Flight control method and system of electric power inspection unmanned aerial vehicle

Abstract

The invention discloses a flight control method and system for an electric power inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection flight control. According to the flight control method of the electric power inspection unmanned aerial vehicle, the current flight position of the unmanned aerial vehicle is obtained in real time, deviation analysis is performed on the current flight position and a target inspection position, and when the deviation exceeds a preset threshold value, disturbance sensing data in a set area is further obtained and a disturbance sensing intensity index is calculated; according to a comparison result of the index and a disturbance attribution judgment threshold value, an offset cause is judged, and a corresponding disturbance response control strategy or a flight error correction strategy is executed respectively, so that the flight stability and the control precision of the electric power inspection unmanned aerial vehicle are dynamically guaranteed; according to the invention, through dual discrimination of the disturbance perception intensity index and the flight error adjustment index, external interference and flight control error causes are accurately distinguished, path offset cause identification and strategy matching are realized, and the flight stability and control precision of the electric power inspection unmanned aerial vehicle in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) inspection flight control, and in particular to a flight control method and system for an electric power inspection UAV. Background Art

[0002] With the growing demand for intelligent transmission line operation and maintenance, drone technology has been widely adopted in power inspections. Unmanned aerial vehicles (UAVs) equipped with high-definition visible light cameras, infrared thermal imaging devices, and a variety of sensors enable long-range, high-precision, non-contact inspections of high-voltage transmission lines, tower structures, insulator components, and other targets, significantly improving inspection efficiency and operational safety. Currently, power inspection drones primarily operate by combining pre-set flight paths with inspections of mission points. The flight control system maintains core flight control functions such as path tracking, attitude stabilization, obstacle avoidance, and positioning and navigation.

[0003] Prior art, such as the invention patent application with publication number CN112327913A, discloses a drone flight control method and system for power inspection, which includes: S1, controlling the drone to fly toward the landing point according to a set return altitude and landing point position; S2, turning on a first imaging device to acquire an image of the landing point and its surrounding environment at a first field of view angle, identifying the landing point therefrom, and obtaining first relative position information between the drone and the landing point in real time through a visual algorithm, etc.; S3, controlling the drone to fly directly above the landing point based on the first relative position information between the drone and the landing point;

[0004] S4: activating a second imaging device to capture an image of the landing point at a second field of view angle, thereby identifying the landing point and obtaining second relative position information between the drone and the landing point; and S5: controlling the drone to land at the landing point based on the second relative position information between the drone and the landing point, thereby achieving precise landing of the drone. The present invention can rapidly identify and locate the landing target using both wide and narrow fields of view during drone landing, achieving precise landing control of the drone and effectively preventing the drone from crashing.

[0005] Existing technology, such as the invention patent application with announcement number: CN112799422B, discloses a drone flight control method and device for power inspection, the method comprising: controlling the drone to fly above the tower to be inspected; adjusting the drone's attitude so that the drone's target heading angle is toward one side of the tower to be inspected; determining the horizontal safety distance of the tower to be inspected; controlling the drone to fly horizontally toward one side of the tower to be inspected by a horizontal safety distance, and then controlling the drone to descend vertically at a preset speed; during the vertical descent, detecting the target object and collecting the target object's image; this method can effectively reduce the manpower and material costs of inspection planning.

[0006] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems: in the process of identifying flight path deviation, the existing technology is difficult to effectively distinguish whether the deviation is caused by external environmental disturbances or by the control error of the flight control system itself. The existing technology mostly relies on the spatial offset distance between the aircraft and the target inspection point as a single judgment basis. Once the deviation value exceeds the preset threshold, a unified return or obstacle avoidance control strategy is triggered, and there is a lack of attribution analysis capability for the cause of the deviation.

[0007] However, in actual complex inspection scenarios, the causes of flight deviations are often highly diverse: external disturbances may come from local magnetic anomalies, hot spot interference or structural vibrations, with spatial concentration and time domain burst characteristics, while flight control errors may come from attitude adjustment delays, speed fluctuations or inertial navigation drift, showing system response instability. If there is a lack of targeted identification mechanism, misjudgment is very likely to occur, such as misjudging flight control errors as environmental interference and activating circling or hovering operations, resulting in reduced mission efficiency, or misjudging real environmental disturbances as general errors and failing to avoid them in time, increasing the risk of aircraft instability or collision. Summary of the Invention

[0008] In response to the shortcomings of the existing technology, the present invention provides a flight control method and system for a power inspection UAV, which solves the problem in the existing technology that it is difficult to distinguish the cause of flight path deviation, resulting in a single response strategy and a high judgment error rate.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a flight control method for an electric power inspection drone, comprising the following steps: obtaining the current flight position coordinates of the electric power inspection drone in real time, analyzing the distance deviation value between the current flight position and the target inspection position, and comparing it with a preset distance offset threshold; if the distance deviation value is higher than the preset distance offset threshold, obtaining the disturbance perception data within the set area of ​​the electric power inspection drone, and analyzing the disturbance perception intensity index; judging whether the disturbance perception intensity index within the set area of ​​the electric power inspection drone is higher than the preset disturbance attribution judgment threshold; if the disturbance perception intensity index within the set area of ​​the electric power inspection drone is higher than the preset disturbance attribution judgment threshold, executing a disturbance response control strategy for the electric power inspection drone; if the disturbance perception intensity index within the set area of ​​the electric power inspection drone is lower than or equal to the preset disturbance attribution judgment threshold, executing a flight error correction control strategy for the electric power inspection drone.

[0010] Furthermore, the disturbance perception data includes the ambient magnetic induction intensity value, the ambient magnetic induction fluctuation value, the ambient hot spot density value, and the ambient sound signal center frequency.

[0011] Furthermore, the specific steps of analyzing the disturbance perception intensity index are as follows: obtaining the current structural state data and structural state reference data of the power inspection drone, the current structural state data including the current structural resonance response amplitude and the current vertical vibration peak value, and the current structural state reference data including the structural resonance response reference amplitude and the vertical vibration reference value; obtaining the disturbance perception reference data within the set area of ​​the power inspection drone, and performing a comprehensive analysis on the current structural state data, structural state reference data, and disturbance perception data within the set area of ​​the power inspection drone to obtain the disturbance perception intensity index, the disturbance perception reference data including the ambient magnetic induction intensity reference value, the ambient magnetic induction fluctuation reference value, the ambient hot spot density reference value, and the ambient sound signal center reference frequency.

[0012] Furthermore, the specific formula for calculating the disturbance perception intensity index is as follows:

[0013]

[0014] Among them, RgQ is the disturbance perception intensity index, Cb is the environmental magnetic induction fluctuation value in the set area of ​​the power inspection drone, Cb′ is the reference value of the environmental magnetic induction fluctuation in the set area of ​​the power inspection drone, Rb is the environmental hot spot density value in the set area of ​​the power inspection drone, Rb′ is the reference value of the environmental hot spot density in the set area of ​​the power inspection drone, λ1 is the thermal magnetic linkage coefficient stored in the database, Cg is the environmental magnetic induction intensity value in the set area of ​​the power inspection drone, Cg′ is the reference value of the environmental magnetic induction intensity in the set area of ​​the power inspection drone, λ2 is the static magnetic offset coefficient stored in the database, Zf is the current structural resonance response amplitude of the power inspection drone, Zf′ is the reference amplitude of the structural resonance response of the power inspection drone, Sp is the center frequency of the environmental sound signal in the set area of ​​the power inspection drone, Sp′ is the center reference frequency of the environmental sound signal in the set area of ​​the power inspection drone, λ3 is the acoustic-vibration linkage coefficient stored in the database, Cz is the current vertical vibration peak value of the power inspection drone, Cz′ is the vertical vibration reference value of the power inspection drone, and λ4 is the vertical vibration surge coefficient stored in the database.

[0015] Furthermore, the specific steps for executing the disturbance response control strategy on the power inspection UAV are as follows: obtaining the current flight status data of the power inspection UAV, the current flight status data including the current flight speed, the current flight direction angle and the current flight attitude angle; obtaining the low threshold value and the high threshold value of the disturbance perception intensity, and inputting them into the flight disturbance adjustment model together with the distance deviation value between the current flight position of the power inspection UAV and the target inspection position, the disturbance perception intensity index, and the current flight status data of the power inspection UAV, to obtain the flight disturbance adjustment index of the power inspection UAV; based on the flight disturbance adjustment index of the power inspection UAV, executing the disturbance response control strategy on the power inspection UAV.

[0016] Furthermore, the flight disturbance adjustment model is specifically as follows: Among them, FrT is the flight disturbance adjustment index of the power inspection UAV, LjP is the distance deviation value between the current flight position of the power inspection UAV and the target inspection position, FxS is the current flight speed of the power inspection UAV, ε is the speed adjustment factor stored in the database, α is the low disturbance adjustment coefficient stored in the database, π is pi, LcT is the path control factor stored in the database, FxJ is the current flight direction angle of the power inspection UAV, ZtJ is the current flight attitude angle of the power inspection UAV, JgY is the angle control factor stored in the database, μ is the angle adjustment coefficient stored in the database, RgQ is the disturbance perception intensity index, DyZ is the low threshold of disturbance perception intensity, e is a natural constant, XcD is the response scale factor stored in the database, δ is the medium disturbance adjustment coefficient stored in the database, GyZ is the high threshold of disturbance perception intensity, and ξ is the high disturbance adjustment coefficient stored in the database.

[0017] Furthermore, based on the flight disturbance adjustment index of the power inspection UAV, the specific steps for executing the disturbance response control strategy for the power inspection UAV are as follows: the flight disturbance adjustment index of the power inspection UAV is judged and analyzed with several preset flight disturbance adjustment intervals, and each flight disturbance adjustment interval corresponds to a flight disturbance adjustment strategy; the flight disturbance adjustment strategy corresponding to the flight disturbance adjustment index in the preset flight disturbance adjustment interval is used as the disturbance response control strategy of the power inspection UAV, and disturbance response control is performed.

[0018] Furthermore, the specific steps for executing the flight error correction control strategy for the power inspection UAV are as follows: obtaining the current flight status change data of the power inspection UAV, the current flight status data including the current flight speed change rate, the current flight direction angle change rate and the current flight attitude angle change rate; conducting a comprehensive analysis of the current flight status change data of the power inspection UAV to obtain the flight error adjustment index of the power inspection UAV; and executing the flight error correction control strategy for the power inspection UAV based on the flight error adjustment index of the power inspection UAV.

[0019] Furthermore, based on the flight error adjustment index of the power inspection UAV, the specific steps for executing the flight error correction control strategy for the power inspection UAV are as follows: the flight error adjustment index of the power inspection UAV is judged and analyzed with several preset flight error adjustment intervals, and each flight error adjustment interval corresponds to a flight error adjustment strategy; the flight error adjustment strategy corresponding to the flight error adjustment index in the preset flight error adjustment interval is used as the flight error correction control strategy of the power inspection UAV, and flight error correction control is performed.

[0020] A flight control system for an electric power inspection drone comprises: a flight deviation analysis unit for acquiring the current flight position coordinates of the electric power inspection drone in real time, analyzing the distance deviation value between the current flight position and the target inspection position, and comparing the value with a preset distance offset threshold; a disturbance perception analysis unit for acquiring disturbance perception data within a set area of ​​the electric power inspection drone and analyzing a disturbance perception intensity index when the distance deviation value is higher than a preset distance offset threshold; a judgment analysis unit for judging whether the disturbance perception intensity index within the set area of ​​the electric power inspection drone is higher than a preset disturbance attribution determination threshold; a disturbance response control unit for executing a disturbance response control strategy for the electric power inspection drone when the disturbance perception intensity index within the set area of ​​the electric power inspection drone is higher than the preset disturbance attribution determination threshold; and an error correction control unit for executing a flight error correction control strategy for the electric power inspection drone when the disturbance perception intensity index within the set area of ​​the electric power inspection drone is lower than or equal to the preset disturbance attribution determination threshold.

[0021] The present invention has the following beneficial effects:

[0022] (1) The flight control method of the power inspection UAV can effectively distinguish whether the cause of the path deviation of the power inspection UAV comes from external environmental disturbance or from the control error of the aircraft itself by introducing a dual-index discrimination mechanism of disturbance perception intensity index and flight error adjustment index, thereby solving the technical shortcomings of the existing technology that only the deviation amplitude can be identified but the cause of the deviation cannot be determined. This method not only monitors the deviation distance between the current position and the target inspection point, but also comprehensively analyzes the multi-source physical perception data such as magnetic induction fluctuation, hot spot density, and acoustic energy center frequency in the set area, and combines structural vibration and attitude response parameters to construct a disturbance perception intensity index; if the index is lower than the disturbance attribution judgment threshold, the system further extracts the dynamic change rate of flight speed, heading angle, and attitude angle to calculate the flight error adjustment index. The dual judgment mechanism enables the system to identify whether the cause behind the same deviation is sudden environmental interference or internal factors such as thrust oscillation and attitude drift, and switch the corresponding control strategy based on the cause type, significantly improving the pertinence, robustness and path return accuracy of the flight control response, and enhancing the mission continuity and control stability of the UAV in complex environments.

[0023] (2) The flight control method of the power inspection UAV integrates the multi-parameter disturbance perception data structure including the environmental magnetic induction intensity value, magnetic induction fluctuation value, hot spot density value and acoustic signal center frequency, and combines the structural vibration and resonance response to construct the disturbance perception intensity index, and sets the low threshold and high threshold intervals of the disturbance perception intensity to achieve fine segmentation classification of the disturbance level, effectively filling the control logic gap of the existing technology where the disturbance signal recognition dimension is insufficient and the response strategy lacks layering, and at the same time matches the corresponding flight disturbance adjustment strategy under different disturbance level intervals, such as in the case of micro-disturbance. It only performs heading fine-tuning and speed maintenance operations, initiates direction-speed coupling control and speed reduction strategy under medium disturbance conditions, and directly triggers hovering avoidance and path exit mechanisms under high disturbance conditions, significantly enhancing the system's dynamic environment adaptability and flight steady-state control capabilities in complex inspection areas such as high-voltage tower gap areas, heat source concentration areas, and magnetic anomaly intersection areas. At the same time, this disturbance segmented adjustment logic not only improves the matching accuracy of the flight control strategy, but also avoids secondary disturbances to the flight path caused by false triggering of the strategy, optimizes energy consumption distribution and flight control efficiency, and has good mission sustainability support performance.

[0024] (3) The flight control method of the power inspection UAV takes the flight speed change rate, direction angle change rate and attitude angle change rate as input variables, and constructs a dynamic weight model through the flight speed change adjustment coefficient, direction angle change adjustment coefficient and attitude angle change adjustment coefficient extracted from the database, thereby realizing a dynamic evaluation of the flight control stability of the aircraft in the absence of external disturbances. Different from the existing technology that only relies on the spatial offset threshold to judge whether to return to the right position, this method can predict potential risks such as flight control lag or attitude oscillation through the state change trend before the system has a significant path offset, and execute micro-bias correction, slow adjustment compensation or attitude and heading joint return strategies in intervals. When the flight error adjustment index is high, the system can automatically suspend the path task, improve the PID sensitivity and synchronously adjust the heading and attitude angle to ensure the control accuracy and flight stability of the flight control system under high dynamic response, significantly improving the system's sensitivity to flight control error scenarios and self-stabilization adjustment capabilities. It is particularly suitable for executing complex power line inspection task scenarios with dense paths, frequent corners and dense electromagnetic interference.

[0025] (4) The flight control system of the power inspection UAV, through a modular functional division structure design, has flight path deviation identification, disturbance perception judgment, cause attribution analysis and response control logic respectively executed by independent functional units, realizing the decoupling deployment and high reliability execution control of the flight control decision process. Among them, the flight deviation analysis unit is responsible for path deviation detection, the disturbance perception analysis unit focuses on the fusion and index analysis of multi-source perception data, the judgment analysis unit classifies the disturbance index into intervals, and the disturbance response control unit and the error correction control unit respectively execute the response strategy switching based on the disturbance and error attribution results. The system structure has a high degree of functional independence and clear coupling boundaries, which is convenient for parallel deployment and scheduling calls on the UAV embedded control platform, improving the real-time, stability and maintainability of the system operation. At the same time, the modular design also facilitates the flexible expansion or upgrade of a sub-unit algorithm logic according to the inspection scenario without affecting the overall system operation architecture. It has good engineering adaptability and system scalability, which is conducive to the subsequent general deployment and technology iteration on multiple UAV platforms.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a flight control method for a power inspection UAV according to the present invention.

[0028] Figure 2 This is a flowchart of the specific steps of executing a disturbance response control strategy on a power inspection UAV in a flight control method of the power inspection UAV of the present invention.

[0029] Figure 3 This is a block diagram of the flight control system of a power inspection UAV of the present invention. DETAILED DESCRIPTION

[0030] See also Figure 1 , an embodiment of the present invention provides a technical solution: a flight control method for an electric power inspection UAV, comprising the following steps: obtaining the current flight position coordinates of the electric power inspection UAV in real time, analyzing the distance deviation value between the current flight position and the target inspection position, and comparing it with a preset distance offset threshold; if the distance deviation value is higher than the preset distance offset threshold, obtaining disturbance perception data within a set area of ​​the electric power inspection UAV (for example, a circular area with a radius of 2m), and analyzing a disturbance perception intensity index; judging whether the disturbance perception intensity index within the set area of ​​the electric power inspection UAV is higher than a preset disturbance attribution determination threshold; if the disturbance perception intensity index within the set area of ​​the electric power inspection UAV is higher than the preset disturbance attribution determination threshold, executing a disturbance response control strategy for the electric power inspection UAV; if the disturbance perception intensity index within the set area of ​​the electric power inspection UAV is lower than or equal to the preset disturbance attribution determination threshold, executing a flight error correction control strategy for the electric power inspection UAV.

[0031] Among them, the distance offset threshold refers to the spatial tolerance standard value used to determine whether the power inspection UAV deviates from the preset inspection path. It is usually set according to factors such as the mission type, the distribution density of the target inspection points, the aircraft control accuracy and the complexity of the environment. When the distance deviation value between the current flight position of the power inspection UAV and the corresponding target inspection position is greater than the threshold, the system considers that there is a significant yaw risk or unexpected trajectory drift, thereby triggering further disturbance attribution analysis and control decision-making process. The threshold can be dynamically generated by the task deployment module according to the inspection path structure, or determined by calling the standard distance tolerance value corresponding to the preset path type in the database (for example, 1.0 meters, 1.5 meters, etc.).

[0032] The disturbance attribution judgment threshold refers to the quantitative judgment boundary used to distinguish whether the path deviation is caused by external environmental disturbance factors (such as magnetic interference, hot spot anomalies, structural vibration, etc.). The threshold is usually extracted based on the statistical distribution of the disturbance perception intensity index in a large amount of mission data. For example, the maximum value within the 95% confidence interval can be selected as the upper limit of the external disturbance judgment. If the disturbance perception intensity index in the current set area of ​​the power inspection drone is higher than the threshold, the system determines that the current deviation is caused by environmental interference and needs to execute the disturbance response control strategy; if it is lower than or equal to the threshold, the deviation is judged to be a flight control error or perturbation, and the flight error correction strategy is executed.

[0033] The disturbance perception data includes the ambient magnetic induction intensity value, the ambient magnetic induction fluctuation value, the ambient hot spot density value, and the ambient sound signal center frequency.

[0034] Among them, the environmental magnetic induction intensity value refers to the total geomagnetic field strength measured by the power inspection UAV in the current set area. The unit is microtesla (μT), which is used to reflect the absolute intensity level of the magnetic field in the flight area. This value is collected in real time by the three-axis geomagnetic sensor carried by the UAV. The sensor is integrated into the flight control system or navigation module, and the geomagnetic steady-state induction value in the current area is obtained after the calibration algorithm removes the sensor zero drift and the magnetic interference of the aircraft body.

[0035] The environmental magnetic induction fluctuation value refers to the variation amplitude or variance of the geomagnetic induction intensity monitored by the power inspection drone within a set time window. It is used to determine whether there are abnormal magnetic disturbance sources in the area, such as magnetic leakage from high-voltage equipment and magnetic eddy currents in substations. This parameter is obtained by sliding window filtering and frequency domain analysis (such as FFT) on the real-time collected magnetic induction intensity values ​​to obtain the magnetic field fluctuation characteristics within a certain frequency range, usually expressed in the form of standard deviation or peak-to-peak value.

[0036] The environmental hot spot density value refers to the number and area ratio of thermal anomaly areas captured by the power inspection drone through thermal imaging equipment in a set area. It is used to characterize the abnormal heat sources in the area (such as cable overload, poor contact or partial discharge, etc.). This value is obtained by obtaining the thermal map through the thermal infrared image acquisition module, and the image segmentation algorithm is used to detect areas above the temperature threshold. The number of hot spots and their proportion to the entire area are counted to obtain the hot spot density value.

[0037] The center frequency of the ambient sound signal refers to the main frequency distribution position with the highest spectral energy concentration in the ambient sound signal collected by the power inspection drone in the set area. The unit is Hz. It is used to identify whether there are abnormal noise sources in the area (such as arc discharge, mechanical jitter, and high-frequency electric vibration). This value is obtained by the microphone array on the drone to obtain the audio waveform. After extracting the spectrum through fast Fourier transform (FFT), the energy center frequency or the maximum amplitude frequency is calculated as the sound energy center frequency of the current area.

[0038] Specifically, the specific steps for analyzing the disturbance perception intensity index are as follows: obtain the current structural state data and structural state reference data of the power inspection drone, the current structural state data includes the current structural resonance response amplitude and the current vertical vibration peak value, and the current structural state reference data includes the structural resonance response reference amplitude and the vertical vibration reference value; obtain the disturbance perception reference data within the set area of ​​the power inspection drone, and conduct a comprehensive analysis based on the current structural state data, structural state reference data, and disturbance perception data within the set area of ​​the power inspection drone to obtain the disturbance perception intensity index, the disturbance perception reference data includes the ambient magnetic induction intensity reference value, the ambient magnetic induction fluctuation reference value, the ambient hot spot density reference value, and the ambient sound signal center reference frequency.

[0039] The structural resonance response reference amplitude refers to the stable amplitude of the structural vibration response in a specific frequency band (such as a typical resonance frequency) as measured by the IMU (Inertial Measurement Unit) when the aircraft is in a static state or during normal flight without external disturbances. This value is statistically extracted from structural response data from "undisturbed flight segments" in a large number of historical missions and serves as a reference amplitude for subsequent identification of excessively strong resonances (for example, due to external interference or loose flight control components).

[0040] The vertical vibration reference value refers to the mean or median of the peak vibration amplitude measured by the accelerometer in the Z-axis direction when the aircraft is in hovering or stable cruising phase, in m / s 2 This reference value is used to determine whether there are abnormal vertical disturbances in the current environment caused by terrain, wind shear, tower coupling, etc. The data source comes from statistics of "low oscillation" sample sections set in multiple flight missions.

[0041] The ambient magnetic induction intensity reference value refers to the typical geomagnetic field strength in the flight area under normal operating conditions, measured in μT. This value is obtained by averaging steady-state magnetic induction values ​​collected multiple times in flight scenarios such as power lines and substations. It can be dynamically referenced during mission deployment, combined with a map magnetic field database or historical mission data, to serve as a comparison benchmark for actual magnetic induction values.

[0042] The ambient magnetic induction fluctuation reference value refers to the historically stable statistical value of the geomagnetic fluctuation amplitude within the flight area in the absence of strong magnetic disturbances. It is typically the maximum fluctuation or mean variance of the magnetic induction intensity within a standard observation time window. The unit is μT. This value is used to determine whether the current magnetic disturbance exceeds the standard background magnetic noise level. The data source is a stable sample accumulated during missions in similar magnetic environments.

[0043] The environmental hot spot density reference value is the statistical mean of the distribution of thermal anomalies detected by infrared imagery within a target area under normal equipment operation. This reference value is derived from a thermal distribution model established by analyzing thermal image features from multiple historical missions with known fault-free equipment. It is used to measure whether the current hot spot is significantly abnormal.

[0044] The center reference frequency of the ambient sound signal is the statistical median or expected value of the main sound energy distribution frequency in the sound field of a specific power equipment or tower structure under normal operating conditions, measured in Hz. This value is extracted by performing spectral cluster analysis on audio data from the same model and structure in historical missions. The center of the main frequency distribution is used as a reference standard to determine whether the current sound signal has offsets or abnormal sound sources.

[0045] The specific formula for calculating the disturbance perception intensity index is as follows:

[0046]

[0047] Among them, RgQ is the disturbance perception intensity index, Cb is the environmental magnetic induction fluctuation value in the set area of ​​the power inspection drone, Cb′ is the reference value of the environmental magnetic induction fluctuation in the set area of ​​the power inspection drone, Rb is the environmental hot spot density value in the set area of ​​the power inspection drone, Rb′ is the reference value of the environmental hot spot density in the set area of ​​the power inspection drone, λ1 is the thermal magnetic linkage coefficient stored in the database, Cg is the environmental magnetic induction intensity value in the set area of ​​the power inspection drone, Cg′ is the reference value of the environmental magnetic induction intensity in the set area of ​​the power inspection drone, λ2 is the static magnetic offset coefficient stored in the database, Zf is the current structural resonance response amplitude of the power inspection drone, Zf′ is the reference amplitude of the structural resonance response of the power inspection drone, Sp is the center frequency of the environmental sound signal in the set area of ​​the power inspection drone, Sp′ is the center reference frequency of the environmental sound signal in the set area of ​​the power inspection drone, λ3 is the acoustic-vibration linkage coefficient stored in the database, Cz is the current vertical vibration peak value of the power inspection drone, Cz′ is the vertical vibration reference value of the power inspection drone, and λ4 is the vertical vibration surge coefficient stored in the database.

[0048] It needs to be explained that the specific steps for obtaining the thermal-magnetic linkage coefficient λ1, static magnetic offset coefficient λ2, acoustic-vibration linkage coefficient λ3, and vertical vibration surge coefficient λ4 stored in the database are: selecting a representative disturbance area sample set, extracting the multi-source perception data and corresponding flight offset records therein, and using the correlation analysis between the disturbance intensity change and the flight stability response to construct a multi-dimensional disturbance influencing factor matrix, respectively evaluating the joint amplification relationship between magnetic field fluctuations and hot spot density, the abnormal triggering ratio between the static magnetic field deviation amplitude and flight stability, the synchronization probability distribution between structural resonance and acoustic spectrum offset, and the nonlinear surge trend of the vibration peak deviation on the flight control disturbance trigger rate, and extracting the thermal-magnetic linkage coefficient, static magnetic offset coefficient, acoustic-vibration linkage coefficient, and vertical vibration surge coefficient through least squares fitting or disturbance intensity attribution analysis.

[0049] In this implementation, quantitative identification and accurate classification of causes of environmental disturbances can be achieved during the flight control process, significantly improving the system's perception accuracy and response judgment capabilities for flight anomalies. By introducing physical parameters such as environmental magnetic induction intensity, magnetic induction fluctuations, hot spot density, and acoustic signal center frequency, and combining aircraft body state data such as structural resonance response and vertical vibration, a composite perception structure for disturbance potential modeling is formed; then, by normalizing and comparing with historical reference data, and introducing correlation weights such as thermal-magnetic linkage coefficient, static magnetic offset coefficient, acoustic-vibration linkage coefficient, and vertical vibration surge coefficient, a multi-dimensional control weight mechanism for the disturbance index is established, thereby avoiding the problems of single environmental disturbance judgment, delayed response, or misjudgment in traditional systems. This mechanism not only enhances the environmental adaptability of the flight control system, but also improves the control matching accuracy of moderate disturbance areas, providing a reliable perception basis and quantitative basis for dynamic flight path correction and safe and stable inspections.

[0050] Specifically, if Figure 2 As shown in the figure, the specific steps of executing the disturbance response control strategy for the power inspection UAV are as follows: obtain the current flight status data of the power inspection UAV, which includes the current flight speed, the current flight direction angle and the current flight attitude angle; obtain the low threshold value and the high threshold value of the disturbance perception intensity, and first perform unit processing on the distance deviation value between the current flight position of the power inspection UAV and the target inspection position, the disturbance perception intensity index, and the current flight status data of the power inspection UAV, and then input them into the flight disturbance adjustment model respectively to obtain the flight disturbance adjustment index of the power inspection UAV; based on the flight disturbance adjustment index of the power inspection UAV, execute the disturbance response control strategy for the power inspection UAV.

[0051] Among them, the specific steps for obtaining the low threshold and high threshold of disturbance perception intensity are as follows: first, typical "offset controllable" and "offset out of control" flight segments are screened out from the power inspection tasks performed by the UAV, and their corresponding disturbance perception intensity index curves are classified and compared; second, the peak value and mean value of the disturbance index in the controllable offset interval are extracted as the candidate low threshold set, and the value after the disturbance index exceeds the interval for the first time in the offset out of control event is taken as the candidate high threshold set; by setting the dividing point between the control strategy response success rate and the failure rate, the disturbance value corresponding to the 95% success control rate is used as the low threshold, and the disturbance value corresponding to the flight offset out of control probability greater than 80% is used as the high threshold; finally, the environmental factor stability factor is combined for dynamic weighted correction to determine the segmented threshold pair suitable for specific power inspection paths and equipment types, which is used as the basis for interval classification judgment of the disturbance index.

[0052] The flight disturbance adjustment model is as follows: Among them, FrT is the flight disturbance adjustment index of the power inspection UAV, LjP is the distance deviation value between the current flight position of the power inspection UAV and the target inspection position, FxS is the current flight speed of the power inspection UAV, ε is the speed adjustment factor stored in the database, which is used to prevent the denominator from being 0, α is the low disturbance adjustment coefficient stored in the database, π is pi, and in this embodiment, the value is 3.14, LcT is the path control factor stored in the database, FxJ is the current flight direction angle of the power inspection UAV, ZtJ is the current flight attitude angle of the power inspection UAV, JgY is the angle control factor stored in the database, μ is the angle adjustment coefficient stored in the database, RgQ is the disturbance perception intensity index, DyZ is the low threshold of disturbance perception intensity, e is a natural constant, and in this embodiment, the value is 2.71, XcD is the response scale factor stored in the database, δ is the medium disturbance adjustment coefficient stored in the database, GyZ is the high threshold of disturbance perception intensity, and ξ is the high disturbance adjustment coefficient stored in the database.

[0053] It should be explained that the specific steps for obtaining the low-disturbance adjustment coefficient α, path control factor LcT, angle control factor JgY, angle adjustment coefficient μ, response scale factor XcD, medium-disturbance adjustment coefficient δ, and high-disturbance adjustment coefficient ξ stored in the database are as follows: select multiple flight process records with known path deviation, speed fluctuation, and attitude instability, and extract their corresponding disturbance perception data, path trajectory deviation data, flight attitude change data, and aircraft control response data; secondly, construct a regression mapping model between disturbance type and flight stability index, and calculate the mean amplification factor between the path response amplitude and the adjustment amplitude under the disturbance state as the path control factor; calculate the mapping function between the change gradient of the flight direction angle and attitude angle in the disturbance event and the change of the disturbance perception intensity, and extract the angle control factor and angle adjustment coefficient; extract the low-disturbance adjustment coefficient, medium-disturbance adjustment coefficient, and high-disturbance adjustment coefficient based on the fitting relationship between the change range of the disturbance index and the controller response amplitude curve; and combine the exponential function input-output error model to extract the characteristic input activation amplitude in the disturbance response process to determine the response scale factor.

[0054] This implementation enables a comprehensive assessment of the cause of flight path deviation, the aircraft's current dynamic state, and the degree of environmental disturbance, resulting in high-resolution disturbance level recognition capabilities. By normalizing key parameters such as distance deviation, flight speed, flight direction angle, attitude angle, and disturbance perception intensity index and inputting them into the model, and combining them with training-derived parameter coefficients from a database such as the low-disturbance adjustment coefficient, path control factor, and angle adjustment factor for mapping analysis, the flight disturbance adjustment index can be accurately calculated, enabling the quantitative triggering logic of the disturbance response strategy. Furthermore, the model introduces a dual-threshold mechanism of low and high disturbance perception intensity, coupled with a response scale factor and exponential activation structure, resulting in excellent nonlinear adjustment capabilities and segmented disturbance level discrimination. This significantly improves the flight control system's strategy matching accuracy under mild, moderate, and high-intensity disturbances. Compared to traditional static threshold judgment and linear compensation methods, this model effectively avoids strategy mis-triggering or over-response while maintaining control sensitivity, demonstrating greater adaptability, stability, and intelligent strategy control.

[0055] Specifically, based on the flight disturbance adjustment index of the power inspection UAV, the specific steps for executing the disturbance response control strategy for the power inspection UAV are as follows: the flight disturbance adjustment index of the power inspection UAV is judged and analyzed with several preset flight disturbance adjustment intervals, and each flight disturbance adjustment interval corresponds to a flight disturbance adjustment strategy; the flight disturbance adjustment strategy corresponding to the flight disturbance adjustment index in the preset flight disturbance adjustment interval is used as the disturbance response control strategy of the power inspection UAV, and disturbance response control is performed.

[0056] The flight disturbance adjustment strategies include but are not limited to the following examples:

[0057] Flight disturbance adjustment index ∈ [0, 1.5) : Perturbation adjustment strategy, indicating that the external disturbance to the aircraft is minimal, the flight state is basically stable, and there is only a slight path deviation. At this time, the system executes the perturbation adjustment strategy, including:

[0058] The control system activates the low-amplitude heading fine-tuning algorithm to slowly correct the yaw with an attitude angle not exceeding ±2°;

[0059] Maintain the current flight speed and altitude to avoid introducing additional disturbances due to excessive adjustments;

[0060] Deactivate the speed adjustment module to ensure stable energy distribution;

[0061] If the system is still in this range after three consecutive detections, the disturbance control process will be automatically terminated.

[0062] Flight Disturbance Adjustment Index ∈ [1.5, 3.5) : This indicates that the aircraft is experiencing a certain level of disturbance, such as magnetic disturbance, thermal flux, or structural resonance, and the path deviation is accumulating. In this case, the system executes the medium disturbance coordination adjustment strategy, including:

[0063] Adjust the direction angle and flight speed in a coordinated manner, and activate the direction-speed coupling control algorithm to suppress jitter feedback;

[0064] Expand the heading adjustment range to ±5-10°, allowing active return to path within 2 seconds;

[0065] Start the speed reduction mode, reduce the current speed by less than 15%, and increase the control response time window;

[0066] The structural vibration mitigation module is activated to monitor and mitigate the feedback trigger of vertical vibration peaks.

[0067] Flight Disturbance Adjustment Index ≥ 3.5: High-disturbance protection and avoidance strategy is activated, indicating that the aircraft is in a highly disturbed environment and there is a risk of flight control imbalance. The system will implement high-disturbance protection and avoidance strategies, including:

[0068] Immediately suspend the current automatic path task and enter the "abnormal flight control state";

[0069] Start the fixed-point hovering protection mode to maintain a stable posture with minimum energy at the current position;

[0070] If the ambient magnetic induction fluctuation value or the hot spot density value exceeds the reference upper limit at the same time, initiate evasive movement in place and perform discrete retreat flight in the opposite direction with a radius of ≤2 meters;

[0071] The system also records disturbance events and marks them with a “high disturbance label” through the flight control module for subsequent mission learning and risk map construction.

[0072] In this implementation plan, a segmented response control strategy based on the flight disturbance adjustment index is constructed to realize the intelligent and adaptive flight control decision-making of the power inspection UAV in a multi-intensity disturbance environment. The mechanism divides the disturbance adjustment index into multiple intervals and presets differentiated flight control strategies for different intervals, forming a set of progressive response logic from micro-disturbance adjustment, medium-disturbance coordinated adjustment to high-disturbance protection avoidance, which can accurately match the flight control behavior according to the degree of disturbance. In the case of micro-disturbance, the system maintains stability with low-amplitude attitude fine-tuning to avoid energy waste or secondary offset caused by overreaction; in the case of medium disturbance, direction-speed adjustment is introduced. The coupling and deceleration mechanism enables the system to have the ability to quickly return to the path and ensure structural stability; in high-disturbance scenarios, the system automatically switches to hovering or avoidance mode, and records disturbance information for subsequent learning and optimization, ensuring that the aircraft enters the protection state in time before the risk of loss of control. This strategy system significantly improves the system's environmental adaptability, flight control stability and mission continuity through dynamic mapping of disturbance level and control intensity, effectively avoiding the "one-size-fits-all" response drawbacks of traditional flight control strategies, and provides an implementable and scalable logical basis for high-safety, high-precision flight control in complex environments in power inspection tasks.

[0073] Specifically, the specific steps for implementing the flight error correction control strategy for the power inspection UAV are as follows: obtain the current flight status change data of the power inspection UAV, the current flight status data includes the current flight speed change rate, the current flight direction angle change rate and the current flight attitude angle change rate; conduct a comprehensive analysis of the current flight status change data of the power inspection UAV to obtain the flight error adjustment index of the power inspection UAV; based on the flight error adjustment index of the power inspection UAV, implement the flight error correction control strategy for the power inspection UAV.

[0074] The specific formula for calculating the flight error adjustment index of the power inspection drone is as follows: Among them, FwC is the flight error adjustment index of the power inspection UAV, SbH is the current flight speed change rate of the power inspection UAV, ω1 is the flight speed change adjustment coefficient stored in the database, FbH is the current flight direction angle change rate of the power inspection UAV, ω2 is the direction angle change adjustment coefficient stored in the database, ZbH is the current flight attitude angle change rate of the power inspection UAV, and ω3 is the flight attitude angle change adjustment coefficient stored in the database.

[0075] It should be explained that the specific steps for obtaining the flight speed change adjustment coefficient ω1, direction angle change adjustment coefficient ω2, and flight attitude angle change adjustment coefficient ω3 stored in the database are: select flight record samples that have been marked as "non-external interference but path deviation or control abnormality" in multiple historical flight missions, extract the flight speed change rate, direction angle change rate, and attitude angle change rate curves under different flight states, and fit them with the flight deviation response amplitude within the period; secondly, construct a multi-dimensional error inducement feature set, and use the least squares fitting method, multi-factor sensitivity analysis or disturbance amplitude attribution modeling method to extract the contribution curve of each feature item to the degree of flight deviation; finally, according to the slope and increase trend of the influence curve of each type of feature variable on path deviation or attitude instability, extract their respective nonlinear adjustment weight factors as the flight speed change adjustment coefficient, direction angle change adjustment coefficient, and flight attitude angle change adjustment coefficient.

[0076] Based on the flight error adjustment index of the power inspection UAV, the specific steps of implementing the flight error correction control strategy for the power inspection UAV are as follows: the flight error adjustment index of the power inspection UAV is judged and analyzed with several preset flight error adjustment intervals, and each flight error adjustment interval corresponds to a flight error adjustment strategy; the flight error adjustment strategy corresponding to the flight error adjustment index in the preset flight error adjustment interval is used as the flight error correction control strategy of the power inspection UAV, and flight error correction control is performed.

[0077] The flight error adjustment strategies include but are not limited to the following examples:

[0078] Flight Error Adjustment Index ∈ [0, 0.8) : Steady-state micro-bias correction strategy. This indicates that the flight state is basically stable, with only slight aircraft control errors or sensor feedback offsets. In this case, the steady-state micro-bias correction strategy is executed, including:

[0079] Call the micro-course return module in the flight control system to adjust the flight direction angle to ≤±3°;

[0080] Maintain the original flight speed and altitude unchanged, improving the energy consumption stability of the aircraft;

[0081] The system marks this deviation as "acceptable error" and terminates the correction process if it remains in the low range continuously.

[0082] Flight Error Adjustment Index ∈ [0.8, 2.0) : Steady-state slow-adjustment compensation strategy, indicating that there is a sustainable cumulative error trend in the aircraft's flight attitude or direction control. The system implements a slow-adjustment compensation strategy, including:

[0083] Turn on the azimuth-thrust linear fine-tuning control module to smoothly adjust the current heading within a range of ±5 to 10°;

[0084] Reduce flight speed by 10-15% to mitigate thrust response errors;

[0085] If the attitude change rate continues to deviate from the normal reference value, the attitude stabilizer adjustment submodule is activated to perform pitch or roll angle correction.

[0086] If the Flight Error Adjustment Index is ≥ 2.0, the system will implement a dynamic return-to-center flight control strategy. This indicates that the flight control system may be experiencing execution delays, control surface error amplification, or inertial guidance feedback error accumulation. The system will then implement a dynamic return-to-center flight control strategy, including:

[0087] Temporarily interrupt the tracking task of the current path segment and start the "path fast return" control;

[0088] Start the flight attitude and heading joint correction algorithm to adjust the attitude angle and direction angle synchronously;

[0089] If the system determines that the speed change rate is abnormal, it resets the flight control response coefficient to improve the PID control sensitivity;

[0090] The system automatically records the deviation event and uploads it to the flight control log for subsequent self-learning model optimization.

[0091] In this implementation plan, by constructing a flight error adjustment index and its segmented control strategy, intelligent identification and dynamic correction of flight control error states caused by non-external disturbances are achieved, which significantly improves the flight stability and control robustness of the UAV system in complex inspection tasks. The mechanism is based on three types of physical state parameters: flight speed change rate, direction angle change rate, and attitude angle change rate. The adjustment coefficient obtained by fitting historical flight mission samples constructs an index model, which can accurately reflect the deviation trend and risk level of the aircraft in the case of flight control device lag, inertial navigation offset, or inconsistent control surface response. The system adopts a micro-heading adjustment strategy in the low error index range to maintain the aircraft's direction. In order to improve energy efficiency, a direction-thrust coupling compensation and speed reduction mechanism is introduced in the medium error range to achieve continuous deviation adjustment. In the high error range, it immediately switches to the dynamic return flight control strategy, and quickly restores the flight stability by jointly adjusting the attitude and direction angle and improving the PID response sensitivity. Compared with the traditional single correction method that relies on the path deviation value, this mechanism has significant predictive, hierarchical and adaptive characteristics. It can complete active response and strategy adjustment before the error causes serious deviation, providing the flight control system with more reliable early warning capabilities and more reasonable control scheduling strategies, ensuring the continuity and safety of UAVs in long-path, high-frequency inspection tasks.

[0092] See also Figure 3An embodiment of the present invention provides a technical solution: a flight control system for a power inspection drone, comprising: a flight deviation analysis unit, configured to obtain the current flight position coordinates of the power inspection drone in real time, analyze the distance deviation value between the current flight position and the target inspection position, and compare the value with a preset distance offset threshold; a disturbance perception analysis unit, configured to obtain disturbance perception data within a set area of ​​the power inspection drone (e.g., a circular area with a radius of 2m) and analyze a disturbance perception intensity index when the distance deviation value is higher than a preset distance offset threshold; a judgment analysis unit, configured to judge whether the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than a preset disturbance attribution determination threshold; a disturbance response control unit, configured to execute a disturbance response control strategy for the power inspection drone when the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than the preset disturbance attribution determination threshold; and an error correction control unit, configured to execute a flight error correction control strategy for the power inspection drone when the disturbance perception intensity index within the set area of ​​the power inspection drone is lower than or equal to the preset disturbance attribution determination threshold.

[0093] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0094] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A flight control method for a power inspection drone, characterized in that: The following steps are involved: Obtain the current flight position coordinates of the power inspection drone in real time, analyze the distance deviation between the current flight position and the target inspection position, and compare it with the preset distance offset threshold; If the distance deviation value is higher than the preset distance offset threshold, the disturbance perception data within the set area of ​​the power inspection drone is obtained and the disturbance perception intensity index is analyzed; Determine whether the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than the preset disturbance attribution judgment threshold; If the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than the preset disturbance attribution judgment threshold, the disturbance response control strategy is executed on the power inspection drone; If the disturbance perception intensity index within the set area of ​​the power inspection UAV is lower than or equal to the preset disturbance attribution judgment threshold, the flight error correction control strategy is executed on the power inspection UAV.

2. The flight control method for a power inspection UAV according to claim 1, characterized in that: The disturbance perception data includes the ambient magnetic induction intensity value, the ambient magnetic induction fluctuation value, the ambient hot spot density value, and the ambient sound signal center frequency.

3. The flight control method for a power inspection UAV according to claim 2, characterized in that: The specific steps for analyzing the disturbance perception intensity index are as follows: Obtaining current structural state data and structural state reference data of the power inspection drone, wherein the current structural state data includes the current structural resonance response amplitude and the current vertical vibration peak value, and the current structural state reference data includes the structural resonance response reference amplitude and the vertical vibration reference value; The disturbance perception reference data within the set area of ​​the power inspection drone is obtained, and a comprehensive analysis is performed on the current structural state data of the power inspection drone, the structural state reference data, and the disturbance perception data in the set area to obtain the disturbance perception intensity index. The disturbance perception reference data includes the ambient magnetic induction intensity reference value, the ambient magnetic induction fluctuation reference value, the ambient hot spot density reference value, and the ambient sound signal center reference frequency.

4. The flight control method for a power inspection UAV according to claim 3, characterized in that: The specific formula for calculating the disturbance perception intensity index is as follows: Among them, RgQ is the disturbance perception intensity index, Cb, Cb′, Rb, Rb′, Cg, Cg′, Sp, Sp′ are the ambient magnetic induction fluctuation value, ambient magnetic induction fluctuation reference value, ambient hot spot density value, ambient hot spot density reference value, ambient magnetic induction intensity value, ambient magnetic induction intensity reference value, ambient sound signal center frequency, and ambient sound signal center reference frequency in the set area of ​​the power inspection drone, respectively; Zf, Zf′, Cz, Cz′ are the current structural resonance response amplitude, structural resonance response reference amplitude, current vertical vibration peak, and vertical vibration reference value of the power inspection drone, respectively; λ1, λ2, λ3, and λ4 are the thermal-magnetic linkage coefficient, static magnetic offset coefficient, acoustic-vibration linkage coefficient, and vertical vibration surge coefficient stored in the database, respectively.

5. The flight control method for a power inspection UAV according to claim 1, characterized in that: The specific steps for implementing the disturbance response control strategy for the power inspection drone are as follows: Obtain the current flight status data of the power inspection drone, wherein the current flight status data includes the current flight speed, the current flight direction angle, and the current flight attitude angle; Obtain the low and high thresholds of disturbance perception intensity, and input them into the flight disturbance adjustment model along with the distance deviation between the current flight position of the power inspection UAV and the target inspection position, the disturbance perception intensity index, and the current flight status data of the power inspection UAV to obtain the flight disturbance adjustment index of the power inspection UAV. Based on the flight disturbance adjustment index of the power inspection UAV, a disturbance response control strategy is implemented for the power inspection UAV.

6. The flight control method for a power inspection UAV according to claim 5, characterized in that: The flight disturbance adjustment model is specifically as follows: Among them, FrT, FxS, FxJ, and ZtJ are the flight disturbance adjustment index, current flight speed, current flight direction angle, and current flight attitude angle of the power inspection UAV, respectively; LjP is the distance deviation between the current flight position and the target inspection position of the power inspection UAV, π is pi, e is a natural constant, RgQ, DyZ, and GyZ are the disturbance perception intensity index, the low threshold of disturbance perception intensity, and the high threshold of disturbance perception intensity, respectively; ε, α, LcT, JgY, μ, XcD, δ, and ξ are the speed adjustment factor, low disturbance adjustment coefficient, path control factor, angle control factor, angle adjustment coefficient, response scale factor, medium disturbance adjustment coefficient, and high disturbance adjustment coefficient stored in the database, respectively.

7. The flight control method for a power inspection UAV according to claim 5, characterized in that: Based on the flight disturbance adjustment index of the power inspection UAV, the specific steps for implementing the disturbance response control strategy for the power inspection UAV are as follows: The flight disturbance adjustment index of the power inspection UAV is judged and analyzed with several preset flight disturbance adjustment intervals, and each flight disturbance adjustment interval corresponds to a flight disturbance adjustment strategy; The flight disturbance adjustment strategy corresponding to the flight disturbance adjustment index being in the preset flight disturbance adjustment range is used as the disturbance response control strategy of the power inspection UAV, and disturbance response control is performed.

8. The flight control method for a power inspection UAV according to claim 1, characterized in that: The specific steps for implementing the flight error correction control strategy for the power inspection UAV are as follows: Obtain the current flight status change data of the power inspection UAV, wherein the current flight status data includes the current flight speed change rate, the current flight direction angle change rate, and the current flight attitude angle change rate; Comprehensively analyze the current flight status change data of the power inspection UAV to obtain the flight error adjustment index of the power inspection UAV; Based on the flight error adjustment index of the power inspection UAV, a flight error correction control strategy is implemented for the power inspection UAV.

9. The flight control method for a power inspection UAV according to claim 8, characterized in that: Based on the flight error adjustment index of the power inspection UAV, the specific steps of implementing the flight error correction control strategy for the power inspection UAV are as follows: The flight error adjustment index of the power inspection UAV is judged and analyzed with several preset flight error adjustment intervals, and each flight error adjustment interval corresponds to a flight error adjustment strategy; The flight error adjustment strategy corresponding to the flight error adjustment index being in the preset flight error adjustment range is used as the flight error correction control strategy of the power inspection UAV, and flight error correction control is performed.

10. A flight control system for a power inspection UAV, applying the flight control method for a power inspection UAV according to any one of claims 1 to 9, characterized in that: include: The flight deviation analysis unit is used to obtain the current flight position coordinates of the power inspection UAV in real time, analyze the distance deviation value between the current flight position and the target inspection position, and compare it with the preset distance deviation threshold; The disturbance perception analysis unit is used to obtain the disturbance perception data within the set area of ​​the power inspection drone when the distance deviation value is higher than the preset distance offset threshold, and analyze the disturbance perception intensity index; A judgment and analysis unit is used to determine whether the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than a preset disturbance attribution judgment threshold; A disturbance response control unit is used to execute a disturbance response control strategy on the power inspection drone when the disturbance perception intensity index within the set area of ​​the power inspection drone is higher than a preset disturbance attribution determination threshold; The error correction control unit is used to execute the flight error correction control strategy for the power inspection UAV when the disturbance perception intensity index in the set area of ​​the power inspection UAV is lower than or equal to the preset disturbance attribution judgment threshold.

Images