Unmanned aerial vehicle nest remote control method and system

Abstract

The invention belongs to the technical field of unmanned aerial vehicle nest control, and discloses an unmanned aerial vehicle nest remote control method and system, and the method comprises the steps: obtaining the remaining electric quantity and real-time position of each unmanned aerial vehicle in an inspection area; obtaining the state of each position of each unmanned aerial vehicle nest; constructing a charging scheduling function based on the residual electric quantity and the real-time position of each unmanned aerial vehicle, the state of each position of each unmanned aerial vehicle nest, the nest position and the inspection task; generating a dispatching instruction and a charging instruction of the unmanned aerial vehicle based on the charging dispatching function; the dispatching instruction is sent to the unmanned aerial vehicle needing to be charged, and the charging instruction is sent to the unmanned aerial vehicle nest with the parking space in the idle state; the unmanned aerial vehicle needing to be charged flies to the unmanned aerial vehicle nest with the parking space in the idle state based on the dispatching instruction, and the unmanned aerial vehicle nest charges the unmanned aerial vehicle entering the nest based on the charging instruction. The situation that the unmanned aerial vehicle queues for charging is avoided, and the inspection efficiency can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of drone nest control, and in particular relates to a drone nest remote control method and system. Background Art

[0002] A drone nest is a facility that provides storage, charging, maintenance, and dispatch services for drones, similar to a traditional drone nest or hangar. It usually includes the following parts:

[0003] Drone storage area: Provides a safe and stable storage environment for drones to avoid interference and damage from external factors.

[0004] Charging facilities: Provide charging services for drones to ensure that drones can take off in time when needed.

[0005] Maintenance and repair area: Provide daily maintenance and repair services for drones to ensure the flight safety and stable performance of drones.

[0006] Dispatch Center: Responsible for real-time monitoring and dispatching of drones, including take-off, flight, landing and other stages.

[0007] Data processing and storage area: used to process and store the data collected by the drone during flight, providing support for subsequent data analysis and applications.

[0008] Drone nests are generally used in situations where a large number of drones are required to perform tasks, such as using drones to inspect power grid lines. By deploying drone nests on the inspection routes of the power grid, drones can inspect more distant lines. Drone inspections have high operating efficiency and can work in harsh environments.

[0009] However, since the drone nest has limited space, and the power consumption rate of the drone will be affected by factors such as ambient temperature, altitude and wind speed, the flight time of a fully charged drone will vary in different environments.

[0010] At present, the control of drones and drone nests is usually to control the drone to fly to the nearest drone nest for charging after the drone’s power reaches the charging threshold. This method does not take into account the drone nest’s position capacity, resulting in some drone nests having more drones entering the nest than the total number of positions. The drones have to wait for charging, which affects the overall inspection efficiency. Summary of the invention

[0011] The purpose of the present invention is to provide a remote control method and system for a drone nest, so as to solve the problem that the prior art does not take into account the position capacity of the drone nest, resulting in the number of drones entering some drone nests exceeding the total number of positions, and the drones have to wait for charging, thereby affecting the overall inspection efficiency.

[0012] In order to achieve the above object, the present invention adopts the following technical solutions:

[0013] In a first aspect, the present invention provides a method for remotely controlling a drone nest, the method comprising:

[0014] Obtain the remaining power and real-time position of each drone in the inspection area; wherein, there are several drone nests deployed in the inspection area, each drone nest is deployed with at least one position, and each drone in the inspection area is pre-assigned with an inspection task;

[0015] Obtaining the status of each position of each drone nest, wherein the status of each position includes: idle state or occupied state;

[0016] Based on the remaining power and real-time location of each drone, the status and location of each drone nest, and the inspection tasks, a charging scheduling function is constructed;

[0017] Generate the dispatching instructions and charging instructions of the drone based on the charging dispatching function;

[0018] A scheduling instruction is sent to a drone that needs to be charged, and a charging instruction is sent to a drone nest with an idle position; the drone that needs to be charged flies to the drone nest with an idle position based on the scheduling instruction, and the drone nest charges the drone after entering the nest based on the charging instruction.

[0019] Preferably, the inspection task includes: multiple inspection routes and multiple inspection locations on each inspection route;

[0020] Based on the remaining power and real-time location of each drone, the status and location of each drone nest, and the inspection tasks, a charging scheduling function is constructed, including:

[0021] Based on the inspection route of each drone, determine the inspection time function of each drone;

[0022] Based on the real-time location of each drone, the location of each drone nest and the location of the next inspection location, determine the round-trip charging time function for each drone;

[0023] Based on the remaining power of each drone, determine a charging time function for charging each drone to a preset power;

[0024] Determine constraints based on the remaining power of each drone and the status of each position of each drone nest;

[0025] A charging scheduling function is constructed based on the inspection time function, charging round-trip time function, charging duration function and constraints.

[0026] Preferably, the charging scheduling function T is expressed as:

[0027] T=min(T c +T r +T p );

[0028] Where, T c is the charging time function, T r is the inspection time function, T p is the round trip time function for charging, and min() is the minimum function.

[0029] Preferably, the constraints include: remaining power constraints during the inspection process, charging starting point constraints, drone nest position constraints and remaining power constraints when the inspection is completed.

[0030] Preferably, the charging time function T c The expression is:

[0031]

[0032] Where, L 0,i is the remaining power of the drone when it reaches the drone nest after the i-th charging, L is the current power of the drone during the charging process, and L G is the amount of electricity corresponding to the inflection point of the charging efficiency curve of the drone during the charging process, L MAX is the maximum charging power of the drone, K1 is the first charging efficiency, K2 is the second charging efficiency, and M is the total number of charging times, where K1>K2.

[0033] Preferably, the charging round trip time function T p The expression is:

[0034]

[0035] In the formula, is the distance between the jth inspection location of the inspection route and the nth drone nest when the drone leaves the inspection route during the i-th charging. is the distance between the UAV leaving the nth UAV nest and the j+1th inspection location on the inspection route after the i-th charging is completed, and v0 is the average flight speed of the UAV.

[0036] Preferably, the inspection time function T r The expression is:

[0037]

[0038] Where Z is the total number of inspection locations on the inspection route, D z,z+1 is the distance between the zth inspection location and the z+1th inspection location on the inspection route, v0 is the average flight speed of the drone, Δt z is the inspection time of the UAV at the zth inspection location on the inspection route, It is the distance between the jth inspection location of the inspection route and the j+1th inspection location of the inspection route when the drone leaves the inspection route for the i-th time.

[0039] Preferably, generating the dispatching instruction and charging instruction of the drone based on the charging dispatching function includes:

[0040] Based on the preset optimization algorithm, the charging scheduling function is optimized and solved with the constraint conditions as the constraint items to obtain the optimal inspection parameters of each drone. The optimal inspection parameters include: the optimal number of charging times, the optimal charging power for each charging, the optimal charging starting point and the optimal drone nest location. The inspection location of the drone when it leaves the inspection route is taken as the optimal charging starting point.

[0041] After any UAV reaches the optimal charging starting point and completes the inspection task at the inspection location corresponding to the optimal charging starting point, a dispatch instruction and a charging instruction for the UAV are generated, wherein the charging instruction includes: an instruction for the UAV to enter the nest and an instruction for the optimal charging power; wherein the instruction for the UAV to enter the nest is used to make the UAV dock at an idle position, and the instruction for the optimal charging power is used to make the UAV charge at an idle position and leave the optimal UAV nest after reaching the optimal charging power.

[0042] Preferably, the inspection task is preset with an emergency priority, and the method further comprises:

[0043] The drone nest counts the inspection tasks of each drone parked at the station and obtains the completed task set and the uncompleted task set;

[0044] The drone nest reallocates the unfinished task set based on the emergency priority of the inspection tasks.

[0045] In a second aspect, the present invention provides a drone nest remote control system, the system comprising:

[0046] Several drones are used to perform pre-assigned inspection tasks within the inspection area;

[0047] A plurality of drone nests, each of which is deployed in the inspection area, and each drone nest is deployed with at least one station. After the drone is docked at the station, it is connected to the drone nest for communication, so as to charge the drone docked at the station.

[0048] The control station is respectively connected to each drone and is respectively connected to each drone nest, and the control station is used to execute the above-mentioned drone nest remote control method.

[0049] Beneficial effects:

[0050] The present invention obtains the status of each position of each drone nest, and constructs a charging scheduling function for the drone based on the status of each position of the drone nest, the remaining power and real-time position of each drone, the nest position, and the inspection task. The charging scheduling function is used to generate scheduling instructions and charging instructions to ensure that the drone will not be dispatched to a drone nest where all positions are occupied, so as to avoid the situation where drones have to queue for charging, thereby improving the efficiency of inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0052] Figure 1 is a flow chart of a method for remotely controlling a drone nest provided by one embodiment of the present invention;

[0053] Figure 2 It is a block diagram of a drone nest remote control system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0055] Embodiment 1

[0056] Figure 1 It is a flow chart of a remote control method for a drone nest provided by an embodiment of the present invention. The application scenarios of the control method include: a control station, several drones and several drone nests. The control station is deployed in an inspection area. There are several power grid lines in the inspection area that need to be inspected regularly. When the drones are not performing inspection tasks, they dock at the control station. After the control station assigns inspection tasks to each drone, each drone departs from the control station and performs inspection tasks along the assigned inspection routes.

[0057] For a single line, the drone nest can be deployed on a single line; for a mesh line, the drone nest can be deployed on part of the line or between lines. Each drone nest can be responsible for charging drones within a certain range. When drones on other lines need to be charged, the drones that need to be charged can be reasonably dispatched through scheduling to achieve charging of the drones.

[0058] Based on the above application scenarios, this embodiment provides a method for remotely controlling a drone nest, such as Figure 1 As shown, the method includes:

[0059] Step S10: Obtain the remaining power and real-time position of each drone in the inspection area; wherein, a number of drone nests are deployed in the inspection area, each drone nest is deployed with at least one position, and each drone in the inspection area is pre-assigned with an inspection task.

[0060] The drone of this embodiment is equipped with a power meter and a positioning module, and the power meter is used to monitor the remaining power of the drone's battery in real time; the positioning module can be a GPS module or a Beidou positioning module, and the positioning module also includes: an accelerometer and a gyroscope. By detecting the acceleration and angular velocity of the drone, the flight speed of the drone can be calculated, and the flight distance of the drone can be calculated in turn. Combined with the positioning signal of the GPS module, the real-time position of each drone can be accurately detected to ensure the accuracy of positioning.

[0061] Each drone is connected to the control station through communication. Each drone can upload the remaining power and real-time position to the control station, and the control station can observe the power, position and other information of each drone.

[0062] The inspection tasks performed by each drone include: multiple inspection routes and multiple inspection locations on each inspection route. In this embodiment, when the inspection area is too large, due to the limited number of drones, one drone can be responsible for the inspection of multiple inspection routes.

[0063] Step S20: Obtain the status of each position of each drone nest, where the status of each position includes: idle state or occupied state.

[0064] In this embodiment, when there is a drone being charged at the position of the drone nest, the state of the position is characterized as an occupied state. When there is no drone at the position of the drone nest or there is a drone but it is fully charged, the state of the position is characterized as an idle state.

[0065] Step S30: construct a charging scheduling function based on the remaining power and real-time position of each drone, the status and position of each drone nest, and the inspection task.

[0066] Specifically, based on the remaining power and real-time location of each drone, the status and location of each drone nest, and the inspection tasks, a charging scheduling function is constructed, including:

[0067] Step S301: determining the inspection time function of each drone based on the inspection route of each drone;

[0068] Step S302: Based on the real-time position of each drone, the position of each drone nest and the position of the next inspection location, determine the round-trip charging time function of each drone;

[0069] Step S303: Based on the remaining power of each drone, determine the charging time function for charging each drone to a preset power;

[0070] Step S304: determining constraint conditions based on the remaining power of each drone and the status of each position of each drone nest;

[0071] Step S305: construct a charging scheduling function based on the inspection time function, the charging round-trip time function, the charging duration function and the constraint conditions.

[0072] In this embodiment, the expression of the charging scheduling function T is:

[0073] T=min(T c +T r +T p );

[0074] Where, T c is the charging time function, T r is the inspection time function, T p is the round trip time function for charging, and min() is the minimum function.

[0075] In this embodiment, the battery usually carried by the drone is a lithium battery. When the lithium battery is charged, the charging time and the charging power are not linear. When the battery power is higher, the charging efficiency of the battery will decrease. Therefore, there is a power inflection point in the charging efficiency curve of the drone's lithium battery. When the current power of the lithium battery is lower than the inflection point, the charging efficiency of the lithium battery is higher and the charging speed is faster. When the current power of the lithium battery is higher than the inflection point, the charging efficiency of the lithium battery will decrease and the charging speed will decrease.

[0076] Therefore, the number of charging times and the amount of electricity charged each time will have an impact on the total duration of the entire inspection of the drone. c The expression is:

[0077]

[0078] Where, L0,i is the remaining power of the drone when it reaches the drone nest after the i-th charging, L is the current power of the drone during the charging process, and L G is the amount of electricity corresponding to the inflection point of the charging efficiency curve of the drone during the charging process, L MAX is the maximum charging power of the drone, K1 is the first charging efficiency, K2 is the second charging efficiency, and M is the total number of charging times, where K1>K2.

[0079] In this embodiment, the charging round trip time function T p The expression is:

[0080]

[0081] In the formula, is the distance between the jth inspection location of the inspection route and the nth drone nest when the drone leaves the inspection route during the i-th charging. is the distance between the UAV leaving the nth UAV nest and the j+1th inspection location on the inspection route after the i-th charging is completed, and v0 is the average flight speed of the UAV.

[0082] In this embodiment, the inspection time function T r The expression is:

[0083]

[0084] Where Z is the total number of inspection locations on the inspection route, D z,z+1 is the distance between the zth inspection location and the z+1th inspection location on the inspection route, v0 is the average flight speed of the drone, Δt z is the inspection time of the UAV at the zth inspection location on the inspection route, It is the distance between the jth inspection location of the inspection route and the j+1th inspection location of the inspection route when the drone leaves the inspection route for the i-th time.

[0085] In this embodiment, the constraints include: remaining power constraints during the inspection process, charging starting point constraints, drone nest position constraints, and remaining power constraints when the inspection is completed.

[0086] The remaining power constraint during the inspection process is: the minimum power of the drone cannot be lower than the minimum power required for the drone to fly from its current location to the nearest drone nest.

[0087] The remaining power constraint when the inspection is completed is: after the drone reaches the last inspection location of a certain inspection route, the remaining power cannot be less than the minimum power required for the drone to fly from the current inspection point to the nearest drone nest.

[0088] The charging starting point constraints include:

[0089] 1. When the current power of the drone is not lower than the minimum power, when the drone needs to be charged, the inspection point is used as the charging starting point, and the drone flies directly from the inspection point to the drone nest;

[0090] 2. When the drone arrives at any inspection point, if the remaining power of the drone when flying to the next inspection point with the current power is lower than the minimum power required for the drone to fly from the next inspection point to the nearest drone nest, the current inspection point will be used as the charging starting point;

[0091] 3. When the UAV reaches the lth inspection point, if the remaining power of the UAV when it flies to the l+1th inspection point with the current power is greater than the minimum power required for the UAV to fly from the l+1th inspection point to the nearest UAV nest; calculate the sum of the distance between the lth inspection point and the UAV nest, the distance between the UAV nest and the l+1th inspection point, and the distance between the l+1th inspection point and the l+2th inspection point to obtain the first distance; and calculate the sum of the distance between the lth inspection point and the l+1th inspection point, the distance between the l+1th inspection point and the UAV nest, and the distance between the UAV nest and the l+2 inspection point to obtain the second distance; when the first distance is less than the second distance, the lth inspection point is used as the charging starting point; when the first distance is greater than the second distance, the l+1th inspection point is used as the charging starting point.

[0092] The position constraints of the drone nest are as follows: all positions of the drone nest cannot be occupied at the same time, and at least one position must be reserved for idle state. When a drone needs to be charged urgently, it will be charged at the reserved position. After the charging time reaches the preset time, the charging of the drone with the highest power needs to be stopped so that the state of the position is updated from occupied state to idle state.

[0093] In this embodiment, without considering charging, the total inspection time of the entire line is constant. During the flight, the drone flies at a constant flight speed (i.e., the average flight speed v0); at this time, the total inspection time includes: the flight time between each two adjacent inspection points and the operation time of staying in the air at each inspection point to perform the inspection operation (i.e., the inspection time Δt l ), the operation time depends on the actual inspection situation.

[0094] Step S40: Generate the dispatching instruction and charging instruction of the drone based on the charging dispatching function.

[0095] Specifically, the dispatching instructions and charging instructions of the drone are generated based on the charging dispatching function, including:

[0096] Step S401: Based on a preset optimization algorithm, the charging scheduling function is optimized and solved with the constraint condition as the constraint item to obtain the optimal inspection parameters of each drone, wherein the optimal inspection parameters include: the optimal number of charging times, the optimal charging power for each charging, the optimal charging starting point and the optimal drone nest position, and the inspection location of the drone when it leaves the inspection route is taken as the optimal charging starting point;

[0097] Step S402: After any UAV reaches the optimal charging starting point and completes the inspection task at the inspection location corresponding to the optimal charging starting point, a dispatch instruction and a charging instruction for the UAV are generated, wherein the charging instruction includes: an instruction for the UAV to enter a nest and an instruction for optimal charging power; wherein the instruction for the UAV to enter a nest is used to make the UAV dock at an idle position, and the instruction for optimal charging power is used to make the UAV charge at an idle position and leave the optimal UAV nest after reaching the optimal charging power.

[0098] In this embodiment, the preset optimization algorithm is a particle swarm algorithm, in which each potential solution (referred to as a particle) represents a point in the solution space. The particle updates its position based on its own experience (individual optimal solution) and the experience of the group (global optimal solution). The particle swarm algorithm includes the following steps:

[0099] Step 1: Initialize the particle swarm: Randomly initialize the position and velocity of a group of particles, each particle represents a potential solution to the charging scheduling function;

[0100] Step 2: Evaluate particle fitness: Calculate the charging scheduling function value for each particle's position to evaluate its fitness;

[0101] Step 3: Update individual optimal solution: Each particle compares its current position with its individual historical best position (pbest). If the current position is better, pbest is updated.

[0102] Step 4: Update the global optimal solution: find the best position in the individual optimal solution in the entire particle swarm and use it as the global historical best position (gbest);

[0103] Step 5: Update speed and position: Update the speed and position of each particle according to the following formula;

[0104]

[0105] In the formula, and represent the velocity and position of the kth particle at time t+1, respectively. and denote the velocity and position of the kth particle at time t, respectively. is the individual optimal solution, gt is the global optimal solution, ω is the inertia weight, c1 and c2 are acceleration constants, r1 and r2 are random numbers in the interval [0,1];

[0106] Step 6: Check termination conditions: Check whether the termination conditions are met, such as reaching the predetermined number of iterations, particle convergence, etc. If met, stop the iteration; otherwise, return to step 2 to continue execution.

[0107] Step S50: Send a scheduling instruction to the drone that needs to be charged, and send a charging instruction to a drone nest with an idle position; the drone that needs to be charged flies to the drone nest with an idle position based on the scheduling instruction, and the drone nest charges the drone after entering the nest based on the charging instruction.

[0108] In this embodiment, the particle swarm algorithm can be used to determine the optimal number of charging times, the optimal charging power for each charge, the optimal charging starting point, and the optimal drone nest location, so as to achieve efficient scheduling of drones and reasonable control of the charging of drone nests, thereby improving the inspection efficiency of drones.

[0109] During the real-time inspection process, weather factors will affect the power consumption of the drone. Therefore, the above parameters are dynamically adjusted according to the current remaining power of each drone to ensure the highest inspection efficiency of the drone.

[0110] As a further optimization of this embodiment, the inspection task is preset with an emergency priority, and the method further includes:

[0111] Step a01: The drone nest counts the inspection tasks of each drone parked at the station and obtains the completed task set and the uncompleted task set;

[0112] Step a02: The drone nest reallocates the unfinished task set based on the emergency priority of the inspection task.

[0113] For some power grid lines, there may be safety hazards, or the fault frequency is high in historical data. At this time, the urgency of the power grid line is high and inspection is required within the preset time. In this case, the emergency priority of the inspection task assigned to the drone is higher. When a drone with a higher emergency priority flies to the drone nest for charging, the drone nest can communicate with all drones parked at the position. The drone nest collects the inspection tasks of each drone, analyzes and processes the inspection tasks, and obtains the unfinished task set. Since each inspection task is preset with an emergency priority; then the unfinished task set is reallocated according to the current power of the drone that is charging at the position.

[0114] The reallocation rule is: unfinished tasks with higher emergency priority are allocated to drones with higher power. The drone nest also records the identification information of the reallocated drones, as well as the inspection tasks before and after the reallocation; and uploads the recorded identification information of the reallocated drones, the inspection tasks before and after the reallocation, to the control station. The control station can observe the execution status of the inspection tasks of each drone after the reallocation.

[0115] The present invention sets emergency priorities to ensure that power grid lines with higher urgency are given priority in performing inspection tasks, reducing the impact of charging on the total inspection time, thereby improving the timeliness of the inspection and enabling timely discovery of power grid faults and safety hazards.

[0116] The present invention obtains the status of each position of each drone nest, and constructs a charging scheduling function for the drone based on the status of each position of the drone nest, the remaining power and real-time position of each drone, the nest position, and the inspection task. The charging scheduling function is used to generate scheduling instructions and charging instructions to ensure that the drone will not be dispatched to a drone nest where all positions are occupied, so as to avoid the situation where drones have to queue for charging, thereby improving the efficiency of inspection.

[0117] Embodiment 2

[0118] Figure 2 is a block diagram of a drone nest remote control system provided by one embodiment of the present invention, such as Figure 2 As shown, this embodiment provides a drone nest remote control system, the system comprising: a plurality of drones, a plurality of drone nests and a control station;

[0119] Several drones are used to perform pre-assigned inspection tasks within the inspection area;

[0120] Each drone nest is deployed in the inspection area, and at least one parking position is deployed on each drone nest. After the drone is parked on the parking position, it is connected to the drone nest for communication, so as to charge the drone parked on the parking position.

[0121] The control station is respectively connected to each drone in communication, and is respectively connected to each drone nest in communication, and the control station is used to execute the drone nest remote control method of embodiment 1.

[0122] The present invention obtains the status of each position of each drone nest, and constructs a charging scheduling function for the drone based on the status of each position of the drone nest, the remaining power and real-time position of each drone, the nest position, and the inspection task. The charging scheduling function is used to generate scheduling instructions and charging instructions to ensure that the drone will not be dispatched to a drone nest where all positions are occupied, so as to avoid the situation where drones have to queue for charging, thereby improving the efficiency of inspection.

[0123] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for remote control of a drone nest, characterized in that: The method comprises: Obtain the remaining power and real-time position of each drone in the inspection area; wherein, there are several drone nests deployed in the inspection area, each drone nest is deployed with at least one position, and each drone in the inspection area is pre-assigned with an inspection task; Obtaining the status of each position of each drone nest, wherein the status of each position includes: idle state or occupied state; Based on the remaining power and real-time location of each drone, the status and location of each drone nest, and the inspection tasks, a charging scheduling function is constructed; Generate the dispatching instructions and charging instructions of the drone based on the charging dispatching function; A scheduling instruction is sent to a drone that needs to be charged, and a charging instruction is sent to a drone nest with an idle position; the drone that needs to be charged flies to the drone nest with an idle position based on the scheduling instruction, and the drone nest charges the drone after entering the nest based on the charging instruction.

2. The drone nest remote control method according to claim 1, characterized in that: The inspection task includes: multiple inspection routes and multiple inspection locations on each inspection route; Based on the remaining power and real-time location of each drone, the status and location of each drone nest, and the inspection tasks, a charging scheduling function is constructed, including: Based on the inspection route of each drone, determine the inspection time function of each drone; Based on the real-time location of each drone, the location of each drone nest and the location of the next inspection location, determine the round-trip charging time function for each drone; Based on the remaining power of each drone, determine a charging time function for charging each drone to a preset power; Determine constraints based on the remaining power of each drone and the status of each position of each drone nest; A charging scheduling function is constructed based on the inspection time function, charging round-trip time function, charging duration function and constraints.

3. The drone nest remote control method according to claim 2, characterized in that: The expression of the charging scheduling function T is: T=min(T c +T r +T p ); Where, T c is the charging time function, T r is the inspection time function, T p is the round trip time function for charging, and min() is the minimum function.

4. The drone nest remote control method according to claim 3, characterized in that: The constraints include: remaining power constraints during the inspection process, charging starting point constraints, drone nest position constraints, and remaining power constraints when the inspection is completed.

5. The drone nest remote control method according to claim 3, characterized in that: The charging time function T c The expression is: Where, L 0,i is the remaining power of the drone when it reaches the drone nest after the i-th charging, L is the current power of the drone during the charging process, and L G is the amount of electricity corresponding to the inflection point of the charging efficiency curve of the drone during the charging process, L MAX is the maximum charging power of the drone, K1 is the first charging efficiency, K2 is the second charging efficiency, and M is the total number of charging times, where K1>K2.

6. The drone nest remote control method according to claim 5, characterized in that: The charging round trip time function T p The expression is: In the formula, is the distance between the jth inspection location of the inspection route and the nth drone nest when the drone leaves the inspection route during the i-th charging. is the distance between the UAV leaving the nth UAV nest and the j+1th inspection location on the inspection route after the i-th charging is completed, and v0 is the average flight speed of the UAV.

7. The drone nest remote control method according to claim 5, characterized in that: The inspection time function T r The expression is: Where Z is the total number of inspection locations on the inspection route, D z,z+1 is the distance between the zth inspection location and the z+1th inspection location on the inspection route, v0 is the average flight speed of the drone, Δt z is the inspection time of the UAV at the zth inspection location on the inspection route, It is the distance between the jth inspection location of the inspection route and the j+1th inspection location of the inspection route when the drone leaves the inspection route for the i-th time.

8. The method for remotely controlling a drone nest according to claim 3, characterized in that: Generate the dispatching instructions and charging instructions of the drone based on the charging dispatching function, including: Based on the preset optimization algorithm, the charging scheduling function is optimized and solved with the constraint conditions as the constraint items to obtain the optimal inspection parameters of each drone. The optimal inspection parameters include: the optimal number of charging times, the optimal charging power for each charging, the optimal charging starting point and the optimal drone nest location. The inspection location of the drone when it leaves the inspection route is taken as the optimal charging starting point. After any UAV reaches the optimal charging starting point and completes the inspection task at the inspection location corresponding to the optimal charging starting point, a dispatch instruction and a charging instruction for the UAV are generated, wherein the charging instruction includes: an instruction for the UAV to enter the nest and an instruction for the optimal charging power; wherein the instruction for the UAV to enter the nest is used to make the UAV dock at an idle position, and the instruction for the optimal charging power is used to make the UAV charge at an idle position and leave the optimal UAV nest after reaching the optimal charging power.

9. The method for remotely controlling a drone nest according to any one of claims 1 to 8, characterized in that: The inspection task is preset with an emergency priority, and the method further comprises: The drone nest counts the inspection tasks of each drone parked at the station and obtains the completed task set and the uncompleted task set; The drone nest reallocates the unfinished task set based on the emergency priority of the inspection tasks.

10. A drone nest remote control system, characterized in that: The system comprises: Several drones are used to perform pre-assigned inspection tasks within the inspection area; A plurality of drone nests, each of which is deployed in the inspection area, and each drone nest is deployed with at least one station. After the drone is docked at the station, it is connected to the drone nest for communication, so as to charge the drone docked at the station. A control station is respectively connected to each drone and is respectively connected to each drone nest, and the control station is used to execute the drone nest remote control method described in any one of claims 1-9.

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