Unmanned aerial vehicle scheduling method and system for air-ground cooperation

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle scheduling method and system for air-ground cooperation, and the method comprises the steps: collecting the power consumption and wind speed of an unmanned aerial vehicle at the same moment; the method comprises the following steps: dividing into a stable wind speed group and an abnormal wind speed group on the basis of the volatility of wind speed collected at each moment along a timeline, establishing a mapping relation between the stable wind speed group and power consumption, calculating an unmanned aerial vehicle scheduling safety coefficient at the current moment, and performing scheduling on the basis of the unmanned aerial vehicle scheduling safety coefficient at the current moment. According to the method, the wind speed collected in real time is screened by using the clustering algorithm, the abnormal wind speed data is screened out and eliminated, the interference of the abnormal wind speed data on the mapping relation between the power consumption and the wind speed is reduced, and the accuracy of subsequent calculation of the unmanned aerial vehicle scheduling safety coefficient is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle technology, and more specifically, to a method and system for dispatching unmanned aerial vehicles for air-ground collaboration. Background Art

[0002] With the development of drone technology, it plays an important role in air-ground collaborative tasks. Air-ground collaborative drones refer to the use of information interaction and collaborative control between ground terminals (such as dispatch centers, drone ground stations, smart vehicles, etc.) and drones to achieve remote monitoring, intelligent dispatch, energy replenishment, task optimization and other functions of drones. The air part (drone): responsible for task execution, such as inspection, mapping, material delivery, etc. The ground part (smart terminal / vehicle / control center): responsible for calculation, monitoring, navigation assistance, data fusion, energy replenishment, etc. Moreover, during the information collection process, drones often consume more electricity due to wind interference, and the actual flight time is much lower than the theoretical value. For example, when flying against the wind, the motor needs to increase power to maintain speed. At the same time, the wind causes attitude fluctuations, and the flight control system needs high-frequency corrections, which further increases power consumption. For example, instantaneous changes in wind speed will cause the instantaneous power consumption of drones to increase.

[0003] In the prior art, when a drone is flying in a complex wind speed environment, it is difficult to accurately predict the power consumption of the drone. In addition, during the flight of the drone, changes in wind speed have a significant impact on the power consumption of the drone, and abnormal wind speed data will interfere with the mapping relationship between power consumption and wind speed. Therefore, when accurately evaluating the return time and power safety of the drone, abnormal wind speed data may cause errors in the safety assessment of the drone, resulting in the risk of the drone crashing due to insufficient power. Summary of the invention

[0004] The present invention provides a method and system for dispatching unmanned aerial vehicles (UAVs) for air-ground collaboration, aiming to solve the problem in related technologies that changes in wind speed have a significant impact on the power consumption of UAVs, and abnormal wind speed data will interfere with the mapping relationship between power consumption and wind speed. Therefore, errors may occur in the accurate assessment of the return time and power safety of the UAV.

[0005] In the first aspect, the present invention provides a method for dispatching drones for air-ground collaboration, including: collecting the power consumption and wind speed of drones at the same time; dividing the collected wind speeds into stable wind speed groups and abnormal wind speed groups based on the volatility of the wind speeds at each time along the timeline, establishing a mapping relationship between the stable wind speed group and the power consumption, and calculating the drone dispatch safety factor at the current time, and dispatching based on the size of the drone dispatch safety factor at the current time; wherein, calculating the drone dispatch safety factor at the current time , the calculation formula is: ; In the formula, is the number of abnormal wind speed data in the target time period, is the number of valid data at the current moment, is the remaining power of the drone at the current moment, The time required for the drone to return to the ground terminal. is the acquisition frequency, The target time period The power consumption corresponding to the wind speed is collected at each moment, wherein the number of valid data is related to the wind speed at the current moment and the number of all data within and between two consecutive stable wind speed groups before the current moment, and the target time period is the current moment and the previous moment. The time period consisting of all the moments between is the number of moments in the target time period, wherein the number of valid data is related to the wind speed at the current moment and the number of all data within and between two consecutive stable wind speed groups before the current moment. The target time period is the time between the current moment and the previous moment. The time period consists of all moments between moments. By mapping the stable wind speed group and the power consumption, the stable wind speed group and the abnormal wind speed group are distinguished, the interference of the violent fluctuation of wind speed on the prediction is reduced, the accuracy of the power consumption prediction is improved, and the historical wind speed data is used to calculate the actual power consumption of the UAV at different wind speeds, avoiding the deviation caused by relying solely on the theoretical model.

[0006] Furthermore, scheduling is performed based on the size of the drone scheduling safety factor at the current moment, including: if the drone scheduling safety factor at the current moment is less than a safety threshold, a return request warning is sent to the ground terminal, wherein the experience value of the safety threshold is 0.3.

[0007] Furthermore, the number of valid data at the current moment is calculated using the following formula: ;in, is the number of valid data at the current moment, is the wind speed at the current moment, is the maximum stable wind speed of the drone, The maximum stable wind speed is set by the parameters of the drone itself. This method can adjust the number of valid data in real time according to the wind speed at the current moment, and can effectively control the number of valid data used to predict the wind speed of the drone.

[0008] Furthermore, the wind speed collected at each moment is divided into multiple wind speed groups, including: starting clustering with the wind speed collected at the i-th moment as the starting point, wherein the clustering method can use the K-means clustering method, and calculating the response function value of the clustering once for each additional moment; if the response function value is less than the clustering threshold, the clustering is completed to obtain a cluster cluster, and a cluster cluster corresponds to a wind speed group, wherein the cluster cluster contains all wind speeds collected between the i-th moment and the moment when the response function value is less than the clustering threshold. When the wind speed changes drastically, the response function value drops rapidly, and the clustering ends, so that the sudden change wind speed is grouped separately; when the wind speed changes steadily, the clustering continues to ensure that the stable wind speed data can be classified into the same group.

[0009] Further, the wind speed group is divided into a stable wind speed group and an abnormal wind speed group, including: judging whether the wind speed group is abnormal according to the number of wind speeds in the wind speed group; if the number of wind speeds in the wind speed group is less than the number threshold, determining that the wind speed group is an abnormal wind speed group; if the number of wind speeds in the wind speed group is greater than or equal to the number threshold, determining that the wind speed group is a stable wind speed group. Distinguishing between a stable wind speed group and an abnormal wind speed group based on the number of wind speed data in a wind speed group does not require complex calculations and is suitable for real-time wind speed monitoring.

[0010] Furthermore, the calculation method of the response function value of the wind speed at each moment is: ; In the formula, For the The response function value of wind speed at a moment, For the Time to The sequence of all wind speed data between the times, For the The wind speed at that moment, The natural constant The exponential function with base , is the mean function, STD is the standard deviation, where the standard deviation reflects the volatility of wind speed. This method combines the mean and standard deviation to measure wind speed changes, and the exponential function adaptively adjusts the sensitivity to improve the accuracy of abnormal wind speed identification, taking into account both short-term and long-term changes, reducing misjudgment, and improving the accuracy of subsequent calculations.

[0011] Furthermore, a mapping relationship between the stable wind speed group and the power consumption is established, including: obtaining the power consumption and normal wind speed at each moment in the stable wind speed group, and obtaining a scatter plot of the power consumption and normal wind speed at each moment; using the spline fitting tool in the matlab tool to fit a relationship curve between the normal wind speed and the power consumption, thereby establishing a mapping relationship between the power consumption and the normal wind speed. In a second aspect of the present invention, a drone scheduling system for air-ground collaboration is also provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the drone scheduling method for air-ground collaboration as described in any one of the above items.

[0012] Beneficial effects: (I) The clustering algorithm is used to screen the wind speed collected in real time, and the abnormal wind speed data is screened out and eliminated, which reduces the interference of abnormal wind speed data on the mapping relationship between power consumption and wind speed, and improves the accuracy of subsequent calculation of the safety factor of drone scheduling.

[0013] (ii) The volatility of wind speed directly affects the power consumption of UAVs. This method can more accurately predict future power consumption by establishing a wind speed-power consumption mapping relationship based on a stable wind speed group. It can also adaptively adjust the prediction model under different wind speed conditions, thereby improving the accuracy of the UAV return time and power safety estimation, reducing the risk of insufficient power due to sudden changes in wind speed, and helping to optimize UAV mission planning and scheduling strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure schematically shows a flow chart of calculating the safety factor of drone dispatching according to an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0016] like Figure 1 As shown, S101: collecting the power consumption and wind speed of the drone at the same time.

[0017] In one embodiment, during a UAV flight mission, the real-time power consumption and real-time wind speed of the UAV are collected (via an ultrasonic anemometer carried by the UAV), and one collection moment includes one wind speed and one power consumption (the power difference between adjacent collection moments).

[0018] S102: Obtain a stable wind speed group.

[0019] In one embodiment, when the wind speed changes drastically, whether the wind speed decreases or increases, the drone's fuselage will fluctuate violently, thereby causing the drone's instantaneous power consumption to increase. This situation will interfere with the determination of the mapping relationship between real-time power consumption and real-time wind speed. Therefore, it is necessary to eliminate the abnormal data and reduce the interference of abnormal wind speed on the mapping relationship between power consumption and wind speed.

[0020] Specifically, based on the volatility of wind speed collected at each moment along the timeline, it is divided into a stable wind speed group and an abnormal wind speed group, wherein the abnormal wind speed group is abnormal data. It should be noted that the division process of the wind speed group is as follows: clustering the wind speed collected in real time, wherein clustering is performed using the K-means clustering method, specifically, clustering is started with the wind speed collected at the i-th moment as the starting point, and the response function value of the clustering is calculated once for each additional moment; if the response function value of the increased moment is less than the clustering threshold, the clustering is completed and a cluster cluster is obtained, and a cluster cluster corresponds to a wind speed group, wherein the cluster cluster contains all wind speeds collected between the i-th moment and the moment when the response function value is less than the clustering threshold (the increased moment). Then, a new clustering is started again according to the above method at the next moment of the increased moment, and at this point, the wind speed group can be divided based on the wind speed collected in real time. Among them, in this embodiment, the empirical value of the clustering threshold is 0.4, and in other embodiments, the empirical value of the clustering threshold can be 0.5 or 0.44, etc., which can be adjusted according to the specific implementation situation.

[0021] Exemplarily, taking the wind speed collected at the i-th moment as the starting point, the response function value of the wind speed at the i+1th moment is calculated to be 0.5, and the response function value at the i+1th moment is greater than the clustering threshold, then the i+2th moment is added to continue clustering, the response function value of the wind speed at the i+2th moment is 0.6, and the response function value at the i+2th moment is also greater than the clustering threshold, then the i+3th moment is added to continue clustering, the response function value of the wind speed at the i+3th moment is 0.45, and the response function value at the i+3th moment is also greater than the clustering threshold, then the i+4th moment is added to continue clustering, and the response function value of the wind speed at the i+4th moment is 0.3. At this time, the response function value at the i+4th moment is less than the clustering threshold, and the clustering is completed, and the wind speeds collected from the i-th moment to the i+4th moment are determined as a wind speed group. Then a new clustering is started with the i+5th moment as the starting point.

[0022] In one embodiment, the response function value of the wind speed at each moment is calculated as follows: ; In the formula, For the The response function value of wind speed at a moment, For the Time to The sequence of all wind speed data between the times, For the The wind speed at that moment, The natural constant The exponential function with base , is the mean function, STD is the standard deviation, where the standard deviation reflects the volatility of wind speed.

[0023] in, For the Time to The stability of all wind speeds between moments. The larger the value, the stronger the wind speed. Time to The more stable the wind speed changes between moments, the Time to The wind speed between moments can be classified into a cluster. For the The wind speed at the time Time to The overall difference in wind speed between the first The wind speed at the moment is relative to Time to The overall change in wind speed between all moments. The larger the value, the greater the change in wind speed. The more the moment does not belong to the Time to The clusters formed by all wind speeds between the times.

[0024] Continuing from the above, after being divided into wind speed groups, the number of wind speeds in the wind speed group can be used to determine whether the wind speed group belongs to a stable wind speed group or an abnormal wind speed group. Specifically, if the number of wind speeds in the wind speed group is less than the number threshold, the wind speed group is determined to be an abnormal wind speed group; if the number of wind speeds in the wind speed group is greater than or equal to the number threshold, the wind speed group is determined to be a stable wind speed group, wherein the empirical value of the number threshold is 15. The reason is that wind speed fluctuations are instantaneous rather than continuous, and each wind speed in the wind speed group corresponds to a moment, so the duration of the wind speed can be judged based on the number of wind speeds in the wind speed group. When the number in the wind speed group is less than the number threshold, it means that the wind speed changes suddenly in a short period of time, and the wind speed group is marked as an abnormal wind speed group. Conversely, when the number of wind speeds in the cluster is greater than or equal to the number threshold, it means that the cluster is a stable wind speed group.

[0025] It should be noted that the determination method of the number threshold can be determined according to the specifications and performance of the drone. For example, the balance recovery time of an ordinary drone affected by a sudden change in wind speed is 1.5 seconds. If the wind speed collection frequency is 10 Hz, the data number threshold is 15, so the wind speed group greater than or equal to the number threshold is determined as a stable wind speed group.

[0026] S103: Calculate the safety factor of drone dispatching.

[0027] In one embodiment, based on the stable wind speed group, the mapping relationship between power consumption and normal wind speed is obtained using matlab, that is, the power consumption of the drone under different wind speeds can be obtained. Specifically, the power consumption and normal wind speed at each moment in the stable wind speed group are obtained, and then the scatter plot established by the power consumption and normal wind speed at each moment is obtained. The spline fitting tool in the matlab tool is used to fit the relationship curve between normal wind speed and power consumption, thereby establishing a mapping relationship between power consumption and normal wind speed. Then, with reference to the wind speed that has occurred, the decline in the power consumption of the drone in the future period is predicted to determine whether the drone can return safely within the return time. Specifically, the time for the drone to return to the ground vehicle is obtained, and the power consumption of the drone during this time is predicted, thereby calculating the safety factor of drone scheduling.

[0028] In one embodiment, the current UAV dispatch safety factor is calculated. , the calculation formula is: ; In the formula, is the number of abnormal wind speed data in the target time period, is the number of valid data at the current moment, is the remaining power of the drone at the current moment, The time required for the drone to return to the ground terminal. is the acquisition frequency, The target time period The power consumption corresponding to the wind speed is collected at each moment, wherein the number of valid data is related to the wind speed at the current moment and the number of all data within and between two consecutive stable wind speed groups before the current moment, and the target time period is the current moment and the previous moment. The time period consists of all the moments between is the number of moments in the target time period, where the target time period is the time between the current moment and the moment before it. The time period consisting of all the moments between the moments.

[0029] in, In order to obtain the average power consumption of the UAV within the range of the above-mentioned number of valid data, as the real-time power consumption during the time when the UAV returns to the ground vehicle in the future, and compare it with the remaining power, the power risk coefficient of the UAV is obtained. The larger the value, the more remaining power, the smaller the risk of the UAV returning, and the higher the safety factor of the UAV scheduling. It is used to limit the number of abnormal wind speed data within the range of valid data. When the number of abnormal wind speed data is relatively large, the UAV dispatch safety factor should be actively reduced, because the more abnormal wind speed data, the more unstable the wind speed is, and the average consumption rate of the remaining power will not be greater than the average power consumption of the UAV within the range of valid data. Therefore, the larger the value, the fewer the number of abnormal wind speed data within the range of valid data, indicating that the power consumption predicted by the UAV is more accurate.

[0030] In one embodiment, the number of valid data at the current moment is related to the wind speed at the current moment and the number of all data within and between two consecutive stable wind speed groups before the current moment. The method for determining the number of valid data at the current moment needs to satisfy the following relationship: ;in, is the number of valid data at the current moment, is the wind speed at the current moment, is the maximum stable wind speed of the drone, is the number of all wind speeds within and between two consecutive stable wind speed groups before the current moment, where the maximum stable wind speed is set by the drone's own parameters, and the wind speed value range at the current moment should be less than the maximum stable wind speed, because when the wind speed at the current moment is equal to or greater than the maximum stable wind speed of the drone, it means that the drone can no longer maintain balance and may fall, and the drone should be alarmed at this time. The number of valid data That is to say, the number of all wind speeds within and between two consecutive stable wind speed groups before the current moment will be affected by the real-time wind speed. The closer the wind speed at the current moment is to the maximum stable wind speed of the drone, the more likely the balance of the drone will be threatened. Therefore, for safety reasons, the number of valid data used to predict the wind speed of the drone should be reduced.

[0031] In one embodiment, the number of all wind speeds within and between two consecutive stable wind speed groups before the current moment is obtained. , for example: the wind speed groups divided before the current moment are stable wind speed group Z1, abnormal wind speed group Z2, stable wind speed group Z3 and stable wind speed group Z4 in order from near to far in time. The two consecutive stable wind speed groups before the current moment are stable wind speed group Z1 and stable wind speed group Z3, so the number The number includes the number of wind speeds in the stable wind speed group Z1 and the stable wind speed group Z3, and also includes the number of wind speeds in the abnormal wind speed group Z2.

[0032] S104: Perform scheduling based on the drone scheduling safety factor.

[0033] In one embodiment, if the current UAV dispatch safety factor is less than the safety threshold, a return warning is sent to the ground terminal. The empirical value of the safety threshold is 0.3. In other embodiments, the empirical value of the safety threshold may be 0.33 or 0.45.

[0034] The present invention also provides a drone scheduling system for air-ground collaboration. The system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a drone scheduling method for air-ground collaboration according to the first aspect of the present invention is implemented.

[0035] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0036] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions stored or otherwise maintained in such a computer-readable medium.

[0037] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for dispatching unmanned aerial vehicles for air-ground collaboration, characterized in that: include: Collect the power consumption and wind speed of the drone at the same time; Based on the volatility of wind speed collected at each moment along the timeline, it is divided into a stable wind speed group and an abnormal wind speed group, a mapping relationship between the stable wind speed group and power consumption is established, and the drone dispatch safety factor at the current moment is calculated, and the dispatch is performed based on the size of the drone dispatch safety factor at the current moment; Among them, the safety factor of drone dispatch at the current moment is calculated , the calculation formula is: ； In the formula, is the number of abnormal wind speed data in the target time period, is the number of valid data at the current moment, is the remaining power of the drone at the current moment, The time required for the drone to return to the ground terminal. is the acquisition frequency, The target time period The power consumption corresponding to the wind speed is collected at each moment, wherein the number of valid data is related to the wind speed at the current moment and the number of all data within and between two consecutive stable wind speed groups before the current moment, and the target time period is the current moment and the previous moment. The time period consists of all the moments between is the number of time points in the target time period.

2. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 1, characterized in that: The dispatch is performed based on the current drone dispatch safety factor, including: If the UAV dispatch safety factor at the current moment is less than the safety threshold, a return warning request is sent to the ground terminal.

3. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 1, characterized in that: Calculate the number of valid data at the current moment. The calculation formula is: ； in, is the number of valid data at the current moment, is the wind speed at the current moment, is the maximum stable wind speed of the drone, It is the number of all wind speeds within and between two consecutive stable wind speed groups before the current moment, where the maximum stable wind speed is set by the drone's own parameters.

4. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 1, characterized in that: The wind speed collected at each moment is divided into multiple wind speed groups, including: Clustering starts with the wind speed collected at the i-th moment, and the clustering response function value is calculated once each time a moment is added; If the response function value is less than the clustering threshold, the clustering is completed to obtain a cluster, and one cluster corresponds to one wind speed group, wherein the cluster contains all wind speeds collected between the i-th moment and the moment when the response function value is less than the clustering threshold.

5. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 4, characterized in that: The wind speed group is divided into a stable wind speed group and an abnormal wind speed group, including: Judging whether the wind speed group is abnormal according to the number of wind speeds in the wind speed group; If the number of wind speeds in the wind speed group is less than the number threshold, the wind speed group is determined to be an abnormal wind speed group; If the number of wind speeds in the wind speed group is greater than or equal to a number threshold, the wind speed group is determined to be a stable wind speed group.

6. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 1, characterized in that: The calculation method of the response function value of wind speed at each moment is: ； In the formula, For the The response function value of wind speed at a moment, For the Time to The sequence of all wind speed data between the times, For the The wind speed at that moment, The natural constant The exponential function with base , is the mean function, STD is the standard deviation, where the standard deviation reflects the volatility of wind speed.

7. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 1, characterized in that: Establish a mapping relationship between the stable wind speed group and power consumption, including: Obtain the power consumption and normal wind speed at each moment in the stable wind speed group, and obtain a scatter plot of the power consumption and normal wind speed at each moment; The spline fitting tool in the MATLAB tool is used to fit the relationship curve between normal wind speed and power consumption, thereby establishing a mapping relationship between power consumption and normal wind speed.

8. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 4, characterized in that: The K-means clustering method is used for clustering.

9. The method for dispatching unmanned aerial vehicles for air-ground collaboration according to claim 2, characterized in that: The empirical value of the safety threshold is 0.

3.

10. A UAV dispatching system for air-ground collaboration, comprising a processor and a memory, characterized in that: The memory stores a computer program, and the processor executes the computer program to implement the drone scheduling method for air-ground collaboration as described in any one of claims 1-9.

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