UAV flight optimization control method, system, UAV and storage medium

Abstract

The present invention relates to the technical field of unmanned aerial vehicle flight control, and in particular to a method, system, unmanned aerial vehicle and storage medium for optimizing unmanned aerial vehicle flight control. The present invention determines the static flight energy consumption during the flight process by considering the influence of obstacles on the flight route, and determines the dynamic flight energy consumption by considering the influence of wind resistance on the flight process on the basis of the static flight energy consumption; thereby, the difference between the dynamic flight energy consumption and the static flight energy consumption is combined to make the energy loss measurement of the flight route under each planning mode more accurate, so that the planned flight route obtained according to the rationality of the energy loss is more accurate, and solves the problem that the influence of wind resistance and obstacles is not considered, resulting in more power loss and lower efficiency when the unmanned aerial vehicle performs power inspection tasks, so that the effect of unmanned aerial vehicle flight control according to the planned flight route obtained by the present application is better.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle flight control, and in particular to a unmanned aerial vehicle flight optimization control method, a system, a unmanned aerial vehicle and a storage medium. Background Art

[0002] UAVs, or unmanned aircraft, have been widely used in many civil fields as UAV technology matures and their costs decrease. For example, UAVs can be used for aerial photography, agriculture, plant protection, express delivery, wildlife observation, disaster relief, and power inspection. In the field of power inspection, UAVs are usually used for high-altitude photography and inspection. UAVs use infrared imaging technology to detect the heat distribution of power transmission equipment and identify potential power faults such as overloads and short circuits. Compared with manual inspections, they are safer and more efficient.

[0003] During the power inspection process, each drone is usually responsible for inspecting the corresponding power inspection area, and each power inspection area usually includes multiple inspection nodes. Therefore, it is necessary to plan the drone flight route in advance so that the planned flight route can traverse all inspection nodes. Since the longer the planned flight route, the more time and power the drone consumes to perform the power inspection task, the existing technology usually uses the shortest route that can traverse all inspection nodes as the planned flight route for drone flight control.

[0004] However, in the actual power inspection process, the flight of the drone will be affected by wind resistance and obstacles. The shortest planned flight route may be greatly affected by wind resistance and obstacles, resulting in a large amount of power consumption when the drone is controlled along the planned flight route. In addition, drones usually have an upper limit on power consumption. When the remaining power cannot fly to the next inspection node, the drone usually needs to be recovered. Therefore, the more power is consumed during the task execution, the greater the frequency of drone recovery, which affects the efficiency of the drone in performing power inspections. That is, when the prior art controls the flight of the drone along the shortest route that can traverse all inspection nodes, the influence of wind resistance and obstacles is not taken into account, resulting in more power consumption and lower efficiency when the drone performs power inspection tasks. That is, the effect of drone flight control based on the planned flight route obtained according to the prior art is poor. Summary of the invention

[0005] The present application provides a UAV flight optimization control method, system, UAV and storage medium. The static flight energy consumption during the flight is determined by considering the influence of obstacles on the flight route. The dynamic flight energy consumption is determined by considering the influence of wind resistance on the flight process on the basis of the static flight energy consumption. The difference between the dynamic flight energy consumption and the static flight energy consumption is combined to make the energy loss measurement of the flight route under each planning method more accurate, so that the planned flight route obtained according to the rationality of the energy loss is more accurate, which solves the problem that the prior art does not consider the influence of wind resistance and obstacles, resulting in more power loss and lower efficiency when the UAV performs power inspection tasks, so that the effect of UAV flight control according to the planned flight route obtained by the present application is better.

[0006] The first aspect of the present application provides a method for optimizing flight control of a drone, comprising:

[0007] Obtain all inspection nodes in the drone power inspection area where the drone needs to perform inspection tasks and the docking nodes for drone charging;

[0008] Plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of the drone in a single flight, and determine all the flight routes of the drone under each planning method; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes;

[0009] The other inspection nodes outside each inspection node are used as comparison nodes for each inspection node; the static energy consumption of the flight from each comparison node to the corresponding inspection node is determined according to the complexity of the flight route and the length of the flight path of the UAV from each comparison node to the corresponding inspection node;

[0010] According to the correlation between the static energy consumption of the flight from all comparison nodes to the corresponding inspection nodes and the length of the flight path, the node movement cost of each inspection node is determined; according to the influence of the wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost, the dynamic energy consumption of the flight from each comparison node to the corresponding inspection node is determined;

[0011] According to the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption, the rationality of the energy consumption loss under each planning method is determined; the planned flight route of the best planning method is determined according to the rationality of the energy consumption loss; and the UAV flight control is performed according to the planned flight route.

[0012] In an embodiment of the first aspect of the present application, the method for acquiring the flight route includes:

[0013] The UAV takes off from the docking node, flies to each inspection node pre-planned in the flight route in sequence, and then returns to the docking node, which is regarded as a flight route; the power consumption of the flight route is less than the power limit of a single flight of the UAV.

[0014] In an embodiment of the first aspect of the present application, the method for obtaining the flight static energy consumption includes:

[0015] Obtain the flight path between each comparison node and the corresponding inspection node after avoiding obstacles through the path planning algorithm, and determine the instantaneous speed direction of the UAV at each reference moment on the flight path; use the positive correlation mapping value of the angle between the instantaneous speed direction of the UAV at each reference moment and the instantaneous speed direction at the previous reference moment as the direction change value of the UAV at each reference moment;

[0016] The product of the accumulated value of the direction change value of the UAV at all reference moments and the length of the flight path is taken as the static energy consumption of the flight from each comparison node to the corresponding inspection node.

[0017] In an embodiment of the first aspect of the present application, the method for obtaining the node movement cost includes:

[0018] Arrange the flight paths from all comparison nodes to the corresponding inspection nodes in ascending order of length to obtain a flight path sequence corresponding to each inspection node; determine the flight static energy consumption sequence and the flight path length sequence according to the flight path arrangement sequence in the flight path;

[0019] The negative correlation mapping value of the Pearson correlation coefficient between the flight static energy consumption sequence and the flight path length sequence is used as the node movement cost of each inspection node.

[0020] In an embodiment of the first aspect of the present application, the method for obtaining the flight dynamic energy consumption includes:

[0021] On the flight path of the UAV from each comparison node to the corresponding inspection node, the instantaneous speed direction at each reference moment and the angle between the wind direction are positively correlated to obtain the drag coefficient of the UAV at each reference moment;

[0022] The average drag coefficient of the drone at all reference times is used as the dynamic energy consumption weight from each comparison node to the corresponding inspection node;

[0023] The product of the dynamic energy consumption weight from each comparison node to the corresponding inspection node, the flight static energy consumption and the node movement cost of the inspection node is used as the flight dynamic energy consumption from each comparison node to the corresponding inspection node.

[0024] In an embodiment of the first aspect of the present application, the method for obtaining the rationality of energy consumption loss includes:

[0025] Arrange the inspection nodes on each flight route in sequence to obtain an inspection node reference sequence for each flight route; calculate the energy consumption difference between the flight dynamic energy consumption from each inspection node to the next inspection node in the inspection node reference sequence and the flight dynamic energy consumption;

[0026] The cumulative value of all energy consumption differences in the inspection node reference sequence of each flight route is used as the flight energy loss value of each flight route; the cumulative value of all flight dynamic energy consumption in the inspection node reference sequence of each flight route is used as the flight energy consumption value of each flight route; the product of the normalized value of the flight energy consumption value and the flight energy loss value is used as the energy consumption loss reference value of each flight route;

[0027] The accumulated values ​​of the energy loss reference values ​​of all flight routes under each planning method are negatively correlated to determine the rationality of the energy loss under each planning method.

[0028] In an embodiment of the first aspect of the present application, the method for acquiring the planned flight route includes:

[0029] All flight routes in the planning method with the maximum reasonable energy loss are taken as the planned flight routes of the optimal planning method.

[0030] In a second aspect, the present application provides a UAV flight optimization control system, the system comprising:

[0031] A data acquisition module is used to obtain all inspection nodes in the UAV power inspection area where the UAV needs to perform inspection tasks and the docking nodes for UAV charging;

[0032] The first determination module is used to plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of a single flight of the drone, and determine all flight routes of the drone under each planning method; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes;

[0033] The second determination module is used to use other inspection nodes outside each inspection node as comparison nodes for each inspection node; according to the complexity of the flight route and the length of the flight path of the UAV from each comparison node to the corresponding inspection node, determine the flight static energy consumption from each comparison node to the corresponding inspection node;

[0034] The third determination module is used to determine the node movement cost of each inspection node according to the correlation between the static energy consumption of the flight from all comparison nodes to the corresponding inspection nodes and the flight path length; determine the dynamic energy consumption of the flight from each comparison node to the corresponding inspection node according to the influence of the wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost;

[0035] The UAV flight control module is used to determine the rationality of energy consumption loss under each planning method based on the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption; determine the planned flight route of the best planning method based on the rationality of the energy consumption loss; and perform UAV flight control according to the planned flight route.

[0036] In a third aspect, the present application provides a drone, comprising a fuselage and a processor; the fuselage is provided with a flight power system for providing flight power; the processor is used to receive route information sent by a drone control device, the route information including a planned flight route obtained by the method of the first aspect of the present application or any embodiment of the first aspect; in response to a control instruction sent by the drone control device, the drone is controlled to fly along the planned flight route.

[0037] In a fourth aspect, the present application provides a computer device, comprising a memory and a processor. The memory is used to store computer program code, and the processor is used to call and run the computer program code from the memory to execute the method of the first aspect of the present application or any embodiment of the first aspect.

[0038] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program code, and when the computer program code is executed, the method of the first aspect or any embodiment of the first aspect of the present application is performed.

[0039] In a sixth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program code. When the computer program code is executed, the method of the first aspect or any embodiment of the first aspect of the present application is executed.

[0040] This application has the following beneficial effects:

[0041] Considering that a single UAV flight needs to take off from a docking node for charging, fly to each inspection node in turn along the predetermined flight route to perform tasks, and return to the docking node for charging before the battery is exhausted, and continue to traverse each inspection node after being fully charged until all inspection nodes are inspected. Therefore, when planning the flight route, it is necessary to consider all inspection nodes, docking nodes, and the upper limit of the power of a single flight of the UAV, and each planning method usually includes multiple flight routes. It is further necessary to consider the influence of power consumption and wind resistance to evaluate the rationality of each planning method, so as to select the optimal planned flight route.

[0042] According to the principle that the shortest distance between two points is a straight line, the shortest flight path between two inspection nodes is a straight line. However, in the actual inspection environment, there are usually obstacles on the straight path, so in order to avoid obstacles, the flight path of the drone usually contains curves. For any two inspection nodes, the greater the fluctuation of the corresponding flight path curve, the more complex the corresponding flight path is in order to avoid obstacles, that is, the higher the difficulty of obstacle avoidance, and the greater the static energy consumption of flight; and the longer the flight path, the greater the corresponding static energy consumption of flight; therefore, the static energy consumption of flight without wind resistance is determined by combining the complexity of the flight route and the length of the flight path.

[0043] Furthermore, for each inspection node, the more obstacles there are near it, the more difficult it is to avoid obstacles when flying to the inspection node, and the greater the corresponding node movement cost. In the absence of obstacles, combined with the principle of obtaining flight static energy consumption, the flight static energy consumption and the flight path length should have an obvious positive correlation; the more obstacles there are near the inspection node, the greater the impact of obstacles on the flight static energy consumption of other inspection nodes when flying to the node, and the worse the correlation between the flight static energy consumption and the flight path length; therefore, the node movement cost of each inspection node is determined based on the correlation between the flight static energy consumption and the flight path length from all comparison nodes to the corresponding inspection node.

[0044] It is further necessary to consider that the angle between the wind direction and the flight direction of the UAV will affect the wind resistance of the UAV flight, thereby affecting the flight dynamic energy consumption of the flight path; and for each inspection node, the greater the corresponding node movement cost, the greater the degree of difficulty in obstacle avoidance when other inspection nodes fly to the inspection node, the more difficult the obstacle avoidance action required by the UAV on the flight path, and the greater the possibility of increased dynamic energy consumption caused by the corresponding random factors; therefore, combined with the impact of wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost, a more accurate flight dynamic energy consumption is determined.

[0045] The greater the difference between the dynamic flight energy consumption and the static flight energy consumption, the greater the power loss due to wind resistance and obstacle avoidance, and the more unreasonable the corresponding energy loss. Similarly, the greater the overall flight energy consumption under the planning method, the greater the corresponding total power consumption, and the worse the rationality of the planning method. Therefore, by combining the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption, the rationality of the energy loss under each planning method is obtained, and then the more accurate planned flight route corresponding to the best planning method is selected according to the rationality of the energy loss, so that less power is lost and the efficiency is higher when flying according to the planned flight route, that is, the effect of UAV flight control according to the planned flight route is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0047] Figure 1 A flow chart of a UAV flight optimization control method provided by one embodiment of the present invention;

[0048] Figure 2 A flow chart of a method for obtaining flight dynamic energy consumption of a UAV flight optimization control method provided by an embodiment of the present invention;

[0049] Figure 3 A structural diagram of a UAV flight optimization control system provided by one embodiment of the present invention;

[0050] Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a UAV flight optimization control method, system, UAV and storage medium proposed in accordance with the present invention, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment, and the specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0052] 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.

[0053] The following is a detailed description of a specific solution of a UAV flight optimization control method, system, UAV and storage medium provided by the present invention in conjunction with the accompanying drawings.

[0054] This application embodiment provides a method for optimizing the flight control of a drone. Figure 1 , which shows a flow chart of a method for optimizing flight control of a drone provided by an embodiment of the present invention, the method comprising:

[0055] Step S101: Obtain all inspection nodes in the drone power inspection area where the drone needs to perform inspection tasks and the docking nodes for charging the drone.

[0056] In the field of power inspection, the target or node that needs to be inspected is usually the support column or power tower of the transmission line. Therefore, in a specific implementation of the embodiment of the present invention, the support column and power tower of the transmission line are used as inspection nodes; and the UAV power inspection area is set to 9 square kilometers. It is further necessary to consider that there is an upper limit on the power of a single flight of the UAV, which makes it usually impossible for the UAV to traverse all inspection nodes at one time, so it is necessary to recycle and charge multiple times; therefore, it is necessary to obtain all the docking nodes in the UAV power inspection area to facilitate the recovery and charging of the UAV. In a specific implementation of the embodiment of the present invention, 9 UAV landing platforms are evenly set in the power inspection area for UAV recovery, charging and adjustment, and the UAV landing platform is used as a docking node. It should be noted that the implementer can adjust the number of UAV landing platforms according to the specific implementation environment, and can adjust the placement of the UAV landing platform by itself, such as random setting, which will not be further described here. In addition, it should be noted that since the present application needs to take into account the influence of wind resistance on the flight of the UAV, before planning, the wind direction and wind speed of the local power inspection area are determined in advance by setting a wind speed and wind direction meter at the center of the area to facilitate subsequent analysis.

[0057] Step S102: Plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of the drone for a single flight, and determine all flight routes of the drone under each planning method; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes.

[0058] Considering that a single UAV flight needs to take off from a docking node for charging, fly to each inspection node in turn along the predetermined flight route to perform tasks, and return to the docking node for charging before the battery is exhausted, and continue to traverse each inspection node after being fully charged until all inspection nodes are inspected. Therefore, the UAV flight route is planned according to all inspection nodes, docking nodes, and the upper limit of the power of a single flight of the UAV, and all the flight routes of the UAV under each planning method are determined. In the subsequent, the rationality of the flight route under each planning method is evaluated, so as to screen out the optimal planned flight route corresponding to the optimal planning method.

[0059] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for obtaining the flight route in step S102 includes:

[0060] The route in which the drone takes off from the docking node, flies to each inspection node pre-planned in the flight route in sequence, and then returns to the docking node is taken as a flight route; the power consumption of the flight route is less than the upper limit of the power of the drone for a single flight. That is, when planning the flight route, it is necessary to ensure that after the task of the last inspection node is completed, the drone has sufficient power to return to the nearest docking node. In a specific implementation of an embodiment of the present invention, all planning methods that can traverse all inspection nodes are obtained, and the total flight length under each planning method is calculated; the planning method with a normalized value of the total flight length greater than the preset length threshold is used as the planning method for subsequent analysis of the embodiment of the present invention; the preset length threshold is set to 0.8, and the implementer can adjust it according to the specific implementation environment. By excluding planning methods with relatively long lengths, the analysis scope of the subsequent planned routes can be smaller and more valuable for reference.

[0061] Step S103: Use other inspection nodes outside each inspection node as comparison nodes for each inspection node; determine the flight static energy consumption from each comparison node to the corresponding inspection node based on the complexity of the flight route and the length of the flight path of the drone from each comparison node to the corresponding inspection node.

[0062] According to the principle that the shortest distance between two points is a straight line, the shortest flight path between two inspection nodes is a straight line. However, in the actual inspection environment, there are usually obstacles on the straight path, so in order to avoid obstacles, the flight path of the drone usually contains curves. For any two inspection nodes, the greater the fluctuation of the corresponding flight path curve, the more complex the corresponding flight path is in order to avoid obstacles, that is, the higher the difficulty of obstacle avoidance, and the greater the static energy consumption of flight; and the longer the flight path, the greater the corresponding static energy consumption of flight; therefore, the static energy consumption of flight without wind resistance is determined by combining the complexity of the flight route and the length of the flight path.

[0063] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for obtaining the flight static energy consumption in step S103 includes:

[0064] Obtain the flight path between each comparison node and the corresponding inspection node after avoiding obstacles through the path planning algorithm, and determine the instantaneous speed direction of the drone at each reference moment on the flight path; the positive correlation mapping value of the angle between the instantaneous speed direction of the drone at each reference moment and the instantaneous speed direction at the previous reference moment is used as the direction change value of the drone at each reference moment; in a specific implementation of the embodiment of the present invention, the time interval between two adjacent reference moments in time sequence is set to once per second, that is, the instantaneous speed direction of the drone on the flight path between the two inspection nodes is detected once every second. It should be noted that in the embodiment of the present invention, the drone flies along the flight path in a uniform speed, so the position of the drone at each reference moment can be determined by performing path planning in advance, so as to judge the instantaneous speed direction according to the position of the drone. In a specific implementation of the embodiment of the present invention, the path planning algorithm adopts the A star algorithm, and the implementer can also adopt other path planning methods according to the specific implementation environment, such as the D star algorithm or the Dijkstra algorithm, and the A star algorithm, the D star algorithm and the Dijkstra algorithm are prior arts well known to those skilled in the art, and are not further defined and elaborated here.

[0065] The larger the overall direction change value corresponding to the drone, the greater the fluctuation of the drone's flight path curve, the more complex the flight path, and the longer the flight path, the greater the energy consumption of the flight path. Therefore, the product of the accumulated value of the drone's direction change value at all reference moments and the length of the flight path is further used as the static energy consumption of the flight from each comparison node to the corresponding inspection node. The larger the static energy consumption of the flight, the more energy is required to fly when obstacles are considered, and the static energy consumption of the flight also represents the energy consumption of the flight path between two inspection nodes without considering the influence of wind resistance.

[0066] In a specific implementation of the embodiment of the present invention, the process of obtaining the static energy consumption of flight is expressed by the formula: ;in, For the Inspection nodes to The static energy consumption of a patrol node in flight, and The values ​​are different; For drones from Inspection nodes to The length of the flight path of each inspection node; For drones from Inspection nodes to The number of reference moments on the flight path of each inspection node; For drones from Inspection nodes to The first inspection node on the flight path The direction change value at the reference moment, that is, the direction change value of the drone at the The angle between the instantaneous velocity direction at a reference moment and the instantaneous velocity direction at the previous reference moment; is an inverse tangent function, which is used to adjust the dimension of the direction change value; it should be noted that, in the embodiment of the present invention, the direction change value at the first reference moment is set to 0.

[0067] Step S104: Determine the node movement cost of each inspection node based on the correlation between the static flight energy consumption and the flight path length from all comparison nodes to the corresponding inspection nodes; determine the dynamic flight energy consumption from each comparison node to the corresponding inspection node based on the impact of wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost.

[0068] Furthermore, for each inspection node, the more obstacles there are near it, the more difficult it is to avoid obstacles when flying to the inspection node, and the greater the corresponding node movement cost. In the absence of obstacles, combined with the principle of obtaining flight static energy consumption, the flight static energy consumption and the flight path length should have an obvious positive correlation; the more obstacles there are near the inspection node, the greater the impact of obstacles on the flight static energy consumption of other inspection nodes when flying to the node, and the worse the correlation between the flight static energy consumption and the flight path length; therefore, the node movement cost of each inspection node is determined based on the correlation between the flight static energy consumption and the flight path length from all comparison nodes to the corresponding inspection node.

[0069] It is also necessary to consider that the angle between the wind direction and the flight direction of the UAV will affect the wind resistance of the UAV flight, thereby affecting the flight dynamic energy consumption of the flight path; and for each inspection node, the greater the corresponding node movement cost, the greater the degree of difficulty in obstacle avoidance when other inspection nodes fly to the inspection node, the more difficult the obstacle avoidance action required by the UAV on the flight path, and the greater the possibility of increased dynamic energy consumption caused by the corresponding random factors; therefore, combined with the impact of wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost, a more accurate flight dynamic energy consumption can be determined.

[0070] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for obtaining the node movement cost in step S104 includes:

[0071] The flight paths from all comparison nodes to the corresponding inspection nodes are arranged in ascending order of length to obtain the flight path sequence corresponding to each inspection node; the flight static energy consumption sequence and the flight path length sequence are determined according to the arrangement order of the flight paths in the flight path; the negative correlation mapping value of the Pearson correlation coefficient between the flight static energy consumption sequence and the flight path length sequence is used as the node movement cost of each inspection node.

[0072] According to the characteristics that the more obstacles there are near the inspection node, the more the static energy consumption of other inspection nodes will be affected by the obstacles when flying to the node, and the worse the correlation between the static energy consumption of flight and the length of the flight path; all flight paths corresponding to the inspection node are arranged in order of length from small to large to obtain a flight path sequence; if the positive correlation between the flight path length sequence and the static energy consumption sequence of the flight path sequence is greater or more obvious, it means that the fewer obstacles there are near the inspection node, the smaller the flight cost of other inspection nodes moving to the inspection node, and vice versa; therefore, the positive correlation between the flight path length sequence and the static energy consumption sequence is characterized by the numerical value of the Pearson correlation coefficient, and according to the relationship between the positive correlation and the flight cost of the inspection node movement, the obtained Pearson correlation coefficient is further negatively mapped to obtain the required node movement cost. As the node movement cost of the inspection node increases, the obstacle avoidance difficulty of other inspection nodes when flying toward the inspection node increases, the obstacle avoidance action required by the UAV on the flight path increases, and the possibility of increased dynamic energy consumption caused by the corresponding random factors increases; that is, the greater the node movement cost, the greater the dynamic energy consumption of the flight on the flight path from other inspection nodes to the inspection node.

[0073] In a specific implementation of the embodiment of the present invention, a method for obtaining the node movement cost of the inspection node is expressed in the formula as follows: ;in, For the The node movement cost of each inspection node; For the The Pearson correlation coefficient between the flight static energy consumption sequence and the flight path length sequence corresponding to the inspection node; The first adjustment parameter is preset to 2, so that the minimum value of the node movement cost is 1, and the subsequently calculated flight dynamic energy consumption is more in line with objective facts. The implementer can adjust the size of the preset first adjustment parameter according to the specific implementation environment, but it must be greater than or equal to 2 to conform to objective facts.

[0074] In other possible implementations of the embodiments of the present invention, the process of obtaining the node movement cost is expressed in the formula as follows: ;in, For the The node movement cost of each inspection node; For the The number of flight paths in the flight path sequence corresponding to the inspection node; For the The first inspection node in the flight path sequence Static energy consumption of the flight path; For the The first inspection node in the flight path sequence Static energy consumption of the flight path; is the absolute value symbol; A second adjustment parameter is preset to prevent the denominator from being 0, and a corresponding value is added to the numerator to keep the numerator and denominator corresponding; is the normalization function, and the normalization method uses linear normalization. When the flight static energy consumption sequence is monotonically increasing, it means that the flight static energy consumption sequence and the flight path length sequence are both monotonically increasing sequences, and therefore have a high positive correlation; therefore The smaller the value is, the more the corresponding flight static energy consumption sequence shows a monotonically increasing trend, the greater the positive correlation between the flight static energy consumption sequence and the flight path length sequence, and the smaller the node movement cost is. Therefore, it is normalized and a positive integer 1 is added to make the value of the node movement cost more in line with objective facts.

[0075] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for obtaining the flight dynamic energy consumption in step S104 includes: Figure 2 , which shows a flow chart of a method for obtaining flight dynamic energy consumption of a UAV flight optimization control method provided by an embodiment of the present invention, the method comprising:

[0076] Step S201: On the flight path of the UAV from each comparison node to the corresponding inspection node, the instantaneous speed direction at each reference moment is positively correlated with the angle of the wind direction to obtain the drag coefficient of the UAV at each reference moment.

[0077] On any flight path, the larger the angle between the flight direction of the drone at each sampling moment, that is, the instantaneous speed direction and the wind direction, the greater the impact of wind resistance on the drone during flight. Therefore, the drag coefficient is obtained to measure the degree to which the drone is affected by wind resistance at each sampling moment.

[0078] Step S202: The average value of the drag coefficient of the UAV at all reference times is used as the dynamic energy consumption weight from each comparison node to the corresponding inspection node.

[0079] The drag coefficients of the drone at all reference moments on the corresponding flight path are combined in the form of an average, so as to characterize the dynamic energy consumption weight on the flight path from each comparison node to the corresponding inspection node at the overall level. In other words, the degree to which the flight path is affected by wind resistance is characterized by the dynamic energy consumption weight.

[0080] Step S203: The product of the dynamic energy consumption weight from each comparison node to the corresponding inspection node, the flight static energy consumption and the node movement cost of the inspection node is used as the flight dynamic energy consumption from each comparison node to the corresponding inspection node.

[0081] The dynamic energy consumption of flight is the energy consumption of the flight path under the influence of wind resistance, and the static energy consumption of flight is the energy consumption of the flight path without considering the influence of wind resistance. The dynamic energy consumption weight is combined to characterize the degree to which the flight path is affected by wind resistance and the relationship between the node movement cost and the dynamic energy consumption of flight. Therefore, the static energy consumption of wind direction is weighted by the dynamic energy consumption weight and the node movement cost, so as to obtain a more accurate energy consumption of the flight path under the influence of wind resistance and obstacles.

[0082] In a specific implementation of the embodiment of the present invention, the process of obtaining the flight dynamic energy consumption is expressed by the formula: ; For the Inspection nodes to The flight dynamic energy consumption of each inspection node, and The values ​​are different; For the Inspection nodes to The static energy consumption of each inspection node in flight; For drones from Inspection nodes to The number of reference moments on the flight path of each inspection node; For drones from Inspection nodes to The first inspection node on the flight path The angle between the instantaneous velocity direction and the wind direction at a reference moment; is the inverse tangent function; For drones from Inspection nodes to The first inspection node on the flight path The drag coefficient at a reference time; right Positive correlation mapping can make the minimum value of the drag coefficient 1; when the instantaneous speed direction at all reference moments on the flight path is consistent with the wind direction, that is, when it is not affected by wind resistance, the calculated dynamic energy consumption weight is 1, which is consistent with objective facts; For the Inspection nodes to The dynamic energy consumption weight of each inspection node.

[0083] Step S105: Determine the rationality of energy consumption loss under each planning method according to the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption; determine the planned flight route of the best planning method according to the rationality of energy consumption loss; and perform UAV flight control according to the planned flight route.

[0084] The greater the difference between the dynamic flight energy consumption and the static flight energy consumption, the greater the power loss due to wind resistance and obstacle avoidance, and the more unreasonable the corresponding energy loss. Similarly, the greater the overall flight energy consumption under the planning method, the greater the corresponding total power consumption, and the worse the rationality of the planning method. Therefore, by combining the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption, the rationality of the energy loss under each planning method is obtained, and then the more accurate planned flight route corresponding to the best planning method is selected according to the rationality of the energy loss, so that less power is lost and the efficiency is higher when flying according to the planned flight route, that is, the effect of UAV flight control according to the planned flight route is better.

[0085] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for obtaining the rationality of energy consumption loss in step S105 includes:

[0086] The inspection nodes on each flight route are arranged in sequence to obtain the inspection node reference sequence of each flight route; the energy consumption difference between the flight dynamic energy consumption from each inspection node to the next inspection node in the inspection node reference sequence and the flight dynamic energy consumption is calculated; the cumulative value of all energy consumption differences in the inspection node reference sequence of each flight route is used as the flight energy loss value of each flight route; the cumulative value of all flight dynamic energy consumption in the inspection node reference sequence of each flight route is used as the flight energy consumption value of each flight route; the product of the normalized value of the flight energy consumption value and the flight energy loss value is used as the energy loss reference value of each flight route; the cumulative value of the energy loss reference values ​​of all flight routes under each planning method is negatively correlated and mapped to determine the rationality of the energy loss under each planning method.

[0087] First, each flight route includes at least two inspection nodes, and according to the flight order of the flight route, two adjacent inspection nodes will correspond to a flight path; therefore, for each flight route, the larger the corresponding cumulative value of all energy consumption differences, that is, the larger the flight energy loss value, the greater the energy loss of the flight route due to wind resistance and obstacles; and for each flight route, the larger the corresponding cumulative value of all flight dynamic energy consumption, that is, the larger the flight energy consumption value, the greater the total energy consumption on the wind direction route; the greater the energy loss and the greater the energy consumption, the more unreasonable the planning of the flight path, and the greater the corresponding energy loss reference value. Each planning method usually includes at least two flight routes, so the larger the cumulative value of the energy loss reference value of all flight routes under each planning method, the larger the total energy consumption and total energy loss corresponding to the planning method, and the more unreasonable the corresponding planning, so the cumulative value is negatively correlated to obtain the rationality of the energy loss of the corresponding planning method.

[0088] In a specific implementation of the embodiment of the present invention, the process of obtaining the rationality of energy consumption loss is expressed by the formula: ;in, For the Reasonableness of energy loss under each planning method; For the The number of flight routes under each planning method; For the The first The number of flight paths in the flight route, that is, the energy consumption difference, the static energy consumption of the flight, or the dynamic energy consumption of the flight, that is, the The first The number of inspection nodes in the flight route minus the positive integer 1; For the The first Flight route Dynamic energy consumption of flight paths; For the The first Flight route Static energy consumption of the flight path; For the The first Flight route The length of the flight path; For the The first The flight energy consumption value of the flight route; For the The first The flight energy loss value of the flight route; For the The first Energy loss reference value for the flight route; is the normalization function, and the normalization method uses linear normalization.

[0089] Preferably, in some possible implementations of the embodiments of the present invention, the specific implementation process of the method for acquiring the planned flight route in step S105 includes:

[0090] All flight routes in the planning method with the greatest rationality of energy consumption loss are used as the planned flight routes of the optimal planning method. Since the greater the rationality of energy consumption loss, the smaller the energy consumption loss of the corresponding planning method, the flight route of the planning method with the greatest rationality of energy consumption loss is used as the planned flight route for UAV flight control, so that the total amount of energy consumed by UAV flight control and the energy loss are as small as possible, so that the power consumption is less and the efficiency is higher when flying according to the planned flight route, that is, the effect of UAV flight control according to the planned flight route is better.

[0091] To summarize, a method for optimizing and controlling UAV flight determines the static energy consumption of the flight during the flight by considering the impact of obstacles on the flight route, and determines the dynamic energy consumption of the flight by considering the impact of wind resistance on the flight process on the basis of the static energy consumption of the flight; thereby combining the difference between the dynamic energy consumption of the flight and the static energy consumption of the flight to make the energy loss measurement of the flight route under each planning method more accurate, thereby making the planned flight route obtained based on the rationality of the energy loss more accurate, solving the problem of more power loss and lower efficiency when the UAV performs power inspection tasks due to not considering the impact of wind resistance and obstacles, and making the effect of UAV flight control according to the planned flight route obtained in this application better.

[0092] This application also provides a UAV flight optimization control system, please refer to Figure 3 , which shows a structural diagram of a drone flight optimization control system provided by an embodiment of the present invention, the system includes: a data acquisition module 301, a first determination module 302, a second determination module 303, a third determination module 304 and a drone flight control module 305.

[0093] The data acquisition module 301 is used to acquire all inspection nodes in the UAV power inspection area where the UAV needs to perform inspection tasks and the docking nodes for charging the UAV;

[0094] The first determination module 302 is used to plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of a single flight of the drone, and determine all flight routes of the drone under each planning mode; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes;

[0095] The second determination module 303 is used to use other inspection nodes outside each inspection node as comparison nodes for each inspection node; and determine the flight static energy consumption from each comparison node to the corresponding inspection node according to the complexity of the flight route and the length of the flight path of the UAV from each comparison node to the corresponding inspection node;

[0096] The third determination module 304 is used to determine the node movement cost of each inspection node according to the correlation between the static energy consumption of the flight from all comparison nodes to the corresponding inspection node and the flight path length; determine the dynamic energy consumption of the flight from each comparison node to the corresponding inspection node according to the influence of the wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost;

[0097] The UAV flight control module 305 is used to determine the rationality of energy consumption loss under each planning method based on the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption; determine the planned flight route of the best planning method based on the rationality of energy consumption loss; and perform UAV flight control according to the planned flight route.

[0098] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the UAV flight optimization control system and the UAV flight optimization control method embodiment provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0099] An embodiment of the present application also provides a drone, which includes a fuselage and a processor; the fuselage is provided with a flight power system for providing flight power; the processor is used to receive route information sent by a drone control device, the route information including a planned flight route obtained by any one of the drone flight optimization control methods introduced above; and in response to a control instruction sent by the drone control device, the drone is controlled to fly along the planned flight route.

[0100] The present application also provides a computer device. Figure 4, which shows a schematic diagram of the structure of a computer device provided by an embodiment of the present invention, the computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the computer device can execute any one of the UAV flight optimization control methods introduced above.

[0101] An embodiment of the present application also provides a computer program product. When the computer program product is run on a computer device, the computer device can execute any one of the UAV flight optimization control methods introduced above.

[0102] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code runs on a computer device, the computer device can execute any of the drone flight optimization control methods introduced above.

[0103] In the embodiments provided in the present application, it should be understood that the provided computer device, computer program product and computer-readable storage medium are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the methods provided above and will not be repeated here.

[0104] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for optimizing the flight control of an unmanned aerial vehicle, characterized in that: The method comprises: Obtain all inspection nodes in the drone power inspection area where the drone needs to perform inspection tasks and the docking nodes for drone charging; Plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of the drone in a single flight, and determine all the flight routes of the drone under each planning method; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes; The other inspection nodes outside each inspection node are used as comparison nodes for each inspection node; the static energy consumption of the flight from each comparison node to the corresponding inspection node is determined according to the complexity of the flight route and the length of the flight path of the UAV from each comparison node to the corresponding inspection node; According to the correlation between the static energy consumption of the flight from all comparison nodes to the corresponding inspection nodes and the length of the flight path, the node movement cost of each inspection node is determined; according to the influence of the wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost, the dynamic energy consumption of the flight from each comparison node to the corresponding inspection node is determined; Determine the rationality of energy loss under each planning method according to the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption; determine the planned flight route of the best planning method according to the rationality of the energy loss; and control the flight of the UAV according to the planned flight route; The method for obtaining the flight static energy consumption includes: Obtain the flight path between each comparison node and the corresponding inspection node after avoiding obstacles through the path planning algorithm, and determine the instantaneous speed direction of the UAV at each reference moment on the flight path; use the positive correlation mapping value of the angle between the instantaneous speed direction of the UAV at each reference moment and the instantaneous speed direction at the previous reference moment as the direction change value of the UAV at each reference moment; The product of the accumulated value of the direction change value of the UAV at all reference moments and the length of the flight path is used as the flight static energy consumption from each comparison node to the corresponding inspection node; The method for obtaining the rationality of the energy consumption loss includes: Arrange the inspection nodes on each flight route in sequence to obtain an inspection node reference sequence for each flight route; calculate the energy consumption difference between the flight dynamic energy consumption from each inspection node to the next inspection node in the inspection node reference sequence and the flight dynamic energy consumption; The cumulative value of all energy consumption differences in the inspection node reference sequence of each flight route is used as the flight energy loss value of each flight route; the cumulative value of all flight dynamic energy consumption in the inspection node reference sequence of each flight route is used as the flight energy consumption value of each flight route; the product of the normalized value of the flight energy consumption value and the flight energy loss value is used as the energy consumption loss reference value of each flight route; The accumulated values ​​of the energy loss reference values ​​of all flight routes under each planning method are negatively correlated to determine the rationality of the energy loss under each planning method; The method for acquiring the planned flight route includes: All flight routes in the planning method with the maximum reasonable energy loss are taken as the planned flight routes of the optimal planning method.

2. The method for optimizing the flight control of an unmanned aerial vehicle according to claim 1, characterized in that: The method for obtaining the flight route includes: The UAV takes off from the docking node, flies to each inspection node pre-planned in the flight route in sequence, and then returns to the docking node, which is regarded as a flight route; the power consumption of the flight route is less than the power limit of a single flight of the UAV.

3. The method for optimizing flight control of an unmanned aerial vehicle according to claim 1, characterized in that: The method for obtaining the node movement cost includes: Arrange the flight paths from all comparison nodes to the corresponding inspection nodes in ascending order of length to obtain a flight path sequence corresponding to each inspection node; determine the flight static energy consumption sequence and the flight path length sequence according to the flight path arrangement sequence in the flight path; The negative correlation mapping value of the Pearson correlation coefficient between the flight static energy consumption sequence and the flight path length sequence is used as the node movement cost of each inspection node.

4. The method for optimizing the flight control of an unmanned aerial vehicle according to claim 1, characterized in that: The method for obtaining the flight dynamic energy consumption includes: On the flight path of the UAV from each comparison node to the corresponding inspection node, the instantaneous speed direction at each reference moment and the angle between the wind direction are positively correlated to obtain the drag coefficient of the UAV at each reference moment; The average of the drag coefficient of the drone at all reference times is used as the dynamic energy consumption weight from each comparison node to the corresponding inspection node; The product of the dynamic energy consumption weight from each comparison node to the corresponding inspection node, the flight static energy consumption and the node movement cost of the inspection node is used as the flight dynamic energy consumption from each comparison node to the corresponding inspection node.

5. A UAV flight optimization control system, characterized in that: The system comprises: A data acquisition module is used to obtain all inspection nodes in the UAV power inspection area where the UAV needs to perform inspection tasks and the docking nodes for UAV charging; The first determination module is used to plan the flight route of the drone according to all inspection nodes, docking nodes and the upper limit of the power of a single flight of the drone, and determine all flight routes of the drone under each planning method; wherein all flight routes include all inspection nodes and each flight route includes at least two inspection nodes; The second determination module is used to use other inspection nodes outside each inspection node as comparison nodes for each inspection node; according to the complexity of the flight route and the length of the flight path of the UAV from each comparison node to the corresponding inspection node, determine the flight static energy consumption from each comparison node to the corresponding inspection node; The method for obtaining the flight static energy consumption includes: Obtain the flight path between each comparison node and the corresponding inspection node after avoiding obstacles through the path planning algorithm, and determine the instantaneous speed direction of the UAV at each reference moment on the flight path; use the positive correlation mapping value of the angle between the instantaneous speed direction of the UAV at each reference moment and the instantaneous speed direction at the previous reference moment as the direction change value of the UAV at each reference moment; The product of the accumulated value of the direction change value of the UAV at all reference moments and the length of the flight path is used as the flight static energy consumption from each comparison node to the corresponding inspection node; The third determination module is used to determine the node movement cost of each inspection node according to the correlation between the static energy consumption of the flight from all comparison nodes to the corresponding inspection nodes and the flight path length; determine the dynamic energy consumption of the flight from each comparison node to the corresponding inspection node according to the influence of the wind direction on the flight of the UAV from each comparison node to the corresponding inspection node and the node movement cost; The UAV flight control module is used to determine the rationality of energy consumption loss under each planning method according to the difference between the overall flight dynamic energy consumption and the overall flight static energy consumption of all flight routes under each planning method and the overall flight energy consumption; determine the planned flight route of the best planning method according to the rationality of the energy consumption loss; and perform UAV flight control according to the planned flight route; The method for obtaining the rationality of the energy consumption loss includes: Arrange the inspection nodes on each flight route in sequence to obtain an inspection node reference sequence for each flight route; calculate the energy consumption difference between the flight dynamic energy consumption from each inspection node to the next inspection node in the inspection node reference sequence and the flight dynamic energy consumption; The cumulative value of all energy consumption differences in the inspection node reference sequence of each flight route is used as the flight energy loss value of each flight route; the cumulative value of all flight dynamic energy consumption in the inspection node reference sequence of each flight route is used as the flight energy consumption value of each flight route; the product of the normalized value of the flight energy consumption value and the flight energy loss value is used as the energy consumption loss reference value of each flight route; The accumulated values ​​of the energy loss reference values ​​of all flight routes under each planning method are negatively correlated to determine the rationality of the energy loss under each planning method; The method for acquiring the planned flight route includes: All flight routes in the planning method with the maximum reasonable energy loss are taken as the planned flight routes of the optimal planning method.

6. A drone, characterized in that: The UAV includes a fuselage and a processor; the fuselage is provided with a flight power system for providing flight power; the processor is used to receive route information sent by a UAV control device, the route information including a planned flight route obtained by a UAV flight optimization control method as described in any one of claims 1 to 4; in response to a control instruction sent by the UAV control device, the UAV is controlled to fly along the planned flight route.

7. A computer-readable storage medium storing computer program code, characterized in that: When the computer program code is executed by a computer, the steps of the unmanned aerial vehicle flight optimization control method as described in any one of claims 1 to 4 are implemented.

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