CN120315455B - Unmanned equipment control method, unmanned equipment control system and storage medium - Google Patents
Unmanned equipment control method, unmanned equipment control system and storage mediumInfo
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- CN120315455B CN120315455B CN202510788503.8A CN202510788503A CN120315455B CN 120315455 B CN120315455 B CN 120315455B CN 202510788503 A CN202510788503 A CN 202510788503A CN 120315455 B CN120315455 B CN 120315455B
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Abstract
本发明涉及一般的控制系统技术领域，具体涉及一种无人设备控制方法、控制系统及存储介质，包括：根据每个偏航时段中每一时刻的偏航距离以及每个偏航时段的时长，得到每个偏航时段的最终偏航严重程度，根据每个偏航时段的偏航起因时段中每一时刻的风速以及风向、每个偏航时段内所有时刻的电池温度中的最大值以及电池温度中的最大值之前的电池温度中的最小值，得到每一时刻的无人机控制动作指令。本发明通过分析无人机偏航的严重程度，进一步分析造成偏航的原因，确定每一时刻的关注因子，令后续无人机控制时，更加关注故障数据，确保优先处理和响应潜在的故障情况，并减少对非故障数据的误报，从而提高无人机的控制效果。
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
The invention relates to the technical field of general control systems, in particular to an unmanned equipment control method, a control system and a storage medium.
Background
Unmanned devices refer to devices that autonomously perform tasks without direct human manipulation. These devices are typically equipped with sensors, processors, and actuators that are capable of sensing the environment, making decisions, and performing actions. The unmanned aerial vehicle is very widely applied, for example in electric power inspection, uses unmanned aerial vehicle to detect power line, and in the unmanned aerial vehicle carries out the in-process that power line detected, need control unmanned aerial vehicle along the route of settlement, and keep a constant speed to cruise. However, due to the influences of factors such as changes of air speed and wind direction, instability of a device power system and the like, unmanned aerial vehicle yaw can be caused, and when unmanned aerial vehicle yaw differentiation caused by faults and non-fault factors is inaccurate, the control effect of the unmanned aerial vehicle can be influenced, so that flight safety cannot be ensured.
Disclosure of Invention
The invention provides an unmanned equipment control method, a control system and a storage medium, which are used for solving the existing problems.
The invention discloses an unmanned equipment control method, a control system and a storage medium, which adopt the following technical scheme:
An embodiment of the present invention provides a method for controlling an unmanned device, including the steps of:
acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle;
according to the position coordinates of the unmanned aerial vehicle at all moments, a plurality of yaw time periods of the unmanned aerial vehicle in the autonomous inspection process are obtained; obtaining the final yaw severity of each yaw period according to the position coordinates of the unmanned aerial vehicle at each moment in each yaw period and the duration of each yaw period;
recording a period formed by a plurality of moments before and a plurality of moments after the initial moment of each yaw period as a yaw cause period of each yaw period, obtaining the possibility of the yaw cause of each yaw period as wind influence according to the wind speed and the wind direction of each moment in the yaw cause period of each yaw period, rotating the yaw cause of each yaw period counterclockwise by 0 degree to the right for one circle to obtain angles corresponding to all directions, and obtaining initial attention factors of each yaw period according to the possibility of the yaw cause of each yaw period as wind influence, the angles corresponding to the direction of each moment in each yaw period and the final yaw severity of each yaw period;
Obtaining a final attention factor of each yaw period 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, and obtaining an unmanned plane control action instruction at each time according to the final attention factor of each yaw period.
Further, according to the position coordinates of the unmanned aerial vehicle at all moments, a plurality of yaw time periods of the unmanned aerial vehicle in the autonomous inspection process are obtained, and the method comprises the following specific steps:
in the current autonomous inspection process, performing straight line fitting by using a least square method according to the position coordinates of the unmanned aerial vehicle at all moments to obtain a fitting straight line and a fitting error at each moment, taking the fitting straight line as a cruising route of the unmanned aerial vehicle, and obtaining a normalized value of the fitting error at each moment by using a minimum maximum normalization method to serve as a yaw distance at each moment;
Presetting a first judgment threshold value, and recording the moment when the yaw distance is greater than the preset first judgment threshold value as yaw moment in the current autonomous inspection process;
the period of time constituted by successive yaw moments is referred to as a yaw period.
Further, the obtaining the final yaw severity of each yaw period according to the position coordinates of the unmanned aerial vehicle at each moment in each yaw period and the duration of each yaw period comprises the following specific steps:
In the first place Sequentially counting yaw distances at each moment in a yaw period to form a yaw distance sequence;
Calculate the first The ratio of the duration of each yaw period to the duration from the beginning to the current moment in the current autonomous inspection process is calculated, and the ratio is calculated to be the first oneThe product of the maximum values in the yaw distance sequence corresponding to the yaw periods is recorded as the firstInitial yaw severity for each yaw period;
Using a wave crest and wave trough detection algorithm to obtain wave crests and wave troughs in a yaw distance sequence corresponding to each yaw period;
And obtaining the final yaw severity of each yaw period according to the initial yaw severity of each yaw period and the peaks and troughs in the corresponding yaw distance sequence.
Further, the step of obtaining the final yaw severity of each yaw period according to the initial yaw severity of each yaw period and the peaks and the troughs in the corresponding yaw distance sequence comprises the following specific steps:
For the first Calculating the difference value between the number of wave peaks in the yaw distance sequence and a preset first number threshold value and a first yaw periodThe ratio of the durations of the yaw periods is taken as a first ratio, and the first ratio in the yaw distance sequence is calculatedYaw distance corresponding to each wave crest and the firstThe difference of yaw distance corresponding to the wave trough adjacent to the wave crest is taken as the first wave crestCalculating the product of the sum of the first differences of all wave peaks in the yaw distance sequence and the first ratio, calculating the sum of the normalized value of the preset first quantity threshold and the first product as the first sum, and combining the first sum with the first ratioThe product of the initial yaw severity of the yaw period as the firstFinal yaw severity for each yaw period.
Further, the method obtains the possibility of influence of wind on the yaw cause of each yaw period according to the wind speed and the wind direction of each moment in the yaw cause period of each yaw period, and comprises the following specific steps:
using a least square method to perform straight line fitting on wind speeds at all moments in a yaw cause period, and recording a normalized value of the slope of the fitted straight line as a wind speed sudden increase factor;
in the yaw cause period, calculating the minimum included angle between the wind direction at each moment and the cruising route, and taking the average value of the minimum included angles corresponding to all moments as a wind direction influence factor;
Calculate the first Wind direction influencing factors of yaw cause periods of the yaw periodsIs the ratio of the ratio to the firstThe product of the wind speed surge factors of the yaw cause periods of the yaw periods is recorded as the firstThe yaw of the individual yaw periods is due to the possibility of wind effects, wherein,Is a preset angle threshold.
Further, the initial attention factor of each yaw period is obtained according to the possibility of wind influence caused by the yaw of each yaw period, the angle corresponding to the direction of each moment in each yaw period and the final yaw severity of each yaw period, and the specific steps are as follows:
For the first Calculating the sum of the normalized value of the standard deviation of the angles corresponding to the directions at all times and the normalized value of the standard deviation of the speed as a second sum, calculating the ratio of the second sum to a preset third quantity threshold as a second ratio, and calculating the second ratio to the third quantity thresholdThe product of the yaw occurrence of the yaw period due to the possibility of wind influence is calculated as a second product, a difference value between a preset third quantity threshold value and a normalized value of the first product is calculated as a second difference value, and the second difference value and the first difference value are calculated as a third difference valueThe product of the final yaw severity of each yaw period, noted as the firstInitial attention factor for each yaw period.
Further, the step of obtaining the final attention factor of each yaw period according to the initial attention factor of each yaw period, the maximum value of the battery temperatures at all times in each yaw period and the minimum value of the battery temperatures before the maximum value of the battery temperatures comprises the following specific steps:
For the first A yaw period in which a difference between a maximum value of the battery temperatures at all times and a minimum value of the battery temperatures before the maximum value is calculated as a third difference, a ratio of the third difference to a time interval between the maximum value of the battery temperatures at all times and the minimum value of the battery temperatures before the maximum value is calculated as a third ratio, and the third ratio to the third ratioThe product of the initial attention factors of each yaw period is recorded as the firstThe final attention factor for each yaw period.
Further, according to the final attention factor of each yaw period, the unmanned aerial vehicle control action instruction at each moment is obtained, and the method comprises the following specific steps:
Let the attention factor of each moment not in the yaw period be Make the firstThe attention factor of each moment in the yaw period is the firstThe normalized value of the final attention factor for each yaw period is added to the sum of 1, thereby yielding the attention factor for each moment, wherein,Is a preset fourth quantity threshold;
And controlling the unmanned aerial vehicle by using model prediction control, wherein the model input data are position coordinates of the unmanned aerial vehicle at each moment, wind speed and direction received by the unmanned aerial vehicle, speed of the unmanned aerial vehicle and battery temperature of the unmanned aerial vehicle, attention factors at each moment are used as weights at each moment, and the model output is an unmanned aerial vehicle control action instruction at each moment.
The unmanned equipment control system adopts the unmanned equipment control method, and comprises the following modules:
the data acquisition module is used for acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle;
The system comprises a final yaw severity acquisition module, a final yaw severity acquisition module and a final yaw severity acquisition module, wherein the final yaw severity acquisition module is used for acquiring a plurality of yaw periods of the unmanned aerial vehicle in an autonomous inspection process according to the position coordinates of the unmanned aerial vehicle at all moments;
The initial focus factor acquisition module is used for recording a time period formed by a plurality of time points before and a plurality of time points after the initial time point of each yaw time period as a yaw factor time period of each yaw time period, obtaining the possibility of the yaw factor of each yaw time period due to wind influence according to the wind speed and the wind direction of each time point in the yaw factor time period of each yaw time period, rotating the yaw factor of each yaw time period counterclockwise by 0 degree from the horizontal to the right to obtain angles corresponding to all directions, and obtaining the initial focus factor of each yaw time period according to the possibility of the yaw factor of each yaw time period due to wind influence, the angles corresponding to the direction of each time point in each yaw time period and the final yaw severity of each yaw time period;
The control instruction acquisition module is used for obtaining a final attention factor of each yaw period 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, and obtaining an unmanned aerial vehicle control action instruction at each time according to the final attention factor of each yaw period.
An unmanned device control storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the unmanned device control method when the computer program is executed.
The technical scheme of the invention has the beneficial effects that:
According to the embodiment of the invention, various time sequence data of the unmanned aerial vehicle in the autonomous inspection process are acquired, the final yaw severity of each yaw period is obtained according to the position coordinates of the unmanned aerial vehicle at each moment in each yaw period and the duration of each yaw period, the yaw condition of the unmanned aerial vehicle in the inspection process can be more accurately evaluated according to the deviation distance between the position data and the cruising route of the unmanned aerial vehicle in the power inspection process, and the comprehensive monitoring and evaluation of the unmanned aerial vehicle inspection process are ensured. According to the wind speed and the wind direction of each moment in the yaw cause period of each yaw period, the initial attention factor of each yaw period is obtained, the initial attention factor is obtained according to the wind speed and the wind direction of each moment in the yaw cause period, the external environment factors causing yaw can be more accurately determined, and an accurate basis is provided for subsequent yaw analysis and adjustment. And obtaining unmanned aerial vehicle control action instructions at each moment 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, and further reducing false alarms of non-fault data according to the initial attention factor of each yaw period and the key factor of the battery temperature. Therefore, the method and the device analyze the severity of the yaw of the unmanned aerial vehicle, further analyze the cause of the yaw, determine the attention factor of each moment, pay more attention to fault data during subsequent unmanned aerial vehicle control, ensure preferential treatment and response to potential fault conditions, reduce false alarm of non-fault data, and improve the control effect of the unmanned aerial vehicle.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of a method for controlling an unmanned device according to the present invention;
FIG. 2 is a block flow diagram of an unmanned device control system of the present invention;
fig. 3 is a schematic diagram of autonomous inspection of a high-voltage cable by an unmanned aerial vehicle in this embodiment.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description refers to the specific implementation, structure, characteristics and effects of an unmanned equipment control method, a unmanned equipment control system and a storage medium according to the invention by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The following specifically describes a control method, a control system and a specific scheme of a storage medium of an unmanned device provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for controlling an unmanned device according to an embodiment of the present invention is shown, where the method includes the following steps:
And S001, acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle.
The unmanned aerial vehicle is used for autonomous inspection of the high-voltage cable, and the cable between the two high-voltage towers is detected as one inspection process.
Because the cable between two high-voltage towers is close to a straight line, the cruise line of unmanned aerial vehicle design is a straight line along the cable direction in this scheme, and unmanned aerial vehicle constant speed cruises. A schematic diagram of autonomous inspection of the high voltage cable by the unmanned aerial vehicle is shown in fig. 3.
According to various sensors carried on the unmanned aerial vehicle, position coordinates (altitude, longitude and latitude) of the unmanned aerial vehicle, wind speed and direction received by the unmanned aerial vehicle, speed of the unmanned aerial vehicle and battery temperature of the unmanned aerial vehicle are collected in real time. Wherein the acquisition frequency is 1 second.
The sensor comprises a GPS positioning sensor, a wind speed sensor, a wind direction sensor, a speed sensor and a temperature sensor. The sensors can help the unmanned aerial vehicle to sense surrounding wind power conditions and the flight condition of the unmanned aerial vehicle, so that the control strategy of the unmanned aerial vehicle can be better adjusted.
Step S002, obtaining a plurality of yaw periods of the unmanned aerial vehicle in the autonomous inspection process according to the position coordinates of the unmanned aerial vehicle at all times, and obtaining the final yaw severity of each yaw period according to the position coordinates of the unmanned aerial vehicle at each time in each yaw period and the duration of each yaw period.
The yaw analysis of the unmanned aerial vehicle is particularly important to control the unmanned aerial vehicle, so that the yaw period is firstly obtained according to the linear characteristic of the unmanned aerial vehicle on the cable cruising path.
In the current autonomous inspection process, according to the position coordinates of the unmanned aerial vehicle at all moments, a least square method is used for carrying out straight line fitting to obtain a fitting straight line and a fitting error at each moment, the fitting straight line is used as a cruising route of the unmanned aerial vehicle, and a minimum maximum normalization method is used for obtaining a normalization value of the fitting error at each moment to be used as a yaw distance at each moment.
And presetting a first judgment threshold value, and recording the moment when the yaw distance is larger than the preset first judgment threshold value as the yaw moment in the current autonomous inspection process.
It should be noted that, in this embodiment, the preset first determination threshold is 0.1, which is described as an example, where the least square method and the least maximum standard method are both known techniques, and specific methods are not described herein.
The period constituted by successive yaw moments is recorded as a yaw period, whereby a plurality of yaw periods are obtained.
It is noted that, regardless of the yaw period of less than 3, the attention factor at each time in the yaw period of less than 3 is given by。
In the first placeFor example, in the first yaw periodAnd counting yaw distances at each moment in sequence in a yaw period to form a yaw distance sequence.
The longer the yaw period, the more serious the yaw distance.
In the first placeFor example, the yaw period is the firstThe calculation formula of the initial yaw severity of each yaw period is as follows:
in the formula, Represent the firstInitial yaw severity for each yaw period,Is the firstThe maximum value in the sequence of yaw distances corresponding to the individual yaw periods,Is the firstThe duration of the individual yaw periods,And the time length from the beginning to the current moment in the current autonomous inspection process is set.
And in the yaw period, if the normal cruising route can be returned only after a plurality of adjustments, the yaw is more serious.
And acquiring the wave crest and the wave trough in the yaw distance sequence corresponding to each yaw period by using a wave crest and wave trough detection algorithm.
It should be noted that the peak-valley detection algorithm is a well-known technique, and the specific method is not described here.
In the first placeFor example, the yaw period is the firstThe final yaw severity for each yaw period is calculated as:
in the formula, Represent the firstThe final yaw severity of the individual yaw periods,Is the firstThe duration of the individual yaw periods,In the first ratio of the values of the first and second values,Represent the firstInitial yaw severity for each yaw period,Is the firstThe number of peaks in the yaw distance sequence corresponding to the individual yaw periods,Is the firstThe yaw distance sequence corresponding to each yaw periodThe yaw distance corresponding to each peak of the wave,Is the firstCorresponding yaw distance sequence of yaw periodThe yaw distance corresponding to the previous trough adjacent to the wave crest,Is the firstA first difference value of the individual peaks,As a result of the first product of the products,Is the first sum.Normalized to between 0 and 1 for a linear normalization function,A first number threshold is preset.
It is to be noted that whenWhen 0, letWhen the firstThe yaw distance sequence corresponding to each yaw periodWhen there is no trough before each peak, then letIs the firstYaw distances corresponding to initial moments of the yaw distance sequences corresponding to the yaw periods.1, When yaw is adjusted once to return to the cruising route, the data in the yaw distance sequence should be increased and then reduced, only one peak exists, whenThe larger the time, the more times the adjustment was made when returning to the cruising route, the more severe the yaw, andThe larger the distance, the farther the re-yaw is, i.e., the more severe the re-yaw, is, the more the way back to the cruising route is. Thereby usingFor a pair ofAnd adjusting to obtain the final yaw severity.
And S003, recording time periods formed by a plurality of time points before and a plurality of time points after the initial time point of each yaw time period as yaw factor time periods of each yaw time period, obtaining the possibility of the yaw factor of each yaw time period due to wind influence according to the wind speed and the wind direction of each time point in the yaw factor time periods of each yaw time period, rotating the yaw factor of each yaw time period in a circle anticlockwise with the horizontal direction to the right of 0 degree, obtaining angles corresponding to all directions, and obtaining initial attention factors of each yaw time period according to the possibility of the yaw factor of each yaw time period due to wind influence, the angles corresponding to the direction of each time point in each yaw time period and the final yaw severity of each yaw time period.
When the unmanned aerial vehicle flies, wind power can directly act on the unmanned aerial vehicle to change the movement track and speed of the unmanned aerial vehicle, and particularly in an outdoor environment, the unmanned aerial vehicle can deviate from a preset path due to sudden increase of wind speed or abrupt change of wind direction. However, instability of the power system of the unmanned aerial vehicle, such as fluctuation of the electric quantity of the battery (the reasons are short circuit or fault inside the battery and poor contact between the battery and equipment), can affect the stability of power supply, and can cause change of the movement track and speed of the unmanned aerial vehicle. Among them, yaw caused by instability of the unmanned aerial vehicle power system is more of a concern than the influence of wind.
Will be the firstBefore the initial moment of the yaw periodTime and afterThe time period formed by each time (i.e. the time period is centered on the initial timeTime period of (a) is recorded as the firstYaw cause periods of the yaw period being insufficient before or after the initial timeThe time period is constituted by the time of existence.
And (3) performing straight line fitting on the wind speeds at all times in the yaw cause period by using a least square method, and recording a normalized value of the slope of the fitted straight line as a wind speed sudden increase factor. UsingThe linear normalization function normalizes the slope.
It should be noted that the second number threshold value preset in this embodimentThis was taken as an example for analysis, 3.
And in the yaw cause period, calculating the minimum included angle between the wind direction at each moment and the cruising route, and taking the average value of the minimum included angles between the wind directions at all moments and the cruising route as a wind direction influence factor.
In the first placeFor example, the yaw period is the firstThe calculation formula of the possibility that the yaw of the yaw period is due to wind is as follows:
in the formula, Represent the firstYaw of the individual yaw periods is due to the possibility of wind effects,Is the firstThe wind direction influencing factors of the yaw cause periods of the individual yaw periods,Is the firstYaw cause of individual yaw periods the wind speed of the period of time increases by a factor,The closer to 90 degrees, the more the wind direction is perpendicular to the cruising route during the period of yaw cause, the greater the influence of the wind on yaw,The larger the yaw cause period is, the larger the wind speed suddenly increases, the larger the influence of wind on yaw is,Is a preset angle threshold.
It should be noted that the preset angle threshold value in this embodimentThis was taken as an example for analysis at 90 °.
And rotating the rotating body counterclockwise by 0 degrees horizontally and rightwards for one circle to obtain angles corresponding to all directions.
In the first placeFor example, the yaw period is the firstThe calculation formula of the initial attention factor of each yaw period is as follows:
in the formula, Represent the firstAn initial focus factor for each yaw period,Is the firstThe final yaw severity of the individual yaw periods,Is the firstNormalized values of standard deviation of the speeds at all times within the individual yaw periods,Is the firstNormalized values of standard deviation of angles corresponding to directions at all times in the individual yaw period,For the second sum value to be the second sum value,As a function of the linear normalization,For a preset third number of thresholds,In the second ratio of the values of the two,Represent the firstYaw of the individual yaw periods is due to the possibility of wind effects,As a result of the second product of the products,Is the second difference.
It should be noted that, in the present embodiment, the preset third number threshold valueThis is exemplified by 2.The larger the yaw, the more serious the need for attention.AndThe larger, then describe the firstThe wind speed and the wind direction change violently in each yaw period, and the more likely the wind causes yaw to occur again in the yaw adjustment process, so thatRepresent the firstThe greater the value of the wind influence of each yaw period, the more likely the yaw is caused by non-fault factors, the smaller the value, the more likely the yaw is caused by power faults, and the more attention is required relatively, thusFor a pair ofAdjusting to obtain initial attention factor, whereinRespectively to the firstStandard deviation of speeds at all moments in each yaw period and the thAnd carrying out normalization processing on standard deviations of angles corresponding to directions of all the moments in each yaw period.
Step S004, obtaining a final attention factor of each yaw period 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, and obtaining an unmanned aerial vehicle control action instruction at each time according to the final attention factor of each yaw period.
Since yaw may be caused by both a change in wind and an unstable power due to a short circuit or malfunction inside the battery and poor contact between the battery and the device, which may cause the battery to rise in temperature. It is therefore necessary to further adjust the initial attention factor according to the temperature change of the battery. Among other things, the instability of the power system is often transient, mainly because the power system of the unmanned aerial vehicle is designed to have a certain self-regulating capacity.
In the first placeFor example, yaw period, the firstThe final attention factor for each yaw period is calculated as:
in the formula, Represent the firstThe final attention factor for the individual yaw periods,Is the firstAn initial focus factor for each yaw period,Is the firstThe maximum of the battery temperatures at all times during the yaw period,Is the firstDuring a yaw periodThe minimum value in the previous battery temperature,For the third difference value, the first difference value,In the third ratio of the number of times,Is thatAnd (3) withTime interval between.
What needs to be described is: Is the first The greater the extent of battery temperature increase over the yaw period, the greater the likelihood of dynamic instability, and the greater the need for attention.
Let the attention factor of each moment not in the yaw period beMake the firstThe attention factor of each moment in each yaw period isThereby obtaining a factor of interest for each moment.
It should be noted that the fourth number threshold value is preset in this embodiment1, And this is taken as an example for analysis.
And controlling the unmanned aerial vehicle by using model prediction control, wherein the model input data are position coordinates of the unmanned aerial vehicle at each moment, wind speed and direction received by the unmanned aerial vehicle, speed of the unmanned aerial vehicle and battery temperature of the unmanned aerial vehicle, attention factors at each moment are used as weights at each moment, and the model output is an unmanned aerial vehicle control action instruction at each moment.
The method has the advantages that the data in yaw caused by unstable power are given larger weight, and the data in yaw caused by unstable power are given larger weight, so that a model can be helped to learn a fault mode better, potential fault signs are detected more sensitively, early warning is carried out in advance, priority processing and response to potential fault conditions are ensured, false alarm of non-fault data is reduced, and overall prediction efficiency is improved. Among them, model Predictive Control (MPC) is a well-known technique, and a specific method is not described here.
In a second aspect, referring to fig. 2, there is shown an unmanned device control system comprising the following modules:
the data acquisition module is used for acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle;
The system comprises a final yaw severity acquisition module, a final yaw severity acquisition module and a final yaw severity acquisition module, wherein the final yaw severity acquisition module is used for acquiring a plurality of yaw periods of the unmanned aerial vehicle in an autonomous inspection process according to the position coordinates of the unmanned aerial vehicle at all moments;
The initial focus factor acquisition module is used for recording a time period formed by a plurality of time points before and a plurality of time points after the initial time point of each yaw time period as a yaw factor time period of each yaw time period, obtaining the possibility of the yaw factor of each yaw time period due to wind influence according to the wind speed and the wind direction of each time point in the yaw factor time period of each yaw time period, rotating the yaw factor of each yaw time period counterclockwise by 0 degree from the horizontal to the right to obtain angles corresponding to all directions, and obtaining the initial focus factor of each yaw time period according to the possibility of the yaw factor of each yaw time period due to wind influence, the angles corresponding to the direction of each time point in each yaw time period and the final yaw severity of each yaw time period;
The control instruction acquisition module is used for obtaining a final attention factor of each yaw period 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, and obtaining an unmanned aerial vehicle control action instruction at each time according to the final attention factor of each yaw period.
In a third aspect, the invention also proposes an unmanned device control storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the steps of the method according to steps S001 to S004 when said computer program is executed.
The present invention has been completed.
In summary, in the embodiment of the invention, the position coordinates of the unmanned aerial vehicle at all times, the wind speed and the wind direction at each time and the battery temperature of the unmanned aerial vehicle are obtained in the autonomous inspection process, and the yaw distance at each time is obtained according to the position coordinates of the unmanned aerial vehicle at all times; the method comprises the steps of obtaining a plurality of yaw time periods of an unmanned aerial vehicle in an autonomous inspection process according to the yaw distance of each time, obtaining final yaw severity of each yaw time period according to the yaw distance of each time in each yaw time period, the duration of each yaw time period and the duration from the beginning to the current time in the current autonomous inspection process, recording time periods formed by a plurality of time points before and a plurality of time points after the initial time of each yaw time period as yaw factor time periods of each yaw time period, obtaining the possibility of the yaw factor of each yaw time period according to the wind speed and the wind direction of each time point in the yaw factor time period, rotating the unmanned aerial vehicle to the right of 0 degree in a anticlockwise direction to obtain angles corresponding to all directions, obtaining initial factors of each yaw time period according to the possibility of the yaw factor of each yaw time period due to the wind, the corresponding angle of each time point in each yaw time period and the final yaw severity of each yaw time period, obtaining the initial factors of each yaw time period according to the initial factors of each yaw time period, the maximum values of battery temperatures in all time periods and the maximum values of the battery temperatures in each time period, and obtaining the final factors of the battery temperatures according to the yaw factors of each yaw command, and obtaining the final factors of each unmanned aerial vehicle according to the yaw factors.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of controlling an unmanned device, the method comprising the steps of:
acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle;
according to the position coordinates of the unmanned aerial vehicle at all moments, a plurality of yaw time periods of the unmanned aerial vehicle in the autonomous inspection process are obtained; obtaining the final yaw severity of each yaw period according to the position coordinates of the unmanned aerial vehicle at each moment in each yaw period and the duration of each yaw period;
recording a period formed by a plurality of moments before and a plurality of moments after the initial moment of each yaw period as a yaw cause period of each yaw period, obtaining the possibility of the yaw cause of each yaw period as wind influence according to the wind speed and the wind direction of each moment in the yaw cause period of each yaw period, rotating the yaw cause of each yaw period counterclockwise by 0 degree to the right for one circle to obtain angles corresponding to all directions, and obtaining initial attention factors of each yaw period according to the possibility of the yaw cause of each yaw period as wind influence, the angles corresponding to the direction of each moment in each yaw period and the final yaw severity of each yaw period;
Obtaining a final attention factor of each yaw period 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, and obtaining an unmanned plane control action instruction at each time according to the final attention factor of each yaw period.
2. The method for controlling the unmanned aerial vehicle according to claim 1, wherein the obtaining a plurality of yaw periods of the unmanned aerial vehicle in the autonomous inspection process according to the position coordinates of the unmanned aerial vehicle at all moments comprises the following specific steps:
in the current autonomous inspection process, performing straight line fitting by using a least square method according to the position coordinates of the unmanned aerial vehicle at all moments to obtain a fitting straight line and a fitting error at each moment, taking the fitting straight line as a cruising route of the unmanned aerial vehicle, and obtaining a normalized value of the fitting error at each moment by using a minimum maximum normalization method to serve as a yaw distance at each moment;
Presetting a first judgment threshold value, and recording the moment when the yaw distance is greater than the preset first judgment threshold value as yaw moment in the current autonomous inspection process;
the period of time constituted by successive yaw moments is referred to as a yaw period.
3. The method for controlling the unmanned aerial vehicle according to claim 2, wherein the obtaining the final yaw severity of each yaw period according to the position coordinates of the unmanned aerial vehicle at each moment in each yaw period and the duration of each yaw period comprises the following specific steps:
In the first place Sequentially counting yaw distances at each moment in a yaw period to form a yaw distance sequence;
Calculate the first The ratio of the duration of each yaw period to the duration from the beginning to the current moment in the current autonomous inspection process is calculated, and the ratio is calculated to be the first oneThe product of the maximum values in the yaw distance sequence corresponding to the yaw periods is recorded as the firstInitial yaw severity for each yaw period;
Using a wave crest and wave trough detection algorithm to obtain wave crests and wave troughs in a yaw distance sequence corresponding to each yaw period;
And obtaining the final yaw severity of each yaw period according to the initial yaw severity of each yaw period and the peaks and troughs in the corresponding yaw distance sequence.
4. A method of controlling an unmanned device according to claim 3, wherein the obtaining the final yaw severity of each yaw period from the initial yaw severity of each yaw period, the peaks and troughs in the corresponding yaw distance sequence comprises the following specific steps:
For the first Calculating the difference value between the number of wave peaks in the yaw distance sequence and a preset first number threshold value and a first yaw periodThe ratio of the durations of the yaw periods is taken as a first ratio, and the first ratio in the yaw distance sequence is calculatedYaw distance corresponding to each wave crest and the firstThe difference of yaw distance corresponding to the wave trough adjacent to the wave crest is taken as the first wave crestCalculating the product of the sum of the first differences of all wave peaks in the yaw distance sequence and the first ratio, calculating the sum of the normalized value of the preset first quantity threshold and the first product as the first sum, and combining the first sum with the first ratioThe product of the initial yaw severity of the yaw period as the firstFinal yaw severity for each yaw period.
5. The unmanned equipment control method according to claim 2, wherein the obtaining the possibility that the yaw of each yaw period is influenced by wind according to the wind speed and the wind direction of each moment in the yaw cause period of each yaw period comprises the following specific steps:
using a least square method to perform straight line fitting on wind speeds at all moments in a yaw cause period, and recording a normalized value of the slope of the fitted straight line as a wind speed sudden increase factor;
in the yaw cause period, calculating the minimum included angle between the wind direction at each moment and the cruising route, and taking the average value of the minimum included angles corresponding to all moments as a wind direction influence factor;
Calculate the first Wind direction influencing factors of yaw cause periods of the yaw periodsIs the ratio of the ratio to the firstThe product of the wind speed surge factors of the yaw cause periods of the yaw periods is recorded as the firstThe yaw of the individual yaw periods is due to the possibility of wind effects, wherein,Is a preset angle threshold.
6. The method for controlling the unmanned equipment according to claim 1, wherein the initial focus factor of each yaw period is obtained according to the possibility that the yaw of each yaw period is influenced by wind, the angle corresponding to the direction of each moment in each yaw period and the final yaw severity of each yaw period, and the method comprises the following specific steps:
For the first Calculating the sum of the normalized value of the standard deviation of the angles corresponding to the directions at all times and the normalized value of the standard deviation of the speed as a second sum, calculating the ratio of the second sum to a preset third quantity threshold as a second ratio, and calculating the second ratio to the third quantity thresholdThe product of the yaw occurrence of the yaw period due to the possibility of wind influence is calculated as a second product, a difference value between a preset third quantity threshold value and a normalized value of the first product is calculated as a second difference value, and the second difference value and the first difference value are calculated as a third difference valueThe product of the final yaw severity of each yaw period, noted as the firstInitial attention factor for each yaw period.
7. The method for controlling the unmanned equipment according to claim 1, wherein the 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 temperatures at all times in each yaw period, and the minimum value of the battery temperatures before the maximum value of the battery temperatures comprises the following specific steps:
For the first A yaw period in which a difference between a maximum value of the battery temperatures at all times and a minimum value of the battery temperatures before the maximum value is calculated as a third difference, a ratio of the third difference to a time interval between the maximum value of the battery temperatures at all times and the minimum value of the battery temperatures before the maximum value is calculated as a third ratio, and the third ratio to the third ratioThe product of the initial attention factors of each yaw period is recorded as the firstThe final attention factor for each yaw period.
8. The unmanned aerial vehicle control method according to claim 1, wherein the unmanned aerial vehicle control action command at each moment is obtained according to the final attention factor of each yaw period, comprising the following specific steps:
Let the attention factor of each moment not in the yaw period be Make the firstThe attention factor of each moment in the yaw period is the firstThe normalized value of the final attention factor for each yaw period is added to the sum of 1, thereby yielding the attention factor for each moment, wherein,Is a preset fourth quantity threshold;
And controlling the unmanned aerial vehicle by using model prediction control, wherein the model input data are position coordinates of the unmanned aerial vehicle at each moment, wind speed and direction received by the unmanned aerial vehicle, speed of the unmanned aerial vehicle and battery temperature of the unmanned aerial vehicle, attention factors at each moment are used as weights at each moment, and the model output is an unmanned aerial vehicle control action instruction at each moment.
9. An unmanned equipment control system employing an unmanned equipment control method according to any one of claims 1 to 8, wherein the system comprises the following modules:
the data acquisition module is used for acquiring position coordinates of the unmanned aerial vehicle at all moments in the autonomous inspection process, wind speed and wind direction at each moment and battery temperature of the unmanned aerial vehicle;
The system comprises a final yaw severity acquisition module, a final yaw severity acquisition module and a final yaw severity acquisition module, wherein the final yaw severity acquisition module is used for acquiring a plurality of yaw periods of the unmanned aerial vehicle in an autonomous inspection process according to the position coordinates of the unmanned aerial vehicle at all moments;
The initial focus factor acquisition module is used for recording a time period formed by a plurality of time points before and a plurality of time points after the initial time point of each yaw time period as a yaw factor time period of each yaw time period, obtaining the possibility of the yaw factor of each yaw time period due to wind influence according to the wind speed and the wind direction of each time point in the yaw factor time period of each yaw time period, rotating the yaw factor of each yaw time period counterclockwise by 0 degree from the horizontal to the right to obtain angles corresponding to all directions, and obtaining the initial focus factor of each yaw time period according to the possibility of the yaw factor of each yaw time period due to wind influence, the angles corresponding to the direction of each time point in each yaw time period and the final yaw severity of each yaw time period;
The control instruction acquisition module is used for obtaining a final attention factor of each yaw period 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, and obtaining an unmanned aerial vehicle control action instruction at each time according to the final attention factor of each yaw period.
10. An unmanned device control storage medium comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of a unmanned device control method as claimed in any one of claims 1 to 8.
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| CN108364159A (en) * | 2018-04-09 | 2018-08-03 | 郑州檀乐科技有限公司 | A kind of unmanned plane logistics face label device and method |
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| US12157568B2 (en) * | 2018-10-18 | 2024-12-03 | Telefonaktiebolaget Lm Ericsson (Publ) | Formation flight of unmanned aerial vehicles |
| CN118999565B (en) * | 2024-08-08 | 2025-03-11 | 郑州航空工业管理学院 | Unmanned aerial vehicle navigation control method based on navigation positioning |
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| CN108364159A (en) * | 2018-04-09 | 2018-08-03 | 郑州檀乐科技有限公司 | A kind of unmanned plane logistics face label device and method |
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