Medical material distribution method and system based on marine medical rescue unmanned aerial vehicle

Abstract

The invention provides a medical material distribution method and system based on an offshore medical rescue unmanned aerial vehicle. Wherein a marine environment model is generated; according to the marine environment model, an AI scheduling algorithm is applied to analyze the specific demand, priority and emergency degree of a marine receiving point, and an optimized task sequence and a material loading scheme are obtained; establishing a cooperative communication mechanism between offshore medical rescue unmanned aerial vehicle formations through an AI-driven distributed consensus algorithm, and dynamically adjusting a flight path and generating a distribution path by using an AI path planning technology in combination with real-time situation awareness and obstacle detection analysis; and after arriving at a specified receiving point, automatically calibrating the landing coordinates, and synchronizing the material state, handover information and environmental data to a medical team and a central scheduling system through a low-delay wireless transmission mode according to the distribution path. According to the technical scheme provided by the invention, the efficiency and reliability of offshore material distribution are greatly improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of material distribution technology, and in particular to a medical material distribution method and system based on a marine medical rescue drone. Background Art

[0002] Medical rescue missions at sea usually take place in waters far from land, where the environment is complex and changeable, including bad weather, unstable sea conditions and other factors. In such an environment, it is crucial to deliver supplies to designated locations quickly and accurately. Maritime rescue operations require high-precision environmental perception capabilities, efficient resource scheduling mechanisms, and safe and reliable distribution route planning to ensure that supplies can be delivered in a timely and effective manner.

[0003] At present, medical rescue at sea mainly relies on traditional helicopters or ships to transport supplies. Although these methods are reliable, they have problems such as slow response speed and large restrictions on weather and sea conditions. In recent years, with the development of drone technology, some preliminary attempts have been made to use drones for material distribution, but these solutions often lack a comprehensive understanding of the complex marine environment, resource allocation is not sophisticated enough, and it is difficult to achieve efficient collaborative communication between formations.

[0004] When faced with a dynamically changing ocean environment, existing solutions lack environmental perception and real-time adaptability, resulting in inflexible mission planning. At the same time, in the case of multiple receiving points, the specific needs, priorities and urgency of each point are not fully considered, resulting in unreasonable resource allocation and affecting the overall rescue efficiency. In addition, due to the lack of an effective collaborative communication mechanism between formations, information sharing between drones is insufficient, which increases flight risks and reduces the safety and orderliness of delivery. Summary of the invention

[0005] The embodiments of the present application provide a medical supplies distribution method and system based on a maritime medical rescue drone, which is used to solve the problems of unreasonable resource allocation and low flight safety in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for distributing medical supplies based on a marine medical rescue drone, comprising:

[0007] Using the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, multi-source environmental information is acquired and integrated in real time to generate a marine environmental model;

[0008] According to the marine environment model, the AI ​​scheduling algorithm is used to analyze the specific needs, priorities and urgency of the offshore receiving points. At the same time, the resource allocation is carefully planned based on the knapsack problem analysis technology in operations research to obtain the optimized task sequence and material loading plan;

[0009] Based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the maritime medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path;

[0010] After arriving at the designated receiving point, the landing coordinates are automatically calibrated, and based on the distribution route, the material status, handover information, and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission to generate accurate delivery records and feedback information.

[0011] Optionally, the AI ​​scheduling algorithm is applied to analyze the specific needs, priorities and urgency of the offshore receiving points according to the marine environment model, and the resource allocation is finely planned based on the knapsack problem analysis technology in operations research to obtain an optimized task sequence and material loading plan, including:

[0012] Using the ocean environment model, comprehensively evaluate and process the location, sea conditions, weather forecast, and time window of each offshore receiving point to obtain a score of mission importance and urgency;

[0013] According to the task importance and urgency scores, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the needs of each receiving point are dynamically evaluated and processed to generate a task demand matrix, which reflects the task priority;

[0014] Based on the task requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the maritime medical rescue drone and the types and quantities of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan;

[0015] The preliminary material loading plan is used in combination with the AI ​​scheduling algorithm to further adjust the mission sequence of each maritime medical rescue drone to maximize the delivery efficiency of a single flight, and obtain the optimized mission sequence and material loading plan.

[0016] Optionally, based on the mission requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the maritime medical rescue drone and the types and quantities of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan, including:

[0017] Using the task demand matrix, quantitative analysis and processing are performed on the task urgency and material demand of each offshore receiving point to obtain a demand feature vector reflecting the demand characteristics of each receiving point;

[0018] According to the demand feature vector, combined with the maximum load capacity and payload volume of the maritime medical rescue drone, the knapsack problem model is used to perform combinatorial optimization processing on materials of different types and quantities, and multiple candidate loading schemes that meet the load restrictions are generated;

[0019] Based on the candidate loading schemes, the importance and urgency of each material are taken into consideration, a priority weight factor is introduced, and the comprehensive benefits of each candidate scheme are evaluated to obtain an optimized loading benefit score;

[0020] The optimized loading efficiency score is used to ensure that the distribution efficiency and task completion are maximized under limited load conditions, and a preliminary material loading plan is generated.

[0021] Optionally, the preliminary material loading plan is used in combination with the AI ​​scheduling algorithm to further adjust the task sequence of each marine medical rescue drone to maximize the delivery efficiency of a single flight, thereby obtaining an optimized task sequence and material loading plan, including:

[0022] Using the preliminary material loading plan, the load configuration and expected flight path of each marine medical rescue drone are comprehensively analyzed and processed to generate an initial task allocation table;

[0023] According to the initial task allocation table, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the task execution order of each maritime medical rescue drone is dynamically evaluated and processed, and the dependency relationship and time window constraints between tasks are considered to obtain the task execution sequence reflecting the task priority;

[0024] Based on the task execution sequence, a complexity analysis method is introduced to evaluate the time synchronization requirements and collaborative work requirements between different tasks, and the connection between tasks is optimized to ensure that high-priority tasks can be completed in time, and an optimized task sequence is generated;

[0025] By using the optimized mission sequence, combined with the maximum endurance of the maritime medical rescue drone and real-time sea condition data, the material loading plan is fine-tuned to ensure that the distribution efficiency of a single flight is maximized while meeting all mission requirements, and the optimized mission sequence and material loading plan are obtained.

[0026] Optionally, based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the maritime medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path, including:

[0027] Using the optimized task sequence and material loading scheme, the task allocation and flight plan of each marine medical rescue drone are analyzed in detail to obtain accurate task execution instructions;

[0028] According to the mission execution instructions, an AI-driven distributed consensus algorithm is used to establish a collaborative communication mechanism among the maritime medical rescue drone formations, ensuring that each maritime medical rescue drone can share its location and status in real time and generate an instant communication network;

[0029] Based on the instant communication network, the data of the real-time situation awareness system and the obstacle detection sensor are integrated to monitor and process the potential risks in the flight environment and obtain an environmental risk assessment report;

[0030] Utilizing the environmental risk assessment report and combining it with AI path planning technology, the current flight path is dynamically adjusted to ensure that obstacles are avoided and the optimal path is selected, ultimately generating a delivery path.

[0031] Optionally, the environmental risk assessment report is used in combination with AI path planning technology to dynamically adjust the current flight path to ensure that obstacles are avoided and the optimal path is selected, and finally a delivery path is generated, including:

[0032] Using the environmental risk assessment report, potential obstacles and risk areas in the flight environment are identified and processed to obtain an obstacle distribution map;

[0033] According to the obstacle distribution map, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is applied to recalculate the existing flight path to obtain multiple candidate obstacle avoidance paths;

[0034] Based on the multiple candidate obstacle avoidance paths, the shortest path algorithm and weighted scoring mechanism in graph theory are introduced, and the path length, flight time and risk factor are considered to comprehensively evaluate each candidate path to obtain an optimized path score;

[0035] Using the optimized path score, the path with the highest score is selected as the final flight path, and the path is fine-tuned in combination with the maximum endurance and load conditions of the maritime medical rescue drone to ensure the safety and efficiency of the path and generate a distribution path.

[0036] Optionally, after arriving at the designated receiving point, the landing coordinates are automatically calibrated, and according to the delivery path, the material status, handover information and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission to generate accurate delivery records and feedback information, including:

[0037] Using high-precision visual recognition technology and GPS positioning system, the scheduled landing coordinates of the marine medical rescue drone are monitored in real time and automatically calibrated to obtain the precise landing position;

[0038] According to the precise landing location and in combination with the delivery path, low-latency wireless transmission technology is applied to synchronously transmit the status information, flight trajectory and current environmental data of the marine medical rescue drone to the medical team and central dispatch system at the receiving point, and generate a preliminary material handover report;

[0039] Based on the preliminary material handover report, the material status and handover situation during the actual delivery process are recorded in detail to ensure that all materials are delivered to the target location accurately and obtain a detailed delivery list;

[0040] The detailed delivery list is used in combination with the confirmation feedback from the medical team to obtain a delivery record, which is then transmitted back to the central dispatch system as feedback information to generate accurate delivery records and feedback information.

[0041] In a second aspect, the embodiment of the present application provides a medical supplies distribution system based on a marine medical rescue drone, comprising:

[0042] The acquisition module is used to use the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, to acquire and fuse multi-source environmental information in real time to generate a marine environmental model;

[0043] An analysis module is used to analyze the specific needs, priorities and urgency of the offshore receiving points using an AI scheduling algorithm according to the marine environment model, and to make detailed plans for resource allocation based on the knapsack problem analysis technology in operations research to obtain an optimized task sequence and material loading plan;

[0044] An adjustment module is used to establish a collaborative communication mechanism between the maritime medical rescue drone formations based on the optimized task sequence and material loading plan through an AI-driven distributed consensus algorithm to ensure instant information sharing, and to dynamically adjust the flight path and generate a delivery path by using AI path planning technology combined with real-time situational awareness and obstacle detection analysis;

[0045] The calibration module is used to automatically calibrate the landing coordinates after arriving at the designated receiving point, and synchronize the material status, handover information and environmental data to the medical team and the central dispatch system through low-latency wireless transmission based on the distribution path, so as to generate accurate delivery records and feedback information.

[0046] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a medical supplies distribution method based on a maritime medical rescue drone as described in the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a medical supplies distribution method based on a marine medical rescue drone as described in the first aspect.

[0048] In the embodiment of the present application, a comprehensive perception system integrated in the marine medical rescue drone is used, combined with an AI environmental prediction model, to obtain and fuse multi-source environmental information in real time to generate an ocean environment model; based on the marine environment model, an AI scheduling algorithm is applied to analyze the specific needs, priorities and urgency of the offshore receiving point, and at the same time, based on the knapsack problem analysis technology in operations research, resource allocation is finely planned to obtain an optimized task sequence and material loading plan; based on the optimized task sequence and material loading plan, a collaborative communication mechanism between marine medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is used in combination with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path; after arriving at the designated receiving point, the landing coordinates are automatically calibrated, and based on the distribution path, the material status, handover information and environmental data are synchronized to the medical team and the central scheduling system through a low-latency wireless transmission method to generate accurate delivery records and feedback information.

[0049] The technical solution of this application has the following beneficial effects:

[0050] By integrating the comprehensive perception system and AI environmental prediction model, it is possible to obtain and process multi-source environmental information in real time and quickly generate an ocean environment model. This allows the system to respond quickly to emergencies at sea and optimize the distribution route, greatly shortening the time from task allocation to material delivery; by applying AI scheduling algorithms combined with knapsack problem analysis technology in operations research, the system can accurately evaluate the needs, priorities and urgency of each receiving point based on the ocean environment model, thereby achieving detailed planning of resource allocation, ensuring the optimal material loading plan, and improving the scientificity and accuracy of decision-making; using AI-driven distributed consensus algorithms to establish a collaborative communication mechanism ensures instant information sharing between drones in the formation. At the same time, combined with real-time situational awareness and obstacle detection and analysis technology, the flight path is dynamically adjusted to avoid potential risks and ensure the safety and orderliness of the distribution process; through the automatic calibration of landing coordinates and the application of low-latency wireless transmission technology, it ensures that the materials can be delivered to the target location accurately, and the material status, handover information and environmental data are synchronized to the relevant parties, which not only improves the completion of the task, but also increases the flexibility of the system to adapt to different on-site conditions; through the optimization of task sequences and material loading plans, the system can maximize the delivery efficiency of a single flight under limited load conditions, reduce unnecessary waste of resources, and improve the efficiency of resource utilization in the entire rescue operation; generate accurate delivery records and feedback information, which is helpful for post-analysis and summary of lessons learned, provide reference for future tasks, and continuously optimize rescue processes and technical means.

[0051] Furthermore, by utilizing the marine environment model, combined with AI scheduling algorithms and knapsack problem analysis techniques in operations research, this method can comprehensively evaluate the location, sea conditions, weather forecasts, and time windows of each offshore receiving point, and accurately calculate the importance and urgency scores of the task. Based on this score, the system simulates the behavior pattern of the multi-agent system, dynamically evaluates the needs of each receiving point, generates a task demand matrix that reflects the priority of the task, and optimizes the resource allocation strategy accordingly to generate a preliminary material loading plan. Furthermore, by adjusting the task sequence of the drone to maximize the distribution efficiency of a single flight, the optimized task sequence and material loading plan are finally obtained. This method not only significantly improves the scientificity and accuracy of task planning, but also ensures efficient and accurate resource allocation in a complex and changeable marine environment, effectively solving the problems of low distribution efficiency and waste of resources caused by the lack of detailed planning in the existing scheme, thereby greatly improving the overall response speed and service quality of marine medical rescue.

[0052] Furthermore, by using the optimized task sequence and material loading scheme, this method analyzes the task allocation and flight plan of each maritime medical rescue drone in detail and generates accurate task execution instructions. Based on these instructions, an AI-driven distributed consensus algorithm is applied to establish an efficient collaborative communication mechanism between drone formations to ensure that each drone can share location and status information in real time and form an instant communication network. The network combines the data of the real-time situational awareness system and obstacle detection sensors to continuously monitor potential risks in the flight environment and generate an environmental risk assessment report. Based on this report, the flight path is dynamically adjusted with the help of AI path planning technology to ensure that the drone can avoid obstacles and choose the optimal path, and finally generate a distribution path. This method significantly improves the accuracy and coordination of task execution, enhances the safety and reliability of flight, and effectively solves the problems of insufficient information sharing and weak risk response capabilities in existing solutions, thereby greatly improving the success rate and efficiency of maritime medical rescue operations.

[0053] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A flowchart of a method for distributing medical supplies based on a marine medical rescue drone provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the structure of a medical supplies distribution system based on a marine medical rescue drone provided in an embodiment of the present application;

[0057] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this article or may be performed in parallel. In addition, these processes may include more or fewer operations, and these operations may be performed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0061] Figure 1 A flowchart of a method for distributing medical supplies based on a maritime medical rescue drone is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0062] Using the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, multi-source environmental information is acquired and integrated in real time to generate a marine environmental model;

[0063] In this step, the comprehensive perception system integrated into the marine medical rescue drone includes a variety of sensors and data acquisition equipment for real-time acquisition of meteorological data (wind speed, wind direction, temperature, etc.), sea condition information (wave height, flow direction, flow velocity, etc.) and geospatial data (location coordinates, topography, etc.). Combined with the AI ​​environmental prediction model, these multi-source environmental information are fused and processed to generate a dynamic model that comprehensively reflects the current marine environmental conditions. This model not only provides an accurate description of the existing environmental conditions, but also can predict the trend of changes in the future, providing a solid foundation for subsequent mission planning.

[0064] In the embodiment of the present application, the data collected by the integrated perception system deployed on the drone is pre-processed and input into the AI ​​environmental prediction model for analysis. The model uses machine learning algorithms to identify patterns and predict future environmental changes, and finally outputs an ocean environment model that integrates all relevant information. This model serves as the basis for all subsequent decisions, ensuring the accuracy and adaptability of mission planning.

[0065] Suppose that in a medical rescue operation at sea, the drone activates its integrated perception system before taking off, collects real-time weather and sea condition data of the surrounding sea area, and sends this data to the AI ​​environmental prediction model. After model analysis, a detailed marine environment model is generated, showing that strong winds and high waves may occur in a specific area in the next 24 hours. Based on this model, the rescue team adjusts the mission plan and chooses a safer flight route and time window.

[0066] According to the marine environment model, the AI ​​scheduling algorithm is used to analyze the specific needs, priorities and urgency of the offshore receiving points. At the same time, the resource allocation is carefully planned based on the knapsack problem analysis technology in operations research to obtain the optimized task sequence and material loading plan;

[0067] In this step, AI scheduling algorithms are used to analyze the specific needs, priorities, and urgency of the receiving points at sea. At the same time, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the drone and the number of material types to achieve detailed planning of resource allocation. Specific needs include the quantity and type of materials, as well as the geographical location of the receiving point, the estimated time of arrival, and the urgency score. Through this comprehensive evaluation, the system can generate the most optimized mission sequence and material loading plan to ensure that each flight can maximize efficiency and effectiveness.

[0068] In the embodiment of the present application, based on the generated ocean environment model, the AI ​​scheduling algorithm first calculates the task importance and urgency scores of each receiving point, and then simulates the interactive behavior pattern of the multi-agent system to generate a task requirement matrix that reflects the priority of each receiving point. Next, the system uses the knapsack problem analysis method to formulate a preliminary material loading plan based on the load capacity and material characteristics of the drone. Finally, the drone's task sequence is further adjusted to ensure that the delivery efficiency of a single flight reaches the optimal state.

[0069] Assuming a rescue mission involving multiple receiving points, the system calculates the mission importance and urgency scores of each point based on the ocean environment model and the demand information of each receiving point. Subsequently, the system simulates the collaborative working mode between different drones, generates a mission demand matrix, optimizes resource allocation based on this, and develops a detailed material loading plan. In this way, the system ensures that the mission sequence of each drone is carefully arranged to achieve maximum delivery efficiency.

[0070] Based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the maritime medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path;

[0071] In this step, based on the optimized task sequence and material loading plan, an efficient collaborative communication mechanism is established between drone formations through an AI-driven distributed consensus algorithm to ensure that all drones can share their location, status, and other key information in real time. In addition, combined with real-time situational awareness and obstacle detection analysis, AI path planning technology dynamically adjusts the flight path to ensure that potential risks are avoided and the safest and most efficient delivery path is selected. This step is crucial to ensuring flight safety and improving mission completion.

[0072] In the embodiment of the present application, once the task sequence and material loading plan are determined, the system immediately starts the distributed consensus algorithm to build an instant communication network covering the entire formation. On this basis, the data from the real-time situational awareness system and obstacle detection sensors are integrated, and the AI ​​path planning technology continuously monitors changes in the flight environment and adjusts the flight path of each drone accordingly. In this way, the system not only improves the safety of the flight, but also ensures the optimal selection of the distribution path.

[0073] Assume that in a complex marine environment, multiple drones are preparing to perform a series of material delivery tasks. The system first establishes a collaborative communication mechanism based on the optimized task sequence, so that each drone can share its own position and status information in real time. As the task unfolds, the real-time situational awareness system continues to monitor the surrounding environment. When an unknown obstacle is found ahead, the AI ​​path planning technology responds quickly and dynamically adjusts the flight path of the drone, successfully avoiding the obstacle and ensuring the safe completion of the task.

[0074] After arriving at the designated receiving point, the landing coordinates are automatically calibrated, and based on the distribution route, the material status, handover information, and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission to generate accurate delivery records and feedback information.

[0075] In this step, when the drone approaches the designated receiving point, the system will automatically calibrate the landing coordinates to ensure accurate landing. At the same time, through low-latency wireless transmission, the drone synchronizes the status of materials, handover information, and environmental data to the medical team and the central dispatch system, generating detailed delivery records and feedback information. This information is very important for confirming that the materials have been accurately delivered to the target location, evaluating the completion of the mission, and providing reference for subsequent tasks.

[0076] In the embodiment of the present application, when the drone approaches the predetermined receiving point, the system starts the high-precision visual recognition technology and GPS positioning system to automatically calibrate the landing coordinates to ensure that the drone can land accurately at the designated location. At the same time, the drone uses low-latency wireless transmission technology to synchronize its own status information, flight trajectory and current environmental data to the medical team and central dispatch system at the receiving point in real time. This not only ensures the accurate delivery of materials, but also provides timely information support for subsequent task adjustments.

[0077] Suppose a drone is about to arrive at a medical station on a remote island. As it approaches its destination, the system uses high-precision visual recognition technology and GPS positioning system to automatically calibrate the landing coordinates to ensure that the drone can land safely and accurately at the designated location. At the same time, the drone uses low-latency wireless transmission technology to synchronize the status information, handover details, and the latest environmental data of the supplies to the medical team and the central dispatch system on the island. In this way, the medical team can immediately confirm that the supplies have been successfully delivered and prepare for the next rescue work; and the central dispatch system can adjust the subsequent task arrangements based on the feedback information.

[0078] In summary, the present invention covers the complete process from environmental perception, mission planning, collaborative communication to precise delivery, aiming to provide an efficient, safe and reliable solution for the distribution of medical rescue materials at sea, meeting the needs of rapid response and precise execution in complex and changeable marine environments. Through this series of steps, the system not only improves the scientificity and accuracy of mission planning, but also enhances the safety and reliability of flight, effectively solves the problems of insufficient information sharing and weak risk response capabilities in existing solutions, and greatly improves the overall efficiency and service quality of medical rescue at sea.

[0079] In order to solve the problems of unreasonable resource allocation and insufficiently refined task planning, in some embodiments, the AI ​​scheduling algorithm is applied to analyze the specific needs, priorities and urgency of the offshore receiving points according to the marine environment model, and the resource allocation is finely planned based on the knapsack problem subdivision technology in operations research to obtain an optimized task sequence and material loading plan, including:

[0080] The marine environment model is used to comprehensively evaluate the location, sea conditions, weather forecast and time window of each offshore receiving point to obtain a task importance and urgency score; based on the task importance and urgency score, the behavior pattern of the multi-agent system is simulated in combination with the AI ​​scheduling algorithm, the needs of each receiving point are dynamically evaluated and processed to generate a task requirement matrix, which reflects the task priority; based on the task requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the marine medical rescue drone and the type and quantity of materials, the resource allocation strategy is optimized, and a preliminary material loading plan is generated; the preliminary material loading plan is used to further adjust the task sequence of each marine medical rescue drone in combination with the AI ​​scheduling algorithm to maximize the delivery efficiency of a single flight, and obtain an optimized task sequence and material loading plan.

[0081] In this embodiment, the marine environment model is used to comprehensively evaluate the location, sea conditions, weather forecast and time window of each receiving point at sea, and obtain the task importance and urgency scores; these scores are used to quantify the demand level of each receiving point. According to the task importance and urgency scores, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the needs of each receiving point are dynamically evaluated and processed to generate a task demand matrix, which reflects the task priority; this matrix helps determine the execution order of each task. Based on the task demand matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the marine medical rescue drone and the type and quantity of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan; this plan ensures that the distribution efficiency is maximized under limited load conditions. Using the preliminary material loading plan, combined with the AI ​​scheduling algorithm, the task sequence of each marine medical rescue drone is further adjusted to maximize the distribution efficiency of a single flight, and the optimized task sequence and material loading plan are obtained; the final plan ensures the best use of resources and efficient completion of tasks.

[0082] In the embodiments of the present application, firstly, the key information of each receiving point (such as location, sea conditions, weather forecast and time window) is comprehensively evaluated through the marine environment model, and the task importance and urgency scores of each receiving point are calculated; secondly, based on these scores, the AI ​​scheduling algorithm is used to simulate the interactive behavior of the multi-agent system, dynamically evaluate the needs of each receiving point, and generate a task requirement matrix reflecting the task priority; thirdly, based on the task requirement matrix, and taking into account the load limit of the UAV and the quantity and type of different materials, the knapsack problem analysis technology is used to optimize the resource allocation strategy, and a preliminary material loading plan is formed; finally, in combination with the preliminary plan, the task sequence of the UAV is further adjusted through the AI ​​scheduling algorithm to ensure that a single flight can maximize the delivery efficiency, thereby obtaining the final optimized task sequence and material loading plan.

[0083] Here is a specific example:

[0084] Suppose that in a complex maritime rescue scenario, there are multiple islands that need emergency supplies. First, the system uses the marine environment model to evaluate the location of each island, the current sea conditions, the weather forecast for the next 24 hours, and the expected arrival time window, and calculates the mission importance and urgency scores for each island. Second, based on these scores, the system simulates the interaction mode between the drone formation and the receiving point, generates a detailed mission requirement matrix, and clearly indicates which tasks have the highest priority. Third, based on the mission requirement matrix, the system considers the maximum load of the drone and the number of different types of materials to be carried, and uses the knapsack problem analysis technology to develop a preliminary material loading plan. Finally, the system adjusts the drone's mission sequence based on the preliminary plan to ensure that each flight can cover more receiving points to the greatest extent, and finally forms an optimized mission sequence and material loading plan. Through the above steps, the system not only achieves the optimal allocation of resources, but also ensures that each flight can complete the task efficiently and accurately, significantly improving the overall rescue efficiency and service quality.

[0085] In order to solve the problems of unreasonable resource allocation and insufficiently refined task planning, in some embodiments, based on the task requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the maritime medical rescue drone and the types and quantities of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan, including:

[0086] The task requirement matrix is ​​used to quantitatively analyze and process the task urgency and material demand of each offshore receiving point, and obtain a demand feature vector that reflects the demand characteristics of each receiving point; based on the demand feature vector, combined with the maximum load capacity and payload volume of the marine medical rescue drone, the knapsack problem model is used to perform combinatorial optimization processing on materials of different types and quantities, and generate multiple candidate loading schemes that meet the load restrictions; based on the candidate loading schemes, the importance and urgency of each material are considered, a priority weight factor is introduced, and the comprehensive benefits of each candidate scheme are evaluated to obtain an optimized loading benefit score; the optimized loading benefit score is used to ensure that the distribution efficiency and task completion are maximized under limited load conditions, and a preliminary material loading plan is generated.

[0087] In this embodiment, the task demand matrix is ​​used to quantitatively analyze and process the task urgency and material demand of each offshore receiving point, and obtain the demand feature vector reflecting the demand characteristics of each receiving point; these vectors are used to accurately describe the demand situation of each receiving point. According to the demand feature vector, combined with the maximum load capacity and payload volume of the marine medical rescue drone, the knapsack problem model is applied to combine and optimize materials of different types and quantities to generate multiple candidate loading schemes that meet the load limit; this process ensures that all possible loading combinations are taken into account. Based on the candidate loading scheme, considering the importance and urgency of each material, the priority weight factor is introduced, and the comprehensive benefits of each candidate scheme are evaluated and processed to obtain the optimized loading benefit score; in this way, the system can select the most effective loading scheme. Using the optimized loading benefit score, the distribution efficiency and task completion are maximized under limited load conditions, and a preliminary material loading plan is generated; the final plan ensures that each flight can complete the task efficiently.

[0088] In the embodiment of the present application, first, the system uses the task requirement matrix to quantitatively analyze the task urgency and the quantity of materials required for each receiving point to form a demand characteristic vector; secondly, based on these characteristic vectors, combined with the maximum load capacity and payload volume of the drone, the knapsack problem model is applied to calculate multiple candidate loading schemes; thirdly, for each candidate scheme, the importance and urgency of the materials are considered, and a priority weight factor is introduced to evaluate its comprehensive benefits and obtain an optimized loading benefit score; finally, based on these scores, the optimal scheme is selected to ensure maximum delivery efficiency and task completion under limited load conditions, and generate a preliminary material loading plan.

[0089] Here is a specific example:

[0090] Assuming that in a multi-island rescue scenario, the system first quantitatively analyzes the mission urgency and the quantity of materials required for each island based on the mission requirement matrix, and forms a detailed demand feature vector; secondly, the system combines the maximum load capacity and payload volume of the drone, and applies the knapsack problem model to calculate multiple possible loading schemes; thirdly, the system considers the importance and urgency of each material, introduces a priority weight factor, evaluates the comprehensive benefits of each candidate scheme, and obtains an optimized loading benefit score; finally, the system selects the highest-scoring scheme to ensure maximum delivery efficiency and mission completion under limited load conditions, and generates a preliminary material loading plan. Through the above steps, the system can not only accurately match the needs of each receiving point, but also maximize the rescue effect under limited resource conditions.

[0091] In order to solve the problem that the preliminary material loading plan fails to fully consider the task sequence, in one or more of the above embodiments, the preliminary material loading plan is used in combination with the AI ​​scheduling algorithm to further adjust the task sequence of each marine medical rescue drone to maximize the delivery efficiency of a single flight, and obtain an optimized task sequence and material loading plan, including:

[0092] Using the preliminary material loading plan, the load configuration and expected flight path of each maritime medical rescue drone are comprehensively analyzed and processed to generate an initial task allocation table; according to the initial task allocation table, the behavior mode of the multi-agent system is simulated in combination with the AI ​​scheduling algorithm, and the task execution order of each maritime medical rescue drone is dynamically evaluated and processed, considering the dependencies between tasks and time window constraints, so as to obtain a task execution sequence reflecting the task priority; based on the task execution sequence, a complexity analysis method is introduced to evaluate the time synchronization requirements and collaborative work requirements between different tasks, and the connection between tasks is optimized to ensure that high-priority tasks can be completed in time, and an optimized task sequence is generated; using the optimized task sequence, combined with the maximum endurance and real-time sea condition data of the maritime medical rescue drone, the material loading plan is fine-tuned to ensure that the distribution efficiency of a single flight is maximized while meeting all task requirements, so as to obtain an optimized task sequence and material loading plan.

[0093] In this embodiment, the preliminary material loading plan is used to comprehensively analyze and process the load configuration and expected flight path of each marine medical rescue drone to generate an initial task allocation table; this table is used to guide the task arrangement of the drone. According to the initial task allocation table, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the task execution sequence of each marine medical rescue drone is dynamically evaluated, and the dependencies and time window constraints between tasks are considered to obtain a task execution sequence reflecting the task priority; this sequence clarifies the execution order of the tasks. Based on the task execution sequence, the complexity analysis method is introduced to evaluate the time synchronization requirements and collaborative operation requirements between different tasks, and the connection between tasks is optimized to ensure that high-priority tasks can be completed in time; this step ensures the consistency and efficiency of the tasks. Using the optimized task sequence, combined with the maximum endurance and real-time sea condition data of the marine medical rescue drone, the material loading plan is fine-tuned to ensure that the distribution efficiency of a single flight is maximized while meeting all task requirements, and the optimized task sequence and material loading plan are obtained; the final plan ensures the smooth completion of the task and the best use of resources.

[0094] In the embodiment of the present application, first, based on the preliminary material loading plan, the system analyzes the load configuration and expected flight path of each UAV, and generates an initial task allocation table; secondly, based on this table, combined with the AI ​​scheduling algorithm to simulate the behavior pattern of the multi-agent system, the task execution order of the UAV is dynamically evaluated and adjusted, and the dependencies between tasks and time window constraints are considered to generate a task execution sequence that reflects the task priority; thirdly, the system introduces a complexity analysis method to evaluate the time synchronization requirements and collaborative work requirements between different tasks, optimizes the connection between tasks, and ensures that high-priority tasks can be completed in a timely manner; finally, the system fine-tunes the material loading plan based on the maximum endurance of the UAV and real-time sea conditions data to ensure that the delivery efficiency of a single flight is maximized while meeting all task requirements, thereby obtaining an optimized task sequence and material loading plan.

[0095] Here is a specific example:

[0096] Assuming that in a complex maritime rescue operation, the system first analyzes the load configuration and expected flight path of each drone based on the preliminary material loading plan, and generates an initial task allocation table; secondly, based on this table, the system combines the AI ​​scheduling algorithm to simulate the behavior pattern of the multi-agent system, dynamically evaluates and adjusts the task execution order of the drone, considers the dependencies between tasks and time window constraints, and generates a task execution sequence that reflects the task priority; thirdly, the system introduces complexity analysis methods, evaluates the time synchronization requirements and collaborative operation requirements between different tasks, optimizes the connection between tasks, and ensures that high-priority tasks can be completed in time; finally, the system combines the maximum endurance of the drone and real-time sea conditions data to fine-tune the material loading plan, ensuring that the distribution efficiency of a single flight is maximized while meeting all task requirements, and finally obtains the optimized task sequence and material loading plan. Through the above steps, the system not only improves the consistency and efficiency of tasks, but also ensures the best use of resources and the successful completion of tasks.

[0097] This application takes into account that in order to further improve the planning accuracy and response speed of medical rescue missions at sea, there are problems in the prior art such as unreasonable resource allocation and inaccurate task priority assessment. Traditional methods are difficult to fully consider factors such as the specific needs of each receiving point, the urgency of the environment, and the payload capacity of drones, resulting in low distribution efficiency and failure to meet emergency rescue needs in a timely manner. Therefore, the embodiment of the present invention proposes this optional solution to solve the above problems by introducing an AI scheduling algorithm to simulate the behavior mode of a multi-agent system, dynamically evaluate and process the needs of each receiving point, and generate a task requirement matrix that reflects the task priority, thereby optimizing resource allocation and improving rescue efficiency and service quality.

[0098] Optionally, according to the task importance and urgency scores, the behavior mode of the multi-agent system is simulated by combining the AI ​​scheduling algorithm, and the needs of each receiving point are dynamically evaluated and processed to generate a task demand matrix, which reflects the task priority and includes:

[0099] In calculating the task urgency score US i Before that, it is necessary to conduct time sensitivity analysis, comprehensive evaluation of the location relationship of adjacent nodes, and evaluation of the environmental urgency; these analyses provide comprehensive and accurate basic data for subsequent calculations;

[0100]

[0101] Among them, US i represents the task urgency score of the i-th receiving point; D i Indicates the estimated material demand time of the receiving point; T irepresents the current time; α is the time sensitivity coefficient; Neighbors(i) is the set of neighbor nodes of the receiving point; W j is the task weight of the neighbor node; S j is the service capacity coefficient of the neighboring node; d ij is the distance from receiving point i to neighbor node j; b is the influence index of distance, which is used to adjust the degree of influence of neighbor nodes; β is the neighbor influence coefficient; E i is the environmental urgency score of the receiving point; E j is the environmental urgency score of the neighboring node; λ is the sensitivity coefficient of urgency difference;

[0102] After calculating the mission urgency score, combined with the specific material requirements of each receiving point, the payload capacity of the maritime medical rescue drone and the material priority, and considering the impact of environmental urgency, the mission requirement matrix element TDME reflecting the requirements of each receiving point for different types of materials is generated. ik ;

[0103]

[0104] TDME ik represents the demand score of the receiving point for material type k in the task demand matrix; γ represents the task importance adjustment coefficient; Q k represents the demand for material type k; C k represents the upper limit of the number of materials type k that can be carried by the maritime medical rescue drone at one time; δ represents the demand ratio sensitivity coefficient; η represents the urgency impact coefficient; P i represents the environmental urgency score of the receiving point; μ represents the periodic fluctuation coefficient; R i Indicates the relative position of the receiving point to the distribution center; R max Indicates the longest delivery distance;

[0105] After calculating the elements of the mission requirement matrix, they are normalized, and the material distribution plan is optimized according to the maximum endurance and actual flight path of the maritime medical rescue drone. A redundancy mechanism is introduced to deal with emergencies, and a mission requirement matrix that comprehensively considers multiple factors is generated.

[0106] This formula aims to calculate the task urgency score US i and the task requirement matrix element TDME ikPreviously, a comprehensive time sensitivity analysis, a comprehensive evaluation of the location relationship of adjacent nodes, and an evaluation of the degree of environmental urgency were required to ensure that subsequent calculations were based on comprehensive and accurate basic data. This not only helps to accurately assess the demand priority of each receiving point, but also optimizes the material distribution plan based on the maximum endurance of the drone and the actual flight path, introduces a redundancy mechanism to deal with emergencies, and ultimately generates a task demand matrix that comprehensively considers multiple factors, thereby improving the scientificity and accuracy of task planning.

[0107] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0108]

[0109] Time Sensitivity Section Indicates the time urgency of the task;

[0110] Neighbor node influence part Indicates the influence of neighbor nodes on the task;

[0111] The following is a brief introduction to how to obtain the parameters of the formula:

[0112] D i and T i are obtained from the task schedule and the system clock respectively; α is set based on historical data analysis; Neighbors (i) is determined through the Geographic Information System (GIS); W j and S j Provided by the task database;d ij Measured using GPS positioning system; b adjusted according to experiments; β set through simulation test; E i and E j From the environmental monitoring system; λ is set according to the law of urgency changes.

[0113] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0114]

[0115] Task Importance Adjustment Sectionγ·US i : Indicates the importance and urgency of the task. This part is used to emphasize the importance and urgency of the task;

[0116] Demand ratio sensitivity section Indicates the ratio of material demand to the UAV payload capacity. This part is used to evaluate the rationality of material demand relative to the UAV payload.

[0117] The urgency degree affects the part 1+η·log(1+P i): represents the impact of the environmental urgency at the receiving point. This part is used to quantify the impact of environmental urgency on the task;

[0118] Periodic fluctuations Indicates the influence of periodic fluctuations of the relative position of the receiving point. This part is used to consider the periodic influence of the position of the receiving point.

[0119] The following is a brief introduction to how to obtain the parameters of the formula:

[0120] γSet by expert experience; US i Calculated by the above formula; Q k and C k Provided by the material requirements list and the drone specifications; δ is set based on historical data analysis of the demand ratio; η is set through simulation testing; P i From the environmental monitoring system; μ is set according to the characteristics of the geographical location; R i and R max Determined using a GIS system.

[0121] Assume that in a rescue scenario in a complex sea area, there are three receiving points A, B, and C. The estimated material demand time is 2 hours, 3 hours, and 4 hours, respectively, and the current time is 0 hours. The neighbor node impact assessment shows that receiving points A and B are adjacent, with a distance of 5 kilometers and a service capacity coefficient of 0.8; receiving point C has no neighboring nodes. The environmental urgency scores are A: 7, B: 6, and C: 5. The maximum load of the drone is 100 kg, and the demand for material type k is A: 30 kg, B: 40 kg, and C: 30 kg. The task importance adjustment coefficient γ = 1.2, the time sensitivity coefficient α = 1, the neighbor influence coefficient β = 0.5, the distance influence index b = 2, the urgency difference sensitivity coefficient λ = 0.5, the demand ratio sensitivity coefficient δ = 0.8, the urgency impact coefficient η = 0.3, the periodic fluctuation coefficient μ = 0.2, and the farthest delivery distance R max =100 km.

[0122] First, calculate the task urgency score US i :

[0123]

[0124] Then calculate the task demand matrix element TDME ik :

[0125]

[0126] Through the above steps, the system not only improves the accuracy and response speed of task planning, but also ensures the optimal allocation and efficient use of resources, significantly improving the overall efficiency and service quality of maritime medical rescue. Since the result is greater than the set threshold, it shows that the system can effectively identify and prioritize the most urgent tasks, ensure the timely delivery of materials, and enhance the success rate and reliability of rescue operations.

[0127] In order to solve the problem of not fully considering collaborative communication and environmental risks in task allocation and flight path planning, in some embodiments, based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the marine medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path, including:

[0128] Utilizing the optimized task sequence and material loading scheme, the task allocation and flight plan of each maritime medical rescue drone are analyzed in detail to obtain accurate task execution instructions; based on the task execution instructions, an AI-driven distributed consensus algorithm is used to establish a collaborative communication mechanism between maritime medical rescue drone formations to ensure that each maritime medical rescue drone can share its position and status in real time and generate an instant communication network; based on the instant communication network, the data of the real-time situational awareness system and obstacle detection sensors are integrated to monitor and process potential risks in the flight environment to obtain an environmental risk assessment report; utilizing the environmental risk assessment report, combined with AI path planning technology, the current flight path is dynamically adjusted to ensure that obstacles are avoided and the optimal path is selected, and finally a distribution path is generated.

[0129] In this embodiment, the optimized task sequence and material loading scheme are used to analyze and process the task allocation and flight plan of each marine medical rescue drone in detail to obtain accurate task execution instructions; these instructions are used to guide the specific actions of each drone. According to the task execution instructions, an AI-driven distributed consensus algorithm is used to establish a collaborative communication mechanism between the marine medical rescue drone formations to ensure that each drone can share its position and status in real time and generate an instant communication network; this network ensures the instant sharing of information within the formation. Based on the instant communication network, the data of the real-time situational awareness system and the obstacle detection sensor are integrated to monitor and process the potential risks in the flight environment to obtain an environmental risk assessment report; these reports provide detailed environmental risk information. Using the environmental risk assessment report, combined with AI path planning technology, the current flight path is dynamically adjusted to ensure that obstacles are avoided and the optimal path is selected, and finally a distribution path is generated; this path ensures the safety and efficiency of each flight.

[0130] In the embodiment of the present application, first, based on the optimized task sequence and material loading plan, the system conducts a detailed analysis of the task allocation and flight plan of each drone, and generates precise task execution instructions; secondly, according to these instructions, the system applies an AI-driven distributed consensus algorithm to establish an efficient collaborative communication mechanism between drone formations, ensuring that each drone can share its position and status in real time, forming an instant communication network; thirdly, based on this communication network, the system integrates data from real-time situational awareness systems and obstacle detection sensors, continuously monitors potential risks in the flight environment, and generates an environmental risk assessment report; finally, the system uses these risk assessment reports, combined with AI path planning technology, to dynamically adjust the current flight path to ensure that obstacles are avoided and the optimal path is selected, and ultimately a delivery path is generated.

[0131] Here is a specific example:

[0132] Assuming that in a complex maritime rescue scenario, the system first analyzes the task allocation and flight plan of each drone in detail based on the optimized task sequence and material loading plan, and generates precise task execution instructions; secondly, based on these instructions, the system applies an AI-driven distributed consensus algorithm to establish an efficient collaborative communication mechanism between drone formations, ensuring that each drone can share its position and status in real time, forming an instant communication network; thirdly, based on this communication network, the system integrates the data of the real-time situational awareness system and obstacle detection sensors, continuously monitors the potential risks in the flight environment, and generates a detailed environmental risk assessment report; finally, the system uses these risk assessment reports and combines AI path planning technology to dynamically adjust the current flight path to ensure that obstacles are avoided and the optimal path is selected, and finally a distribution path is generated. Through the above steps, the system not only achieves efficient information sharing and collaborative work, but also ensures that each flight can avoid potential risks and select the safest and most effective path, greatly improving the success rate and reliability of rescue missions.

[0133] In order to solve the problem of insufficient consideration of environmental risks in flight path planning, in some embodiments, the environmental risk assessment report is used in combination with AI path planning technology to dynamically adjust the current flight path to ensure that obstacles are avoided and the optimal path is selected, and finally a delivery path is generated, including:

[0134] Using the environmental risk assessment report, potential obstacles and risk areas in the flight environment are identified and processed to obtain an obstacle distribution map; based on the obstacle distribution map, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is used to recalculate the existing flight path to obtain multiple candidate obstacle avoidance paths; based on the multiple candidate obstacle avoidance paths, the shortest path algorithm and weighted scoring mechanism in graph theory are introduced, and the path length, flight time and risk factor are considered to comprehensively evaluate each candidate path to obtain an optimized path score; using the optimized path score, the path with the highest score is selected as the final flight path, and combined with the maximum endurance and load conditions of the maritime medical rescue drone, the path is fine-tuned to ensure the safety and efficiency of the path, and a distribution path is generated.

[0135] In this embodiment, the environmental risk assessment report is used to identify potential obstacles and risk areas in the flight environment to obtain an obstacle distribution map; these data are used to accurately describe the physical obstacles and risk areas that may be encountered in the flight environment. According to the obstacle distribution map, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is applied to recalculate the existing flight path to obtain multiple candidate obstacle avoidance paths; this process ensures that every possible obstacle avoidance solution is taken into account. Based on the multiple candidate obstacle avoidance paths, the shortest path algorithm and weighted scoring mechanism in graph theory are introduced, and the path length, flight time and risk factor are considered to comprehensively evaluate each candidate path to obtain an optimized path score; in this way, the system can select the most effective flight path. Using the optimized path score, the path with the highest score is selected as the final flight path, and the path is fine-tuned in combination with the maximum endurance and load conditions of the marine medical rescue drone to ensure the safety and efficiency of the path and generate a distribution path; the final path ensures that each flight can complete the task efficiently and safely.

[0136] In the embodiment of the present application, first, the system uses the environmental risk assessment report to identify potential obstacles and risk areas in the flight environment and form a detailed obstacle distribution map; secondly, based on these distribution maps, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is applied to calculate multiple possible obstacle avoidance paths; thirdly, based on these candidate paths, the system uses the shortest path algorithm and weighted scoring mechanism in graph theory, taking into account the path length, flight time and risk factor, to conduct a comprehensive evaluation of each candidate path and obtain an optimized path score; finally, the system selects the path with the highest score as the final flight path, and fine-tunes it according to the maximum endurance and load conditions of the drone to ensure the safety and efficiency of the path and generate a delivery path.

[0137] Here is a specific example:

[0138] Assuming that in a complex maritime rescue scenario, the system first uses the environmental risk assessment report to identify potential obstacles and risk areas in the flight environment, and forms a detailed obstacle distribution map; secondly, based on these distribution maps, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is applied to calculate multiple possible obstacle avoidance paths; thirdly, based on these candidate paths, the system uses the shortest path algorithm and weighted scoring mechanism in graph theory, taking into account path length, flight time and risk factor, and conducts a comprehensive evaluation of each candidate path to obtain an optimized path score; finally, the system selects the path with the highest score as the final flight path, and fine-tunes it according to the maximum endurance and load conditions of the drone to ensure the safety and efficiency of the path, and generates a delivery path. Through the above steps, the system can not only avoid obstacles, but also select the optimal path to ensure that each flight can complete the mission efficiently and safely, thereby improving the success rate and reliability of the overall rescue operation.

[0139] This application takes into account that in order to further improve the efficiency of collaborative communication between drone formations in maritime medical rescue missions, the existing technology has problems such as insufficient information sharing, communication bottlenecks and lack of real-time performance. Traditional methods are difficult to fully consider the relative position, motion status, mission requirements, urgency and environmental factors of each drone, resulting in poor communication quality and inability to respond to complex changes in the marine environment in a timely manner. Therefore, the embodiment of the present invention proposes this optional solution to solve the above problems. By introducing an AI-driven distributed consensus algorithm, an efficient collaborative communication mechanism is established between drone formations to ensure that each drone can share its position, status and other key information in real time, generate an instant communication network, and thus optimize the information sharing strategy and improve information interaction efficiency and service quality.

[0140] Optionally, according to the task execution instruction, an AI-driven distributed consensus algorithm is applied to establish a collaborative communication mechanism among the maritime medical rescue drone formations to ensure that each maritime medical rescue drone can share its location, status and other key information in real time and generate an instant communication network;

[0141] Calculating the CCQE score for cooperative communication of marine medical rescue drones j Previously, it was necessary to analyze the communication environment of the maritime medical rescue drone formation, evaluate the relative position, motion status, mission requirements, urgency, and the impact of environmental factors, and provide basic data for subsequent scoring;

[0142]

[0143] Here CCQE j represents the collaborative communication quality score of the first maritime medical rescue UAV; ζ is the communication efficiency sensitivity coefficient; N is the total number of UAVs participating in the mission; W iis the weight of the i-th task, and W i ≥0;D ij is the data transmission delay from the UAV to the mission, and D ij ≥0; T j is the data processing threshold of UAV j, and T j ≥0; is the urgency impact coefficient; E j is the environmental urgency score of the drone, and E j ≥0; ω is the relative velocity influence coefficient; V j is the speed of drone j, and V j ≥0; is the average speed of the formation; V max is the maximum permissible speed, and V max >0;

[0144] Completed the CCQE score for the coordinated communication quality of maritime medical rescue drones j After calculation, the information sharing strategy is optimized based on the score, the communication bandwidth allocation is adjusted, and the future information sharing mode is predicted to reduce communication bottlenecks and provide the information sharing index RSI for maritime medical rescue drones. j Prepare for calculations and improve the efficiency of information interaction;

[0145]

[0146] Among them, RSI j represents the information sharing index of the jth marine medical rescue drone; M is the number of information types; C k is the importance of information type k, and C k ≥0; S jk is the sharing success rate of drone j for information type k, and 0≤S jk ≤1; B jk (t) = B0 + αt is the change of the communication bandwidth of information type k over time, where B0 is the initial bandwidth and α is the rate of change of the bandwidth over time; Neighbors(j) is the set of drones adjacent to the first drone; d jl is the distance between UAV j and l, and d jl ≥0; J is the distance attenuation index; η is the environmental change impact coefficient; F l (t) = βe -γt is the environmental change factor that changes over time, where β and γ are parameters that adjust the rate of environmental change; ψ is the periodic fluctuation coefficient; R j is the relative position of the drone to the distribution center; R max is the longest delivery distance, and R max >0; χ is the impact coefficient of environmental urgency change; ΔE j=E j (t)-E j (0) is the change in the environmental urgency of the drone; E max is the maximum environmental urgency score, and E max >0.

[0147] Calculate the information sharing index RSI of the maritime medical rescue drone j Then, an instant communication network is built based on the information sharing index, the optimal path and timing are selected for data transmission, a distributed consensus algorithm is applied to ensure real-time information sharing, a redundancy mechanism is introduced to deal with emergencies, and the network health status is checked regularly to maintain stable operation.

[0148] Calculating the CCQE score for cooperative communication of marine medical rescue drones j and Information Sharing Index (RSI) j Previously, it was necessary to analyze the communication environment of the drone formation, evaluate the relative position, motion status, mission requirements, urgency, and the impact of environmental factors, and provide basic data for subsequent scoring. This not only helps to accurately evaluate the communication quality and information sharing capabilities of each drone, but also optimizes the allocation of communication bandwidth based on actual conditions, predicts future information sharing modes, reduces communication bottlenecks, and ultimately builds an efficient and stable instant communication network to ensure the smooth completion of the mission.

[0149] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0150]

[0151] Communication efficiency section represents the communication efficiency; ζ is the communication efficiency sensitivity coefficient; N is the total number of UAVs participating in the mission; W i is the weight of the i-th task; D ij is the data transmission delay from the UAV to the mission; T j is the data processing threshold of UAV j; this part is used to measure the communication efficiency;

[0152] The impact of urgency is 1+φ·log(1+E j ): indicates the impact of urgency on communication quality; φ is the urgency impact coefficient; E j is the environmental urgency score of the drone; this part is used to quantify the impact of urgency on communication quality;

[0153] Relative speed influence part Represents the influence of relative speed on communication quality; ω is the relative speed influence coefficient; V j is the speed of drone j; is the average speed of the formation; V maxis the maximum allowed speed; this part is used to evaluate the impact of relative speed on communication quality.

[0154] The following is a brief introduction to how to obtain the parameters of the formula:

[0155] ζ is set based on historical data analysis of communication efficiency; N is determined by the task schedule; W i and D ij From the communication log; T j Preset by the system; φ is set through simulation test; E j From the environmental monitoring system; ω is adjusted according to the experiment; V j Measured using GPS positioning system; V avg Calculated from the speed of all drones; V max Courtesy of dronespecs.

[0156] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0157]

[0158] Information Type Importance Section represents the importance of different types of information; M is the number of information types; C k is the importance of information type k; S jk is the sharing success rate of drone j for information type k; B jk (t) = B0 + αt is the communication bandwidth of information type k over time; this part is used to evaluate the importance of different types of information;

[0159] Neighbor node influence part represents the influence of neighbor nodes on information sharing; Neighbors(j) is the set of drones adjacent to drone j; d jl is the distance between UAV j and l; J is the distance attenuation index; η is the environmental change influence coefficient; F l (t) = βe -γt It is the environmental change factor that changes over time; this part is used to evaluate the impact of neighbor nodes on information sharing;

[0160] Periodic fluctuations represents the impact of periodic fluctuations on information sharing; ψ is the periodic fluctuation coefficient; R j is the relative position of the drone to the distribution center; R max is the farthest delivery distance; this part is used to consider the impact of periodic fluctuations;

[0161] Impact of changes in environmental urgency represents the impact of environmental urgency changes on information sharing; χ is the impact coefficient of environmental urgency changes; ΔE j =E j (t)-E j (0) is the change in the environmental urgency of the drone; E max is the maximum environmental urgency score; this part is used to evaluate the impact of changes in environmental urgency on information sharing.

[0162] The following is a brief introduction to how to obtain the parameters of the formula:

[0163] M is determined by the task requirements; C k and S jk From information sharing records; B jk (t) provided by the communication bandwidth management module; Neighbors (j) determined by the Geographic Information System (GIS); d jl Measured using the GPS positioning system; J adjusted based on experiments; η set through simulation tests; β and γ set based on environmental change rules; ψ set based on geographical location characteristics; R j and R max Determined using a GIS system; χ set by expert experience; ΔE j and E max From environmental monitoring systems.

[0164] Assume that in a rescue scenario in a complex sea area, there are 5 drones involved in the mission, numbered A, B, C, D, and E. The mission weight of drone A is W. A =0.8, data transmission delay D Aj = 2 seconds, data processing threshold T A = 3 seconds, environmental urgency score E A =7, speed V A =10 m / s, average speed of the formation V avg =12 m / s, maximum permissible speed V max = 20 m / s. The number of information types M = 3, the importance of information types are C1 = 0.6, C2 = 0.4, C3 = 0.5, and the sharing success rates are S A1 =0.9, S A2 =0.8, S A3 =0.7, initial bandwidth B0 = 5MHz, bandwidth change rate over time α = 0.1MHz / s. Neighbor node set Neighbors(A) = {B, C}, distances d AB =5 m, d AC =7 meters, distance attenuation index J = 2, environmental change influence coefficient η = 0.3, environmental change factor time variation parameters β = 0.8 and γ = 0.1, periodic fluctuation coefficient ψ = 0.2, the farthest delivery distance Rmax = 100 meters, environmental urgency change impact coefficient χ = ​​0.4, environmental urgency change ΔE A =2, maximum environmental urgency score E max =10.

[0165] First, calculate the collaborative communication quality score CCQE A :

[0166]

[0167] Substituting the values ​​into CCQE A ≈0.92

[0168] Then calculate the information sharing index RSI A :

[0169]

[0170] Substituting the values ​​into the RSI A ≈0.88

[0171] Through the above steps, the system not only improves the quality of collaborative communication and information sharing efficiency, but also ensures the optimal allocation and efficient use of resources, significantly improving the overall efficiency and service quality of maritime medical rescue. Since the result is greater than the set threshold, it shows that the system can effectively identify and prioritize drones with high communication quality and strong information sharing capabilities, ensuring timely and accurate information transmission, and enhancing the success rate and reliability of rescue operations.

[0172] In order to solve the problems of insufficient accuracy and untimely information feedback during the material handover process, in some embodiments, after arriving at the designated receiving point, the landing coordinates are automatically calibrated, and according to the delivery path, the material status, handover information and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission, generating accurate delivery records and feedback information, including:

[0173] Using high-precision visual recognition technology and GPS positioning system, the scheduled landing coordinates of the maritime medical rescue drone are monitored in real time and automatically calibrated to obtain the precise landing position; based on the precise landing position and in combination with the distribution path, low-latency wireless transmission technology is used to synchronously transmit the status information, flight trajectory and current environmental data of the maritime medical rescue drone to the medical team and the central dispatch system at the receiving point to generate a preliminary material handover report; based on the preliminary material handover report, the material status and handover situation during the actual delivery process are recorded in detail to ensure that all materials are delivered to the target location accurately and to obtain a detailed delivery list; using the detailed delivery list and in combination with the confirmation feedback of the medical team, a delivery record is obtained, and the delivery record is transmitted back to the central dispatch system as feedback information to generate accurate delivery records and feedback information.

[0174] In this embodiment, high-precision visual recognition technology and GPS positioning system are used to monitor and automatically calibrate the scheduled landing coordinates of the marine medical rescue drone in real time to obtain the precise landing position; these technologies ensure that the drone can land accurately at the predetermined position. According to the precise landing position, combined with the distribution path, low-latency wireless transmission technology is applied to synchronously transmit the status information, flight trajectory and current environmental data of the marine medical rescue drone to the medical team and the central dispatch system at the receiving point to generate a preliminary material handover report; this report provides an initial overview of the handover situation. Based on the preliminary material handover report, the material status and handover situation in the actual delivery process are recorded in detail to ensure that all materials are delivered to the target location accurately and accurately, and a detailed delivery list is obtained; this list ensures the transparency and accuracy of the material handover. Using the detailed delivery list, combined with the confirmation feedback of the medical team, a delivery record is obtained, and the delivery record is transmitted back to the central dispatch system as feedback information to generate accurate delivery records and feedback information; this information ensures the traceability of the entire delivery process.

[0175] In the embodiment of the present application, first, the system uses high-precision visual recognition technology and GPS positioning system to monitor and automatically calibrate the scheduled landing coordinates of the drone in real time to ensure that the drone can land accurately at the predetermined location; secondly, based on the precise landing location and the previously planned safe and orderly delivery path, the system uses low-latency wireless transmission technology to synchronously transmit the drone's status information, flight trajectory and current environmental data to the medical team and central dispatch system at the receiving point, and generate a preliminary material handover report; thirdly, based on this preliminary report, the system records the status and handover of materials during the actual delivery process in detail to ensure that all materials are delivered to the target location accurately and form a detailed delivery list; finally, the system combines the confirmation feedback from the medical team to generate a final delivery record, and transmits this record as feedback information back to the central dispatch system to ensure the transparency and traceability of the entire delivery process.

[0176] Here is a specific example:

[0177] Assuming that in an emergency medical rescue scenario on a remote island, the system first uses high-precision visual recognition technology and GPS positioning system to monitor and automatically calibrate the drone's scheduled landing coordinates in real time to ensure that the drone can land accurately at the predetermined location; secondly, based on the precise landing location and the previously planned safe and orderly delivery path, the system uses low-latency wireless transmission technology to synchronously transmit the drone's status information, flight trajectory and current environmental data to the island's medical team and central dispatch system to generate a preliminary material handover report; thirdly, based on this preliminary report, the system records the material status and handover situation in detail during the actual delivery process to ensure that all materials are delivered to the target location accurately and form a detailed delivery list; finally, the system combines the confirmation feedback of the medical team to generate the final delivery record, and transmits this record as feedback information back to the central dispatch system to ensure the transparency and traceability of the entire delivery process. Through the above steps, the system not only ensures the accuracy and efficiency of material handover, but also provides a complete delivery record, enhancing the transparency and reliability of task execution.

[0178] Figure 2 A structural schematic diagram of a medical supplies distribution system based on a maritime medical rescue drone is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0179] The acquisition module 21 is used to utilize the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environment prediction model, to acquire and fuse multi-source environmental information in real time to generate a marine environment model;

[0180] The analysis module 22 is used to analyze the specific needs, priorities and urgency of the offshore receiving points by applying the AI ​​scheduling algorithm according to the marine environment model, and to make a detailed plan for resource allocation based on the knapsack problem analysis technology in operations research to obtain an optimized task sequence and material loading plan;

[0181] The adjustment module 23 is used to establish a collaborative communication mechanism between the maritime medical rescue drone formations based on the optimized task sequence and material loading plan through an AI-driven distributed consensus algorithm to ensure instant information sharing, and dynamically adjust the flight path and generate a delivery path by using AI path planning technology combined with real-time situational awareness and obstacle detection analysis;

[0182] The calibration module 24 is used to automatically calibrate the landing coordinates after arriving at the designated receiving point, and synchronize the material status, handover information and environmental data to the medical team and the central dispatch system through low-latency wireless transmission according to the distribution path, so as to generate accurate delivery records and feedback information.

[0183] Figure 2 The medical supplies distribution system based on the marine medical rescue drone can be implemented Figure 1 The implementation principle and technical effect of the medical supplies distribution method based on a medical rescue drone at sea described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the medical supplies distribution system based on a medical rescue drone at sea in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0184] In one possible design, Figure 2 A medical supplies distribution system based on a marine medical rescue drone in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0185] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0186] The processing component 32 is used to: utilize the comprehensive perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, to acquire and fuse multi-source environmental information in real time to generate a marine environment model; according to the marine environment model, apply the AI ​​scheduling algorithm to analyze the specific needs, priorities and urgency of the marine receiving point, and at the same time, based on the knapsack problem analysis technology in operations research, finely plan the resource allocation to obtain an optimized task sequence and material loading plan; based on the optimized task sequence and material loading plan, establish a collaborative communication mechanism between the marine medical rescue drone formations through an AI-driven distributed consensus algorithm to ensure instant information sharing, and use AI path planning technology combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path; after arriving at the designated receiving point, automatically calibrate the landing coordinates, and synchronize the material status, handover information and environmental data to the medical team and the central scheduling system through a low-latency wireless transmission method based on the distribution path to generate accurate delivery records and feedback information.

[0187] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0188] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0189] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0190] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0191] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0192] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0193] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for distributing medical supplies based on a marine medical rescue drone.

[0194] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0195] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0196] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for distributing medical supplies based on a marine medical rescue drone, characterized in that: include: Using the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, multi-source environmental information is acquired and integrated in real time to generate a marine environmental model; According to the marine environment model, the AI ​​scheduling algorithm is used to analyze the specific needs, priorities and urgency of the offshore receiving points. At the same time, the resource allocation is carefully planned based on the knapsack problem analysis technology in operations research to obtain the optimized task sequence and material loading plan; Based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the maritime medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path; After arriving at the designated receiving point, the landing coordinates are automatically calibrated, and based on the distribution route, the material status, handover information, and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission to generate accurate delivery records and feedback information.

2. The method according to claim 1, characterized in that: According to the marine environment model, the AI ​​scheduling algorithm is used to analyze the specific needs, priorities and urgency of the offshore receiving points, and the resource allocation is carefully planned based on the knapsack problem analysis technology in operations research to obtain the optimized task sequence and material loading plan, including: Using the ocean environment model, comprehensively evaluate and process the location, sea conditions, weather forecast, and time window of each offshore receiving point to obtain a score of mission importance and urgency; According to the task importance and urgency scores, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the needs of each receiving point are dynamically evaluated and processed to generate a task demand matrix, which reflects the task priority; Based on the task requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the maritime medical rescue drone and the types and quantities of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan; The preliminary material loading plan is used in combination with the AI ​​scheduling algorithm to further adjust the mission sequence of each maritime medical rescue drone to maximize the delivery efficiency of a single flight, and obtain the optimized mission sequence and material loading plan.

3. The method according to claim 2, characterized in that Based on the task requirement matrix, the knapsack problem analysis technology in operations research is introduced to consider the load limit of the marine medical rescue drone and the types and quantities of materials, optimize the resource allocation strategy, and generate a preliminary material loading plan, including: Using the task demand matrix, quantitative analysis and processing are performed on the task urgency and material demand of each offshore receiving point to obtain a demand feature vector reflecting the demand characteristics of each receiving point; According to the demand feature vector, combined with the maximum load capacity and payload volume of the maritime medical rescue drone, the knapsack problem model is used to perform combinatorial optimization processing on materials of different types and quantities, and multiple candidate loading schemes that meet the load restrictions are generated; Based on the candidate loading schemes, the importance and urgency of each material are taken into consideration, a priority weight factor is introduced, and the comprehensive benefits of each candidate scheme are evaluated to obtain an optimized loading benefit score; The optimized loading efficiency score is used to ensure that the distribution efficiency and task completion are maximized under limited load conditions, and a preliminary material loading plan is generated.

4. The method according to claim 2, characterized in that: The preliminary material loading plan is used in combination with the AI ​​scheduling algorithm to further adjust the task sequence of each marine medical rescue drone to maximize the delivery efficiency of a single flight, and obtain an optimized task sequence and material loading plan, including: Using the preliminary material loading plan, the load configuration and expected flight path of each marine medical rescue drone are comprehensively analyzed and processed to generate an initial task allocation table; According to the initial task allocation table, combined with the AI ​​scheduling algorithm to simulate the behavior mode of the multi-agent system, the task execution order of each maritime medical rescue drone is dynamically evaluated and processed, and the dependency relationship and time window constraints between tasks are considered to obtain the task execution sequence reflecting the task priority; Based on the task execution sequence, a complexity analysis method is introduced to evaluate the time synchronization requirements and collaborative work requirements between different tasks, and the connection between tasks is optimized to ensure that high-priority tasks can be completed in time, and an optimized task sequence is generated; By using the optimized mission sequence, combined with the maximum endurance of the maritime medical rescue drone and real-time sea condition data, the material loading plan is fine-tuned to ensure that the distribution efficiency of a single flight is maximized while meeting all mission requirements, and the optimized mission sequence and material loading plan are obtained.

5. The method according to claim 1, characterized in that Based on the optimized task sequence and material loading plan, a collaborative communication mechanism between the maritime medical rescue drone formations is established through an AI-driven distributed consensus algorithm to ensure instant information sharing, and AI path planning technology is combined with real-time situational awareness and obstacle detection analysis to dynamically adjust the flight path and generate a distribution path, including: Using the optimized task sequence and material loading scheme, the task allocation and flight plan of each marine medical rescue drone are analyzed in detail to obtain accurate task execution instructions; According to the mission execution instructions, an AI-driven distributed consensus algorithm is used to establish a collaborative communication mechanism among the maritime medical rescue drone formations, ensuring that each maritime medical rescue drone can share its location and status in real time and generate an instant communication network; Based on the instant communication network, the data of the real-time situation awareness system and the obstacle detection sensor are integrated to monitor and process the potential risks in the flight environment and obtain an environmental risk assessment report; Utilizing the environmental risk assessment report and combining it with AI path planning technology, the current flight path is dynamically adjusted to ensure that obstacles are avoided and the optimal path is selected, ultimately generating a delivery path.

6. The method according to claim 5, characterized in that The environmental risk assessment report is used in combination with AI path planning technology to dynamically adjust the current flight path to ensure that obstacles are avoided and the optimal path is selected, and finally a delivery path is generated, including: Using the environmental risk assessment report, potential obstacles and risk areas in the flight environment are identified and processed to obtain an obstacle distribution map; According to the obstacle distribution map, combined with real-time sea conditions and meteorological data, the AI ​​path planning algorithm is applied to recalculate the existing flight path to obtain multiple candidate obstacle avoidance paths; Based on the multiple candidate obstacle avoidance paths, the shortest path algorithm and weighted scoring mechanism in graph theory are introduced, and the path length, flight time and risk factor are considered to comprehensively evaluate each candidate path to obtain an optimized path score; Using the optimized path score, the path with the highest score is selected as the final flight path, and the path is fine-tuned in combination with the maximum endurance and load conditions of the maritime medical rescue drone to ensure the safety and efficiency of the path and generate a distribution path.

7. The method according to claim 1, characterized in that After arriving at the designated receiving point, the landing coordinates are automatically calibrated, and according to the delivery path, the material status, handover information and environmental data are synchronized to the medical team and the central dispatch system through low-latency wireless transmission to generate accurate delivery records and feedback information, including: Using high-precision visual recognition technology and GPS positioning system, the scheduled landing coordinates of the marine medical rescue drone are monitored in real time and automatically calibrated to obtain the precise landing position; According to the precise landing location and in combination with the delivery path, low-latency wireless transmission technology is applied to synchronously transmit the status information, flight trajectory and current environmental data of the marine medical rescue drone to the medical team and central dispatch system at the receiving point, and generate a preliminary material handover report; Based on the preliminary material handover report, the material status and handover situation during the actual delivery process are recorded in detail to ensure that all materials are delivered to the target location accurately and obtain a detailed delivery list; The detailed delivery list is used in combination with the confirmation feedback from the medical team to obtain a delivery record, which is then transmitted back to the central dispatch system as feedback information to generate accurate delivery records and feedback information.

8. A medical supplies distribution system based on marine medical rescue drones, characterized in that: include: The acquisition module is used to use the integrated perception system integrated in the marine medical rescue drone, combined with the AI ​​environmental prediction model, to acquire and fuse multi-source environmental information in real time to generate a marine environmental model; An analysis module is used to analyze the specific needs, priorities and urgency of the offshore receiving points using an AI scheduling algorithm according to the marine environment model, and to make detailed plans for resource allocation based on the knapsack problem analysis technology in operations research to obtain an optimized task sequence and material loading plan; An adjustment module is used to establish a collaborative communication mechanism between the maritime medical rescue drone formations based on the optimized task sequence and material loading plan through an AI-driven distributed consensus algorithm to ensure instant information sharing, and to dynamically adjust the flight path and generate a delivery path by using AI path planning technology combined with real-time situational awareness and obstacle detection analysis; The calibration module is used to automatically calibrate the landing coordinates after arriving at the designated receiving point, and synchronize the material status, handover information and environmental data to the medical team and the central dispatch system through low-latency wireless transmission based on the distribution path, so as to generate accurate delivery records and feedback information.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a medical supplies distribution method based on a marine medical rescue drone as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a medical supplies distribution method based on a marine medical rescue drone as described in any one of claims 1 to 7 is implemented.

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