Rotor unmanned aerial vehicle attitude control method and system

Abstract

The embodiment of the invention relates to the technical field of unmanned aerial vehicles, and particularly discloses a rotor unmanned aerial vehicle attitude control method and system. According to the embodiment of the invention, the direct working unmanned aerial vehicle and the auxiliary monitoring unmanned aerial vehicle are selected; performing basic flight control; performing in-place flight control; the working space position of the direct working unmanned aerial vehicle is obtained in real time, and follow-up adjustment of the monitoring attitude of the auxiliary monitoring unmanned aerial vehicle is carried out; and analyzing the follow-up monitoring data, and correspondingly adjusting the working attitude of the direct working unmanned aerial vehicle. The follow-up adjustment of the monitoring attitude of the auxiliary monitoring unmanned aerial vehicle can be performed according to the working space position of the direct working unmanned aerial vehicle, and the working attitude of the direct working unmanned aerial vehicle can be correspondingly adjusted according to the follow-up monitoring of the auxiliary monitoring unmanned aerial vehicle on the direct working unmanned aerial vehicle, so that the problem that the working attitude of the direct working unmanned aerial vehicle cannot be adjusted in a complex fire-fighting environment is solved. The problem that attitude adjustment and control are difficult to timely and effectively carry out when the rotor unmanned aerial vehicle has a monitoring blind area is solved, and the execution efficiency and effect of fire-fighting tasks are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles (UAVs), and in particular relates to a method and system for controlling the attitude of a rotary-wing UAV. Background Art

[0002] A rotor drone is a type of drone that generates lift through the rotation of its rotors, thereby achieving flight and performing various tasks. It usually relies on an electric motor to drive the rotor rotation to generate power. It is mainly composed of a rotor system, fuselage, power system, flight control system and sensor system. It is widely used in aerial photography, geographic surveying and mapping, agricultural planting, logistics distribution and emergency rescue.

[0003] In the existing technology, the attitude adjustment of rotary-wing UAVs mainly relies on the data monitored and obtained by the various sensors carried by them, and corresponding adjustment and control operations are implemented according to the analysis results. For rotary-wing UAVs in firefighting operations, their working environment is extremely complex. The firefighting operation site is often filled with a large amount of thick smoke, which not only seriously blocks the line of sight, but also interferes with the normal operation of the sensors. At the same time, the high temperature environment will affect the performance of the UAV's electronic components and sensors, reducing its monitoring accuracy and reliability. In addition, various obstacles that may exist at the scene, such as collapsed buildings, burning debris, etc., will further increase the difficulty and risk of flight. In such a complex environment, rotary-wing UAVs are very likely to have monitoring blind spots, and it is difficult to timely and effectively perform attitude adjustment control based on the limited and possibly inaccurate data obtained by themselves, which in turn affects the efficiency and effectiveness of the firefighting mission. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for controlling the attitude of a rotary-wing UAV, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for controlling the attitude of a rotary-wing UAV comprises the following steps: Receive firefighting work requirements, plan target firefighting routes, and select direct work drones and auxiliary monitoring drones from multiple rotorcraft drones; Performing basic flight control on the direct-working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receiving basic environmental data transmitted by the auxiliary monitoring UAV; Analyze the basic environmental data, plan the direct working area and auxiliary monitoring location, and perform on-site flight control of the direct working UAV and the auxiliary monitoring UAV; Acquire the working space position of the direct working UAV in real time, perform follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and acquire the follow-up monitoring data of the auxiliary monitoring UAV in real time; The follow-up monitoring data is analyzed to plan and generate work adjustment data, and the working posture of the direct working UAV is correspondingly adjusted according to the work adjustment data.

[0006] A rotary wing UAV attitude control system, comprising a firefighting UAV selection unit, a basic flight control unit, an in-position flight control unit, a monitoring follow-up adjustment unit, and a work corresponding adjustment unit, wherein: A firefighting drone selection unit is used to receive firefighting work requirements, plan target firefighting routes, and select direct-work drones and auxiliary monitoring drones from multiple rotorcraft drones; A basic flight control unit, configured to perform basic flight control on the direct-working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receive basic environmental data transmitted by the auxiliary monitoring UAV; An on-site flight control unit, configured to analyze the basic environmental data, plan the direct working area and the auxiliary monitoring location, and perform on-site flight control of the direct working UAV and the auxiliary monitoring UAV; A monitoring follow-up adjustment unit is used to obtain the working space position of the direct working UAV in real time, perform follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and obtain the follow-up monitoring data of the auxiliary monitoring UAV in real time; The work corresponding adjustment unit is used to analyze the follow-up monitoring data, plan and generate work adjustment data, and make corresponding adjustments to the working posture of the direct working UAV according to the work adjustment data.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention selects a direct-working UAV and an auxiliary-monitoring UAV; performs basic flight control; performs in-position flight control; obtains the working space position of the direct-working UAV in real time, performs follow-up adjustment of the monitoring attitude of the auxiliary-monitoring UAV; analyzes the follow-up monitoring data, and adjusts the working attitude of the direct-working UAV accordingly. The embodiment of the present invention can follow-up adjustment of the monitoring attitude of the auxiliary-monitoring UAV according to the working space position of the direct-working UAV, and adjust the working attitude of the direct-working UAV accordingly based on the follow-up monitoring of the direct-working UAV by the auxiliary-monitoring UAV. This solves the problem of rotor UAVs having monitoring blind spots in complex firefighting environments, making it difficult to perform timely and effective attitude adjustment control, thereby improving the efficiency and effectiveness of firefighting tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0009] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0010] Figure 2 A flowchart of selecting a direct working drone and an auxiliary monitoring drone in the method provided by an embodiment of the present invention is shown.

[0011] Figure 3 A flow chart of basic flight control in the method provided by an embodiment of the present invention is shown.

[0012] Figure 4 A flow chart of performing in-place flight control in the method provided by an embodiment of the present invention is shown.

[0013] Figure 5 A flow chart of the follow-up adjustment of the monitoring posture in the method provided by an embodiment of the present invention is shown.

[0014] Figure 6 A flow chart showing the corresponding adjustment of the working posture in the method provided by an embodiment of the present invention is shown.

[0015] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0016] Figure 8 A structural block diagram of a firefighting drone selection unit in a system provided by an embodiment of the present invention is shown.

[0017] Figure 9 The structure block diagram of the in-situ flight control unit in the system provided by an embodiment of the present invention is shown.

[0018] Figure 10 The structure block diagram of the monitoring and follow-up adjustment unit in the system provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] It is understandable that in the existing technology, the attitude adjustment of the rotor UAV mainly depends on the data monitored and obtained by the various sensors carried by the UAV, and the corresponding adjustment and control operations are implemented according to the analysis results. For the rotor UAV in firefighting operations, the working environment is extremely complex. The firefighting operation site is often filled with a large amount of thick smoke, which not only seriously blocks the line of sight, but also interferes with the normal operation of the sensors. At the same time, the high temperature environment will affect the performance of the UAV's electronic components and sensors, reducing its monitoring accuracy and reliability. In addition, various obstacles that may exist at the scene, such as collapsed buildings, burning debris, etc., will further increase the difficulty and risk of flight. In such a complex environment, the rotor UAV is prone to monitoring blind spots, and it is difficult to timely and effectively perform attitude adjustment control based on the limited and possibly inaccurate data obtained by itself, thereby affecting the efficiency and effectiveness of the firefighting mission.

[0021] To address the above-mentioned issues, an embodiment of the present invention receives firefighting work requirements, plans a target firefighting route, and selects a direct-working drone and an auxiliary monitoring drone from multiple rotorcraft drones. According to the target firefighting route, basic flight control is performed on the direct-working drone and the auxiliary monitoring drone, and basic environmental data transmitted by the auxiliary monitoring drone is received. The basic environmental data is analyzed to plan a direct working area and an auxiliary monitoring position, and the direct-working drone and the auxiliary monitoring drone are controlled in-place. The working space position of the direct-working drone is obtained in real time, and the monitoring attitude of the auxiliary monitoring drone is adjusted accordingly. The auxiliary monitoring drone is also obtained in real time by analyzing the monitoring data to generate work adjustment data. The working attitude of the direct-working drone is adjusted accordingly based on the work adjustment data. The direct-working drone can also adjust its working attitude accordingly based on the auxiliary monitoring drone's monitoring of the direct-working drone. This solves the problem of rotorcraft drones having monitoring blind spots in complex firefighting environments, making it difficult to perform timely and effective attitude adjustment control, thereby improving the efficiency and effectiveness of firefighting tasks.

[0022] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0023] Specifically, the rotary wing UAV attitude control method includes the following steps: Step S101: Receive firefighting work requirements, plan a target firefighting route, and select a direct working drone and an auxiliary monitoring drone from multiple rotor drones.

[0024] In an embodiment of the present invention, by receiving firefighting work requirements, and then performing location identification on the firefighting work requirements, the target firefighting location is determined, and then according to the target firefighting location, the target firefighting route is planned, and the drone status data of multiple standby rotorcraft drones is obtained, and according to the target firefighting route, the endurance requirements are determined, and then according to the endurance requirements, the status data of multiple drones are compared and analyzed, and direct working drones and auxiliary monitoring drones are selected from multiple rotorcraft drones, among which the direct working drone is a rotorcraft drone that directly performs firefighting work; the auxiliary monitoring drone is a rotorcraft drone that does not participate in direct firefighting work but performs real-time monitoring.

[0025] Specifically, Figure 2 A flowchart of selecting a direct working drone and an auxiliary monitoring drone in the method provided by an embodiment of the present invention is shown.

[0026] In a preferred embodiment of the present invention, the steps of receiving firefighting work requirements, planning a target firefighting route, and selecting a direct working drone and an auxiliary monitoring drone from a plurality of rotary-wing drones specifically include the following steps: Step S1011, receiving firefighting work requirements; Step S1012, identifying the firefighting work requirements and determining the target firefighting location; Step S1013, planning a target firefighting route according to the target firefighting location; Step S1014, obtaining drone status data of multiple rotary-wing drones; Step S1015: compare the status data of the plurality of UAVs, and select a direct working UAV and an auxiliary monitoring UAV from the plurality of rotor UAVs.

[0027] In a preferred embodiment of the present invention, comparing the status data of the plurality of UAVs and selecting the direct working UAV and the auxiliary monitoring UAV from the plurality of rotary-wing UAVs specifically includes the following steps: Obtain drone status data and target firefighting routes. Drone status data includes battery charge, flight speed, and load capacity. Set the load adjustment coefficient based on the drone's load capacity. Calculate the route demand quantification indicator based on the target firefighting route. The effective endurance time is calculated by multiplying the battery capacity by the load regulation factor; The theoretical flight time is calculated using the target firefighting route distance and flight speed; The comprehensive score of each drone is calculated based on the effective flight time and theoretical flight time; Sort all drones’ comprehensive scores in descending order and label the sensor type of each drone to obtain a priority list with device attributes; Set the minimum flight time threshold of the target firefighting route, and based on the minimum flight time threshold of the target firefighting route, select the first drone from the priority list with device attributes that meets the effective flight time greater than the minimum flight time threshold to select the direct working drone; If the top 30% of drones in the priority list with device attributes do not meet the threshold, a manual intervention alarm will be triggered to guide the operator to select the working drone directly; Using camera resolution as a screening indicator, several qualified drones are selected from a priority list with device attributes as auxiliary monitoring drones.

[0028] In this embodiment of the present invention, a dynamic coefficient is used to adjust battery life calculations in real time, incorporating the drone's actual payload into the battery evaluation system. This prevents high-load drones from crashing mid-flight due to false battery ratings. A score is generated by integrating multiple parameters, including flight speed, route distance, and battery efficiency, transcending the limitations of single-dimensional selection. Furthermore, differentiated criteria (primary payload endurance threshold and auxiliary payload image performance) are employed to form a complementary selection logic, allowing the direct-operating drone to focus on firefighting, while the auxiliary monitoring drone specializes in auxiliary detection. Furthermore, asymmetric selection is employed for the primary and secondary drones. By combining the dynamic payload attenuation coefficient with the asymmetric selection rule, a "1+1>2" effect is achieved.

[0029] Furthermore, the rotary wing UAV attitude control method further includes the following steps: Step S102: performing basic flight control on the direct working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receiving basic environmental data transmitted by the auxiliary monitoring UAV.

[0030] In an embodiment of the present invention, basic flight control is performed on the direct working UAV and the auxiliary monitoring UAV according to the target fire-fighting route, so that the direct working UAV and the auxiliary monitoring UAV arrive at the target fire-fighting location, and arrival feedback of the direct working UAV and the auxiliary monitoring UAV is received. After that, environmental monitoring instructions are generated and sent to the auxiliary monitoring UAV, and then the basic environmental data transmitted by the auxiliary monitoring UAV is received.

[0031] Specifically, Figure 3 A flow chart of basic flight control in the method provided by an embodiment of the present invention is shown.

[0032] In a preferred embodiment of the present invention, performing basic flight control on the direct-operating UAV and the auxiliary monitoring UAV according to the target firefighting route and receiving basic environmental data transmitted by the auxiliary monitoring UAV specifically include the following steps: Step S1021: performing basic flight control on the direct working UAV and the auxiliary monitoring UAV according to the target firefighting route; Step S1022: receiving arrival feedback of the direct working UAV and the auxiliary monitoring UAV; Step S1023: Generate and send an environmental monitoring instruction to the auxiliary monitoring drone; Step S1024: receiving basic environmental data transmitted by the auxiliary monitoring UAV.

[0033] In a preferred embodiment of the present invention, performing basic flight control on the direct-operating UAV and the auxiliary monitoring UAV according to the target firefighting route specifically includes the following steps: Calculate the remaining distance based on the total length of the target firefighting route and the current flight distance to obtain the remaining flight distance; Use the auxiliary drone to obtain the real-time wind speed vector and the average wind speed reference value of the current area, and use the real-time wind speed vector and the average wind speed reference value of the current area to calculate the effective wind speed; Obtain the flight performance records of historical missions and the terrain feature codes of the current area, and obtain the terrain feature weights through machine learning model fitting, and convert the terrain feature weights into environmental attenuation factors; Obtain the task urgency rating, and map the discretized task urgency rating into a continuous numerical factor to obtain the emergency coefficient; The load adjustment coefficient is calculated based on the current liquid load and the maximum load of the drone; Obtain the current remaining battery power of the drone and calculate the initial value of the flight speed using the emergency factor, load adjustment factor, and the current remaining battery power; The wind resistance attenuation term is constructed using the effective wind speed and the environmental attenuation factor, and the distance attenuation term is constructed using the remaining flight distance and the environmental attenuation factor; The initial value of the flight speed is adjusted using the wind resistance attenuation term and the distance attenuation term to obtain the optimal flight speed, and the optimal flight speed is used as the basic flight control instruction for the direct working UAV and the auxiliary monitoring UAV.

[0034] Furthermore, the rotary wing UAV attitude control method further includes the following steps: Step S103: Analyze the basic environmental data, plan the direct working area and the auxiliary monitoring position, and perform in-situ flight control on the direct working UAV and the auxiliary monitoring UAV.

[0035] In an embodiment of the present invention, the fire hazard area is determined by analyzing the basic environmental data, and based on the fire hazard area, a nearby direct working area is selected, and based on the direct working area and preset auxiliary monitoring parameters, an auxiliary monitoring position is planned, and then the direct working UAV is controlled in place according to the direct working area, so that the direct working UAV flies to the direct working area to perform firefighting operations, and the auxiliary monitoring UAV is controlled in place according to the auxiliary monitoring position, so that the auxiliary monitoring UAV flies to the auxiliary monitoring position for real-time monitoring.

[0036] Specifically, Figure 4 A flow chart of performing in-place flight control in the method provided by an embodiment of the present invention is shown.

[0037] In a preferred embodiment of the present invention, the analysis of the basic environmental data, planning of the direct working area and the auxiliary monitoring location, and in-situ flight control of the direct working UAV and the auxiliary monitoring UAV specifically include the following steps: Step S1031, analyzing the basic environmental data to determine fire risk areas; Step S1032: planning a direct work area based on the fire hazard area; Step S1033: planning auxiliary monitoring positions based on the direct working area; Step S1034, performing in-position flight control on the direct working UAV according to the direct working area; Step S1035: performing in-place flight control on the auxiliary monitoring UAV according to the auxiliary monitoring position.

[0038] In a preferred embodiment of the present invention, planning the auxiliary monitoring position based on the direct working area specifically includes the following steps: Obtaining data collected in the direct working area, including real-time temperature distribution data, fire spread vectors, and wireless signal strength distribution maps; Analyze the core coordinates of the fire source according to the fire spread vector, and establish a polar coordinate system with the core coordinates of the fire source as the center of the circle; In the polar coordinate system, the fire impact weight of each location point is calculated according to the distance between the fire source and each location point, and a hazard level gradient map centered on the fire source is obtained; Perform second-order differential calculations on the temperature distribution data to locate the temperature mutation boundary; Based on the temperature mutation boundary, the combustion front at the edge of the fire is identified, the potential explosion risk area is marked, and an enhanced thermal map with key thermal characteristics is obtained; Based on the wireless signal strength distribution map, morphological expansion processing is performed on the signal blind area to construct a communication security buffer zone, and a regional segmentation map with communication security weights is obtained; A weighted superposition algorithm is used to synthesize a multi-physics field coupling equivalent potential field by combining a hazard level gradient map centered on the fire source, an enhanced thermal map with key thermal characteristics marked, and a regional segmentation map with communication assurance weights. This yields a composite potential field that includes fire threat, thermal characteristics, and communication assurance. The horizontal position coordinates of each drone are encoded as the probability amplitude of quantum bits, and a dedicated quantum register is assigned to each drone. A two-dimensional quantum grid is established to obtain the initial quantum state probability cloud distribution map. Based on the initial quantum state probability cloud distribution map, the composite potential field is injected into the Hamiltonian of the quantum system, and the fire field potential energy constraint term is embedded in the quantum state wave function to generate the optimized quantum state probability cloud distribution map. Based on the optimized quantum state probability cloud distribution map, a controlled NOT gate is applied between adjacent drone qubits to establish a correlation entanglement relationship between the qubits; The cosine function of the viewing angle difference is used as the phase rotation factor, and phase modulation under the viewing angle difference constraint is applied to the entangled state to generate the quantum state evolution equation; Based on the quantum state evolution equation, an adjustable rotation gate is inserted into the quantum circuit to establish a mapping channel between quantum states and classical parameters, resulting in a trainable hybrid quantum-classical computing model. Based on a trainable hybrid quantum-classical computing model, the expected energy of the quantum state in the composite potential field is calculated to evaluate the fidelity of the monitoring network coverage and obtain the system energy evaluation value under the current parameters. The revolving door parameters are iteratively optimized using the gradient descent method according to the system energy evaluation value under the current parameters. After the optimization is completed, the quantum state probability distribution under the optimal parameters is obtained; According to the quantum state probability distribution under the optimal parameters, the area with the maximum quantum state amplitude is extracted as the candidate coordinates, and the probability amplitude is converted into the actual geographic coordinate offset to adjust the candidate coordinates to obtain a high-precision monitoring point coordinate set; The high-precision monitoring point coordinates are input into the real-time path planner and fine-tuned in combination with wind speed and obstacle data to generate a flight trajectory instruction set with a speed curve. A simulated monitoring perspective is generated according to a flight trajectory instruction set with a speed curve, and a three-dimensional overlapping area of ​​the simulated monitoring perspective is calculated. An auxiliary monitoring position is selected according to the coverage rate of the three-dimensional overlapping area of ​​the simulated monitoring perspective.

[0039] In an embodiment of the present invention, by unifying the dimensions of three types of heterogeneous data—fire dynamics, thermal characteristics, and communication constraints—a quantifiable physical field model is established to intelligently identify and avoid communication blind spots. Furthermore, quantum entanglement is used to enforce collaborative constraints between drones. A phase rotation factor is introduced to ensure the rigid requirements of the viewing angle difference of the monitoring network, thereby reducing monitoring blind spots and ensuring that the selected auxiliary monitoring locations have better auxiliary effects. Geographic coordinates are converted into quantum state probability amplitudes to achieve a precise mapping between physical space and quantum computing space. Environmental parameters are loaded through the Hamiltonian, establishing a strong correlation between quantum computing and physical reality, improving positioning accuracy, and maintaining a stable position coding benchmark even in the event of sudden changes in fire intensity.

[0040] Furthermore, the rotary wing UAV attitude control method further includes the following steps: Step S104: acquiring the working space position of the direct working UAV in real time, performing follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and acquiring the follow-up monitoring data of the auxiliary monitoring UAV in real time.

[0041] In an embodiment of the present invention, by obtaining the workspace position of the direct working drone in the direct working area in real time, monitoring planning is performed on the auxiliary monitoring drone according to the workspace position, and the attitude adjustment angle is determined. Then, according to the attitude adjustment angle, the monitoring attitude of the auxiliary monitoring drone is adjusted, so that the auxiliary monitoring drone can monitor the direct working drone in real time and obtain the follow-up monitoring data of the auxiliary monitoring drone in real time.

[0042] Specifically, Figure 5 A flow chart of the follow-up adjustment of the monitoring posture in the method provided by an embodiment of the present invention is shown.

[0043] Among them, in the preferred embodiment provided by the present invention, the real-time acquisition of the working space position of the direct working drone, the follow-up adjustment of the monitoring posture of the auxiliary monitoring drone, and the real-time acquisition of the follow-up monitoring data of the auxiliary monitoring drone specifically include the following steps: Step S1041, obtaining the working space position of the direct working drone in real time; Step S1042: performing monitoring planning for the auxiliary monitoring UAV according to the workspace position and determining an attitude adjustment angle; Step S1043, performing follow-up adjustment of the monitoring posture of the auxiliary monitoring UAV according to the posture adjustment angle; Step S1044: Acquire the follow-up monitoring data of the auxiliary monitoring drone in real time.

[0044] In a preferred embodiment of the present invention, the monitoring planning of the auxiliary monitoring drone according to the workspace position and the determination of the attitude adjustment angle specifically include the following steps: Perform threshold segmentation on real-time temperature distribution data to identify the contour of the temperature core combustion area and obtain a temperature distribution segmentation map; Perform morphological closing operations on the temperature distribution segmentation map to eliminate noise, extract the shape features of the continuous fire front line, and generate a fire scene dynamic evolution map with geometric feature annotations; Based on the dynamic evolution diagram of the fire scene with geometric features, the pre-trained fire migration prediction model is combined with real-time wind speed to predict the future fire point coordinates; Based on the current and future fire point coordinates, obtain the combustible material density distribution map around the fire point to calculate the combustion diffusion energy accumulation value, and normalize the combustion diffusion energy accumulation value to generate the fire point threat weight; Construct a three-dimensional space grid and record the real-time wind speed vector of each grid point in the three-dimensional space grid; Perform Kriging interpolation calculation on the area beyond the sensor measurement range to obtain the wind speed vector distribution matrix of the entire field; The wind speed modulus at each grid point in the full-field wind speed vector distribution matrix is ​​normalized to a probability amplitude, and the three-dimensional wind field is mapped to the probability space of quantum bits through amplitude encoding to obtain a quantum superposition state that represents the full-space wind field distribution. Perform quantum Fourier transform on the quantum superposition state representing the wind field distribution in the entire space to obtain the quantum Fourier transform result; Measure the quantum state frequency components of the quantum Fourier transform results, identify the dominant periodic characteristics, and extract the wind field main frequency characteristic parameters that affect the observation stability; Use auxiliary drones to perform three-dimensional laser radar scanning of obstacles in the workspace to obtain laser point cloud data of the workspace position; The laser point cloud data of the workspace position is input into the graph convolutional network to predict the future occlusion probability of each spatial point in the workspace position. The future occlusion probability of each spatial point in the workspace position is dynamically adjusted using meteorological parameters to obtain a dynamic occlusion probability cloud map. Based on the dynamic occlusion probability cloud map, the coverage efficiency indicator is defined as the information entropy value of the occlusion probability. A variational autoencoder is used to search for the observation path with the minimum entropy value in the posture space to obtain the optimal observation path sequence and the corresponding posture angle set. Substitute the main frequency characteristic parameters of the wind farm into the basis function generator to obtain the anti-wind disturbance basis function with phase compensation; The attitude angle set is decomposed into Fourier descriptors to extract the path geometric characteristic frequencies. The path geometric characteristic frequencies are then subjected to a tensor product operation with the anti-wind disturbance basis function with phase compensation to obtain the anti-interference basis function that integrates the path constraints. Project the fire point coordinates into the drone observation view space and calculate the visibility index of each fire point; The visibility index of each fire point is integrated with the corresponding fire point threat weight to obtain the fire point coverage weight matrix with spatiotemporal characteristics. Taking fire point coverage, wind resistance stability, and energy efficiency as optimization objectives, a multi-objective optimization function is constructed using attitude angle sets, fire point coverage weight matrices with spatiotemporal characteristics, and anti-interference basis functions fused with path constraints. The alternating direction multiplier method is used to perform multi-objective collaborative optimization on the multi-objective optimization function. After the optimization is completed, the global optimal attitude angle is obtained.

[0045] In an embodiment of the present invention, a dynamic occlusion propagation model is constructed based on LiDAR point clouds and deep learning to predict future blind spot evolution trends. Furthermore, with the goal of minimizing information entropy, an observation path with the lowest occlusion risk is searched in attitude space. By integrating spatiotemporal prediction, quantum wind field decomposition, and occlusion entropy suppression to optimize multimodal collaborative observation attitudes, this approach addresses the difficulty of predicting blind spots in complex environments and enables more efficient allocation of multi-UAV mission execution.

[0046] Furthermore, the rotary wing UAV attitude control method further includes the following steps: Step S105: Analyze the follow-up monitoring data, plan and generate work adjustment data, and adjust the working posture of the direct working UAV accordingly according to the work adjustment data.

[0047] In an embodiment of the present invention, the fire-fighting requirement position in the direct working area is determined by analyzing the follow-up monitoring data, and flight adjustment and attitude adjustment planning are performed based on the working space position and the fire-fighting requirement position to generate work adjustment data. Then, based on the work adjustment data, flight adjustment instructions and attitude adjustment instructions are generated, and then the flight position of the directly working UAV is adjusted according to the flight adjustment instructions, and the flight attitude of the directly working UAV is adjusted according to the attitude adjustment instructions.

[0048] Specifically, Figure 6 A flow chart showing the corresponding adjustment of the working posture in the method provided by an embodiment of the present invention is shown.

[0049] In a preferred embodiment of the present invention, analyzing the follow-up monitoring data, planning and generating work adjustment data, and adjusting the working posture of the direct working drone accordingly according to the work adjustment data specifically include the following steps: Step S1051, analyzing the tracking monitoring data to determine the location where firefighting is required; Step S1052: Based on the workspace position and the fire-fighting required position, flight adjustment and attitude adjustment planning are performed to generate work adjustment data; Step S1053, generating a flight adjustment instruction and an attitude adjustment instruction according to the work adjustment data; Step S1054: adjusting the flight position of the direct working UAV according to the flight adjustment instruction; Step S1055: Adjust the flight attitude of the direct working UAV according to the attitude adjustment instruction.

[0050] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0051] Among them, in another preferred embodiment provided by the present invention, the rotary wing UAV attitude control system includes: The firefighting drone selection unit 101 is used to receive firefighting work requirements, plan a target firefighting route, and select a direct working drone and an auxiliary monitoring drone from multiple rotor drones.

[0052] In an embodiment of the present invention, the firefighting drone selection unit 101 receives firefighting work requirements, then identifies the location of the firefighting work requirements, determines the target firefighting location, and then plans the target firefighting route according to the target firefighting location, and obtains the drone status data of multiple standby rotorcraft drones, determines the endurance requirements according to the target firefighting route, and then compares and analyzes the status data of multiple drones based on the endurance requirements, and selects direct working drones and auxiliary monitoring drones from multiple rotorcraft drones, among which the direct working drone is a rotorcraft drone that directly performs firefighting work; the auxiliary monitoring drone is a rotorcraft drone that does not participate in direct firefighting work but performs real-time monitoring.

[0053] Specifically, Figure 8 The structure block diagram of the firefighting drone selection unit 101 in the system provided by an embodiment of the present invention is shown.

[0054] In a preferred embodiment of the present invention, the firefighting drone selection unit 101 specifically includes: The demand receiving module 1011 is used to receive firefighting work demands; A demand identification module 1012 is used to identify the firefighting work demand and determine the target firefighting location; Route planning module 1013, used to plan a target firefighting route according to the target firefighting location; A status data acquisition module 1014 is used to acquire drone status data of multiple rotary-wing drones; The drone selection module 1015 is used to compare the status data of the multiple drones and select a direct working drone and an auxiliary monitoring drone from the multiple rotorcraft drones.

[0055] Furthermore, the rotary wing UAV attitude control system also includes: The basic flight control unit 102 is used to perform basic flight control on the direct working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receive basic environmental data transmitted by the auxiliary monitoring UAV.

[0056] In an embodiment of the present invention, the basic flight control unit 102 performs basic flight control on the direct working UAV and the auxiliary monitoring UAV according to the target fire-fighting route, so that the direct working UAV and the auxiliary monitoring UAV arrive at the target fire-fighting location, and receives arrival feedback of the direct working UAV and the auxiliary monitoring UAV. After that, it generates environmental monitoring instructions and sends the environmental monitoring instructions to the auxiliary monitoring UAV, and then receives the basic environmental data transmitted by the auxiliary monitoring UAV.

[0057] The on-site flight control unit 103 is used to analyze the basic environmental data, plan the direct working area and the auxiliary monitoring position, and perform on-site flight control of the direct working UAV and the auxiliary monitoring UAV.

[0058] In an embodiment of the present invention, the on-site flight control unit 103 determines the fire hazard area by analyzing the basic environmental data, selects the nearby direct working area based on the fire hazard area, and plans the auxiliary monitoring position based on the direct working area and preset auxiliary monitoring parameters, and then performs on-site flight control on the direct working UAV according to the direct working area, so that the direct working UAV flies to the direct working area to perform firefighting operations, and performs on-site flight control on the auxiliary monitoring UAV according to the auxiliary monitoring position, so that the auxiliary monitoring UAV flies to the auxiliary monitoring position for real-time monitoring.

[0059] Specifically, Figure 9 FIG. 1 shows a structural block diagram of the in-situ flight control unit 103 in the system provided by an embodiment of the present invention.

[0060] Among them, in the preferred embodiment provided by the present invention, the in-situ flight control unit 103 specifically includes: The environmental analysis module 1031 is used to analyze the basic environmental data and determine the fire risk area; A work area planning module 1032 is used to plan a direct work area based on the fire hazard area; A monitoring location planning module 1033 is configured to plan auxiliary monitoring locations based on the direct working area; A first in-place flight control module 1034 is configured to perform in-place flight control on the direct working UAV according to the direct working area; The second in-place flight control module 1035 is used to perform in-place flight control on the auxiliary monitoring UAV according to the auxiliary monitoring position.

[0061] Furthermore, the rotary wing UAV attitude control system also includes: The monitoring follow-up adjustment unit 104 is used to obtain the working space position of the direct working UAV in real time, perform follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and obtain the follow-up monitoring data of the auxiliary monitoring UAV in real time.

[0062] In an embodiment of the present invention, the monitoring follow-up adjustment unit 104 obtains the working space position of the direct working UAV in the direct working area in real time, and plans the monitoring of the auxiliary monitoring UAV according to the working space position, determines the attitude adjustment angle, and then adjusts the monitoring attitude of the auxiliary monitoring UAV according to the attitude adjustment angle, so that the auxiliary monitoring UAV can monitor the direct working UAV in real time and obtain the follow-up monitoring data of the auxiliary monitoring UAV in real time.

[0063] Specifically, Figure 10 FIG. 1 shows a structural block diagram of the monitoring and following adjustment unit 104 in the system provided by an embodiment of the present invention.

[0064] In a preferred embodiment of the present invention, the monitoring and following adjustment unit 104 specifically includes: A real-time positioning module 1041 is used to obtain the working space position of the direct working drone in real time; A monitoring planning module 1042 is configured to perform monitoring planning for the auxiliary monitoring UAV according to the workspace position and determine an attitude adjustment angle; A monitoring attitude adjustment module 1043 is used to adjust the monitoring attitude of the auxiliary monitoring UAV according to the attitude adjustment angle; The monitoring data acquisition module 1044 is used to obtain the follow-up monitoring data of the auxiliary monitoring drone in real time.

[0065] Furthermore, the rotary wing UAV attitude control system also includes: The work corresponding adjustment unit 105 is used to analyze the follow-up monitoring data, plan and generate work adjustment data, and make corresponding adjustments to the working posture of the direct working UAV according to the work adjustment data.

[0066] In an embodiment of the present invention, the work corresponding adjustment unit 105 determines the fire-fighting requirement position in the direct working area by analyzing the follow-up monitoring data, performs flight adjustment and attitude adjustment planning based on the working space position and the fire-fighting requirement position, generates work adjustment data, and then generates flight adjustment instructions and attitude adjustment instructions based on the work adjustment data, and then adjusts the flight position of the directly working UAV according to the flight adjustment instructions, and adjusts the flight attitude of the directly working UAV according to the attitude adjustment instructions.

[0067] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0068] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0069] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned 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.

[0070] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the attitude of a rotary-wing UAV, characterized in that: The method specifically comprises the following steps: Receive firefighting work requirements, plan target firefighting routes, and select direct work drones and auxiliary monitoring drones from multiple rotorcraft drones; Performing basic flight control on the direct-working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receiving basic environmental data transmitted by the auxiliary monitoring UAV; Analyze the basic environmental data, plan the direct working area and auxiliary monitoring location, and perform on-site flight control of the direct working UAV and the auxiliary monitoring UAV; Acquire the working space position of the direct working UAV in real time, perform follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and acquire the follow-up monitoring data of the auxiliary monitoring UAV in real time; Analyze the follow-up monitoring data, plan and generate work adjustment data, and adjust the working posture of the direct working UAV accordingly according to the work adjustment data; The steps of receiving firefighting work requirements, planning a target firefighting route, and selecting a direct working drone and an auxiliary monitoring drone from a plurality of rotorcraft drones specifically include the following steps: Receive firefighting work requests; Identify the firefighting needs and determine the target firefighting location; Plan target firefighting routes according to the target firefighting locations; Obtain drone status data for multiple rotary-wing drones; The status data of the plurality of UAVs are compared, and a direct working UAV and an auxiliary monitoring UAV are selected from the plurality of rotor UAVs.

2. The method for controlling the attitude of a rotary-wing UAV according to claim 1, wherein: The step of comparing the status data of the plurality of drones and selecting the direct working drone and the auxiliary monitoring drone from the plurality of rotary-wing drones specifically includes the following steps: Obtain drone status data and target firefighting routes. Drone status data includes battery charge, flight speed, and load capacity. Set the load adjustment coefficient based on the drone's load capacity. Calculate the route demand quantification indicator based on the target firefighting route. The effective endurance time is calculated by multiplying the battery capacity by the load regulation factor; The theoretical flight time is calculated using the target firefighting route distance and flight speed; The comprehensive score of each drone is calculated based on the effective flight time and theoretical flight time; Sort all drones’ comprehensive scores in descending order and label the sensor type of each drone to obtain a priority list with device attributes; Set the minimum flight time threshold of the target firefighting route, and based on the minimum flight time threshold of the target firefighting route, select the first drone from the priority list with device attributes that meets the effective flight time greater than the minimum flight time threshold to select the direct working drone; If the top 30% of drones in the priority list with device attributes do not meet the threshold, a manual intervention alarm will be triggered to guide the operator to select the working drone directly; Using camera resolution as a screening indicator, several qualified drones are selected from a priority list with device attributes as auxiliary monitoring drones.

3. The method for controlling the attitude of a rotary-wing UAV according to claim 2, wherein: The step of performing basic flight control on the direct working UAV and the auxiliary monitoring UAV according to the target firefighting route and receiving basic environmental data transmitted by the auxiliary monitoring UAV specifically includes the following steps: Performing basic flight control on the direct-working UAV and the auxiliary monitoring UAV according to the target firefighting route; receiving arrival feedback of the direct working UAV and the auxiliary monitoring UAV; Generate and send environmental monitoring instructions to the auxiliary monitoring drone; Receive basic environmental data transmitted by the auxiliary monitoring drone.

4. The method for controlling the attitude of a rotary-wing UAV according to claim 3, wherein: The basic flight control of the direct working UAV and the auxiliary monitoring UAV according to the target firefighting route specifically includes the following steps: Calculate the remaining distance based on the total length of the target firefighting route and the current flight distance to obtain the remaining flight distance; Use the auxiliary drone to obtain the real-time wind speed vector and the average wind speed reference value of the current area, and use the real-time wind speed vector and the average wind speed reference value of the current area to calculate the effective wind speed; Obtain the flight performance records of historical missions and the terrain feature codes of the current area, and obtain the terrain feature weights through machine learning model fitting, and convert the terrain feature weights into environmental attenuation factors; Obtain the task urgency rating, and map the discretized task urgency rating into a continuous numerical factor to obtain the emergency coefficient; The load adjustment coefficient is calculated based on the current liquid load and the maximum load of the drone; Obtain the current remaining battery power of the drone and calculate the initial value of the flight speed using the emergency factor, load adjustment factor, and the current remaining battery power; The wind resistance attenuation term is constructed using the effective wind speed and the environmental attenuation factor, and the distance attenuation term is constructed using the remaining flight distance and the environmental attenuation factor; The initial value of the flight speed is adjusted using the wind resistance attenuation term and the distance attenuation term to obtain the optimal flight speed, and the optimal flight speed is used as the basic flight control instruction for the direct working UAV and the auxiliary monitoring UAV.

5. The method for controlling the attitude of a rotary-wing UAV according to claim 4, wherein: The analyzing of the basic environmental data, planning of the direct working area and the auxiliary monitoring location, and performing in-situ flight control of the direct working UAV and the auxiliary monitoring UAV specifically include the following steps: Analyze the basic environmental data to determine fire hazard areas; Plan the immediate working area based on the fire hazard areas; Planning auxiliary monitoring locations based on the direct working area; Performing in-place flight control on the direct working UAV according to the direct working area; The auxiliary monitoring UAV is controlled to fly in place according to the auxiliary monitoring position.

6. The method for controlling the attitude of a rotary-wing UAV according to claim 5, wherein: The planning of the auxiliary monitoring position based on the direct working area specifically includes the following steps: Obtaining data collected in the direct working area, including real-time temperature distribution data, fire spread vectors, and wireless signal strength distribution maps; Analyze the core coordinates of the fire source according to the fire spread vector, and establish a polar coordinate system with the core coordinates of the fire source as the center of the circle; In the polar coordinate system, the fire impact weight of each location point is calculated according to the distance between the fire source and each location point, and a hazard level gradient map centered on the fire source is obtained; Perform second-order differential calculations on the temperature distribution data to locate the temperature mutation boundary. Based on the temperature mutation boundary, identify the combustion front at the edge of the fire, mark the potential deflagration risk area, and obtain an enhanced thermal map with key thermal characteristics marked. Based on the wireless signal strength distribution map, morphological dilation processing is performed on the signal blind area to construct a communication security buffer zone, and a regional segmentation map with communication security weights is obtained; A weighted superposition algorithm is used to synthesize a multi-physics field coupling equivalent potential field by combining a hazard level gradient map centered on the fire source, an enhanced thermal map with key thermal characteristics marked, and a regional segmentation map with communication assurance weights. This yields a composite potential field that includes fire threat, thermal characteristics, and communication assurance. The horizontal position coordinates of each drone are encoded as the probability amplitude of quantum bits, and a dedicated quantum register is assigned to each drone. A two-dimensional quantum grid is established to obtain the initial quantum state probability cloud distribution map. Based on the initial quantum state probability cloud distribution map, the composite potential field is injected into the Hamiltonian of the quantum system, and the fire field potential energy constraint term is embedded in the quantum state wave function to generate the optimized quantum state probability cloud distribution map. Based on the optimized quantum state probability cloud distribution map, a controlled NOT gate is applied between adjacent drone qubits to establish a correlation entanglement relationship between the qubits; The cosine function of the viewing angle difference is used as the phase rotation factor, and phase modulation under the viewing angle difference constraint is applied to the entangled state to generate the quantum state evolution equation; Based on the quantum state evolution equation, an adjustable rotation gate is inserted into the quantum circuit to establish a mapping channel between quantum states and classical parameters, resulting in a trainable hybrid quantum-classical computing model. Based on a trainable hybrid quantum-classical computing model, the expected energy of the quantum state in the composite potential field is calculated to evaluate the fidelity of the monitoring network coverage and obtain the system energy evaluation value under the current parameters. The revolving door parameters are iteratively optimized using the gradient descent method according to the system energy evaluation value under the current parameters. After the optimization is completed, the quantum state probability distribution under the optimal parameters is obtained; According to the quantum state probability distribution under the optimal parameters, the area with the maximum quantum state amplitude is extracted as the candidate coordinates, and the probability amplitude is converted into the actual geographic coordinate offset to adjust the candidate coordinates to obtain a high-precision monitoring point coordinate set; Input the high-precision monitoring point coordinates into the real-time path planner and fine-tune it in combination with wind speed and obstacle data to generate a flight trajectory instruction set with a speed curve; A simulated monitoring perspective is generated according to a flight trajectory instruction set with a speed curve, and a three-dimensional overlapping area of ​​the simulated monitoring perspective is calculated. An auxiliary monitoring position is selected according to the coverage rate of the three-dimensional overlapping area of ​​the simulated monitoring perspective.

7. The method for controlling the attitude of a rotary-wing UAV according to claim 6, wherein: The real-time acquisition of the working space position of the direct working UAV, the follow-up adjustment of the monitoring posture of the auxiliary monitoring UAV, and the real-time acquisition of the follow-up monitoring data of the auxiliary monitoring UAV specifically include the following steps: Acquiring the working space position of the direct working drone in real time; Performing monitoring planning for the auxiliary monitoring UAV according to the workspace position and determining an attitude adjustment angle; According to the attitude adjustment angle, the auxiliary monitoring UAV is adjusted to follow the monitoring attitude; The follow-up monitoring data of the auxiliary monitoring drone is obtained in real time.

8. The method for controlling the attitude of a rotary-wing UAV according to claim 7, wherein: The monitoring planning of the auxiliary monitoring UAV according to the workspace position and determining the attitude adjustment angle specifically include the following steps: Perform threshold segmentation on real-time temperature distribution data to identify the contour of the temperature core combustion area and obtain a temperature distribution segmentation map; Perform morphological closing operations on the temperature distribution segmentation map to eliminate noise, extract the shape features of the continuous fire front line, and generate a fire scene dynamic evolution map with geometric feature annotations; Based on the dynamic evolution diagram of the fire scene with geometric features, the pre-trained fire migration prediction model is combined with real-time wind speed to predict the future fire point coordinates; Based on the current and future fire point coordinates, obtain the combustible material density distribution map around the fire point to calculate the combustion diffusion energy accumulation value, and normalize the combustion diffusion energy accumulation value to generate the fire point threat weight; Construct a three-dimensional space grid and record the real-time wind speed vector of each grid point in the three-dimensional space grid; Perform Kriging interpolation calculation on the area beyond the sensor measurement range to obtain the wind speed vector distribution matrix of the entire field; The wind speed modulus at each grid point in the full-field wind speed vector distribution matrix is ​​normalized to a probability amplitude, and the three-dimensional wind field is mapped to the probability space of quantum bits through amplitude encoding to obtain a quantum superposition state that represents the full-space wind field distribution. Perform quantum Fourier transform on the quantum superposition state representing the wind field distribution in the entire space to obtain the quantum Fourier transform result; Measure the quantum state frequency components of the quantum Fourier transform results, identify the dominant periodic characteristics, and extract the wind field main frequency characteristic parameters that affect the observation stability; Use auxiliary drones to perform three-dimensional laser radar scanning of obstacles in the workspace to obtain laser point cloud data of the workspace position; The laser point cloud data of the workspace position is input into the graph convolutional network to predict the future occlusion probability of each spatial point in the workspace position. The future occlusion probability of each spatial point in the workspace position is dynamically adjusted using meteorological parameters to obtain a dynamic occlusion probability cloud map. Based on the dynamic occlusion probability cloud map, the coverage efficiency indicator is defined as the information entropy value of the occlusion probability. A variational autoencoder is used to search for the observation path with the minimum entropy value in the posture space to obtain the optimal observation path sequence and the corresponding posture angle set. Substitute the main frequency characteristic parameters of the wind farm into the basis function generator to obtain the anti-wind disturbance basis function with phase compensation; The attitude angle set is decomposed into Fourier descriptors to extract the path geometric characteristic frequencies. The path geometric characteristic frequencies are then subjected to a tensor product operation with the anti-wind disturbance basis function with phase compensation to obtain the anti-interference basis function that integrates the path constraints. Project the fire point coordinates into the drone observation view space and calculate the visibility index of each fire point; The visibility index of each fire point is integrated with the corresponding fire point threat weight to obtain the fire point coverage weight matrix with spatiotemporal characteristics. Taking fire point coverage, wind resistance stability, and energy efficiency as optimization objectives, a multi-objective optimization function is constructed using attitude angle sets, fire point coverage weight matrices with spatiotemporal characteristics, and anti-interference basis functions fused with path constraints. The alternating direction multiplier method is used to perform multi-objective collaborative optimization on the multi-objective optimization function. After the optimization is completed, the global optimal attitude angle is obtained.

9. The method for controlling the attitude of a rotary-wing UAV according to claim 8, wherein: The analyzing the follow-up monitoring data, planning and generating work adjustment data, and adjusting the working posture of the direct working UAV accordingly according to the work adjustment data specifically include the following steps: Analyze the follow-up monitoring data to determine the location where firefighting is required; Based on the workspace position and the fire-fighting requirement position, flight adjustment and attitude adjustment planning are performed to generate work adjustment data; generating flight adjustment instructions and attitude adjustment instructions according to the work adjustment data; Adjusting the flight position of the direct working UAV according to the flight adjustment instruction; According to the attitude adjustment instruction, the flight attitude of the direct working UAV is adjusted.

10. A rotary wing UAV attitude control system, using the rotary wing UAV attitude control method according to any one of claims 1 to 9, characterized in that: The system includes a firefighting drone selection unit, a basic flight control unit, an in-place flight control unit, a monitoring follow-up adjustment unit, and a work corresponding adjustment unit, wherein: A firefighting drone selection unit is used to receive firefighting work requirements, plan target firefighting routes, and select direct-work drones and auxiliary monitoring drones from multiple rotorcraft drones; A basic flight control unit, configured to perform basic flight control on the direct-working UAV and the auxiliary monitoring UAV according to the target firefighting route, and receive basic environmental data transmitted by the auxiliary monitoring UAV; An on-site flight control unit, configured to analyze the basic environmental data, plan the direct working area and the auxiliary monitoring location, and perform on-site flight control of the direct working UAV and the auxiliary monitoring UAV; A monitoring follow-up adjustment unit is used to obtain the working space position of the direct working UAV in real time, perform follow-up adjustment on the monitoring posture of the auxiliary monitoring UAV, and obtain the follow-up monitoring data of the auxiliary monitoring UAV in real time; The work corresponding adjustment unit is used to analyze the follow-up monitoring data, plan and generate work adjustment data, and make corresponding adjustments to the working posture of the direct working UAV according to the work adjustment data.

Images