Geological surveying and mapping method and system based on unmanned aerial vehicle and storage medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a geological surveying and mapping method and system based on an unmanned aerial vehicle and a storage medium. The method comprises the following steps: processing real-time wind field data and a compensation control instruction set to obtain a wind field compensation model, and optimizing the wind field compensation model to obtain adaptive route data; analyzing and processing the battery state data, the adaptive route data and the task completion degree data of the unmanned aerial vehicle through an energy consumption prediction model to obtain task optimization parameters, and dynamically distributing and processing the task optimization parameters to obtain a task execution instruction set; and processing the state information and the task execution instruction set of the multiple unmanned aerial vehicles to obtain cooperative control data, and performing real-time calculation processing on the cooperative control data to obtain a multi-vehicle cooperative control instruction set. According to the invention, by establishing a distributed cooperative control mechanism, intelligent task allocation and anti-collision control during multi-machine cooperative operation are realized, and the efficiency and reliability of geological surveying and mapping are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of cooperative control of unmanned aerial vehicles (UAVs), and in particular to a geological surveying and mapping method, system and storage medium based on unmanned aerial vehicles (UAVs). Background Art

[0002] With the development of geological surveying and mapping technology, drones are widely used in the field of geological surveying and mapping due to their advantages such as strong mobility and high surveying and mapping efficiency. The existing drone geological surveying and mapping systems mainly adopt a single-machine operation mode, collect data through preset routes, and then use ground control points to coordinate drone control and analysis. This surveying and mapping method has achieved good application results in areas with relatively simple terrain conditions such as plains and hills. At the same time, some systems also integrate GPS / IMU combined navigation, real-time image transmission and other functions, which enhances the controllability of the surveying and mapping process and the reliability of data.

[0003] However, the existing technology still has some problems under complex terrain conditions: first, the single-machine operation mode is difficult to cope with large-scale, complex terrain surveying and mapping tasks, and the operation efficiency is low; second, the preset routes cannot be dynamically adjusted according to real-time wind field conditions, which can easily affect flight safety and data quality; third, the lack of accurate prediction and management of UAV energy consumption makes it difficult to ensure long-term and continuous surveying and mapping operations; finally, there is a lack of effective task allocation and anti-collision mechanisms when multiple machines work together, which increases the risk of operations. Summary of the invention

[0004] The present application provides a geological mapping method, system and storage medium based on a drone, which are used to solve the technical problem that the existing drone geological mapping system lacks intelligent control and multi-machine coordination capabilities under complex terrain conditions.

[0005] In the first aspect, the present application provides a geological mapping method based on a drone, and the geological mapping method based on a drone includes: performing fuzzy controller processing on the terrain feature data of the survey area and the surveying parameter requirements to obtain an initial control parameter group and a controller gain matrix; processing the digital elevation model of the survey area and the initial control parameter group through a partitioning algorithm to obtain partition control data, and performing closed-loop control processing on the partition control data and the controller gain matrix through a cascade PID algorithm to obtain a posture control instruction set; fusing the position data collected by multiple sensors and the posture control instruction set through an extended Kalman filter algorithm to obtain posture fusion data, and compensating the posture fusion data through a model predictive control algorithm to obtain a compensation control instruction set. Instruction set; the real-time wind field data collected by the airborne sensor and the compensation control instruction set are processed by a three-dimensional modeling algorithm to obtain a wind field compensation model, and the wind field compensation model is optimized by a dynamic programming algorithm to obtain adaptive route data; the battery status data of the UAV, the adaptive route data and the task completion data are analyzed and processed by an energy consumption prediction model to obtain task optimization parameters, and the task optimization parameters are dynamically allocated by a task planning algorithm to obtain a task execution instruction set; the status information of multiple UAVs and the task execution instruction set are processed by a distributed collaborative algorithm to obtain collaborative control data, and the collaborative control data is calculated and processed in real time by a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

[0006] In a second aspect, the present application provides a geological mapping system based on a drone, and the geological mapping system based on a drone includes:

[0007] A processing module is used to perform fuzzy controller processing on the terrain feature data of the survey area and the surveying and mapping parameter requirements to obtain an initial control parameter group and a controller gain matrix;

[0008] A control module is used to process the digital elevation model of the measurement area and the initial control parameter group through a partition algorithm to obtain partition control data, and to perform closed-loop control processing on the partition control data and the controller gain matrix through a cascade PID algorithm to obtain a posture control instruction set;

[0009] A fusion module is used to fuse the position data collected by multiple sensors and the attitude control instruction set through an extended Kalman filter algorithm to obtain attitude fusion data, and to compensate the attitude fusion data through a model predictive control algorithm to obtain a compensation control instruction set;

[0010] An optimization module is used to process the real-time wind field data collected by the airborne sensor and the compensation control instruction set through a three-dimensional modeling algorithm to obtain a wind field compensation model, and optimize the wind field compensation model through a dynamic programming algorithm to obtain adaptive route data;

[0011] An analysis module is used to analyze and process the drone battery status data, the adaptive route data and the task completion data through an energy consumption prediction model to obtain task optimization parameters, and dynamically allocate the task optimization parameters through a task planning algorithm to obtain a task execution instruction set;

[0012] The computing module is used to process the status information of multiple UAVs and the task execution instruction set through a distributed collaborative algorithm to obtain collaborative control data, and to perform real-time computation and processing on the collaborative control data through a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

[0013] In a third aspect, the present application provides a computer-readable storage medium, which stores instructions, and when the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned UAV-based geological mapping method.

[0014] In the technical solution provided by the present application, the terrain feature data of the survey area and the surveying and mapping parameter requirements are processed by a fuzzy controller, so as to realize the intelligent generation of the initial control parameters under complex terrain conditions, and improve the accuracy and adaptability of the parameter setting. The digital elevation model of the survey area and the initial control parameter group are processed by the partition algorithm and the cascade PID algorithm, so as to realize the precise control of different terrain feature areas and enhance the flight stability of the UAV under complex terrain conditions. The position data and attitude control instruction set collected by multiple sensors are fused and processed by the extended Kalman filter algorithm, and the compensation processing is combined with the model predictive control algorithm, so as to improve the accuracy of the position and attitude estimation and enhance the robustness of the control system. The real-time wind field data and the compensation control instruction set collected by the airborne sensor are processed by the three-dimensional modeling algorithm and the dynamic programming algorithm, so as to realize the precise compensation of the wind field influence and the real-time optimization of the route, and improve the execution reliability of the surveying and mapping task under complex wind field conditions. The battery status data, adaptive route data and task completion data of the UAV are analyzed and processed based on the energy consumption prediction model and the task planning algorithm, so as to realize the optimization of energy utilization efficiency and the reasonable planning of task execution. The distributed collaborative algorithm and the safe distance field algorithm are used to process the status information and task execution instruction sets of multiple drones, realizing intelligent task allocation and collision risk control in the multi-machine collaborative process, and improving the efficiency and safety of multi-machine collaborative mapping. Overall, the method of the present invention significantly improves the adaptability, reliability and efficiency of UAV geological mapping in complex environments through multi-level closed-loop control and multi-algorithm collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of a geological mapping method based on a drone in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of an embodiment of a geological mapping system based on a drone in an embodiment of the present application. DETAILED DESCRIPTION

[0018] Embodiments of the present application provide a method, system and storage medium for geological mapping based on drones. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the geological mapping method based on a drone includes:

[0020] Step S101, fuzzy controller processing is performed on the terrain feature data of the survey area and the surveying and mapping parameter requirements to obtain an initial control parameter group and a controller gain matrix;

[0021] Step S102: Processing the digital elevation model of the survey area and the initial control parameter group by a partition algorithm to obtain partition control data, and performing closed-loop control processing on the partition control data and the controller gain matrix by a cascade PID algorithm to obtain a posture control instruction set;

[0022] Step S103, fusing the position data and attitude control instruction set collected by the multi-sensor through an extended Kalman filter algorithm to obtain attitude fusion data, and compensating the attitude fusion data through a model predictive control algorithm to obtain a compensation control instruction set;

[0023] Step S104: Process the real-time wind field data and compensation control instruction set collected by the airborne sensor through a three-dimensional modeling algorithm to obtain a wind field compensation model, and optimize the wind field compensation model through a dynamic programming algorithm to obtain adaptive route data;

[0024] Step S105: Analyze and process the drone battery status data, adaptive route data, and mission completion data through an energy consumption prediction model to obtain mission optimization parameters, and dynamically allocate and process the mission optimization parameters through a mission planning algorithm to obtain a mission execution instruction set;

[0025] Step S106: The status information and task execution instruction sets of multiple UAVs are processed by a distributed collaborative algorithm to obtain collaborative control data, and the collaborative control data is processed in real time by a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

[0026] It is understandable that the execution subject of the present application may be a geological surveying and mapping system based on a drone, or may be a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0027] Optionally, the terrain feature data and surveying and mapping parameter requirements of the survey area are first processed, and the initial control parameter group and controller gain matrix are generated by fuzzy controller processing. The terrain feature data includes elevation data, slope data and surface cover type data, and the surveying and mapping parameter requirements include parameters such as spatial resolution, overlap and heading angle. The fuzzy controller calculates the gradient of the elevation data to obtain the terrain slope distribution, and establishes a fuzzy rule base in combination with the resolution requirements in the surveying and mapping parameter requirements. For example, when the terrain slope is greater than 30 degrees and the resolution is required to be higher than 5 cm, the flight altitude needs to be set below 80 meters, and the speed needs to be controlled within 15 meters / second. These parameters constitute the initial control parameter group. Through parameter sensitivity analysis, the influence weight of each parameter on the control effect is obtained to form a controller gain matrix. The digital elevation model of the survey area and the initial control parameter group are processed by a partition algorithm to obtain partition control data. The partition algorithm first performs mean filtering on the elevation model to eliminate abnormal points, and then divides the survey area into a gentle area, a transition area and a complex area based on the terrain characteristics. Different control strategies are adopted for different partitions, and closed-loop control is achieved through the cascade PID algorithm. The cascade PID algorithm includes an inner attitude loop and an outer position loop, which correspond to fast response and stable tracking respectively. The attitude control instruction set includes the control quantities of pitch angle, roll angle and yaw angle, which can ensure the flight stability of the UAV under complex terrain conditions.

[0028] After obtaining the multi-sensor position data and attitude control instruction set, the extended Kalman filter algorithm is used for data fusion. The extended Kalman filter algorithm estimates the state of the nonlinear system through two stages: state prediction and measurement update. The state vector contains position, velocity and attitude information, and the observation vector contains GPS position data and IMU attitude data. The attitude fusion data is compensated by the model predictive control algorithm. The predictive control time domain is usually 2-3 seconds. During this period, the state of the drone is predicted and optimized to generate a compensation control instruction set. The wind field data and compensation control instruction set collected by the airborne sensor are processed by three-dimensional modeling to establish a wind field compensation model. The wind field data contains information such as wind speed, wind direction and turbulence intensity. The continuous three-dimensional wind field distribution is constructed by the interpolation algorithm. The dynamic programming algorithm is used to optimize the wind field compensation model and generate adaptive route data. The dynamic programming algorithm selects the optimal route by calculating the route cost matrix and comprehensively considering the flight distance, energy consumption and wind field impact.

[0029] Energy consumption prediction and analysis is performed on the battery status data, adaptive route data and task completion data of the drone. The battery status data contains voltage, current and temperature information, and the remaining available time is estimated through the energy consumption prediction model. The task planning algorithm dynamically allocates tasks based on task optimization parameters and generates task execution instruction sets. The allocation process takes into account factors such as task priority, energy balance and time constraints. Finally, the status information and task execution instruction sets of multiple drones are processed in a distributed collaborative manner. The distributed collaborative algorithm realizes information interaction between multiple drones by building a communication topology. The safe distance field algorithm calculates the relative distance and speed between drones, builds a collision risk assessment model, and generates a multi-machine collaborative control instruction set.

[0030] For example: the survey area is 2 square kilometers, the terrain height difference reaches 200 meters, and the spatial resolution is required to be better than 5 centimeters. First, the terrain slope distribution is calculated based on the elevation data, and the maximum slope is 45 degrees. The fuzzy controller generates initial control parameters based on these data: the flight altitude in the gentle area is 100 meters, and the speed is 20 meters / second; the flight altitude in the transition area is 80 meters, and the speed is 15 meters / second; the flight altitude in the complex area is 60 meters, and the speed is 10 meters / second. The partition control data shows that the survey area contains 40% gentle areas, 35% transition areas, and 25% complex areas. The extended Kalman filter algorithm integrates GPS and IMU data, with a position accuracy of better than 10 centimeters and an attitude accuracy of better than 0.5 degrees. The wind field data shows that there is a wind speed change of 5-8 meters / second in the survey area. The adaptive route generated by the dynamic programming algorithm saves 15% of the flight distance compared to the conventional route. The task is assigned to 3 drones for execution. The remaining power of each drone is between 75% and 85%. The energy consumption forecast shows that the endurance time is sufficient to complete their respective mission areas. Through the distributed collaborative algorithm, drones maintained a safe distance of 50-100 meters, successfully avoiding the risk of flight trajectory deviation due to wind field changes. The entire surveying and mapping task was completed within 2 hours, meeting the spatial resolution and data quality requirements.

[0031] In the embodiment of the present application, the terrain feature data of the survey area and the surveying and mapping parameter requirements are processed by the fuzzy controller, so as to realize the intelligent generation of the initial control parameters under complex terrain conditions, and improve the accuracy and adaptability of the parameter setting. The digital elevation model of the survey area and the initial control parameter group are processed by the partition algorithm and the cascade PID algorithm, so as to realize the precise control of different terrain feature areas and enhance the flight stability of the UAV under complex terrain conditions. The position data and attitude control instruction set collected by multiple sensors are fused and processed by the extended Kalman filter algorithm, and the compensation processing is combined with the model predictive control algorithm to improve the accuracy of position and attitude estimation and enhance the robustness of the control system. The real-time wind field data and compensation control instruction set collected by the airborne sensor are processed by the three-dimensional modeling algorithm and the dynamic programming algorithm, so as to realize the precise compensation of the wind field influence and the real-time optimization of the route, and improve the execution reliability of the surveying and mapping task under complex wind field conditions. The battery status data, adaptive route data and task completion data of the UAV are analyzed and processed based on the energy consumption prediction model and the task planning algorithm, so as to realize the optimization of energy utilization efficiency and the reasonable planning of task execution. The distributed collaborative algorithm and the safe distance field algorithm are used to process the status information and task execution instruction sets of multiple drones, realizing intelligent task allocation and collision risk control in the multi-machine collaborative process, and improving the efficiency and safety of multi-machine collaborative mapping. Overall, the method of the present invention significantly improves the adaptability, reliability and efficiency of UAV geological mapping in complex environments through multi-level closed-loop control and multi-algorithm collaboration.

[0032] In an optional embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) Perform gradient calculation on the elevation data in the survey area terrain feature data to obtain terrain slope data, and perform interval division on the terrain slope data to obtain terrain complexity classification data;

[0034] (2) Quantify the spatial resolution data in the surveying and mapping parameter requirements to obtain the reference flight altitude data, and associate the reference flight altitude data with the terrain complexity classification data to obtain the altitude adjustment coefficient;

[0035] (3) Analyze and process the overlap data in the surveying and mapping parameter requirements to obtain speed constraints, and define the flight speed range based on the speed constraints to obtain speed range data;

[0036] (4) Performing fuzzy rule generation processing on the speed interval data and the height adjustment coefficient to obtain a control rule base, and constructing a membership function on the control rule base to obtain a fuzzy reasoning set;

[0037] (5) Defuzzifying the fuzzy inference set to obtain original control parameters, and performing equilibrium constraint processing on the original control parameters to obtain an initial control parameter group;

[0038] (6) Perform sensitivity analysis on the parameters in the initial control parameter group to obtain the parameter influence weights, and generate the controller gain matrix based on the parameter influence weights.

[0039] Optionally, the elevation data in the terrain feature data of the survey area is processed by gradient calculation. The elevation data is obtained through a digital elevation model, which contains the altitude value of each grid point in the survey area. The elevation data is calculated by gradient, and the terrain slope data is calculated by dividing the elevation difference between adjacent grid points by the horizontal distance. According to the statistical distribution characteristics of the terrain slope data, the slope is divided into multiple intervals: 0-15 degrees is a gentle area, 15-30 degrees is a transition area, and above 30 degrees is a steep area, thereby obtaining terrain complexity classification data. For the spatial resolution data in the surveying and mapping parameter requirements, quantification processing is required to obtain the benchmark flight altitude data. The spatial resolution indicates the actual distance corresponding to one pixel on the ground, which is directly related to the flight altitude and camera parameters. The calculation of the benchmark flight altitude data takes into account parameters such as camera focal length and sensor size, and is calculated using the following height adjustment coefficient formula:

[0040]

[0041] Among them, H adj represents the height adjustment factor, GSD represents the target ground resolution (meters / pixels), f represents the camera focal length (mm), p s represents the sensor pixel size (micrometers), α represents the terrain slope (radians), and K t Represents the terrain complexity correction factor. For the overlap data in the surveying and mapping parameter requirements, it is necessary to obtain the speed constraint conditions through analytical processing. The overlap data includes heading overlap and lateral overlap. Usually, the heading overlap is required to be more than 80%, and the lateral overlap is required to be more than 60%. According to the overlap requirements and the camera shooting interval, the maximum flight speed limit can be determined. By comprehensively considering the flight altitude, overlap requirements and camera performance parameters, the speed range data is obtained.

[0042] The speed interval data and altitude adjustment coefficient are processed by fuzzy rule generation to establish a control rule base. The fuzzy rule divides the speed and altitude parameters into several linguistic variables, such as "low", "medium", "high", etc., and establishes a mapping relationship between input and output. A membership function is constructed for the control rule base, and the membership value is calculated using the following formula:

[0043]

[0044] Among them, μ(x) represents the membership function value, x represents the input variable, c represents the membership function center value, σ represents the standard deviation parameter, β represents the shape adjustment factor, and γ represents the weight coefficient. In this way, the fuzzy inference set is obtained. The fuzzy inference set is defuzzified, and the fuzzy inference results are converted into clear control quantities to obtain the original control parameters. Defuzzification uses the centroid method to perform balanced constraint processing on the original control parameters to ensure that the parameter values ​​are within a reasonable range, thereby obtaining the initial control parameter group. A sensitivity analysis is performed on the parameters in the initial control parameter group. By changing a single parameter to observe the change in system response, the parameter influence weight is calculated, and finally the controller gain matrix is ​​generated.

[0045] For example, the survey area is 3 square kilometers, and the elevation data resolution is 5 meters. The elevation data is gradient calculated to obtain the terrain slope distribution: the gentle area accounts for 45%, with a slope between 0-15 degrees; the transition area accounts for 35%, with a slope between 15-30 degrees; the steep area accounts for 20%, with a slope of more than 30 degrees. The target ground resolution is set to 3 cm / pixel, and a camera with a focal length of 35 mm and a pixel size of 3.76 microns is used. The base flight altitude of the gentle area is calculated to be 120 meters. Through the height adjustment coefficient formula, the flight altitudes of the transition area and the steep area are adjusted to 90 meters and 60 meters respectively. The heading overlap is set to 85%, the lateral overlap is 65%, and the minimum camera shooting interval is 0.5 seconds. According to these parameters, the speed constraints are calculated: the maximum speed in the gentle area is 18 meters / second, the maximum speed in the transition area is 14 meters / second, and the maximum speed in the steep area is 10 meters / second. The fuzzy rule base is established, which contains 27 rules (3 levels of speed and height, 3 levels of terrain complexity). Through the membership function calculation, a complete fuzzy reasoning set is obtained.

[0046] After defuzzification, the specific control parameters of the flat area, transition area and steep area are obtained. Sensitivity analysis shows that the influence weight of flight altitude on surveying and mapping accuracy is 0.45, the influence weight of flight speed is 0.35, and the influence weight of overlap is 0.20. Based on this, the controller gain matrix is ​​generated for subsequent flight control. The entire processing process realizes the automatic generation of flight control parameters from terrain data, ensuring the reliable execution of surveying and mapping tasks.

[0047] In an optional embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) Perform mean filtering on the elevation data in the digital elevation model of the survey area to obtain smooth elevation data, and perform slope calculation on the smooth elevation data to obtain terrain slope distribution data;

[0049] (2) Performing cluster analysis on the terrain slope distribution data to obtain terrain feature partitions, and performing boundary extraction on the terrain feature partitions to obtain partition boundary data;

[0050] (3) performing parameter matching processing on the partition boundary data and the initial control parameter group to obtain a partition parameter set, and performing interpolation processing on the partition parameter set to obtain a continuous parameter field;

[0051] (4) performing boundary constraint processing on the continuous parameter field to obtain constraint parameter data, and generating partition control data based on the constraint parameter data;

[0052] (5) performing error calculation processing on the partition control data to obtain position error data, and performing differentiation processing on the position error data to obtain speed error data;

[0053] (6) Integrate the speed error data to obtain integrated error data, and perform proportional operation on the integrated error data according to the controller gain matrix to obtain the basic control amount;

[0054] (7) The basic control quantity is limited to obtain a bounded control quantity, and the bounded control quantity is subjected to attitude solution processing to obtain an attitude control instruction set.

[0055] Optionally, the processing of the digital elevation model data of the survey area first needs to be mean filtered to eliminate outliers and noise. The mean filter uses a sliding window method to average the data within the surrounding 3×3 or 5×5 grid range for each elevation point to obtain smoothed elevation data. The smoothed elevation data is used for slope calculation, and the terrain slope distribution data is obtained by calculating the ratio of the elevation difference between adjacent grid points to the horizontal distance. The terrain slope distribution data is analyzed using the K-means clustering algorithm to cluster areas with similar slope characteristics to form terrain feature partitions. The K-means clustering process includes four steps: initial cluster center selection, distance calculation, category division, and center update, and finally terrain partitions of different categories are obtained. Subsequently, the boundaries of the terrain feature partitions are extracted, and the Sobel operator is used to detect the regional boundaries to obtain the partition boundary data.

[0056] The partition boundary data is matched with the initial control parameter group, and the control parameters of different partitions are associated with the boundary data to obtain the partition parameter set. The partition parameter set contains parameters such as the flight altitude, speed, and heading angle of each partition. In order to eliminate the sudden change of parameters between partitions, the partition parameter set is bilinearly interpolated to generate a continuous parameter field to ensure smooth transition of parameters at the partition boundary.

[0057] The continuous parameter field is processed with boundary constraints, mainly considering the performance limitations and flight safety requirements of the UAV, including the maximum flight altitude, minimum flight altitude, maximum speed and other constraints, to obtain the constraint parameter data. The partition control data is generated according to the constraint parameter data, including the specific control strategy for each partition. The partition control data is compared with the actual flight position to calculate the position error data. The position error represents the deviation between the current position of the UAV and the predetermined route position. The position error data is differentiated to obtain the speed error data, which reflects the rate of change of the position deviation. The speed error data is integrated to obtain the integral error data, which is used to eliminate the steady-state error. The integral error data is proportionally operated according to the controller gain matrix to obtain the basic control quantity.

[0058] The basic control quantity is limited to ensure that the control command is within the executable range of the drone and obtain the bounded control quantity. The calculation formula of the bounded control quantity is:

[0059]

[0060] Among them, U bound represents the bounded control quantity, K1, K2, K3 represent the weight coefficients of position error, speed error and integral error respectively, E p represents the normalized position error, E v represents the normalized velocity error, E i represents the normalized integral error, U max Indicates the maximum control amount.

[0061] Finally, the bounded control quantity is processed by attitude solution, and the control quantity is converted into a specific attitude angle instruction. The calculation formula of attitude solution is:

[0062]

[0063] in, θ, ψ represent the roll angle, pitch angle and yaw angle respectively, W1, W2, W3, W4 and W5 are attitude weight coefficients, ω is the angular velocity, t is the time parameter, U x , U y , U z is a three-axis bounded control quantity.

[0064] For example, in a geological mapping task with a survey area of ​​4 square kilometers, the resolution of the digital elevation model is 2 meters. Through 5×5 mean filtering, 98% of abnormal elevation values ​​are eliminated. The slope is calculated for the smoothed elevation data, and the distribution data with a slope range of 0-65 degrees is obtained. K-means clustering (K=3) is used to divide the terrain into a gentle area (0-20 degrees), a transition area (20-40 degrees) and a steep area (above 40 degrees). The boundary extraction obtains the partition boundary data with a total length of about 25 kilometers. The initial control parameter group includes a flight altitude of 120 meters and a speed of 18 meters / second in the gentle area, a flight altitude of 90 meters and a speed of 15 meters / second in the transition area, and a flight altitude of 70 meters and a speed of 12 meters / second in the steep area. A continuous parameter field is generated by bilinear interpolation, and the parameter change rate at the partition boundary is controlled to no more than 2% per meter. Considering the performance constraints of the drone, the maximum flight altitude is set to 150 meters, the minimum flight altitude is 50 meters, and the maximum speed is 20 meters / second.

[0065] During the actual flight, the position error is controlled within the range of ±3 meters, and the speed error is controlled within the range of ±2 meters per second. After the integral error is weighted, the basic control amount obtained is between -1 and 1. The limit processing ensures that the roll angle is within the range of ±30 degrees, the pitch angle is within the range of ±25 degrees, and the yaw angle is within the range of ±180 degrees. The attitude control instruction set finally generated is updated at a frequency of 50 Hz, ensuring the stable flight of the drone under complex terrain conditions.

[0066] In an optional embodiment, the process of executing step S103 may specifically include the following steps:

[0067] (1) Performing timestamp alignment processing on the position data collected by multiple sensors to obtain synchronized position data, and constructing state vectors on the synchronized position data and attitude control instruction sets to obtain initial state estimation;

[0068] (2) performing state prediction processing on the initial state estimate to obtain a priori state quantity, and performing covariance transfer processing on the priori state quantity to obtain a priori covariance;

[0069] (3) Perform observation prediction processing on the prior state quantity to obtain the predicted observation value, and perform residual calculation processing on the predicted observation value and the actual observation value to obtain the observation residual;

[0070] (4) Performing gain calculation processing on the observation residual and the prior covariance to obtain the Kalman gain, and performing state update processing on the Kalman gain to obtain the posture fusion data;

[0071] (5) Performing error prediction processing on the pose fusion data to obtain a prediction error sequence, and constructing a cost function on the prediction error sequence to obtain a control objective function;

[0072] (6) performing constraint setting processing on the control objective function to obtain a constraint condition set, and performing rolling time domain partition processing on the constraint condition set to obtain a time domain control interval;

[0073] (7) Performing sequence prediction processing on the state within the time domain control interval to obtain a predicted control quantity, and performing feedback correction processing on the predicted control quantity to obtain a compensation control instruction set.

[0074] Optionally, the processing of multi-sensor position data first requires timestamp alignment. Multi-sensors include GPS, IMU, LiDAR and other devices, and the data sampling frequency of each sensor is different: GPS data frequency is 10Hz, IMU data frequency is 200Hz, and LiDAR data frequency is 20Hz. Timestamp alignment uses an interpolation algorithm to unify data of different frequencies to the same time base to obtain synchronized position data. The state vector is constructed for the synchronized position data and attitude control instruction set. The state vector contains 12 components such as position coordinates, velocity, attitude angle and angular velocity, which constitute the initial state estimate.

[0075] The initial state estimate is processed for state prediction, and the UAV dynamics model is used to predict the state at the next moment. State prediction takes into account the UAV's motion characteristics and external forces, including gravity, thrust, and aerodynamics. The predicted prior state quantity represents the best estimate of the system before the measurement update. The prior state quantity is processed for covariance transfer, and the uncertainty of the state prediction is calculated to obtain the prior covariance matrix. The covariance matrix reflects the correlation between the state quantities and the credibility of the prediction. The prior state quantity is substituted into the observation equation, and the observation prediction process is performed to obtain the predicted observation value. The observation equation describes the relationship between the state quantity and the sensor measurement value. The predicted observation value is compared with the actual observation value, and the observation residual is calculated. The observation residual reflects the deviation between the predicted value and the actual measurement value, which is an important indicator for evaluating the accuracy of the prediction.

[0076] The gain is calculated based on the observation residual and the prior covariance to obtain the Kalman gain. The Kalman gain determines the weight distribution of the predicted value and the observed value. The larger the value, the more inclined to believe the observed value. The Kalman gain is processed for state update, and the predicted value and the observed value are weighted fused to obtain the pose fusion data. The pose fusion data combines the advantages of the prediction model and the observation data to provide a more accurate state estimate. The pose fusion data is processed for error prediction, and the state error at future moments is predicted based on the system model to obtain the prediction error sequence. The prediction error sequence is used to evaluate the control effect and optimize the control strategy. By setting the error weight and control cost, the cost function is constructed for the prediction error sequence to obtain the control objective function. The control objective function comprehensively considers multiple aspects such as tracking accuracy and control energy consumption.

[0077] Constraints are set for the control objective function, including state constraints and control constraints. The state constraints ensure that the UAV motion state is within a safe range, and the control constraints ensure that the control output does not exceed the actuator capacity. The constraint set is divided into a rolling time domain to obtain a time domain control interval. The time domain control interval is usually selected to be 2-3 seconds, during which prediction and optimization are performed. The state within the time domain control interval is sequence predicted, and the dynamic programming method is used to search for the optimal control sequence to obtain the predicted control quantity. The predicted control quantity is feedback corrected to compensate for the influence of model errors and external interference, and finally a compensation control instruction set is generated.

[0078] For example, in a complex terrain mapping mission, the drone was equipped with three sensors: GPS, IMU, and LiDAR. The raw data sampling shows that the GPS position accuracy is ±2 meters, and the update frequency is 10Hz; the IMU attitude accuracy is ±0.1 degrees, and the update frequency is 200Hz; the LiDAR ranging accuracy is ±0.05 meters, and the update frequency is 20Hz. Through timestamp alignment processing, all data are unified to a frequency of 50Hz. The initial values ​​of the constructed state vector are: position (100,200,50) meters, speed (5,0,0) meters / second, attitude angle (2,3,30) degrees, and angular velocity (0,0,2) degrees / second. In the state prediction stage, considering the prediction step of 0.02 seconds, the prior state is calculated: position (100.1,200,50) meters, speed (4.8,0.2,0) meters / second, attitude angle (2,3.1,30.04) degrees.

[0079] Comparing the actual observation value with the predicted value, the observation residual is obtained: position deviation 0.3 meters, speed deviation 0.2 meters / second, attitude angle deviation 0.2 degrees. The calculated Kalman gain makes the position estimation 75% using the observation value and 25% using the predicted value; attitude estimation 60% using the observation value and 40% using the predicted value. The position accuracy after fusion is improved to ±0.5 meters, and the attitude accuracy is improved to ±0.05 degrees. Based on the fusion data, the error trend within the next 2 seconds is predicted, and the control objective function is set: position error weight 0.5, speed error weight 0.3, attitude error weight 0.2. Constraints include: maximum speed 20 meters / second, maximum acceleration 4 meters / square second, maximum attitude angle change rate 60 degrees / second. The time domain division adopts a control cycle of 0.1 seconds to generate 20 control points. The final generated compensation control instruction set controls the route tracking error within 0.8 meters and the attitude stability is better than 0.5 degrees.

[0080] In an optional embodiment, the process of executing step S104 may specifically include the following steps:

[0081] (1) Performing component decomposition processing on the wind speed data collected by the airborne sensor to obtain three-dimensional wind speed components, and performing interpolation processing on the three-dimensional wind speed components to obtain continuous wind field data;

[0082] (2) Perform density analysis on the continuous wind field data to obtain the wind field density distribution, and perform gradient calculation on the wind field density distribution to obtain the wind field change characteristics;

[0083] (3) Correlation processing is performed on the wind field change characteristics and the compensation control instruction set to obtain the wind field influence coefficient, and threshold division processing is performed on the wind field influence coefficient to obtain a wind field compensation model;

[0084] (4) Quantifying the change amplitude in the wind farm compensation model to obtain a route cost matrix, and performing path search processing on the route cost matrix to obtain an initial route set;

[0085] (5) Calculate the energy consumption of the initial route set to obtain energy consumption distribution data, and sort the energy consumption distribution data to obtain a route sequence;

[0086] (6) The route sequence is traversed to obtain a set of candidate routes, and the set of candidate routes is smoothed to obtain adaptive route data.

[0087] Optionally, the wind speed data collected by the airborne sensor is decomposed into components. The airborne sensors include an anemometer and a barometer, which measure the wind speed magnitude and direction data. The wind speed data is decomposed into an east component, a north component, and a vertical component to form a three-dimensional wind speed component. The three-dimensional wind speed components are processed by cubic spline interpolation to expand the discrete measurement points into continuous wind field data with an interpolation interval of 10 meters to ensure the continuity and smoothness of the wind field data. Density analysis is performed on the continuous wind field data to calculate the wind speed change within the unit space to obtain the wind field density distribution. The wind field density distribution reflects the concentration degree and change trend of the wind field. The wind field density distribution data is gradient calculated to obtain the wind field change characteristics. The calculation formula for the wind field change characteristics is:

[0088]

[0089] Among them, G wind represents the wind field variation characteristics, λ i Represents the weight coefficient of each direction, V i Represents the wind speed components in three directions, ρ air Indicates the air density.

[0090] The wind field change characteristics are correlated with the compensation control instruction set to calculate the wind field influence coefficient. The wind field influence coefficient indicates the degree of influence of the wind field on flight control. The wind field influence coefficient is divided into thresholds, and multiple risk levels are set to form a wind field compensation model. The wind field compensation model is used to guide route planning and flight control. The change amplitude in the wind field compensation model is quantitatively analyzed to establish a route cost matrix. The route cost matrix considers factors such as wind field intensity, change gradient, and flight direction. The route cost matrix is ​​subjected to an A* algorithm path search. The search process takes into account the starting point, end point, and obstacle avoidance requirements to obtain an initial route set that meets the surveying and mapping requirements.

[0091] The energy consumption of each route in the initial route set is calculated to obtain energy consumption distribution data. The formula for energy consumption calculation is:

[0092]

[0093] Among them, E total represents the total energy consumption, m represents the mass of the drone, g represents the acceleration of gravity, h j Indicates the altitude change of the flight segment, v j represents the segment speed, ξ j Indicates the drag coefficient, F drag represents resistance, d j represents the flight distance, η j represents the energy conversion efficiency, and n represents the number of flight segments.

[0094] The energy consumption distribution data is sorted and arranged from low to high energy consumption to obtain a route sequence. The route sequence is traversed to select a route combination that meets the surveying and mapping requirements and has low energy consumption to form an alternative route group. The alternative route group is smoothed with cubic splines to eliminate the sudden changes at the route inflection points, and finally the adaptive route data is obtained.

[0095] For example, the survey area covers an area of ​​5 square kilometers with an altitude difference of 300 meters. The airborne anemometer collects wind speed data at a frequency of 5 Hz, and the raw data shows that the wind speed ranges from 3 to 12 meters per second. The wind speed data is decomposed into three components, with an average east wind speed of 6 meters per second, an average north wind speed of 4 meters per second, and an average vertical wind speed of 1 meter per second. Through cubic spline interpolation, continuous wind field data of 100 meters × 100 meters grid is generated. Density analysis shows that there are three main wind field dense areas in the survey area, located at the ridge and valley positions, with a maximum wind speed gradient of 0.2 meters per second per meter. The calculation results of wind field change characteristics show that the wind field at the ridge changes most violently, with an influence coefficient of 0.8, which needs to be considered. The wind field compensation model divides the survey area into four risk levels, of which the high-risk area accounts for 15% of the total area. The route cost matrix constructed based on the wind field compensation model is 500×500 in size, and the A* algorithm searches for 12 feasible initial routes. Energy consumption calculations show that the energy consumption of these routes ranges from 800 to 1,200 watt-hours. After sorting the routes, the top five routes were selected as alternatives. After smoothing, the total length of the optimal route is 15 kilometers, the estimated flight time is 45 minutes, and the energy consumption is 950 watt-hours, which meets the requirements of surveying and mapping accuracy and coverage.

[0096] In an optional embodiment, the process of executing step S105 may specifically include the following steps:

[0097] (1) performing data standardization processing on the voltage, current and temperature data in the battery status data to obtain standardized battery parameters, and performing feature extraction processing on the standardized battery parameters to obtain battery status features;

[0098] (2) Correlation processing is performed on the battery status characteristics and the adaptive route data to obtain route energy consumption data, and statistical analysis and processing is performed on the route energy consumption data to obtain a unit energy consumption index;

[0099] (3) Performing interval segmentation processing on the task completion data to obtain segmented task data, and performing weight calculation processing on the segmented task data to obtain a task weight coefficient;

[0100] (4) Comprehensively calculate and process the unit energy consumption index and the task weight coefficient to obtain weighted energy consumption data, and perform threshold analysis on the weighted energy consumption data to obtain task optimization parameters;

[0101] (5) The task optimization parameters are arranged in sequence to obtain a task sequence table, and the task sequence table is time-allocated to obtain a time-series task table;

[0102] (6) Performing task partitioning on the sequential task table to obtain task subsets, and performing priority allocation on the task subsets to obtain a task execution instruction set.

[0103] Optionally, the battery status data includes three parameters: voltage, current and temperature, where the voltage range is 20-25V, the current range is 0-40A, and the temperature range is 10-50℃. Data normalization uses the maximum and minimum method to normalize all parameters to the range of 0-1. The standardized battery parameters are subjected to feature extraction, including calculating statistical features such as mean value, variance, and rate of change to obtain battery status features. Feature extraction highlights key indicators of the battery working state, such as remaining capacity, discharge rate, and temperature stability. The battery status features and adaptive route data are correlated and analyzed, and the energy consumption of each route is calculated to obtain route energy consumption data. Route energy consumption data reflects the energy consumption in different flight stages, including the energy consumption characteristics of processes such as climbing, cruising, and turning. Statistical analysis is performed on the route energy consumption data to calculate the energy consumption per unit distance and per unit time to obtain the unit energy consumption index. The unit energy consumption index reflects the energy utilization efficiency and provides an important basis for mission planning.

[0104] The task completion data is segmented into intervals, and the tasks are divided into different completion stages according to indicators such as the surveying area coverage rate and data collection quality. The segmented task data contains the work content, completion status and remaining workload of each stage. The segmented task data is weighted, and the task weight coefficient is obtained by considering factors such as task importance, time urgency and resource consumption. The task weight coefficient is used to guide resource allocation and task priority sorting. The unit energy consumption index and the task weight coefficient are comprehensively calculated, and the weighted energy consumption data is obtained by weighted summation. The weighted energy consumption data comprehensively considers energy consumption and task value, providing a decision-making basis for task optimization. The weighted energy consumption data is threshold analyzed, and multiple energy consumption levels are set to obtain task optimization parameters. The task optimization parameters guide the reasonable allocation of tasks and the arrangement of execution order. The task optimization parameters are arranged in order, sorted from high to low priority, and a task sequence table is generated. The task sequence table contains the execution order, estimated time and resource requirements of each task. The task sequence table is time-allocated, and the execution time window of each task is determined according to the drone's endurance and the urgency of the task, and a time-series task table is obtained. The time-series task table provides a detailed task execution plan.

[0105] The task table is divided into tasks, and the large tasks are decomposed into independently executable subtasks to obtain task subsets. Task subsets facilitate multi-machine collaborative execution and dynamic task adjustment. Priority is assigned to task subsets, and task execution instruction sets are generated considering task dependencies and resource constraints. The task execution instruction set provides clear work guidance for drones. For example, a drone powered by a lithium battery performs a task. The initial battery status shows: voltage 24.5V, current 5A, temperature 25℃. After data standardization, the voltage standard value is 0.9, the current standard value is 0.125, and the temperature standard value is 0.375. Feature extraction shows that the battery capacity remains 85%, the temperature change rate is less than 0.5℃ / minute, and the working condition is good.

[0106] The total length of the adaptive route is 20 kilometers, divided into 25 segments. The energy consumption data analysis of the route shows that the average power consumption is 380W, the peak power of the climbing segment is 550W, and the power of the cruising segment is 320W. The unit energy consumption index calculation results are: 70 watt-hours per kilometer and 1150 watt-hours per hour. The task completion data shows that 40% of the survey area has been covered. After the interval is divided, three task stages are obtained: undulating terrain segment (weight 0.4), gentle terrain segment (weight 0.3), and transition terrain segment (weight 0.3). The weighted energy consumption calculation results show that the unit area energy consumption of the undulating terrain segment is 1.2 watt-hours / square meter, the gentle terrain segment is 0.8 watt-hours / square meter, and the transition terrain segment is 1.0 watt-hours / square meter. Threshold analysis divides the tasks into three energy consumption levels: high, medium, and low. The task sequence table is arranged in the order of "high energy consumption-low energy consumption-medium energy consumption", and the time allocation considers the execution of high energy consumption tasks during the morning period when the temperature is lower.

[0107] The final task execution instruction set contains 15 subtasks, and the priority allocation takes into account the remaining power, weather conditions and task urgency. High-priority tasks are scheduled during periods of sufficient battery capacity (remaining power greater than 70%), and 20% of power is reserved as a safety margin to ensure the safe completion of the task. The entire surveying and mapping mission is expected to take 3 hours and consume a total energy of 3,500 watt-hours, meeting the survey area coverage and data quality requirements.

[0108] In an optional embodiment, the process of executing step S106 may specifically include the following steps:

[0109] (1) Performing data synchronization processing on the position and velocity data of multiple UAVs to obtain synchronization status data, and performing association processing on the synchronization status data and the task execution instruction set to obtain initial coordination parameters;

[0110] (2) performing communication topology construction processing on the initial coordination parameters to obtain communication link data, and performing bandwidth allocation processing on the communication link data to obtain a resource allocation table;

[0111] (3) performing time slot division processing on the resource allocation table to obtain a communication timing table, and performing conflict detection processing on the communication timing table to obtain an available time slot set;

[0112] (4) performing information interaction processing on the data in the available time slot set to obtain interaction status data, and performing consistency verification processing on the interaction status data to obtain collaborative control data;

[0113] (5) Perform distance calculation on the position information in the collaborative control data to obtain a relative distance matrix, and perform safety domain construction on the relative distance matrix to obtain collision risk data;

[0114] (6) Classifying the collision risk data to obtain a risk level table, and performing track planning on the risk level table to obtain a set of alternative tracks;

[0115] (7) Perform interference assessment on the alternative trajectory set to obtain a multi-aircraft collaborative control instruction set.

[0116] Optionally, the realization of multi-machine cooperative control first requires data synchronization processing. The position and speed data of each drone are collected at different frequencies. The position data is obtained through GPS at a frequency of 10Hz, and the speed data is measured through a pitot tube at a frequency of 20Hz. The data synchronization process adopts the time window method, takes 50 milliseconds as the synchronization time window, and linearly interpolates the data within the window to obtain the synchronization state data under a unified time reference. The synchronization state data is associated with the task execution instruction set, and the task information, position information and speed information of each drone are extracted to form the initial cooperative parameters. The communication topology is constructed for the initial cooperative parameters, and the minimum spanning tree algorithm is used to establish the communication connection relationship between drones. The communication topology takes into account the distance and communication quality between drones and optimizes the layout of the communication link. The communication link data is allocated bandwidth. According to the data transmission requirements and link quality, appropriate bandwidth resources are allocated to each communication link to generate a resource allocation table. The bandwidth allocation adopts a dynamic adjustment strategy to ensure the timely transmission of key data.

[0117] The resource allocation table is divided into time slots, and the communication is organized in TDMA (time division multiple access) mode. The time slot length is set to 10 milliseconds, and a specific data transmission task is arranged in each time slot to generate a communication schedule. The communication schedule is checked for conflicts to check whether the same time slot is occupied by multiple communication tasks. The conflict detection adopts the sliding window method with a window size of 100 milliseconds. The detected conflicts are adjusted to obtain a set of available time slots. Information exchange is performed on the data in the set of available time slots, including the exchange of information such as position, speed, and task status. The information exchange adopts a distributed consistency protocol to ensure the synchronization and consistency of information between multiple machines. The interaction status data is verified for consistency, the timeliness and reliability of the data are checked, and abnormal data is eliminated to obtain collaborative control data. The collaborative control data contains verified global status information, which provides a basis for subsequent multi-machine collaboration.

[0118] The distance calculation is performed on the position information in the collaborative control data, and the relative distance between drones is calculated using the Euclidean distance to form a relative distance matrix. The relative distance matrix reflects the spatial distribution status of the drone group. The safety domain is constructed for the relative distance matrix, the minimum safety distance threshold is set, the collision risk between drones is analyzed, and the collision risk data is obtained. The safety domain takes into account the motion characteristics and environmental factors of the drone and dynamically adjusts the safety distance requirements. The collision risk data is graded, and the risk is divided into three levels: high, medium, and low according to the relative distance, relative speed, and environmental conditions, and a risk level table is generated. The risk level table is used for trajectory planning, and the artificial potential field method is used to generate obstacle avoidance trajectories to form an alternative trajectory set. The artificial potential field method regards other drones as repulsive sources and the target point as a gravitational source, and generates a safe trajectory through force field superposition.

[0119] Interference assessment is performed on the candidate track set, the mutual influence between tracks and the task execution effect are analyzed, and a multi-machine collaborative control instruction set is generated after comprehensive assessment. The multi-machine collaborative control instruction set contains information such as track planning, speed control and task allocation for each UAV.

[0120] For example, three drones are dispatched to perform tasks at the same time. The initial positions of the three drones are (0, 0, 100) meters, (200, 0, 100) meters, and (100, 200, 100) meters, respectively, and the flight speed is 15 meters per second. During data synchronization, the data in the 50-ms time window is unified to a frequency of 20 Hz through linear interpolation. The communication topology is constructed using the minimum spanning tree algorithm, and three main communication links are established with a total bandwidth of 10 Mbps, which is allocated to the three links in a ratio of 4:3:3. The time slot division divides the 100-ms communication cycle into 10 time slots, each of which is 10 milliseconds. Conflict detection finds two time slot conflicts, which are resolved by adjusting the timing to obtain 8 available time slots. During information interaction, position data occupies 4 time slots, speed data occupies 2 time slots, and task status data occupies 2 time slots.

[0121] Relative distance calculations show that the minimum distance between the three drones is 200 meters and the maximum distance is 282 meters. The safety distance threshold is set at 100 meters, and the collision risk analysis shows that all relative distances are within the safe range. During track planning, considering that a drone encountered a crosswind of 5 meters per second, its track was adjusted to deviate from the original route by 30 meters to ensure flight safety. The final generated multi-machine collaborative control instruction set assigned different mapping areas to the three drones. The working areas were 150 meters apart from each other, the flight altitude difference was set to 20 meters, and an interlaced "S"-shaped mapping route was adopted to avoid mutual interference. The entire mapping task was completed within 45 minutes, with a data transmission success rate of 99.5% and a position control accuracy of better than ±2 meters, meeting the accuracy requirements of geological mapping.

[0122] The above describes the geological surveying method based on the UAV in the embodiment of the present application. The following describes the geological surveying system based on the UAV in the embodiment of the present application. Figure 2 In the embodiments of the present application, an embodiment of a geological surveying and mapping system based on a drone includes:

[0123] A processing module is used to perform fuzzy controller processing on the terrain feature data of the survey area and the surveying and mapping parameter requirements to obtain an initial control parameter group and a controller gain matrix;

[0124] A control module is used to process the digital elevation model of the measurement area and the initial control parameter group through a partition algorithm to obtain partition control data, and to perform closed-loop control processing on the partition control data and the controller gain matrix through a cascade PID algorithm to obtain a posture control instruction set;

[0125] A fusion module is used to fuse the position data collected by multiple sensors and the attitude control instruction set through an extended Kalman filter algorithm to obtain attitude fusion data, and to compensate the attitude fusion data through a model predictive control algorithm to obtain a compensation control instruction set;

[0126] An optimization module is used to process the real-time wind field data collected by the airborne sensor and the compensation control instruction set through a three-dimensional modeling algorithm to obtain a wind field compensation model, and optimize the wind field compensation model through a dynamic programming algorithm to obtain adaptive route data;

[0127] An analysis module is used to analyze and process the drone battery status data, the adaptive route data and the task completion data through an energy consumption prediction model to obtain task optimization parameters, and dynamically allocate the task optimization parameters through a task planning algorithm to obtain a task execution instruction set;

[0128] The computing module is used to process the status information of multiple UAVs and the task execution instruction set through a distributed collaborative algorithm to obtain collaborative control data, and to perform real-time computation and processing on the collaborative control data through a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

[0129] Through the cooperation of the above components, the terrain feature data and mapping parameter requirements of the survey area are processed by the fuzzy controller, and the intelligent generation of initial control parameters under complex terrain conditions is realized, which improves the accuracy and adaptability of parameter settings. The digital elevation model of the survey area and the initial control parameter group are processed by the partition algorithm and the cascade PID algorithm, which realizes the precise control of different terrain feature areas and enhances the flight stability of the UAV under complex terrain conditions. The position data and attitude control instruction set collected by multiple sensors are fused and processed by the extended Kalman filter algorithm, and the compensation processing is combined with the model predictive control algorithm to improve the accuracy of position and attitude estimation and enhance the robustness of the control system. The real-time wind field data and compensation control instruction set collected by the airborne sensors are processed by the three-dimensional modeling algorithm and the dynamic programming algorithm, which realizes the precise compensation of the wind field impact and the real-time optimization of the route, and improves the execution reliability of the mapping task under complex wind field conditions. Based on the energy consumption prediction model and the task planning algorithm, the battery status data, adaptive route data and task completion data of the UAV are analyzed and processed, which realizes the optimization of energy utilization efficiency and the reasonable planning of task execution. The distributed collaborative algorithm and the safe distance field algorithm are used to process the status information and task execution instruction sets of multiple drones, realizing intelligent task allocation and collision risk control in the multi-machine collaborative process, and improving the efficiency and safety of multi-machine collaborative mapping. Overall, the method of the present invention significantly improves the adaptability, reliability and efficiency of UAV geological mapping in complex environments through multi-level closed-loop control and multi-algorithm collaboration.

[0130] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the UAV-based geological mapping method.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A geological surveying method based on drone, characterized in that: The geological surveying and mapping method based on UAV includes: The terrain feature data and surveying and mapping parameter requirements of the survey area are processed by fuzzy controller to obtain the initial control parameter group and controller gain matrix; The digital elevation model of the survey area and the initial control parameter group are processed by a partition algorithm to obtain partition control data, and the partition control data and the controller gain matrix are closed-loop controlled by a cascade PID algorithm to obtain a posture control instruction set; The position data collected by the multi-sensor and the attitude control instruction set are fused by an extended Kalman filter algorithm to obtain attitude fusion data, and the attitude fusion data are compensated by a model predictive control algorithm to obtain a compensation control instruction set; The real-time wind field data collected by the airborne sensor and the compensation control instruction set are processed by a three-dimensional modeling algorithm to obtain a wind field compensation model, and the wind field compensation model is optimized by a dynamic programming algorithm to obtain adaptive route data; The battery status data of the drone, the adaptive route data and the task completion data are analyzed and processed by an energy consumption prediction model to obtain task optimization parameters, and the task optimization parameters are dynamically allocated and processed by a task planning algorithm to obtain a task execution instruction set; The status information of multiple UAVs and the task execution instruction set are processed by a distributed collaborative algorithm to obtain collaborative control data, and the collaborative control data is processed in real time by a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

2. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The fuzzy controller is used to process the terrain feature data of the survey area and the surveying and mapping parameter requirements to obtain an initial control parameter group and a controller gain matrix, including: Performing gradient calculation processing on the elevation data in the survey area terrain feature data to obtain terrain slope data, and performing interval division processing on the terrain slope data to obtain terrain complexity classification data; Quantifying the spatial resolution data in the surveying and mapping parameter requirements to obtain reference flight altitude data, and correlating the reference flight altitude data with the terrain complexity classification data to obtain an altitude adjustment coefficient; Analyze and process the overlap data in the surveying and mapping parameter requirements to obtain speed constraint conditions, and define the flight speed range according to the speed constraint conditions to obtain speed range data; Performing fuzzy rule generation processing on the speed interval data and the altitude adjustment coefficient to obtain a control rule base, and performing membership function construction processing on the control rule base to obtain a fuzzy inference set; Defuzzifying the fuzzy inference set to obtain original control parameters, and performing equilibrium constraint processing on the original control parameters to obtain an initial control parameter group; A sensitivity analysis is performed on the parameters in the initial control parameter group to obtain parameter influence weights, and a controller gain matrix is ​​generated according to the parameter influence weights.

3. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The digital elevation model of the measurement area and the initial control parameter group are processed by a partition algorithm to obtain partition control data, and the partition control data and the controller gain matrix are closed-loop controlled by a cascade PID algorithm to obtain a posture control instruction set, including: Performing mean filtering processing on the elevation data in the digital elevation model of the survey area to obtain smooth elevation data, and performing slope calculation processing on the smooth elevation data to obtain terrain slope distribution data; Performing cluster analysis on the terrain slope distribution data to obtain terrain feature partitions, and performing boundary extraction on the terrain feature partitions to obtain partition boundary data; Performing parameter matching processing on the partition boundary data and the initial control parameter group to obtain a partition parameter set, and performing interpolation processing on the partition parameter set to obtain a continuous parameter field; Performing boundary constraint processing on the continuous parameter field to obtain constraint parameter data, and generating partition control data according to the constraint parameter data; Performing error calculation processing on the partition control data to obtain position error data, and performing differentiation processing on the position error data to obtain speed error data; Integrating the speed error data to obtain integrated error data, and performing proportional operation processing on the integrated error data according to the controller gain matrix to obtain a basic control amount; The basic control amount is subjected to a limiting process to obtain a bounded control amount, and the bounded control amount is subjected to an attitude solution process to obtain an attitude control instruction set.

4. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The position data collected by the multi-sensor and the attitude control instruction set are fused by an extended Kalman filter algorithm to obtain attitude fusion data, and the attitude fusion data are compensated by a model predictive control algorithm to obtain a compensation control instruction set, including: Performing timestamp alignment processing on the position data collected by the multiple sensors to obtain synchronized position data, and performing state vector construction processing on the synchronized position data and the attitude control instruction set to obtain an initial state estimate; Performing state prediction processing on the initial state estimate to obtain a priori state quantity, and performing covariance transfer processing on the priori state quantity to obtain a priori covariance; Performing observation prediction processing on the priori state quantity to obtain a predicted observation value, and performing residual calculation processing on the predicted observation value and the actual observation value to obtain an observation residual; Performing gain calculation processing on the observation residual and the prior covariance to obtain Kalman gain, and performing state update processing on the Kalman gain to obtain posture fusion data; Performing error prediction processing on the posture fusion data to obtain a prediction error sequence, and performing cost function construction processing on the prediction error sequence to obtain a control objective function; Performing constraint condition setting processing on the control objective function to obtain a constraint condition set, and performing rolling time domain division processing on the constraint condition set to obtain a time domain control interval; The state within the time domain control interval is subjected to sequence prediction processing to obtain a predicted control amount, and the predicted control amount is subjected to feedback correction processing to obtain a compensation control instruction set.

5. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The real-time wind field data collected by the airborne sensor and the compensation control instruction set are processed by a three-dimensional modeling algorithm to obtain a wind field compensation model, and the wind field compensation model is optimized by a dynamic programming algorithm to obtain adaptive route data, including: Performing component decomposition processing on the wind speed data collected by the airborne sensor to obtain three-dimensional wind speed components, and performing interpolation processing on the three-dimensional wind speed components to obtain continuous wind field data; Performing density analysis processing on the continuous wind field data to obtain wind field density distribution, and performing gradient calculation processing on the wind field density distribution to obtain wind field change characteristics; Performing correlation processing on the wind field change characteristics and the compensation control instruction set to obtain a wind field influence coefficient, and performing threshold division processing on the wind field influence coefficient to obtain a wind field compensation model; Quantifying the change amplitude in the wind farm compensation model to obtain a route cost matrix, and performing path search processing on the route cost matrix to obtain an initial route set; Performing energy consumption calculation processing on the initial route set to obtain energy consumption distribution data, and sorting the energy consumption distribution data to obtain a route sequence; The route sequence is traversed to obtain an alternative route group, and the alternative route group is smoothed to obtain adaptive route data.

6. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The drone battery status data, the adaptive route data and the task completion data are analyzed and processed by the energy consumption prediction model to obtain task optimization parameters, and the task optimization parameters are dynamically allocated and processed by the task planning algorithm to obtain a task execution instruction set, including: Performing data standardization processing on the voltage, current and temperature data in the battery status data to obtain standardized battery parameters, and performing feature extraction processing on the standardized battery parameters to obtain battery status features; Correlation processing is performed on the battery status characteristics and the adaptive route data to obtain route energy consumption data, and statistical analysis is performed on the route energy consumption data to obtain a unit energy consumption index; Performing interval segmentation processing on the task completion data to obtain segmented task data, and performing weight calculation processing on the segmented task data to obtain a task weight coefficient; Performing comprehensive calculation processing on the unit energy consumption index and the task weight coefficient to obtain weighted energy consumption data, and performing threshold analysis processing on the weighted energy consumption data to obtain task optimization parameters; The task optimization parameters are sequentially arranged to obtain a task sequence table, and the task sequence table is time-allocated to obtain a time-series task table; The sequential task table is processed by task division to obtain task subsets, and the task subsets are processed by priority allocation to obtain a task execution instruction set.

7. The method for geological surveying and mapping based on an unmanned aerial vehicle according to claim 1, characterized in that: The state information of the multiple drones and the task execution instruction set are processed by a distributed collaborative algorithm to obtain collaborative control data, and the collaborative control data is processed in real time by a safe distance field algorithm to obtain a multi-machine collaborative control instruction set, including: Performing data synchronization processing on the position and speed data of the multiple UAVs to obtain synchronization state data, and performing association processing on the synchronization state data and the task execution instruction set to obtain initial coordination parameters; Performing communication topology construction processing on the initial coordination parameters to obtain communication link data, and performing bandwidth allocation processing on the communication link data to obtain a resource allocation table; Performing time slot division processing on the resource allocation table to obtain a communication timing table, and performing conflict detection processing on the communication timing table to obtain an available time slot set; Performing information interaction processing on the data in the available time slot set to obtain interaction state data, and performing consistency verification processing on the interaction state data to obtain collaborative control data; Performing distance calculation processing on the position information in the collaborative control data to obtain a relative distance matrix, and performing safety domain construction processing on the relative distance matrix to obtain collision risk data; Performing a classification process on the collision risk data to obtain a risk level table, and performing a track planning process on the risk level table to obtain an alternative track set; Interference assessment processing is performed on the alternative track set to obtain a multi-machine cooperative control instruction set.

8. A geological mapping system based on an unmanned aerial vehicle, used to implement the geological mapping method based on an unmanned aerial vehicle as claimed in any one of claims 1 to 7, characterized in that: The UAV-based geological surveying and mapping system includes: A processing module is used to perform fuzzy controller processing on the terrain feature data of the survey area and the surveying and mapping parameter requirements to obtain an initial control parameter group and a controller gain matrix; A control module is used to process the digital elevation model of the measurement area and the initial control parameter group through a partition algorithm to obtain partition control data, and to perform closed-loop control processing on the partition control data and the controller gain matrix through a cascade PID algorithm to obtain a posture control instruction set; A fusion module is used to fuse the position data collected by multiple sensors and the attitude control instruction set through an extended Kalman filter algorithm to obtain attitude fusion data, and to compensate the attitude fusion data through a model predictive control algorithm to obtain a compensation control instruction set; An optimization module is used to process the real-time wind field data collected by the airborne sensor and the compensation control instruction set through a three-dimensional modeling algorithm to obtain a wind field compensation model, and optimize the wind field compensation model through a dynamic programming algorithm to obtain adaptive route data; An analysis module is used to analyze and process the drone battery status data, the adaptive route data and the task completion data through an energy consumption prediction model to obtain task optimization parameters, and dynamically allocate the task optimization parameters through a task planning algorithm to obtain a task execution instruction set; The computing module is used to process the status information of multiple UAVs and the task execution instruction set through a distributed collaborative algorithm to obtain collaborative control data, and to perform real-time computation and processing on the collaborative control data through a safe distance field algorithm to obtain a multi-machine collaborative control instruction set.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the UAV-based geological mapping method as described in any one of claims 1-7 is implemented.

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