the water. The visual sensor 131 includes 4 high-definition cameras, which are installed at the four corners of the hull 110. Each camera has a 30-100 times optical zoom capability and a 360-degree rotation function. A deep learning algorithm is used for image processing to identify human contours, movements and facial features. Under daylight conditions, the recognition range is 2000 meters. The thermal imaging sensor 132 includes 2 high-sensitivity thermal imaging cameras, which are located at the bow and stern respectively. The temperature resolution reaches 0.05°C, which can accurately capture the heat source of the human body. At night or in low visibility conditions, the recognition range is 1500 meters.

[0144] In terms of underwater drowning detection, this embodiment uses an advanced multi-beam sonar system as the sonar sensor 133, which is installed in the center of the bottom of the ship. The operating frequency can be adjusted between 30kHz and 200kHz to adapt to different water depths and detection requirements. The detection range is 360 degrees in the horizontal direction and can reach 50-100 meters in the vertical direction. Within a range of 50 meters, the resolution can reach 5 cm, which can clearly distinguish human targets.

[0145] The process of locating drowned people first involves sensor data fusion, combining the data from the visual sensor 131 and the thermal imaging sensor 132 to improve the accuracy of surface target recognition, while using sonar data to confirm underwater targets. Then, the Kalman filter algorithm is used to track each identified drowned person in real time, and the drift trajectory of the drowned person is predicted by taking into account the influence of water flow and wind.

[0146] In terms of position calculation, the positioning sensor 134 (GPS / Beidou dual-mode system) provides the precise position of the intelligent unmanned rescue boat 100 with an accuracy of centimeters. The system combines the azimuth and distance information obtained by the sensor to calculate the position of each drowning person relative to the intelligent unmanned rescue boat 100, and obtains the absolute geographic coordinates (latitude and longitude) of the drowning person through coordinate conversion. The system updates the position information of all drowning people every 0.1 seconds, and establishes a dynamic database to record the ID, position, movement speed and direction of each drowning person.

[0147] For the identification of obstacles on the water surface, this embodiment uses the aforementioned four high-definition cameras to scan the water surface and adopts a target detection algorithm (such as YOLO v5) to identify various obstacles on the water surface, including floating ice, floating objects, other ships, etc. The obstacle identification range is consistent with the drowning person identification range, which is 2000 meters. The system can classify and identify different types of obstacles and assess their potential danger level.

[0148] For underwater obstacle detection, this embodiment uses the aforementioned multi-beam sonar system for underwater scanning and adopts acoustic image processing technology to identify underwater obstacles such as reefs, shipwrecks, and large marine life. The detection range is 360 degrees horizontally and 50-100 meters vertically. The system can distinguish between fixed obstacles and mobile obstacles (such as large marine life such as whales).

[0149] The obstacle position and size calculation process is as follows:

[0150] In terms of data collection, the visual sensor 131 and the sonar sensor 133 continuously scan and update obstacle data every 0.5 seconds. When calculating the position, the system uses the position data of the intelligent unmanned rescue boat 100 provided by the positioning sensor 134 as a reference point. For surface obstacles, the relative position is calculated by combining the azimuth and estimated distance of the visual sensor 131; for underwater obstacles, the relative position is calculated using sonar data. The relative position is then converted into absolute geographic coordinates.

[0151] In terms of size estimation, for surface obstacles, the system uses the image data of the visual sensor 131 and the known size of a reference object (such as the intelligent unmanned rescue boat 100 itself) to estimate the length, width and height of the obstacle. For underwater obstacles, the sonar echo intensity and duration are used to estimate the size and shape of the obstacle.

[0152] For moving obstacles, the system will record their moving speed and direction and predict their future position. Based on the type, size, movement characteristics and relative position of the obstacle to the intelligent unmanned rescue boat 100, the system will assess the danger level of each obstacle.

[0153] Finally, all identified obstacle information is integrated into a dynamic environment map, including the location, size, movement characteristics and degree of danger of the obstacles.

[0154] Through the above steps, the intelligent unmanned rescue boat 100 can fully understand the obstacles in the surrounding environment and provide accurate and real-time data support for subsequent path planning and obstacle avoidance decisions. This all-round environmental perception capability greatly improves the safety and efficiency of the rescue process.

[0155] The U-shaped hull 110 design in this embodiment, combined with the advanced multi-sensor system 130 and data processing algorithm, enables the intelligent unmanned rescue boat 100 to perform rescue missions more efficiently and safely under complex sea conditions, significantly improving the success rate and reliability of maritime rescue.

[0156] Embodiment 3

[0157] This embodiment provides an intelligent unmanned rescue boat 100, whose processor 150 executes a computer program to determine the rescue priority. This function is crucial to improving the rescue efficiency and success rate, especially in complex situations where multiple people fall into the water.

[0158] For the identification of the number of people who have fallen into the water, the processor 150 accurately identifies the number of people who have fallen into the water by analyzing the data of the multi-sensor system 130. The visual sensor 131 and the thermal imaging sensor 132 work together to scan the people who have fallen into the water on the surface, while the sonar sensor 133 is used to detect possible people who have fallen into the water underwater. The system uses advanced target detection algorithms, such as the improved YOLO (You Only Look Once) algorithm, which can accurately distinguish and count people who have fallen into the water in complex sea conditions. At the same time, the system will continue to track the identified people who have fallen into the water to avoid repeated counting or omissions.

[0159] In order to obtain the location information of the drowning person, the system uses the positioning sensor 134 (GPS / Beidou dual-mode system) to obtain the precise location of the rescue boat, and then combines the data of the visual sensor 131, thermal imaging sensor 132 and sonar sensor 133 to calculate the position of each drowning person relative to the rescue boat. Through coordinate conversion, the system obtains the absolute geographic coordinates (latitude and longitude) of each drowning person. In addition, the system will also predict the drift trajectory of the drowning person, taking into account factors such as ocean currents and wind direction, so as to plan the rescue route more accurately.

[0160] Regarding the vital sign detection of the drowning person, this embodiment obtains the vital sign information of the drowning person through the thermal imaging sensor 132 and the visual sensor 131. The thermal imaging sensor 132 is used to detect the body temperature of the drowning person, and its temperature resolution reaches 0.05°C, which can accurately capture the heat source of the human body. The system identifies heat sources in the range of 36-37°C as normal body temperature. Temperatures below this range may indicate hypothermia and require priority rescue. The visual sensor 131 is used to observe the breathing movements of the drowning person. Through high frame rate video and advanced image processing algorithms, the system can detect tiny chest fluctuations and determine whether the drowning person is breathing.

[0161] To assess the meteorological conditions of the surrounding environment, the intelligent unmanned rescue boat 100 is equipped with advanced meteorological sensors 135, including anemometers, thermometers, hygrometers and barometers. These sensors monitor the meteorological conditions of the surrounding environment in real time, including wind speed, wind direction, temperature, humidity, air pressure and wave height. The system inputs this data into a specially designed meteorological assessment model to assess the impact of current meteorological conditions on the survival of the drowning person.

[0162] Based on all the information collected above, the processor 150 will execute a multi-factor decision-making algorithm to determine the rescue priority. The algorithm comprehensively considers factors such as the number of people who fall into the water, location information, vital signs and meteorological conditions. When there are multiple people who fall into the water, the system will give priority to how to rescue everyone most effectively to ensure that each person who falls into the water can be rescued as soon as possible. Location information is also a key factor. People who fall into the water who are closer to the intelligent unmanned rescue boat 100 or in a dangerous position (such as close to a reef) will receive a higher priority. In terms of vital signs, people who fall into the water with hypothermia or no breathing movement detected will receive the highest priority because they are in more urgent danger of life. Changes in meteorological conditions will also affect the rescue strategy. Under severe weather conditions, the system will increase the overall rescue urgency and adjust the rescue order in real time according to weather changes to ensure the safety and effectiveness of the rescue operation. By combining these factors, the system can formulate the best rescue plan to maximize the success rate of rescue.

[0163] The rescue priority determination algorithm executed by the processor 150 includes the following steps: Convert different types of data (such as distance, body temperature, meteorological index, etc.) into a unified scoring standard. Assign weights to each factor based on the preset importance. For example, vital signs receive higher weights. Perform a comprehensive score on each drowning person, and the calculation formula is: total score = Σ(factor score × weight). Sort all drowning people according to the comprehensive score to obtain the rescue priority. The system will continuously update the data and adjust the rescue priority in real time based on new information.

[0164] In terms of output results, the processor 150 generates a priority list, including the ID, location, vital signs status and recommended rescue order of each drowning person. This list will be updated in real time and transmitted to the navigation system of the intelligent unmanned rescue boat 100 and the external rescue command center. At the same time, the system will also generate a visual rescue map, clearly showing the location and priority of each drowning person, to assist in rescue decision-making.

[0165] Through this intelligent rescue priority determination system, the intelligent unmanned rescue boat 100 can quickly make the best rescue decision in complex situations where multiple people fall into the water, greatly improving the rescue efficiency and success rate, and maximizing the safety of the lives of those who fall into the water.

[0166] Embodiment 4

[0167] This embodiment provides an intelligent unmanned rescue boat 100, whose processor 150 executes a computer program to implement the function of sending the location information of the drowning person and the rescue progress report to the external device through the communication system 140. This function is crucial for coordinating rescue operations, improving rescue efficiency, and cooperating with external rescue forces.

[0168] The intelligent unmanned rescue boat 100 is equipped with an advanced multi-mode communication system 140, including a satellite communication module, a 4G / 5G mobile communication module, a shortwave communication system 140 and a hydroacoustic communication system 140. The satellite communication module uses the latest low-orbit satellite communication technology to ensure a stable communication link even in offshore areas. The 4G / 5G module is used to achieve high-speed data transmission in offshore areas. The shortwave communication system 140 is used as a backup communication method when other communication methods fail. The hydroacoustic communication system 140 is used to communicate with underwater equipment or other rescue vessels. The system automatically selects the best communication method based on the current location and communication quality.

[0169] After the processor 150 identifies and locates the drowning person through the multi-sensor system 130 (including the visual sensor 131, the thermal imaging sensor 132 and the sonar sensor 133), it will immediately send the location information of the drowning person to the external device through the communication system 140. This location information includes the precise geographic coordinates (latitude and longitude) of each drowning person, the direction and distance relative to the rescue boat, and the estimated drift speed and direction. The location information is sent in the form of encrypted data packets to ensure the security and accuracy of the information. The system will update the location information every 10 seconds to reflect the possible location changes of the drowning person.

[0170] The processor 150 will count the total number of drowning people identified by the multi-sensor system 130 in real time. This number will be dynamically updated as the search and rescue process progresses, including newly discovered drowning people and drowning people who may leave the monitoring range for various reasons (such as entering other rescue equipment 180). The system will immediately report to the external device every time the number changes, with a timestamp, so that the external rescue force can understand the latest situation.

[0171] The system defines the standard of "approached" as the distance between the rescue boat and the drowning person is less than 5 meters. The processor 150 will continuously calculate the distance between the rescue boat and each identified drowning person, and count the number of drowning people who meet the "approached" standard. This data reflects the progress of the rescue operation. Whenever a new drowning person is approached or an approached drowning person leaves the approach range, the system will update and send the latest data.

[0172] The processor 150 determines whether the drowning person has been successfully rescued (i.e., boarded a rescue boat or transferred to other rescue equipment 180) by analyzing the data from the visual sensor 131 and the pressure sensor. The system counts the number of people successfully rescued in real time and sends an update report to the external device immediately after each successful rescue. This information is crucial for evaluating the effectiveness of the rescue operation and planning follow-up actions.

[0173] The rescue boat is equipped with a high-precision GPS / Beidou dual-mode positioning system, which can obtain its precise geographic location in real time. The processor 150 sends the current location of the rescue boat to the external device every 5 seconds, including latitude and longitude coordinates, altitude (relative to sea level), heading and speed. This information enables external rescue forces to accurately grasp the movement of the rescue boat and facilitate the coordination of the overall rescue operation.

[0174] The processor 150 uses a prediction algorithm to estimate the estimated time to complete the rescue based on the number of people currently identified, the number of people rescued, the speed of the rescue boat, the location distribution of the remaining people to be rescued, and the current sea and weather conditions. This estimate takes into account a variety of factors, including the time required to approach each person, the average time to perform a rescue operation, and possible unexpected situations. The system updates this estimate every minute and sends it to an external device.

[0175] The processor 150 integrates all the above information (the number of people identified, the number of people approached, the number of people successfully rescued, the current location of the unmanned rescue boat and the estimated time to complete the rescue) into a structured rescue progress report. This report uses a standardized data format to facilitate rapid parsing and processing by external systems. The report is automatically generated and sent every 60 seconds, and is also generated and sent immediately when a key event occurs (such as a new person found in the water, a person successfully rescued, etc.).

[0176] Considering the importance and sensitivity of rescue information, the system uses multiple encryption and data verification mechanisms. All sent data is encrypted with AES-256 bit and digital signatures are used to ensure data integrity. The system also implements an automatic retransmission mechanism, which will immediately retransmit when a data transmission failure is detected to ensure timely delivery of information.

[0177] Through this comprehensive communication and reporting system, the Intelligent Unmanned Rescue Boat 100 can maintain close contact with external rescue forces and share real-time and accurate rescue progress information. This not only improves the coordination and efficiency of the overall rescue operation, but also provides decision makers with valuable real-time data support, helping to optimize rescue strategies and maximize rescue success rates.

[0178] Embodiment 5

[0179] This embodiment provides an intelligent unmanned rescue boat 100, whose processor 150 executes a computer program and uses a machine learning algorithm to optimize the accuracy of identifying and locating drowning people, as well as optimizing the rescue path. This method significantly improves the rescue efficiency and success rate.

[0180] The intelligent unmanned rescue boat 100 is equipped with a high-performance embedded AI processor 150, which is specially designed to execute complex machine learning algorithms. The processor 150 adopts a 64-bit architecture, has a main frequency of 3.5GHz, is equipped with 256GB high-speed RAM and a 2TB solid-state drive, and supports real-time processing of large amounts of sensor data and execution of complex deep learning and reinforcement learning algorithms. The system adopts a modular design, and the algorithm modules can be updated or replaced as needed, ensuring the scalability and long-term effectiveness of the system.

[0181] This embodiment uses a deep learning model based on a convolutional neural network (CNN) for image recognition. Specifically, an improved YOLOv5 (You Only Look Once version 5) algorithm is used, which performs well in real-time target detection. The model has been trained with a large amount of image data of marine rescue scenes and can accurately identify drowning people under various complex sea conditions and lighting conditions. The input of the model includes image data from the visual sensor 131 and the thermal imaging sensor 132, and the output is the position, number and confidence of the identified drowning people.

[0182] The deep learning model is trained using a transfer learning approach, first pre-trained using a large-scale general image dataset, and then fine-tuned using a specific dataset for maritime rescue scenarios. The training data includes images of drowning people in various weather conditions and lighting conditions, as well as images of other marine objects that may be confused with drowning people (such as driftwood, life buoys, etc.). The model uses data enhancement techniques such as rotation, scaling, and adding noise to improve the generalization ability of the model. In addition, the system also implements an online learning mechanism that can continuously optimize model parameters in actual rescue missions.

[0183] For the drowning person positioning algorithm, this embodiment uses a multi-sensor fusion algorithm for precise positioning based on the results of deep learning model recognition. The algorithm combines the data of the visual sensor 131, the thermal imaging sensor 132, the sonar sensor 133, and the data of the GPS / Beidou positioning system. The extended Kalman filter is used to process the noise and uncertainty in the nonlinear system to improve the positioning accuracy. The system can track the position of the drowning person in real time and predict its drift trajectory.

[0184] In view of the use of reinforcement learning algorithms to optimize rescue paths, this embodiment uses a deep Q-network (DQN) as the core of the reinforcement learning algorithm to optimize rescue paths. DQN combines the value iteration method of Q learning and the function approximation ability of deep neural networks, and is particularly suitable for handling decision-making problems in large-scale state spaces. The system models the rescue task as a Markov decision process (MDP), in which the states include the location of the rescue boat, the location of the drowning person, the location of obstacles, weather conditions, etc.; the actions include the direction and speed of the rescue boat; the reward function is designed based on factors such as rescue time, fuel consumption, and the number of successful rescues.

[0185] The training process of the reinforcement learning algorithm is carried out in a simulated environment that simulates various complex sea conditions, weather conditions, and multiple drowning scenarios. Experience Replay and Target Network technologies are used to improve training stability and efficiency. After training, the model can quickly generate the optimal rescue path based on the current state. In actual applications, the system will continue to collect new experience data and regularly update model parameters to achieve continuous optimization.

[0186] During the actual rescue process, the system receives various sensor data in real time, including the latest location of the drowning person, changes in sea conditions, and other information. The deep learning model continuously performs image recognition and target tracking, while the reinforcement learning algorithm dynamically adjusts the rescue path based on the latest status. The system evaluates the effectiveness of the current path every second and makes adjustments when necessary to respond to changes in the waters or newly discovered drowning people.

[0187] The system is equipped with a self-evaluation module. After each rescue mission is completed, the entire rescue process will be comprehensively evaluated, including indicators such as recognition accuracy, positioning accuracy, and path efficiency. The evaluation results will be used to further optimize the deep learning and reinforcement learning models. The system also has a remote update mechanism that can receive the latest model parameters trained in the cloud to ensure that the algorithm is always in the best state.

[0188] By combining advanced deep learning and reinforcement learning algorithms, the intelligent unmanned rescue boat 100 can quickly and accurately identify and locate drowning people in complex and changing marine environments, and generate the optimal rescue path. This machine learning-based method greatly improves the efficiency and success rate of rescue.

[0189] Embodiment 6

[0190] like Figure 2As shown, this embodiment provides an intelligent unmanned rescue boat 100, which is equipped with a connection interface 170 for detachably electrically connecting an externally mounted rescue device 180. This design greatly improves the flexibility and adaptability of the rescue boat, enabling it to quickly configure the required rescue equipment 180 according to different rescue scenarios.

[0191] The connection interface 170 is arranged on the shell of the rescue boat, adopts a waterproof design, and the protection level reaches the IP68 standard, which can maintain a reliable electrical connection in harsh sea conditions. The interface adopts a quick locking mechanism, which is convenient for quickly installing or replacing the rescue equipment 180 in an emergency. Each interface is equipped with an intelligent identification chip, which can automatically identify the type of connected equipment and adjust the power supply parameters and communication protocols accordingly. The connection interface 170 is electrically connected to the central processor 150 of the rescue boat, realizing intelligent control and data exchange of external devices.

[0192] The searchlight is a key device used for nighttime lighting and rescue. The searchlight in this embodiment uses a high-brightness LED light source with a maximum luminous flux of 50,000 lumens. The searchlight supports 360-degree rotation and pitch adjustment from -30 degrees to 90 degrees, and can cover a large area around the rescue boat. The searchlight is equipped with an automatic focusing system that can automatically adjust the beam width according to the target distance. In addition, the searchlight also has a strobe mode that can be used to send a distress signal to the drowning person. The searchlight is connected to the processor 150 via the connection interface 170, and can automatically align with the position of the drowning person based on the data of the visual sensor 131 and the thermal imaging sensor 132.

[0193] The loudspeaker is used for voice communication with the drowning person, which is crucial for calming the drowning person and conveying rescue instructions. The loudspeaker of this embodiment adopts a waterproof design, with a maximum output power of 150 watts, which can ensure clear voice transmission even in harsh marine environments. The loudspeaker supports multi-language automatic translation function, which can translate the rescuer's instructions into multiple languages ​​in real time. In addition, the loudspeaker is also equipped with a high-sensitivity microphone array, which can capture the faint cries for help of the drowning person and determine the approximate location of the drowning person through the sound source localization algorithm.

[0194] The life rope launcher is an efficient close-range rescue device 180. The launcher of this embodiment uses compressed air drive technology to accurately launch the rescue rope to the target location within a range of 50 meters. The rescue rope is made of high-strength, lightweight aramid fiber material, with a diameter of 8 mm, a length of 100 meters, and a maximum load-bearing capacity of 2 tons. A special gripping ring with a diameter of 30 cm and filled with buoyant material is designed at the end of the rope, which is easy to grab even when the drowning person is physically exhausted. After the launcher is connected to the processor 150, the launch angle and force can be automatically adjusted according to the precise position of the drowning person, thereby improving the success rate of rescue.

[0195] The medical equipment box is used to store various first aid supplies to meet different medical needs. The medical equipment box of this embodiment adopts a waterproof and shockproof design, and the internal temperature is kept constant at 18-22°C to ensure the effectiveness of the medicine. The equipment box contains first aid equipment such as tourniquets, first aid bandages, disinfectants, cardiac defibrillators, breathing masks, and commonly used first aid medicines such as adrenaline. In addition, it is also equipped with high-energy-density nutritious foods and purified drinking water. The medical equipment box is connected to the processor 150 via the connection interface 170, which can monitor the inventory and expiration date of medicines in real time, and request supplies from the external rescue center when necessary.

[0196] The life buoy launcher is used to quickly deliver a life buoy to a drowning person who is far away or in a dangerous area. The launcher of this embodiment can accurately launch a standard life buoy with a diameter of 80 cm to a target position within a range of 100 meters. The launch system adopts electromagnetic acceleration technology, which can complete the launch within 0.5 seconds, and the launch angle can be adjusted between -10 degrees and 45 degrees. The life buoy is made of highly visible orange material, with a built-in LED flash and GPS positioning device, which is convenient for night search. After the launcher is connected to the processor 150, the optimal launch parameters can be automatically calculated based on meteorological data such as wind speed and wind direction to ensure that the life buoy accurately reaches the vicinity of the drowning person.

[0197] The inflatable life raft provides a temporary safety platform for multiple people who fall into the water, and is particularly suitable for large-scale rescue operations. The life raft of this embodiment is made of high-strength, lightweight composite materials. It is small in size and light in weight when stored, and is easy to carry by the intelligent unmanned rescue boat 100. The life raft can be automatically inflated within 10 seconds. The maximum size is 5 meters × 3 meters, and it can accommodate 10 adults at the same time. The life raft is equipped with an automatic inflation system, an anti-flip design, an insulation layer, a rainwater collection device, and an emergency positioning beacon. After being connected to the processor 150 through the connection interface 170, the life raft can automatically select the best placement position and time according to the number and distribution of people who fall into the water.

[0198] The central processor 150 intelligently controls all external rescue equipment 180 through the connection interface 170. The processor 150 runs a special equipment management software that can automatically identify the type of connected equipment and formulate the best equipment use strategy according to the current rescue situation. For example, during night rescue, the system will give priority to the use of searchlights and thermal imaging equipment; in the case of multiple people falling into the water, it will coordinate the use of lifebuoy launchers and inflatable life rafts. The system can also monitor the working status and energy consumption of each device in real time to ensure the continuity and effectiveness of the rescue operation.

[0199] The power module 190 is used to supply power to the above-mentioned power-consuming components of the intelligent unmanned rescue boat 100. The power module 190 is the core power supply system of the intelligent unmanned rescue boat 100, and is used to provide a stable and reliable power supply to all power-consuming components on the boat. The design of this module fully considers the particularity of the marine rescue environment and the power requirements of each system, ensuring that the entire rescue boat can perform rescue missions for a long time and efficiently. The main components of the power module 190 include: a high-capacity lithium-ion battery pack, which adopts the latest lithium-ion battery technology and has high energy density, long life and fast charging characteristics, and can support the rescue boat to work continuously for more than several hours; a solar charging system, a high-efficiency flexible solar panel is installed on the top of the hull 110, which can continuously charge the battery pack under sunlight conditions to extend the working time of the rescue boat; an intelligent power management system, which monitors the power consumption of each power-consuming device in real time and dynamically adjusts the power supply strategy according to mission requirements and power status to achieve optimal energy utilization efficiency; waterproof and anti-corrosion design, the power module 190 adopts IP68-level waterproof and dustproof design, and uses special anti-corrosion materials to ensure stable operation even in harsh marine environments; multiple protection mechanisms, including overcharge protection, over-discharge protection, short circuit protection and temperature protection, etc., to ensure the safety and reliability of the power system; an emergency backup power supply, equipped with a small fuel cell as a backup power supply, which can provide an additional 8 hours of working time when the main battery is exhausted, further improving the endurance of the intelligent unmanned rescue boat.

[0200] Through this modular and scalable design, the intelligent unmanned rescue boat 100 can quickly configure appropriate rescue equipment 180 according to different rescue needs. This not only improves the flexibility and efficiency of rescue, but also greatly expands the application scenarios of the rescue boat, enabling it to cope with various complex marine rescue situations. At the same time, the introduction of the intelligent control system in the processor 150 ensures that these rescue equipment 180 can work together in the most optimized way to maximize the success rate of rescue.

[0201] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0202] Embodiment 7

[0203] like Figure 6 As shown, this embodiment provides a rescue control method for an intelligent unmanned rescue boat 100, which implements efficient and accurate marine rescue operations through a series of steps. The following are the detailed steps of the method:

[0204] Step S210: receiving rescue mission information from an external device.

[0205] The intelligent unmanned rescue boat 100 receives rescue mission information from external devices (such as a maritime search and rescue center or a command ship) through its high-performance communication system 140. This information usually includes the geographical coordinate range of the search and rescue area, the expected number of people falling into the water, the last known location of falling into the water, the type of accident (such as ship sinking, people falling into the water, etc.) and the current weather conditions. The communication system 140 uses encryption protocols to ensure the security of information transmission and uses multiple confirmation mechanisms to ensure the integrity and accuracy of the information. Once the mission information is received, the system will immediately parse and verify it, and input the key parameters into the navigation system and mission planning module of the rescue boat.

[0206] Step S220: Navigate to the designated search and rescue area according to the rescue mission information.

[0207] Based on the rescue mission information received, the navigation system of the intelligent unmanned rescue boat 100 will plan an optimal route to the designated search and rescue area. The navigation process uses the GPS / Beidou dual-mode positioning system to determine the current position, and combines electronic charts and real-time meteorological data to select the safest and fastest route. During the voyage, the system will continue to monitor changes in sea conditions and automatically adjust the route if encountering bad weather or dangerous waters. At the same time, the rescue boat will maintain communication with the external command center and report the navigation status and estimated arrival time in real time.

[0208] Step S230: Control the multi-sensor system 130 to scan surrounding waters within the designated search and rescue area.

[0209] After arriving at the designated search and rescue area, the rescue boat starts the all-round scanning mode. The visual sensor 131 performs a 360-degree panoramic scan with a coverage range of 2,000 meters; the thermal imaging sensor 132 works in low visibility conditions and can detect human heat sources within a range of 1,500 meters; the sonar system performs underwater scanning with a detection depth of 200 meters. These three sensors work together to form an all-round, three-dimensional search network. During the scanning process, the system will automatically adjust the scanning frequency and accuracy according to the sea conditions to ensure that no possible targets are missed.

[0210] Step S240 : Identify and locate one or more persons who have fallen into the water based on the data of the multi-sensor system 130 .

[0211] The data collected by the multi-sensor system 130 is input into an advanced target recognition algorithm. The algorithm uses a deep learning model to accurately identify human targets in complex sea conditions. For people on the surface, the system combines visual and thermal imaging data for identification; for people who may sink into the water, it mainly relies on sonar data. Once the target is identified, the system will immediately calculate its precise position and absolute geographic coordinates relative to the rescue boat. At the same time, the system will also evaluate the status of each person in the water, such as mobility, whether there are signs of life, etc., to provide a basis for subsequent rescue priority judgment.

[0212] Step S250 : determining obstacle avoidance requirements based on data from the multi-sensor system 130 .

[0213] While identifying the person who has fallen into the water, the system will also conduct a comprehensive scan of the surrounding environment to identify possible obstacles. This includes floating objects and other ships on the water surface, as well as underwater reefs, sunken ships, etc. For each identified obstacle, the system will calculate its location, size, and moving speed (if it is a moving obstacle), and evaluate its potential impact on the rescue operation. This data will be used for subsequent path planning to ensure the safety of the rescue process.

[0214] Step S260: Generate an optimal rescue path based on the location information of the drowning person, the number of drowning people, weather conditions and obstacle avoidance requirements.

[0215] Based on all the information collected, including the location information of the drowning person, the number of people, current weather conditions (such as wind direction, wind speed, wave height) and obstacle avoidance requirements, the rescue boat's artificial intelligence system will generate the optimal rescue path. The path planning uses an improved A* algorithm combined with a dynamic programming algorithm, taking into account multiple factors: the shortest total distance, avoiding dangerous areas, considering the influence of wind direction and ocean currents, and reserving a safe distance. The system will generate multiple feasible solutions at the same time and select the best solution through a comprehensive scoring mechanism. This process is dynamic and will continuously adjust and optimize the path according to real-time conditions.

[0216] Step S270: Control the power system 120 to move the hull 110 according to the optimal rescue path to approach one or more drowning persons, and rescue the one or more drowning persons.

[0217] After determining the optimal path, the system controls the power system 120 to move the hull 110 along the planned path. The rescue boat uses an electric propulsion system to achieve precise speed and direction control. In the process of approaching the drowning person, the system will continuously adjust the speed and course to ensure safe and effective approach to the target. When the distance to the drowning person is close enough (usually within 10 meters), the system will start a special rescue procedure, such as reducing the speed of the boat, adjusting the posture of the hull 110, and starting the corresponding rescue equipment 180 (such as lifebuoy launcher, life rope launcher, etc.) according to the situation.

[0218] Step S280: Control the communication system 140 to report the rescue progress to an external device.

[0219] During the entire rescue process, the intelligent unmanned rescue boat 100 will continue to report the rescue progress to external devices (such as the search and rescue center) through the communication system 140. The report content includes: the number of people who have been identified, the number of people who have approached, the number of people who have been successfully rescued, the current location of the rescue boat, the estimated time to complete the rescue, etc. This information is automatically sent every 60 seconds in a structured data format, and updates will be sent immediately when key events occur (such as newly discovered people who have fallen into the water, successfully rescued people who have fallen into the water, etc.). In addition, if special circumstances are encountered or additional support is required, the system will immediately issue an alarm to the outside.

[0220] Through this systematic and intelligent rescue control method, the intelligent unmanned rescue boat 100 can efficiently and safely complete complex marine rescue tasks. The method makes full use of advanced sensing technology, artificial intelligence algorithms and communication systems 140, greatly improving the success rate and efficiency of marine rescue and gaining precious time for saving lives.

[0221] In some embodiments, the steps of identifying and locating one or more people who fall into the water based on the data of the multi-sensor system 130, and determining the obstacle avoidance requirements specifically include: controlling the visual sensor 131 and the thermal imaging sensor 132 to identify people who fall into the water on the water surface; controlling the sonar sensor 133 to detect people who fall into the water underwater; combining the position data of the positioning sensor 134, calculating the position and absolute geographic coordinates of each identified person who falls into the water relative to the intelligent unmanned rescue boat 100, thereby locating multiple people who fall into the water and obtaining the position information of the people who fall into the water; controlling the visual sensor 131 to identify obstacles on the water surface; controlling the sonar sensor 133 to detect underwater obstacles; combining the position data of the positioning sensor 134, calculating the position and size of the identified obstacles relative to the intelligent unmanned rescue boat 100.

[0222] In some embodiments, the rescue control method of the intelligent unmanned rescue boat 100 further includes the following steps: determining the rescue priority based on the number of identified people who have fallen into the water, the location information of the people who have fallen into the water, the vital signs of the people who have fallen into the water, and the meteorological conditions of the surrounding environment; wherein the vital signs are obtained by detecting body temperature through the thermal imaging sensor 132 and / or observing breathing movements through the visual sensor 131.

[0223] In some embodiments, the step of controlling the communication system 140 to report the rescue progress to the external device specifically includes: sending the location information of the person who fell into the water and a rescue progress report to the external device, wherein the rescue progress report includes the number of people who have been identified, the number of people who have approached the water, the number of people who have been successfully rescued, the current location of the unmanned rescue boat and the estimated time to complete the rescue.

[0224] In some embodiments, the rescue control method of the intelligent unmanned rescue boat 100 also includes the following steps: using a machine learning algorithm to optimize the accuracy of identifying and locating the person falling into the water, wherein the machine learning algorithm includes a deep learning model for image recognition and a reinforcement learning algorithm for optimizing the rescue path.

[0225] In some embodiments, the rescue control method of the intelligent unmanned rescue boat 100 further includes the following steps: detachably and electrically connecting an externally mounted rescue device 180 via a connection interface 170 , wherein the connection interface 170 is electrically connected to the processor 150 .

[0226] In some embodiments, the rescue equipment 180 includes at least one of the following: a searchlight for nighttime lighting rescue; a loudspeaker for voice communication with the person who falls into the water; a life rope launcher for launching a rescue rope to the person who falls into the water, the end of the rescue rope having a ring for grasping; a medical equipment box for storing tourniquets, first aid medicines and nutritional foods; a life buoy launcher for launching a life buoy to the person who falls into the water; an inflatable life raft for providing a temporary safety floating platform for multiple people who fall into the water; wherein the method also includes controlling the processor 150 to operate the corresponding rescue equipment 180 to assist in rescue.

[0227] In some embodiments, receiving rescue mission information from an external device includes: receiving the geographical coordinate range of the search and rescue area, the estimated number of people falling into the water, the last known location of falling into the water, the type of accident, and the current weather conditions.

[0228] In some embodiments, the step of generating the optimal rescue path includes: performing data collection and preprocessing to collect the location information of the person who falls into the water, meteorological conditions and obstacle distribution data; evaluating the rescue priority of each person who falls into the water based on the collected data; using an improved A* algorithm to generate an initial rescue path; applying a dynamic programming algorithm to optimize the initial rescue path; performing a safety assessment on the optimized rescue path; generating and evaluating multiple optional rescue path plans, and selecting the rescue path plan with the highest comprehensive score as the final optimal rescue path; and setting a real-time adjustment mechanism for the rescue path.

[0229] In some embodiments, the execution of data collection and preprocessing includes: collecting the location information of the identified person who fell into the water, including the direction and distance relative to the intelligent unmanned rescue boat 100; obtaining the current meteorological conditions, including wind direction, wind speed, wave height and visibility; sorting out obstacle avoidance requirements, and determining the location and size of the identified surface and underwater obstacles.

[0230] In some embodiments, the rescue priority assessment for each drowning person includes: considering the distance between the drowning person and the rescue boat, the concentration of the drowning persons, and the estimated survival time; and determining the rescue priority for each drowning person based on a comprehensive assessment of these factors.

[0231] In some embodiments, the use of the improved A* algorithm to generate an initial rescue path includes: setting the current position of the intelligent unmanned rescue boat 100 as the starting point; setting the position of the person who fell into the water as a waypoint in order of priority; considering the position of obstacles during the search process and automatically bypassing unsafe areas.

[0232] In some embodiments, applying a dynamic programming algorithm to optimize the initial path includes:

[0233] According to the impact of wind direction and ocean currents on the drift of rescue boats and people who fall into the water, the rescue route is dynamically adjusted to minimize the total travel distance, maximize the rescue success rate and minimize fuel consumption.

[0234] In some embodiments, the applying a dynamic programming algorithm to optimize the initial path comprises the following steps:

[0235] Step 1: Obtain and analyze real-time meteorological data. Specifically, first, collect meteorological data such as wind direction, wind speed, ocean current direction and speed in the current area. These data can be obtained through equipment such as weather stations, satellites and buoys. Subsequently, use meteorological models to predict the trend of meteorological changes in the short term (such as 2-4 hours) in the future. These forecast data will provide key references for path optimization.

[0236] Step 2: Predict the drift trajectory of the rescue boat and the drowning person. Specifically, based on the acquired meteorological data, the drift prediction model is used to calculate the expected position of the rescue boat at different time points. Similarly, the possible position of each drowning person at different time points is predicted. Through these predictions, the rescue path can be planned more accurately to ensure effective rescue operations in a changing environment.

[0237] Step 3: Build a time-varying environment model. Specifically, this step integrates the predicted drift trajectories of the rescue boat and the drowning person into the environment model, while taking into account the dynamic changes of obstacles, such as the movement of floating objects. This model will provide a dynamic view of the environmental changes that the rescue operation may face in the future, helping to formulate more accurate rescue strategies.

[0238] Step 4: Apply the dynamic programming algorithm. Specifically, this step uses the dynamic programming algorithm to calculate the optimal path based on the constructed time-varying environment model. At each decision point, multiple objective functions are considered, including minimizing the total travel distance, maximizing the rescue success rate, and minimizing fuel consumption. By combining these factors, the dynamic programming algorithm can generate a rescue path that takes into account both efficiency and safety.

[0239] Step 5: Path segment optimization. Specifically, this step divides the entire rescue process into multiple time periods. The path of each time period is optimized separately, taking into account the specific environmental conditions within that time period. This segment optimization method can more flexibly respond to dynamically changing environments and ensure that the rescue path at each stage is optimal.

[0240] Step 6: Integrate the optimization results. Specifically, this step integrates the optimization results of each time period to form a complete rescue path. Ensure the continuity and smoothness of the path and avoid sudden changes in direction, which can improve the stability and safety of the rescue operation and reduce the occurrence of unexpected situations.

[0241] Step 7: Evaluate the optimization results. Specifically, this step calculates the expected total travel distance, estimated rescue success rate, and fuel consumption of the optimized path. These results are compared with the initial path to ensure that the optimized path is improved in all aspects. The purpose of this step is to verify the optimization results and ensure that it meets the needs of the rescue mission.

[0242] Step 8: Iterative optimization. Specifically, if the optimization result does not meet the preset standard, return to step 4, adjust the parameters or constraints, and optimize again. Repeat this process until a satisfactory optimization result is achieved or the preset number of iterations is reached. This iterative optimization method can continuously improve the path planning and ultimately generate the best rescue path.

[0243] In some embodiments, the safety assessment of the optimized path includes: ensuring a safe distance from all obstacles throughout the rescue process; and making necessary path adjustments based on the risks that may be brought about by meteorological conditions, such as areas with large waves.

[0244] In some embodiments, the generating and evaluating of multiple optional rescue path plans includes: using a comprehensive scoring mechanism to evaluate each plan, considering multiple factors including total rescue time, fuel consumption, safety factor and rescue success rate during the evaluation; selecting the plan with the highest comprehensive score as the final optimal rescue path.

[0245] In some embodiments, the real-time adjustment mechanism of the path setting includes: continuously monitoring environmental changes during the rescue process; when a new drowning person or a new obstacle is found, quickly re-execute the rescue path generation process to update the optimal rescue path.

[0246] like Figure 7 As shown, an embodiment of the present invention further provides a computer-readable storage medium 700 on which a computer program 710 is stored. When the program 710 is executed by a processor, the rescue control method of any one of the above-mentioned intelligent unmanned rescue boats is implemented.

[0247] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. Of course, there are other ways of readable storage media, such as quantum memory, graphene memory, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0248] Embodiment 9

[0249] The present invention also provides a rescue control device for an intelligent unmanned rescue boat. The rescue control device for an intelligent unmanned rescue boat of an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the rescue control method for the intelligent unmanned rescue boat provided by the present invention.

[0250] Reference below Figure 8 , which shows a structural schematic diagram of a rescue control device 800 suitable for implementing an intelligent unmanned rescue boat according to an embodiment of the present invention. Figure 8 The rescue control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0251] like Figure 8As shown, the rescue control device 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the computer system 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0252] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0253] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the main step diagram. In the above embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, the above functions defined in the system of the present invention are executed.

[0254] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0255] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs a specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions. The units involved in the embodiments of the present invention described in the embodiment can be implemented by software or by hardware. The described unit can also be set in a processor.

[0256] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent unmanned rescue boat, characterized in that: include: hull; A power system, used for driving the hull to move on the water surface; Multi-sensor system including: Vision sensors for identifying people and obstacles on the water surface in daylight conditions; Thermal imaging sensors, used to identify body temperature characteristics of drowning people in night conditions; Sonar sensors to detect people who have sunk into the water and underwater obstacles; A positioning sensor, used to determine the position data of the intelligent unmanned rescue boat; Meteorological sensors, used to detect the meteorological conditions of the surrounding environment; Communication system, used for exchanging data with external devices; a processor connected to the multi-sensor system and the communication system; a memory, connected to the processor, storing a computer program; The processor is used to execute the computer program to implement the following steps: Controlling the multi-sensor system to scan surrounding waters; Identify and locate one or more people in the water based on the data scanned and acquired by the multi-sensor system, and determine obstacle avoidance requirements; Generate an optimal rescue path according to the position information of the drowning person, the number of the drowning person, the meteorological conditions and the obstacle avoidance requirements; control the power system to move the hull along the optimal rescue path to approach the one or more drowning persons, and rescue the one or more drowning persons.

2. The intelligent unmanned rescue boat according to claim 1, characterized in that: The hull includes a U-shaped shell; the identification and positioning of multiple drowning persons based on the data scanned and acquired by the multi-sensor system specifically includes: Controlling the visual sensor and the thermal imaging sensor to identify a person who falls into the water on the water surface; Controlling the sonar sensor to detect a person who falls into the water; Combined with the position data of the positioning sensor, the position and absolute geographic coordinates of each identified drowning person relative to the intelligent unmanned rescue boat are calculated, thereby locating multiple drowning people and obtaining their position information; The determining of obstacle avoidance requirements based on the data scanned and acquired by the multi-sensor system specifically includes: Controlling the visual sensor to identify obstacles on the water surface; Controlling the sonar sensor to detect underwater obstacles; Combined with the position data of the positioning sensor, the position and size of the identified obstacle relative to the intelligent unmanned rescue boat are calculated.

3. The intelligent unmanned rescue boat according to claim 1, characterized in that: The processor is further configured to execute the computer program to implement the following steps: Determine the rescue priority based on the number of people identified, their location information, their vital signs and the meteorological conditions of the surrounding environment; The vital signs are obtained by detecting body temperature with the thermal imaging sensor and / or observing breathing movements with the visual sensor.

4. The intelligent unmanned rescue boat according to claim 1, characterized in that: The processor is further configured to execute the computer program to implement the following steps: The communication system sends location information of drowning persons and a rescue progress report to an external device, wherein the rescue progress report includes the number of drowning persons identified, the number of drowning persons approaching, the number of drowning persons successfully rescued, the current location of the unmanned rescue boat and the estimated time to complete the rescue.

5. The intelligent unmanned rescue boat according to claim 1, characterized in that: The processor is further configured to execute the computer program to implement the following steps: The accuracy of identifying and locating people who have fallen into the water is optimized using a machine learning algorithm, wherein the machine learning algorithm includes a deep learning model for image recognition and a reinforcement learning algorithm for optimizing the rescue path.

6. The intelligent unmanned rescue boat according to claim 1, characterized in that: It also includes a connection interface, which is arranged on the hull and is used for detachably and electrically connecting externally mounted rescue equipment, and the connection interface is electrically connected to the processor.

7. The intelligent unmanned rescue boat according to claim 6, characterized in that: The rescue equipment includes at least one of the following: Searchlight, used for nighttime lighting and rescue; A loudspeaker for voice communication with the drowning person; A lifeline launcher, used to launch a rescue rope to a person who falls into the water, wherein the end of the rescue rope has a ring for grasping; Medical equipment boxes for storing tourniquets, first aid medicines and nutritional foods; Lifebuoy launcher, used to launch a lifebuoy to a person who falls into the water; An inflatable life raft used to provide a temporary safe floating platform for multiple people who fall into the water.

8. A rescue control method for an intelligent unmanned rescue boat according to any one of claims 1 to 7, characterized in that: The following steps are involved: receiving rescue mission information from an external device; Navigate to a designated search and rescue area according to the rescue mission information; Controlling the multi-sensor system to scan the surrounding waters within the designated search and rescue area; Identifying and locating one or more persons in the water based on data from the multi-sensor system, and determining obstacle avoidance requirements; Generate the optimal rescue path based on the location information of the drowning person, the number of drowning people, weather conditions and obstacle avoidance requirements; Controlling the power system to move the hull according to the optimal rescue path to approach the one or more drowning persons, and rescuing the one or more drowning persons; The communication system is controlled to report the rescue progress to the external device.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 8 is implemented.

10. A rescue control device for an intelligent unmanned rescue boat, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 8.

Images