A flight control method and system for geographical surveying and mapping UAV

Abstract

The present application relates to the technical field of attitude control of unmanned aerial vehicles (UAVs), and proposes a flight control method and system for a geographic surveying and mapping UAV, including: obtaining an attitude control state sequence; calculating an attitude control deviation characteristic value through the difference in local attitude change characteristics between different attitude control state sequences, calculating an attitude information influence ratio coefficient for each attitude information data based on the attitude control deviation characteristic value, obtaining an attitude information control weight for each attitude information data through the attitude information influence ratio coefficient; obtaining the attitude information of the UAV based on the attitude information control weights of different attitude information data in combination with a Mahony filter, and accurately and quickly controlling the attitude of the UAV based on the obtained UAV attitude information. The present application determines the weight of each attitude information by comparing the differences in local dynamic characteristics of different UAV attitude information data, thereby improving the accuracy of controlling the UAV by obtaining attitude information using the Mahony filter.

Description

Technical Field

[0001] The present application relates to the technical field of UAV attitude control, and in particular to a flight control method and system for a geographic mapping UAV. Background Art

[0002] With the rapid development of artificial intelligence, information processing, and automatic control technologies, drones are increasingly being used in geographic surveying and mapping. When performing surveying and mapping missions, drones face diverse and complex flight tasks. The randomness, ambiguity, and uncertainty of attitude anomalies pose significant challenges to attitude control during flight.

[0003] During UAV (UAV) surveying and mapping flights, if an attitude anomaly occurs, traditional ground control or remote control stabilization methods often struggle to achieve timely and effective attitude adjustments. While Mahony filters or Kalman filters are currently widely used to estimate UAV attitude information, these filters rely on key sensors such as gyroscopes, accelerometers, and magnetometers. These sensors are susceptible to temperature drift, accumulated errors, body vibration, and magnetic field interference in complex and changing surveying environments. This can lead to deviations in attitude information estimation, compromising the accuracy of UAV flight attitude control. Summary of the Invention

[0004] This application provides a flight control method and system for a geographic surveying and mapping UAV to solve the problem of low accuracy in UAV flight attitude control. The technical solutions adopted are as follows:

[0005] The present invention provides a method for controlling the flight of a geographical surveying and mapping UAV, which includes the following steps:

[0006] Obtain the attitude control state sequence of each attitude information data during the UAV flight;

[0007] The attitude control state sequence of each attitude information data during the flight of the UAV is divided and the time sequence interval is determined, and the attitude control state sequence of other attitude information data is divided according to the determined time sequence interval;

[0008] Determining the attitude data difference coefficient of each divided subsequence according to the division result of the attitude control state sequence of each attitude information data, and determining the attitude control deviation characteristic value of each subsequence;

[0009] Constructing a posture deviation influence characteristic curve of each posture control state sequence according to the change relationship of the posture control deviation characteristic value of the subsequence in each posture control state sequence;

[0010] Obtaining a posture information influence weight coefficient for each posture information data based on relative differences in posture deviation influence characteristic curves of different posture control state sequences;

[0011] The attitude control of the UAV is completed through the attitude information influence ratio coefficient of each attitude information data during the flight of the UAV.

[0012] In some embodiments of the present application, the method of dividing the attitude control state sequence of each attitude information data during the flight of the drone and determining the time sequence interval, and dividing the attitude control state sequence of other attitude information data according to the determined time sequence interval is:

[0013] For the posture control state sequence of each posture information data, a sequence segmentation method is used to obtain a subsequence of the posture control state sequence, and the time interval of each subsequence is determined according to the acquisition time range of the subsequence, and the sequence composed of data within the same time interval in the posture control state sequence of each other posture information data is used as a subsequence of the posture control state sequence of each other posture information data.

[0014] In some embodiments of the present application, the method for obtaining the posture control state sequence is:

[0015] Sensors are used to obtain attitude information data of the drone during flight. The attitude information data includes angular velocity data, acceleration data, and magnetometer measurement data. The sequence composed of the normalized results of the data synthesis corresponding to each acquisition moment of the three types of data is used as the attitude control state sequence.

[0016] In some embodiments of the present application, the posture data difference coefficient is determined as follows:

[0017] For each subsequence in the posture control state sequence of each posture information data, the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences is taken as a first difference, the mapping result of the first difference in the exponential function is calculated, and the trend statistic of the subsequence and the subsequences in the same time series interval of other posture information control state sequences is determined using a trend verification algorithm;

[0018] The posture data difference coefficient of the subsequence consists of two parts: the mapping result and the trend statistic. The posture data difference coefficient is positively correlated with the mapping result, and negatively correlated with the trend statistic.

[0019] In some embodiments of the present application, the method for determining the posture control deviation characteristic value is:

[0020] For each subsequence in the posture control state sequence of each posture information data, obtain a difference comparison result of the posture data difference coefficient between the subsequence and the subsequences in the same time series interval of other posture information control state sequences, and obtain a comparison result of the change characteristics of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences;

[0021] The posture control deviation characteristic value of the subsequence consists of two parts: the difference comparison result and the contrast result, wherein the posture control deviation characteristic value is positively correlated with the difference comparison result and the contrast result respectively.

[0022] In some embodiments of the present application, the process of constructing the posture deviation impact characteristic curve includes:

[0023] For each posture control state sequence of posture information data, the sequence composed of the posture control deviation characteristic values ​​of all subsequences in the posture control state sequence is used as the posture control deviation characteristic sequence. Probabilistic statistics are performed on the posture control deviation characteristic sequence to obtain a probability distribution curve, and the probability distribution curve is used as the posture deviation influence characteristic curve of the posture control state sequence.

[0024] In some embodiments of the present application, the specific method for obtaining the attitude information influence weight coefficient is:

[0025] For each posture information data, obtaining a measurement result of the difference between the posture control state sequence of the posture information data and the posture deviation influence characteristic curve of the posture control state sequence of other posture information data, and calculating the ratio of the mean of all elements in the posture control state sequence of the posture information data and the posture control deviation data sequence corresponding to the other posture information data;

[0026] The posture information influence weight coefficient of the posture information data consists of two parts: the measurement result of the difference and the ratio. The posture information influence weight coefficient is positively correlated with the measurement result of the difference and the ratio respectively.

[0027] In some embodiments of the present application, the method for controlling the attitude of the drone by using the attitude information influence weight coefficient of each attitude information data during the flight of the drone is:

[0028] The posture information control weight of each posture information data is obtained by the relative relationship between the posture information influence weight coefficients of different posture information data;

[0029] The collected attitude information data and the corresponding attitude information control weight are used as input, and the Mahony filter is used to obtain the attitude value represented by the quaternion during the flight of the UAV. The attitude value is converted into Euler angle as the input of the PI controller, and the output is the corrected angular velocity value. The UAV motor speed is adjusted according to the corrected angular velocity value to control the UAV attitude.

[0030] In some embodiments of the present application, the method for obtaining the posture information control weight is:

[0031] The sum of the posture information weight coefficients of all types of posture information data is calculated, and the ratio of the posture information weight coefficient of each type of posture information data to the sum is used as the posture information control weight of each type of posture information data.

[0032] An embodiment of the present application also provides a geographic mapping UAV flight control system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0033] As can be seen from the above embodiments, the flight control method for a geographic surveying and mapping UAV provided in the embodiments of the present application has at least the following beneficial effects:

[0034] Considering the complex change characteristics of each attitude information data when the UAV responds to different flight environments during flight, which leads to large deviations in the control of the UAV's flight attitude, by collecting relevant data on the UAV's flight attitude in different flight environments, the same division is performed on other attitude information data based on the division results of one type of attitude information data. Compared with the existing attitude information data analysis method in complex scenes, it can accurately reflect the relative impact characteristics of environmental changes on attitude line data; further, according to the analysis results of the relative impact characteristics of each attitude information data, the degree to which each attitude information data is affected by the flight environment in different environments can be determined. Based on the analysis results, the attitude information of the UAV in complex flight environments can be accurately adjusted, thereby improving the control accuracy of the UAV's attitude when the UAV responds to complex flight environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1A flowchart of a flight control method for a geographic mapping UAV provided in one embodiment of the present application;

[0037] Figure 2 A schematic diagram of a process for obtaining a control weight for each type of posture information provided by an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the process of controlling the attitude of a drone provided in one embodiment of the present application. DETAILED DESCRIPTION

[0039] To further illustrate the technical means and effects employed by this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for controlling a geographical mapping drone flight, including its specific implementation, structure, features, and effects. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The following describes in detail a specific solution of a geographic mapping UAV flight control method and system provided by the present application with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flow chart of a flight control method for a geographic mapping UAV provided by one embodiment of the present application, the method comprising the following steps:

[0043] S001, obtain the attitude control state sequence obtained during the flight of the UAV.

[0044] Geographic surveying and mapping requires extremely high data accuracy. UAV flight control ensures that the drone flies precisely along a predetermined route, thereby obtaining high-precision, high-resolution surveying and mapping data, which is crucial for applications such as topographic mapping and resource surveys.

[0045] The purpose of the embodiments of the present application is to improve the accuracy of attitude control of a geographic mapping UAV during flight, thereby enabling the UAV to fly more stably.

[0046] The main sensors for obtaining drone attitude information data include angular velocity meter, accelerometer and magnetometer. Therefore, during the flight of the drone, the angular velocity data, acceleration data and magnetometer measurement data of the drone are collected through the angular velocity meter, accelerometer and magnetometer.

[0047] Preferably, in one embodiment of the present application, the data acquisition device is a nine-axis gyroscope, wherein the nine-axis gyroscope includes a three-axis angular velocity meter, a three-axis accelerometer, and a three-axis magnetometer. As other implementations, implementers can select the specific gyroscope model based on the actual application scenario.

[0048] Furthermore, the collected drone angular velocity data, acceleration data, and magnetometer measurement data include angular velocity, acceleration, and magnetometer measurement data rotating around the X-axis, Y-axis, and Z-axis in space, respectively. Therefore, each type of data collected is a vector composed of three-axis data. Specifically, for example, the angular velocity data collected at a certain moment is ,in 、 and They represent the angular velocity of rotation around the X-axis, Y-axis, and Z-axis at that moment, respectively.

[0049] Furthermore, the angular velocity data, acceleration data and magnetometer measurement data collected during the flight of the UAV up to the current flight moment are sequenced in ascending time order as the angular velocity data sequence, acceleration data sequence and magnetometer measurement data sequence respectively. Since each element in the above three sequences is a vector composed of three-axis data, the composite amount of each element in the above three data is calculated respectively, that is, the modulus value of each element is used as the composite amount of each element, and the sequence composed of the composite amount of all elements in each of the above data sequences is used as a posture feature sequence, and the normalized results of each posture feature sequence obtained by using the Z-score normalization algorithm are respectively used as a posture control state sequence; wherein the Z-score normalization algorithm is a well-known technology and will not be described in detail.

[0050] It should be understood that, as an embodiment of the present application, the Z-score normalization algorithm is used to normalize the collected data sequence. The above only provides a method for normalizing the posture feature sequence. As other implementation methods, on the premise of achieving the purpose of normalizing the posture feature sequence, other normalization algorithms in the prior art can be used to normalize the posture feature sequence.

[0051] At this point, the attitude control state sequence of the three attitude information data of the UAV has been obtained.

[0052] S002, determining a posture control deviation characteristic value of each posture control state sequence based on the difference in local posture change characteristics between different posture control state sequences.

[0053] Generally speaking, when using a nine-axis gyroscope to collect attitude information data during the flight of a drone, due to the different flight missions performed by the drone and the different flight environments it faces, the temperature drift in the nine-axis gyroscope, the degree to which the accelerometer is affected by the vibration of the body, and the degree to which the magnetometer is affected by the environmental magnetic field are all different. When analyzing the attitude information of the drone by fusing the collected attitude information data, the degree of change in the characteristics of different data in different scenarios is different. Therefore, the influence change characteristics of different attitude information during the flight of the drone can be analyzed by the difference in the local change characteristics of the collected different attitude information data during the flight of the drone.

[0054] Furthermore, since the overall change characteristics of the attitude information of the entire drone body after being affected by the flight environment during flight are extremely small, and the attitude differences of different attitude information of the drone are mainly manifested in the differences in local attitude characteristics affected by the flight environment, that is, different attitude information is manifested as asynchronous or synchronous attitude information differences; therefore, the local characteristics of different attitude information are analyzed by dividing the time interval.

[0055] Specifically, each posture control state sequence is taken as input, and the posture control state sequence is divided using the Variable-length SubsequenceClustering algorithm to obtain subsequences of each posture control state sequence, wherein the minimum length of the divided subsequence is 50, and the maximum length is the result of rounding up one-third of the total number of elements in the posture control state sequence; the specific division process of the Variable-length Subsequence Clustering algorithm is a well-known technology and will not be described in detail; the time interval of each subsequence is determined according to the maximum and minimum values ​​of all elements in each subsequence in the posture control state sequence at the acquisition moment, for example, one of the subsequences is , the sequence of acquisition times corresponding to each element in the subsequence is [5,6,7,8,9], that is, the acquisition time corresponding to data 10 in the subsequence is 5. Therefore, based on the maximum and minimum values ​​of the acquisition times of all elements in the subsequence, the time series interval of each subsequence is determined to be [5,9], that is, the time series interval is between 5 and 9 seconds;

[0056] Since the time interval is divided into the acquisition time range based on the division result of one of the attitude control state sequences, and all attitude control state sequences are collected synchronously, the acquisition time range of all attitude control state sequences is the same. When one of the attitude control state sequences is further analyzed, the other two attitude control state sequences are divided according to the time intervals of all subsequences in one of the attitude control state sequences. For example, the data in the same divided time interval of each other attitude control state sequence is used as a subsequence of each other attitude control state sequence, thereby completing the division of each other attitude control state sequence according to the determined time interval.

[0057] It should be understood that, as an embodiment of the present application, the Variable-length SubsequenceClustering algorithm is used to divide the collected data sequence. The above only provides a method for dividing the posture control state sequence. As other implementation methods, on the premise of achieving the purpose of posture control state sequence division, other sequence division algorithms in the prior art can be used to divide the posture feature sequence.

[0058] Generally, when the UAV flight attitude information data deviates, it manifests as the local relative change characteristics of different attitude information data. Therefore, the local attitude information relative change characteristics of different attitude control state sequences are analyzed through the time series interval determined by one of the attitude control state sequences. Based on the analysis results, the attitude data difference coefficient is calculated. The specific process is as follows:

[0059] For each subsequence in the posture control state sequence of each posture information data, the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences is taken as a first difference, the mapping result of the first difference in the exponential function is calculated, and the trend statistic of the subsequence and the subsequences in the same time series interval of other posture information control state sequences is determined using a trend verification algorithm;

[0060] In one embodiment of the present application, the average of the cumulative results of the ratio of the mapping result to the trend statistic in the posture control state sequence is used as the posture data difference coefficient of the subsequence. It should be noted that when the trend statistic is 0, the sum of the trend statistic and any positive number less than 0.01 can be used as the denominator.

[0061] Preferably, the trend verification algorithm may be the Mann-Kendall algorithm, and the exponential function may be an exponential function with a natural constant as the base. As other embodiments, on the basis of achieving the purpose of sequence trend change analysis, the implementer may adopt other methods of the prior art to obtain the trend change analysis results of the sequence, and this application does not impose any special restrictions.

[0062] Generally, the difference in the attitude data difference coefficients of the subsequences in different attitude control state sequences can further reflect the local state characteristic deviation of the attitude information data. Therefore, the attitude control deviation characteristic value of each subsequence in the attitude control state sequence of each attitude information data can be calculated based on the attitude data difference coefficients of the local attitude information of different attitude control state sequences. The attitude control deviation characteristic value accurately reflects the local change difference of the attitude information data. The specific acquisition process of the attitude control deviation characteristic value includes:

[0063] For each subsequence in the posture control state sequence of each posture information data, obtain a difference comparison result of the posture data difference coefficient between the subsequence and the subsequences in the same time series interval of other posture information control state sequences, and obtain a comparison result of the change characteristics of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences;

[0064] In one embodiment of the present application, the difference comparison result can be calculated by calculating the absolute value of the difference between the posture data difference coefficient of the subsequence and the subsequence of the same time interval in other posture information control state sequences; the comparison result can be calculated by calculating the Manhattan distance between the subsequence and the subsequence of the same time interval in other posture information control state sequences; and the average of the accumulated results of the product of the difference comparison result and the comparison result over all posture control state sequences is used as the posture control deviation characteristic value of the subsequence.

[0065] At this point, the attitude control deviation characteristic value is obtained.

[0066] S003, obtaining a posture information influence ratio coefficient for each posture information data through the posture control deviation characteristic value of each posture control state sequence, and obtaining a posture information control weight for each posture information data through the posture information influence ratio coefficient.

[0067] The process of obtaining the control weight of each posture information is as follows: Figure 2 As shown, the specific steps include:

[0068] S1, obtaining a posture deviation influence characteristic curve of a posture control state sequence of each posture information data.

[0069] As an embodiment of the present application, the specific process of obtaining the posture deviation impact characteristic curve includes:

[0070] Take the first attitude control state sequence as an example for analysis. The attitude control deviation eigenvalues ​​of all subsequences in the attitude control state sequence are arranged in ascending order as the first The posture control deviation data sequence of the posture control state sequence is obtained by performing probability statistics on all elements in the posture control deviation data sequence. A probability distribution curve of a posture control state sequence is prepared, wherein the horizontal axis of the probability distribution curve is each element in the posture control deviation data sequence, and the vertical axis is the probability of each element appearing in the sequence; the probability distribution curve is used as the posture deviation influence characteristic curve.

[0071] It should be noted that, in one embodiment of the present application, The attitude deviation influence characteristic curve of each attitude control state sequence can be obtained in the same way as the attitude deviation influence characteristic curve of the attitude control state sequence. The time interval determined by each attitude control state sequence is obtained based on the local attitude data characteristics of the attitude control state sequence currently analyzed. For example, In the process of the attitude deviation of the attitude control state sequence affecting the characteristic curve, the The timing interval is determined by dividing the subsequences of the attitude control state sequence into two subsequences, and the subsequences of each other attitude control state sequence are obtained according to the determined timing interval. The attitude control deviation characteristic value is further obtained by the calculation method in S002 to obtain the attitude deviation influence characteristic curves of the two attitude control state sequences.

[0072] For one embodiment of the present application, the method for obtaining the probability distribution curve of the posture control deviation feature sequence can be: performing probability distribution statistics on the posture control deviation feature sequence, and constructing a two-dimensional rectangular coordinate system based on the statistical results, with the horizontal axis being each posture control deviation feature value and the vertical axis being the probability of occurrence of each posture control deviation feature value, and using the curve formed by all data points in the two-dimensional rectangular coordinate system as the probability distribution curve.

[0073] S2, constructing a posture information influence weight coefficient through a posture control deviation data sequence and a posture deviation influence characteristic curve of each posture information data.

[0074] The attitude information influence ratio coefficient of different attitude information data during the UAV flight process is determined based on the difference between the attitude deviation influence characteristic curves of each attitude control state sequence. The attitude information influence ratio coefficient reflects the degree of change of attitude information data caused by the flight environment during the UAV flight process. The specific construction process includes:

[0075] For each type of posture information data, obtaining a measurement result of the difference between the posture deviation influence characteristic curves of the posture control state sequence of the posture information data and the posture control state sequence of other posture information data, and calculating the ratio of the mean of all elements in the posture control deviation data sequence of the posture control state sequence of the posture information data and the posture control state sequence of other posture information data;

[0076] In one embodiment of the present application, the average of the accumulated results of the product of the difference measurement result and the ratio over all attitude information data is used as the attitude information influence weight coefficient of the attitude information data, and the larger the attitude information influence weight coefficient, the greater the degree of influence of the attitude information data during the flight of the drone relative to other attitude information data.

[0077] Preferably, the measurement result of the difference between the posture deviation affecting the characteristic curve of the posture control state sequence of the posture information data and the posture control state sequence of other posture information data is used as the third multiplication factor, wherein the calculation method of the measurement result of the difference can be KL (Kullback-Leibler) divergence. As other embodiments, on the basis of achieving the purpose of measuring the difference between the two curves, the implementer can adopt other methods in the prior art to obtain the measurement result of the difference between the two curves, and this application does not make any special limitations.

[0078] S3, obtaining a posture information control weight for each type of posture information data according to the posture information weight coefficient of each type of posture information data.

[0079] The specific acquisition process includes: calculating the sum of the attitude information weight coefficients of all types of attitude information data, and taking the ratio of the attitude information weight coefficient of each attitude information data to the sum as the attitude information control weight of each attitude information data; if the attitude information control weight is greater, it means that the relative change and influence of the corresponding attitude information data during the flight of the drone are greater.

[0080] At this point, the posture information control weight is obtained through the above calculation.

[0081] S004, accurately and quickly controlling the attitude of the UAV according to the attitude information control weights of different attitude information data.

[0082] In order to accurately control the attitude of the UAV during flight, the attitude information control weight of each attitude information data is obtained by the calculation from S001 to S003 above, wherein the attitude information data includes angular velocity data, acceleration data and magnetometer measurement data. Based on the attitude information control weight of the obtained attitude information data, the attitude information of the UAV is controlled and adjusted in combination with the Mahony filter and PI control strategy. The specific control process is as follows:

[0083] The collected angular velocity data, acceleration data, magnetometer measurement data and the attitude information control weight corresponding to each attitude information data are used as input, and the Mahony filter is used to obtain the attitude value represented by the quaternion during the flight of the UAV. The attitude value is converted into the Euler angle as the input of the PI controller, and the output is the corrected angular velocity value. The UAV motor speed is adjusted according to the corrected angular velocity value, wherein the proportional gain and integral gain in the PI controller are set to 1.5 and 0.005 respectively. The specific process of controlling the UAV attitude is as follows. Figure 3 As shown, Mahony filter and PI control strategy are technologies well known to those skilled in the art, and the specific calculation process of the posture value will not be described in detail.

[0084] Based on the same inventive concept as the above method, an embodiment of the present application also provides a geographic mapping UAV flight control system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned geographic mapping UAV flight control methods are implemented.

[0085] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the principles of this application shall be included in the scope of protection of this application.

Claims

1. A flight control method for a geographic surveying and mapping UAV, characterized in that: The method comprises the following steps: Obtain the attitude control state sequence of each attitude information data during the UAV flight; The attitude control state sequence of each attitude information data during the flight of the UAV is divided and the time sequence interval is determined, and the attitude control state sequence of other attitude information data is divided according to the determined time sequence interval; Determining the attitude data difference coefficient of each divided subsequence according to the division result of the attitude control state sequence of each attitude information data, and determining the attitude control deviation characteristic value of each subsequence; For each attitude control state sequence of attitude information data, a sequence composed of attitude control deviation characteristic values ​​of all subsequences in the attitude control state sequence is used as an attitude control deviation characteristic sequence. Probabilistic statistics are performed on the attitude control deviation characteristic sequence to obtain a probability distribution curve, and the probability distribution curve is used as an attitude deviation influence characteristic curve of the attitude control state sequence. For each type of posture information data, obtaining a measurement result of the difference between the posture control state sequence of the posture information data and the posture deviation influence characteristic curve of the posture control state sequence of other posture information data, and calculating the ratio of the mean of all elements in the posture control state sequence of the posture information data and the posture control deviation data sequence corresponding to the other posture information data; the posture information influence weighting coefficient of the posture information data is composed of the measurement result of the difference and the ratio, wherein the posture information influence weighting coefficient is positively correlated with the measurement result of the difference and the ratio respectively; The attitude information control weight of each attitude information data is obtained by the relative relationship between the attitude information influence coefficients of different attitude information data; each collected attitude information data and each corresponding attitude information control weight are used as input, and a Mahony filter is used to obtain the attitude value represented by the quaternion during the flight of the UAV. The attitude value is converted into Euler angle as the input of the PI controller, and the output is a corrected angular velocity value. The UAV motor speed is adjusted according to the corrected angular velocity value to control the UAV attitude; The method for obtaining the posture information control weight is: calculating the sum of the posture information weight coefficients of all types of posture information data, and taking the ratio of the posture information weight coefficient of each posture information data to the sum as the posture information control weight of each posture information data.

2. A method for controlling a geographic surveying and mapping UAV flight according to claim 1, characterized in that: The method of dividing the attitude control state sequence of each attitude information data during the flight of the UAV and determining the time sequence interval, and dividing the attitude control state sequence of other attitude information data according to the determined time sequence interval is as follows: For the posture control state sequence of each posture information data, a sequence segmentation method is used to obtain a subsequence of the posture control state sequence, and the time interval of each subsequence is determined according to the acquisition time range of the subsequence, and the sequence composed of data within the same time interval in the posture control state sequence of each other posture information data is used as a subsequence of the posture control state sequence of each other posture information data.

3. A method for controlling the flight of a geographic surveying and mapping UAV according to claim 2, characterized in that: The method for obtaining the posture control state sequence is: Sensors are used to obtain attitude information data of the drone during flight. The attitude information data includes angular velocity data, acceleration data, and magnetometer measurement data. The sequence composed of the normalized results of the data synthesis corresponding to each acquisition moment of the three types of data is used as the attitude control state sequence.

4. The flight control method for a geographic surveying and mapping UAV according to claim 1, characterized in that: The determination method of the attitude data difference coefficient is as follows: For each subsequence in the posture control state sequence of each posture information data, the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences is taken as a first difference, the mapping result of the first difference in the exponential function is calculated, and the trend statistic of the subsequence and the subsequences in the same time series interval of other posture information control state sequences is determined using a trend verification algorithm; The posture data difference coefficient of the subsequence consists of two parts: the mapping result and the trend statistic. The posture data difference coefficient is positively correlated with the mapping result, and negatively correlated with the trend statistic.

5. The flight control method for a geographic surveying and mapping UAV according to claim 1, characterized in that: The method for determining the attitude control deviation characteristic value is: For each subsequence in the posture control state sequence of each posture information data, obtain a difference comparison result of the posture data difference coefficient between the subsequence and the subsequences in the same time series interval of other posture information control state sequences, and obtain a comparison result of the change characteristics of all elements in the subsequence and the subsequences in the same time series interval of other posture information control state sequences; The posture control deviation characteristic value of the subsequence consists of two parts: the difference comparison result and the contrast result, wherein the posture control deviation characteristic value is positively correlated with the difference comparison result and the contrast result respectively.

6. A flight control system for a geographic surveying and mapping UAV, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the flight control method for a geographic mapping UAV as described in any one of claims 1 to 5 are implemented.

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