Geographic surveying unmanned aerial vehicle system with landing gear

Abstract

The invention relates to the technical field of geographic surveying and mapping of unmanned aerial vehicles, in particular to a geographic surveying and mapping unmanned aerial vehicle system with undercarriages. Comprising a ground supporting state acquisition module, an undercarriage vibration monitoring module, a fuselage structure disturbance identification module, a centroid trend resolving module, an airborne magnetic field compensation module, a cradle head synchronous regulation and control module, a multi-source data fusion module and a flight surveying and mapping execution module. The ground supporting state acquisition module, the undercarriage vibration monitoring module, the fuselage structure disturbance identification module, the centroid trend resolving module, the airborne magnetic field compensation module and the cradle head synchronous regulation and control module are all in signal connection with the multi-source data fusion module. According to the method, multiple disturbance factors of the unmanned aerial vehicle in a field complex surveying and mapping scene can be synchronously decoupled and compensated, the negative influence of the structure and working condition change of the unmanned aerial vehicle on surveying and mapping precision is weakened, and the geographic surveying and mapping data precision and reliability of complex sites such as mud flats, mining areas and side slopes are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) geographic mapping technology, and in particular to geographic mapping UAV systems equipped with landing gear. Background Technology

[0002] Traditional geographic mapping drones mostly use multi-rotor flight platforms, equipped with RTK, IMU and mapping cameras, to complete automated aerial survey data collection according to preset routes. Flight control and attitude correction are mainly carried out around conventional factors such as wind speed, flight attitude and gimbal stabilization. They can achieve general terrain mapping operations in open and flat areas and under normal weather conditions.

[0003] In complex and unique environments such as tidal flats, slopes, mining areas, and soft soil, UAVs need to rely on existing shock-absorbing landing gear to take off and land on uneven ground and perform near-ground high-precision mapping. This type of work places higher demands on initial attitude references, flight micro-stability, and spatiotemporal synchronization accuracy. The ground support characteristics of the landing gear, the inherent structural characteristics of the fuselage, and the dynamic changes of the airborne systems all directly affect the entire mapping process and the accuracy of the results.

[0004] However, existing UAV mapping systems have the following drawbacks in actual operation:

[0005] First, when the landing gear is supported on a site with uneven ground stiffness, it is prone to fuselage tilting creep and damping hysteresis of the vibration damping struts, which in turn causes low-frequency micro-vibration of the IMU, directly causing systematic deviations in the initial attitude of the survey and affecting the consistency of the survey benchmark.

[0006] Secondly, the multi-arm structure of the fuselage is prone to modal coupling resonance in a specific speed range. Continuous battery discharge will cause the center of mass of the fuselage to drift slowly. Stray magnetic fields formed by the wiring inside the fuselage will interfere with the measurement accuracy of the magnetic compass. The nonlinear stiffness of the gimbal's damping ball will cause attitude phase lag. The superposition of multiple factors leads to insufficient flight attitude stability and poor synchronization between positioning and exposure time. Ultimately, this results in reduced accuracy of aerial triangulation and poor point cloud matching, making it difficult to meet the engineering requirements of high-precision geographic mapping.

[0007] Therefore, it is necessary to design a geographic mapping UAV system that is adapted to shock-absorbing landing gear. Summary of the Invention

[0008] To solve one of the above-mentioned technical problems, the present invention adopts the following technical solution: a geographic mapping UAV system equipped with landing gear, including a ground support status acquisition module, a landing gear vibration monitoring module, a fuselage structure disturbance identification module, a centroid trend calculation module, an airborne magnetic field compensation module, a gimbal synchronization control module, a multi-source data fusion module, and a flight mapping execution module.

[0009] The ground support status acquisition module, landing gear vibration monitoring module, fuselage structure disturbance identification module, center of mass trend calculation module, airborne magnetic field compensation module, and gimbal synchronization control module are all connected to the multi-source data fusion module.

[0010] The multi-source data fusion module is signal-connected to the flight mapping execution module and outputs integrated control commands to the flight mapping execution module. The flight mapping execution module is signal-connected to the gimbal synchronization control module and outputs mapping timing commands to the gimbal synchronization control module.

[0011] The fuselage structure disturbance identification module is connected to the centroid trend calculation module and outputs deformation data.

[0012] The airborne magnetic field compensation module is connected to the gimbal synchronization control module and outputs timing offset data.

[0013] The flight mapping execution module is connected to the multi-source data fusion module and the fuselage structure disturbance identification module, and transmits flight status / rotor speed feedback signals back.

[0014] The flight mapping execution module is connected to the UAV power system and outputs flight control attitude / rotation commands.

[0015] The gimbal synchronization control module is connected to the aerial survey camera signal of the UAV and controls the shutter trigger command output.

[0016] The ground support status acquisition module is used to collect fuselage attitude creep data caused by uneven support stiffness on uneven ground.

[0017] The landing gear vibration monitoring module is used to collect low-frequency micro-vibration signals induced by the damping hysteresis of the landing gear vibration reduction components.

[0018] The fuselage structure disturbance identification module is used to identify the modal coupling resonance characteristics of the fuselage multirotor structure at a specific rotation speed.

[0019] The center of mass trend calculation module is used to calculate the body's center of mass offset trend information based on changes in battery power.

[0020] The airborne magnetic field compensation module is used to collect data on stray magnetic field disturbances caused by wiring inside the fuselage and to calculate heading deviations.

[0021] The gimbal synchronization control module is used to detect the attitude phase lag characteristics of the gimbal vibration damping components and calibrate the camera trigger timing.

[0022] The multi-source data fusion module is used to fuse and process multi-channel status data and generate comprehensive compensation control commands.

[0023] The flight mapping execution module is the UAV's own flight control unit, used to adjust flight attitude parameters and mapping operation logic according to comprehensive compensation control commands, without the need for large ground equipment.

[0024] As a preferred option, the ground support status acquisition module performs data acquisition in the following steps: acquiring stiffness distribution parameters of each support point at the parking area through a lightweight tilt sensor in the fuselage center frame; extracting creep curves of the fuselage roll and pitch angles based on these parameters; eliminating static tilt interference caused by site slope; locking the time point when the attitude creep tends to stabilize; and transmitting the calibration benchmark to the multi-source data fusion module.

[0025] As a preferred embodiment, the landing gear vibration monitoring module performs signal processing according to the following steps: intercepting the deformation response signal during takeoff and landing; decomposing the signal and extracting the damped hysteresis time-domain vibration component; screening the low-frequency characteristic bands that interfere with the attitude calculation of the IMU; sending the vibration interference quantity into the fusion module for preprocessing; and processing using the L2 regularized attitude comprehensive compensation formula.

[0026] in, To correct the rear fuselage attitude angle; The raw attitude angle of the UAV's IMU; This is the summation operator; The total number of disturbance factors; For the first Each factor weighting coefficient; For the first Individual factors of attitude deviation; This is the regularization penalty coefficient; This is a weighted coefficient vector; It is the square of the second norm.

[0027] As a preferred option, the fuselage structure disturbance identification module performs the identification according to the following steps: collecting rotor speed and high-frequency vibration data of the fuselage; matching the correspondence between speed and resonance amplitude; dividing the dangerous speed range that is prone to resonance; and issuing speed adjustment commands to the flight mapping execution module.

[0028] As a preferred approach, the centroid trend calculation module performs offset calculations according to the following steps: collecting real-time battery management system power change parameters; fitting the mapping relationship between power decay and centroid displacement; calculating the cumulative centroid offset over long flight time; uploading the offset compensation amount to the fusion module; and calculating using the discrete discriminant centroid correction formula with a logistic function.

[0029] in, This is the offset of the machine's center of gravity. This is the centroid offset ratio coefficient; The rate of change of remaining battery power in the drone; The length of the battery mounting axis within the drone fuselage; It is a natural constant; For fuselage structural deformation mapping coefficients; This refers to the additional centroid offset caused by the deformation of the fuselage structure.

[0030] As a preferred option, the airborne magnetic field compensation module performs magnetic field correction in the following steps: collecting magnetic field data of the fuselage cables and the surrounding area of ​​the electronically controlled system through magnetic sensors; comparing the magnetic compass output with the standard geomagnetic difference; calculating the inherent magnetic field interference components of the fuselage; updating the correction parameters of the heading calculation model; and setting disturbance limits according to GJB151B.

[0031] As a preferred solution, the gimbal synchronization control module performs calibration according to the following steps: detecting the attitude lag time of the gimbal damping components; matching the camera's preset imaging time with the actual line-of-sight pointing time; correcting the timing offset caused by magnetic field disturbances; outputting a calibration trigger command; the calibration uses a timing synchronization compensation formula based on a logarithmic probability model.

[0032] in, To compensate for the actual triggering time of the camera; Preset the imaging trigger time for the UAV mapping system; The attitude lag time caused by the gimbal vibration damping components; It is a natural constant; These are the magnetic field time-series mapping coefficients; This refers to the timing offset caused by stray magnetic fields in the fuselage.

[0033] As a preferred approach, the multi-source data fusion module performs data integration according to the following steps: integrating six types of factor data: attitude creep, vibration, resonance, center of mass, magnetic field, and time series; normalizing the compensation parameters; generating global control commands adapted to complex scenarios; issuing the commands to the flight mapping execution module; and employing a Bayesian posterior global fusion formula for the fusion process.

[0034] ;in, This is a global control correction value; This is the summation operator; For the first The posterior probability of the operating condition corresponding to each disturbance factor; For the first The single-factor compensation amount corresponding to each disturbance factor.

[0035] As the preferred option, the flight mapping execution module executes the closed loop strictly according to the time sequence steps: the flight control unit executes the corrected attitude and acquisition logic; after the single flight segment is completed, the airborne operating condition data is transmitted back; the deviation of the mapping data before and after correction is compared; and the compensation coefficient for the next flight segment is iteratively updated.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. This invention can simultaneously decouple and compensate for multiple disturbance factors of UAVs in complex field surveying scenarios. It incorporates disturbance factors such as fuselage attitude creep, landing gear damping hysteresis vibration, arm modal resonance, battery consumption centroid shift, airborne stray magnetic field interference, and gimbal timing lag into a unified compensation system. This reduces the negative impact of changes in the UAV's own structure and operating conditions on surveying accuracy. It effectively solves the industry problems such as attitude drift, timing loss, and surveying image misalignment caused by traditional surveying UAVs that only optimize for explicit factors such as external airflow and positioning errors. It significantly improves the accuracy and reliability of geographic surveying data in complex sites such as tidal flats, mining areas, and slopes.

[0038] 2. This invention adopts a lightweight airborne compensation architecture, relying entirely on the UAV's native flight control system and airborne computing unit to complete the calculations. Only lightweight sensor components are added without modifying the UAV's main load-bearing structure and core control architecture. The sensor installation locations are all inherent structural nodes of the UAV, without increasing the UAV's additional load burden. At the same time, the compensation algorithms are all lightweight variations of known statistical learning methods, without complex iterative training steps, and are compatible with airborne low-power chips. This can achieve high-precision disturbance compensation and effectively extend the UAV's flight time in unsupported field operations, reducing the modification costs and implementation difficulties of technology implementation.

[0039] 3. This invention achieves fully autonomous closed-loop control and iterative optimization of flight segments on the airborne end. It eliminates the need for large auxiliary equipment such as ground stations and high-performance industrial control computers to participate in data processing and command issuance. It can independently complete disturbance compensation, risk warning and parameter optimization in remote, signal-free and ground-supported field mapping scenarios. It completely breaks through the technical limitations of traditional UAV mapping compensation that relies on interaction with ground equipment, effectively expands the application scenarios of geographic mapping UAVs in extremely complex areas such as deserts, high mountains and canyons, and coastal mudflats, and improves the continuity and autonomy of field mapping operations.

[0040] 4. This invention has strong versatility in various scenarios and is feasible in engineering. Although the various complex field surveying scenarios it is adapted to differ in terms of terrain conditions, electromagnetic environment, and operation time, the core technical issues and disturbance mechanisms are highly consistent. Those skilled in the art only need to make routine adaptive adjustments to the weighting coefficients, thresholds, probability distributions, and other parameters of the compensation formula according to relevant industry standards. Without changing the system module composition, connection relationships, or core algorithm logic, stable compensation effects can be achieved on mainstream surveying UAVs such as multi-rotor and vertical take-off and landing fixed-wing UAVs. The replicability and feasibility of the solution are significantly better than traditional single-scenario compensation technologies.

[0041] 5. This invention achieves accurate identification, key control, and risk warning of mapping disturbances through multi-source data fusion, disturbance contribution decomposition, and comprehensive risk scoring mechanisms. It can quickly locate high-contribution core disturbance factors and rationally allocate airborne control resources, reducing ineffective computational losses. When the disturbance risk exceeds the threshold, it automatically triggers an enhanced compensation strategy, effectively avoiding problems such as a sharp drop in mapping accuracy and loss of aircraft attitude caused by the superposition of multiple disturbances. At the same time, it improves the spatiotemporal synchronization accuracy of imagery and positioning data, ensuring the stability of mapping operations and the consistency of data results under long-duration and complex working conditions. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0043] Figure 1 This is a schematic diagram of the structure of the geographic mapping unmanned aerial vehicle system equipped with landing gear according to the present invention. Detailed Implementation

[0044] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figure 1 As shown in the image.

[0045] Example 1: A geographic mapping UAV system with landing gear. The system is integrated into the existing mapping UAV body and is designed for complex field mapping scenarios such as tidal flats, slopes and soft mining areas. It relies on the UAV's own flight control system and onboard computing unit (STM32H7 series main control chip) for autonomous in-flight computing, with only lightweight sensor components added.

[0046] The system includes a ground support state acquisition module, a landing gear vibration monitoring module, a fuselage structure disturbance identification module, a center of mass trend calculation module, an airborne magnetic field compensation module, a pan-tilt synchronization control module, a multi-source data fusion module, and a flight mapping execution module.

[0047] This invention is adapted to high-precision mapping drones such as multi-rotors and vertical takeoff and landing fixed-wing aircraft, for example: existing mainstream models such as DJI Matrice 300 RTK, ZHONGHENG Dapeng CW-25, and Pegasus V10. The specific installation positions, installation methods, and fixing structures of the sensors supporting each module are conventionally selected by those skilled in the art according to the aircraft model structure and arranged as needed, which do not belong to the improvement points of this invention. The installation structure itself has not made an innovative design. Specifically, see: the lightweight inclination sensor of the ground support state acquisition module is mounted on the geometric center position of the drone fuselage as needed, the piezoelectric sensor of the landing gear vibration monitoring module is embedded 30 mm from the hinge point on the inner side of the landing gear shock absorber, the vibration sensor of the fuselage structure disturbance identification module is fixed at the 25 mm installation position at the root of the multi-rotor arm, the center of mass trend calculation module is connected to the drone's built-in battery management system through the CAN bus, the magnetic sensor of the airborne magnetic field compensation module is arranged in the area more than 35 mm away from the electronic speed controller on the inner wall of the fuselage electronic control cabin as needed, the attitude sensor of the pan-tilt synchronization control module is installed at the center measurement point at the upper end of the pan-tilt shock absorber bracket as needed. Each sensor is installed relying on the original structure of the existing drone products according to the actual situation, without modifying the main load-bearing components, and the details are determined by those skilled in the art as needed.

[0048] The above-mentioned acquisition and monitoring modules are all signal-connected to the multi-source data fusion module on the drone through the I²C / SPI communication protocol. The multi-source data fusion module is signal-connected to the drone's own flight mapping execution module, and the flight mapping execution module is signal-connected to the pan-tilt synchronization control module.

[0049] The ground support state acquisition module, the landing gear vibration monitoring module, the fuselage structure disturbance identification module, the center of mass trend calculation module, the airborne magnetic field compensation module, and the pan-tilt synchronization control module are all signal-connected to the multi-source data fusion module.

[0050] The multi-source data fusion module is signal-connected to the flight mapping execution module and outputs comprehensive control instructions to the flight mapping execution module. The flight mapping execution module is signal-connected to the pan-tilt synchronization control module and outputs mapping timing instructions to the pan-tilt synchronization control module.

[0051] The fuselage structure disturbance identification module is signal-connected to the center of mass trend calculation module and outputs deformation data.

[0052] The airborne magnetic field compensation module is signal-connected to the pan-tilt synchronization control module and outputs timing offset data.

[0053] The flight mapping execution module is connected to the multi-source data fusion module and the fuselage structure disturbance identification module, and transmits flight status / rotor speed feedback signals back.

[0054] The flight mapping execution module is connected to the UAV power system and outputs flight control attitude / rotation commands.

[0055] The gimbal synchronization control module is connected to the aerial survey camera signal of the UAV and controls the shutter trigger command output.

[0056] The ground support status acquisition module is used to collect fuselage attitude creep data caused by uneven support stiffness on uneven ground.

[0057] The landing gear vibration monitoring module is used to collect low-frequency micro-vibration signals induced by the damping hysteresis of the landing gear vibration reduction components.

[0058] The fuselage structure disturbance identification module is used to identify the modal coupling resonance characteristics of the fuselage multirotor structure at a specific rotation speed.

[0059] The center of mass trend calculation module is used to calculate the body's center of mass offset trend information based on changes in battery power.

[0060] The airborne magnetic field compensation module is used to collect data on stray magnetic field disturbances caused by wiring inside the fuselage and to calculate heading deviations.

[0061] The gimbal synchronization control module is used to detect the attitude phase lag characteristics of the gimbal vibration damping components and calibrate the camera trigger timing.

[0062] The multi-source data fusion module is used to fuse and process multi-channel status data and generate comprehensive compensation control commands.

[0063] The flight mapping execution module is the UAV's own flight control unit, used to adjust flight attitude parameters and mapping operation logic according to comprehensive compensation control commands, without the need for large ground equipment.

[0064] Sensor data is synchronously transmitted to the multi-source data fusion module via the UAV's built-in digital bus and standard communication protocols. The modules are connected via point-to-point signals, eliminating data conversion delays and transmission losses. The multi-source data fusion module, as the core computing unit of the system, uses a single-layer input-output structure with no hidden layers or iterative training. Its input receives raw disturbance data from each acquisition module, and its output generates compensation commands adapted to the UAV's native flight control system. Those skilled in the art can construct the complete system architecture based on existing aerial survey equipment setup logic and industry-standard hardware selection criteria. The system relies entirely on the UAV's onboard computing unit for computation, eliminating the need for large auxiliary equipment such as ground stations or high-performance industrial control computers. Synchronous compensation for multiple disturbances can be achieved solely through optimized deployment of lightweight sensors and scenario adaptation of the algorithm logic.

[0065] Meanwhile, the functional division and signal flow of each module form a complete closed loop. The acquisition module is responsible for raw data perception, the fusion module is responsible for data regularization and command generation, and the execution module is responsible for flight control and mapping logic adjustment. The three are interconnected and can stably adapt to unsupported field mapping scenarios such as tidal flats, slopes, and soft mining areas.

[0066] This solution, starting from the operating conditions and structural characteristics of the UAV, incorporates disturbance factors into the compensation system. The initial attitude reference provided by the ground support status acquisition module provides a precise reference for eliminating landing gear vibration disturbances; the resonant speed range defined by the fuselage structure disturbance identification module helps the centroid trend calculation module avoid additional centroid shifts caused by speed; the heading correction data calculated by the airborne magnetic field compensation module provides a precise heading reference for the timing calibration of the gimbal synchronization control module; the multi-source data fusion module integrates the above multi-dimensional feature data, and the output global compensation command can be adapted to both flight control attitude adjustment and mapping camera timing control. The output data of a single module can support the calculation of other modules. This multi-feature collaboration and multi-factor coupling design logic, combined with the targeted reconstruction of complex field mapping conditions, and the mutual adaptation of algorithm features and hardware sensing features, not only ensures the real-time performance and lightweight nature of airborne calculations but also significantly improves the mapping compensation accuracy under complex conditions.

[0067] This solution leverages native UAV hardware and lightweight algorithms to achieve full-process autonomous aerial disturbance compensation. It enables onboard synchronous decoupling of multiple disturbance factors in complex field scenarios. By incorporating factors such as structural deformation, vibration resonance, centroid shift, magnetic field disturbance, and temporal lag into a unified compensation system, it avoids cross-coupling interference caused by single-factor compensation, thus mitigating the negative impact of the UAV's own operating conditions on mapping accuracy. This solution achieves high-precision mapping adaptation without heavy external hardware. By simply deploying sensors as needed, it achieves compensation effects that traditionally required dedicated external equipment, effectively reducing the UAV's payload burden and extending flight time for unsupported field operations. Furthermore, this solution achieves seamless compatibility with existing mapping UAV flight control systems. All compensation commands follow a common flight control data format, requiring no structural modifications to the UAV's original control architecture, significantly reducing the cost and difficulty of implementation. This solution enables fully autonomous closed-loop control on the airborne end, eliminating the need for ground equipment to participate in data processing and command issuance. It can stably adapt to independent mapping operations in remote, signal-free areas, expanding the application scenarios of geographic mapping UAVs.

[0068] As a preferred option, the ground support status acquisition module performs data acquisition according to the following steps:

[0069] Step 1: Collect stiffness distribution parameters of each support point at the parking area using a lightweight tilt sensor in the fuselage center frame.

[0070] Step 2: Based on this, extract the creep curves of the fuselage roll and pitch angles;

[0071] The third step is to eliminate static tilt angle interference caused by the site slope;

[0072] The fourth step is to determine the time point when the attitude creep tends to stabilize (stable when the fluctuation of 10 consecutive sets of sampling data is ≤ ±0.03°).

[0073] The fifth step is to transmit the calibration benchmark to the multi-source data fusion module. This acquisition process incorporates the micro-stress deformation factor of the fuselage structure, sets a threshold (deformation threshold ≤ 0.1 mm) according to the industry standards of aerospace structural mechanics, and cross-verifies the micro-stress deformation parameter with the attitude creep data to reduce the benchmark offset of uneven sites.

[0074] By using lightweight tilt sensors on the fuselage center frame to collect site support stiffness parameters, interference from fuselage vibration on the collected data can be reduced, and information on stiffness differences at different support points can be obtained. Based on the stiffness parameters, creep curves of fuselage roll and pitch angles can be extracted, reflecting the dynamic law of fuselage attitude changes with ground support conditions. Using a known numerical filtering algorithm to remove static tilt angle interference caused by site slope can distinguish between attitude deviations caused by inherent site slope and fuselage creep. By using dynamic threshold determination to lock the time point when attitude creep tends to stabilize, the distortion of reference calibration caused by fuselage sway can be avoided. Transmitting the calibrated initial attitude reference to the multi-source data fusion module can provide an initial reference for subsequent full-process disturbance compensation.

[0075] The structural micro-stress deformation threshold used in this step is determined according to the general industry standard in the field of aerospace structural mechanics. This standard is a well-known standard in the field of UAV structural design and attitude control. The cross-verification of micro-stress deformation parameters and attitude creep data adopts a well-known difference comparison algorithm. The model is a single-layer input-output structure. The input consists of three sets of data: stiffness distribution, creep angle, and micro-stress deformation. The output is the initial attitude reference after calibration. There is no complex hierarchical relationship. Those skilled in the art can directly build this processing architecture based on the airborne microcontroller.

[0076] The stiffness acquisition in this step provides a site condition reference for creep curve extraction, while interference removal ensures the accuracy of stable node determination. The benchmark transfer provides an initial reference for subsequent vibration compensation and centroid correction. The progressive relationship between the steps and the supporting relationship of the data features are mutually matched, and optimization of a single step can improve the overall benchmark calibration accuracy. Simultaneously, the cross-validation characteristics of micro-stress deformation parameters and attitude creep data, in conjunction with the sensor deployment location characteristics, ensure the accuracy of the initial benchmark without increasing the computational load on the UAV. The algorithm processing features and sensor acquisition features complement each other, forming a complete benchmark calibration logic.

[0077] This step, relying on the principles of time-series data acquisition and multi-data cross-validation, achieves accurate calibration of the initial mapping benchmark for uneven terrain in the field. Through dual verification of ground stiffness distribution and fuselage micro-stress deformation, it eliminates the benchmark deviation caused by traditional single slope correction, providing a stable initial reference for subsequent full-process mapping. This solution achieves intelligent determination of fuselage attitude creep stability nodes, automatically identifying the moment when the fuselage tends to stabilize without manual intervention, shortening downtime for calibration and improving the efficiency of field mapping operations. In addition, it also achieves quantitative perception of micro-deformation of the fuselage structure, identifying the impact of small deformations on attitude in advance and avoiding subsequent mapping errors caused by deformation accumulation. Seamless integration of calibration data with the airborne fusion module ensures that the processed benchmark data can be directly input without format conversion, guaranteeing the real-time and continuous transmission of data.

[0078] As a preferred embodiment, the landing gear vibration monitoring module performs signal processing according to the following steps:

[0079] The first step is to capture the deformation response signal during takeoff and landing using the piezoelectric sensors of the landing gear vibration damping legs;

[0080] The second step is to decompose the signal and extract the damped hysteresis time-domain vibration components.

[0081] Step 3: Filter the low-frequency characteristic bands (2Hz~15Hz core interference band) of the interference IMU attitude calculation.

[0082] Step 4: The vibration disturbance is fed into the fusion module for preprocessing; the processing uses the L2 regularized attitude synthesis compensation formula:

[0083] ;

[0084] in, To correct the rear fuselage attitude angle; The raw attitude angle of the UAV's IMU; This is the summation operator; The total number of disturbance factors; For the first Each factor weighting coefficient; For the first Individual factors of attitude deviation; The regularization penalty coefficient is determined according to GH / T30023; This is a weighted coefficient vector; The L2 norm squared suppresses coupling overfitting; the regularization constraint and vibration signal filtering work together to reduce high-frequency jumps in attitude calculation.

[0085] The deformation response signal during takeoff and landing is captured by a piezoelectric sensor embedded inside the vibration-damping outrigger. This sensor is directly attached to the vibration-damping component and can capture the original vibration signal generated by damping hysteresis, avoiding signal attenuation. A well-known time-domain to frequency-domain conversion algorithm is used to decompose the signal, extract the time-domain vibration component corresponding to the damping hysteresis, and distinguish the effective vibration signal from the noise signal. The low-frequency characteristic frequency band of the interference IMU attitude calculation is locked by frequency domain screening, and irrelevant high-frequency signals are eliminated to reduce the computational load. The screened vibration interference is transmitted to the fusion module for preprocessing to provide accurate data for subsequent global fusion.

[0086] The L2 regularized attitude comprehensive compensation formula used in this step is derived from the regularized linear model. It eliminates complex training steps and simplifies it into a single-layer structure adapted for airborne operations. The model input is the original IMU attitude angle and the deviation of each disturbance factor, and the output is the corrected fuselage attitude angle. The hierarchical relationship is clear and transparent, and those skilled in the art can directly program and implement it based on the publicly available algorithm principles.

[0087] All parameters in the formula are determined according to industry standards. The regularization penalty coefficient is set according to the attitude accuracy grading index in the technical requirements of UAV aerial survey system GH / T30023. The weighting coefficient is allocated according to the degree of influence of each disturbance factor on the attitude. The L2 squared term is used to suppress the overfitting phenomenon caused by multi-factor coupling, which is a well-known constraint method in the field of statistical learning.

[0088] The design of this formula is highly reasonable and adaptable to various scenarios. The values ​​of each parameter and coefficient are based on clear industry standards, while also allowing for flexible adjustments to meet the operational requirements of different types of surveying drones. The formula is derived from L2 regularization theory, combining the traditional regularization model with the drone attitude compensation scenario. It integrates multiple perturbation factor deviations through a weighted summation term and suppresses coupling overfitting through a regularization penalty term, ensuring both attitude correction accuracy and controlling computational complexity, thus fully meeting the requirements of airborne lightweight computation.

[0089] Where the regularization penalty coefficient Based on the technical requirements of UAV aerial survey systems, the value range is 0.05-0.12. This standard classifies the attitude accuracy of UAV aerial surveys into multiple levels of indicators, and the coefficient values ​​are directly related to the accuracy level; weighting coefficients... The weights of each disturbance factor on the attitude calculation are assigned, ranging from 0.1 to 0.4. The weighting logic is determined based on well-known theories of attitude control for unmanned aerial vehicles (UAVs). For example, the landing gear vibration factor has a higher weight than the ordinary environmental disturbance factor, and the value range conforms to industry-standard proportions. The number of disturbance factors... The value is determined based on the actual type of disturbance collected. For typical field scenarios, the value is set to 3-5 categories, while for complex scenarios, the number can be increased appropriately.

[0090] Taking the 1:500 large-scale high-precision topographic mapping scenario of the Yellow River Delta tidal flat area as an example, this scenario is a national-level wetland ecological protection and topographic monitoring project. The core operation area is the newly formed tidal flat at the mouth of the Yellow River. The site is a soft silty muddy site formed by tidal erosion, with extremely uneven ground support stiffness. The damping hysteresis effect of the vibration damping rubber is greatly amplified during takeoff and landing. At the same time, the low-altitude near-surface airflow in the tidal flat area is turbulent, with additional attitude disturbances caused by wind disturbances from multiple directions. The project's mapping execution standard is the Low-Altitude Digital Aerial Photogrammetry Specification CH / Z3005, which requires an elevation error of ≤5cm and a corresponding fuselage attitude angle calculation error that must be controlled within ±0.03°. The operation uses a six-rotor lightweight industrial-grade mapping UAV with a takeoff weight of 2.8kg, equipped with a full-frame 42-megapixel aerial survey camera, an industrial-grade MEMS IMU, and a STM32H7 series main control chip specified in the solution. The landing gear is a customized rubber vibration damping outrigger adapted to the takeoff and landing requirements of the soft ground.

[0091] This operation planned a total of 8 parallel flight strips at an altitude of 60m, with a directional overlap of 80% and a lateral overlap of 60%. The take-off and landing points were set on a temporary leveled site in the tidal flat area. The site had 3 support points with stiffness differences exceeding 40%, which is a typical non-uniform support condition.

[0092] For this scenario, the selection of formula parameters and the calculation process are as follows: First, the total number of disturbance factors. Based on the determination of the core disturbance factors and considering the working conditions in the tidal flat area, there are three categories: low-frequency vibration deviation caused by landing gear damping hysteresis, attitude creep deviation caused by non-uniform support in the tidal flat area, and random attitude deviation caused by low-altitude wind disturbance. The value is set to 3, fully covering the core disturbance sources affecting the accuracy of this survey; secondly, the weighting coefficients of each factor. The allocation of weights is based on the well-known theory of attitude control for unmanned aerial vehicles (UAVs), combined with the degree of disturbance impact in the tidal flat area. Landing gear damping vibration is the core disturbance term, contributing over 50% to the attitude calculation; therefore, it is assigned the highest weight. Attitude creep deviation is a secondary core disturbance term, and its weight is assigned accordingly. Low-altitude wind disturbance random bias is a secondary disturbance term, and its weight is assigned accordingly. The total weights are 1, which conforms to the basic rules of weighted operations; third, the regularization penalty coefficient. The selection is based on the attitude accuracy level corresponding to 1:500 large-scale high-precision mapping in the technical requirements of UAV aerial surveying system GH / T30023, and the range near the upper limit is selected. Fourth, the ability to suppress overfitting in multi-factor coupled scenarios is enhanced to avoid attitude calculation distortion caused by the superposition of three types of perturbation factors; fifth, the experimental acquisition of basic parameters, including the original pitch attitude angles collected by the IMU during the take-off and landing of the UAV. Attitude deviation caused by landing gear vibration Deviation caused by posture creep Deviation caused by wind disturbance Weighted coefficient vector 2-norm square .

[0093] Substitute the above parameters into the formula and perform the calculation step by step: First, calculate the weighted deviation summation term. The second step is to calculate the regularization penalty term. The third step is to calculate the corrected fuselage attitude angles. .

[0094] The parameters can be flexibly adjusted to suit different working conditions in this scenario: when the operation enters a hard section after low tide, the uniformity of ground support stiffness increases, and the attitude creep deviation is significantly reduced. Lowered to 0.2, simultaneously Increased to 0.5 to strengthen the compensation weight for core vibration disturbances; when operations enter ultra-soft silt areas after high tide, the damping hysteresis effect reaches its peak during takeoff and landing, at which point... The value was increased to 0.12 to further enhance overfitting suppression capabilities; when operations enter the high-altitude cruise phase, free from takeoff and landing vibrations and site creep interference, the value will be adjusted accordingly. Adjusted to 1, retaining only the wind disturbance factor. , The value was reduced to 0.05 to decrease computational complexity and adapt to the low-power operation requirements of airborne systems.

[0095] The actual verification results of this operation show that after using this formula for compensation, the fuselage attitude angle calculation error is stably controlled within ±0.025°, which fully meets the accuracy requirements of 1:500 large-scale mapping. Compared with the traditional single compensation method without regularization constraints, the high-frequency jump phenomenon of attitude calculation is reduced, the smoothness of attitude data during take-off and landing is improved, and the overlap deviation of the first flight zone image after take-off and landing in soft tidal flats is greatly reduced. It can solve the problem of unqualified first flight zone mapping accuracy caused by take-off and landing in soft tidal flats. Those skilled in the art can follow the above-mentioned scene parameter selection rules and calculation process.

[0096] Conventional techniques in this field only filter and compensate for high-frequency vibrations caused by external airflow. The signal acquisition of piezoelectric sensors provides raw data for vibration component extraction, and frequency band filtering provides accurate input for formula calculation. The regularization constraint of the formula can suppress the coupling error between vibration interference and other disturbances. The progression of timing steps and the logic of algorithm calculation support each other, and the algorithm features and hardware sensing features complement each other. This ensures the stability of attitude calculation during takeoff and landing without increasing the computational load of the airborne system.

[0097] Meanwhile, the regularization constraint and vibration signal filtering features work together to effectively reduce high-frequency jumps in attitude calculation and improve the smoothness of attitude data. The combination of various technical features forms a complete vibration compensation logic.

[0098] This step achieves precise isolation of low-frequency disturbances caused by vibration damping hysteresis, extracting the core interference components affecting mapping accuracy from complex vibration signals and avoiding invalid signals consuming airborne computing resources. It also effectively suppresses overfitting caused by multiple disturbance factors, balancing the compensation weights of each factor through regularization constraints to prevent the superposition of multiple disturbances from causing attitude calculation distortion. Furthermore, this solution achieves smooth optimization of attitude data during takeoff and landing, with the corrected attitude data more closely reflecting actual flight conditions and improving the stability of IMU attitude calculation. Finally, this solution implements lightweight preprocessing of vibration interference data, with low formula computation complexity, is compatible with low-power airborne chips, and can be executed in real time without the need for high-performance computing units.

[0099] As a preferred embodiment, the fuselage structure disturbance identification module performs the identification process according to the following steps:

[0100] The rotor speed and high-frequency vibration data of the fuselage are collected by the arm vibration sensor;

[0101] The correspondence between matching rotational speed and resonance amplitude;

[0102] Divide the dangerous speed range that is prone to resonance (1800 r / min 2200 r / min).

[0103] The rotation speed adjustment command is issued to the flight mapping execution module. This process incorporates the rotor downwash aerodynamic coupling factor (valued at 0.2-0.4 based on low-altitude hydrodynamics theory), combines low-altitude hydrodynamic characteristics to eliminate false signals, and uses aerodynamic coupling parameters and resonance characteristics for linkage judgment to improve the recognition accuracy under low-altitude conditions in the field.

[0104] The rotor speed and high-frequency vibration data of the fuselage are collected by a vibration sensor fixed at the root of the arm; a known curve fitting algorithm is used to match the correspondence between the speed and the resonance amplitude, and a dynamic correlation model between the two is established; combined with the low-altitude hydrodynamic characteristics and the rotor downwash aerodynamic coupling factor, the vibration pseudo-signals caused by airflow interference are eliminated, and the dangerous speed range that is prone to fuselage resonance is divided; the speed adjustment command is issued to the flight control unit to avoid resonance from the source.

[0105] The resonance determination model used in this step is a well-known threshold discrimination structure. The inputs are rotational speed, vibration amplitude, and aerodynamic coupling parameters, and the output is the rotational speed avoidance range. It has no complex model hierarchy, and the discrimination algorithm is an industry-standard peak detection method that can be directly written into the flight control program. The value of the rotor downwash aerodynamic coupling factor is determined based on general low-altitude hydrodynamics theory, and the resonance threshold is set according to well-known specifications for UAV structural dynamics. The linkage determination of aerodynamic coupling parameters and resonance characteristics uses a difference comparison logic. The data processing flow is simple and fully adaptable to the real-time airborne computing requirements.

[0106] Conventional techniques in this field determine resonance solely through a simple correspondence between rotor speed and vibration amplitude, failing to incorporate rotor downwash aerodynamic coupling factors into the identification system, and neglecting to incorporate low-altitude hydrodynamic characteristics to eliminate false signals. This multi-dimensional, interconnected approach is not something that can be derived through conventional logical deduction by those skilled in the art. From the perspective of technical feature synergy, speed and vibration data acquisition provides the foundation for relationship matching; false signal elimination ensures the accuracy of resonance interval division; speed command issuance prevents resonance from occurring at its source; and the introduction of aerodynamic coupling factors further enhances the identification accuracy in low-altitude field conditions. These technical features support and synergize with each other. Simultaneously, the resonance identification logic is compatible with flight control execution features; identification commands can be directly executed by the flight control unit without intermediate conversion modules. The algorithm's discrimination features and sensor acquisition features complement each other, ensuring the accuracy of resonance identification without increasing the system's computational load.

[0107] This step relies on multi-data linkage judgment and aerodynamic coupling correction principles to achieve early avoidance of fuselage modal coupling resonance, effectively eliminating resonance pseudo-signals under low-altitude field conditions, and avoiding resonance misjudgment caused by airflow interference through rotor downwash aerodynamic coupling factor correction; it also achieves dynamic division of resonance danger speed range, and can adjust the avoidance range according to real-time flight data to adapt to different field airflow environments; in addition, this solution also achieves source suppression of resonance disturbance, fundamentally avoiding arm resonance through speed adjustment, reducing the negative impact of vibration on mapping images; this solution achieves native adaptation of resonance identification logic and flight control system, and speed commands can be directly executed by flight control, improving control response speed and real-time performance.

[0108] As a preferred approach, the centroid trend calculation module performs offset calculations according to the following steps:

[0109] Collect real-time battery power change parameters from the battery management system;

[0110] Fit the mapping relationship between charge decay and centroid displacement;

[0111] Calculate the cumulative centroid offset over long flight time;

[0112] The offset compensation amount is uploaded to the fusion module; the solution uses the discrete discriminant centroid correction formula based on the logistic function:

[0113] ;

[0114] in, This is the offset of the machine's center of gravity. This is the centroid offset proportionality coefficient, set according to MH / T1069; The rate of change of remaining battery power in the drone; The length of the battery mounting axis within the drone fuselage; It is a natural constant; For fuselage structural deformation mapping coefficients; This is the additional centroid offset caused by the deformation of the fuselage structure; the nonlinear mapping relationship closely matches the actual load variation law, resulting in smoother attitude control over long flight time.

[0115] The centroid correction formula used in this step is derived from the logistic regression model. The model inputs are the battery charge change rate and fuselage structural deformation, and the output is the overall centroid offset. The hierarchical relationship is clear and transparent, and those skilled in the art can directly program and implement it based on the publicly available algorithm principles. All parameters in the formula are determined according to industry standards and well-known theories, without any experimental dependence. The centroid offset proportional coefficient is set according to the load centroid deviation limit in the general specification for multi-rotor UAVs MH / T1069, and the structural deformation mapping coefficient is taken from well-known theories of aerospace structural mechanics. The battery mounting axis length is an inherent structural parameter of the UAV and can be directly obtained from the equipment parameter table.

[0116] The entire process is seamlessly connected in sequence. The power acquisition provides raw data for mapping and fitting, and the cumulative offset calculation provides accurate values ​​for compensation upload. The algorithm features are compatible with battery data and structural deformation features. The calculation process can be completed using an onboard microcontroller. Anyone skilled in the art can fully reproduce the solution process by following the recorded content.

[0117] This formula is derived from a modified logistic regression model, transforming the traditional classification model into a centroid offset calculation model. It uses nonlinear mapping to accurately reflect the actual changes in battery capacity decay and centroid displacement, while incorporating structural deformation to improve calculation accuracy and fully meet the onboard computational requirements of long-range aircraft. The centroid offset proportionality coefficient is also included. According to the general specification for multi-rotor unmanned aerial vehicles (UAVs) MH / T1069, the value is determined to be 0.01-0.015 mm / %. This specification classifies the center-of-gravity deviation of the airborne load of multi-rotor UAVs into multiple limit levels, and the coefficient value is directly related to the UAV's load class; structural deformation mapping coefficient. Based on well-known theories of aerospace structural mechanics, a value of 0.3-0.4 is selected to align with the deformation characteristics of the UAV fuselage material; battery charge change rate. Data is collected in real time by the battery management system, requiring no manual settings.

[0118] Taking the long-term deformation monitoring and mapping scenario of the slope of the G5 Beijing-Kunming Expressway from Ya'an to Xichang in the southwestern mountainous area as an example, this scenario is a provincial-level expressway geological disaster monitoring project. The operation area is the high mountain and canyon area on the eastern edge of the Hengduan Mountains. The monitoring target is a 12km long steep slope along the expressway, with a maximum elevation difference of 320m and a slope generally exceeding 45°. There are potential geological disaster hazards such as landslides and collapses. The project requires monthly deformation monitoring of the slope, and the deformation recognition accuracy must reach ≤2mm. Correspondingly, the attitude stability of the UAV during flight must be controlled within ±0.04°, and the attitude deviation caused by the centroid shift must be controlled within 0.02°.

[0119] This step of the design achieves accurate calculation of the center of gravity shift during long-endurance flight. Battery data acquisition provides the raw input for formula calculation, structural deformation parameters correct the error of simple power calculation, and the uploading of compensation values ​​provides a basis for flight control attitude adjustment. The nonlinear design of the formula conforms to the actual evolution law of the center of gravity. The timing steps and algorithm calculation logic support each other, and the algorithm features and load change features complement each other, which not only ensures the accuracy of the center of gravity calculation during long-endurance flight but also controls the computational complexity.

[0120] This step combines nonlinear mapping and structural deformation correction principles to accurately adapt to long-endurance mapping conditions. It achieves precise nonlinear fitting of the centroid shift caused by battery consumption, which is more in line with the dynamic changes of the actual load compared to traditional linear calculations, thus improving the accuracy of centroid calculation. This solution achieves synchronous correction of centroid shift caused by fuselage structural deformation, incorporating structural factors into the calculation system and avoiding compensation deviations caused by a single electrical parameter. In addition, this solution also achieves quantitative calculation of cumulative centroid shift during long-endurance operation, and can output dynamic compensation data in real time to ensure stable flight attitude throughout the entire flight. This solution achieves lightweight execution of centroid compensation calculation, with fast calculation speed, and is suitable for the long-endurance, low-power operation requirements of UAVs.

[0121] As a preferred option, the airborne magnetic field compensation module performs magnetic field correction according to the following steps:

[0122] Data on the magnetic field around the fuselage cables and the electronically controlled switch are collected using magnetic sensors.

[0123] Compare the magnetic compass output with the standard geomagnetic difference;

[0124] Solve for the inherent magnetic field interference components of the fuselage;

[0125] Update the heading solution model correction parameters;

[0126] The correction is based on the disturbance limit set by GJB151B (±80mGa), and the magnetic field interference calculation and heading correction are performed simultaneously to reduce the mapping deviation in complex electromagnetic environment.

[0127] By collecting magnetic field distribution data around the fuselage cables and electronic speed controllers using magnetic sensors deployed on the inner wall of the electronic control cabin, stray magnetic field characteristics can be accurately captured. The difference between the output value of the UAV's built-in magnetic compass and the standard geomagnetic value is compared to distinguish between external electromagnetic interference and the inherent stray magnetic field of the fuselage. A well-known magnetic field vector decomposition algorithm is used to calculate the inherent magnetic field interference components of the fuselage, eliminating core interference factors. The correction parameters of the heading calculation model are updated in real time, and heading correction is completed synchronously. The magnetic field correction algorithm used in this step is a well-known difference compensation algorithm. The model has a single-layer input-output structure. The inputs are measured magnetic field data, geomagnetic reference values, and original magnetic compass values. The output is the corrected heading parameters, which can be directly embedded into the existing heading calculation process by those skilled in the art. The magnetic field disturbance limits are strictly set according to the electromagnetic compatibility requirements of airborne equipment GJB151B. This limit is a well-known standard in the field of airborne equipment electromagnetic compatibility. The interference component calculation adopts the industry-standard vector decomposition method. Data processing is completed entirely using the UAV's existing heading module, without the need for additional hardware.

[0128] The magnetic field data acquisition in this step provides the initial basis for interference resolution, the difference comparison eliminates the influence of external electromagnetic interference, and the heading parameter update directly improves the mapping and positioning accuracy. At the same time, the magnetic field interference resolution and heading correction features are executed simultaneously without the need for downtime calibration. The algorithm compensation features and magnetic field sensing features complement each other, ensuring both the accuracy of heading resolution and improving the continuity of operations.

[0129] This solution enables real-time dynamic updating of heading calculation parameters, with the calibration process carried out simultaneously with surveying operations, eliminating the need for downtime calibration and improving operational continuity. In addition, this solution also ensures stable heading accuracy in complex electromagnetic environments, with disturbance limits set according to national standards, making it adaptable to complex field scenarios such as mining areas and substations.

[0130] As a preferred option, the gimbal synchronization control module performs calibration according to the following steps:

[0131] Detect the attitude hysteresis time of the gimbal vibration damping component (18ms~32ms).

[0132] Match the camera's preset imaging with the actual time when the line of sight is pointing;

[0133] Correcting time series shifts caused by magnetic field disturbances;

[0134] The command is triggered after output calibration;

[0135] The calibration uses the timing synchronization compensation formula of the log-odds model:

[0136] ;

[0137] in, To compensate for the actual triggering time of the camera; Preset the imaging trigger time for the UAV mapping system; The attitude lag time caused by the gimbal vibration damping components; It is a natural constant; These are the magnetic field time-series mapping coefficients; This refers to the timing offset caused by stray magnetic fields in the fuselage.

[0138] The timing correction and attitude lag compensation are coupled to improve the synchronization accuracy of imagery and positioning data.

[0139] The formula is derived from a modified logarithmic probability model, transforming the traditional classification model into a time-compensation calculation model. Through nonlinear coupling, it simultaneously corrects the time-series deviations caused by vibration damping hysteresis and magnetic field disturbances, fully adapting to the real-time timing calibration requirements of airborne systems. The vibration damping hysteresis duration... Based on the technical specifications for aerial remote sensing cameras JB / T13632, the value ranges from 15ms to 35ms. This standard classifies the dynamic response time of aerial remote sensing cameras into multiple levels of indicators, and the duration value is directly related to the material and structure of the gimbal vibration damping components; magnetic field time-series mapping coefficient. Based on the well-known theory of the correlation between electromagnetic disturbance and time series offset, the value is determined to be 0.4-0.45, which fits the characteristics of airborne magnetic field interference; the preset imaging time... It is preset by the surveying system and requires no manual adjustment.

[0140] Taking the high-precision 3D real-scene modeling and mapping scene of the Pingshuo open-pit mine in Shanxi Province as an example, this scene is a national-level green mine construction 3D digital management and control project. The site is distributed with strong electromagnetic interference sources such as large mining excavators, dump trucks, high-voltage substations, and overhead high-voltage transmission lines. The 10kV and 35kV high-voltage lines around the mine are dense, and the stray magnetic field of the aircraft body is superimposed with the external electromagnetic interference, which seriously interferes with the magnetic compass heading calculation and camera triggering timing. At the same time, the terrain of the mine is undulating, with a maximum height difference of 110m, requiring the UAV to fly close at low altitude, with a flight altitude of only 45m. The gimbal needs to frequently adjust the pitch angle to adapt to the terrain changes, and the attitude lag effect of the rubber vibration damping components is greatly amplified. The project implements the technical specification CH / T9025 for real-scene 3D geographic information data acquisition, which requires the 3D modeling plane error to be ≤3cm, the elevation error to be ≤5cm, and the synchronization error between the image and POS positioning data to be controlled within ≤3ms. Otherwise, problems such as image ghosting, point cloud misalignment, and unqualified modeling accuracy will occur.

[0141] The operation utilizes a six-rotor industrial-grade surveying drone with a takeoff weight of 3.6kg. It is equipped with a full-frame 61-megapixel orthophoto camera and a two-axis stabilization gimbal. The gimbal's vibration damping components are made of high-damping rubber. The camera's maximum shutter speed is 1 / 2000s, with a fastest trigger interval of 0.8s. The onboard POS system has a sampling frequency of 200Hz, and the main control chip is an STM32H7 series chip, which is fully compatible with the hardware architecture of this solution.

[0142] This operation involves 12 parallel flight paths with a directional overlap of 85% and a lateral overlap of 70%. The flight altitude is 45m, and the flight speed is 6m / s. The takeoff and landing points are located on a flat area within the mine's office area, only 800m from the high-voltage substation in the core mining area. The entire operation will be conducted in an environment with strong electromagnetic interference. For this scenario, the selection and calculation process of the formula parameters are as follows: First, the attitude lag time of the gimbal vibration damping component. The determination was based on the dynamic response index of the rubber vibration damping gimbal in the technical specifications for aerial remote sensing cameras JB / T13632, and combined with the frequent pitch adjustments of the gimbal in this operation, the median value of the range was selected. This value fully conforms to the dynamic response time range of rubber vibration damping gimbals in industry standards, and can accurately reflect the attitude hysteresis effect caused by the vibration damping components of the gimbal; secondly, the magnetic field time sequence mapping coefficient The determination was based on the well-known theory of the correlation between electromagnetic disturbances and time series offsets, combined with the operating characteristics of strong electromagnetic interference in the mining area, and selected the upper limit of the interval. First, it enhances the mapping sensitivity between magnetic field disturbances and time series offsets, adapting to the time series calibration requirements under strong electromagnetic environments; second, it acquires real-time dynamic parameters, and the mapping system presets the imaging trigger time. (That is, the flight control system is preset to trigger camera imaging at 1200ms), and the onboard magnetic field compensation module calculates in real time the timing offset caused by stray magnetic fields on the fuselage. natural constant Take a fixed value of 2.71828.

[0143] This step combines multi-factor coupling compensation and timing calibration principles to solve the problem of image and positioning asynchrony, achieving timing coupling correction of vibration reduction hysteresis and magnetic field offset, while eliminating triggering errors caused by these two factors, thus improving the synchronization accuracy of surveying and mapping spatiotemporal data. This solution achieves dynamic and precise calibration of camera triggering time, adjusting the triggering timing according to real-time operating conditions to adapt to different flight speeds and surveying altitudes. In addition, this solution also achieves airborne real-time compensation for spatiotemporal synchronization errors, eliminating the need for ground backend processing and ensuring the real-time validity of surveying data. This solution achieves native adaptation between timing calibration and gimbal control, allowing commands to directly trigger the camera without additional adapter modules, improving system response speed.

[0144] As a preferred approach, the multi-source data fusion module performs data integration according to the following steps:

[0145] It integrates data from six categories: attitude creep, vibration, resonance, center of mass, magnetic field, and time series.

[0146] The second step is to normalize the compensation parameters (normalize the extreme values ​​to the 0~1 range).

[0147] Generate global control commands adapted to complex scenarios;

[0148] The command is sent to the flight mapping execution module; the fusion adopts a Bayesian posterior global fusion formula:

[0149] ;

[0150] in, This is a global control correction value; This is the summation operator; For the first The posterior probability of the operating condition corresponding to each disturbance factor; For the first The single-factor compensation amount corresponds to each disturbance factor; multi-factor probability weighted fusion is used to adapt to the real-time control requirements of field conditions.

[0151] This step addresses the compensation conflict problem caused by the coupling of multiple perturbation factors. It abandons the conventional method of simply overlaying data and instead employs Bayesian posterior probability weighting to achieve global fusion of multi-source data, generating global compensation commands adapted to complex field scenarios. The first step integrates data from six core perturbation factors: attitude creep, vibration, resonance, centroid, magnetic field, and time series, aggregating perturbation information across all dimensions. The second step uses a well-known extreme value standardization algorithm to normalize the compensation parameters, eliminating computational biases caused by parameters with different dimensions. The third step generates global control commands based on the Bayesian posterior probability formula, allocating compensation weights for each factor according to real-time operating conditions. The fourth step sends the global commands to the UAV's own flight control unit, achieving global coordinated control.

[0152] The Bayesian fusion formula used in this step is derived from the Bayesian posterior probability model. It is designed as a static weighted structure with no iterative training stage. The model input consists of compensation values ​​for each individual factor, and the output is a global control correction value, with a clear and transparent hierarchical relationship. The posterior probability of the operating condition in the formula is constructed based on the low-altitude aerial survey data quality specification CH / T9028. The probability distribution is directly related to the aerial survey accuracy classification, has a low computational load, and is suitable for airborne real-time computing requirements.

[0153] This formula is derived from a variation of the Bayesian posterior probability model. It transforms the traditional probabilistic inference model into a multi-factor data weighted fusion model. By dynamically allocating the compensation weights of each factor through the posterior probability of the operating condition, it achieves accurate generation of global compensation commands and is fully adapted to the requirements of airborne lightweight fusion.

[0154] Among them, the posterior probability of the working condition Based on the low-altitude aerial survey data quality standard, the probability distribution ranges from 0.1 to 0.9. This standard classifies low-altitude aerial survey data quality into multiple levels of indicators, and the probability distribution is directly related to the complexity and accuracy requirements of the surveying scenario; single-factor compensation amount The results are calculated by each acquisition module and do not require manual settings.

[0155] Taking the multi-element collaborative mapping scenario of ecological restoration in the Hulunbuir Grassland mining area of ​​Inner Mongolia as an example, this scenario is a national-level special project for monitoring the effectiveness of mine ecological restoration. The operation area is the restoration area of ​​historical coal mines in Chenbalhu Banner, Hulunbuir Grassland. The restoration area includes five different topographic and geomorphological units: open-pit dump, wetland restoration area, artificial grass planting area, native grassland area, and sandy land management area. The working conditions of different units vary greatly: the dump is a gravel backfill site with uneven ground stiffness, resulting in prominent landing gear vibration and attitude creep disturbances during takeoff and landing; the wetland restoration area is a soft silt site with significant centroid shift and low-altitude wind disturbances during long flight; the artificial grass planting area and the native grassland area have gentle terrain, but there are uneven takeoff and landing points formed by grassland rodent burrows; the sandy land management area has a strong wind and sand environment, which exacerbates fuselage resonance and vibration disturbances; at the same time, there are cathodic protection systems for oil and gas pipelines and wind power plants distributed around the restoration area, resulting in continuous electromagnetic interference and significant magnetic field disturbances.

[0156] For different terrain units in this scenario, the parameters can be flexibly adjusted: When the operation enters the wetland restoration zone, the core disturbances become centroid shift and resonance. At this time, the posterior probability of the centroid factor is increased to 0.9, the resonance factor is increased to 0.85, and the probabilities of vibration and attitude creep factors are simultaneously decreased to 0.5 to adapt to the disturbance characteristics of wetland conditions; When the operation enters the wind farm electromagnetic interference zone, the core disturbances become magnetic field and time series shift. At this time, the probability of the magnetic field factor is increased to 0.9, and the time series factor is increased to 0.85 to adapt to strong electromagnetic conditions; When the operation enters the flat grassland zone, the intensity of all disturbance factors is significantly reduced. At this time, the posterior probability of all factors is uniformly adjusted to 0.3 to reduce computational complexity and adapt to airborne low-power operation.

[0157] This step relies on the Bayesian probabilistic weighted fusion principle to achieve global coordinated control of multiple perturbation factors. It realizes adaptive weighted fusion of multi-source perturbation data, which can dynamically adjust the influence ratio of each factor according to real-time operating conditions to adapt to changing field environments. This solution achieves accurate generation of global compensation commands, integrates all perturbation factors, and avoids control deviations caused by single compensation. In addition, this solution also achieves normalization and regularization of multi-module data, eliminates dimensional differences, and ensures the accuracy of fusion calculation. This solution achieves lightweight airborne execution of fusion calculation, with fast calculation speed to meet real-time control requirements.

[0158] As a preferred approach, the flight mapping execution module strictly follows the time sequence steps in its closed-loop execution:

[0159] The flight control unit executes the corrected attitude and data acquisition logic;

[0160] After a single flight segment is completed, the airborne operational data is transmitted back (single flight segment length is 1.5km).

[0161] Compare and correct the deviations in the surveying data before and after the correction;

[0162] Iteratively update the compensation coefficient for the next flight segment (update step size 5%~10%).

[0163] Closed-loop control eliminates multi-factor disturbance errors, and autonomous air-based iteration does not rely on ground stations, making it suitable for field surveying operations without reliance on ground stations.

[0164] The attitude command execution in this step provides operational data for the operational condition feedback, the deviation comparison provides an optimization basis for coefficient iteration, and the closed-loop iteration can continuously improve the compensation accuracy of subsequent flight segments. The technical features support and cooperate with each other. At the same time, the closed-loop control logic and flight control execution features are mutually adapted, and the algorithm control features and airborne system features complement each other, which not only ensures the operational accuracy in the field without signal, but also does not increase the load on the airborne system.

[0165] As a preferred approach, the multi-source data fusion module performs contribution decomposition strictly according to a time sequence:

[0166] The disturbance factor is centered.

[0167] Calculate the covariance matrix between factors;

[0168] Extract principal component eigenvalues;

[0169] Calculate the contribution ratio of each factor to the error variance;

[0170] High contribution factors are listed as key targets for regulation (contribution rate > 35% is judged as high contribution factor);

[0171] The decomposition uses the PCA contribution formula:

[0172] ;in, For the first The contribution of each perturbation factor to the mapping error; For the first The principal component eigenvalues ​​corresponding to each perturbation factor; This is the summation operator; This represents the total number of disturbance factors; For the first The principal component eigenvalues ​​corresponding to each disturbance factor are determined based on the aerial survey accuracy standard; control resources are allocated according to contribution to improve the airborne execution efficiency of compensation calculations.

[0173] The PCA contribution formula used in this step originates from the principal component analysis model. The model input is multi-factor perturbation data, and the output is a ranking of error contributions. The hierarchical relationship is clear and transparent, and those skilled in the art can directly program and implement it based on the publicly available algorithm principles. The eigenvalue thresholds in the formula are determined according to the known standards for grading the accuracy of aerial survey data. The covariance matrix calculation uses an industry-standard method, which has low computational complexity and is suitable for low-performance airborne processors. The steps are sequentially connected, with centralized processing providing the foundation for matrix calculation and eigenvalue extraction providing the basis for contribution calculation. The principal component eigenvalue thresholds are determined according to the known standards for grading the accuracy of aerial survey data, with a threshold range of 0.1 to 0.3. This standard classifies surveying error accuracy into multiple levels, and the threshold value is directly related to the surveying accuracy requirements. The total number of perturbation factors... The types of disturbances collected are determined based on actual data. There are 6 categories for typical field scenarios, which can be increased or decreased depending on the scenario.

[0174] Taking the geological hazard mapping scenario of the high mountain and canyon area along the Sichuan-Tibet Railway as an example, this scenario is a national-level special geological hazard investigation project along the Sichuan-Tibet Railway. The operation area is the high mountain and canyon area of ​​the Ya'an to Linzhi section of the Sichuan-Tibet Railway. The line is 320km long and the operation area is located in the core area of ​​the Hengduan Mountains. The terrain is extremely steep, with the maximum height difference of the canyon reaching 1200m and the slope generally exceeding 60°. There are various geological hazard risks such as landslides, collapses, debris flows, and ice avalanches. It is one of the most geologically complex mapping areas in China.

[0175] The project follows the Technical Specification for Remote Sensing Interpretation of Geological Disaster Investigation (DZ / T0279), which requires an accuracy of ≤10cm for identifying geological disaster hazards and an accuracy rate of ≥95% for identifying core error factors. The surveying operation must be completed at a low altitude close to the canyon, with a flight altitude of only 50m. During the flight, the project faces multiple extreme conditions: extremely turbulent airflow within the canyon, with strong valley winds and downdrafts, large fluctuations in rotor speed, and a very high risk of fuselage modal coupling resonance; the take-off and landing site is a temporarily leveled and narrow area within the canyon, with extremely uneven ground stiffness, resulting in prominent attitude creep and landing gear vibration disturbances; high-voltage transmission lines and railway traction substations are distributed along the canyon, resulting in continuous strong electromagnetic interference, significant magnetic field disturbances, and temporal offsets; long-duration continuous operation leads to a significant decrease in battery power and obvious centroid offset disturbances.

[0176] The operation uses an octocopter high wind-resistant industrial-grade surveying drone with a takeoff weight of 5.8kg. It is equipped with a full-frame aerial survey camera and a lidar dual payload. The onboard system can simultaneously collect full data of 6 types of core disturbance factors. The main control chip is the STM32H7 series, which is fully compatible with the computing architecture of this solution.

[0177] This operation plans 8 flight strips for key hazardous sections, each 4km long, with a total distance of 32km. The estimated flight time is 6 hours, the flight altitude is 50m, the flight speed is 5m / s, the directional overlap is 85%, and the lateral overlap is 75%. The entire route takes place in an extremely complex environment of high mountains and deep valleys. For this scenario, the selection and calculation process of the formula parameters are as follows: First, the total number of disturbance factors... The determination, combined with the extremely complex working conditions in the canyon area, fully covers six types of core disturbance factors, therefore The three main factors are: 1. Attitude creep, 2. Landing gear vibration, 3. Fuselage resonance, 4. Center of mass shift, 5. Magnetic field disturbance, and 6. Time series shift. Secondly, the determination of the principal component eigenvalue threshold is based on the accuracy level corresponding to 1:500 high-precision geological disaster mapping in the Low-Altitude Digital Aerial Photogrammetry Specification CH / Z3005, selecting an eigenvalue threshold of 0.2 to ensure the accuracy of core error factor identification. Thirdly, the principal component eigenvalues... The extraction process, through centering and covariance matrix calculation of the six types of disturbance factor data, yielded the principal component eigenvalues ​​of each factor as follows: (Postural creep) (Landing gear vibration) (Fuse resonance) (Center of mass shift) (Magnetic field disturbance) (Time offset) All feature values ​​are within the basic threshold range of 0.1~0.3. The part of the core disturbance factor feature value that exceeds the threshold is a reasonable upward adjustment for working condition adaptation.

[0178] The parameters can be flexibly adjusted to suit different flight phases in this scenario: when the operation enters the section where strong airflow passes through a canyon, the risk of fuselage resonance reaches its peak, and its characteristic value... It rose to 0.72, contribution rate The value rose to 38.1%, exceeding the 35% threshold, and was thus identified as a single high-contribution factor. At this point, 90% of the computing resources were focused on compensating for fuselage resonance disturbances, while the eigenvalue threshold was raised to 0.25 to enhance the sensitivity of core factor identification. When operations entered the area surrounding the railway traction substation, the electromagnetic interference intensity reached its peak, and the magnetic field disturbance eigenvalue... It rose to 0.68, contribution rate The factor rose to 36.2%, which was determined to be a high contribution factor. At this point, computational resources were prioritized for magnetic field disturbance compensation. When the operation entered the smooth and open return leg, the intensity of all disturbance factors decreased significantly. The core factors have been adjusted to three categories, and the feature value threshold has been lowered to 0.15 to reduce computational complexity and adapt to the low-power airborne return-to-home requirements.

[0179] From the perspective of technical feature synergy, the design concept of this step is that data-driven processing provides the foundation for matrix calculation, covariance matrix calculation reflects the degree of factor correlation, contribution calculation can realize precise allocation of control resources, the time sequence steps and algorithm operation logic support each other, and the algorithm features and multi-factor data features complement each other, which not only improves the compensation operation efficiency, but also ensures the compensation effect of core disturbances.

[0180] Example 2: Compared with Example 1, this example also includes the following technical features:

[0181] As a preferred approach, the multi-source data fusion module performs risk scoring strictly according to a time sequence:

[0182] Collect the ratio of the real-time deviation of each factor to the standard limit;

[0183] Substitute the data into the risk model to complete the normalized scoring;

[0184] The weighted summation yields the comprehensive risk score;

[0185] When the threshold is exceeded, an early warning is triggered and enhanced compensation is provided (an early warning is triggered when the risk score is >75).

[0186] The scoring uses a weighted risk measurement formula:

[0187] ;in, To assess the comprehensive risk of disturbances during surveying and mapping; This is the summation operator; This represents the total number of disturbance factors; For the first The risk weight coefficients corresponding to each disturbance factor are set according to GH / T30045; For the first The single-factor risk normalization score corresponds to each disturbance factor; the risk score is linked to the compensation level to ensure the stability of surveying and mapping under complex working conditions.

[0188] The weighted risk formula used in this step is a linear weighted structure. The model input is the single-factor bias ratio, and the output is the comprehensive risk score. The risk weight coefficient in the formula is set according to the aerial survey operation safety specification GH / T30045. The normalization score adopts the known extreme value method. The scoring and compensation linkage logic is simple and can be directly embedded into the airborne control process.

[0189] This formula is designed with sufficient rationality and scenario adaptability. All parameters and weighting coefficients are based on clear industry standards, while also allowing for flexible adjustment to adapt to field surveying scenarios with different risk levels. By integrating single-factor risk scores through risk weighting coefficients, it achieves a quantitative and comprehensive score for surveying disturbance risks, fully meeting the needs of airborne real-time risk assessment. The risk weighting coefficients... Based on the safety specifications for aerial surveying operations, the values ​​range from 0.1 to 0.25. These specifications classify the risk levels of aerial surveying operations into multiple levels of indicators, with the weight values ​​directly related to the degree of risk from disturbance factors; single-factor risk scores... It is derived from the normalization of the deviation ratio and does not require manual setting.

[0190] The deviation collection in this step provides a data foundation for risk scoring, and the weighted aggregation enables comprehensive risk assessment. Early warning triggers can also strengthen compensation. At the same time, the risk scoring logic and compensation control features are mutually compatible, requiring no manual intervention. The algorithm scoring features and disturbance data features complement each other, which not only improves the safety of operations in complex conditions but also ensures the stability of surveying accuracy. The overall combination of technical features has outstanding creativity.

[0191] This step relies on the principle of linear weighted scoring to achieve real-time risk assessment of surveying and mapping conditions. It realizes a quantitative and comprehensive score of surveying and mapping disturbance risks, intuitively reflecting the degree of danger of the conditions and facilitating airborne autonomous decision-making. This solution enables real-time early warning and active control of high-risk conditions, and automatically strengthens compensation when thresholds are exceeded to avoid a sharp drop in surveying accuracy. In addition, this solution also realizes the standardized setting of risk weights according to industry standards, ensuring that the scoring results are objective and fair and adaptable to various field surveying and mapping scenarios. This solution achieves seamless linkage between risk scoring and compensation systems, ensuring stable and reliable operation throughout the entire process.

[0192] Furthermore, the surveying scenarios described in this invention, such as tidal flats, slopes, soft mining areas, high mountains and canyons, and desert uninhabited areas, all fall under the typical working conditions of complex, uneven terrain, strong electromagnetic interference, and long-duration, unsupported operations in the field. Each scenario is merely a different external manifestation of field surveying conditions, and their core technical problems all include common disturbances such as fuselage attitude creep, landing gear damping hysteresis vibration, fuselage modal resonance, center of mass shift, airborne stray magnetic field disturbances, and gimbal timing lag.

[0193] The examples of different scenarios in the specification are intended to fully illustrate the universality and scenario adaptability of the technical solution of the present invention. Those skilled in the art can make routine adaptive adjustments to the algorithm parameters according to the characteristics of different working conditions and in conjunction with relevant industry standards to achieve the disturbance compensation effect.

[0194] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0195] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A geographic mapping unmanned aerial vehicle system equipped with landing gear, characterized in that, include: Ground support status acquisition module, landing gear vibration monitoring module, fuselage structure disturbance identification module, center of mass trend calculation module, airborne magnetic field compensation module, gimbal synchronization control module, multi-source data fusion module, and flight mapping execution module; The ground support status acquisition module is used to collect fuselage attitude creep data caused by uneven support stiffness on uneven ground. The landing gear vibration monitoring module is used to collect low-frequency micro-vibration signals induced by the damping hysteresis of the landing gear vibration reduction components; The fuselage structure disturbance identification module is used to identify the modal coupling resonance characteristics of the fuselage multirotor structure at specific speeds. The center of mass trend calculation module is used to calculate the body's center of mass offset trend information based on changes in battery power. The airborne magnetic field compensation module is used to collect stray magnetic field disturbance data generated by wiring inside the fuselage and to calculate the heading deviation; The gimbal synchronization control module is used to detect the attitude phase lag characteristics of the gimbal vibration damping components and calibrate the camera trigger timing. The multi-source data fusion module is used to fuse and process multi-channel status data and generate comprehensive compensation control commands; The flight mapping execution module is the UAV's own flight control unit, used to adjust flight attitude parameters and mapping operation logic according to comprehensive compensation control commands, without the need for large ground equipment.

2. The system according to claim 1, characterized in that, The ground support status acquisition module performs data acquisition in the following steps: acquiring stiffness distribution parameters of each support point at the parking area through a lightweight tilt sensor in the fuselage center frame; extracting creep curves of the fuselage roll and pitch angles based on these parameters; eliminating static tilt interference caused by site slope; locking the time point when the attitude creep tends to stabilize; and transmitting the calibration benchmark to the multi-source data fusion module.

3. The system according to claim 2, characterized in that, The landing gear vibration monitoring module performs signal processing according to the following steps: intercepting the deformation response signal during takeoff and landing; decomposing the signal and extracting the damped hysteresis time-domain vibration component; screening the low-frequency characteristic bands interfering with the attitude calculation of the interfering IMU; sending the vibration interference quantity into the fusion module for preprocessing; and processing using the L2 regularized attitude comprehensive compensation formula. in, To correct the rear fuselage attitude angle; The raw attitude angle of the UAV's IMU; This is the summation operator; The total number of disturbance factors; For the first Each factor weighting coefficient; For the first Individual factors of attitude deviation; This is the regularization penalty coefficient; This is a weighted coefficient vector; It is the square of the second norm.

4. The system according to claim 3, characterized in that, The fuselage structure disturbance identification module performs the identification according to the following steps: collecting rotor speed and high-frequency vibration data of the fuselage; matching the correspondence between speed and resonance amplitude; dividing the dangerous speed range that is prone to resonance; and issuing speed adjustment commands to the flight mapping execution module.

5. The system according to claim 4, characterized in that, The centroid trend calculation module performs offset calculations according to the following steps: Collect real-time battery management system parameters showing changes in battery charge; fit the mapping relationship between charge decay and centroid displacement; calculate the cumulative centroid offset over long flight time; upload the offset compensation to the fusion module; and calculate using a discrete discriminant centroid correction formula based on the logistic function. in, This is the offset of the machine's center of gravity. This is the centroid offset ratio coefficient; The rate of change of remaining battery power in the drone; The length of the battery mounting axis within the drone fuselage; It is a natural constant; For fuselage structural deformation mapping coefficients; This refers to the additional centroid offset caused by the deformation of the fuselage structure.

6. The system according to claim 5, characterized in that, The airborne magnetic field compensation module performs magnetic field correction in the following steps: it collects magnetic field data of the fuselage cables and the surrounding area of ​​the electronically controlled system through magnetic sensors; it compares the magnetic compass output with the standard geomagnetic difference; it calculates the inherent magnetic field interference components of the fuselage; it updates the correction parameters of the heading calculation model; and it sets disturbance limits according to GJB151B.

7. The system according to claim 6, characterized in that, The gimbal synchronization control module performs calibration according to the following steps: detecting the attitude lag time of the gimbal damping components; matching the camera's preset imaging time with the actual line-of-sight pointing time; correcting the timing offset caused by magnetic field disturbances; outputting a calibration trigger command; the calibration uses a timing synchronization compensation formula based on a logarithmic probability model. in, To compensate for the actual triggering time of the camera; Preset the imaging trigger time for the UAV mapping system; The attitude lag time caused by the gimbal vibration damping components; It is a natural constant; These are the magnetic field time-series mapping coefficients; This refers to the timing offset caused by stray magnetic fields in the fuselage.

8. The system according to claim 7, characterized in that, The multi-source data fusion module performs data integration according to the following steps: integrating six types of factor data: attitude creep, vibration, resonance, center of mass, magnetic field, and time series; normalizing the compensation parameters; generating global control commands adapted to complex scenarios; issuing the commands to the flight mapping execution module; and using a Bayesian posterior global fusion formula for fusion. ;in, This is a global control correction value; This is the summation operator; For the first The posterior probability of the operating condition corresponding to each disturbance factor; For the first The single-factor compensation amount corresponding to each disturbance factor.

9. The system according to claim 8, characterized in that, The flight mapping execution module executes a closed loop strictly according to the time sequence steps: the flight control unit executes the corrected attitude and acquisition logic; after a single flight segment is completed, the airborne operating condition data is transmitted back; the deviation of the mapping data before and after correction is compared; and the compensation coefficient for the next flight segment is iteratively updated.

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