Unmanned equipment control method, control system and storage medium

Abstract

The present invention relates to the general field of control system technology, and more specifically to an unmanned equipment control method, control system, and storage medium. The method comprises: obtaining the final yaw severity of each yaw period based on the yaw distance at each moment in each yaw period and the duration of each yaw period; and obtaining the drone control action instruction at each moment based on the wind speed and wind direction at each moment in the yaw cause period of each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value. The method analyzes the severity of the drone yaw, further analyzes the cause of the yaw, and determines the focus factor at each moment. This method allows the subsequent drone control to pay more attention to fault data, ensures priority processing and response to potential fault conditions, and reduces false alarms of non-fault data, thereby improving the drone control effect.

Description

Technical Field

[0001] The present invention relates to the general field of control system technology, and in particular to an unmanned equipment control method, a control system and a storage medium. Background Art

[0002] Unmanned equipment refers to devices that can autonomously perform tasks without direct human control. These devices are typically equipped with sensors, processors, and actuators, enabling them to sense their environment, make decisions, and execute actions. Unmanned equipment is widely used. For example, in power line inspections, drones are used to inspect power lines. During this process, the drones must be controlled to follow a set route and maintain a constant speed. However, factors such as fluctuating wind speed and direction and instability in the equipment's power system can cause the drone to yaw. Inaccurately distinguishing between yaw caused by faults and non-faults can affect the drone's control effectiveness, making flight safety impossible. Summary of the Invention

[0003] The present invention provides an unmanned equipment control method, a control system and a storage medium to solve the existing problems.

[0004] The unmanned equipment control method, control system and storage medium of the present invention adopt the following technical solutions:

[0005] An embodiment of the present invention provides a method for controlling an unmanned device, the method comprising the following steps:

[0006] Obtain the drone's position coordinates at all times during the autonomous inspection process, the wind speed and direction at each moment, and the drone's battery temperature;

[0007] According to the position coordinates of the UAV at all times, several yaw periods of the UAV during the autonomous inspection process are obtained; according to the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period, the final yaw severity of each yaw period is obtained;

[0008] The period consisting of several moments before and after the initial moment of each yaw period is recorded as the yaw cause period of each yaw period. According to the wind speed and wind direction at each moment in the yaw cause period of each yaw period, the possibility that the yaw of each yaw period is caused by wind influence is obtained; with the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angle corresponding to each direction. According to the possibility that the yaw of each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, the initial attention factor of each yaw period is obtained;

[0009] According to the initial attention factor of each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature, the final attention factor of each yaw period is obtained; according to the final attention factor of each yaw period, the drone control action command at each moment is obtained.

[0010] Furthermore, the method of obtaining several yaw periods of the drone during the autonomous inspection process based on the position coordinates of the drone at all times includes the following specific steps:

[0011] In the current autonomous inspection process, the least squares method is used to perform straight line fitting based on the position coordinates of the UAV at all times to obtain the fitting line and the fitting error at each moment. The fitting line is used as the UAV's cruise route, and the minimum and maximum norm method is used to obtain the normalized value of the fitting error at each moment as the yaw distance at each moment.

[0012] A first judgment threshold is preset, and during the current autonomous inspection process, the moment when the yaw distance is greater than the preset first judgment threshold is recorded as the yaw moment;

[0013] The period consisting of continuous yaw moments is recorded as the yaw period.

[0014] Furthermore, the final yaw severity of each yaw period is obtained according to the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period, including the following specific steps:

[0015] In the During the yaw period, the yaw distance at each moment is counted in turn to form a yaw distance sequence;

[0016] Calculate the The ratio of the duration of the yaw period to the duration from the beginning to the current moment of the current autonomous inspection process is used to calculate the ratio of the duration of the yaw period to the duration of the current autonomous inspection process. The product of the maximum value in the yaw distance sequence corresponding to the yaw period is recorded as The initial yaw severity of each yaw period;

[0017] Use the peak and trough detection algorithm to obtain the peaks and troughs in the yaw distance sequence corresponding to each yaw period;

[0018] According to the initial yaw severity of each yaw period and the corresponding peaks and troughs in the yaw distance sequence, the final yaw severity of each yaw period is obtained.

[0019] Furthermore, the final yaw severity of each yaw period is obtained according to the initial yaw severity of each yaw period and the corresponding peaks and troughs in the yaw distance sequence, including the following specific steps:

[0020] For the yaw period, calculate the difference between the number of peaks in the yaw distance sequence and the preset first number threshold and the The ratio of the duration of the yaw period is used as the first ratio to calculate the first ratio in the yaw distance sequence. The yaw distance corresponding to the first peak is The difference in yaw distances corresponding to the previous trough adjacent to the first peak is taken as the The first difference of the peaks is calculated, and the product of the sum of the first difference values of all the peaks in the yaw distance sequence and the first ratio is calculated as the first product. The sum of the preset first quantity threshold and the normalized value of the first product is calculated as the first sum value. The first sum value is added to the first sum value. The product of the initial yaw severity of the yaw period is taken as the The final yaw severity for the yaw period.

[0021] Furthermore, the method of obtaining the possibility that the yaw cause of each yaw period is caused by wind influence according to the wind speed and wind direction at each moment in the yaw cause period of each yaw period includes the following specific steps:

[0022] The least square method is used to fit the wind speed at all times during the yaw-causing period to a straight line, and the normalized value of the slope of the fitted straight line is recorded as the wind speed sudden increase factor.

[0023] During the yaw-causing period, calculate the minimum angle between the wind direction and the cruise route at each moment, and take the average of the minimum angles corresponding to all moments as the wind direction influencing factor;

[0024] Calculate the The wind direction influencing factor of the yaw-causing period in the yaw period is The ratio of The product of the wind speed sudden increase factor of the yaw-causing period and the yaw period is recorded as The possibility that the yaw is caused by wind during the yaw period is: is the preset angle threshold.

[0025] Furthermore, the initial attention factor for each yaw period is obtained based on the possibility that the yaw in each yaw period is caused by wind, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, including the following specific steps:

[0026] For the yaw period, calculate the sum of the normalized value of the standard deviation of the angle corresponding to the direction at all times and the normalized value of the standard deviation of the speed as the second sum, calculate the ratio of the second sum to the preset third quantity threshold as the second ratio, calculate the ratio of the second ratio to the first The product of the probability that the yaw is caused by the wind in the yaw period is used as the second product, and the difference between the preset third quantity threshold and the normalized value of the first product is calculated as the second difference. The product of the final yaw severity of the yaw period is recorded as The initial attention factor for the yaw period.

[0027] Furthermore, the final attention factor of each yaw period is obtained according to the initial attention factor of each yaw period, the maximum value of the battery temperature at all times in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature, including the following specific steps:

[0028] For the In a yaw period, the difference between the maximum value of the battery temperature at all times and the minimum value of the battery temperature before the maximum value is calculated as a third difference, the ratio of the third difference to the time interval between the maximum value of the battery temperature at all times and the minimum value of the battery temperature before the maximum value is calculated as a third ratio, and the third ratio is added to the first The product of the initial attention factors of the yaw period is recorded as The final attention factor for the yaw period.

[0029] Furthermore, the method of obtaining the drone control action instruction at each moment according to the final attention factor of each yaw period includes the following specific steps:

[0030] Let the attention factor at each moment not in the yaw period be , order The attention factor at each moment in the yaw period is The sum of the normalized values of the final attention factors of the yaw periods plus 1 is used to obtain the attention factor at each moment, where is a preset fourth quantity threshold;

[0031] The UAV is controlled using model predictive control, where the model input data includes the position coordinates of the UAV at each moment, the wind speed and direction the UAV is exposed to, the speed of the UAV, and the battery temperature of the UAV. The attention factor at each moment is used as the weight at each moment, and the output of the model is the UAV control action instruction at each moment.

[0032] An unmanned equipment control system adopts the unmanned equipment control method described above, and the system includes the following modules:

[0033] The data acquisition module is used to obtain the position coordinates of the drone at all times during the autonomous inspection process, the wind speed and direction at each moment, and the battery temperature of the drone;

[0034] The final yaw severity acquisition module is used to obtain several yaw periods of the UAV during the autonomous inspection process based on the position coordinates of the UAV at all times; and obtain the final yaw severity of each yaw period based on the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period;

[0035] An initial concern factor acquisition module is used to record a period consisting of several moments before and after the initial moment of each yaw period as the yaw cause period of each yaw period, and obtain the possibility that the yaw of each yaw period is caused by wind influence based on the wind speed and wind direction at each moment in the yaw cause period of each yaw period; with the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angle corresponding to each direction, and obtain the initial concern factor of each yaw period based on the possibility that the yaw of each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period;

[0036] The control instruction acquisition module is used to obtain the final attention factor of each yaw period based on the initial attention factor of each yaw period, the maximum value of the battery temperature at all times in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature; and obtain the drone control action instruction at each moment based on the final attention factor of each yaw period.

[0037] An unmanned equipment control storage medium includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the unmanned equipment control method are implemented.

[0038] The beneficial effects of the technical solution of the present invention are:

[0039] In this embodiment of the present invention, various time-series data from a drone during an autonomous inspection are acquired. The final yaw severity for each yaw period is determined based on the drone's position coordinates at each moment in each yaw period and the duration of each yaw period. Based on the deviation distance between the drone's position data and the cruise route during the power inspection, the yaw condition of the drone during the inspection can be more accurately assessed, ensuring comprehensive monitoring and assessment of the drone inspection process. An initial concern factor for each yaw period is derived based on the wind speed and direction at each moment in the yaw-causing period. Determining the initial concern factor based on the wind speed and direction at each moment in the yaw-causing period more accurately identifies the external environmental factors causing the yaw, providing a precise basis for subsequent yaw analysis and adjustment. The drone control action command for each moment is derived based on the initial concern factor for each yaw period, the maximum battery temperature at all moments in each yaw period, and the minimum battery temperature before the maximum battery temperature. This initial concern factor for each yaw period, combined with the critical factor of battery temperature, further reduces false positives for non-fault data. At this point, the present invention first analyzes the severity of the drone's yaw, further analyzes the cause of the yaw, and determines the focus factor at each moment. This allows the subsequent drone control to pay more attention to fault data, ensure priority processing and response to potential fault conditions, and reduce false alarms of non-fault data, thereby improving the control effect of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flowchart of the steps of an unmanned equipment control method of the present invention;

[0042] Figure 2 This is a module flow chart of an unmanned equipment control system of the present invention;

[0043] Figure 3 This is a schematic diagram of the autonomous inspection of high-voltage cables by a drone in this embodiment. DETAILED DESCRIPTION

[0044] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an unmanned equipment control method, control system, and storage medium proposed in accordance with the present invention. In the following description, references to "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.

[0045] 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 invention belongs.

[0046] The following describes in detail a specific solution of an unmanned equipment control method, control system and storage medium provided by the present invention with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for controlling an unmanned device according to an embodiment of the present invention, the method comprising the following steps:

[0048] Step S001: Obtain the position coordinates of the drone at all times during the autonomous inspection process, the wind speed and wind direction at each moment, and the battery temperature of the drone.

[0049] Use drones to conduct autonomous inspections of high-voltage cables, with cable inspections between two high-voltage towers considered one inspection process.

[0050] Since the cable between the two high-voltage towers is close to a straight line, the drone's cruise route in this solution is a straight line along the cable direction, and the drone cruises at a constant speed. Figure 3 shown.

[0051] The drone's sensors collect real-time data on its location (altitude, longitude, and latitude), wind speed and direction, speed, and battery temperature. The data is collected every second.

[0052] It should be noted that the sensors include GPS positioning, wind speed, wind direction, velocity, and temperature sensors. These sensors can help the drone sense the surrounding wind conditions and its own flight status, thereby better adjusting its control strategy.

[0053] Step S002: Based on the position coordinates of the UAV at all times, several yaw periods of the UAV during the autonomous inspection process are obtained; based on the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period, the final yaw severity of each yaw period is obtained.

[0054] The yaw analysis of the UAV is particularly important for its control, so the yaw period is first obtained based on the linear characteristics of the UAV's cruising path for the cable.

[0055] In the current autonomous inspection process, the least squares method is used to perform straight line fitting based on the position coordinates of the UAV at all times to obtain the fitting line and the fitting error at each moment. The fitting line is used as the cruise route of the UAV, and the minimum and maximum norm method is used to obtain the normalized value of the fitting error at each moment as the yaw distance at each moment.

[0056] A first judgment threshold is preset, and during the current autonomous inspection process, the moment when the yaw distance is greater than the preset first judgment threshold is recorded as the yaw moment.

[0057] It should be noted that the preset first judgment threshold in this embodiment is 0.1, which is used as an example for description. Among them, the least square method and the minimum and maximum norm method are both well-known technologies, and the specific methods are not introduced here.

[0058] A period consisting of continuous yaw moments is recorded as a yaw period, thereby obtaining a plurality of yaw periods.

[0059] It should be noted that the yaw period with a duration of less than 3 is not considered, and the attention factor of each moment in the yaw period with a duration of less than 3 is .

[0060] First For example, in the yaw period The yaw distance at each moment is counted in sequence to form a yaw distance sequence.

[0061] It should be noted that the longer the yaw duration and the farther the yaw distance, the more serious the yaw.

[0062] First As an example, the first The calculation formula for the initial yaw severity of a yaw period is:

[0063]

[0064] Where, Indicates the The initial yaw severity of the yaw period, For the The maximum value in the yaw distance sequence corresponding to the yaw period, For the The duration of the yaw period, The duration from the start of the current autonomous inspection process to the current moment.

[0065] During the deviation period, if multiple adjustments are required to return to the normal cruising route, the deviation is more serious.

[0066] The peak and trough detection algorithm is used to obtain the peaks and troughs in the yaw distance sequence corresponding to each yaw period.

[0067] It should be noted that the peak and trough detection algorithm is a well-known technology and the specific method will not be introduced here.

[0068] First As an example, the first The calculation formula for the final yaw severity of a yaw period is:

[0069]

[0070] Where, Indicates the The final yaw severity of the yaw period, For the The duration of the yaw period, is the first ratio, Indicates the The initial yaw severity of the yaw period, For the The number of peaks in the yaw distance sequence corresponding to the yaw period, For the The first yaw distance sequence corresponding to the yaw period The yaw distance corresponding to the wave crest is For the The yaw distance sequence corresponding to the yaw period is the same as the The yaw distance corresponding to the previous trough adjacent to the peak, For the The first difference of the peaks, is the first product, is the first sum value. is a linear normalization function, normalized to between 0 and 1, A first quantity threshold is preset.

[0071] What needs to be explained is: When is 0, let , when the The first yaw distance sequence corresponding to the yaw period When there is no trough before a peak, let For the The yaw distance corresponding to the initial moment of the yaw distance sequence corresponding to the yaw period. When the yaw returns to the cruise route after one adjustment, the data in the yaw distance sequence should increase first and then decrease, with only one peak. The larger the value, the more times the adjustment is made when returning to the cruise route, and the more serious the deviation is. The larger the value is, the longer the deviation distance is when returning to the cruise route, that is, the more serious the deviation is. right Make adjustments to get the final yaw severity.

[0072] Step S003: record the period consisting of several moments before and after the initial moment of each yaw period as the yaw cause period of each yaw period, and obtain the possibility that the yaw of each yaw period is caused by wind according to the wind speed and wind direction at each moment in the yaw cause period of each yaw period; take the horizontal right as 0 degrees, rotate counterclockwise for one circle, and obtain the angles corresponding to each direction; according to the possibility that the yaw of each yaw period is caused by wind, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, obtain the initial attention factor of each yaw period.

[0073] During drone flight, wind can directly affect the drone, altering its trajectory and speed. Especially in outdoor environments, sudden increases in wind speed or sharp changes in wind direction can cause the drone to deviate from its intended path. Furthermore, instabilities in the drone's power system, such as fluctuations in battery charge (caused by internal battery short circuits or failures, or poor contact between the battery and the device), can affect power supply stability and cause changes in the drone's trajectory and speed. Compared to the effects of wind, yaw caused by power system instability is of greater concern.

[0074] The first Before the initial moment of the yaw period moment and after The time period is composed of moments (i.e., the time period is centered at the initial moment and lasts for The period of time) is recorded as The yaw cause period of the yaw period, when there is less than When a moment exists, the time period is composed of the existing moments.

[0075] Use the least squares method to fit the wind speed at all times during the yaw-causing period, and record the normalized value of the slope of the fitting line as the wind speed surge factor. The linear normalization function normalizes this slope.

[0076] It should be noted that: the second quantity threshold preset in this embodiment The value is 3, so we take this as an example for analysis.

[0077] During the yaw-causing period, the minimum angle between the wind direction and the cruise route at each moment is calculated, and the average of the minimum angles between the wind direction and the cruise route at all moments is used as the wind direction influencing factor.

[0078] First As an example, the first The calculation formula for the probability that the yaw is caused by wind during a yaw period is:

[0079]

[0080] Where, Indicates the The possibility that the yaw in the yaw period is caused by wind influence, For the The wind direction influencing factor of the yaw-causing period in each yaw period, For the The wind speed sudden increase factor of the yaw-causing period during the yaw period, The closer it is to 90 degrees, the more the wind direction is perpendicular to the cruise route during the yaw-causing period, and the greater the impact of the wind on the yaw. The larger it is, the greater the sudden increase in wind speed during the yaw-causing period, and the greater the impact of wind on yaw. is the preset angle threshold.

[0081] It should be noted that: in this embodiment, the preset angle threshold is 90°, and this is used as an example for analysis.

[0082] Taking the horizontal right as 0 degrees, rotate counterclockwise for one circle to get the corresponding angles in each direction.

[0083] First As an example, the first The calculation formula of the initial attention factor of a yaw period is:

[0084]

[0085] Where, Indicates the The initial attention factor of the yaw period, For the The final yaw severity of the yaw period, For the The normalized value of the standard deviation of the velocity at all moments in a yaw period, For the The normalized value of the standard deviation of the angle corresponding to the direction at all moments in a yaw period, is the second sum, is the linear normalization function, is a preset third quantity threshold, is the second ratio, Indicates the The possibility that the yaw in the yaw period is caused by wind influence, is the second product, is the second difference.

[0086] It should be noted that the third quantity threshold preset in this embodiment is The value is 2, and this is used as an example for description. The larger it is, the more severe the yaw is and the more attention it needs. and The larger the The wind speed and direction change dramatically during the yaw period. The stronger the wind, the more likely it is that the yaw will occur again during the yaw adjustment process. Indicates the The wind influence degree of each yaw period. The larger the value, the more likely the yaw is caused by non-fault factors. The smaller the value, the more likely the yaw is caused by power failure, which requires more attention. right Adjust to get the initial attention factor, where Respectively The standard deviation of the speed at all moments in the yaw period and the The standard deviation of the angles corresponding to the directions at all moments in a yaw period is normalized.

[0087] Step S004: Obtain the final attention factor of each yaw period based on the initial attention factor of each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature; obtain the drone control action instructions at each moment based on the final attention factor of each yaw period.

[0088] Yaw can be caused by both wind fluctuations and power instability. Power instability can be caused by internal battery short circuits or failures, as well as poor contact between the battery and the device. Both of these factors can cause battery temperature to rise. Therefore, the initial focus factor needs to be further adjusted based on battery temperature fluctuations. Power system instability is typically short-lived, primarily because the drone's power system is designed to have a certain degree of self-regulation.

[0089] First As an example, the yaw period The calculation formula for the final attention factor of a yaw period is:

[0090]

[0091] Where, Indicates the The final attention factor of the yaw period, For the The initial attention factor of the yaw period, For the The maximum value of the battery temperature at all times during the yaw period, For the During the yaw period The minimum value of the previous battery temperature, is the third difference, is the third ratio, for and The time interval between.

[0092] What needs to be explained is: For the The greater the increase in battery temperature during a yaw period, the greater the possibility of power instability and the more attention it requires.

[0093] Let the attention factor at each moment not in the yaw period be , order The attention factor at each moment in the yaw period is , thereby obtaining the attention factor at each moment.

[0094] It should be noted that: in this embodiment, the fourth quantity threshold is preset is 1, and this is used as an example for analysis.

[0095] The UAV is controlled using model predictive control, where the model input data includes the position coordinates of the UAV at each moment, the wind speed and direction the UAV is exposed to, the speed of the UAV, and the battery temperature of the UAV. The attention factor at each moment is used as the weight at each moment, and the output of the model is the UAV control action instruction at each moment.

[0096] It should be noted that this solution assigns greater weight to data during yaw, and further weights data during yaw caused by power instability. This helps the model better learn failure modes, more sensitively detect potential failure signs, and provide early warnings. This ensures that potential failures are prioritized and responded to, while reducing false positives for non-fault data, thereby improving overall prediction efficiency. Model Predictive Control (MPC) is a well-known technology, and the specific methods will not be described here.

[0097] Second, see Figure 2 , which shows an unmanned equipment control system, the system includes the following modules:

[0098] The data acquisition module is used to obtain the position coordinates of the drone at all times during the autonomous inspection process, the wind speed and direction at each moment, and the battery temperature of the drone;

[0099] The final yaw severity acquisition module is used to obtain several yaw periods of the UAV during the autonomous inspection process based on the position coordinates of the UAV at all times; and obtain the final yaw severity of each yaw period based on the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period;

[0100] An initial concern factor acquisition module is used to record a period consisting of several moments before and after the initial moment of each yaw period as the yaw cause period of each yaw period, and obtain the possibility that the yaw of each yaw period is caused by wind influence based on the wind speed and wind direction at each moment in the yaw cause period of each yaw period; with the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angle corresponding to each direction, and obtain the initial concern factor of each yaw period based on the possibility that the yaw of each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period;

[0101] The control instruction acquisition module is used to obtain the final attention factor of each yaw period based on the initial attention factor of each yaw period, the maximum value of the battery temperature at all times in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature; and obtain the drone control action instruction at each moment based on the final attention factor of each yaw period.

[0102] In the third aspect, the present invention also proposes an unmanned equipment control storage medium, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the steps of the method described in steps S001 to S004.

[0103] So far, the present invention is completed.

[0104] In summary, in an embodiment of the present invention, the position coordinates of the drone at all times during the autonomous inspection process, the wind speed and wind direction at each moment, and the battery temperature of the drone are obtained. According to the position coordinates of the drone at all times, the yaw distance at each moment is obtained; according to the yaw distance at each moment, several yaw periods of the drone during the autonomous inspection process are obtained; according to the yaw distance at each moment in each yaw period, the duration of each yaw period, and the duration from the beginning to the current moment of the current autonomous inspection process, the final yaw severity of each yaw period is obtained; the period consisting of several moments before and several moments after the initial moment of each yaw period is recorded as the yaw cause period of each yaw period, and the yaw severity of each yaw period is recorded according to the yaw distance at each moment in each yaw period. The wind speed and wind direction at each moment in the induced period are used to obtain the possibility that the yaw in each yaw period is caused by wind influence; with the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angles corresponding to each direction, and according to the possibility that the yaw in each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, the initial attention factor of each yaw period is obtained; according to the initial attention factor of each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature, the final attention factor of each yaw period is obtained; according to the final attention factor of each yaw period, the drone control action instructions at each moment are obtained.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling unmanned equipment, characterized in that: The method comprises the following steps: Obtain the drone's position coordinates at all times during the autonomous inspection process, the wind speed and direction at each moment, and the drone's battery temperature; According to the position coordinates of the UAV at all times, several yaw periods of the UAV during the autonomous inspection process are obtained; according to the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period, the final yaw severity of each yaw period is obtained; The period consisting of several moments before and after the initial moment of each yaw period is recorded as the yaw cause period of each yaw period. According to the wind speed and wind direction at each moment in the yaw cause period of each yaw period, the possibility that the yaw of each yaw period is caused by wind influence is obtained; with the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angle corresponding to each direction. According to the possibility that the yaw of each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, the initial attention factor of each yaw period is obtained; According to the initial attention factor of each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature, the final attention factor of each yaw period is obtained; according to the final attention factor of each yaw period, the drone control action command at each moment is obtained.

2. The unmanned equipment control method according to claim 1, characterized in that: The specific steps of obtaining several yaw periods of the UAV during the autonomous inspection process based on the position coordinates of the UAV at all times are as follows: In the current autonomous inspection process, the least squares method is used to perform straight line fitting based on the position coordinates of the UAV at all times to obtain the fitting line and the fitting error at each moment. The fitting line is used as the UAV's cruise route, and the minimum and maximum norm method is used to obtain the normalized value of the fitting error at each moment as the yaw distance at each moment. A first judgment threshold is preset, and during the current autonomous inspection process, the moment when the yaw distance is greater than the preset first judgment threshold is recorded as the yaw moment; The period consisting of continuous yaw moments is recorded as the yaw period.

3. The unmanned equipment control method according to claim 2, characterized in that: The method of obtaining the final yaw severity of each yaw period according to the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period includes the following specific steps: In the During the yaw period, the yaw distance at each moment is counted in turn to form a yaw distance sequence; Calculate the The ratio of the duration of the yaw period to the duration from the beginning of the current autonomous inspection process to the current moment, and the ratio is added to the The product of the maximum value in the yaw distance sequence corresponding to the yaw period is recorded as The initial yaw severity of each yaw period; Use the peak and trough detection algorithm to obtain the peaks and troughs in the yaw distance sequence corresponding to each yaw period; According to the initial yaw severity of each yaw period and the corresponding peaks and troughs in the yaw distance sequence, the final yaw severity of each yaw period is obtained.

4. The unmanned equipment control method according to claim 3, characterized in that: The method of obtaining the final yaw severity of each yaw period according to the initial yaw severity of each yaw period and the corresponding peaks and troughs in the yaw distance sequence includes the following specific steps: For the yaw period, calculate the difference between the number of peaks in the yaw distance sequence and the preset first number threshold and the The ratio of the duration of the yaw period is used as the first ratio to calculate the first ratio in the yaw distance sequence. The yaw distance corresponding to the first peak is The difference in yaw distances corresponding to the previous trough adjacent to the first peak is taken as the The first difference of the peaks is calculated, and the product of the sum of the first difference values of all the peaks in the yaw distance sequence and the first ratio is calculated as the first product. The sum of the preset first quantity threshold and the normalized value of the first product is calculated as the first sum value. The first sum value is added to the first sum value. The product of the initial yaw severity of the yaw period is taken as the The final yaw severity for the yaw period.

5. The unmanned equipment control method according to claim 2, characterized in that: The method of obtaining the possibility that the yaw cause of each yaw period is the influence of wind according to the wind speed and wind direction at each moment in the yaw cause period of each yaw period includes the following specific steps: The least square method is used to fit the wind speed at all times during the yaw-causing period to a straight line, and the normalized value of the slope of the fitted straight line is recorded as the wind speed sudden increase factor. During the yaw-causing period, calculate the minimum angle between the wind direction and the cruise route at each moment, and take the average of the minimum angles corresponding to all moments as the wind direction influencing factor; Calculate the The wind direction influencing factor of the yaw-causing period in the yaw period is The ratio of The product of the wind speed sudden increase factor of the yaw-causing period and the yaw period is recorded as The possibility that the yaw is caused by wind during the yaw period is: is the preset angle threshold.

6. The unmanned equipment control method according to claim 1, characterized in that: The initial concern factor of each yaw period is obtained according to the possibility that the yaw of each yaw period is caused by wind, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, including the following specific steps: For the yaw period, calculate the sum of the normalized value of the standard deviation of the angle corresponding to the direction at all times and the normalized value of the standard deviation of the speed as the second sum, calculate the ratio of the second sum to the preset third quantity threshold as the second ratio, calculate the ratio of the second ratio to the first The product of the probability that the yaw is caused by the wind in the yaw period is used as the second product, and the difference between the preset third quantity threshold and the normalized value of the first product is calculated as the second difference. The product of the final yaw severity of the yaw period is recorded as The initial attention factor for the yaw period.

7. The unmanned equipment control method according to claim 1, characterized in that: The method of obtaining the final attention factor for each yaw period according to the initial attention factor for each yaw period, the maximum value of the battery temperature at all moments in each yaw period, and the minimum value of the battery temperature before the maximum value of the battery temperature includes the following specific steps: For the In a yaw period, the difference between the maximum value of the battery temperature at all times and the minimum value of the battery temperature before the maximum value is calculated as a third difference, the ratio of the third difference to the time interval between the maximum value of the battery temperature at all times and the minimum value of the battery temperature before the maximum value is calculated as a third ratio, and the third ratio is added to the first The product of the initial attention factors of the yaw period is recorded as The final attention factor for the yaw period.

8. The unmanned equipment control method according to claim 1, characterized in that: The specific steps of obtaining the drone control action instructions at each moment based on the final attention factor of each yaw period are as follows: Let the attention factor at each moment not in the yaw period be , order The attention factor at each moment in the yaw period is The sum of the normalized values of the final attention factors of the yaw periods plus 1 is used to obtain the attention factor at each moment, where is a preset fourth quantity threshold; The UAV is controlled using model predictive control, where the model input data includes the position coordinates of the UAV at each moment, the wind speed and direction the UAV is exposed to, the speed of the UAV, and the battery temperature of the UAV. The attention factor at each moment is used as the weight at each moment, and the output of the model is the UAV control action instruction at each moment.

9. An unmanned equipment control system, using an unmanned equipment control method according to any one of claims 1 to 8, characterized in that: The system includes the following modules: The data acquisition module is used to obtain the position coordinates of the drone at all times during the autonomous inspection process, the wind speed and direction at each moment, and the battery temperature of the drone; The final yaw severity acquisition module is used to obtain several yaw periods of the UAV during the autonomous inspection process based on the position coordinates of the UAV at all times; According to the position coordinates of the UAV at each moment in each yaw period and the duration of each yaw period, the final yaw severity of each yaw period is obtained; An initial attention factor acquisition module is used to record a period consisting of several moments before and after the initial moment of each yaw period as the yaw cause period of each yaw period, and obtain the possibility that the yaw cause of each yaw period is the influence of wind based on the wind speed and wind direction at each moment in the yaw cause period of each yaw period; With the horizontal right as 0 degrees, rotate counterclockwise for one circle to obtain the angles corresponding to each direction. Based on the possibility that the yaw in each yaw period is caused by wind influence, the angle corresponding to the direction at each moment in each yaw period, and the final yaw severity of each yaw period, the initial attention factor of each yaw period is obtained; a control instruction acquisition module, configured to obtain a final attention factor for each yaw period based on an initial attention factor for each yaw period, a maximum value of battery temperatures at all times in each yaw period, and a minimum value of battery temperatures before the maximum value of the battery temperatures; According to the final attention factor of each yaw period, the UAV control action instructions at each moment are obtained.

10. An unmanned equipment control storage medium, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the unmanned equipment control method according to any one of claims 1 to 8 are implemented.

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