Search and rescue type rescue unmanned aerial vehicle control method

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a search and rescue type rescue unmanned aerial vehicle control method, which comprises the following steps of collecting meteorological data of a rescue area through a meteorological station and a satellite, integrating temperature, humidity, wind speed and terrain height data, performing data comparison and analysis, and generating a comprehensive environment data set. According to the invention, through real-time simulation and flight condition analysis feedback of the unmanned aerial vehicle in a search and rescue task, task adaptability is improved, operation flexibility is increased, potential obstacles in a search and rescue area are dynamically identified and evaluated, the unmanned aerial vehicle is allowed to adjust a flight path in real time, the collision risk is significantly reduced, and the safety of the rescue task is improved. The optimal obstacle avoidance distance and angle between the unmanned aerial vehicle and the obstacle are calculated in real time, it is ensured that the unmanned aerial vehicle can keep the highest operation efficiency in an unsafe flight area, the unmanned aerial vehicle can more quickly position victims and effectively support ground rescue actions, dependence on human resources is reduced, and the rescue effect and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to a control method for a search and rescue UAV. Background Art

[0002] The technical field of UAV control refers to the means of commanding and controlling the flight path, mission execution, and behavior patterns of UAVs, including the automatic flight control system, remote operation technology, real-time data transmission, and flight plan scheduling. It mainly relies on advanced sensors, GPS, inertial measurement units (IMUs), and ground control stations to ensure precise and safe operations. With the integration of artificial intelligence and machine learning technologies, the autonomous capabilities of UAVs have been significantly enhanced, enabling them to complete complex tasks with minimal human intervention.

[0003] Among them, the control method for a search and rescue UAV aims to improve the efficiency and effectiveness of the UAV in search and rescue scenarios through specific control strategies and system optimizations. Such UAVs are mainly used in search and rescue operations after disasters, searching for missing persons or analyzing the disaster area through sensors and cameras, thereby providing key information and support to rescue personnel. The control methods include optimization of the flight path, real-time data analysis, and rapid response mechanisms, ensuring that the UAV can quickly locate and provide necessary rescue support in complex and changing environments, significantly improving the safety and efficiency of rescue operations and reducing the dependence on human resources.

[0004] Existing UAV control technologies have deficiencies in adapting to complex or rapidly changing environments. Especially under extreme weather conditions, the real-time adjustment capabilities of traditional control systems are limited, affecting the flexibility of operations and rescue efficiency. In terms of obstacle handling, existing systems often rely on preset programs, lacking in-depth real-time data analysis and immediate response mechanisms, increasing the risk of collisions in unknown or highly variable rescue environments. This is particularly evident when performing complex rescue tasks that require rapid response, resulting in the ineffective utilization of rescue resources at critical moments, thereby reducing the overall efficiency and safety of rescue operations. Summary of the Invention

[0005] The purpose of the present invention is to address the drawbacks existing in the prior art and propose a control method for a search and rescue UAV.

[0006] To achieve the above objective, the present invention adopts the following technical solution: A control method for a search and rescue UAV, comprising the following steps:

[0007] S1: Collect meteorological data of the rescue area through meteorological stations and satellites, integrate temperature, humidity, wind speed, and terrain height data, conduct data comparison and analysis, and generate a comprehensive environmental data set;

[0008] S2: Use the comprehensive environmental dataset to preliminarily set the flight path of the drone. By simulating the flight effects under different weather changes, plan the drone's flight path, match the weather changes, optimize the flight path, and generate a preliminary planning result for the drone search and rescue path.

[0009] S3: Based on the preliminary planning result of the drone search and rescue path, start the drone flight simulation. Fly according to the preliminary planned path, record the simulated flight data, analyze the flight efficiency and safety, and generate a drone flight rehearsal result.

[0010] S4: Analyze the drone flight rehearsal result, identify potential obstacle information in the rescue area, including trees and buildings. Evaluate the height and distance of the obstacles, and comprehensively evaluate the collision risk in combination with the speed and direction of the drone to generate an obstacle identification record.

[0011] S5: Based on the obstacle identification record, adjust the dynamic flight path of the drone, calculate the optimal obstacle avoidance distance and angle between the drone and the obstacles, re-plan the flight route, adjust the drone's heading, and generate a dynamically adjusted drone flight route.

[0012] S6: Apply the dynamically adjusted drone flight route, synchronously update the flight instructions and the flight route, start the real flight of the drone, execute the search and rescue mission, synchronously record the drone's flight status and path adjustment situation, and generate a record of the drone rescue mission execution.

[0013] As a further solution of the present invention, the comprehensive environmental dataset includes the integration result of meteorological data and the analysis record of environmental data. The preliminary planning result of the drone search and rescue path includes the preliminary flight route setting result, the preliminary flight simulation effect, and the path optimization strategy. The drone flight rehearsal result includes the drone simulation flight data and the drone flight stability evaluation result. The obstacle identification record includes the obstacle type, the obstacle height and distance, and the collision risk level. The dynamically adjusted drone flight route includes the adjusted obstacle avoidance distance, the adjusted obstacle avoidance angle, and the re-planned drone flight route. The record of the drone rescue mission execution includes the record of the actual flight status of the drone, the details of the real-time path adjustment, and the record of the completion of the search and rescue mission.

[0014] As a further solution of the present invention, the specific steps of S1 are as follows:

[0015] S101: According to the temperature, humidity, wind speed, and terrain height data of the rescue area collected by the weather station and the satellite, verify the matching degree of the time stamp and the geographical coordinates, and obtain the original meteorological and terrain dataset.

[0016] S102: Based on the original meteorological and terrain data set, standardize the temperature, humidity, and wind speed data, identify and remove abnormal data points, confirm the consistency and reliability of the data, and obtain a meteorological quality control data set;

[0017] S103: Merge the meteorological quality control data set with the terrain data, fill in the missing terrain height data with the weighted average of adjacent point heights, confirm the complete coverage of all data points in the geographical space, and generate a comprehensive environment data set.

[0018] As a further solution of the present invention, the specific steps of S2 are as follows:

[0019] S201: Use the comprehensive environment data set to extract terrain height and wind speed information, select the area with moderate height and the lowest wind speed as the preferred flight path, and obtain the preliminary flight path setting result;

[0020] S202: Based on the preliminary flight path setting result, conduct a preliminary simulation of the UAV flight under weather conditions, adjust the flight path, test and verify the feasibility and safety of the flight path, and generate a preliminary adjusted UAV flight path;

[0021] S203: Compare and analyze the preliminary adjusted UAV flight path with the continuously updated weather forecast data, optimize the flight path to match the weather changes in the rescue area in the future time period, and generate a preliminary planning result of the UAV search and rescue path.

[0022] As a further solution of the present invention, the specific steps of S3 are as follows:

[0023] S301: According to the preliminary planning result of the UAV search and rescue path, start the UAV flight simulation, load the flight path and configure the simulation environment settings to match the actual flight conditions, and output the initialization simulation flight start configuration result;

[0024] S302: Based on the initialization simulation flight start configuration result, monitor the flight parameters of the UAV in real time, record the flight data in real time, monitor the continuity and stability of the UAV flight, and detect any behavior deviating from the predetermined flight path to obtain the simulation flight data record;

[0025] S303: According to the simulation flight data record, analyze the flight efficiency and flight safety of the UAV, identify the risk areas and mark the risk points, and make necessary adjustments to the flight path to generate the UAV flight rehearsal result.

[0026] As a further solution of the present invention, the specific steps of S4 are as follows:

[0027] S401: Based on the UAV flight rehearsal results, analyze the geographical data of the rescue area, calibrate the positions of obstacles, including trees and buildings, extract the height and spatial coordinates of each obstacle, verify the accuracy and integrity of the obstacle information, and generate an obstacle geographical information record;

[0028] S402: Use the obstacle geographical information record to conduct a spatial analysis of the obstacles, calculate the shortest path distance between the UAV flight path and the obstacles, analyze potential spatial conflict points, and generate an obstacle distance and height evaluation record;

[0029] S403: Based on the obstacle distance and height evaluation record, combined with the real-time speed and flight direction data of the UAV, conduct a collision risk assessment of the obstacles, analyze and identify the risk collision areas in the flight path, and generate an obstacle identification record.

[0030] As a further solution of the present invention, the shortest path distance is calculated according to the formula

[0031]

[0032] where D represents the shortest path distance between the UAV flight path and the obstacle, and x i , y i , z i respectively represent the three-dimensional coordinates of the current position of the UAV, and x j , y j , z j respectively represent the three-dimensional coordinates of the obstacle, and S x , S y , S z respectively represent the unit length scaling factors in the x, y, and z directions for adjusting the sensitivity of the coordinate axes, W represents the path weight coefficient for adjusting the path priority, and K is a normalization constant.

[0033] As a further solution of the present invention, the specific steps of S5 are as follows:

[0034] S501: Based on the obstacle identification record, extract the relative position data of the target obstacle and the current flight path of the UAV, conduct a spatial distance and angle analysis, calculate the optimal avoidance distance between the target obstacle and the UAV, and generate an obstacle avoidance parameter calculation result;

[0035] S502: Adopt the obstacle avoidance parameter calculation result to adjust the flight route, modify the node coordinates of the UAV flight path to ensure avoiding the obstacles, and smooth the flight path to confirm the continuity of the flight route and the stable flight of the UAV, and generate a new flight route planning record;

[0036] S503: Based on the new route planning record, test the adaptability of the new route through a simulation environment, verify the accuracy of the UAV heading adjustment, monitor the UAV response situation, confirm the consistency with the planned path, and generate a dynamically adjusted route for the UAV.

[0037] As a further solution of the present invention, the optimal avoidance distance is calculated according to the formula

[0038]

[0039] where D opt represents the optimal avoidance distance, x i , y i , z i represent the three-dimensional coordinates of the current position of the UAV, x j , y j , z j represent the three-dimensional coordinates of the target obstacle, β represents the basic obstacle avoidance sensitivity adjustment coefficient, γ is the line-of-sight angle adjustment coefficient, φ is the angle between the actual line of sight and the obstacle, obtained through a gyroscope and a magnetometer, and ∈ is the environmental complexity coefficient.

[0040] As a further solution of the present invention, the specific steps of S6 are as follows:

[0041] S601: Apply the dynamically adjusted route of the UAV, update the UAV flight path and instructions, verify the matching of the path nodes with the current state of the UAV, and automatically correct the route parameters to generate a flight instruction update record;

[0042] S602: Combine the flight instruction update record, start the actual flight of the UAV, monitor the flight state of the UAV in real time, collect real-time data during flight through sensors, record the flight altitude, speed, and direction, and generate a UAV flight path record;

[0043] S603: Based on the UAV flight path record, track the adjustment of the UAV flight path, confirm the stability of the flight path, integrate the navigation trajectory, flight time, and obstacle avoidance data, and output a UAV rescue mission execution record.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, through real-time simulation of the drone in the search and rescue mission and analysis and feedback of flight conditions, not only is the mission adaptability improved, but also the operation flexibility is increased. Potential obstacles within the search and rescue area are dynamically identified and evaluated, allowing the drone to adjust its flight path in real time, significantly reducing the collision risk, enhancing the safety of the rescue mission. By calculating the optimal obstacle avoidance distance and angle from the obstacles in real time, it is ensured that the drone can maintain the highest operation efficiency in an unsafe flight area, enabling the drone to locate the victim faster and effectively support the ground rescue operation, reducing the dependence on human resources, and improving the rescue effect and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a detailed schematic diagram of S1 of the present invention;

[0048] Figure 3 It is a detailed schematic diagram of S2 of the present invention;

[0049] Figure 4 It is a detailed schematic diagram of S3 of the present invention;

[0050] Figure 5 It is a detailed schematic diagram of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] In order to make the objectives, 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 for explaining the present invention and are not used to limit the present invention.

[0054] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0055] Please refer to Figure 1, A control method for a search and rescue drone, comprising the following steps:

[0056] S1: Collect meteorological data of the rescue area through a weather station and satellites, integrate data on temperature, humidity, wind speed, and terrain height, conduct data comparison and analysis, and generate a comprehensive environmental data set;

[0057] S2: Use the comprehensive environmental data set to preliminarily set the flight path of the drone. By simulating the flight effects under different weather changes, plan the drone's flight path, match the weather changes, optimize the flight path, and generate a preliminary planning result for the drone's search and rescue path;

[0058] S3: Based on the preliminary planning result of the drone's search and rescue path, start the drone flight simulation, fly according to the preliminary planned path, record the simulated flight data, analyze the flight efficiency and safety, and generate a drone flight rehearsal result;

[0059] S4: Analyze the drone flight rehearsal result, identify potential obstacle information in the rescue area, including trees and buildings, evaluate the height and distance of the obstacles, and comprehensively evaluate the collision risk in combination with the speed and direction of the drone to generate an obstacle identification record;

[0060] S5: Based on the obstacle identification record, adjust the dynamic flight path of the drone, calculate the optimal obstacle avoidance distance and angle between the drone and the obstacle, re-plan the flight route, adjust the drone's heading, and generate a dynamically adjusted flight route for the drone;

[0061] S6: Apply the dynamically adjusted flight route of the drone, synchronously update the flight instructions and route, start the real flight of the drone, execute the search and rescue mission, synchronously record the flight status of the drone and the path adjustment situation, and generate a record of the execution of the drone rescue mission.

[0062] The comprehensive environmental data set includes the integration result of meteorological data and the record of environmental data analysis. The preliminary planning result of the drone's search and rescue path includes the preliminary flight route setting result, the preliminary flight simulation effect, and the path optimization strategy. The drone flight rehearsal result includes the drone's simulated flight data and the evaluation result of the drone's flight stability. The obstacle identification record includes the obstacle type, the height and distance of the obstacle, and the collision risk level. The dynamically adjusted flight route of the drone includes the adjusted obstacle avoidance distance, the adjusted obstacle avoidance angle, and the re-planned flight route of the drone. The record of the execution of the drone rescue mission includes the record of the actual flight status of the drone, the details of the real-time path adjustment, and the record of the completion of the search and rescue mission.

[0063] Please refer to Figure 2 , The specific steps of S1 are:

[0064] S101: According to the temperature, humidity, wind speed and terrain height data of the rescue area collected by meteorological stations and satellites, verify the matching degree of timestamps and geographical coordinates, and obtain the original meteorological and terrain data set;

[0065] Among them, to verify the matching degree of timestamps and geographical coordinates, according to the formula

[0066] calculate the matching degree M(i) of each data point i.

[0067] In the formula, t i represents the timestamp of the i-th data point, t avg represents the average value of timestamps, x i and y i represent the geographical coordinates of the i-th data point, x avg and y avg respectively represent the average values of the geographical coordinates of all data points.

[0068] The square root operation of the sum of squares in the formula is used to calculate the deviation degree of the time and space coordinates of each data point from the average value of the overall data set. First, calculate the square of the deviation of each dimension of the timestamp and geographical coordinates respectively, then sum these squared values, and finally take the square root to obtain the matching degree M(i) of a single data point. By comparing the values of M(i), the proximity of the data point to the average value can be determined. A larger value of M(i) indicates a larger deviation of the data point in time or space.

[0069] Collect three data points, and the specific data are as follows:

[0070] Data point 1: Timestamp t1 = 10:00, coordinates (x1 = 30, y1 = 50).

[0071] Data point 2: Timestamp t2 = 10:05, coordinates (x2 = 31, y2 = 51).

[0072] Data point 3: Timestamp t3 = 10:10, coordinates (x3 = 29, y3 = 49).

[0073] Calculate the average values of timestamps and coordinates:

[0074]

[0075] Calculate the matching degree M(i) of each data point:

[0076]

[0077] The calculation results show that data points 1 and 3 have a large deviation from the average value, while data point 2 is closer to the average value.

[0078] S102: Based on the original meteorological and terrain datasets, standardize the temperature, humidity, and wind speed data, identify and remove abnormal data points, confirm the consistency and reliability of the data, and obtain the meteorological quality control dataset;

[0079] Among them, when standardizing the temperature, humidity, and wind speed data, according to the formula Calculate the standardized data value Z i .

[0080] In the formula, X i represents the original data value, μ represents the mean of the dataset, and σ represents the standard deviation.

[0081] Standardization is a common technique for data preprocessing, used to transform data into a form with zero mean and unit variance, enabling data of different magnitudes or units to be compared and analyzed on the same basis. In the formula, X i represents the original data to be standardized, such as the observed values of temperature or wind speed; μ and σ are the mean and standard deviation of these data respectively.

[0082] Collect a simple dataset on temperature (unit: degree Celsius):

[0083] Temperature data: X = [20, 22, 21, 23, 19]

[0084] Calculate the mean and standard deviation of the data:

[0085]

[0086]

[0087] Use the formula for standardization:

[0088]

[0089] The calculation results show that after standardization, the data value Z_i describes the deviation of each temperature observation value from the average temperature, measured in units of the standard deviation. Such a processing method is helpful for subsequent data analysis and outlier detection, because the standardized values intuitively show the position of each data point relative to the overall distribution.

[0090] S103: Merge the meteorological quality control dataset with the terrain data, fill in the missing terrain height data with the weighted average of adjacent point heights, confirm the complete coverage of all data points in the geographical space, and generate the comprehensive environmental dataset;

[0091] Among them, when filling in the missing terrain height data with the weighted average of adjacent point heights, according to the formula Calculate the filled height Hi 。

[0092] In the formula, H j represents the terrain height of adjacent points, w j represents the weight factor relative to the terrain height H j and n represents the number of adjacent points participating in the weighted average.

[0093] When dealing with blank or missing parts in terrain data, it is an effective method to estimate using the known terrain height data points around through the weighted average method. H in the formula j refers to the terrain heights of each adjacent point around, and the weight w j can be determined according to the distance of each point from the missing point, and the points closer to the missing point are given higher weights.

[0094] If a terrain height point H i is missing, collect three terrain height data around it:

[0095] The height of adjacent point A is 300 meters and the distance is 2 kilometers.

[0096] The height of adjacent point B is 310 meters and the distance is 1 kilometer.

[0097] The height of adjacent point C is 320 meters and the distance is 3 kilometers.

[0098] Determine the weights and use the reciprocal of the distance as the weights:

[0099]

[0100] Calculate the sum of weights:

[0101]

[0102] Calculate the estimated height of the missing point:

[0103]

[0104] The calculation results show that through the weighted average method, the missing terrain height point H i can be estimated to be 309.66 meters. This method effectively utilizes the information of surrounding terrain points and improves the integrity and accuracy of the data.

[0105] Please refer to Figure 3 , and the specific steps of S2 are as follows:

[0106] S201: Use the comprehensive environmental dataset to extract terrain height and wind speed information, select the area with moderate height and the lowest wind speed as the preferred waterway, and obtain the preliminary route setting result;

[0107] In the use of the comprehensive environmental dataset, feature extraction is performed on terrain height and wind speed information. Based on these data, the most suitable flight area is selected. In this process, the accuracy of the extracted data and the calculation method are crucial. Usually, digital maps and meteorological station data are used. Through geographic information systems (GIS) and remote sensing technology, the terrain height can be mapped with high precision. At the same time, using surface wind speed monitoring data and combining statistical analysis, the average value and fluctuation range of the wind speed are determined. According to the obtained wind speed and terrain height data, the area with the lowest wind speed is selected as the preferred flight path, and finally, the result of the preliminary flight path setting is formed.

[0108] S202: Based on the result of the preliminary flight path setting, conduct a preliminary simulation of the UAV flight under weather conditions, adjust the flight path, test and verify the feasibility and safety of the flight path, and generate the preliminary adjusted UAV flight path;

[0109] Based on the result of the preliminary flight path setting, conduct a UAV flight simulation under weather conditions through simulation software, and calculate the force received by the UAV during the flight according to the formula F = ma + cd. In the formula, F represents the total force received by the UAV during the flight, m represents the mass of the UAV, a represents the acceleration, c represents the air resistance coefficient, and d represents the influence of the wind speed on the UAV flight.

[0110] Considering that the mass m of the UAV is 1.5 kg, the standard gravitational acceleration a is 9.8 m / s 2 , the air resistance coefficient c is 0.05, and the wind speed d is 5 m / s, calculate the total force received by the UAV under specific conditions:

[0111] F = (1.5 kg × 9.8 m / s 2 )+(0.05 × 5 m / s)

[0112] F = 14.7 N + 0.25 N

[0113] F = 14.95 N

[0114] The result shows that under the given wind speed and other flight conditions, the force received by the UAV is 14.95 N. The magnitude of this force is one of the prerequisite conditions for safe flight, indicating that this flight simulation is crucial for verifying the feasibility and safety of the flight path.

[0115] S203: Compare and analyze the preliminary adjusted UAV flight path with the continuously updated weather forecast data, optimize the flight path to match the weather changes in the rescue area in the future time period, and generate the preliminary planning result of the UAV search and rescue path;

[0116] Based on the adjusted UAV flight path, a comparative analysis is carried out with continuously updated weather forecast data. In this process, an automated weather update system is usually adopted. By connecting to the global meteorological information network, updated data including wind speed, wind direction, temperature, humidity, etc. are received regularly. Through dedicated weather analysis software, the data can be quickly parsed and preprocessed. Then, combined with historical weather data, the weather changes in the next period of time are predicted. Finally, the flight path is optimized to match the weather changes in the rescue area in the future time period, and a preliminary planning result of the UAV search and rescue path is generated.

[0117] Please refer to Figure 4 , the specific steps of S3 are as follows:

[0118] S301: According to the preliminary planning result of the UAV search and rescue path, start the UAV flight simulation, load the flight path and configure the simulation environment settings to match the actual flight conditions, and output the initialization simulation flight start configuration result;

[0119] Before starting the UAV flight simulation, configure the simulation environment that matches the actual flight conditions, which involves detailed settings of weather conditions, terrain, and possible flight obstacles. The settings of the simulation environment use virtual reality technology and advanced flight simulation software. Through these technologies, a flight environment close to the real situation can be simulated, including but not limited to meteorological elements such as wind speed, air pressure, and air temperature. At the same time, the coordinates of each point on the flight route need to be accurately input into the flight control system. An emergency response mechanism also needs to be set up during this process to deal with technical failures or sudden climate changes, ensuring the continuity of the simulation flight and the reliability of the data, and outputting the result of the initialization simulation flight start configuration.

[0120] S302: Based on the initialization simulation flight start configuration result, monitor the flight parameters of the UAV in real time, including position, speed, and energy consumption, record the flight data in real time, monitor the continuity and stability of the UAV flight, detect any behavior deviating from the predetermined flight path, and obtain the simulation flight data record;

[0121] Based on the initialization simulation flight start configuration result, real-time monitoring of the UAV flight parameters becomes the core of the monitoring task, including but not limited to continuous tracking of the UAV's position, speed, and energy consumption. Technologies used include real-time analysis of global positioning system (GPS) and other sensor data. The real-time data stream is transmitted to the ground control station through a high-speed network. The operator of the control station uses this data for dynamic flight trajectory analysis, while detecting whether the UAV deviates from the preset flight path, recording all flight data, and performing data post-processing to obtain the simulation flight data record.

[0122] S303: Analyze the flight efficiency and flight safety of the UAV based on the simulated flight data record, identify the risk areas and mark the risk points, make necessary adjustments to the flight route, and generate the UAV flight rehearsal result.

[0123] Analyze the flight efficiency and flight safety of the UAV based on the simulated flight data record, including a comprehensive evaluation of various parameters during the flight, analyze the response of the UAV under different environmental conditions, identify potential risk points and mark them, predict and identify high-risk areas. Based on this, the flight route adjustment depends on the risk assessment results and the output of the prediction model to ensure that the UAV avoids these high-risk areas, thereby improving the overall flight safety, and generate the UAV flight rehearsal result.

[0124] Please refer to Figure 5 , and the specific steps of S4 are as follows:

[0125] S401: Based on the UAV flight rehearsal result, analyze the geographical data of the rescue area, mark the positions of obstacles, including trees and buildings, extract the height and spatial coordinates of each obstacle, and verify the accuracy and integrity of the obstacle information to generate the obstacle geographical information record.

[0126] When analyzing the geographical data of the rescue area, it includes accurately extracting the spatial coordinates and heights of obstacles such as trees and buildings, using Geographic Information System (GIS) technology for high-precision scanning. The scanned data needs to go through data correction and verification steps to ensure the accuracy and integrity of each data point. Data correction includes integrating and verifying the accuracy of data from multiple data sources, such as by comparing satellite data and on-site measurement data to identify and correct any possible errors. In addition, synchronously mark the specific positions of obstacles. The generated obstacle geographical information record provides key data for subsequent flight path planning.

[0127] S402: Use the obstacle geographical information record to conduct spatial analysis of the obstacles, calculate the shortest path distance between the UAV flight path and the obstacles, analyze potential spatial conflict points, and generate the obstacle distance and height evaluation record.

[0128] The shortest path distance is calculated according to the formula

[0129]

[0130] where D represents the shortest path distance between the UAV flight path and the obstacle, and x i , y i , z i represent the three-dimensional coordinates of the current position of the UAV respectively, and x j , y j , z jRespectively represent the three-dimensional coordinates of the obstacle, S x 、S y 、S z respectively represent the unit length scaling factors in the x, y, and z directions, which are used to adjust the sensitivity of the coordinate axes. W represents the path weight coefficient, which is used to adjust the path priority. K is the normalization constant, which is used to adjust the scale of the calculation result.

[0131] Obtain that the current position of the drone is (100, 150, 30) meters, and the position of the obstacle is (120, 180, 50) meters.

[0132] Set S x = 1.0, S y = 1.2, S z = 0.8, W = 1.5, K = 2.0.

[0133] Substitute into the formula and calculate:

[0134] Calculate the distances between the drone and the obstacle on each axis:

[0135] |x j -x i | = |120 - 100| = 20

[0136] |y j -y i | = |180 - 150| = 30

[0137] |z j -z i | = |50 - 30| = 20

[0138] Apply the scaling factors and calculate the weighted distances in each direction:

[0139]

[0140] Calculate the sum of squares and the square root:

[0141]

[0142] Apply the normalization constant and the path weight:

[0143]

[0144] The result shows that the weighted distance between the drone and the obstacle is approximately 38.25 units. The value indicates the effective shortest path distance between the drone and the obstacle after considering the obstacle type and environmental factors, providing a quantitative data basis for flight planning and obstacle avoidance decision-making.

[0145] S403: Based on the evaluation records of the obstacle distance and height, combined with the real-time speed and flight direction data of the UAV, conduct a collision risk assessment on the obstacles, analyze and identify the risk collision areas in the flight path, and generate obstacle recognition records;

[0146] When conducting a collision risk assessment based on the evaluation records of the obstacle distance and height, the core task is to combine the real-time speed and flight direction data of the UAV to analyze and identify the risk collision areas in the flight path. In this process, dynamic data analysis is carried out. Based on the flight parameters of the UAV and the position data of the obstacles, the possible collision risk areas on the flight path are identified. In addition, the UAV will receive the analysis results in real time to ensure the safety of the UAV flight. The generated obstacle recognition records provide real-time data support for flight control.

[0147] Please refer to Figure 6 , the specific steps of S5 are as follows:

[0148] S501: Based on the obstacle recognition records, extract the relative position data of the target obstacle and the current flight path of the UAV, conduct spatial distance and angle analysis, calculate the optimal avoidance distance between the target obstacle and the UAV, and generate the calculation results of the obstacle avoidance parameters;

[0149] The optimal avoidance distance is calculated according to the formula

[0150]

[0151] For the calculation, where D opt represents the optimal avoidance distance, x i , y i , z i represent the three-dimensional coordinates of the current position of the UAV, x j , y j , z j represent the three-dimensional coordinates of the target obstacle, β represents the basic obstacle avoidance sensitivity adjustment coefficient, γ is the line-of-sight angle adjustment coefficient, φ is the angle between the actual line of sight and the obstacle, obtained through the gyroscope and magnetometer, and ∈ is the environmental complexity coefficient.

[0152] The current coordinates of the UAV are obtained as (100, 200, 50) through GPS and gyroscope, and the coordinates of the obstacle are (120, 210, 55) determined by lidar.

[0153] Set β to 1.5 and γ to 0.3 to reflect the influence of the line-of-sight angle on the obstacle avoidance sensitivity.

[0154] The angle φ between the actual line of sight and the obstacle is obtained as 30°, that is radians.

[0155] The environmental complexity coefficient ∈ is set to 2, which reflects the obstacle density and risk level of the surrounding environment.

[0156] Calculate the Euclidean distance between the drone and the obstacle:

[0157]

[0158]

[0159] Calculate the obstacle avoidance sensitivity factor:

[0160]

[0161] Insert the environmental complexity and calculate the optimal avoidance distance:

[0162]

[0163] The results show that considering the influence of the line-of-sight angle and environmental complexity, the drone should maintain a distance of approximately 26.02 meters to avoid the currently detected obstacle. This distance ensures a sufficient safety buffer while taking into account environmental factors and the flight characteristics of the drone.

[0164] S502: Adopt the calculation results of the obstacle avoidance parameters to adjust the flight route, modify the node coordinates of the drone flight path, ensure avoiding obstacles, smooth the flight path, confirm the continuity of the flight route and the stable flight of the drone, and generate a new flight route planning record;

[0165] Using the calculation results of the obstacle avoidance parameters, the flight path of the drone needs to be adjusted accordingly. During this process, the operator modifies the node coordinates of the drone flight path to ensure that the drone can effectively avoid the detected obstacles. At the same time, the flight path is smoothed to ensure that the drone can still maintain a stable flight when avoiding obstacles and avoid potential safety risks caused by sharp orbit changes. After each adjustment of the flight path, the system will automatically check the continuity of the flight route to ensure that there are no breaks in the modified flight route, thereby generating a new flight route planning record.

[0166] S503: Based on the new flight route planning record, test the adaptability of the new flight route through a simulated environment, verify the accuracy of the drone heading adjustment, monitor the drone response, confirm the consistency with the planned path, and generate a dynamically adjusted flight route for the drone;

[0167] Based on the new route planning records, the adaptability of the new route is tested in a simulated environment. This process includes verifying the adjustment of the drone's heading and monitoring the drone's response to ensure its consistency with the planned path. During this process, the simulation environment provides various conditions similar to actual flight, such as wind speed, temperature, etc., to ensure the reliability of the test results. The operator will make necessary fine-tuning of the route based on the test results. These adjustments are based on the drone's performance data and flight dynamics. The final result is a drone dynamically adjusted route that can accurately adapt to actual flight conditions.

[0168] See also Figure 7 , the specific steps of S6 are:

[0169] S601: Dynamically adjust the flight path of the drone, update the flight path and instructions of the drone, verify the matching of the path nodes with the current state of the drone, automatically correct the route parameters, and generate a flight instruction update record;

[0170] In the process of updating the UAV's flight path and instructions, the key is to ensure that the path nodes perfectly match the UAV's current status, including analyzing the real-time data of the UAV's position, speed and direction. The data comes from the UAV's onboard sensor system, which can provide real-time feedback on the UAV's flight status. The operator evaluates the UAV's current flight status based on this data and compares it with the preset path. When there is a large difference, the route parameters are automatically corrected. This correction process relies on the UAV's automatic flight control system. The system automatically adjusts the route to adapt to the current flight environment based on the deviation between the real-time data and the preset parameters, and finally generates a flight instruction update record.

[0171] S602: Combine the flight command update record, start the actual flight of the UAV, monitor the flight status of the UAV in real time, collect real-time data during the flight through sensors, record the flight altitude, speed and direction, and generate the UAV flight path record;

[0172] In actual flight, the flight status of the drone is monitored in real time, including continuous recording of flight altitude, speed and direction. The operator uses the various sensors on the drone to collect data in real time, and the data is sent to the ground control center in real time via wireless transmission. The control center monitors and displays these flight parameters in real time, allowing the operator to immediately understand the flight status of the drone and make adjustments when necessary. The recorded data not only provides a guarantee for flight safety, but also serves as basic data storage for flight analysis for subsequent analysis and use, generating a record of the drone's flight path.

[0173] S603: Based on the drone flight path record, track the adjustment of the drone flight path, confirm the stability of the flight path, integrate the navigation trajectory, flight time, and obstacle avoidance data, and output the execution record of the drone rescue mission;

[0174] Based on the drone flight path record, the operator tracks the adjustment of the drone flight path. By comprehensively considering various data such as the navigation trajectory, flight time, and obstacle avoidance, the stability of the flight path is confirmed. During this process, the control center uses flight record data to analyze the behavior pattern of the drone, especially its adaptability in complex environments, to ensure that the drone can maintain the optimal flight state under various environmental conditions. Through the analysis of comprehensive data, the performance record of the drone during the rescue mission is output, which not only helps to understand the performance of the drone but also ensures the efficient execution of the rescue mission.

[0175] The above are only the preferred embodiments of the present invention and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A control method for a search and rescue type rescue drone, characterized in that, It includes the following steps: Collect meteorological data of the rescue area through meteorological stations and satellites, integrate temperature, humidity, wind speed and terrain height data, conduct data comparison and analysis, and generate a comprehensive environmental dataset; Use the comprehensive environmental dataset to preliminarily set the flight route of the UAV. By simulating the flight effects under different weather changes, plan the UAV route, match the weather changes, optimize the flight path, and generate the preliminary planning result of the UAV search and rescue path; Based on the preliminary planning result of the UAV search and rescue path, start the UAV flight simulation, fly according to the preliminary planned path, record the simulated flight data, analyze the flight efficiency and safety, and generate the UAV flight rehearsal result; Analyze the UAV flight rehearsal result, identify potential obstacle information in the rescue area, including trees and buildings, evaluate the height and distance of the obstacles, and comprehensively evaluate the collision risk in combination with the speed and direction of the UAV to generate an obstacle identification record; Based on the obstacle identification record, adjust the dynamic flight path of the UAV, calculate the optimal obstacle avoidance distance and angle between the UAV and the obstacle, re-plan the flight route, adjust the UAV heading, and generate the UAV dynamically adjusted route; Apply the UAV dynamically adjusted route, synchronously update the flight instructions and route, start the real flight of the UAV, execute the search and rescue mission, synchronously record the UAV flight status and path adjustment situation, and generate the UAV rescue mission execution record.

2. The search and rescue type rescue UAV control method according to claim 1, wherein The comprehensive environmental dataset includes the meteorological data integration result and the environmental data analysis record. The preliminary planning result of the UAV search and rescue path includes the preliminary route setting result, the preliminary flight simulation effect and the path optimization strategy. The UAV flight rehearsal result includes the UAV simulated flight data and the UAV flight stability evaluation result. The obstacle identification record includes the obstacle type, the obstacle height and distance and the collision risk level. The UAV dynamically adjusted route includes the adjusted obstacle avoidance distance, the adjusted obstacle avoidance angle and the re-planned UAV flight route. The UAV rescue mission execution record includes the UAV actual flight status record, the real-time path adjustment details and the search and rescue mission completion situation record.

3. The search and rescue type rescue UAV control method according to claim 1, wherein The specific steps of collecting meteorological data of the rescue area through meteorological stations and satellites, integrating temperature, humidity, wind speed and terrain height data, and conducting data comparison and analysis to generate a comprehensive environmental dataset are as follows: According to the temperature, humidity, wind speed and terrain height data collected by the meteorological station and satellite in the rescue area, verify the matching degree of the timestamp and geographical coordinates, and obtain the original meteorological and terrain dataset; Based on the original meteorological and terrain dataset, standardize the temperature, humidity and wind speed data, identify and remove abnormal data points, and confirm the consistency and reliability of the data to obtain the meteorological quality control dataset; Merge the meteorological quality control dataset with the terrain data, fill in the missing terrain height data with the weighted average height of adjacent points, and confirm the complete coverage of all data points in the geographical space to generate a comprehensive environmental dataset.

4. The search and rescue type rescue UAV control method according to claim 1, characterized in that The specific steps for initially setting the flight route of the UAV using the comprehensive environmental dataset, planning the UAV route by simulating the flight effects under different weather changes, matching the weather changes, optimizing the flight path, and generating the initial planning result of the UAV search and rescue path are as follows: Using the comprehensive environmental dataset, extract the terrain height and wind speed information, select the area with moderate height and the lowest wind speed as the preferred flight path, and obtain the initial flight route setting result; Based on the initial flight route setting result, conduct a preliminary simulation of the UAV flight under weather conditions, adjust the flight route, test and verify the feasibility and safety of the flight route, and generate the initially adjusted UAV flight route; Compare and analyze the initially adjusted UAV flight route with the continuously updated weather forecast data, optimize the flight route to match the weather changes in the rescue area in the future time period, and generate the initial planning result of the UAV search and rescue path.

5. The search and rescue type rescue UAV control method according to claim 1, characterized in that Based on the initial planning result of the UAV search and rescue path, start the UAV flight simulation, fly according to the initial planned path, record the simulation flight data, analyze the flight efficiency and safety, and the specific steps for generating the UAV flight rehearsal result are as follows: According to the initial planning result of the UAV search and rescue path, start the UAV flight simulation, load the flight route and configure the simulation environment settings to match the actual flight conditions, and output the initialization simulation flight start configuration result; Based on the initialization simulation flight start configuration result, monitor the flight parameters of the UAV in real time, record the flight data in real time, monitor the continuity and stability of the UAV flight, detect any behavior deviating from the predetermined flight route, and obtain the simulation flight data record; According to the simulation flight data record, analyze the UAV flight efficiency and flight safety, identify the risk areas and mark the risk points, and make necessary adjustments to the flight route to generate the UAV flight rehearsal result.

6. The search and rescue type rescue UAV control method according to claim 1, characterized in that Analyze the UAV flight rehearsal result, identify the potential obstacle information in the rescue area, including trees and buildings, evaluate the height and distance of the obstacles, and comprehensively evaluate the collision risk in combination with the speed and direction of the UAV to generate the specific steps for the obstacle identification record are as follows: Based on the UAV flight rehearsal result, analyze the geographical data of the rescue area, mark the positions of the obstacles, including trees and buildings, extract the height and spatial coordinates of each obstacle, and verify the accuracy and integrity of the obstacle information to generate the obstacle geographical information record; Use the obstacle geographical information record to conduct a spatial analysis of the obstacles, calculate the shortest path distance between the UAV flight path and the obstacles, analyze the potential spatial conflict points, and generate the obstacle distance and height evaluation record; Based on the obstacle distance and height evaluation record, combine the real-time speed and flight direction data of the UAV to conduct a collision risk assessment of the obstacles, analyze and identify the risk collision areas in the flight path, and generate the obstacle identification record.

7. The search and rescue drone control method according to claim 6, characterized in that, The shortest path distance, according to the formula, Perform calculations, where D represents the shortest path distance between the UAV flight path and the obstacle, x i , y i , z i respectively represent the three-dimensional coordinates of the current position of the UAV, x j , y j , z j respectively represent the three-dimensional coordinates of the obstacle, S x , S y , S z respectively represent the unit length scaling factors in the x, y, and z directions, which are used to adjust the sensitivity of the coordinate axes. W represents the path weight coefficient, which is used to adjust the path priority, and K is a regularization constant.

8. The search and rescue type rescue UAV control method according to claim 1, wherein, Based on the obstacle identification record, conduct dynamic flight path adjustment of the UAV, calculate the optimal obstacle avoidance distance and angle between the UAV and the obstacles, re-plan the flight route, and adjust the UAV heading. The specific steps for generating the UAV dynamic adjustment flight route are as follows: Based on the obstacle recognition record, extract the relative position data of the target obstacle and the current flight path of the drone, conduct spatial distance and angle analysis, calculate the optimal avoidance distance between the target obstacle and the drone, and generate the calculation result of the obstacle avoidance parameters; Adopt the calculation result of the obstacle avoidance parameters to adjust the flight route, modify the node coordinates of the drone flight path to ensure avoiding obstacles, smooth the flight path, confirm the continuity of the flight route and the stable flight of the drone, and generate a new flight route planning record; Based on the new flight route planning record, test the adaptability of the new flight route through a simulation environment, verify the accuracy of the drone heading adjustment, monitor the drone response situation, confirm the consistency with the planned path, and generate the dynamically adjusted flight route of the drone.

9. The search and rescue type rescue UAV control method according to claim 8, characterized in that The optimal avoidance distance is calculated according to the formula Perform calculations, where D opt represents the optimal avoidance distance, x i , y i , z i represent the three-dimensional coordinates of the current position of the UAV, x j , y j , z j represent the three-dimensional coordinates of the target obstacle, β represents the basic obstacle avoidance sensitivity adjustment coefficient, γ is the line-of-sight angle adjustment coefficient, φ is the angle between the actual line of sight and the obstacle, obtained through the gyroscope and magnetometer, and ∈ is the environmental complexity coefficient.

10. The search and rescue type rescue UAV control method according to claim 1, characterized in that, The specific steps for applying the dynamically adjusted flight route of the drone, synchronously updating the flight instruction and the flight route, starting the actual flight of the drone, executing the search and rescue mission, and synchronously recording the flight state of the drone and the path adjustment situation to generate the drone rescue mission execution record are as follows: Apply the dynamically adjusted flight route of the drone, update the drone flight path and instruction, verify the matching of the path node and the current state of the drone, and automatically correct the flight route parameters to generate a flight instruction update record; Combined with the flight instruction update record, start the actual flight of the drone, monitor the flight state of the drone in real time, collect the real-time data during flight through sensors, record the flight altitude, speed and direction, and generate a drone flight path record; Based on the drone flight path record, track the adjustment situation of the drone flight path, confirm the stability of the flight path, integrate the navigation trajectory, flight time and obstacle avoidance data, and output the drone rescue mission execution record.

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