Multi-source data fusion and dynamic adjustment algorithm and system for complex flight environment

Abstract

The application belongs to the technical field of aviation flight, and particularly relates to a multi-source data fusion and dynamic adjustment algorithm and system suitable for complex flight environment, which comprises the following steps: data acquisition and preprocessing; multi-source data fusion; path planning and dynamic adjustment; adaptive control and execution; exception handling and redundancy compensation. Based on the multi-source data fusion and dynamic adjustment algorithm, the flight strategy is dynamically adjusted in real time by fusing multiple sensor data, and the navigation ability and safety of the aircraft in the complex environment are significantly improved. Compared with the traditional navigation system relying on a single sensor, the self-navigation and control ability of the aircraft in the complex flight environment is significantly improved through the multi-source data fusion and dynamic adjustment algorithm, and the uncertainty problem caused by data loss and environmental changes in the prior art is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of aviation flight technology, and particularly relates to a multi-source data fusion and dynamic adjustment algorithm and system suitable for complex flight environments, especially for dynamic optimization processing of pilot operation data and flight state data. The system fuses multi-source data (such as pilot operation input, aircraft sensor data, environmental data, etc.) and adjusts flight path and operation parameters in real time to ensure high-precision, stable flight control and safe operation in complex flight environments. BACKGROUND

[0002] In modern aircraft systems, especially in complex flight tasks such as long-distance transportation, military training or emergency rescue tasks, pilots need to face diversified flight states and operation scenarios, and the state information changes rapidly and complexly during flight. These task scenarios often involve high-altitude, high-speed, long-time flight, accompanied by complex weather conditions such as strong wind, turbulence, extreme temperature changes, etc. In addition, the physiological state and operation behavior of the pilot during flight are crucial to the success and safety of the flight mission. However, the traditional flight data monitoring system mainly relies on single sensor data, such as flight control system, attitude sensor or atmospheric condition monitor, which cannot fully reflect the operation dynamics of the pilot and the overall flight state. The existing technology usually cannot process and optimize multi-source data in real time, and it is difficult to deal with the complex interaction between pilot operation data and flight state data. With the continuous complication of aircraft systems, the technical problem that needs to be solved by the existing technology is how to dynamically optimize the processing of pilot operation data and flight state data during flight, and adjust the flight strategy and operation in real time to improve the safety, stability and efficiency of the flight mission.

[0003] Therefore, how to effectively fuse multi-source data and dynamically adjust the pilot's operation behavior and the aircraft's state under the condition of guaranteeing the limitation of computing resources to ensure the smooth completion of the flight mission has become a key problem that needs to be solved in the current technical field. SUMMARY

[0004] In view of the problems existing in the prior art, the present application provides a multi-source data fusion and dynamic adjustment algorithm and system suitable for complex flight environments.

[0005] The present application is implemented as follows: a multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environments, the algorithm comprising:

[0006] S1: Data acquisition and preprocessing: the system first acquires multi-source data in the flight environment of the aircraft through the sensor module in real time; for each kind of sensor data, the system will perform basic preprocessing such as filtering, denoising, etc. to ensure the accuracy and consistency of the data;

[0007] S2: Multi-source data fusion: The preprocessed data enters the multi-source data fusion module; first, the extended Kalman filter EKF performs preliminary fusion and state estimation on the sensor data; the system predicts the current position, attitude and speed of the aircraft by establishing a state transition model and combining the current observation data with the historical state; then, the system dynamically adjusts the weight of each sensor in the fusion process according to the confidence of different sensors; for example, when the GPS signal is disturbed, the system will increase the weight of the IMU and LiDAR to ensure the accuracy of the fusion result;

[0008] S3: Path planning and dynamic adjustment: The system performs local path planning based on the fused data; in this process, the system detects obstacles in the flight path in real time, calculates a safe path, and dynamically adjusts the flight trajectory of the aircraft; at the same time, the system optimizes the flight path according to the global task requirements to ensure that the aircraft completes the flight task with the optimal path; in complex environments such as building groups and forests, the system will preferentially avoid obstacles to ensure flight safety;

[0009] S4: Adaptive control and execution: After path planning is completed, the system executes flight control through the adaptive control module; the PID controller adjusts the attitude and speed of the aircraft in real time according to sensor feedback to ensure that the aircraft flies smoothly along the planned path; at the same time, the system dynamically adjusts the control strategy according to environmental changes; for example, when the wind speed changes or the number of obstacles increases, the system will reduce the flight speed and enhance the obstacle avoidance capability;

[0010] S5: Abnormal processing and redundancy compensation: The system detects the integrity of sensor data in real time; when some sensor data is lost or abnormal, the system compensates for the loss or abnormality using other sensor data or historical data through a data redundancy compensation mechanism; for example, when LiDAR data is unavailable due to weather, the system will combine IMU and visual sensors for environmental perception.

[0011] Another object of the present application is to provide a multi-source data fusion and dynamic adjustment system suitable for complex flight environments based on the multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environments, which specifically comprises:

[0012] The sensor module provides global position information, but when the signal is blocked or disturbed, the data will be incomplete or distorted; the accelerometer and gyroscope provide attitude and motion information of the aircraft, but the IMU will have drift error during long-time flight; the laser scanner generates a three-dimensional point cloud of the surrounding environment to provide high-precision environmental perception and is suitable for obstacle detection; the camera acquires environmental images for identifying specific markers or environmental features;

[0013] Multi-source data fusion module, connected with sensor module, used to combine GPS, IMU and LiDAR sensor data to generate unified flight state estimation; EKF can predict state and correct errors through historical data and model in the case of imperfect sensor data; dynamically adjust the weight of sensors according to the confidence of different sensors; for example, when GPS signal is weak, the system will reduce the dependence on GPS data and increase the weight of IMU and LiDAR data;

[0014] Path planning and dynamic adjustment module, connected with multi-source data fusion module, combines sensor data to generate safe flight path based on current environment in real time; this module detects and avoids obstacles in flight path according to sensor feedback; based on global flight task requirements, this module dynamically optimizes flight path combined with multi-source data to ensure that the aircraft completes the flight task efficiently and safely in complex environment;

[0015] Adaptive control module, connected with multi-source data fusion module, adjusts the attitude, speed and direction of the aircraft in real time based on sensor feedback to ensure smooth flight of the aircraft according to the planned path; the system will dynamically adjust the flight strategy according to the changes of the flight environment, such as wind speed, obstacle distribution, etc.; for example, in the area with dense obstacles, the system will reduce the flight speed to increase the obstacle avoidance time;

[0016] Data anomaly processing module, connected with sensor module, detects the integrity and validity of sensor data in real time; when some sensor data is missing or abnormal, the system will issue a warning and adjust the data fusion strategy; when some sensor data is missing, the system can compensate for redundancy through the data of other sensors and historical flight information; for example, in the case of GPS signal loss, IMU and visual sensor can be used for position estimation.

[0017] Further, the implementation of the sensor module is as follows:

[0018] GPS module: the system integrates GPS receiver to obtain global position information through satellite signals; the data preprocessing module filters the received GPS signals to remove noise and jump data, and calculates the geographic coordinates of the current position; since GPS signal is blocked or interfered, it needs to be fused with other sensor data to supplement the positioning accuracy;

[0019] IMU module: IMU module includes accelerometer and gyroscope, used to measure the linear acceleration and angular velocity of the aircraft; through high sampling rate data collection, real-time attitude and motion information of the aircraft is provided; the system uses IMU data to track the instantaneous state of the aircraft and calculates displacement information through integration; however, IMU data has drift problem, which is corrected by EKF;

[0020] LiDAR Module: LiDAR generates three-dimensional point cloud data of the surrounding environment by emitting laser light and measuring its return time; this module is suitable for high-precision environmental perception, especially obstacle detection; during the flight of the aircraft, LiDAR continuously scans the environment, providing accurate distance and orientation of obstacles; the system uses these data for local path planning and dynamic obstacle avoidance;

[0021] Visual Sensor Module: includes one or more cameras for capturing environmental images; the system uses visual data to identify specific markers or environmental features, assisting the aircraft in positioning and navigation; visual sensors play a key role especially in weak or lost GPS signal situations, and visual inertial navigation system (VINS) can combine IMU data for environmental perception and positioning.

[0022] Further, the implementation of the multi-source data fusion module is as follows:

[0023] Extended Kalman Filter (EKF): is the core data fusion algorithm of the system, and its implementation steps include:

[0024] (1) Prediction Step: Based on the instantaneous motion data provided by IMU and LiDAR, the system predicts the current position and attitude of the aircraft through the state transition equation. This step uses historical state data and IMU's instantaneous motion information to estimate the current state;

[0025] (2) Update Step: When GPS, LiDAR or visual sensors provide new observation data, EKF uses these observations to correct the predicted state and calculate the error covariance matrix of the state estimate; the updated state includes the accurate position, attitude and velocity of the aircraft;

[0026] Data Weighting Strategy: The data weighting strategy in EKF adjusts the data weight of each sensor according to the confidence of different sensors; the system reduces the weight of GPS data when the signal is weak or lost by monitoring the strength and quality of the GPS signal, and enhances the reliance on IMU, LiDAR and visual data; this dynamic weighting method ensures the robustness and accuracy of data fusion.

[0027] Further, the implementation of the path planning and dynamic adjustment module is as follows:

[0028] Local Path Planning: The local path planning module is based on the three-dimensional point cloud data generated by LiDAR, which detects obstacles in the flight path in real time and calculates a safe path; the implementation steps of this module are as follows:

[0029] (1) Obstacle Detection: By processing LiDAR point cloud data, the system identifies obstacles in the current environment and labels their position and size.

[0030] (2) Path Calculation: Based on the current flight target of the aircraft, the system uses A* algorithm or RRT fast random tree search algorithm to calculate the optimal path and avoid detected obstacles.

[0031] (3) Path Adjustment: If the aircraft encounters new obstacles during flight, the system will dynamically adjust the flight path to avoid obstacles in real time, ensuring the safe forward movement of the aircraft.

[0032] Global Path Optimization: The global path planning module optimizes the flight path based on task requirements to ensure that the aircraft completes the task with the optimal path; global path optimization considers time and energy constraints of flight tasks, combines multi-source data to calculate the optimal flight route before the start of the flight task, and dynamically adjusts during the flight process according to the actual environment.

[0033] Further, the implementation of the adaptive control module is as follows:

[0034] PID Controller: The PID controller adjusts the attitude, speed and direction of the aircraft to ensure smooth flight according to the planned path, and the implementation steps are as follows:

[0035] (1) Attitude Control: The system adjusts the pitch angle, roll angle and yaw angle of the aircraft in real time based on the feedback of IMU and visual sensors;

[0036] (2) Speed Control: The PID controller adjusts the forward speed of the aircraft according to the obstacles and target position in the flight path to ensure fast and safe flight;

[0037] (3) Position Control: The controller adjusts the motion trajectory of the aircraft based on GPS and fused position information to maintain on the planned path;

[0038] Adaptive Strategy Adjustment: During flight, the system adjusts flight parameters according to changes in external environment such as wind speed changes, obstacle density, etc.; for example, when detecting dense obstacles, the system will reduce flight speed to increase obstacle avoidance time, or increase LiDAR scanning frequency to improve environmental perception accuracy.

[0039] Further, the implementation of the data anomaly processing module is as follows:

[0040] Data Integrity Detection: The system monitors the integrity and validity of sensor data in real time, this module detects whether the sensor has data loss, abnormal value or noise interference, and issues a warning or adjusts the data fusion algorithm; for example, when the GPS signal fails, the system will issue an alarm and automatically switch to IMU and visual sensors for positioning;

[0041] Data Redundancy Compensation: When certain sensor data is missing, the system utilizes data from other sensors and historical information for redundancy compensation; for example, in the case of GPS signal loss, the system calculates the current position of the aircraft through the displacement integration of the IMU, while using visual sensors to identify landmarks or environmental features to further correct the position estimate.

[0042] Another object of the present application is to provide a computer device comprising a memory and a processor, said memory storing a computer program, said computer program being executed by said processor to cause said processor to perform the steps of the multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environments.

[0043] Another object of the present application is to provide a computer-readable storage medium storing a computer program, said computer program being executed by a processor to cause said processor to perform the steps of the multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environments.

[0044] Another object of the present application is to provide an information data processing terminal for implementing the multi-source data fusion and dynamic adjustment system suitable for complex flight environments.

[0045] In combination with the above technical solutions and the technical problems solved, the technical solutions to be protected by the present application have the following advantages and positive effects:

[0046] 1. High robustness of multi-source data fusion: The system uses an extended Kalman filter (EKF) and a dynamic data weighting strategy to provide accurate state estimation even when sensor data is incomplete or noisy. This multi-source data fusion greatly improves the robustness of the system in complex environments, ensuring the stability and navigation accuracy of the aircraft in different flight environments. Through EKF and dynamic weighting strategy, the system effectively fuses multiple sensor data, even if some sensor data is missing or abnormal, it can still provide high-precision state estimation. Compared with single-sensor-dependent navigation systems, multi-source data fusion makes the system more reliable in complex environments.

[0047] 2. Dynamic path planning and real-time adjustment: The system can dynamically adjust the flight path according to the real-time changes of the flight environment. Whether it is obstacle detection or environmental changes, the system can respond in real time and automatically adjust the flight trajectory. This allows the aircraft to be flexible in complex environments and not limited by static planning methods.

[0048] 3. Adaptive control enhances flight safety: Through adaptive control strategies, the system can adjust flight parameters in real time according to changes in the flight environment. For example, when encountering sudden wind speed or obstacles, the system can automatically reduce flight speed and enhance obstacle avoidance capability. This significantly improves the safety of the aircraft in complex flight environments.

[0049] 4. Data anomaly processing and redundancy compensation mechanism: The system has strong data anomaly processing capability. When some sensor data is missing or abnormal, the system can compensate through other sensors or historical data to ensure the continuity and reliability of navigation. This redundancy compensation mechanism enables the system to maintain good performance even in the case of incomplete data.

[0050] 5. Flexibility of dynamic path planning: Adaptive control enhances flight safety: The adaptive control module adjusts through PID controller and adaptive strategy to ensure stable flight of the aircraft in complex environmental conditions, and timely adjusts flight parameters according to changes in the external environment, reducing flight risks.

[0051] This multi-source data fusion and dynamic adjustment algorithm significantly improves the navigation ability and safety of the aircraft in complex environments by fusing multiple sensor data and dynamically adjusting flight strategies. Compared with traditional single-sensor-dependent navigation systems, this system significantly improves the autonomous navigation and control ability of the aircraft in complex flight environments, effectively solving the uncertainty problems caused by data loss and environmental changes in existing technologies. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is the multi-source data fusion and dynamic adjustment algorithm flowchart provided by the embodiment of the present application, which is suitable for complex flight environments;

[0053] Figure 2 is the multi-source data fusion and dynamic adjustment system structure diagram provided by the embodiment of the present application, which is suitable for complex flight environments;

[0054] Figure 3 is the sensor module structure diagram provided by the embodiment of the present application;

[0055] In the figure: 1, sensor module; 2, multi-source data fusion module; 3, path planning and dynamic adjustment module; 4, adaptive control module; 5, data anomaly processing module; 6, GPS module; 7, IMU module; 8, LiDAR module; 9, visual sensor module; 10, GPS receiver; 11, accelerometer; 12, gyroscope. DETAILED DESCRIPTION

[0056] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with examples. It should be understood that the specific examples described herein are only used to explain the present application and not to limit the present application.

[0057] As shown in Figure 1 The embodiment of the present application provides a multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment, which comprises:

[0058] S1: data acquisition and preprocessing: the system first acquires multi-source data in the flight environment of the aircraft in real time through the sensor module; for each kind of sensor data, the system will perform basic preprocessing such as filtering, denoising and the like to ensure the accuracy and consistency of the data;

[0059] S2: multi-source data fusion: the preprocessed data enters the multi-source data fusion module; first, the extended Kalman filter EKF will preliminarily fuse and estimate the state of the sensor data; the system predicts the current position, attitude and speed of the aircraft by establishing a state transition model and combining the current observation data with the historical state; subsequently, the system dynamically adjusts the weight of each sensor in the fusion process according to the confidence of different sensors; for example, when the GPS signal is disturbed, the system will increase the weight of the IMU and the LiDAR to ensure the accuracy of the fusion result;

[0060] S3: path planning and dynamic adjustment: the system performs local path planning according to the fused data. In this process, the system will detect obstacles in the flight path in real time, calculate a safe path and dynamically adjust the flight trajectory of the aircraft; at the same time, the system optimizes the flight path according to the global task requirements to ensure that the aircraft completes the flight task with the optimal path; in complex environments such as building groups, forests and the like, the system will preferentially avoid obstacles to ensure flight safety;

[0061] S4: adaptive control and execution: after the path planning is completed, the system executes flight control through the adaptive control module; the PID controller adjusts the attitude and speed of the aircraft in real time according to the sensor feedback to ensure that the aircraft flies smoothly according to the planned path; at the same time, the system will dynamically adjust the control strategy according to the environmental changes; for example, when the wind speed changes or the number of obstacles increases, the system will reduce the flight speed and enhance the obstacle avoidance capability;

[0062] S5: abnormality processing and redundancy compensation: the system detects the integrity of the sensor data in real time; when some sensor data is lost or abnormal, the system compensates by using the data of other sensors or historical data through the data redundancy compensation mechanism; for example, when the LiDAR data is unavailable due to weather reasons, the system will combine the IMU and the vision sensor to perceive the environment.

[0063] AsFigure 2 As shown, this embodiment of the invention provides a multi-source data fusion and dynamic adjustment system suitable for complex flight environments, based on the aforementioned multi-source data fusion and dynamic adjustment algorithm for complex flight environments. The system specifically includes:

[0064] Sensor module 1 provides global position information, but the data may be incomplete or distorted when the signal is blocked or interfered with; it provides attitude and motion information of the aircraft through accelerometers and gyroscopes, but the IMU may drift during long-term flight; it generates 3D point clouds by scanning the surrounding environment with lasers to provide high-precision environmental perception, which is suitable for obstacle detection; and it acquires environmental images through cameras to identify specific landmarks or environmental features.

[0065] The multi-source data fusion module 2, connected to the sensor module 1, is used to combine sensor data from GPS, IMU, and LiDAR to generate a unified flight state estimate. EKF can perform state prediction and error correction using historical data and models when sensor data is imperfect. It dynamically adjusts the weight of sensors based on the confidence level of different sensors. For example, when the GPS signal is weak, the system will reduce its reliance on GPS data and increase the weight of IMU and LiDAR data.

[0066] The path planning and dynamic adjustment module 3 is connected to the multi-source data fusion module 2. It combines sensor data to generate a safe flight path based on the current environment in real time. This module will detect and avoid obstacles in the flight path based on sensor feedback. Based on the global flight mission requirements, this module combines multi-source data to dynamically optimize the flight path to ensure that the aircraft can complete the flight mission efficiently and safely in complex environments.

[0067] The adaptive control module 4, connected to the multi-source data fusion module 2, adjusts the aircraft's attitude, speed, and direction in real time based on sensor feedback to ensure that the aircraft flies smoothly along the planned path. The system will dynamically adjust the flight strategy according to changes in the flight environment, such as wind speed and obstacle distribution. For example, in areas with dense obstacles, the system will reduce the flight speed to increase obstacle avoidance time.

[0068] The data anomaly processing module 5 is connected to the sensor module 1 and detects the integrity and validity of sensor data in real time. When some sensor data is lost or abnormal, the system will issue a warning and adjust the data fusion strategy. When some sensor data is lost, the system can perform redundancy compensation using data from other sensors and historical flight information. For example, in the case of GPS signal loss, position estimation can be performed using IMU and visual sensors.

[0069] like Figure 3 As shown, the sensor module is implemented in the following specific way:

[0070] GPS module 6: The system integrates a GPS receiver 10 to obtain global position information through satellite signals; the data preprocessing module filters the received GPS signals, eliminates noise and jump data, and calculates the geographic coordinates of the current position; due to the shielding or interference of GPS signals, data fusion with other sensors is needed to supplement the positioning accuracy;

[0071] IMU module 7: The IMU module 7 includes an accelerometer 11 and a gyroscope 12 for measuring the linear acceleration and angular velocity of the aircraft; through high sampling rate data collection, real-time aircraft attitude and motion information is provided; the system uses IMU data to track the instantaneous state of the aircraft and calculates displacement information by integration; however, there is a drift problem in IMU data, and the system corrects the error through EKF;

[0072] LiDAR module 8: LiDAR generates three-dimensional point cloud data of the surrounding environment by emitting laser and measuring its return time; this module is suitable for high-precision environment perception, especially obstacle detection; during the flight of the aircraft, LiDAR continuously scans the environment to provide accurate distance and orientation of obstacles; the system uses these data for local path planning and dynamic obstacle avoidance;

[0073] Vision sensor module 9: includes one or more cameras 13 for capturing environmental images; the system uses visual data to identify specific markers or environmental features to assist the aircraft in positioning and navigation; vision sensors play a key role especially in weak or lost GPS signal conditions, and visual inertial navigation system VINS can combine IMU data for environment perception and positioning.

[0074] The implementation of the multi-source data fusion module is as follows:

[0075] Extended Kalman filter EKF: is the core data fusion algorithm of the system, and its implementation steps include:

[0076] (1) Prediction step: based on the instantaneous motion data provided by IMU and LiDAR, the system predicts the current position and attitude of the aircraft through the state transition equation. This step uses historical state data and IMU instantaneous motion information to estimate the current state;

[0077] (2) Update step: when GPS, LiDAR or vision sensor provides new observation data, EKF uses these observation values to correct the predicted state and calculate the error covariance matrix of state estimation; the updated state includes the accurate position, attitude and velocity of the aircraft;

[0078] Data weighting strategy: The data weighting strategy in the EKF adjusts the data weight of each sensor according to the confidence level of different sensors; the system reduces the weight of GPS data when the signal is weak or lost by monitoring the strength and quality of the GPS signal, and enhances the reliance on IMU, LiDAR and visual data; this dynamic weighting method ensures the robustness and accuracy of data fusion.

[0079] The implementation of the path planning and dynamic adjustment module is as follows:

[0080] Local path planning: The local path planning module generates three-dimensional point cloud data based on LiDAR, detects obstacles in the flight path in real time, and calculates a safe path, the implementation steps of which are as follows:

[0081] (1) Obstacle detection: By processing LiDAR point cloud data, the system identifies obstacles in the current environment and labels their location and size.

[0082] (2) Path calculation: According to the current flight target of the aircraft, the system uses A* algorithm or RRT fast random tree search algorithm to calculate the optimal path and avoid detected obstacles.

[0083] (3) Path adjustment: If the aircraft encounters new obstacles during flight, the system will dynamically adjust the flight path to avoid obstacles in real time and ensure the safe forward movement of the aircraft.

[0084] Global path optimization: The global path planning module optimizes the flight path based on task requirements to ensure that the aircraft completes the task with the optimal path; global path optimization considers the time and energy constraints of the flight task, combines multi-source data, and calculates the optimal flight route before the flight task begins, and dynamically adjusts the flight path during the flight process.

[0085] The implementation of the adaptive control module is as follows:

[0086] PID controller: The PID controller adjusts the attitude, speed and direction of the aircraft to ensure smooth flight according to the planned path, the implementation steps of which are as follows:

[0087] (1) Attitude control: The system adjusts the pitch angle, roll angle and yaw angle of the aircraft in real time according to the feedback of the IMU and visual sensor;

[0088] (2) Speed control: The PID controller adjusts the forward speed of the aircraft according to the obstacles and target position in the flight path to ensure fast and safe flight;

[0089] (3) Position control: The controller adjusts the motion trajectory of the aircraft according to the GPS and fused position information to keep it on the planned path;

[0090] Adaptive strategy adjustment: During flight, the system adjusts flight parameters according to changes in external environment, such as wind speed changes, obstacle density, etc.; for example, when detecting dense obstacles, the system will reduce flight speed to increase obstacle avoidance time, or increase LiDAR scanning frequency to improve environmental perception accuracy.

[0091] The implementation of the data anomaly processing module is as follows:

[0092] Data integrity detection: The system monitors the integrity and validity of sensor data in real time, and this module detects whether the sensor has data loss, abnormal value or noise interference, and issues a warning or adjusts the data fusion algorithm; for example, when the GPS signal is invalid, the system will issue an alarm and automatically switch to IMU and visual sensor for positioning;

[0093] Data redundancy compensation: When some sensor data is lost, the system will use the data of other sensors and historical information for redundancy compensation; for example, in the case of GPS signal loss, the system calculates the current position of the aircraft through the displacement integration of the IMU, and uses the visual sensor to identify landmarks or environmental features to further correct the position estimate.

[0094] The embodiment of the application provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment.

[0095] The embodiment of the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment.

[0096] The embodiment of the application provides an information data processing terminal for realizing the multi-source data fusion and dynamic adjustment system suitable for complex flight environment.

[0097] Specific implementation scheme: multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment

[0098] Through the fusion processing of multi-source sensor data, the precise positioning, path planning and flight control of the aircraft in complex flight environment are ensured, and the ability of abnormal data processing and dynamic adjustment is provided, so as to ensure the flight safety and stability of the aircraft.

[0099] Implementation steps:

[0100] S1: Data acquisition and preprocessing

[0101] 1) Multi-source data collection:

[0102] Install multiple sensors such as IMU (Inertial Measurement Unit), LiDAR (Light Detection and Ranging), GPS, and visual sensors on the aircraft to collect real-time data in the flight environment.

[0103] IMU provides acceleration and angular velocity data of the aircraft; LiDAR provides distance and obstacle information of the environment; GPS is used to obtain the global position coordinates of the aircraft; visual sensors capture images of the flight environment through cameras to assist in perceiving the surrounding environment.

[0104] 2) Data preprocessing:

[0105] Filtering: Perform basic filtering on collected sensor data, such as using Kalman filter (KF) or low-pass filter to remove noise and sudden abnormal data, ensuring the smoothness and continuity of sensor data.

[0106] Data synchronization: Due to different sampling frequencies of different sensors, time synchronization of multi-source sensor data is needed. Through timestamp alignment, ensure that the data of each sensor can be fused at the same time, avoiding errors caused by data delay.

[0107] S2: Multi-source data fusion

[0108] 1) Preliminary fusion and state estimation:

[0109] Extended Kalman filter (EKF): Input the preprocessed sensor data into EKF, use its nonlinear system state estimation ability to estimate the real-time position, attitude and speed of the aircraft. EKF combines historical state and current observation data through state transition model and observation model, iteratively updates, and finally outputs the accurate state of the aircraft.

[0110] 2) Dynamic weight adjustment:

[0111] The system dynamically adjusts the weight of each sensor data in the fusion process according to the confidence of the sensor (such as GPS signal strength, IMU stability, etc.). For example, when the GPS signal is weak or interfered, the system will increase the weight of IMU and LiDAR to ensure the accuracy of position estimation; conversely, when LiDAR is affected by weather and cannot be used, the system will rely more on GPS and visual sensor data.

[0112] S3: Path planning and dynamic adjustment

[0113] 1) Local path planning:

[0114] The system performs local path planning in real-time flight based on fused multi-source data. Obstacles in the front flight path are detected by LiDAR and visual sensors, combined with state estimation data (aircraft position, attitude, speed), and an obstacle avoidance path is calculated in real-time.

[0115] A safe flight path is generated using A* algorithm or RRT (Rapidly-exploring Random Tree) algorithm to ensure the aircraft can avoid obstacles in complex environments.

[0116] 2) Dynamic path adjustment:

[0117] In complex environments, such as flying through a building complex or forest, the system will preferentially avoid obstacles and reduce flight speed if necessary to enhance obstacle avoidance capability. The system combines flight mission requirements and adjusts the flight trajectory in real-time based on global path planning to ensure the aircraft completes the mission and ensures safety.

[0118] S4: Adaptive control and execution

[0119] 1) Adaptive control system:

[0120] A PID controller is used to dynamically adjust the flight attitude and speed of the aircraft based on real-time sensor data feedback. The PID controller continuously adjusts the control input (such as motor speed) to ensure the aircraft flies smoothly along the planned path.

[0121] 2) Dynamic adjustment strategy:

[0122] The system dynamically adjusts the control strategy according to changes in the flight environment (such as an increase in wind speed, an increase in the number of obstacles, etc.). For example, when encountering strong winds, the system will reduce flight speed and improve attitude stability; when encountering complex obstacle environments, the system will enhance obstacle avoidance strategies to prioritize flight safety.

[0123] S5: Exception handling and redundancy compensation

[0124] 1) Sensor data integrity detection:

[0125] The system detects the integrity and accuracy of sensor data in real-time. If it finds that the data of a certain sensor is missing or abnormally fluctuating (such as GPS signal loss, LiDAR data affected by weather and unable to use, etc.), the system will immediately activate the redundancy compensation mechanism.

[0126] 2) Data redundancy compensation mechanism:

[0127] GPS signal loss: When the GPS signal is lost due to obstruction or interference, the system combines IMU and visual sensor data for position estimation, uses visual SLAM (Simultaneous Localization and Mapping) algorithm to capture environmental features with the camera, and combines IMU acceleration and angular velocity data for positioning and dead reckoning.

[0128] LiDAR data unavailability: When LiDAR data is compromised due to adverse weather conditions such as heavy fog, rain, or snow, the system compensates for the data loss using IMU and visual sensors, ensuring the aircraft continues to perceive the surrounding environment and maintains safe flight.

[0129] Historical data compensation: If multiple sensors simultaneously experience anomalies, the system will use historical flight data (such as the prediction results of state transition models) for short-term data compensation, ensuring uninterrupted flight missions.

[0130] Implementation effects:

[0131] 1) High-precision data fusion: Through the multi-source data fusion method of Extended Kalman Filter (EKF), the aircraft can obtain real-time high-precision position, attitude, and speed estimates, ensuring the accuracy of the flight path.

[0132] 2) Enhanced dynamic path planning and obstacle avoidance: The system combines real-time data from multiple sensors, enabling dynamic adjustment of flight paths, especially in complex environments, effectively avoiding obstacles and ensuring flight safety.

[0133] 3) Stability of adaptive control: Through dynamic adjustment strategies of PID controllers, the aircraft can respond promptly to environmental changes (such as changes in wind speed, increased obstacles, etc.), maintaining flight stability and task execution accuracy.

[0134] 4) Redundancy compensation ensures reliability: The system has the ability to handle sensor data loss and anomalies, ensuring that flight missions can still be successfully completed in complex and adverse environments, enhancing the robustness and fault tolerance of the system.

[0135] This implementation scheme effectively improves the aircraft's data processing capability, path planning accuracy, and flight safety in complex flight environments, with broad application prospects.

[0136] Specific implementation scheme: Multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environments

[0137] Through multi-source data fusion and dynamic adjustment algorithms, precise positioning, path planning, adaptive control, and anomaly handling are achieved for aircraft in complex flight environments, ensuring safe flight and task execution.

[0138] S1: Data acquisition and preprocessing

[0139] 1. Multi-source data acquisition:

[0140] - The system uses multiple sensors to collect flight data of the aircraft in real time. Sensors include IMU (Inertial Measurement Unit), LiDAR (Light Detection and Ranging), GPS, visual sensor, etc.

[0141] - IMU provides acceleration and angular velocity information; LiDAR measures obstacle distance in the flight environment; GPS obtains global position; visual sensor captures images of the flight environment.

[0142] 2. Data preprocessing:

[0143] - Filter the collected data (use Kalman filter or low-pass filter) to eliminate noise and sudden data anomalies.

[0144] - Time synchronization: synchronize the sampling frequency of different sensors to ensure that the data can be matched at the same timestamp.

[0145] Python implementation example:

[0146] ```python

[0147] import numpy as np

[0148] from pykalman import KalmanFilter

[0149] def preprocess_data(sensor_data):

[0150] # Use Kalman filter to denoise

[0151] kf = KalmanFilter(initial_state_mean=0, n_dim_obs=1)

[0152] filtered_data = kf.em(sensor_data).smooth(sensor_data)[0]

[0153] return filtered_data

[0154] # Example data

[0155] imu_data = np.array([1, 2, 3, 4, 5])

[0156] lidar_data = np.array([10, 12, 14, 16, 18])

[0157] gps_data = np.array([100, 102, 104, 106, 108]) ```

[0158] # Preprocess data

[0159] filtered_imu = preprocess_data(imu_data)

[0160] filtered_lidar = preprocess_data(lidar_data)

[0161] filtered_gps = preprocess_data(gps_data)

[0162] ```

[0163] S2: Multi-source data fusion

[0164] 1. Extended Kalman Filter (EKF) preliminary fusion:

[0165] - Establish state transition model, define state variables (such as aircraft position, velocity, attitude) and observation model (each sensor observation value).

[0166] - Use EKF to estimate the state of the aircraft according to the historical state and current sensor data. Python implements the core algorithm of EKF:

[0167] ```python

[0168] import numpy as npdef ekf_predict(state, control, P, F, Q): # State prediction state_pred = F @ state + control P_pred = F @ P @ F.T + Q return state_pred, P_preddef ekf_update(state_pred, P_pred, Z, H, R):# Kalman gain K = P_pred @ H.T @ np.linalg.inv(H @ P_pred @ H.T + R) # State update state_upd = state_pred + K @ (Z - H @ state_pred) return state_upd ```

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] state_upd = state_pred + K @ (Z - H @ state_pred)

[0177] P_upd = (np.eye(len(K)) - K @ H) @ P_pred

[0178] return state_upd, P_upd

[0179] # Initialize state and covariance matrix

[0180] state = np.array([0, 0, 0]) # Assume state variables [x, y, theta]

[0181] P = np.eye(3)

[0182] F = np.eye(3) # State transition matrix

[0183] Q = np.eye(3) * 0.01 # Process noise

[0184] H = np.eye(3) # Observation matrix

[0185] R = np.eye(3) * 0.05 # Observation noise

[0186] # Sensor measurements

[0187] Z = np.array([10, 5, 0.1]) # Assume [x, y, theta]

[0188] control = np.array([0.1, 0.05, 0.01])

[0189] # EKF prediction and update

[0190] state_pred, P_pred = ekf_predict(state, control, P, F, Q)

[0191] state_upd, P_upd = ekf_update(state_pred, P_pred, Z, H, R)

[0192] ```

[0193] 2. Dynamic weight adjustment:

[0194] - Adjust the weights of sensors in EKF fusion dynamically based on their confidence. For example, when GPS signal is weak, increase the weights of IMU and LiDAR.

[0195] Python implementation of weight adjustment:

[0196] ```python

[0197] def adjust_weights(sensor_data, sensor_confidence):

[0198] # Sensor confidence weight adjustment

[0199] weighted_data = sensor_data * sensor_confidence

[0200] return np.sum(weighted_data) / np.sum(sensor_confidence)

[0201] # Hypothetical confidence of different sensors

[0202] imu_confidence = 0.8

[0203] gps_confidence = 0.6

[0204] lidar_confidence = 0.7

[0205] # Dynamically adjusted fusion result

[0206] sensor_data = np.array([filtered_imu, filtered_gps, filtered_lidar])

[0207] confidences = np.array([imu_confidence, gps_confidence, lidar_confidence])

[0208] fused_data = adjust_weights(sensor_data, confidences)

[0209] S3: Path planning and dynamic adjustment

[0210] 1. Local path planning:

[0211] - Use the fused data to perform local path planning using A* or RRT algorithms for real-time obstacle avoidance.

[0212] - Python implementation of A* path planning:

[0213]

[0214]

[0215] 2. Dynamic path adjustment:

[0216] - Adjust the flight path in real time according to sensor feedback and environmental changes to ensure the aircraft can avoid obstacles.

[0217] S4: Adaptive control and execution

[0218] 1. PID controller:

[0219] - Use the PID controller to adjust the attitude and speed of the aircraft according to sensor feedback.

[0220] Python implementation of PID controller:

[0221]

[0222]

[0223] # Use PID control attitude

[0224] pid_controller = PIDController(1.0, 0.1, 0.05)

[0225] setpoint = 10 # target height

[0226] measured_value = 8 # actual height

[0227] control_signal = pid_controller.compute(setpoint, measured_value)

[0228] S5: Exception handling and redundancy compensation

[0229] 1. Redundancy compensation mechanism:

[0230] - When some sensor data is lost, use other sensor data or historical data for compensation. For example, when LiDAR data is lost, combine IMU and visual sensor data for environmental perception.

[0231] Compensation mechanism implementation:

[0232]

[0233] Implementation effect:

[0234] 1. High precision fusion: Through EKF, realize efficient fusion of multi-source data, and dynamically adjust sensor weights, improve data fusion precision in complex environment.

[0235] 2. Real-time path planning and dynamic adjustment: Real-time path planning is achieved using A* or RRT algorithms, and the flight path is dynamically adjusted according to sensor feedback to ensure that the aircraft can effectively avoid obstacles.

[0236] 3. Adaptive control: The PID controller dynamically adjusts the attitude and speed of the aircraft to ensure the stability and accuracy of the flight.

[0237] 4. Redundancy compensation: Through the redundancy compensation mechanism, when a sensor fails, the system can quickly compensate to maintain the continuity and safety of the flight mission.

[0238] Example 1: Aircraft data fusion and dynamic adjustment in complex weather conditions

[0239] In complex flight environments such as stormy or windy weather, the aircraft needs to cope with severe airflow disturbances and uncertain environmental changes. The system collects data from multiple sensors in real time through S1, including barometers, accelerometers, wind speed sensors, and GPS sensors. Through data preprocessing, noise and sudden outliers are filtered out to ensure data accuracy. After entering the S2 multi-source data fusion stage, the system uses an extended Kalman filter (EKF) to estimate the state of the aircraft, combining the outputs of multiple sensors to estimate the precise position, speed, and attitude of the aircraft. The system will dynamically weight each sensor according to the current wind speed, wind direction, and GPS signal strength to cope with the uncertain effects of weather conditions on sensor data.

[0240] When the aircraft encounters strong winds or weather changes, the system automatically adjusts the aircraft's heading to avoid areas with high wind speeds through the path planning and dynamic adjustment function of S3, while optimizing the flight path according to the task requirements. During execution (S4), the PID controller adjusts in real time according to the changes in the aircraft's attitude and speed to ensure stable flight. When sensor data is abnormal or signal is lost (S5), the system uses historical wind speed data or inertial measurement data of the aircraft to compensate through the redundancy compensation mechanism, ensuring the continuous and stable flight of the aircraft.

[0241] Example 2: Obstacle avoidance and path adjustment in high-altitude flight

[0242] In complex flight environments such as urban high-altitude flight or mountain flight, the aircraft needs to avoid obstacles such as buildings and mountains in real time. In this scenario, the system collects data from laser radar, cameras, GPS, and inertial measurement units (IMU) through S1 to perceive the surrounding environment in real time. The preprocessed data is combined with the distance information detected by the laser radar and the visual data from the camera through the EKF fusion estimation module in S2 to accurately estimate the distance, position, and relative motion state of the surrounding obstacles.

[0243] In the S3 path planning and dynamic adjustment process, the system calculates the safety distance with the obstacles in real time, and adjusts the path of the aircraft to avoid collision. In the complex high-altitude flight scene, the system preferentially avoids obstacles, and plans the path in the local environment while optimizing the global flight route. In the S4 adaptive control execution process, the PID controller automatically adjusts the attitude of the aircraft according to the feedback of the sensor, to ensure that the aircraft passes through the obstacle-dense area along a safe path. If a sensor such as a laser radar signal is lost or a camera fails, the system compensates through the S5 redundancy compensation mechanism, uses the data of the inertial measurement unit or GPS for compensation, to ensure the normal flight of the aircraft until the sensor recovers to normal.

[0244] It should be noted that the embodiments of the present application can be realized by hardware, software or a combination of software and hardware. The hardware part can be realized by special logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above-mentioned devices and methods can be realized by computer executable instructions and / or included in processor control code, such as provided on a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The devices of the present application and their modules can be realized by hardware circuits, such as very large scale integrated circuits or gate arrays, semiconductors, such as logic chips, transistors, etc., or programmable hardware devices, such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0245] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any modification, equivalent replacement and improvement within the technical range disclosed by the present application, which is within the spirit and principle of the present application, should be covered within the protection scope of the present application.

Claims

1. A multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment, characterized in that, The algorithm comprises: S1: data acquisition and preprocessing: the system first acquires multi-source data in the flight environment of the aircraft in real time through the sensor module; for each kind of sensor data, the system will perform basic preprocessing to ensure the accuracy and consistency of the data; S2: multi-source data fusion: the preprocessed data enters the multi-source data fusion module; first, the extended Kalman filter EKF will preliminarily fuse the sensor data and estimate the state; the system predicts the current position, attitude and speed of the aircraft by establishing a state transition model and combining the current observation data with the historical state; then, the system dynamically adjusts the weight of each sensor in the fusion process according to the confidence of different sensors; S3: path planning and dynamic adjustment: the system performs local path planning based on the fused data; in this process, the system will detect obstacles in the flight path in real time, calculate a safe path, and dynamically adjust the flight trajectory of the aircraft; at the same time, the system optimizes the flight path according to the global task requirements to ensure that the aircraft completes the flight task with the optimal path; in complex environments, the system will prioritize obstacle avoidance to ensure flight safety; S4: adaptive control and execution: after path planning is completed, the system executes flight control through the adaptive control module; the PID controller adjusts the attitude and speed of the aircraft in real time according to sensor feedback to ensure smooth flight of the aircraft along the planned path; at the same time, the system will dynamically adjust the control strategy according to environmental changes; S5: exception handling and redundancy compensation: the system detects the integrity of sensor data in real time; when some sensor data is missing or abnormal, the system compensates for it using data from other sensors or historical data through a data redundancy compensation mechanism.

2. The multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment according to claim 1, characterized in that, The data acquisition and preprocessing in the S1 step includes using a sensor module to acquire multi-source data in the flight environment of the aircraft in real time, and performing filtering and denoising operations on the data of each sensor to improve the accuracy and consistency of the data, ensuring that the sensor data can provide reliable input for subsequent processing.

3. The multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment according to claim 1, characterized in that, The multi-source data fusion in the S2 step uses an extended Kalman filter (EKF) to preliminarily fuse multi-source sensor data, and predicts the real-time state of the aircraft by establishing a state transition model and combining the current sensor observation data with the historical state; this process dynamically adjusts the weight according to the confidence of different sensors to improve the data fusion accuracy in complex environments.

4. The multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment according to claim 1, characterized in that, The path planning and dynamic adjustment in the S3 step performs local path planning based on the fused data, detects obstacles in the flight path in real time, calculates a safe path, and dynamically adjusts the path according to obstacles, flight tasks and real-time state of the aircraft; the system prioritizes obstacle avoidance according to changes in sensor data in complex environments to ensure flight safety.

5. The multi-source data fusion and dynamic adjustment algorithm suitable for complex flight environment according to claim 1, characterized in that, The exception handling and redundancy compensation mechanism in the S5 step monitors the integrity of sensor data in real time, compensates for missing or abnormal data using data from other sensors when some sensor data is missing or abnormal; when the LiDAR signal is lost, the system combines the data from the IMU and vision sensors or historical data to perceive the environment, ensuring data continuity and reliability during flight.

6. A multi-source data fusion and dynamic flight control system for complex flight environments, comprising: a sensor module providing global position information, but data may be incomplete or distorted when signals are blocked or interfered; providing attitude and motion information of the aircraft through accelerometers and gyroscopes, but IMU may have drift errors during long flights; generating three-dimensional point clouds of the surrounding environment through laser scanning, providing high-precision environmental perception, suitable for obstacle detection; acquiring environmental images through cameras for identifying specific markers or environmental features; The multi-source data fusion and dynamic adjustment system suitable for complex flight environment of the adjustment algorithm is characterized in that, a multi-source data fusion module connected to the sensor module, combining GPS, IMU, and LiDAR sensor data to generate a unified flight state estimate; EKF can predict the state and correct errors through historical data and models in the case of imperfect sensor data; dynamically adjusting the weight of sensors based on the confidence of different sensors; a path planning and dynamic adjustment module connected to the multi-source data fusion module, combining sensor data to generate a safe flight path based on the current environment in real time; detecting and avoiding obstacles in the flight path based on sensor feedback; dynamically optimizing the flight path based on global flight task requirements to ensure efficient and safe completion of flight tasks in complex environments; an adaptive control module connected to the multi-source data fusion module, adjusting the attitude, speed, and direction of the aircraft in real time based on sensor feedback to ensure smooth flight along the planned path; the system adjusts the flight path based on changes in the flight environment; a data anomaly processing module connected to the sensor module, detecting the integrity and validity of sensor data in real time; when some sensor data is missing or abnormal, the system will issue a warning and adjust the data fusion strategy; when some sensor data is missing, the system can compensate for redundancy through other sensor data and historical flight information. The implementation of the sensor module is as follows: GPS module: the system integrates a GPS receiver to obtain global position information through satellite signals; the data preprocessing module filters the received GPS signals, removes noise and jump data, and calculates the geographic coordinates of the current position; due to signal blocking or interference, data fusion with other sensors is needed to supplement the positioning accuracy; 7. The multi-source data fusion and dynamic adjustment system for complex flight environment according to claim 6, wherein, IMU module: the IMU module includes accelerometers and gyroscopes for measuring linear acceleration and angular velocity of the aircraft; through high sampling rate data collection, real-time attitude and motion information of the aircraft is provided; the system uses IMU data to track the instantaneous state of the aircraft and calculates displacement information through integration; however, IMU data has drift problems, which are corrected by EKF; LiDAR module: LiDAR generates three-dimensional point cloud data of the surrounding environment by emitting laser and measuring its return time; this module is suitable for high-precision environmental perception and obstacle detection; during flight, LiDAR continuously scans the environment to provide accurate distance and orientation of obstacles; the system uses these data for local path planning and dynamic obstacle avoidance. ​ ​ Visual sensor module: includes one or more cameras for capturing environmental images; the system uses visual data to identify specific markers or environmental features, assisting the aircraft in localization and navigation; visual sensors play a crucial role in weak or lost GPS signal situations, and visual-inertial navigation system (VINS) can combine IMU data for environmental perception and localization.

8. The multi-source data fusion and dynamic adjustment system for complex flight environment according to claim 6, wherein, The implementation of the multi-source data fusion module is as follows: Extended Kalman filter (EKF): is the core data fusion algorithm of the system, and its implementation steps include: (1) Prediction step: based on the instantaneous motion data provided by IMU and LiDAR, the system predicts the current position and attitude of the aircraft through the state transition equation; this step uses historical state data and IMU instantaneous motion information to estimate the current state; (2) Update step: when GPS, LiDAR or visual sensor provides new observation data, EKF uses these observation values to correct the predicted state and calculate the error covariance matrix of the state estimation; the updated state includes the accurate position, attitude and velocity of the aircraft; Data weighting strategy: the data weighting strategy in EKF adjusts the data weight of each sensor according to the confidence of different sensors; the system reduces the weight of GPS data when the signal is weak or lost by monitoring the strength and quality of the GPS signal, and enhances the dependence on IMU, LiDAR and visual data; this dynamic weighting method ensures the robustness and accuracy of data fusion.

9. The multi-source data fusion and dynamic adjustment system for complex flight environment according to claim 6, wherein, The implementation of the path planning and dynamic adjustment module is as follows: Local path planning: the local path planning module generates three-dimensional point cloud data based on LiDAR, detects obstacles in the flight path in real time, and calculates a safe path, and the implementation steps of this module are as follows: (1) Obstacle detection: by processing LiDAR point cloud data, the system identifies obstacles in the current environment and labels their position and size; (2) Path calculation: according to the current flight target of the aircraft, the system uses A* algorithm or RRT fast random tree search algorithm to calculate the optimal path and avoid detected obstacles; (3) Path adjustment: if the aircraft encounters new obstacles during flight, the system will dynamically adjust the flight path to avoid obstacles in real time and ensure the safe forward movement of the aircraft; Global path optimization: the global path planning module optimizes the flight path based on task requirements to ensure that the aircraft completes the task with the optimal path; global path optimization considers the time and energy constraints of the flight task, combines multi-source data, and calculates the optimal flight route before the flight task begins, and dynamically adjusts the flight route during the flight process.

10. The multi-source data fusion and dynamic adjustment system for complex flight environment according to claim 6, wherein, The implementation of the adaptive control module is as follows: PID controller: the PID controller adjusts the attitude, speed and direction of the aircraft to ensure smooth flight according to the planned path, and its implementation steps are as follows: (1) Attitude control: the system adjusts the pitch angle, roll angle and yaw angle of the aircraft in real time according to the feedback of IMU and visual sensor; (2) Speed control: the PID controller adjusts the forward speed of the aircraft according to the obstacles and target position in the flight path to ensure that it can fly quickly and safely; (3) Position control: the controller adjusts the flight trajectory of the aircraft according to the GPS and the fused position information, and keeps on the planned path; Adaptive strategy adjustment: during flight, the system will adjust the flight parameters according to the changes of external environment, wind speed, and obstacle density; when detecting a dense obstacle, the system will reduce the flight speed to increase the obstacle avoidance time, or increase the LiDAR scanning frequency to improve the environmental perception accuracy.

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