A control method, device and equipment for unmanned aerial vehicle power

Abstract

The present application is applicable to the field of equipment control technology, and provides a control method, device and equipment for drone power, including: in response to a control instruction initiated by a user to a drone, determining an initial power vector corresponding to the control instruction; determining the shortest distance between the first position and the no-fly zone corresponding to the first position according to the current first position of the drone; importing the initial power vector and the shortest distance into a preset power correction algorithm, calculating the correction power vector corresponding to the initial power vector; and controlling the movement of the drone based on the correction power vector. The above method can be used to perform power correction on the initial power vector corresponding to the control instruction, so as to avoid the drone engine from being kept running at high power when approaching a no-fly zone, and avoid the situation of mistakenly entering a no-fly zone, thereby improving the accuracy and safety of drone control, and reducing the situation of control failure caused by the drone entering a no-fly zone.

Description

Technical Field

[0001] The present application belongs to the field of equipment control technology, and in particular, relates to a control method, device and equipment applied to unmanned aerial vehicle power. Background Art

[0002] Drones, as one of the most popular flying devices today, are widely used in many fields, such as material transportation, aerial photography, and rescue. How to effectively and accurately control drones has become a focus of users. With existing drone control technology, users can send control instructions to drones through a controller to control the drone to move in a specified direction. However, with existing drone control technology, if the drone continues to run at a high speed when approaching a no-fly zone, it is easy for the drone to enter the no-fly zone, which may cause the drone to be uncontrollable, reducing the safety of drone operation and the accuracy of control. Summary of the invention

[0003] The embodiments of the present application provide a control method, device and equipment for drone power, which can solve the problem of existing drone control technology that when a drone is approaching a no-fly zone, it continues to maintain high-speed operation, which can easily cause the drone to enter the no-fly zone and may cause the drone to be uncontrollable, resulting in low safety of drone operation and low control accuracy.

[0004] In a first aspect, an embodiment of the present application provides a control method for a power source of a drone, the method comprising:

[0005] In response to a control instruction issued by a user to the drone, determining an initial power vector corresponding to the control instruction;

[0006] Determine, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position;

[0007] Importing the initial power vector and the shortest distance into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV;

[0008] The drone is controlled to move based on the corrected momentum vector.

[0009] In a possible implementation manner of the first aspect, before the initial power vector and the shortest distance are introduced into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector, the method further includes:

[0010] In response to the start-up instruction of the drone, determining the load characteristic data of the drone for this take-off; the load characteristic data includes a load value and a load size;

[0011] Acquire, through the environmental sensor configured on the drone, first environmental information corresponding to the moment when the start instruction is received; the first environmental information includes: wind force level and air composition data;

[0012] Calibrate the reference power curve of the engine according to the load value and the air composition data to generate a calibrated power curve of the engine; the reference power curve is generated based on historical operation records;

[0013] Based on the load size and the wind force level, generate a current resistance function of the drone;

[0014] The power calibration algorithm is generated according to the resistance function and the calibration power curve.

[0015] In a possible implementation manner of the first aspect, calibrating the reference power curve of the engine according to the load value and the air composition data to generate the calibrated power curve of the engine includes:

[0016] Determining a load ratio corresponding to the drone according to the rated load of the drone and the load value;

[0017] Obtaining a resistance factor corresponding to the engine through the particle density and water vapor density in the air composition data;

[0018] Based on the load ratio and the resistance factor, the reference power curve is calibrated to obtain the calibration power curve; the calibration power curve is:

[0019]

[0020] Among them, Power adj (x) is the calibration power curve; Power base (x) is the reference power curve; WeightRA is the load ratio; Resist is the resistance factor; β and γ are the preset speed resistance coefficients; V1 is the preset first speed threshold; V2 is the preset second speed threshold; x is the current speed of the UAV.

[0021] In a possible implementation manner of the first aspect, generating a current resistance function of the drone based on the load size and the wind force level includes:

[0022] Determining a resistance surface corresponding to a moving direction of the drone according to a placement posture of a load on the drone;

[0023] Based on the load size, determining a resistance area corresponding to the resistance surface;

[0024] The resistance function is generated according to the resistance area and the wind force level.

[0025] In a possible implementation manner of the first aspect, determining, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position includes:

[0026] Determining a position delay factor corresponding to the signal strength according to the signal strength corresponding to the first position;

[0027] Acquiring second environmental information corresponding to the drone through an environmental sensor configured on the drone;

[0028] Determining an environmental floating factor corresponding to the second environmental information based on a preset environmental floating conversion algorithm;

[0029] The first position is calibrated according to the position delay factor and the environmental fluctuation factor to obtain a second position; the second position is specifically:

[0030]

[0031] Wherein, Post2 is the vector representation of the second position in three-dimensional space; Post1 is the vector representation of the first position in three-dimensional space; Speed ​​is the vector representation of the current moving speed of the drone in three-dimensional space; DelayFactor is the position delay factor; SurdFoctor is the environmental floating factor; Basetime is the preset base time coefficient, and θ is the preset angle constant;

[0032] The shortest distance is determined according to the no-fly zones corresponding to the second position and the first position.

[0033] In a possible implementation manner of the first aspect, determining, according to the signal strength corresponding to the first position, a position delay factor corresponding to the signal strength includes:

[0034] Determine the signal delay corresponding to the signal strength according to the corresponding relationship between the signal strength and the delay;

[0035] The location delay factor is determined based on the signal delay and the amount of information data corresponding to the first location.

[0036] In a possible implementation manner of the first aspect, controlling the movement of the drone based on the corrected power vector includes:

[0037] controlling an engine of the drone to operate at a target driving force corresponding to the corrected power vector;

[0038] If it is detected that the real-time distance between the UAV and the no-fly zone is less than a preset distance threshold, the engine is controlled to run at a minimum driving force; the minimum driving force is the driving force required to keep the UAV aloft.

[0039] In a second aspect, an embodiment of the present application provides a control device for drone power, the device comprising:

[0040] An instruction receiving unit, configured to respond to a control instruction issued by a user to the UAV and determine an initial power vector corresponding to the control instruction;

[0041] a distance calculation unit, configured to determine, according to a current first position of the UAV, a shortest distance between the first position and a no-fly zone corresponding to the first position;

[0042] A power correction unit, used to import the initial power vector and the shortest distance into a preset power correction algorithm, and calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV;

[0043] A movement control unit is used to control the movement of the drone based on the corrected power vector.

[0044] In a third aspect, an embodiment of the present application provides a drone, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects above is implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a drone, enables the drone to execute any of the methods described in the first aspect above.

[0047] Compared with the prior art, the embodiments of the present application have the following beneficial effects: when receiving a control instruction for a drone sent by a user, the initial power vector corresponding to the control instruction can be determined, and the current positional relationship between the drone and the no-fly zone can be determined to obtain the corresponding shortest distance; the initial power vector is subjected to power correction according to the shortest distance to obtain the corresponding corrected power vector, and the drone is controlled to move by the corrected power vector to achieve the purpose of correcting the power according to the distance from the no-fly zone. Compared with the existing drone control technology, in the embodiments of the present application, the initial power vector corresponding to the control instruction can be subjected to power correction to avoid the drone engine from being kept running at high power when approaching the no-fly zone, thereby avoiding the situation of mistakenly entering the no-fly zone, improving the accuracy and safety of drone control, and reducing the situation of control failure caused by the drone entering the no-fly zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 is a schematic diagram of the structure of a drone control system provided by an embodiment of the present application;

[0050] Figure 2 It is a schematic diagram of an implementation of a control method for drone power provided in an embodiment of the present application;

[0051] Figure 3 is a schematic diagram of drone control provided by an embodiment of the present application;

[0052] Figure 4 It is a schematic diagram for determining the shortest distance provided by an embodiment of the present application;

[0053] Figure 5 This is a specific implementation flow chart of a control method for drone power provided in the second embodiment of the present application before S203;

[0054] Figure 6 This is a specific implementation flow chart of a control method for drone power in S202 provided in the third embodiment of the present application;

[0055] Figure 7 This is a specific implementation flow chart of a control method for drone power in S204 provided in the fourth embodiment of the present application;

[0056] Figure 8 is a schematic diagram of the structure of a control device applied to UAV power provided in an embodiment of the present application;

[0057] Fig. 9 It is a schematic diagram of the structure of the drone provided in the embodiment of the present application. DETAILED DESCRIPTION

[0058] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0059] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0060] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0061] A control method for drone power provided in an embodiment of the present application can be applied to a drone control system. For example, Figure 1 FIG. 1 shows a schematic diagram of the structure of a drone control system provided by an embodiment of the present application. Figure 1 The drone control system includes a drone 11 and a controller 12. The drone 11 and the controller 12 can be connected by wireless communication. The user can control the drone 11 by operating the controller 12 to control the drone 11 to move in three-dimensional space, such as moving up or down. The drone 11 includes an engine 111, which provides power to control the wings 112 on the drone so that the drone 11 moves in the direction specified by the user.

[0062] See also Figure 2 , Figure 2A schematic diagram of an implementation of a control method for drone power provided by an embodiment of the present application is shown. The control method for drone power is applied to the above-mentioned drone 11. The execution subject of the embodiment of the present application can be the above-mentioned drone 11. In some implementations, if the controller 12 is also configured with a processing module, for example, the above-mentioned controller is a smart phone, then the above-mentioned power control method can also be applied to the above-mentioned controller 12. The specific settings can be made according to actual conditions and are not limited here. For the convenience of description, the subsequent execution subject is explained by taking a drone as an example. Specifically, the method includes the following steps:

[0063] In S201, in response to a control instruction issued by a user to the drone, an initial power vector corresponding to the control instruction is determined.

[0064] In this embodiment, the user can send control instructions to the drone through the controller. For example, Figure 3 A schematic diagram of drone control provided by an embodiment of the present application is shown, see Figure 3 As shown, the controller is equipped with control modules for multiple moving directions, such as a control module 31 for moving in the horizontal direction and a control module 32 for moving in the vertical direction. The user can control the drone to move in the corresponding direction through the corresponding control module. The control instruction includes two parameters, namely, the moving direction and the moving speed. The controller can determine the moving direction according to the direction in which the user pushes the control module, determine the moving speed according to the amplitude of pushing the control module, and generate a control instruction based on the above two parameters and send it to the drone.

[0065] In some implementations, the drone may be automatically controlled. In this case, the control instructions may be generated based on a control process preset by a user. Similarly, the control instructions generated based on the control process preset by the user may also include two parameters: moving direction and moving speed.

[0066] In this embodiment, after receiving the above control instruction, the UAV can extract the moving direction and moving speed carried in the control instruction, and import the above two parameters into the power conversion algorithm to obtain the initial power vector required by the engine. Among them, the vector direction of the initial power vector is determined based on the above moving direction, and the vector amplitude of the initial power vector is determined based on the above moving speed.

[0067] In S202, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position is determined.

[0068] In this embodiment, the drone may be equipped with a positioning module. During the movement of the drone, the positioning module may receive a positioning signal in real time to determine the real-time position of the drone, that is, the first position mentioned above. In some implementations, the drone may be provided with a positioning cycle, according to which the current first position of the drone is periodically received by the positioning module.

[0069] In this embodiment, for reasons of safety and confidentiality, some areas are set as no-fly zones, that is, users are prohibited from controlling the drone to enter the corresponding areas. The drone can be configured with a corresponding flight map, which records the no-fly zones set in the area. The drone can mark the above-mentioned flight area based on the first position obtained by the acquisition, and then determine the point closest to the first position as the target reference point based on the boundary information of the no-fly zone, and calculate the distance between the target reference point and the first position to obtain the above-mentioned shortest distance.

[0070] For example, Figure 4 FIG. 2 shows a schematic diagram of determining the shortest distance provided by an embodiment of the present application. Figure 4 As shown, the first position where the drone is currently located is position 41, and the no-fly zone is area 42. The boundary of area 42 is an irregularly shaped boundary. Therefore, the drone needs to determine the first position and the closest target reference point on the boundary, that is, position 43, and then determine the distance between position 43 and position 41, so as to obtain the above-mentioned shortest distance. In some methods, the drone can calculate the distance value between position 41 and each boundary point, and then select the one with the smallest value as the above-mentioned shortest distance value.

[0071] In some implementations, if the area where the drone is located includes multiple no-fly zones, the drone can determine the no-fly zones within the effective monitoring range as target no-fly zones based on a preset effective monitoring range, and determine the shortest distance between the drone and each target no-fly zone.

[0072] In S203, the initial power vector and the shortest distance are introduced into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV.

[0073] In this embodiment, the drone may be provided with a corresponding power correction algorithm, which is used to reduce the power of the drone when the drone is close to the no-fly zone, thereby preventing the drone from entering the no-fly zone due to inertia when in high-speed motion. Based on this, the drone may import the above-mentioned initial power vector and the shortest distance into the above-mentioned power correction algorithm, perform vector correction on the initial power vector corresponding to the control instruction, and thus obtain the corresponding corrected power vector.

[0074] In some implementations, the drone can also obtain the current motion speed through a motion sensor, and import the motion speed, initial power vector and shortest distance into the above-mentioned power correction algorithm to obtain the corresponding correction power vector. Among them, the motion speed is used to determine the starting point of the motion trajectory curve corresponding to the drone, and the above-mentioned initial power vector is used to determine the slope of the motion trajectory curve. According to the above two parameters, the corresponding motion trajectory curve can be constructed on a preset coordinate system. If the motion trajectory curve exceeds the safe flight range determined based on the shortest distance, it is necessary to correct the initial power vector, that is, adjust the slope of the motion trajectory curve so that the motion trajectory of the motion trajectory curve in the preset time period is within the above-mentioned safe flight range.

[0075] In this embodiment, the smaller the value of the shortest distance is, the larger the corresponding correction amplitude is; conversely, if the value of the shortest distance is larger, the corresponding correction amplitude is smaller. In particular, if the shortest distance is greater than a preset calibration distance threshold, no calibration is required, that is, the calibration power vector is equal to the initial power vector.

[0076] In this embodiment, the above correction includes correcting the vector amplitude of the initial power vector, and may also include correcting the vector direction, which may be specifically determined according to actual conditions.

[0077] In this embodiment, the corresponding driving curves may differ depending on the running time and model of the engine. In order to improve the accuracy of the above-mentioned power correction algorithm, the UAV may determine the correspondence between the driving force and the power vector according to the historical running records corresponding to the engine, and generate the above-mentioned power correction algorithm based on the above-mentioned correspondence.

[0078] In S204, the movement of the drone is controlled based on the corrected power vector.

[0079] In this embodiment, after determining the corresponding correction power vector, the UAV can convert the correction power vector into driving information corresponding to the UAV engine according to the corresponding relationship between the preset power and the driving force, and control the engine operation based on the driving information, wherein the above-mentioned driving information includes the driving power of the engine and the corresponding direction of the aircraft to which the engine is connected.

[0080] As can be seen from the above, the control method for drone power provided in the embodiment of the present application can determine the initial power vector corresponding to the control command when receiving the control command for the drone sent by the user, and determine the current positional relationship between the drone and the no-fly zone to obtain the corresponding shortest distance; perform power correction on the above initial power vector according to the shortest distance to obtain the corresponding corrected power vector, and control the movement of the drone by correcting the power vector to achieve the purpose of correcting the power according to the distance from the no-fly zone. Compared with the existing drone control technology, in the embodiment of the present application, the initial power vector corresponding to the control command can be used to perform power correction to avoid the drone engine from being kept running at high power when approaching the no-fly zone, and avoid the situation of mistakenly entering the no-fly zone, thereby improving the accuracy and safety of drone control, and reducing the situation of control failure caused by the drone entering the no-fly zone.

[0081] Figure 5 The specific implementation flow chart of a control method for drone power provided by the second embodiment of the present application before S203 is shown. Figure 5 , relative to Figure 2 In the embodiment, the control method for drone power provided in the embodiment further includes, before S203: S501 to S505, which are described in detail as follows:

[0082] Furthermore, before the initial power vector and the shortest distance are introduced into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector, the method further includes:

[0083] In S501, in response to the start instruction of the drone, the load characteristic data of the drone for this takeoff is determined; the load characteristic data includes a load value and a load size.

[0084] In this embodiment, the drone is equipped with a carrying component, and the user can place the items to be transported on the carrying component to control the drone to transport the items to a designated area. In this case, when the user starts the drone to fly, the drone can determine the load characteristic data corresponding to the items placed in the carrying component. Optionally, if the drone does not carry any items when the drone is started to fly, the load characteristic data can be empty, that is, the load value is 0 and the load size is 0.

[0085] In this embodiment, the above-mentioned load characteristic data includes parameters of two dimensions, namely, the load value of the carried items and the size of the carried items, i.e., the above-mentioned load size. The greater the weight of the item, the greater the corresponding load value; the greater the size of the above-mentioned item, the greater the corresponding load size. Optionally, if there are multiple items carried, the above-mentioned load size is determined based on the sizes of the multiple items.

[0086] In some implementations, a gravity sensor is configured on the carrying component, and the drone can determine the weight value of the carried items through the sensing value fed back by the gravity sensor. The carrying component can be provided with a photosensitive component, and the weight size of the items placed on the carrying component can be determined based on the area of ​​the area blocked by the photosensitive component.

[0087] In some implementations, a camera module may be provided on the carrying component, and the drone may use the camera module to capture an area image of the area where the carrying component is located, determine the contour of the carried items in the area image through a contour recognition algorithm, and determine the above-mentioned load size based on the contour of the item.

[0088] In S502, first environmental information corresponding to the moment when the start instruction is received is obtained through an environmental sensor configured on the drone; the first environmental information includes: wind force level and air composition data.

[0089] In this embodiment, the drone is equipped with an environmental sensor, through which the first environmental information in the environment where the drone is located can be collected. According to the number of dimensions of the environmental information to be collected, different types of environmental sensors can be configured to obtain environmental information of corresponding dimensions. For example, in this embodiment, it is necessary to collect wind force level and air composition data, and the above two environmental dimensions can be obtained by two different environmental sensors, wherein the air composition data can be collected by the air composition detection module; and the wind force level can be collected by the wind force detection module.

[0090] In this embodiment, when the UAV receives the start command, in addition to determining the load characteristic data corresponding to the carried components, it can also send a collection command to the environmental sensor. After receiving the collection command, the environmental sensor can feedback the corresponding first environmental information to the UAV. The above-mentioned first environmental information includes wind force level and air composition data; the larger the value of the wind force level, the higher the wind speed in the scene where the UAV is currently located, and the greater the impact on the flight; conversely, the smaller the value of the wind force level, the lower the wind speed in the scene where the UAV is currently located, and the smaller the impact on the flight. The above-mentioned air composition data is used to determine the particle size in the air. If the size of the particles contained in the air is larger and the density is higher, it will affect the operation of the UAV's engine. Therefore, when correcting the power vector, the air composition data still needs to be considered.

[0091] In S503, the reference power curve of the engine is calibrated according to the load value and the air composition data to generate a calibrated power curve of the engine; the reference power curve is generated based on historical operation records.

[0092] In this embodiment, the electronic device can calibrate the weight value in the weight characteristic data and the air composition data in the first environmental information to a preset baseline power curve. The baseline power curve contains two calibration variables. The two calibration variables collected this time are imported into the above-mentioned baseline power curve to obtain the above-mentioned calibration power curve.

[0093] Further, as another embodiment of the present application, the above S503 may specifically include the following steps:

[0094] In S503.1, the load ratio corresponding to the drone is determined according to the rated load of the drone and the load value.

[0095] In this embodiment, different models of drones may correspond to different rated loads, which are the maximum load values ​​of the drone during operation. If the actual load value of the drone during flight exceeds the rated load, the drone is in an overloaded operation state, and being in an overloaded operation state for a long time will affect the service life of the engine in the drone.

[0096] In this embodiment, the drone can calculate the ratio between the current load value and the rated load value, and use the ratio as the load ratio of the drone. When the drone is in a non-overloaded operating state, the load ratio is a value less than 1; when the drone is in an overloaded operating state, the load ratio is a value greater than 1.

[0097] In S503.2, the resistance factor corresponding to the engine is obtained through the particle density and water vapor density in the air composition data.

[0098] In this embodiment, the air composition data collected by the drone through the environmental sensor can record the values ​​corresponding to different air components, such as water vapor density, oxygen concentration, particle density, particle size, etc. The drone can extract the relevant parameters that affect the engine operation from the above air composition data, namely the above-mentioned particle density and water vapor density. Among them, the higher the particle density, the greater the resistance generated during the engine rotation; similarly, the higher the water vapor density, the greater the probability of water droplets condensing on the surface of the engine during the engine rotation, which will also affect the normal operation of the engine. Based on this, the drone can import the preset resistance conversion model based on the above two parameters to obtain the corresponding resistance factor under the current air quality.

[0099] In S503.3, based on the load ratio and the resistance factor, the reference power curve is calibrated to obtain the calibrated power curve; the calibrated power curve is:

[0100]

[0101] Among them, Power adj (x) is the calibration power curve; Power base (x) is the reference power curve; WeightRA is the load ratio; Resist is the resistance factor; β and γ are the preset speed resistance coefficients; V1 is the preset first speed threshold; V2 is the preset second speed threshold; x is the current speed of the UAV.

[0102] In this embodiment, the degree of influence of air resistance varies according to the speed of the drone, that is, the faster the speed of the drone, the higher the degree of influence of air resistance. Therefore, when calibrating the reference power curve, different adjustment methods can be used according to the different speed ranges of the drone. In the case of a low speed, the value of the corresponding drag coefficient β is small; conversely, in the case of a high speed, the value of the corresponding drag coefficient γ is large, and the calculated drag factor and load ratio are used to calibrate the reference power curve to obtain the corresponding calibrated power curve.

[0103] In an embodiment of the present application, the UAV can calibrate the reference power curve in a corresponding manner according to different speeds, thereby improving the accuracy of the calibrated power curve, and then improving the accuracy of subsequent UAV engine control.

[0104] In S504, a current resistance function of the UAV is generated based on the load size and the wind force level.

[0105] In this embodiment, the larger the size of the carried object, the greater the degree of influence of the wind during movement. Since the larger the size, the larger the corresponding resistance surface, the drone can determine the influence of wind speed on power according to the load size and the wind level at the current position, that is, obtain the above-mentioned resistance function. Among them, the above-mentioned resistance function is used to determine the corresponding relationship between driving force and resistance. Since the size of the driving force affects the speed of the drone, that is, there is a certain corresponding relationship between the speed of the drone and the driving force, and the speed of the drone is proportional to its wind resistance, the above-mentioned two functions can be combined to obtain the corresponding relationship between the driving force and the wind resistance, that is, the above-mentioned resistance function.

[0106] Further, as another embodiment of the present application, the above S504 may include the following steps:

[0107] In S504.1, a resistance surface corresponding to the moving direction of the drone is determined according to the placement posture of the load of the drone.

[0108] In this embodiment, different surfaces of the items (i.e., loads) placed on the carrying component may correspond to different areas. Therefore, the corresponding resistance surfaces of the drone when subjected to wind force are different according to the different postures of the loads. Based on this, when determining the resistance function, the drone may first determine the placement posture corresponding to the load, i.e., the corresponding posture, and determine the surface subjected to wind force in the posture according to the direction of movement of the drone, and use the surface as the above-mentioned resistance surface.

[0109] In S504.2, based on the load size, the resistance area corresponding to the resistance surface is determined.

[0110] In this embodiment, the drone can determine the resistance area corresponding to the resistance surface according to the load size corresponding to the load object. Specifically, the drone can obtain the load image corresponding to the load object through the camera module, and construct a three-dimensional model corresponding to the load object based on the load image, and then determine the resistance area corresponding to the resistance surface through the three-dimensional model.

[0111] In S504.3, the resistance function is generated according to the resistance area and the wind force level.

[0112] In this embodiment, the UAV obtains a reference drag coefficient corresponding to different speeds according to the wind force level, and multiplies the reference drag coefficient by the above-mentioned resistance area to construct a corresponding relationship between speed and resistance, and then obtains the above-mentioned resistance function according to the relationship between speed and driving force.

[0113] In the embodiment of the present application, by determining the posture corresponding to the weight, determining the resistance area of ​​the resistance surface, and then constructing a resistance function, the accuracy of the resistance function can be improved.

[0114] In S505, the power calibration algorithm is generated according to the resistance function and the calibration power curve.

[0115] In this embodiment, since the resistance function is used to determine the correspondence between the driving force and the wind resistance, and the calibration power curve is used to determine the correspondence between the driving force and the speed under the current load, the above two functions can be used to further refine the composition of the driving force, consider the influence of the resistance on the driving force actually output by the engine, and then obtain the corresponding power calibration algorithm, thereby reducing the influence of resistance and load on the power calibration process and improving the accuracy of the subsequent calibration power vector.

[0116] Figure 6 The specific implementation flow chart of a control method for drone power provided in the third embodiment of the present application in S202 is shown. Figure 6 , relative to Figure 2-5 In any of the embodiments, a control method for drone power provided in this embodiment includes in S202: S2021 to S2025 are described in detail as follows:

[0117] Further, determining, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position includes:

[0118] In S2021, a position delay factor corresponding to the signal strength is determined according to the signal strength corresponding to the first position.

[0119] In this embodiment, in order to improve the accuracy of drone control, drones often operate at locations with weak signal strength. Therefore, when obtaining the first position, it is necessary to determine the deviation between the first position and the actual position of the drone, that is, to determine the above-mentioned position delay factor. The higher the above-mentioned signal strength, the shorter the time required to obtain the first position, and the smaller the corresponding position delay factor. Therefore, the drone can determine the above-mentioned position delay factor through the corresponding relationship between signal strength and delay.

[0120] Further, as another embodiment of the present application, the above S2021 specifically includes the following two steps:

[0121] In S2021.1, the signal delay corresponding to the signal strength is determined according to the corresponding relationship between the signal strength and the delay.

[0122] In this embodiment, the drone may store a correspondence between signal strength and delay, and the correspondence may be determined by historically received location information. The historically received location information may record the location request time, location reception time, and signal strength at the corresponding time. The delay may be calculated based on the location request time and location reception time, thereby establishing a correspondence between signal strength and delay time.

[0123] In S2021.2, the position delay factor is determined based on the signal delay and the amount of information data corresponding to the first position.

[0124] In this embodiment, since the delay in receiving the position information is related to the amount of data corresponding to the position information in addition to the signal strength, based on this, the drone can determine the amount of information data corresponding to the first position, and calculate the above-mentioned position delay factor based on the above-mentioned signal delay and information data amount. Among them, the larger the amount of information data, the larger the value of the corresponding position delay factor.

[0125] In an embodiment of the present application, when determining the location delay factor, not only the impact of signal strength on the delay is taken into account, but also the transmission delay required for the amount of data to send and receive location information is taken into account, thereby improving the accuracy of the location delay factor.

[0126] In S2022, second environmental information corresponding to the drone is obtained through an environmental sensor configured on the drone.

[0127] In this embodiment, the drone can determine the second environmental information of the scene where the drone is located in real time through the environmental sensor configured thereon. The method of obtaining the second environmental information is exactly the same as the implementation method of S502, except that the two collection timings are different. S502 is collected at startup, while S2022 is collected when a control command is received. For a specific description, please refer to the relevant description of S502, which will not be repeated here.

[0128] In S2023, based on a preset environmental floating conversion algorithm, an environmental floating factor corresponding to the second environmental information is determined.

[0129] In this embodiment, the above-mentioned second environmental information may include wind force level or wind speed, etc. The drone can determine the impact of environmental factors on position acquisition based on the above-mentioned wind force level or wind speed, which may cause the drone to move in the direction of the wind, thereby causing a certain deviation in the position. Based on this, the electronic device can import the wind force level or wind speed in the second environmental information into the environmental floating conversion algorithm to calculate the corresponding environmental floating factor.

[0130] In S2024, the first position is calibrated according to the position delay factor and the environmental fluctuation factor to obtain a second position; the second position is specifically:

[0131]

[0132] Among them, Post2 is the vector representation of the second position in the three-dimensional space; Post1 is the vector representation of the first position in the three-dimensional space; Speed ​​is the vector representation of the current moving speed of the drone in the three-dimensional space; DelayFactor is the position delay factor; SurdFoctor is the environmental floating factor; Basetime is the preset base time coefficient, and θ is the preset angle constant.

[0133] In this embodiment, the drone can import the position delay and the above-mentioned environmental fluctuation factor into the corresponding position calibration algorithm, so as to perform position calibration on the first position, that is, determine the actual position of the drone at the time of receiving the control command, reduce the influence of signal delay and environmental factors on position acquisition, and obtain the corresponding second position. When calibrating the first position to obtain the second position, the corresponding reference time coefficient can be determined according to the historical position information, that is, the reference delay can be determined, so as to improve the compatibility between the above-mentioned calibration algorithm and the drone.

[0134] In S2025, the shortest distance is determined according to the no-fly zones corresponding to the second position and the first position.

[0135] In this embodiment, after the first position is calibrated to obtain the second position, the shortest distance to the boundary of the no-fly zone may be determined based on the boundary between the second position and the no-fly zone.

[0136] In an embodiment of the present application, the position of the first position can be calibrated according to the time delay and environmental information to improve the accuracy of subsequent shortest distance calculations, which can further improve the accuracy of drone control.

[0137] Figure 7 The specific implementation flow chart of a control method for drone power provided by the fourth embodiment of the present application in S204 is shown. Figure 7 , relative to Figure 2-5 In any of the embodiments, a control method for drone power provided in this embodiment includes: S2041-S2042, which are described in detail as follows:

[0138] Further, before generating fault prompt information if the time domain reactive power change curve meets the preset reactive circulating current condition, and performing reactive compensation adjustment on the target power grid according to the fault handling process corresponding to the reactive circulating current condition, it also includes:

[0139] In S701, the engine of the drone is controlled to operate at a target driving force corresponding to the corrected power vector.

[0140] In this embodiment, the UAV can store the conversion relationship between the engine and the power. After determining the corresponding calibration power vector, the corresponding orientation of the aircraft can be controlled according to the vector direction of the calibration power vector, and then the corresponding driving force of the engine, i.e., the target driving force mentioned above, can be determined according to the vector amplitude corresponding to the calibration power vector, and the UAV can be controlled to operate with the corresponding target driving force.

[0141] In S702, if it is detected that the real-time distance between the UAV and the no-fly zone is less than a preset distance threshold, the engine is controlled to operate at a minimum driving force; the minimum driving force is the driving force required to keep the UAV aloft.

[0142] In this embodiment, in the process of controlling the movement of the drone, the real-time distance between the current position of the drone and the boundary of the no-fly zone can be continuously monitored. If the real-time distance is greater than or equal to the distance threshold, the corresponding target driving force can be maintained; conversely, if the real-time distance is less than the distance threshold, the engine can be controlled to run at the lowest driving force to keep the drone in a suspended state to avoid accidentally entering the no-fly zone.

[0143] In the embodiment of the present application, by monitoring the distance between the drone and the no-fly zone, the operation of the drone can be accurately controlled, thereby improving the accuracy of the drone operation and avoiding entering the no-fly zone.

[0144] In this embodiment, Figure 8 The structure block diagram of a control device for drone power provided by an embodiment of the present application is shown. The control device for drone power includes various units for executing Figure 2 The steps implemented by the first device in the corresponding embodiment. For details, please refer to Figure 2 and Figure 2 For the convenience of explanation, only the parts related to this embodiment are shown.

[0145] See also Figure 8 , used in the control device of UAV power, including:

[0146] The command receiving unit 81 is used to respond to the control command issued by the user to the UAV and determine the initial power vector corresponding to the control command;

[0147] a distance calculation unit 82, configured to determine, according to a current first position of the UAV, a shortest distance between the first position and a no-fly zone corresponding to the first position;

[0148] A power correction unit 83, used to import the initial power vector and the shortest distance into a preset power correction algorithm, and calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV;

[0149] The movement control unit 84 is used to control the movement of the UAV based on the corrected power vector.

[0150] It should be understood that Figure 8 In the structural block diagram of the device shown, each module is used to execute Figures 2 to 7 The steps in the corresponding embodiments, and for Figures 2 to 7 Each step in the corresponding embodiment has been explained in detail in the above embodiment. Figures 2 to 7 as well as Figures 2 to 7 The relevant descriptions in the corresponding embodiments are not repeated here.

[0151] Fig. 9 is a structural block diagram of a drone provided by another embodiment of the present application. Fig. 9 The drone 900 of this embodiment includes: a processor 910, a memory 920, and a computer program 930 stored in the memory 920 and executable on the processor 910, such as a program for a control method for drone power. When the processor 910 executes the computer program 930, the steps in each embodiment of the control method for drone power are implemented, such as Figure 2 Alternatively, the processor 910 implements the above when executing the computer program 930 Figure 8 The functions of each module in the corresponding embodiment are, for example, Figure 8 For details on the functions of the units 81 to 84, please refer to Figure 8 Related description in the corresponding embodiment.

[0152] Exemplarily, the computer program 930 may be divided into one or more modules, one or more modules are stored in the memory 920, and are executed by the processor 910 to complete the present application. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 930 in the drone 900. For example, the computer program 930 may be divided into various unit modules, and the specific functions of each module are as described above.

[0153] The drone 900 may include, but is not limited to, a processor 910 and a memory 920. Those skilled in the art will appreciate that Fig. 9 This is only an example of the drone 900 and does not constitute a limitation of the drone 900. The drone 900 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the drone may also include input and output devices, network access devices, buses, etc.

[0154] The processor 910 may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0155] The memory 920 may be an internal storage unit of the drone 900, such as a hard disk or memory of the drone 900. The memory 920 may also be an external storage device of the drone 900, such as a plug-in hard disk, a smart memory card, a flash memory card, etc. equipped on the drone 900. Furthermore, the memory 920 may also include both an internal storage unit of the drone 900 and an external storage device.

[0156] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A control method for unmanned aerial vehicle power, characterized in that: include: In response to a control instruction issued by a user to the drone, determining an initial power vector corresponding to the control instruction; Determine, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position; Importing the initial power vector and the shortest distance into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV; Controlling movement of the drone based on the corrected power vector; Before the initial power vector and the shortest distance are introduced into a preset power correction algorithm to calculate a correction power vector corresponding to the initial power vector, the method further includes: In response to the start-up instruction of the drone, determining the load characteristic data of the drone for this take-off; the load characteristic data includes a load value and a load size; Acquire, through the environmental sensor configured on the drone, first environmental information corresponding to the moment when the start instruction is received; the first environmental information includes: wind force level and air composition data; Calibrate the reference power curve of the engine according to the load value and the air composition data to generate a calibrated power curve of the engine; the reference power curve is generated based on historical operation records; Based on the load size and the wind force level, generate a current resistance function of the drone; generating the power calibration algorithm according to the resistance function and the calibration power curve; The step of calibrating the reference power curve of the engine according to the load value and the air composition data to generate the calibrated power curve of the engine includes: Determining a load ratio corresponding to the drone according to the rated load of the drone and the load value; Obtaining a resistance factor corresponding to the engine through the particle density and water vapor density in the air composition data; Based on the load ratio and the resistance factor, the reference power curve is calibrated to obtain the calibration power curve; the calibration power curve is: Among them, Power adj (x) is the calibration power curve; Power base (x) is the reference power curve; WeightRA is the load ratio; Resist is the resistance factor; β and γ are the preset speed resistance coefficients; V1 is the preset first speed threshold; V2 is the preset second speed threshold; x is the current speed of the UAV.

2. The control method according to claim 1, characterized in that: The generating the current resistance function of the drone based on the load size and the wind force level includes: Determining a resistance surface corresponding to a moving direction of the drone according to a placement posture of a load on the drone; Based on the load size, determining a resistance area corresponding to the resistance surface; The resistance function is generated according to the resistance area and the wind force level.

3. The control method according to any one of claims 1 to 2, characterized in that: The determining, according to the current first position of the drone, the shortest distance between the first position and a no-fly zone corresponding to the first position includes: Determining a position delay factor corresponding to the signal strength according to the signal strength corresponding to the first position; Acquiring second environmental information corresponding to the drone through an environmental sensor configured on the drone; Determining an environmental floating factor corresponding to the second environmental information based on a preset environmental floating conversion algorithm; The first position is calibrated according to the position delay factor and the environmental fluctuation factor to obtain a second position; the second position is specifically: Wherein, Post2 is the vector representation of the second position in three-dimensional space; Post1 is the vector representation of the first position in three-dimensional space; Speed ​​is the vector representation of the current moving speed of the drone in three-dimensional space; DelayFactor is the position delay factor; SurdFoctor is the environmental floating factor; Basetime is the preset base time coefficient, and θ is the preset angle constant; The shortest distance is determined according to the no-fly zones corresponding to the second position and the first position.

4. The control method according to claim 3, characterized in that: The determining, according to the signal strength corresponding to the first position, a position delay factor corresponding to the signal strength includes: Determine the signal delay corresponding to the signal strength according to the corresponding relationship between the signal strength and the delay; The location delay factor is determined based on the signal delay and the amount of information data corresponding to the first location.

5. The control method according to any one of claims 1 to 2, characterized in that: The controlling the movement of the drone based on the corrected power vector comprises: controlling an engine of the drone to operate at a target driving force corresponding to the corrected power vector; If it is detected that the real-time distance between the UAV and the no-fly zone is less than a preset distance threshold, the engine is controlled to run at a minimum driving force; the minimum driving force is the driving force required to keep the UAV aloft.

6. A control device for unmanned aerial vehicle power, characterized in that: The control device is used to execute the control method for drone power according to claim 1, and the control device includes: An instruction receiving unit, configured to respond to a control instruction issued by a user to the UAV and determine an initial power vector corresponding to the control instruction; a distance calculation unit, configured to determine, according to a current first position of the UAV, a shortest distance between the first position and a no-fly zone corresponding to the first position; A power correction unit, used to import the initial power vector and the shortest distance into a preset power correction algorithm, and calculate a correction power vector corresponding to the initial power vector; the power correction algorithm is generated based on the historical operation record of the engine of the UAV; A movement control unit is used to control the movement of the UAV based on the corrected power vector.

7. A drone comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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