Control method and device of high-altitude rescue unmanned aerial vehicle and computer equipment

Abstract

The invention relates to a control method and device of a high-altitude rescue unmanned aerial vehicle and computer equipment. The method comprises the following steps: acquiring an area environment image of a rescue area and area airflow data of the rescue area, and identifying a target rescue range of the rescue area and flight influence distribution data corresponding to the target rescue range based on the area environment image; based on the regional airflow data and the flight influence distribution data corresponding to the target rescue range, generating a regional rescue evacuation route of the target rescue range, and based on the regional rescue evacuation route of the target rescue range, generating a rescue evacuation control strategy of the high-altitude rescue unmanned aerial vehicle; in the execution process of the rescue evacuation control strategy, load real-time change information of the high-altitude rescue unmanned aerial vehicle is collected, and based on the load real-time change information, a load real-time compensation control strategy of the high-altitude rescue unmanned aerial vehicle is generated through a self-adaptive load compensation program. By adopting the method, the protection effect on rescue safety can be comprehensively improved.

Description

Technical Field

[0001] The present application relates to the field of emergency rescue equipment control technology, and in particular to a control method, device and computer equipment for a high-altitude rescue drone. Background Art

[0002] With the acceleration of urbanization and the increasing height of urban buildings, higher demands are being placed on emergency rescue equipment for high-altitude building fires. Traditional rescue methods rely primarily on fire truck ladders (usually operating at heights ≤ 60 meters) and manual rappelling. These methods suffer from slow response times, are subject to significant terrain restrictions, and can easily cause injuries to both rescuers and those being rescued. While the recent emergence of rescue drones offers rapid arrival capabilities, practical applications still face significant safety flaws and technical bottlenecks.

[0003] Mainstream single-rotor, single-propeller, or centrally symmetrical multi-rotor designs suffer from design flaws in their balancing systems. Single-rotor systems lack a torque compensation mechanism, resulting in asymmetric aerodynamic torque when subjected to offset loads. While multi-rotor systems achieve theoretical torque balance through a symmetrical layout, their PID-based speed control systems have inherent delays. This significantly impacts rescue safety. Summary of the Invention

[0004] Based on this, it is necessary to provide a control method, device, computer equipment, computer-readable storage medium and computer program product for a high-altitude rescue drone to address the above technical problems.

[0005] In a first aspect, the present application provides a control method for a high-altitude rescue drone, comprising:

[0006] Collecting a regional environment image of a rescue area and regional airflow data of the rescue area, and identifying a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image;

[0007] Generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range;

[0008] During the execution of the rescue and evacuation control strategy, the real-time load change information of the high-altitude rescue UAV is collected, and based on the real-time load change information, a real-time load compensation control strategy of the high-altitude rescue UAV is generated through an adaptive load compensation program.

[0009] Optionally, the identifying, based on the regional environment image, a target rescue range of the rescue area and each flight impact distribution data corresponding to the target rescue range includes:

[0010] Based on the regional environment image, identifying the target rescue range of the rescue target in the rescue area through an image recognition network;

[0011] In the regional environment image, the target range image of the target rescue range is screened, and based on the target range image, the characteristic distribution data of each flight impact type of the target rescue range is extracted respectively through the image feature extraction network, and the characteristic distribution data of each flight impact type is used as the flight impact distribution data corresponding to the target rescue range.

[0012] Optionally, before generating the regional rescue evacuation route of the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, the method further includes:

[0013] Based on the regional airflow data, identifying airflow data distribution information of each airflow type in the rescue area;

[0014] The airflow data distribution information of each airflow type is respectively subjected to data distribution mapping processing in the regional environment image corresponding to the rescue area to obtain a three-dimensional distribution map of the airflow data of the rescue area.

[0015] Optionally, generating a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range includes:

[0016] Based on the three-dimensional distribution map of airflow data in the rescue area, identifying the airflow distribution range of each airflow type in the rescue area, and obtaining airflow limit information of the high-altitude rescue drone;

[0017] Based on the airflow limit information, identifying the airflow condition range of the high-altitude rescue drone, and based on the airflow condition range of the high-altitude rescue drone, identifying the target airflow type applicable to the high-altitude rescue drone;

[0018] Based on the target rescue range and the airflow distribution range of the target airflow type, identifying a first rescue evacuation route corresponding to the high-altitude rescue drone through a route planning network;

[0019] Based on the flight impact distribution data of the target rescue range, identifying condition distribution information of each rescue condition in the target rescue range, and generating a second rescue evacuation route for the high-altitude rescue drone through a rescue evacuation condition planning network based on the condition distribution information of each rescue condition;

[0020] The first rescue evacuation route corresponding to the high-altitude rescue drone and the second rescue evacuation route of the high-altitude rescue drone are used as regional rescue evacuation routes within the target rescue range.

[0021] Optionally, the generating of a rescue evacuation control strategy for a high-altitude rescue drone based on the regional rescue evacuation route of the target rescue range includes:

[0022] Based on the regional rescue evacuation route, identifying the rescue evacuation trajectory of the high-altitude rescue drone;

[0023] Identifying the direction of the force corresponding to each trajectory point of the rescue evacuation trajectory and the strength of the force corresponding to each trajectory point through a linear integration algorithm;

[0024] Based on the force direction corresponding to each of the trajectory points and the force intensity corresponding to each of the trajectory points, generating force control information of the high-altitude rescue drone corresponding to each of the trajectory points;

[0025] Based on the force control information of the high-altitude rescue drone corresponding to each of the trajectory points, a rescue and evacuation control strategy of the high-altitude rescue drone is generated.

[0026] Optionally, the generating of a real-time load compensation control strategy for the high-altitude rescue UAV through an adaptive load compensation program based on the real-time load change information includes:

[0027] Based on the real-time load change information, load change data of each load detection type is identified, and based on the load change data of each load detection type, synergistic force distribution information of each synergistic layer is generated through a multi-modal control architecture of an adaptive load compensation program;

[0028] Based on the synergistic force distribution information of each of the synergistic frictions, a dual-redundant balance adjustment system control strategy of the high-altitude rescue drone is generated through a balance adjustment program;

[0029] The dual-redundant balance adjustment system control strategy of the high-altitude rescue UAV is used as the real-time load compensation control strategy of the high-altitude rescue UAV.

[0030] In a second aspect, the present application also provides a control device for a high-altitude rescue drone, comprising:

[0031] an acquisition module, configured to acquire a regional environment image of a rescue area and regional airflow data of the rescue area, and identify a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image;

[0032] a generating module for generating a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generating a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range;

[0033] The compensation module is used to collect the real-time load change information of the high-altitude rescue drone during the execution of the rescue and evacuation control strategy, and based on the real-time load change information, generate a real-time load compensation control strategy for the high-altitude rescue drone through an adaptive load compensation program.

[0034] Optionally, the acquisition module is specifically used to:

[0035] Based on the regional environment image, identifying the target rescue range of the rescue target in the rescue area through an image recognition network;

[0036] In the regional environment image, the target range image of the target rescue range is screened, and based on the target range image, the characteristic distribution data of each flight impact type of the target rescue range is extracted respectively through the image feature extraction network, and the characteristic distribution data of each flight impact type is used as the flight impact distribution data corresponding to the target rescue range.

[0037] Optionally, the device further includes:

[0038] an identification module, configured to identify airflow data distribution information of each airflow type in the rescue area based on the regional airflow data;

[0039] The mapping module is used to perform data distribution mapping processing on the airflow data distribution information of each airflow type in the regional environment image corresponding to the rescue area to obtain a three-dimensional distribution map of the airflow data of the rescue area.

[0040] Optionally, the generating module is specifically configured to:

[0041] Based on the three-dimensional distribution map of airflow data in the rescue area, identifying the airflow distribution range of each airflow type in the rescue area, and obtaining airflow limit information of the high-altitude rescue drone;

[0042] Based on the airflow limit information, identifying the airflow condition range of the high-altitude rescue drone, and based on the airflow condition range of the high-altitude rescue drone, identifying the target airflow type applicable to the high-altitude rescue drone;

[0043] Based on the target rescue range and the airflow distribution range of the target airflow type, identifying a first rescue evacuation route corresponding to the high-altitude rescue drone through a route planning network;

[0044] Based on the flight impact distribution data of the target rescue range, identifying condition distribution information of each rescue condition in the target rescue range, and generating a second rescue evacuation route for the high-altitude rescue drone through a rescue evacuation condition planning network based on the condition distribution information of each rescue condition;

[0045] The first rescue evacuation route corresponding to the high-altitude rescue drone and the second rescue evacuation route of the high-altitude rescue drone are used as regional rescue evacuation routes within the target rescue range.

[0046] Optionally, the generating module is specifically configured to:

[0047] Based on the regional rescue evacuation route, identifying the rescue evacuation trajectory of the high-altitude rescue drone;

[0048] Identifying the direction of the force corresponding to each trajectory point of the rescue evacuation trajectory and the strength of the force corresponding to each trajectory point through a linear integration algorithm;

[0049] Based on the force direction corresponding to each of the trajectory points and the force intensity corresponding to each of the trajectory points, generating force control information of the high-altitude rescue drone corresponding to each of the trajectory points;

[0050] Based on the force control information of the high-altitude rescue drone corresponding to each of the trajectory points, a rescue and evacuation control strategy of the high-altitude rescue drone is generated.

[0051] Optionally, the compensation module is specifically configured to:

[0052] Based on the real-time load change information, load change data of each load detection type is identified, and based on the load change data of each load detection type, synergistic force distribution information of each synergistic layer is generated through a multi-modal control architecture of an adaptive load compensation program;

[0053] Based on the synergistic force distribution information of each of the synergistic frictions, a dual-redundant balance adjustment system control strategy of the high-altitude rescue drone is generated through a balance adjustment program;

[0054] The dual-redundant balance adjustment system control strategy of the high-altitude rescue UAV is used as the real-time load compensation control strategy of the high-altitude rescue UAV.

[0055] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0057] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0058] The control method, device and computer equipment of the above-mentioned high-altitude rescue drone collect regional environmental images of the rescue area and regional airflow data of the rescue area, and based on the regional environmental images, identify the target rescue range of the rescue area and the flight impact distribution data corresponding to the target rescue range; based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, generate the regional rescue evacuation route of the target rescue range, and based on the regional rescue evacuation route of the target rescue range, generate the rescue evacuation control strategy of the high-altitude rescue drone; during the execution of the rescue evacuation control strategy, collect the real-time load change information of the high-altitude rescue drone, and based on the real-time load change information, generate the real-time load compensation control strategy of the high-altitude rescue drone through an adaptive load compensation program. This solution uses real-time collected regional environmental images and regional airflow data to conduct a comprehensive analysis of the flight impact distribution data and airflow data of the rescue area, thereby generating a rescue and evacuation control strategy for the high-altitude rescue drone, thereby avoiding the difficulty in achieving effective fit with the facade of the high-altitude building due to the interference of downwash airflow when implementing high-altitude rescue. This method can be adjusted in real time as the collected regional environmental images and regional airflow data change, thereby improving the rescue success rate of the high-altitude rescue drone. Then, this solution combines the real-time collected real-time load change information of the high-altitude rescue drone, and generates a real-time load compensation control strategy for the high-altitude rescue drone through an adaptive load compensation program, thereby improving the efficient processing capability of the fast, highly dynamic, unbalanced load generated by the rescued personnel going up and down or moving in the cabin, and realizing high-dynamic and high-precision balance regulation, improving the balance control stability of the high-altitude rescue drone, thereby comprehensively improving the protection effect of rescue safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only 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.

[0060] Figure 1 This is an overall structural diagram of a high-altitude rescue drone in one embodiment;

[0061] Figure 2 is a front view of a high-altitude rescue drone in one embodiment;

[0062] Figure 3 A top view of a high-altitude rescue drone in one embodiment;

[0063] Figure 4 A side view of a high-altitude rescue drone in one embodiment;

[0064] Figure 5 A plan view of a high-altitude rescue drone (including internal integrated units) in one embodiment;

[0065] Figure 6 This is an overall structural diagram of a retractable rescue cabin and a rescue platform in one embodiment;

[0066] Figure 7 A bottom view of a retractable rescue cabin and a rescue platform in one embodiment;

[0067] Figure 8 1 is a flow chart of a method for controlling a high-altitude rescue drone according to an embodiment;

[0068] Figure 9 1 is a structural block diagram of a control device for a high-altitude rescue drone in one embodiment. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0070] The control method of the high altitude rescue drone provided in the embodiment of the present application can be applied to Figure 1-7In the application environment of the high-altitude rescue drone shown, the drone comprises a main frame; a first main power rotor module and a second main power rotor module symmetrically arranged on either side of the longitudinal axis; a retractable rescue cabin and rescue platform integrated into the front of the main frame; a dynamic balance adjustment system, two coaxial counter-rotating rotors arranged at the rear of the main frame; and a control center. The control center is used to generate instructions corresponding to all hardware structures and the system's operational control strategies. The control center can be a terminal, which can be, but is not limited to, various personal computers, laptops, smartphones, tablets, Internet of Things devices, and portable wearable devices. Among them, the terminal uses the real-time collected regional environmental images and regional airflow data to conduct a comprehensive analysis of the flight impact distribution data and airflow data of the rescue area, thereby generating a rescue and evacuation control strategy for the high-altitude rescue drone, thereby avoiding the difficulty in achieving effective fit with the facade of the high-altitude building due to the interference of downwash airflow when implementing high-altitude rescue. In addition, this method can be adjusted in real time as the collected regional environmental images and regional airflow data change, thereby improving the rescue success rate of the high-altitude rescue drone. Then, this solution combines the real-time collected real-time load change information of the high-altitude rescue drone, and generates a real-time load compensation control strategy for the high-altitude rescue drone through an adaptive load compensation program, thereby improving the efficient processing capability of the fast, highly dynamic, unbalanced load generated by the rescued personnel going up and down or moving in the cabin, and realizing high-dynamic and high-precision balance regulation, improving the balance control stability of the high-altitude rescue drone, thereby comprehensively improving the protection effect of rescue safety.

[0071] In an exemplary embodiment, Figure 8 As shown, a control method for a high-altitude rescue drone is provided, and the method is applied to Figure 1 The control center application in the embodiment is described as a terminal, and the process includes the following steps S801 to S803.

[0072] Step S801 : collecting a regional environment image of the rescue area and regional airflow data of the rescue area, and identifying a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image.

[0073] In this embodiment, the terminal uses a camera installed at the front end of a high-altitude rescue drone to capture real-time images of the rescue area's environment. This image is a high-definition display capable of displaying the rescue area's environment in high definition. For example, this image includes, but is not limited to, information such as the extent of the flames in the fire area, the extent of the dense smoke in the fire area, the extent of the building structures in the fire area, and the extent of people awaiting rescue in the fire area. The terminal then generates regional airflow data for the rescue area using airflow sensors installed at various locations within the rescue area. This airflow data includes information on changes in airflow intensity, airflow range, and airflow type. Based on the image, the terminal identifies the target rescue area and the flight impact distribution data corresponding to the target rescue area. The flight impact distribution data includes sub-flight impact distribution data for different flight impact types, including, but not limited to, airflow impact type, smoke impact type, temperature impact type, building structure impact type, and impact point impact type.

[0074] Step S802: Generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range.

[0075] In this embodiment, the terminal generates a regional rescue evacuation route for the target rescue range based on regional airflow data and the distribution data of various flight impacts corresponding to the target rescue range. Based on this regional rescue evacuation route, the terminal also generates a rescue evacuation control strategy for the high-altitude rescue drone. The regional rescue evacuation route includes the route from the high-altitude rescue drone to / from the target rescue range, as well as the route the high-altitude rescue drone takes when approaching / leaving the rescue target within the target rescue range. Specifically, before reaching the target rescue range, the high-altitude rescue drone is primarily influenced by airflow data, resulting in different paths when approaching / leaving the target rescue range. While within the target rescue range, the high-altitude rescue drone is subject to flight impacts including not only environmental airflow data but also downwash interference caused by high temperatures, temperature effects, alignment issues caused by structural deformation, and the impact of smoke and dust on the high-altitude rescue drone's equipment operation. Therefore, different routes need to be generated for different ranges to ensure efficient rescue operations and mitigate interference.

[0076] Step S803, during the execution of the rescue and evacuation control strategy, collect the real-time load change information of the high-altitude rescue UAV, and based on the real-time load change information, generate the real-time load compensation control strategy of the high-altitude rescue UAV through an adaptive load compensation program.

[0077] In this embodiment, during the execution of the rescue and evacuation control strategy, the terminal collects real-time load change information of the high-altitude rescue drone and generates a real-time load compensation control strategy for the high-altitude rescue drone based on the real-time load change information through an adaptive load compensation program. The adaptive load compensation program is a dynamic balance adjustment system (or dual-redundant balance adjustment system) for the high-altitude rescue drone, i.e., a balance adjustment strategy for the two coaxial counter-rotating rotors arranged at the rear of the main frame. The adaptive load compensation program has three operating modes: an unloaded stabilization mode, in which the upper rotor of the dual-redundant balance rotor rotates forward to output lift, and the total power ratio is dynamically maintained at 18% ± 2% of the total power of the active rotor module; a load compensation mode, in which the system automatically adjusts the speed of the dual-redundant balance rotors, generates a sinking torque through differential regulation, and forms a torque to compensate for the center of gravity offset to maintain the balance of the fuselage; a fault-tolerant control mechanism, a dual-redundant CAN bus communication architecture is used to ensure that the control command transmission delay is ≤ 5μs, and an anti-disturbance strategy based on sliding mode variable structure control is used to suppress the rolling moment fluctuation caused by sudden wind disturbances. The drone's rear-mounted dual power units utilize a coaxial, counter-rotating design, integrating two brushless motors with independent electronic control systems. This system compensates for center-of-gravity shifts through steering and speed regulation. The system employs a multimodal control architecture for stable control. In basic hover mode, the upper rotor maintains baseline lift at 19% ± 2% of nominal power. In payload interaction mode, the system activates a dual-channel differential adaptive compensation mechanism based on a payload positioning network constructed using a high-precision inertial navigation unit and a distributed laser Doppler vibrometer array. Torque generation involves three synergistic layers: differential torque compensation, reverse thrust vector synthesis, and dynamic inertia compensation. A specially designed anti-stall protection module uses Hall sensors to capture rotor phase information in real time. This, combined with a dynamic sliding mode control algorithm, sets the critical speed threshold, effectively mitigating the nonlinear torque attenuation caused by aerodynamic coupling in the low-speed range.

[0078] Based on the above scheme, through the real-time collection of regional environmental images and regional airflow data, the flight impact distribution data and airflow data of the rescue area that affect the flight are comprehensively analyzed, thereby generating a rescue and evacuation control strategy for the high-altitude rescue drone, thereby avoiding the difficulty in achieving effective fit with the facade of the high-altitude building due to the interference of downwash airflow when implementing high-altitude rescue. In addition, this method can be adjusted in real time as the collected regional environmental images and regional airflow data change, thereby improving the rescue success rate of the high-altitude rescue drone. Then, this scheme combines the real-time load change information of the high-altitude rescue drone collected in real time, and generates the real-time load compensation control strategy of the high-altitude rescue drone through an adaptive load compensation program, thereby improving the efficient processing capability of the fast, highly dynamic, unbalanced load generated by the rescued personnel going up and down or moving in the cabin, and realizing high-dynamic and high-precision balance regulation, improving the balance control stability of the high-altitude rescue drone, thereby comprehensively improving the protection effect of rescue safety.

[0079] Optionally, based on the regional environmental image, the target rescue range of the rescue area and the flight impact distribution data corresponding to the target rescue range are identified, including: based on the regional environmental image, through an image recognition network, the target rescue range of the rescue target in the rescue area is identified; in the regional environmental image, the target range image of the target rescue range is screened, and based on the target range image, through an image feature extraction network, the feature distribution data of each flight impact type of the target rescue range are extracted respectively, and the feature distribution data of each flight impact type are used as the flight impact distribution data corresponding to the target rescue range.

[0080] In this embodiment, the terminal uses an image recognition network based on the regional environment image to identify the target rescue range of the rescue target in the rescue area. The image recognition network is a convolutional neural network based on a deep learning model. After identifying the location information of the rescue target in the rescue area, the area within the preset range length of the rescue target area is used as the target rescue range.

[0081] In the regional environment image, the terminal selects the target range image of the target rescue range and, based on the target range image, uses an image feature extraction network to extract feature distribution data for each flight impact type within the target rescue range. This feature distribution data for each flight impact type is then used as the flight impact distribution data corresponding to the target rescue range. The image feature extraction network is a convolutional neural network based on an attention mechanism. The terminal adjusts the flight impact type corresponding to each attention mechanism to extract feature data for each flight impact type. This feature data is then distributed and arranged within the image according to its distribution within the target rescue range to obtain feature distribution data.

[0082] Based on the above scheme, by first extracting the target rescue range and then extracting the feature data of the flight impact type, the comprehensiveness and accuracy of the recognition of the distribution data of each flight impact within the target rescue range are improved.

[0083] Optionally, before generating the regional rescue evacuation route of the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, it also includes: identifying the airflow data distribution information of each airflow type in the rescue area based on the regional airflow data; performing data distribution mapping processing on the airflow data distribution information of each airflow type in the regional environment image corresponding to the rescue area to obtain a three-dimensional distribution map of the airflow data of the rescue area.

[0084] In this embodiment, the terminal identifies airflow data distribution information for various airflow types within the rescue area based on regional airflow data. Airflow types include, but are not limited to, rising airflow, falling airflow, steady airflow, kicking airflow, foehn wind, canyon wind, and bulrush. This airflow data distribution information includes information on the range and intensity of each airflow type.

[0085] The terminal performs data distribution mapping processing on the airflow data distribution information of each airflow type in the regional environment image corresponding to the rescue area, and obtains a three-dimensional distribution map of the airflow data in the rescue area.

[0086] Based on the above scheme, by performing data distribution mapping processing on the airflow data distribution information of different airflow types, a three-dimensional distribution map of the airflow data in the rescue area is obtained, which improves the accuracy and comprehensiveness of the recognition of the airflow data distribution in the rescue area.

[0087] Optionally, based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, a regional rescue evacuation route for the target rescue range is generated, including: based on the three-dimensional distribution map of the airflow data of the rescue area, identifying the airflow distribution range of each airflow type in the rescue area, and obtaining the airflow limit information of the high-altitude rescue UAV; based on the airflow limit information, identifying the airflow condition range of the high-altitude rescue UAV, and based on the airflow condition range of the high-altitude rescue UAV, identifying the target airflow type applicable to the high-altitude rescue UAV; based on the target rescue range and the airflow distribution range of the target airflow type, identifying the first rescue evacuation route corresponding to the high-altitude rescue UAV through the route planning network; based on the flight impact distribution data of the target rescue range, identifying the condition distribution information of each rescue condition of the target rescue range, and based on the condition distribution information of each rescue condition, generating a second rescue evacuation route for the high-altitude rescue UAV through the rescue evacuation condition planning network; using the first rescue evacuation route corresponding to the high-altitude rescue UAV and the second rescue evacuation route of the high-altitude rescue UAV as the regional rescue evacuation route for the target rescue range.

[0088] In this embodiment, the terminal identifies the airflow distribution range of each airflow type in the rescue area based on the three-dimensional distribution map of airflow data in the rescue area and obtains the airflow limit information of the high-altitude rescue drone. The airflow limit information is the airflow intensity range of each airflow type that the high-altitude rescue drone can withstand.

[0089] The terminal identifies the airflow condition range of the high-altitude rescue drone based on the airflow limit information, wherein the airflow condition range is the range within which the high-altitude rescue drone can fly.

[0090] The terminal identifies the target airflow type applicable to the high-altitude rescue drone based on the airflow condition range of the high-altitude rescue drone. Then, based on the target rescue range and the airflow distribution range of the target airflow type, the terminal uses a route planning network to identify a first rescue evacuation route corresponding to the high-altitude rescue drone. The first rescue evacuation route is the flight path from the high-altitude rescue drone to / from the target rescue range. The route planning network is an artificial neural network based on a radial basis function (RBF) network.

[0091] Based on the flight impact distribution data for the target rescue range, the terminal identifies the conditional distribution information for each rescue condition within the target rescue range. Based on this conditional distribution information, the terminal generates a second rescue evacuation route for the high-altitude rescue drone using a rescue evacuation condition planning network. This rescue evacuation condition planning network uses each rescue condition as a reward function and the conditional distribution information for each rescue condition as the environmental range. Using an ant colony algorithm or a fish swarm algorithm, the network generates route information for the high-altitude rescue drone to and from the target rescue location. These rescue conditions include, but are not limited to, temperature, airflow, smoke, and structural fit.

[0092] The terminal uses the first rescue evacuation route corresponding to the high-altitude rescue drone and the second rescue evacuation route of the high-altitude rescue drone as the regional rescue evacuation routes of the target rescue range.

[0093] Based on the above scheme, different strategies are used to identify the first rescue evacuation route and the second rescue evacuation route corresponding to the high-altitude rescue drone, thereby improving the accuracy and comprehensiveness of the identification of the regional rescue evacuation routes within the target rescue range.

[0094] Optionally, based on the regional rescue evacuation route of the target rescue range, a rescue evacuation control strategy for the high-altitude rescue UAV is generated, including: identifying the rescue evacuation trajectory of the high-altitude rescue UAV based on the regional rescue evacuation route; identifying the force direction corresponding to each trajectory point of the rescue evacuation trajectory and the force intensity corresponding to each trajectory point through a linear integration algorithm; generating force control information of the high-altitude rescue UAV corresponding to each trajectory point based on the force direction corresponding to each trajectory point and the force intensity corresponding to each trajectory point; generating a rescue evacuation control strategy for the high-altitude rescue UAV based on the force control information of the high-altitude rescue UAV corresponding to each trajectory point.

[0095] In this embodiment, the terminal identifies the rescue evacuation trajectory of the high-altitude rescue drone based on the regional rescue evacuation route; and uses a linear integration algorithm to identify the force direction and force intensity corresponding to each trajectory point of the rescue evacuation trajectory. The linear integration algorithm is a line integration algorithm, and the algorithm calculation formula is:

[0096]

[0097] W can be interpreted as the total work done by the force over the distance a particle moves under the action of an external force from t1 to t2. F is the force intensity, r is the force direction, and t is time. The trajectory points are the trajectory points obtained by integrating the rescue evacuation trajectory.

[0098] The terminal generates force control information of the high-altitude rescue drone corresponding to each trajectory point based on the force direction corresponding to each trajectory point and the force intensity corresponding to each trajectory point.

[0099] Finally, the terminal generates a rescue and evacuation control strategy for the high-altitude rescue drone based on the force control information corresponding to each trajectory point. This rescue and evacuation control strategy controls the high-altitude rescue drone's dual-power rotors, thereby controlling the high-altitude rescue drone to the rescue target within the target rescue range.

[0100] In another embodiment, the terminal collects new regional airflow data and new regional environmental images in real time, and then returns to execute the steps of identifying the target rescue range of the rescue area and the flight impact distribution data corresponding to the target rescue range based on the regional environmental image, so as to adjust the rescue and evacuation control strategy of the high-altitude rescue drone in real time, thereby ensuring that the high-altitude rescue drone can adapt to and respond to environmental changes and airflow changes in a timely manner.

[0101] Based on the above scheme, the rescue evacuation control strategy of the high-altitude rescue UAV is generated by integrating the regional rescue evacuation routes, thereby improving the accuracy and timeliness of the rescue evacuation control of the high-altitude rescue UAV.

[0102] Optionally, based on the real-time load change information, a real-time load compensation control strategy for the high-altitude rescue UAV is generated through an adaptive load compensation program, including: based on the real-time load change information, identifying the load change data of each load detection type, and based on the load change data of each load detection type, generating the synergistic force distribution information of each synergistic layer through the multi-modal control architecture of the adaptive load compensation program; based on the synergistic force distribution information of each synergistic layer, generating a dual redundant balance adjustment system control strategy for the high-altitude rescue UAV through a balance adjustment program; and using the dual redundant balance adjustment system control strategy of the high-altitude rescue UAV as the real-time load compensation control strategy for the high-altitude rescue UAV.

[0103] In this embodiment, the terminal identifies load change data for each load detection type based on real-time load change information. Based on this data, the terminal generates synergistic force distribution information for each synergistic layer using a multimodal control architecture with an adaptive load compensation program. Specifically, the system employs a multimodal control architecture to achieve stable control. In basic hover mode, the upper rotor maintains a baseline lift at 19% ± 2% of nominal power. Upon entering load interaction mode, the system activates a dual-channel differential adaptive compensation mechanism based on a load positioning network constructed using a high-precision inertial navigation unit and a distributed laser Doppler vibrometer array. Torque generation consists of three synergistic layers: differential torque compensation, reverse thrust vector synthesis, and dynamic inertia compensation. A specially designed anti-stall protection module uses a Hall sensor array to capture rotor phase information in real time. This module, combined with a dynamic sliding mode control algorithm, sets a critical speed threshold, effectively mitigating the nonlinear torque attenuation caused by aerodynamic coupling effects in the low-speed range.

[0104] Based on the synergistic force distribution information of each synergistic force, the terminal generates a dual-redundant balance adjustment system control strategy for the high-altitude rescue drone through a balance adjustment program. Finally, the terminal uses this dual-redundant balance adjustment system control strategy as the high-altitude rescue drone's real-time load compensation control strategy.

[0105] Based on the above scheme, the application set by this scheme is as follows Figure 1-7 The adaptive load compensation program of the high-altitude rescue UAV shown in the figure can compensate and control the load of the high-altitude rescue UAV in real time, ensuring high dynamic and high precision balance control.

[0106] In an exemplary embodiment, Figure 1As shown, a high-altitude rescue UAV is provided, which includes a control center, a UAV main frame, a first main power rotor module, a second main power rotor module, a telescopic rescue cabin and a rescue platform, and a dynamic balance adjustment system, wherein: the first main power rotor module and the second main power rotor module are respectively arranged on both sides of the longitudinal axis of the UAV main frame, the telescopic rescue cabin and the rescue platform are arranged at the front of the main frame, and the dynamic balance adjustment system is arranged at the rear of the main frame; the UAV main frame, the first main power rotor module, the second main power rotor module, the telescopic rescue cabin and the rescue platform, and the dynamic balance adjustment system are wirelessly connected to the control center; the control center is used to collect regional environmental images of the rescue area and regional airflow data of the rescue area, and identify the target rescue range of the rescue area and the flight impact distribution data corresponding to the target rescue range based on the regional environmental images; generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a regional rescue evacuation route for the target rescue range based on the regional rescue evacuation route. Evacuation route, generate a rescue evacuation control strategy for the high-altitude rescue UAV; during the execution of the rescue evacuation control strategy, collect the real-time change information of the load of the high-altitude rescue UAV, and based on the real-time change information of the load, generate the real-time compensation control strategy for the load of the high-altitude rescue UAV through an adaptive load compensation program; the first main power rotor module and the second main power rotor module are used to execute the rescue evacuation control strategy of the high-altitude rescue UAV; the UAV theme framework is used to connect the first main power rotor module, the second main power rotor module, the telescopic rescue cabin and the rescue platform, and the power balance adjustment system; the telescopic rescue cabin and the rescue platform, including the telescopic rescue cabin and the rescue platform, are used to carry the rescue target; the dynamic balance adjustment system includes two coaxial counter-rotating rotors, which generate adjustable balancing force through differential drive to assist the first main power rotor module and the second main power rotor module to execute the rescue evacuation control strategy of the high-altitude rescue UAV and execute the real-time compensation control strategy for the load of the high-altitude rescue UAV; the real-time compensation control strategy for the load is used for high dynamics and the center of gravity spans the first and second power lines.

[0107] Among them, the telescopic rescue cabin and rescue platform are equipped with a high-precision electric linear actuation system, which includes a three-stage silicon carbide ceramic guide rail nested structure, a double closed-loop controlled ball screw transmission mechanism (to enhance positioning accuracy) and a self-compensating guide device (with built-in silicon nitride ceramic bearings and graphene composite lubrication layer to ensure telescopic action in high temperature environment). The surface of the rescue cabin and rescue platform is made of silicon carbide fiber reinforced yttrium aluminum garnet ceramic-based composite material, the outer layer is a porous gradient ceramic layer, the middle layer is an aerogel thermal insulation buffer layer, and the inner layer is an antibacterial and anti-slip composite material, forming a gradient composite protection structure.

[0108] The retractable rescue pod and platform utilize electrically driven rails for free extension and retraction. When a rescue is underway, they fully deploy, balancing the drone's rear counterweight beam. The rescue platform features a symmetrical U-shaped cross-section and safety rails. A graphene oxide-modified silicon carbide ceramic-based fireproof interface layer is applied to the platform's base. This allows it to be installed on the exterior facade of a high-rise building, creating a high-friction, flame-retardant rescue platform that ensures the safety of rescued personnel and the stability of the rescue boarding process. The top of the rescue pod is equipped with an intelligent fire-extinguishing integrated module and a dynamic fire response system.

[0109] The dynamic balancing system's operating logic includes the following mechanisms. In no-load stabilization mode, the upper rotor of the dual-redundant balancing rotor rotates forward to generate lift, dynamically maintaining the total power ratio at 18% ± 2% of the total power of the active rotor module. In load compensation mode, the system automatically adjusts the speed of the dual-redundant balancing rotors, generating a downward moment through differential regulation, forming a couple moment to compensate for center of gravity shift and maintain fuselage balance. Furthermore, a fault-tolerant control mechanism uses a dual-redundant CAN bus communication architecture to ensure control command transmission delays of ≤ 5μs, and an anti-disturbance strategy based on sliding mode variable structure control to suppress rolling moment fluctuations caused by sudden wind disturbances.

[0110] The balancing and adjustment system integrates a multimodal perception fusion architecture (including a high-precision inertial navigation unit and a distributed laser Doppler vibrometer array) and a robust adaptive control system (including a dynamic threshold decision module and a reference model adaptive controller) to achieve high-precision and adaptive control of the dual redundant balanced propellers.

[0111] The dynamic balancing system features two rotors, one rotating to the left and one rotating to the right. Each is driven by a bidirectional, continuously adjustable power module. The drive modes include dual positive, positive-negative, and reverse drive modes. In the dual positive mode, the forces generated by left and right rotations are opposite, and the resultant force is the difference between the forces of the two propellers. By adjusting the difference in the driving force of the two propellers, the direction and magnitude of the resultant force can be adjusted. This method overcomes the low-speed dead zone of a single propeller, making the resultant force continuously adjustable. This method is suitable for the project's requirement for continuous, highly dynamic directional change forces. In the positive and reverse modes, the power of both propellers is directed upward simultaneously, generating a wider range of upward balancing forces. In the reverse mode, the power of both propellers is directed downward simultaneously, generating a wider range of downward balancing forces, thereby increasing the drone's balancing ability.

[0112] In one embodiment, the control center of the high-altitude rescue drone controls the flight process of the high-altitude rescue drone and the example of the balance dynamic adjustment process is as follows (eg Figure 1-7 shown):

[0113] Drone launch phase.

[0114] To make the drone take off, the power supply 11 is started, the controller 12 autonomously detects and controls the drone, and the main power rotor is provided with a reference vertical thrust by the coaxial dual-rotor power units (rotor 3 and rotor 4) symmetrically distributed on both sides of the fuselage. At the same time, the foldable bracket 8 of the fuselage is folded and the bracket 9 maintains its original posture.

[0115] Because it contains a tail counterweight structure 7, in order to ensure the no-load balance of the UAV, the flight control system starts the differential control strategy based on the initial attitude parameters. The upper rotor 5 in the dual redundant balanced rotor group starts the forward lift mode, and the corresponding lower rotor 6 enters the standby hold state.

[0116] This asymmetric thrust distribution scheme effectively offsets the pitching moment caused by the rearward shift of the center of gravity, enabling the main body 2 and the central payload compartment 1 to achieve stable vertical climb under the action of lift coupling, while maintaining the attitude stability of the roll axis and yaw axis.

[0117] UAV docking phase: After the UAV approaches the high-rise building and completes hovering attitude calibration, the integrated rescue system initiates the multimodal docking procedure.

[0118] To assess and suppress fires outside high-rise buildings, a multispectral fire perception array (including visible light / infrared vision modules) integrated into the freely retractable rescue capsule 16 uses a convolutional neural network to analyze the thermal radiation distribution of the building's facade in real time and locate the core fire source area outside the building. Simultaneously, the integrated directional fire extinguishing unit is activated, mobilizing the fire extinguishing agent storage tank 10 and initiating a gradient injection strategy (dual-mode release of dry powder / aerosol) based on the fire intensity. A turbulent field simulation algorithm is also used to optimize the injection trajectory to ensure that the fire extinguishing agent penetrates the flame plume and reaches the burning surface directly.

[0119] Regarding the construction of the rescue channel, when the thermal imaging sensor detects that the temperature of the target area drops to a safe threshold, the guide rail driver 13 drives the telescopic rescue cabin 16 and the telescopic rescue platform 15, including the integrated guardrail 14, to be extended with millimeter-level precision positioning through the electric-driven linear guide rail 18. The hinged support rail 17 is used to ensure the stability of the telescopic process. The rescue platform 15 adaptively attaches to the facade of the high-rise building (including window sills, balconies, etc.).

[0120] The rescue phase involves high-speed and highly dynamic load changes, and the dual redundant balanced rotors implement a dynamic torque compensation strategy.

[0121] The rotor mode switches, rotor 6 switches to the forward thrust mode, rotor 5 executes the linear deceleration instruction, and after its speed reaches 0, rotor 5 switches to the reverse torque mode.

[0122] The voice guidance protocol is activated through the multimodal human-computer interaction system to guide the rescued personnel to log onto the telescopic rescue platform 15. The telescopic rescue cabin 16 is integrated with a pre-tightened electromagnetic restraint device, and the mechanical coupling between the personnel and the cabin structure is achieved through a self-locking buckle mechanism.

[0123] To achieve high-dynamic and high-precision balance control, a multi-source sensor array (including MEMS inertial navigation unit, distributed fiber Bragg grating weight sensor, and Hall effect motor monitoring module) collects data streams, which are fused through extended Kalman filtering to generate dynamic balancing coefficients. The reverse rotation of rotor 5 and the forward rotation of rotor 6 form a differential thrust dipole, generating a controllable sink vector to suppress pitch oscillations caused by sudden lift changes.

[0124] In order to deal with the extreme load, when the extreme load is reached, the load threshold alarm protocol is triggered and the recovery procedure of the telescopic rescue platform 15 is activated.

[0125] When the rescue platform 15 and the rescue cabin 16 are recovered, sound and light warnings are turned on to guide the rescued personnel. The guide rail driver 13 drives the telescopic rescue platform 15 to retract gradually. If the limit load is not reached, there is no need to trigger the alarm protocol. The telescopic rescue platform 15 adopts adaptive retraction.

[0126] In order to achieve return and landing, the anti-disturbance balance algorithm is activated during the return phase. The differential speed ratio of the dual-compensation rotor 5 and the rotor 6 is dynamically optimized through the Lyapunov stability criterion to offset the inertial disturbance caused by the high-speed load change caused by the movement of personnel in the cabin. The main power rotor 2 and the main power rotor 3 execute the lift system according to the exponential decay thrust curve, combined with the landing cushion control law, and coordinated to control the opening of the foldable bracket 8, and finally achieve a soft landing.

[0127] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0128] Based on the same inventive concept, the present application also provides a control device for a high-altitude rescue drone for implementing the aforementioned control method for a high-altitude rescue drone. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the one or more control device embodiments for high-altitude rescue drones provided below can be found in the above-mentioned limitations of the control method for high-altitude rescue drones and will not be repeated here.

[0129] In an exemplary embodiment, Figure 9 As shown, a control device for a high-altitude rescue drone is provided, including: a collection module 910, a generation module 920 and a compensation module 930, wherein:

[0130] The acquisition module 910 is configured to acquire a regional environment image of a rescue area and regional airflow data of the rescue area, and identify a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image;

[0131] A generating module 920 is configured to generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range;

[0132] The compensation module 930 is used to collect the real-time load change information of the high-altitude rescue drone during the execution of the rescue and evacuation control strategy, and based on the real-time load change information, generate a real-time load compensation control strategy for the high-altitude rescue drone through an adaptive load compensation program.

[0133] Optionally, the acquisition module 910 is specifically configured to:

[0134] Based on the regional environment image, identifying the target rescue range of the rescue target in the rescue area through an image recognition network;

[0135] In the regional environment image, the target range image of the target rescue range is screened, and based on the target range image, the characteristic distribution data of each flight impact type of the target rescue range is extracted respectively through the image feature extraction network, and the characteristic distribution data of each flight impact type is used as the flight impact distribution data corresponding to the target rescue range.

[0136] Optionally, the device further includes:

[0137] an identification module, configured to identify airflow data distribution information of each airflow type in the rescue area based on the regional airflow data;

[0138] The mapping module is used to perform data distribution mapping processing on the airflow data distribution information of each airflow type in the regional environment image corresponding to the rescue area to obtain a three-dimensional distribution map of the airflow data of the rescue area.

[0139] Optionally, the generating module 920 is specifically configured to:

[0140] Based on the three-dimensional distribution map of airflow data in the rescue area, identifying the airflow distribution range of each airflow type in the rescue area, and obtaining airflow limit information of the high-altitude rescue drone;

[0141] Based on the airflow limit information, identifying the airflow condition range of the high-altitude rescue drone, and based on the airflow condition range of the high-altitude rescue drone, identifying the target airflow type applicable to the high-altitude rescue drone;

[0142] Based on the target rescue range and the airflow distribution range of the target airflow type, identifying a first rescue evacuation route corresponding to the high-altitude rescue drone through a route planning network;

[0143] Based on the flight impact distribution data of the target rescue range, identifying condition distribution information of each rescue condition in the target rescue range, and generating a second rescue evacuation route for the high-altitude rescue drone through a rescue evacuation condition planning network based on the condition distribution information of each rescue condition;

[0144] The first rescue evacuation route corresponding to the high-altitude rescue drone and the second rescue evacuation route of the high-altitude rescue drone are used as regional rescue evacuation routes within the target rescue range.

[0145] Optionally, the generating module 920 is specifically configured to:

[0146] Based on the regional rescue evacuation route, identifying the rescue evacuation trajectory of the high-altitude rescue drone;

[0147] Identifying the direction of the force corresponding to each trajectory point of the rescue evacuation trajectory and the strength of the force corresponding to each trajectory point through a linear integration algorithm;

[0148] Based on the force direction corresponding to each of the trajectory points and the force intensity corresponding to each of the trajectory points, generating force control information of the high-altitude rescue drone corresponding to each of the trajectory points;

[0149] Based on the force control information of the high-altitude rescue drone corresponding to each of the trajectory points, a rescue and evacuation control strategy of the high-altitude rescue drone is generated.

[0150] Optionally, the compensation module 930 is specifically configured to:

[0151] Based on the real-time load change information, load change data of each load detection type is identified, and based on the load change data of each load detection type, synergistic force distribution information of each synergistic layer is generated through a multi-modal control architecture of an adaptive load compensation program;

[0152] Based on the synergistic force distribution information of each of the synergistic frictions, a dual-redundant balance adjustment system control strategy of the high-altitude rescue drone is generated through a balance adjustment program;

[0153] The dual-redundant balance adjustment system control strategy of the high-altitude rescue UAV is used as the real-time load compensation control strategy of the high-altitude rescue UAV.

[0154] Each module in the control device for the high-altitude rescue drone described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0155] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps corresponding to the control method of the high-altitude rescue drone when executing the computer program.

[0156] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps corresponding to the control method of the high-altitude rescue drone are implemented.

[0157] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements steps corresponding to a method for controlling a high-altitude rescue drone.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0160] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0161] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A control method for a high-altitude rescue drone, characterized in that: The method comprises: Collecting a regional environment image of a rescue area and regional airflow data of the rescue area, and identifying a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image; Generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range; During the execution of the rescue and evacuation control strategy, the real-time load change information of the high-altitude rescue UAV is collected, and based on the real-time load change information, a real-time load compensation control strategy of the high-altitude rescue UAV is generated through an adaptive load compensation program.

2. The method according to claim 1, characterized in that The identifying, based on the regional environment image, a target rescue range of the rescue area and each flight impact distribution data corresponding to the target rescue range includes: Based on the regional environment image, identifying the target rescue range of the rescue target in the rescue area through an image recognition network; In the regional environment image, the target range image of the target rescue range is screened, and based on the target range image, the characteristic distribution data of each flight impact type of the target rescue range is extracted respectively through the image feature extraction network, and the characteristic distribution data of each flight impact type is used as the flight impact distribution data corresponding to the target rescue range.

3. The method according to claim 1, characterized in that Before generating the regional rescue evacuation route of the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, the method further includes: Based on the regional airflow data, identifying airflow data distribution information of each airflow type in the rescue area; The airflow data distribution information of each airflow type is respectively subjected to data distribution mapping processing in the regional environment image corresponding to the rescue area to obtain a three-dimensional distribution map of the airflow data of the rescue area.

4. The method according to claim 3, characterized in that Generating a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range includes: Based on the three-dimensional distribution map of airflow data in the rescue area, identifying the airflow distribution range of each airflow type in the rescue area, and obtaining airflow limit information of the high-altitude rescue drone; Based on the airflow limit information, identifying the airflow condition range of the high-altitude rescue drone, and based on the airflow condition range of the high-altitude rescue drone, identifying the target airflow type applicable to the high-altitude rescue drone; Based on the target rescue range and the airflow distribution range of the target airflow type, identifying a first rescue evacuation route corresponding to the high-altitude rescue drone through a route planning network; Based on the flight impact distribution data of the target rescue range, identifying condition distribution information of each rescue condition in the target rescue range, and generating a second rescue evacuation route for the high-altitude rescue drone through a rescue evacuation condition planning network based on the condition distribution information of each rescue condition; The first rescue evacuation route corresponding to the high-altitude rescue drone and the second rescue evacuation route of the high-altitude rescue drone are used as regional rescue evacuation routes within the target rescue range.

5. The method according to claim 1, wherein The regional rescue evacuation route based on the target rescue range generates a rescue evacuation control strategy for the high-altitude rescue drone, including: Based on the regional rescue evacuation route, identifying the rescue evacuation trajectory of the high-altitude rescue drone; Identifying the direction of the force corresponding to each trajectory point of the rescue evacuation trajectory and the strength of the force corresponding to each trajectory point through a linear integration algorithm; Based on the force direction corresponding to each of the trajectory points and the force intensity corresponding to each of the trajectory points, generating force control information of the high-altitude rescue drone corresponding to each of the trajectory points; Based on the force control information of the high-altitude rescue drone corresponding to each of the trajectory points, a rescue and evacuation control strategy of the high-altitude rescue drone is generated.

6. The method according to claim 1, characterized in that The method of generating a real-time load compensation control strategy for the high-altitude rescue UAV based on the real-time load change information through an adaptive load compensation program includes: Based on the real-time load change information, load change data of each load detection type is identified, and based on the load change data of each load detection type, synergistic force distribution information of each synergistic layer is generated through a multi-modal control architecture of an adaptive load compensation program; Based on the synergistic force distribution information of each of the synergistic frictions, a dual-redundant balance adjustment system control strategy of the high-altitude rescue drone is generated through a balance adjustment program; The dual-redundant balance adjustment system control strategy of the high-altitude rescue UAV is used as the real-time load compensation control strategy of the high-altitude rescue UAV.

7. A control device for a high-altitude rescue drone, characterized in that: The device comprises: an acquisition module, configured to acquire a regional environment image of a rescue area and regional airflow data of the rescue area, and identify a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environment image; a generating module for generating a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generating a rescue evacuation control strategy for the high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range; The compensation module is used to collect the real-time load change information of the high-altitude rescue drone during the execution of the rescue and evacuation control strategy, and based on the real-time load change information, generate a real-time load compensation control strategy for the high-altitude rescue drone through an adaptive load compensation program.

8. A high-altitude rescue drone, characterized in that: The high-altitude rescue drone includes a control center, a drone main frame, a first main power rotor module, a second main power rotor module, a telescopic rescue cabin and a rescue platform, and a power balance adjustment system, wherein: The first main power rotor module and the second main power rotor module are respectively arranged on both sides of the longitudinal axis of the UAV main frame, the telescopic rescue cabin and the rescue platform are arranged at the front of the main frame, and the dynamic balance adjustment system is arranged at the rear of the main frame; The UAV main frame, the first main power rotor module, the second main power rotor module, the telescopic rescue cabin and rescue platform, and the power balance adjustment system are wirelessly connected to the control center; The control center is configured to collect a regional environmental image of a rescue area and regional airflow data of the rescue area, and identify a target rescue range of the rescue area and flight impact distribution data corresponding to the target rescue range based on the regional environmental image; generate a regional rescue evacuation route for the target rescue range based on the regional airflow data and the flight impact distribution data corresponding to the target rescue range, and generate a rescue evacuation control strategy for a high-altitude rescue drone based on the regional rescue evacuation route for the target rescue range; during the execution of the rescue evacuation control strategy, collect real-time load change information of the high-altitude rescue drone, and generate a real-time load compensation control strategy for the high-altitude rescue drone based on the real-time load change information through an adaptive load compensation program; The first main power rotor module and the second main power rotor module are used to execute the rescue and evacuation control strategy of the high-altitude rescue drone; The UAV theme framework is used to connect the first main power rotor module, the second main power rotor module, the telescopic rescue cabin and the rescue platform, and the power balance adjustment system; The telescopic rescue cabin and rescue platform include a telescopic rescue cabin and a rescue platform, which are used to carry the rescue target; The dynamic balance adjustment system includes two coaxial counter-rotating rotors, which generate an adjustable balancing force through differential drive to assist the first main power rotor module and the second main power rotor module to execute the rescue and evacuation control strategy of the high-altitude rescue drone and the real-time load compensation control strategy of the high-altitude rescue drone; the real-time load compensation control strategy is used for high dynamics and the center of gravity spans the first and second power lines.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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