Offshore-based rapid response deployment method for drones and applications

Abstract

The application discloses a near-shore-based unmanned aerial vehicle rapid response deployment method and application, relates to the technical field of near-shore unmanned aerial vehicle emergency scheduling, and comprises the following steps: receiving a task request containing an incident location, an event type and a level, dynamically screening a candidate unmanned aerial vehicle airport and obtaining a real-time state of the candidate unmanned aerial vehicle airport. A plurality of alternative deployment schemes are generated based on multiple information, risk assessment and timeliness calculation are performed on each scheme, and the optimal scheme is selected through comprehensive trade-off. Detailed instructions containing a target unmanned aerial vehicle airport, an accurate flight path and a task load configuration are generated according to the optimal scheme, and the detailed instructions are sent to the target unmanned aerial vehicle airport, so that the unmanned aerial vehicle automatically completes the whole process operation of taking off, route flying and load activation. The method realizes multi-target intelligent optimization of a response scheme and full-link automation of an execution process, and improves the speed and decision reliability of emergency response in a near-shore task scene.

Description

Technical Field

[0001] This invention belongs to the field of near-shore unmanned aerial vehicle (UAV) emergency dispatch technology, specifically a method and application for rapid response deployment of UAVs in near-shore waters. Background Technology

[0002] Currently, drone response in near-shore areas for mission scenarios mainly relies on manual scheduling or semi-automatic systems based on preset rules. Conventional technologies typically assign missions based on the geographical distance between the incident location and the fixed drone airport, or the drone's preset range.

[0003] The existing solution has shortcomings. The decision-making process fails to integrate multi-dimensional information such as the specific type of event, the required mission payload, the real-time resource status of the UAV airport, and environmental dynamics. This may result in the dispatch plan not being optimal in terms of timeliness and mission adaptability, affecting the handling effect. Furthermore, the command issuance and equipment execution are disconnected, and manual operation is still required for flight path planning, payload checking, and takeoff confirmation. The response process is subject to human delays, making it difficult to meet the requirements of minute-level or even second-level response in emergency situations.

[0004] There is a need for a technical method that can automatically integrate dynamic information from multiple sources to optimize and select deployment schemes, and seamlessly convert the selected schemes into automated execution commands. This method needs to overcome the shortcomings of existing technologies, such as singular decision-making and fragmented processes, to achieve rapid and autonomous response throughout the entire process from task analysis to UAV deployment, thereby improving the efficiency and reliability of near-shore emergency operations. This invention aims to provide such a solution. Summary of the Invention

[0005] This invention aims to solve one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a rapid response deployment method for unmanned aerial vehicles (UAVs) based on near-shore waters, including:

[0007] Receive task request information from the nearshore monitoring network, the task request information including the geographical location of the incident, the type of the incident, and the preliminary situation level;

[0008] Based on the mission request information, candidate drone airports that meet the preset response conditions are selected from multiple near-shore drone airports, and real-time status data and a list of available drone resources of the candidate drone airports are obtained.

[0009] Based on the geographical location of the incident, the type of the incident, and the real-time status data of the candidate drone airports, multiple alternative drone deployment plans are generated, and risk assessment and timeliness calculation are performed for each deployment plan.

[0010] Based on the combined risk assessment results and timeliness calculation results, the optimal deployment scheme is selected from the alternative drone deployment schemes, and detailed deployment instructions containing the target drone airport, drone flight path and mission payload configuration are generated according to the optimal deployment scheme.

[0011] Detailed deployment instructions are sent to the target drone airport, triggering the target drone airport to perform automatic take-off, en-route flight, and mission payload activation operations according to the deployment instructions.

[0012] Furthermore, the step of selecting candidate drone airports that meet preset response conditions from multiple near-shore drone airports based on the task request information specifically includes:

[0013] Analyze the geographical location and event type in the task request information to determine the minimum requirements for drone model, mission payload type, and endurance for responding to the task;

[0014] Obtain the geographical distribution of all near-shore drone airports, the operational status of the drone airports, and a list of drone models and payload configurations under the jurisdiction of each drone airport;

[0015] Calculate the estimated flight time from the airport to the incident location for each near-shore drone, and adjust the estimated flight time based on the current sea conditions and meteorological data;

[0016] Nearshore drone airports that meet the minimum requirements, are in a ready operational status, and have a corrected estimated flight time less than a preset threshold are marked as candidate drone airports, forming a candidate set.

[0017] From the candidate set, extract the real-time status data of each candidate drone airport. The real-time status data includes the number of drones remaining available at the drone airport, the current charging status, and the communication link quality.

[0018] Furthermore, based on the geographical location of the incident, the type of the incident, and the real-time status data of the candidate drone airports, multiple alternative drone deployment schemes are generated, specifically including:

[0019] For each candidate drone airport, multiple alternative flight paths are planned, with the airport as the starting point and the geographical location of the incident as the target point. Each path takes into account different flight altitude levels and avoidance areas.

[0020] Based on the event type, a suitable combination of mission payloads is matched from the mission payload library. The combination of mission payloads includes optical reconnaissance equipment, thermal imagers, megaphones, or material mounting devices.

[0021] Based on the number of available drones in the real-time status data of candidate drone airports, different mission execution modes are calculated, including single-drone execution, multi-drone formation execution, or batch relay execution.

[0022] Each alternative flight path, each combination of mission payload, and each mission execution mode is arranged and combined to generate an initial deployment plan for each candidate UAV airport.

[0023] Assign a unique scheme identifier to each initial deployment scheme and associate it with the corresponding candidate UAV airport information, flight path details, payload configuration and execution mode.

[0024] Furthermore, the risk assessment and timeliness calculation for each deployment plan specifically includes:

[0025] For each deployment plan, obtain its associated flight path details, and extract real-time meteorological data, air traffic density data, and known communication blind spot information along the path;

[0026] Based on the extracted real-time meteorological data, assess the probability of the UAV encountering wind shear, precipitation, or insufficient visibility while flying along the flight path.

[0027] Based on air traffic density data, assess the risk level of air traffic conflict when the UAV flies along the stated flight path;

[0028] Based on communication blind spot information, assess the period and duration of communication interruptions that occurred on the UAV's flight path;

[0029] Based on the combined risk probability, risk level, and communication interruption assessment results, a multi-factor weighted model is used to calculate the comprehensive risk assessment score of the deployment scheme.

[0030] Based on the flight path length, drone cruising speed, and mission execution mode of the deployment plan, the estimated total time from takeoff to arrival at the incident area and commencement of operations is calculated, serving as an indicator of the timeliness of the deployment plan.

[0031] Furthermore, the comprehensive risk assessment results and timeliness calculation results are used to select the optimal deployment scheme from the alternative drone deployment schemes, specifically including:

[0032] The comprehensive risk assessment scores of all alternative deployment options and the estimated total time are normalized to comparable values ​​under the same dimension.

[0033] Based on the urgency of the response task, a dynamic weight is assigned to the timeliness indicator, and another complementary dynamic weight is assigned to the risk assessment indicator.

[0034] For each deployment plan, calculate its weighted comprehensive score. The weighted comprehensive score is equal to the normalized estimated total time multiplied by the timeliness dynamic weight, plus the normalized comprehensive risk assessment score multiplied by the risk assessment dynamic weight.

[0035] All deployment schemes are sorted in ascending order according to their weighted comprehensive scores, and the deployment scheme with the smallest weighted comprehensive score is selected as the initial optimal scheme.

[0036] The preliminary optimal solution undergoes resource conflict verification, which involves checking whether the drones and drone airport resources used conflict with other assigned or pending tasks. If there are no conflicts, it is confirmed as the final optimal deployment solution.

[0037] Furthermore, the generation of detailed deployment instructions based on the optimal deployment scheme, including the target UAV airport, UAV flight path, and mission payload configuration, specifically includes:

[0038] Extract the unique identifier, geographic coordinates, and communication channel parameters of the target UAV airport from the optimal deployment scheme;

[0039] Extract the details of the confirmed flight path from the optimal deployment plan and encode it into a waypoint sequence format that can be recognized by the UAV flight control system. Each waypoint contains latitude, longitude, altitude and speed information.

[0040] Extract the specific configuration of the task load combination from the optimal deployment scheme and generate a load control parameter list, which includes the power-on / off sequence, working mode and data return settings of each load;

[0041] The target UAV's airport information, coded flight path, and payload control parameter list are integrated and encapsulated into a structured, detailed deployment instruction data package according to a preset instruction protocol.

[0042] Add the task verification code and instruction effective timestamp to the detailed deployment instruction data package to complete the generation of deployment instructions;

[0043] The specific configuration for extracting the task load combination from the optimal deployment scheme and generating a load control parameter list specifically includes:

[0044] Read the task payload combination specified in the optimal deployment scheme, including the type and model of the main payload and auxiliary payload;

[0045] Based on the event type and mission objectives, set initial operating parameters for each payload, such as setting zoom magnification, shooting frequency and image format for optical reconnaissance equipment;

[0046] Plan the collaborative workflow of multiple payloads, and determine the start-up sequence, working period, and data acquisition synchronization point of each payload;

[0047] For each parameter in the load control parameter list, specify the triggering conditions for its effectiveness, including triggering based on geographical location, triggering based on flight time, or triggering based on command.

[0048] The payload type, initial operating parameters, collaborative workflow, and parameter activation trigger conditions are compiled into a payload control parameter list that can be parsed by the UAV mission management system, according to the payload control protocol format.

[0049] Furthermore, the detailed deployment instructions are sent to the target UAV airport, triggering the target UAV airport to perform automatic UAV takeoff, en-route flight, and mission payload activation operations according to the deployment instructions, specifically including:

[0050] Detailed deployment instruction data packets are sent to the ground control unit at the target UAV airport via an encrypted command and control link.

[0051] After receiving the detailed deployment instruction data packet, the ground control unit verifies the validity of the mission verification code and the instruction effective timestamp. Once the verification is successful, the instruction content is parsed.

[0052] The ground control unit performs self-matching based on the parsed target UAV airport information to confirm itself as the subject of command execution. Then, based on the flight path and payload configuration in the command, it prepares for the UAV pre-takeoff check.

[0053] Upon reaching the command effective timestamp or receiving manual execution confirmation, the ground control unit controls the designated UAV to automatically take off along the coded waypoint sequence and execute the flight route.

[0054] When the drone flies to the preset payload activation area, the ground control unit remotely activates the mission payload carried by the drone according to the payload control parameter list, so that it enters the working state.

[0055] Furthermore, the calculation of the estimated flight time from the airport to the incident location for each near-shore UAV, and the correction of the estimated flight time based on current sea conditions and meteorological data, specifically includes:

[0056] Obtain the straight-line distance between each near-shore drone airport and the geographical location of the incident, and calculate the theoretical flight time based on the drone's standard cruising speed;

[0057] The system queries the current sea state level data of the sea area covered by the flight path between the candidate UAV airport and the geographical location of the incident, as well as the real-time weather forecast data of the geographical area of ​​the incident, including wind direction, wind speed, and temperature.

[0058] Based on wind direction and wind speed data, calculate the tailwind or headwind components encountered by the drone on its flight path, and adjust the effective flight speed of the drone by increasing or decreasing the speed according to the wind components.

[0059] Based on sea state and temperature data, assess the impact of sea surface vapor or low cloud cover on visibility and safety of low-altitude flight. If the impact is significant, increase the detour distance of the flight path or reduce the cruising speed.

[0060] The additional time due to wind speed correction and path / speed adjustment is added to the theoretical flight time to obtain the corrected estimated flight time.

[0061] Furthermore, the assessment of the probability of the UAV encountering wind shear, precipitation, or insufficient visibility while flying along the flight path, based on the extracted real-time meteorological data, specifically includes:

[0062] Obtain gridded meteorological data of the airspace traversed by the flight path, and extract the vertical wind speed gradient data, precipitation data and visibility data of each grid point;

[0063] Analyze vertical wind speed gradient data to identify gradient abrupt change regions that exceed the safe flight threshold of drones, and count the frequency of occurrence of the gradient abrupt change regions in the same historical period to use as the probability of wind shear risk.

[0064] Analyze precipitation data to determine the intensity and coverage of precipitation along the flight path during the flight period, and calculate the probability of performance degradation or failure due to precipitation in combination with the waterproof rating of the UAV.

[0065] Analyze visibility data to identify flight segments where visibility is below the minimum requirements of the UAV's visual navigation or obstacle avoidance system, and calculate the probability of risk caused by insufficient visibility by combining the terrain and obstacle information of the flight segments.

[0066] The comprehensive meteorological risk probability of the flight path is obtained by integrating the wind shear risk probability, precipitation risk probability, and insufficient visibility risk probability.

[0067] Furthermore, the present invention also includes an application of the above-described near-shore-based rapid response deployment method for unmanned aerial vehicles in near-shore target surveillance or maritime emergency rescue scenarios.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] After receiving a task, the system synchronously acquires real-time status and resource data of eligible UAV airports. Based on the incident location, event type, and this dynamic data, the system automatically generates multiple alternative deployment plans using algorithms. Each plan undergoes independent and quantified risk assessment and timeliness calculation, with assessment factors including path safety, resource load, and task suitability. The decision-making mechanism comprehensively weighs and compares the risk assessment and timeliness calculation results for each plan, thereby selecting the deployment plan that achieves the optimal balance between time efficiency and execution reliability. This process transforms response decision-making from a fixed rule-based or experience-based model to an intelligent model driven by real-time data and multi-objective optimization models, improving the rationality of the initial action plan and the accuracy of task adaptation.

[0070] This solution defines and transmits structured, detailed deployment instructions, achieving end-to-end automation of the response process. The instructions integrate the target UAV's airport identification, planned flight path, and specific mission payload configuration parameters. Once issued to the target UAV airport, the instructions directly trigger the automatic execution sequence of the UAV airport control system. This sequence, according to the instructions, controls the UAV to complete autonomous takeoff, fly along the predetermined route, and automatically activate or configure the designated onboard mission payload during the mission phase. This design seamlessly integrates decision-making instructions with the execution actions of physical equipment, eliminating delays and operational errors caused by manual intervention after instruction issuance. This results in an order-of-magnitude improvement in system response speed and provides stable and reliable instantaneous triggering and execution capabilities. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating the steps of the rapid response deployment method for unmanned aerial vehicles (UAVs) based on nearshore waters as described in this invention.

[0072] Figure 2 A flowchart for screening candidate drone airports;

[0073] Figure 3 A flowchart for generating alternative drone deployment plans;

[0074] Figure 4 A multi-dimensional indicator radar chart for the optimal solution P05;

[0075] Figure 5 Comparison chart of near-shore drone deployment schemes. Detailed Implementation

[0076] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] See Figure 1 The system receives a mission request from a near-shore monitoring network, which includes the geographical location of the incident, the type of the incident, and the initial situation level. Based on this mission request, the system selects candidate drone airports that meet preset response conditions from multiple drone airports deployed in the near-shore area, and simultaneously acquires the real-time status data of these candidate drone airports and their available drone resource lists. Based on the geographical location of the incident, the type of the incident, and the real-time status data of the candidate drone airports, the system calculates and generates multiple alternative drone deployment schemes, and independently performs risk assessment and timeliness calculation for each deployment scheme. The system comprehensively weighs the risk assessment results and timeliness calculation results of each scheme, uses a decision algorithm to select the optimal deployment scheme, and generates a structured detailed deployment instruction based on the optimal scheme. This instruction specifies the target drone airport, the specific flight path of the drone, and the detailed configuration of the mission payload. The system sends the generated detailed deployment instruction to the selected target drone airport, triggering the drone airport to automatically control the drone to complete a series of operations according to the instruction, including take-off, flight along the predetermined route, and activation of the mission payload in the designated area.

[0078] See Figure 2 In one embodiment of the present invention, the system parses the geographical location and event type in the task request information to determine the minimum requirements for the UAV model, mission payload type, and endurance for responding to the task. For example, for a search and rescue mission for personnel falling into the water at sea, the minimum requirements are that the UAV model must have water take-off and landing capabilities, the mission payload must include optical reconnaissance equipment and a thermal imager, and the endurance must be no less than 60 minutes. The system obtains the geographical distribution of all near-shore UAV airports, the operational status of the UAV airports, and a list of UAV models and payload configurations under the jurisdiction of each near-shore UAV airport. The geographical distribution of near-shore UAV airports includes near-shore UAV airport A located at 120.0 degrees east longitude and 30.0 degrees north latitude, and near-shore UAV airport B located at 121.0 degrees east longitude... At 30.5 degrees North latitude, the drone airport is in either ready or busy status. Nearshore drone airport A operates model X drones with payloads including optical reconnaissance equipment and a loudspeaker. Nearshore drone airport B operates model Y drones with payloads including a thermal imager and a cargo mounting device. Calculate the straight-line distance from each nearshore drone airport to the incident location. Based on the drones' standard cruising speed, calculate the theoretical flight time. For example, if the straight-line distance from nearshore drone airport A to the incident location is 50 kilometers, and the drone's standard cruising speed is 20 meters per second, the theoretical flight time is 2500 seconds (approximately 41.7 minutes). If the straight-line distance from nearshore drone airport B to the incident location is 60 kilometers, the theoretical flight time is 3000 seconds (approximately 50 minutes).

[0079] In the actual deployment of this invention, the near-shore UAV airport is not a simple open-air take-off and landing point, but a prefabricated, fixed UAV airport. Each airport contains a temperature-controlled, salt-spray-proof, enclosed hangar, which integrates UAV storage racks, automatic charging devices, and necessary maintenance equipment. The hangar environment is controlled, ensuring that UAVs and their payloads can be on standby for extended periods in the high-humidity, high-salt-spray coastal environment, maintaining stable status. A weather sensing terminal is integrated on the top of the airport to collect real-time data such as wind speed and visibility for take-off decisions. A ground control unit workstation is located within the airport, connected to the flight service center via a wired network to receive commands and control the UAVs for automatic operation. This design ensures that the UAVs are always in a ready state, eliminating the need for manual on-site equipment transportation, assembly, and power-on testing during response, thus achieving a rapid response foundation for immediate flight upon startup.

[0080] In some embodiments, the current sea state level data of the sea area covered by the flight path from the UAV airport to the incident location along the nearshore coast is queried, as well as real-time weather forecast data for the incident location. The weather forecast data includes wind direction, wind speed, and temperature. For example, if the current sea state level is 3, and the real-time weather forecast data shows a southeast wind direction, a wind speed of 8 meters per second, and a temperature of 25 degrees Celsius, the tailwind or headwind component encountered by the UAV on the flight path is calculated based on the wind direction and wind speed data. The effective flight speed of the UAV is then adjusted by increasing or decreasing the wind speed based on the wind component. For example, if the planned flight direction of the UAV is northeast and the wind direction is southeast, then the angle between the wind component and the flight direction is 45 degrees. The tailwind component increases the effective flight speed. The wind speed correction formula is as follows:

[0081]

[0082] in: It is the effective flight speed. That is the standard cruising speed. It's wind speed. It is the angle between the wind direction and the flight direction. Based on sea state level and temperature data, the potential impact of sea surface vapor or low cloud cover on visibility and safety of low-altitude flight is assessed. If the impact is significant, the flight path detour distance is increased or the cruise speed is reduced in the calculation. For example, if sea state level 3 and temperature 25 degrees Celsius may produce slight sea fog, the detour distance is increased by 5% after assessment.

[0083] Optionally, the increased time due to wind speed correction and path speed adjustment can be added to the theoretical flight time to obtain the corrected estimated flight time. For example, the corrected effective flight speed of near-shore UAV airport A is 21 meters per second, and the detour distance increases to 52.5 kilometers, resulting in a corrected estimated flight time of 2600 seconds (approximately 43.3 minutes). The corrected effective flight speed of near-shore UAV airport B is 19 meters per second, and the detour distance increases to 63 kilometers, resulting in a corrected estimated flight time of 3300 seconds (approximately 55 minutes). In some embodiments, near-shore UAV airports that meet the minimum mission requirements, are in a ready operational state, and have a corrected estimated flight time less than a preset threshold are marked as candidate UAV airports. Ports are used to form a candidate set. For example, if the preset threshold is 60 minutes, the estimated flight time of near-shore drone airport A after correction is 43.3 minutes, which meets the condition. The estimated flight time of near-shore drone airport B after correction is 55 minutes, which also meets the condition. Both are marked as candidate drone airports. Real-time status data of each candidate drone airport is extracted from the candidate set. The real-time status data includes the number of drones remaining available at the drone airport, the current charging status, and the communication link quality. For example, near-shore drone airport A has 2 drones remaining available, the current charging status is full, and the communication link quality is good. Near-shore drone airport B has 1 drone remaining available, the current charging status is charging, and the communication link quality is medium.

[0084] It is understandable that data comparison shows the difference in estimated flight time before and after correction. For example, the theoretical flight time for near-shore UAV airport A is 41.7 minutes, which is corrected to 43.3 minutes, an increase of 1.6 minutes. The theoretical flight time for near-shore UAV airport B is 50 minutes, which is corrected to 55 minutes, an increase of 5 minutes. The correction process takes into account actual environmental factors and improves the accuracy of time estimation. In specific implementation, through the above steps, the system selects candidate UAV airports that meet the response conditions and provides a basis for subsequent scheme generation. Optionally, when calculating the corrected estimated flight time, the meaning of the characters in the formula is clear, including the effective flight speed. Standard cruise speed used for subsequent flight time calculations These are the design parameters for the drone, including wind speed. Obtained from meteorological data, the angle between wind direction and flight direction It was derived through geometric calculations.

[0085] See Figure 3In one embodiment of the present invention, for each selected candidate drone airport, multiple alternative flight paths are planned based on its geographical location as the starting point and the location of the incident as the target point. For example, for candidate drone airport C, located at 122.2 degrees east longitude and 31.5 degrees north latitude, and the incident location located at 122.5 degrees east longitude and 31.8 degrees north latitude, the system plans three alternative flight paths: flight path A is a straight route with a flight altitude set at 100 meters above sea level; flight path B is a route along the coastline with a flight altitude set at 150 meters above sea level to avoid a known bird sanctuary; and flight path C is a zigzag route with a flight altitude at 80 meters above sea level. The altitude varies between 100 meters and 120 meters to avoid a temporarily designated no-fly zone. For candidate UAV airport D, located at 121.8 degrees east longitude and 31.3 degrees north latitude, the system also plans three alternative flight paths with different altitude layers and avoidance areas. In some embodiments, based on the event type in the mission request information, the system matches an appropriate combination of mission payloads from the mission payload library. For example, for an oil spill monitoring event, the matched mission payload combination includes a high-resolution optical reconnaissance device for observing the oil slick area and a thermal imager for assisting nighttime or low-visibility monitoring. For a maritime patrol event, the matched mission payload combination includes an optical reconnaissance device, a thermal imager, and a loudspeaker.

[0086] Based on the number of available drones in the real-time status data of candidate drone airports, different mission execution modes are calculated, including single-drone execution, multi-drone formation execution, and batch relay execution. For example, the real-time status data of candidate drone airport C shows that there are 3 available drones. The calculated mission execution modes include Mode 1: a single drone carrying a complete payload combination; Mode 2: two drones in formation, one carrying optical reconnaissance equipment and the other carrying a thermal imager; and Mode 3: three drones relay execution in batches to extend the overall monitoring time. The real-time status data of candidate drone airport D shows that there is 1 available drone. The calculated mission execution modes only include the single-drone execution mode. It can be understood that the number of available drones directly limits the range of calculable mission execution modes. For each candidate drone airport, the planned alternative flight paths, matched payload combinations, and calculated mission execution modes are permuted and combined to generate an initial deployment scheme for each candidate drone airport. For example, to generate an initial deployment scheme for candidate drone airport C, 3 alternative flight paths are planned, 2 payload combinations are matched, and 3 feasible mission execution modes are calculated. The total number of initial deployment schemes generated by permutation and combination is 3 x 2 x 3 = 18. To generate an initial deployment scheme for candidate drone airport D, 3 alternative flight paths are planned, 2 payload combinations are matched, and 1 mission execution mode is calculated. The total number of initial deployment schemes generated is 3 x 2 x 1 = 6. Data comparison shows that candidate drone airport C, due to its more abundant resources, can generate far more initial deployment schemes than candidate drone airport D.

[0087] Each initial deployment plan is assigned a unique plan identifier, which is then associated with its corresponding candidate UAV airport information, flight path details, payload configuration details, and mission execution mode. For example, the first initial deployment plan for candidate UAV airport C is assigned the identifier "Plan_C_001". The associated information includes the coordinates of candidate UAV airport C, the detailed waypoint sequence of flight path A, the payload configuration of a combination of high-resolution optical reconnaissance equipment and thermal imager, and the mission execution mode of a two-aircraft formation. In specific implementation, the formula for calculating the total number of initial deployment plans is:

[0088]

[0089] in: This represents the total number of initial deployment schemes generated for a candidate drone airport. This represents the number of alternative flight paths planned for the airport for the candidate drone. This represents the number of task payload combinations matched based on the event type. This represents the number of feasible mission execution modes calculated based on the real-time airport status data of the candidate UAV. Optionally, the encoding rules for the scheme identifier include the candidate UAV airport code, generation sequence number, and timestamp. It can be understood that by systematically generating initial deployment schemes through permutations and combinations, a comprehensive set of options is provided for subsequent evaluation and decision-making. In some embodiments, the matching of mission payload combinations strictly follows a mapping relationship library between event types and payload functions; for example, maritime search and rescue events are mapped to a combination of optical reconnaissance equipment and thermal imagers, and maritime communication relay events are mapped to communication relay payloads. Optionally, the different flight altitude layer data considered during flight path planning originates from real-time layered information released by airspace management agencies, and the avoidance area data includes spatial geographic information of permanent no-fly zones, temporary activity areas, and nature reserves.

[0090] In one embodiment of the present invention, for each generated deployment scheme, its associated flight path details are obtained, and real-time meteorological data, air traffic density data, and known communication blind spot information along the path are extracted. For example, for the deployment scheme with the identifier "Plan_E_001", its flight path details include a segment crossing near-shore waters. Real-time meteorological data extracted from this segment shows scattered precipitation, air traffic density data shows light general aviation aircraft activity in this airspace, and communication blind spot information shows a weak signal area in the middle of the path due to terrain obstruction. Gridded meteorological data of the airspace traversed by the flight path is obtained, and vertical wind speed gradient data, precipitation data, and visibility data for each grid point are extracted. For example, if the path is divided into 10 grids, the vertical wind speed gradient data for grid point G5 is 5 meters per second per 100 meters, the precipitation data for grid point G7 is 2 millimeters per hour, and the visibility data for grid point G8 is 1500 meters. Analyzing vertical wind speed gradient data identifies gradient abrupt change regions exceeding the drone's safe flight threshold. The frequency of these gradient abrupt change regions during the same historical period is then used as the wind shear risk probability. For example, if the drone's safe flight threshold is set at 4 meters per second per 100 meters, and grid point G5 is identified as a gradient abrupt change region, a historical database query reveals that this point occurs 30% of the time under similar weather conditions, thus the wind shear risk probability is 0.30. Analyzing precipitation data determines the precipitation intensity and coverage along the flight path. Combined with the drone's waterproof rating, the probability of performance degradation or failure due to precipitation is calculated. For example, if 30% of the grid points along the flight path experience precipitation greater than 1 millimeter per hour, and the drone has an IP54 waterproof rating, the calculated precipitation risk probability is 0.15. Analyze visibility data to identify flight segments where visibility is below the minimum requirements of UAV visual navigation or obstacle avoidance systems. Combine this with terrain and obstacle information for these flight segments to calculate the probability of risk caused by insufficient visibility. For example, if the minimum visibility requirement for a UAV visual navigation system is 1000 meters, and the flight segment where grid point G8 is located is identified as having insufficient visibility, and this flight segment is close to coastal wind turbines, the calculated probability of insufficient visibility risk is 0.25.

[0091] The probability of wind shear risk, the probability of precipitation risk, and the probability of insufficient visibility risk are fused to obtain the comprehensive meteorological risk probability of the flight path. In some embodiments, the fusion calculation uses a weighted summation method, such as the comprehensive meteorological risk probability. The calculation formula is:

[0092]

[0093] in: It is a comprehensive meteorological risk probability. It is the probability of wind shear risk. It is the probability of precipitation risk. It is the probability of insufficient visibility risk. , , It is a preset weighting coefficient and satisfies Based on calculations, the overall meteorological risk probability of deployment plan "Plan_E_001" is 0.23. Based on extracted air traffic density data, the risk level of air traffic conflict when the UAV flies along this flight path is assessed. For example, if there is a 10-minute overlap between the path and the airspace occupied by general aviation aircraft, the air traffic conflict risk level is assessed as "medium" based on the volume and traffic flow density of the overlapping airspace. Based on communication blind spot information, the possible communication interruption period and duration of the UAV along this flight path are assessed. For example, a weak signal area may cause a communication link interruption lasting 90 seconds. Combining the overall meteorological risk probability, air traffic conflict risk level, and communication interruption assessment results, a multi-factor weighted model is used to calculate the overall risk assessment score of this deployment plan. For example, the "medium" risk level is quantified as 0.5, and the 90-second communication interruption duration is quantified as 0.3. The overall risk assessment score of deployment plan "Plan_E_001" is calculated to be 0.35 using the weighted model. Simultaneously, based on the flight path length of the deployment plan, the drone's cruising speed, and the mission execution mode it adopts, the estimated total time from takeoff to arrival at the incident area and commencement of operations is calculated as an indicator of the deployment plan's timeliness. For example, the flight path length of deployment plan "Plan_E_001" is 55 kilometers, the drone's cruising speed is 18 meters per second, and it adopts a single-machine execution mode. The calculated estimated total time is 3056 seconds, approximately 50.9 minutes.

[0094] Understandably, the data comparison reflects the differences in risk and timeliness indicators among different deployment schemes. For example, another deployment scheme, "Plan_E_002," chooses a flight path that detours around the meteorological area. Its comprehensive meteorological risk probability calculation result is 0.10, but the flight path length increases to 65 kilometers, and the estimated total time increases to 3611 seconds, approximately 60.2 minutes. Deployment schemes "Plan_E_001" and "Plan_E_002" show a clear trade-off between risk and time. In specific implementation, the calculation of the estimated total time needs to consider the impact of the mission execution mode. For example, for a multi-aircraft formation execution mode, the estimated total time is based on the time when the last UAV in the formation arrives at the incident area. For a batch relay execution mode, the estimated total time also needs to include the takeoff interval time of subsequent batches of UAVs. Optionally, the assessment of the air traffic conflict risk level is based on the comparison results of real-time broadcast automatic dependent surveillance data and flight plans, and the communication interruption assessment needs to be combined with the propagation model of the different frequency band communication links used by the UAVs. In some embodiments, the spatial resolution and temporal update frequency of gridded meteorological data directly affect the accuracy of risk probability calculation, such as using a data source with a 1 km x 1 km grid and updates every 5 minutes. Optionally, the weight coefficients of each factor in the multi-factor weighted model can be dynamically configured according to different event types. For example, for personnel search and rescue missions, the weight of the communication interruption factor can be appropriately increased.

[0095] In one embodiment of the present invention, the system obtains the comprehensive risk assessment score and estimated total time of all alternative deployment schemes. For example, in a marine oil spill monitoring and response mission, five alternative deployment schemes are generated, numbered from P01 to P05. The original data of the comprehensive risk assessment score and estimated total time for each deployment scheme are as follows: Scheme P01 comprehensive risk assessment score 0.35, estimated total time 3056 seconds; Scheme P02 comprehensive risk assessment score 0.28, estimated total time 3300 seconds; Scheme P03 comprehensive risk assessment score 0.45, estimated total time 2800 seconds; Scheme P04 comprehensive risk assessment score 0.45, estimated total time 2800 seconds; Scheme P04 comprehensive risk assessment score 0.45, estimated total time 2800 seconds; Scheme P04 comprehensive risk assessment score 0.45, estimated total time 2800 seconds; Scheme P05 ... The evaluation score is 0.20, with an estimated total time of 3600 seconds. The comprehensive risk assessment score for scheme P05 is 0.50, with an estimated total time of 2500 seconds. The comprehensive risk assessment scores and estimated total times of all alternative deployment schemes are normalized and converted to comparable values ​​under the same dimension. The min-max normalization method is used. For the estimated total time index, the time of all schemes is mapped to the interval [0,1]. The smaller the value, the shorter the time. For the comprehensive risk assessment score index, it is also mapped to the interval [0,1]. The smaller the value, the lower the risk. The normalized data are compared and referred to Table 1.

[0096] Table 1: Deployment Scheme Normalized Data and Weighted Comprehensive Score Table

[0097] Scheme Number Original estimated total time Original comprehensive risk assessment score Normalization estimated total time Normalized Comprehensive Risk Assessment Score Timeliness dynamic weight Risk assessment dynamic weighting Weighted composite score P01 3056 0.35 0.50 0.50 0.7 0.3 0.50 P02 3300 0.28 0.73 0.27 0.7 0.3 0.58 P03 2800 0.45 0.27 0.83 0.7 0.3 0.43 P04 3600 0.20 1.00 0.00 0.7 0.3 0.70 P05 2500 0.50 0.00 1.00 0.7 0.3 0.30

[0098] Based on the urgency of the response task reflected in the preliminary situation level in the task request information, a dynamic weight is assigned to the timeliness indicator, and a complementary dynamic weight is assigned to the risk assessment indicator. For example, in this oil spill monitoring task, the situation level is "urgent," so the system sets the dynamic weight for timeliness to 0.7 and the dynamic weight for risk assessment to 0.3. It can be understood that the higher the urgency of the task, the larger the dynamic weight for timeliness, and the smaller the dynamic weight for risk assessment. For each deployment plan, its weighted comprehensive score is calculated. The weighted comprehensive score equals the normalized estimated total time multiplied by the dynamic weight for timeliness, plus the normalized comprehensive risk assessment score multiplied by the dynamic weight for risk assessment. The formula for calculating the weighted comprehensive score is:

[0099]

[0100] in: The weighted overall score representing the deployment plan, This represents the normalized estimated total time for the scheme. This represents the normalized overall risk assessment score of the scheme. Represents the dynamic weight of timeliness. Representing the dynamic weight of risk assessment, based on the data in the table, the weighted comprehensive score of scheme P01 is 0.7*0.50+0.3*0.50=0.50.

[0101] After the calculation is completed, all deployment schemes are sorted in ascending order according to their weighted comprehensive scores. The deployment scheme with the lowest weighted comprehensive score is selected as the preliminary optimal scheme. For example, after sorting the data in the table, scheme P05 has the lowest weighted comprehensive score of 0.30, scheme P03 has the second lowest weighted comprehensive score of 0.43, scheme P01 has a weighted comprehensive score of 0.50, scheme P02 has a weighted comprehensive score of 0.58, and scheme P04 has the highest weighted comprehensive score of 0.70. Therefore, scheme P05 is selected as the preliminary optimal scheme. In some embodiments, the dynamic weight is set based on the mapping table between the event level and the weight. For example, the dynamic weight of timeliness is 0.3 for the "normal" event level and the dynamic weight of risk assessment is 0.7 for the "emergency" event level. The preliminary optimal solution undergoes resource conflict verification to check whether the planned drones and drone airport resources conflict with other assigned or pending tasks in the system. For example, the preliminary optimal solution P05 plans to use UAV_001 from the near-shore drone airport F. If the task scheduling log is checked and it is found that UAV_001 has been assigned another patrol task during the target time period, then there is a resource conflict. If there is no conflict, it is confirmed as the final optimal deployment solution. Optionally, resource conflict verification can be achieved by accessing the central task database and comparing the resource occupancy time window.

[0102] In implementing this invention, a routine drone monitoring and rapid response mode was established to achieve rapid response. Fixed drone airfields are pre-positioned in key coastal areas, such as fishing ports. These airfields are equipped with temperature-controlled, salt-spray-proof, enclosed hangars where drones and their payloads, such as optical reconnaissance equipment and thermal imagers, are always on standby, in an automatic charging or ready-to-go state. The airfields continuously report their operational status, the number of available drones under their jurisdiction, their current charging status, and local real-time meteorological data collected through integrated meteorological terminals to the flight service center via a communication network through a ground control unit. The system maintains a real-time status list of drone resources across the entire region. When a mission request is received, the system immediately initiates a screening, evaluation, and decision-making process based on this real-time list, directly selecting the optimal solution from the ready-to-go resources and remotely triggering automated execution, thereby eliminating the time delays caused by personnel deployment, equipment transportation, and on-site preparation in the traditional model.

[0103] In practical implementation, normalization ensures the comparability of indicators with different dimensions. Data comparison shows that scheme P05 has the shortest original estimated total time but the highest original comprehensive risk assessment score, while scheme P04 has the lowest original comprehensive risk assessment score but the longest original estimated total time. After normalization and weighting, scheme P05 has the lowest score due to its high timeliness weight. It can be understood that dynamic weight adjustment changes the scheme selection tendency. Optionally, when the resource conflict verification of the preliminary optimal scheme fails, the system automatically selects the scheme with the second smallest weighted comprehensive score as the new preliminary optimal scheme and repeats the verification process. For example, if scheme P05 is in conflict, scheme P03 is selected for verification. In some embodiments, the weighted comprehensive score calculation uses a linear weighted sum model, and the meaning of the characters in the formula is clear. The normalized estimated total time... and comprehensive risk assessment score All are dimensionless, with dynamic weights reflecting timeliness. With risk assessment dynamic weight A coefficient set according to the urgency of the task and satisfying the following conditions. .

[0104] See Figure 4This is a multi-dimensional radar chart of the optimal solution P05, used to visually demonstrate its performance across three core dimensions. The original time consumption (normalized) value is 0.00, indicating that among all alternative solutions, P05 has the shortest estimated total time consumption and the best timeliness. The original risk (normalized) value is 1.00, indicating that among all alternative solutions, P05 has the highest comprehensive risk assessment score and the greatest risk. The weighted score is 0.30, the lowest among all solutions, making it the optimal choice after considering both timeliness (weight 0.7) and risk (weight 0.3). This clearly reveals the "high timeliness, high risk" characteristics of the P05 solution. In the "urgent" task scenario, the high timeliness weight (0.7) amplifies its advantage of having the shortest consumption time, thus winning in the comprehensive score.

[0105] In one embodiment of the present invention, the unique identifier, geographical coordinates, and communication channel parameters of the target UAV airport are extracted from the confirmed optimal deployment scheme. For example, in the optimal deployment scheme "OptPlan_001", the unique identifier of the target UAV airport is "Base_Alpha", the geographical coordinates are 123.45 degrees east longitude and 32.10 degrees north latitude, and the communication channel parameters are UHF band, frequency 430MHz, and bandwidth 2MHz. The confirmed flight path details are extracted from the optimal deployment scheme. The flight path details include five waypoints, each of which contains latitude, longitude, altitude, and speed information. The specific configuration of the specified mission payload combination is extracted from the optimal deployment scheme. The types and models of the specified primary and secondary payloads are read. For example, the mission payload combination includes a high-definition optical pod model "Cam_HD01" as the primary payload and a thermal imager model "Thermal_IR05" as the secondary payload. Initial operating parameters are set for each payload based on the event type and mission objective. For example, the zoom magnification is set to 8x, the shooting frequency to 2 frames per second, and the image format to JPEG for the HD optical pod; the temperature range is set to -20 to 150 degrees Celsius and the image output to heatmap mode for the thermal imager. The collaborative workflow of multiple payloads is planned, and the start-up sequence, working period, and data acquisition synchronization point of each payload are determined. For example, the collaborative workflow stipulates that the HD optical pod will start wide-angle scanning when the UAV arrives at waypoint 3, and the thermal imager will start and synchronize the data acquisition timestamps of both when it arrives at waypoint 4. The trigger conditions for each parameter in the payload control parameter list are specified, including trigger conditions based on geographical location, flight time, or command. For example, the zoom parameter of the HD optical pod will be adjusted to 12x when the longitude of the UAV exceeds 123.44 degrees, and the working mode of the thermal imager will be switched to high sensitivity mode 300 seconds after takeoff. The payload type, initial operating parameters, collaborative workflow, and parameter activation trigger conditions are compiled into a payload control parameter list that can be parsed by the UAV mission management system according to the payload control protocol format.

[0106] The system integrates the target UAV's airport information, coded flight path, and generated payload control parameter list, and encapsulates them into a structured, detailed deployment command data packet according to a preset command protocol. The preset command protocol uses JSON format. The data packet includes a header containing the task ID and version number, and a body containing the UAV airport identifier, waypoint sequence, and payload parameter list. A task verification code and a command activation timestamp are added to the detailed deployment command data packet. The task verification code is generated using a hash algorithm; for example, using the SHA-256 algorithm to calculate the verification code "a1b2c3d4" from the task ID and the current timestamp. The command activation timestamp is set to a fixed delay after the current time; for example, the formula for calculating the command activation timestamp is:

[0107]

[0108] in: It is the timestamp when the instruction takes effect. This is the current system time. The deployment command is generated after a preset delay time, such as 60 seconds. Detailed deployment command data packets are sent to the ground control unit at the target UAV airport via an encrypted command and control link. The encrypted link uses the AES-256 encryption algorithm. Upon receiving the detailed deployment command data packet, the ground control unit verifies the validity of the task verification code and the command's effective timestamp. If the verification is successful, the command content is parsed. For example, the ground control unit calculates whether the received task verification code matches the locally recalculated value and checks whether the command's effective timestamp has expired. Based on the parsed target UAV airport information, the ground control unit performs self-matching to confirm itself as the command executor. Then, based on the flight path and payload configuration in the command, it prepares for pre-takeoff checks on the UAV, including battery level, sensor status, and communication link testing. Upon reaching the command's effective timestamp or receiving manual execution confirmation, the ground control unit controls the designated UAV to automatically take off along the coded waypoint sequence and execute the flight path. When the UAV reaches the preset payload activation area, the ground control unit remotely activates the UAV's payload according to the payload control parameter list, putting it into working condition. For example, when the UAV reaches longitude 123.44, the ground control unit sends a command to activate the zoom function of the high-definition optical pod.

[0109] To achieve stable and reliable low-altitude communication coverage, fixed communication and navigation UAV base stations are deployed along the coastline at preset intervals. These UAV base stations integrate L / S dual-band data links, are optimized for multipath effects over the sea surface, and are connected to each other via fiber optic or 5G private networks, forming a near-shore low-altitude communication network. The Flight Service Center, as the system's sole command and dispatch hub, gathers all UAV airport status, UAV resources, communication UAV base station link quality, and mission information from the near-shore monitoring network. After the Service Center's mission scheduling engine generates the optimal deployment plan, its structured detailed deployment instructions are transmitted via the aforementioned encrypted command and control links, through the fixed communication UAV base station network, to the ground control unit at the target UAV airport. This infrastructure system ensures reliable and low-latency communication throughout the entire process from instruction generation and transmission to receipt and execution in complex near-shore environments, supporting remote control and automatic execution.

[0110] In some embodiments, data comparison is reflected in the trigger condition settings for different mission payload configurations. For example, for a marine oil spill monitoring mission, the payload control parameter list may set the optical equipment to operate in visible light mode after sunrise and switch to multispectral mode before sunset. For a nighttime search and rescue mission, the thermal imager is set to start immediately after takeoff and operate continuously. It can be understood that the setting of trigger conditions is directly related to the timeliness and effectiveness of mission execution. Optionally, the delay time of the command effective timestamp... The settings can be dynamically adjusted according to the urgency of the task; emergency task settings. 10 seconds, standard task settings The transmission time is 60 seconds. In some embodiments, the key for the encrypted link is stored and retrieved through a secure hardware module to ensure the security of the transmission process. Optionally, the pre-flight checklist of the ground control unit can be predefined according to the UAV model and mission type, such as including propeller fastening checks, GPS signal strength checks, and payload power supply checks. It can be understood that the conversion from scheme to instruction realizes the automation and precise control of mission execution.

[0111] See Figure 5This is a comparison chart of six near-shore UAV deployment schemes, comparing their performance across three core metrics. Scheme 4 has the longest path length and execution time of all schemes, but also the highest payload capacity, indicating it's a "heavy-duty, long-duration" scheme, suitable for complex scenarios requiring multi-payload coordination. Scheme 3 has the shortest path length and execution time of all schemes, with a payload capacity of 2, making it a "lightweight and efficient" scheme, ideal for urgent tasks with extremely high timeliness requirements. Schemes 2 and 5 both have a payload capacity of 3, with path length and execution time at a moderate level, representing a trade-off between mission capability and response speed. The chart clearly shows the absolute differences in path length and execution time, while the line graph highlights the trend of payload capacity changes across different schemes, facilitating the rapid identification of high-payload schemes.

[0112] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A rapid response deployment method for unmanned aerial vehicles (UAVs) based on near-shore environments, characterized in that: Includes the following steps: Receive task request information from the nearshore monitoring network, the task request information including the geographical location of the incident, the type of the incident, and the preliminary situation level; Based on the mission request information, candidate drone airports that meet the preset response conditions are selected from multiple near-shore drone airports, and real-time status data and available drone resource lists of the candidate drone airports are obtained. Based on the geographical location of the incident, the type of the incident, and the real-time status data of the candidate drone airports, multiple alternative drone deployment plans are generated, and risk assessment and timeliness calculation are performed for each deployment plan. Based on the combined risk assessment results and timeliness calculation results, the optimal deployment scheme is selected from the alternative drone deployment schemes, and detailed deployment instructions containing the target drone airport, drone flight path and mission payload configuration are generated according to the optimal deployment scheme. Detailed deployment instructions are sent to the target drone airport, triggering the target drone airport to perform automatic take-off, en-route flight, and mission payload activation operations according to the deployment instructions.

2. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, The step of selecting candidate drone airports that meet preset response conditions from multiple near-shore drone airports based on task request information specifically includes: Analyze the geographical location and event type in the task request information to determine the minimum requirements for drone model, mission payload type, and endurance for responding to the task; Obtain the geographical distribution of all near-shore drone airports, the operational status of the drone airports, and a list of drone models and payload configurations under the jurisdiction of each drone airport; Calculate the estimated flight time from the airport to the incident location for each near-shore drone, and adjust the estimated flight time based on the current sea conditions and meteorological data; Nearshore drone airports that meet the minimum requirements, are in a ready operational status, and have a corrected estimated flight time less than a preset threshold are marked as candidate drone airports, forming a candidate set. Real-time status data of each candidate drone airport is extracted from the candidate set. The real-time status data includes the number of drones remaining available at the drone airport, the current charging status, and the quality of the communication link.

3. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, Based on the geographical location of the incident, the type of the incident, and the real-time status data of candidate drone airports, multiple alternative drone deployment schemes are generated, specifically including: For each candidate drone airport, multiple alternative flight paths are planned, with the airport as the starting point and the geographical location of the incident as the target point. Each path takes into account different flight altitude levels and avoidance areas. Based on the event type, a suitable combination of mission payloads is matched from the mission payload library. The combination of mission payloads includes optical reconnaissance equipment, thermal imagers, megaphones, or material mounting devices. Based on the number of available drones in the real-time status data of candidate drone airports, different mission execution modes are calculated, including single-drone execution, multi-drone formation execution, or batch relay execution. Each alternative flight path, each mission payload combination, and each mission execution mode is arranged and combined to generate an initial deployment plan for each candidate UAV airport. Assign a unique scheme identifier to each initial deployment scheme and associate it with the corresponding candidate UAV airport information, flight path details, payload configuration and execution mode.

4. The rapid response deployment method for unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, The risk assessment and timeliness calculation for each deployment plan specifically includes: For each deployment plan, obtain its associated flight path details, and extract real-time meteorological data, air traffic density data, and known communication blind spot information along the path; Based on the extracted real-time meteorological data, assess the probability of the UAV encountering wind shear, precipitation, or insufficient visibility while flying along the flight path. Based on air traffic density data, assess the risk level of air traffic conflict when the UAV flies along the stated flight path; Based on communication blind spot information, assess the period and duration of communication interruptions that occurred on the UAV's flight path; Based on the combined risk probability, risk level, and communication interruption assessment results, a multi-factor weighted model is used to calculate the comprehensive risk assessment score of the deployment scheme. Based on the flight path length, drone cruising speed, and mission execution mode of the deployment plan, the estimated total time from takeoff to arrival at the incident area and commencement of operations is calculated, serving as an indicator of the timeliness of the deployment plan.

5. The rapid response deployment method for unmanned aerial vehicles (UAVs) based on near-shore waters according to claim 1, characterized in that, Based on the comprehensive risk assessment results and timeliness calculation results, the optimal deployment scheme is selected from the alternative drone deployment schemes, specifically including: The comprehensive risk assessment scores of all alternative deployment options and the estimated total time are normalized to comparable values ​​under the same dimension. Based on the urgency of the response task, a dynamic weight is assigned to the timeliness indicator, and another complementary dynamic weight is assigned to the risk assessment indicator. For each deployment plan, calculate its weighted comprehensive score. The weighted comprehensive score is equal to the normalized estimated total time multiplied by the timeliness dynamic weight, plus the normalized comprehensive risk assessment score multiplied by the risk assessment dynamic weight. All deployment schemes are sorted in ascending order according to their weighted comprehensive scores, and the deployment scheme with the smallest weighted comprehensive score is selected as the initial optimal scheme. The preliminary optimal solution undergoes resource conflict verification, which involves checking whether the drones and drone airport resources used there are conflicts with other assigned or pending tasks. If there are no conflicts, it is confirmed as the final optimal deployment solution.

6. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, The detailed deployment instructions generated based on the optimal deployment scheme, including the target UAV airport, UAV flight path, and mission payload configuration, specifically include: Extract the unique identifier, geographic coordinates, and communication channel parameters of the target UAV airport from the optimal deployment scheme; Extract the details of the confirmed flight path from the optimal deployment plan and encode them into a waypoint sequence format that can be recognized by the UAV flight control system. Each waypoint contains latitude, longitude, altitude and speed information. Extract the specific configuration of the task load combination from the optimal deployment scheme and generate a load control parameter list, which includes the power-on / off sequence, working mode and data return settings of each load; The target UAV's airport information, coded flight path, and payload control parameter list are integrated and encapsulated into a structured, detailed deployment instruction data package according to a preset instruction protocol. Add the task verification code and instruction effective timestamp to the detailed deployment instruction data package to complete the generation of deployment instructions; The specific configuration for extracting the task load combination from the optimal deployment scheme and generating a load control parameter list specifically includes: Read the task payload combination specified in the optimal deployment plan, including the type and model of the main payload and auxiliary payload; Based on the event type and mission objectives, set initial operating parameters for each payload, such as setting zoom magnification, shooting frequency and image format for optical reconnaissance equipment; Plan the collaborative workflow of multiple payloads, and determine the start-up sequence, working period, and data acquisition synchronization point of each payload; For each parameter in the load control parameter list, specify the triggering conditions for its effectiveness, including triggering based on geographical location, triggering based on flight time, or triggering based on command. The payload type, initial operating parameters, collaborative workflow, and parameter activation trigger conditions are compiled into a payload control parameter list that can be parsed by the UAV mission management system, according to the payload control protocol format.

7. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, The detailed deployment instructions are sent to the target UAV airport, triggering the target UAV airport to perform automatic UAV takeoff, en-route flight, and mission payload activation operations according to the deployment instructions. Specifically, this includes: Detailed deployment instruction data packets are sent to the ground control unit of the target UAV airport via an encrypted command and control link; After receiving the detailed deployment instruction data packet, the ground control unit verifies the validity of the mission verification code and the instruction effective timestamp. Once the verification is successful, the instruction content is parsed. The ground control unit performs self-matching based on the parsed target UAV airport information to confirm itself as the subject of command execution. Then, based on the flight path and payload configuration in the command, it prepares for the UAV pre-takeoff check. Upon reaching the command effective timestamp or receiving manual execution confirmation, the ground control unit controls the designated UAV to automatically take off along the coded waypoint sequence and execute the flight route. When the drone flies to the preset payload activation area, the ground control unit remotely activates the mission payload carried by the drone according to the payload control parameter list, so that it enters the working state.

8. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 2, characterized in that, The calculation of the estimated flight time from the airport to the incident location for each near-shore UAV, and the correction of the estimated flight time based on current sea conditions and meteorological data, specifically includes: Obtain the straight-line distance between each near-shore drone airport and the geographical location of the incident, and calculate the theoretical flight time based on the drone's standard cruising speed; The system queries the current sea state level data of the sea area covered by the flight path between the candidate UAV airport and the geographical location of the incident, as well as the real-time weather forecast data of the geographical area of ​​the incident, including wind direction, wind speed, and temperature. Based on wind direction and wind speed data, calculate the tailwind or headwind components encountered by the drone on its flight path, and adjust the effective flight speed of the drone by increasing or decreasing the speed according to the wind components. Based on sea state and temperature data, assess the impact of sea surface vapor or low cloud cover on visibility and safety of low-altitude flight. If the impact is significant, increase the detour distance of the flight path or reduce the cruising speed. The additional time due to wind speed correction and path / speed adjustment is added to the theoretical flight time to obtain the corrected estimated flight time.

9. The method for rapid response deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 4, characterized in that, The assessment of the probability of encountering wind shear, precipitation, or insufficient visibility when the UAV encounters such risks while flying along the flight path, based on the extracted real-time meteorological data, specifically includes: Obtain gridded meteorological data of the airspace traversed by the flight path, and extract the vertical wind speed gradient data, precipitation data and visibility data of each grid point; Analyze vertical wind speed gradient data to identify gradient abrupt change regions that exceed the safe flight threshold of drones, and count the frequency of occurrence of the gradient abrupt change regions in the same historical period to use as the probability of wind shear risk. Analyze precipitation data to determine the intensity and coverage of precipitation along the flight path during the flight period, and calculate the probability of performance degradation or failure due to precipitation in combination with the waterproof rating of the UAV. Analyze visibility data to identify flight segments where visibility is below the minimum requirements of the UAV's visual navigation or obstacle avoidance system, and calculate the probability of risk caused by insufficient visibility by combining the terrain and obstacle information of the flight segments. The comprehensive meteorological risk probability of the flight path is obtained by integrating the wind shear risk probability, precipitation risk probability, and insufficient visibility risk probability.

10. The method for rapid deployment of unmanned aerial vehicles (UAVs) based on near-shore environments according to claim 1, characterized in that, The application of a rapid response deployment method for unmanned aerial vehicles (UAVs) based on nearshore areas, as described in any one of claims 1 to 9, in nearshore target surveillance or maritime emergency rescue scenarios.

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