GIS-based unmanned aerial vehicle collaborative inspection scheduling method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a GIS-based unmanned aerial vehicle collaborative inspection scheduling method and system, and the method comprises the steps: obtaining the geographic information data of a target inspection region and the real-time state data of a plurality of unmanned aerial vehicles; generating an initial task set according to the geographic information data and a preset inspection task, and mapping each inspection point in the initial task set to a coordinate grid corresponding to the geographic information data; judging whether the initial task set meets a preset distribution condition or not; and determining a target task packet corresponding to each unmanned aerial vehicle according to the initial task set and the real-time state data of the plurality of unmanned aerial vehicles in response to the initial task set satisfying a preset distribution condition. According to the invention, by acquiring the state and geographic information data of the unmanned aerial vehicle in real time, the inspection task can be reasonably allocated according to the electric quantity and position of each unmanned aerial vehicle, and the efficiency and timeliness of task execution are ensured.

Description

A GIS-based method and system for collaborative inspection and scheduling of unmanned aerial vehicles (UAVs). Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, specifically to a GIS-based UAV collaborative inspection and scheduling method and system. Background Technology

[0002] Currently, traditional methods typically employ a static task allocation mechanism, which cannot dynamically adjust inspection tasks based on real-time battery level and location. This results in some drones being unable to complete their assigned tasks due to insufficient battery power or unfavorable location, thereby reducing inspection efficiency. Furthermore, traditional methods often struggle to make timely adjustments in the event of environmental changes or drone status changes, leading to inflexible execution of inspection tasks and impacting overall operational effectiveness.

[0003] Furthermore, traditional methods fail to effectively identify no-fly zones and restricted flight zones, which may cause drones to accidentally enter these areas, increasing safety risks during mission execution. Moreover, without an emergency return-to-home mechanism, drones may not be able to return safely in the event of an emergency. Additionally, when multiple drones are performing missions simultaneously, traditional methods may lack effective coordination strategies, leading to conflicts or interference between drones, thereby affecting overall operational efficiency and safety. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a GIS-based UAV collaborative inspection and scheduling method, comprising:

[0005] Acquire geographic information data of the target inspection area and real-time status data of multiple drones; generate an initial task set based on the geographic information data and preset inspection tasks, and map each inspection point in the initial task set to the coordinate grid corresponding to the geographic information data;

[0006] Determine whether the initial task set meets the preset distribution conditions; wherein, the initial task set meets the preset distribution conditions if the total number of tasks in the initial task set is greater than a first quantity threshold and the average remaining power of multiple drones is greater than the power threshold and the duration of maintenance is greater than a first preset duration.

[0007] If the initial task set meets the preset distribution conditions, the target task package corresponding to each drone is determined based on the initial task set and the real-time status data of multiple drones. Otherwise, the initial task set is sorted according to the preset priority rules and the drones are controlled to perform inspections in sequence according to the sorting results.

[0008] Based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data, determine the path planning data corresponding to the current scheduling cycle; based on the path planning data and the real-time position coordinates of multiple UAVs, determine the first adjustment command of the collaborative adjustment strategy corresponding to the current scheduling cycle;

[0009] Based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, determine the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle, control the UAV to operate according to the first and second adjustment instructions, and return to the step of judging whether the initial task set meets the preset distribution conditions.

[0010] Preferably, the real-time status data includes the drone's location coordinates and remaining battery power;

[0011] Based on the initial task set and real-time status data of multiple drones, the target task package corresponding to each drone is determined, including:

[0012] The quotient of the number of inspection points corresponding to the initial task set and the number of multiple drones is used as the basic task load for each drone.

[0013] The product of the difference between the remaining battery power of multiple drones and the average battery power, and the preset weighting coefficient, is used as the battery power correction amount for each drone.

[0014] The sum of the base task quantity and the power correction quantity is used as the task limit of the target task package for each drone.

[0015] Based on the task limit of the target task package and the coordinate distance of the inspection points in the initial task set, the inspection points in the initial task set are assigned to the corresponding drones.

[0016] Preferably, the collaborative adjustment strategy includes spacing maintenance items and conflict avoidance items;

[0017] Based on path planning data and the real-time position coordinates of multiple drones, the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle is determined, including:

[0018] The difference between the path nodes corresponding to the path planning data and the real-time position coordinates of multiple drones is used as the position deviation for the current scheduling cycle.

[0019] The product of the difference between the position deviation and the preset spacing threshold and the spacing maintenance coefficient is used as the spacing maintenance term of the collaborative adjustment strategy;

[0020] The product of the time difference between the arrival times of multiple drones at path nodes and the preset time threshold and the conflict avoidance coefficient is used as the conflict avoidance term of the collaborative adjustment strategy.

[0021] The sum of the spacing maintenance term and the conflict avoidance term is used as the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle.

[0022] Preferably, the flight control strategy includes speed limits and altitude limits;

[0023] Based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, determine the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle, including:

[0024] The angle between the target heading corresponding to the first adjustment instruction and the no-fly zone boundary in the geographic information data is taken as the heading deviation for the current scheduling cycle;

[0025] The product of the heading deviation and the heading proportionality coefficient is used as the speed limit term of the flight control strategy;

[0026] The product of the difference between the distance between the drone and the boundary of the no-fly zone and the safe distance threshold and the altitude restriction coefficient is used as the altitude restriction term of the flight control strategy;

[0027] The sum of the speed limit and the altitude limit is used as the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle.

[0028] Preferably, after controlling the drone to operate according to the first adjustment instruction and the second adjustment instruction, before returning to the step of determining whether the initial task set meets the preset distribution conditions, the method further includes:

[0029] If the drone's real-time location coordinates fall within the no-fly zone boundary in the geographic information data, or if the drone's remaining battery power is less than the second battery threshold for a duration longer than the second preset duration, then the target mission package for controlling the drone is an emergency return-to-home mission.

[0030] Preferably, the sum of the speed limit and the altitude limit is used as the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle, including:

[0031] Use the sum of the speed limit and the altitude limit as the initial adjustment command;

[0032] If the initial adjustment instruction is greater than or equal to the first instruction threshold, then the initial adjustment instruction is set to the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle.

[0033] If the initial adjustment instruction is less than the first instruction threshold, then the first instruction threshold is set to the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle.

[0034] Preferably, after determining the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, the method further includes:

[0035] If the duration of the second adjustment command being less than the third command threshold is greater than the third preset duration, then the output of the coordinated adjustment strategy and the flight control strategy will be turned off.

[0036] After controlling the operation of the drone according to the first adjustment command and the second adjustment command, the method further includes:

[0037] If a new inspection task instruction is received, the conflict avoidance item of the control coordination adjustment strategy will be gradually reduced to zero according to the preset number of times.

[0038] Preferably, the geographic information data includes the distribution of terrain obstacles and the boundaries of no-fly zones;

[0039] After acquiring geographic information data of the target inspection area and real-time status data of multiple drones, the method further includes:

[0040] Identify no-fly zones and restricted-fly zones within the target inspection area based on the distribution of terrain obstacles in geographic information data;

[0041] Determine whether each drone is located in a no-fly zone or a restricted-fly zone based on the position coordinates in the real-time status data of multiple drones;

[0042] If a drone is located in a no-fly zone, a forced landing command is generated and sent to the drone; if a drone is located in a restricted-fly zone, the drone's flight altitude limit parameters are adjusted.

[0043] Preferably, the path planning data includes path nodes and obstacle avoidance height;

[0044] Based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data, the path planning data corresponding to the current scheduling cycle is determined, including:

[0045] Extract the node coordinates of each inspection point in the coordinate grid from the target task package;

[0046] Based on the distribution of terrain obstacles in the node coordinates and geographic information data, a path search algorithm is used to generate a preliminary path connecting each node.

[0047] The vertical height of the initial path is corrected based on the height data of terrain obstacles to obtain path planning data including path nodes and obstacle avoidance heights.

[0048] A GIS-based UAV collaborative inspection and scheduling system, applicable to the aforementioned GIS-based UAV collaborative inspection and scheduling method, includes:

[0049] The data acquisition unit is used to acquire geographic information data of the target inspection area and real-time status data of multiple drones; it generates an initial task set based on the geographic information data and preset inspection tasks, and maps each inspection point in the initial task set to the coordinate grid corresponding to the geographic information data.

[0050] The distribution judgment unit is used to determine whether the initial task set meets the preset distribution conditions; wherein, the initial task set meets the preset distribution conditions when the total number of tasks in the initial task set is greater than a first quantity threshold and the average remaining power of multiple drones is greater than the power threshold and the duration of maintenance is greater than a first preset duration.

[0051] The task determination unit is used to determine the target task package corresponding to each UAV based on the initial task set and the real-time status data of multiple UAVs when the initial task set meets the preset distribution conditions; otherwise, it sorts the initial task set according to the preset priority rules and controls the UAVs to perform inspections in sequence according to the sorting results.

[0052] The adjustment strategy unit is used to determine the path planning data corresponding to the current scheduling cycle based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data; and to determine the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle based on the path planning data and the real-time position coordinates of multiple UAVs.

[0053] The collaborative inspection unit is used to determine the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction and the no-fly zone boundary in the geographic information data, and to control the operation of the UAV according to the first and second adjustment instructions, and return to the step of judging whether the initial task set meets the preset distribution conditions.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] (1) By acquiring UAV status and geographic information data in real time, this invention can reasonably allocate inspection tasks according to the battery level and location of each UAV, ensuring the efficiency and timeliness of task execution. Moreover, it can evaluate and adjust inspection tasks in real time, flexibly adjust tasks according to the actual status of the UAV and environmental changes, improve the system's adaptability, and significantly improve the safety of UAVs during inspection by identifying no-fly zones and restricted flight zones and setting emergency return missions, reducing the risks caused by flying into no-fly zones or insufficient battery power.

[0056] (2) By introducing a collaborative adjustment strategy, this invention enables multiple drones to effectively avoid conflicts and interference with each other when performing tasks, thereby improving overall operational efficiency. Moreover, by using a path planning algorithm, this method can generate the optimal path to avoid obstacles, ensuring that the drones can reach each inspection point smoothly and safely during the inspection process. Furthermore, through the collaborative operation of multiple drones, the coverage and efficiency of the inspection area are improved, enabling large-scale inspection tasks to be completed in a shorter time. Attached Figure Description

[0057] Figure 1 is a schematic flowchart of the overall method in an embodiment of the present invention;

[0058] Figure 2 is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0059] In the diagram: 1. Data acquisition unit; 2. Distribution and judgment unit; 3. Task determination unit; 4. Strategy adjustment unit; 5. Collaborative inspection unit. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1, please refer to Figure 1, the present invention provides a technical solution: a GIS-based UAV collaborative inspection and scheduling method, comprising:

[0062] S1. Acquire geographic information data of the target inspection area and real-time status data of multiple drones; generate an initial task set based on the geographic information data and preset inspection tasks, and map each inspection point in the initial task set to the coordinate grid corresponding to the geographic information data.

[0063] S2. Determine whether the initial task set meets the preset distribution conditions; wherein, the initial task set meets the preset distribution conditions when the total number of tasks in the initial task set is greater than the first quantity threshold and the average remaining power of multiple drones is greater than the power threshold and the duration of maintenance is greater than the first preset duration.

[0064] S3. In response to the initial task set meeting the preset distribution conditions, the target task package corresponding to each drone is determined based on the initial task set and the real-time status data of multiple drones. Otherwise, the initial task set is sorted according to the preset priority rules and the drones are controlled to perform inspections in sequence according to the sorting results.

[0065] S4. Determine the path planning data corresponding to the current scheduling cycle based on the coordinate grid corresponding to the target task package and the terrain obstacle distribution in the geographic information data; determine the first adjustment command of the collaborative adjustment strategy corresponding to the current scheduling cycle based on the path planning data and the real-time position coordinates of multiple UAVs;

[0066] S5. Determine the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction and the no-fly zone boundary in the geographic information data, control the UAV to operate according to the first and second adjustment instructions, and return to the step of judging whether the initial task set meets the preset distribution conditions.

[0067] It should be noted that the system acquires geographic information data (such as terrain, obstacles, etc.) of the target inspection area and real-time status data (such as location, battery level, status, etc.) of multiple drones. Based on the acquired geographic information data and the preset inspection tasks, the system generates an initial task set. This task set contains multiple inspection points and maps these inspection points to the corresponding geographic coordinate grid to facilitate subsequent path planning.

[0068] Check whether the initial task set meets certain preset distribution conditions; these conditions include: the total number of tasks must be greater than a specified quantity threshold; the average remaining battery power of multiple drones must exceed the battery threshold, and they must have the ability to maintain flight for more than the set duration;

[0069] If the initial task set meets the above conditions, the system will assign a corresponding target task package to each drone based on the initial task set and the drone's real-time status data; if the conditions are not met, the system will sort the initial task set according to the preset priority rules and control the drones to execute the inspection tasks in sequence according to this sorting result.

[0070] Based on the coordinate grid in the target mission package and the distribution of terrain obstacles in the geographic information data, the system will formulate path planning data for the current scheduling cycle; ensuring that the UAV can effectively avoid obstacles during flight; through the path planning data of the current scheduling cycle and the real-time position coordinates of the UAV, the system will determine the first adjustment instruction of the collaborative adjustment strategy; this may involve adjusting the flight path of the UAV or the task allocation.

[0071] Based on the first adjustment instruction and the no-fly zone boundary in the geographic information data, the system generates a second adjustment instruction corresponding to the current scheduling cycle. This is to ensure that the drone does not enter the no-fly zone. Based on the first and second adjustment instructions, the system controls the drone to fly according to the instructions and returns to step S2 to re-determine whether the initial task set meets the preset distribution conditions. This loop ensures the dynamic adaptability and efficiency of the scheduling process.

[0072] In one alternative embodiment, the real-time status data includes the drone's location coordinates and remaining battery power;

[0073] Based on the initial task set and real-time status data of multiple drones, the target task package corresponding to each drone is determined, including:

[0074] The quotient of the number of inspection points corresponding to the initial task set and the number of multiple drones is used as the basic task load for each drone.

[0075] The product of the difference between the remaining battery power of multiple drones and the average battery power, and the preset weighting coefficient, is used as the battery power correction amount for each drone.

[0076] The sum of the base task quantity and the power correction quantity is used as the task limit of the target task package for each drone.

[0077] Based on the task limit of the target task package and the coordinate distance of the inspection points in the initial task set, the inspection points in the initial task set are assigned to the corresponding drones.

[0078] It should be noted that the position coordinates refer to the drone's current specific location in three-dimensional space, which is crucial for path planning and mission execution; the remaining battery power indicates the drone's current available battery power, affecting its flight time and mission execution capability.

[0079] The following steps are used to assign target mission packages to each drone:

[0080] Divide the number of inspection points in the initial task set (e.g., the number of locations to be inspected) by the number of currently available drones. The result is the basic number of inspection tasks that each drone should perform without any other adjustments; this value represents a fair initial task allocation.

[0081] To take into account the impact of each drone's remaining battery power on its mission performance, the system calculates the difference between each drone's remaining battery power and the average battery power of all drones; then, it multiplies this difference by a preset weighting coefficient to obtain the battery power correction amount.

[0082] If a drone's remaining battery power is higher than the average, the difference is positive; otherwise, it is negative. This means that drones with high battery power can handle more tasks, while drones with low battery power need to reduce their task load. The weighting coefficient can be set according to the actual situation to adjust the degree of influence of battery power on task allocation.

[0083] The base workload is added to the power correction amount to obtain the workload limit for each drone; this limit determines the maximum number of inspection tasks that the drone can perform in this round of scheduling.

[0084] Based on the task limit of each drone's target task package and the coordinate distance of the initial task set inspection points, the system assigns the inspection points in the initial task set to the corresponding drones. In this process, the distance between inspection points is usually considered to optimize the drone's flight path and reduce flight time and energy consumption. The system may select those inspection points that are closer together to ensure that the task can be effectively completed within the range allowed by the drone's battery power.

[0085] In one alternative embodiment, the collaborative adjustment strategy includes a spacing maintenance term and a conflict avoidance term;

[0086] Based on path planning data and the real-time position coordinates of multiple drones, the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle is determined, including:

[0087] The difference between the path nodes corresponding to the path planning data and the real-time position coordinates of multiple drones is used as the position deviation for the current scheduling cycle.

[0088] The product of the difference between the position deviation and the preset spacing threshold and the spacing maintenance coefficient is used as the spacing maintenance term of the collaborative adjustment strategy;

[0089] The product of the time difference between the arrival times of multiple drones at path nodes and the preset time threshold and the conflict avoidance coefficient is used as the conflict avoidance term of the collaborative adjustment strategy.

[0090] The sum of the spacing maintenance term and the conflict avoidance term is used as the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle.

[0091] It should be noted that a certain safe distance should be maintained between multiple drones to avoid collisions or interference; potential time conflicts that may occur when multiple drones are performing tasks should be handled to ensure that they can reach their respective inspection targets on time.

[0092] The first adjustment instruction of the collaborative adjustment strategy for the current scheduling period is calculated through the following steps:

[0093] The path nodes (i.e., the specific locations that the drone needs to reach) in the path planning data are compared with the real-time location coordinates of multiple drones, and the difference between them is calculated; this difference reflects the degree of deviation of each drone from its target location.

[0094] The difference between the positional deviation and a preset spacing threshold (e.g., the minimum distance that drones must maintain) is calculated; this indicates whether the current distance between the drone and other drones meets safety standards; the importance of the adjustment is determined by multiplying the result of this difference by a preset spacing maintenance coefficient; the spacing maintenance coefficient can be used to adjust the degree of importance of maintaining a safe distance, and a higher coefficient makes maintaining the distance more important;

[0095] Calculate the difference between the estimated time for each drone to arrive at its path node and a preset time threshold; this difference reflects the degree to which the drone arrives at the node ahead of schedule or behind schedule; multiply this time difference by a conflict avoidance coefficient to measure the priority of conflict avoidance; a higher conflict avoidance coefficient will cause the system to pay more attention to avoiding time conflicts between drones.

[0096] The sum of the distance maintenance term and the conflict avoidance term is the first adjustment instruction of the coordinated adjustment strategy corresponding to the current scheduling cycle. This instruction will provide specific operational guidance for the UAVs to maintain a safe flight distance and coordinate their arrival at their respective destinations.

[0097] In one alternative embodiment, the flight control strategy includes a speed limit and an altitude limit;

[0098] Based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, determine the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle, including:

[0099] The angle between the target heading corresponding to the first adjustment instruction and the no-fly zone boundary in the geographic information data is taken as the heading deviation for the current scheduling cycle;

[0100] The product of the heading deviation and the heading proportionality coefficient is used as the speed limit term of the flight control strategy;

[0101] The product of the difference between the distance between the drone and the boundary of the no-fly zone and the safe distance threshold and the altitude restriction coefficient is used as the altitude restriction term of the flight control strategy;

[0102] The sum of the speed limit and the altitude limit is used as the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle.

[0103] It should be noted that the speed limit is used to restrict the drone's flight speed to ensure safe and stable flight; the altitude limit ensures that the drone stays within a reasonable altitude range during flight and avoids entering no-fly zones.

[0104] The second adjustment instruction for the flight control strategy in the current scheduling cycle is calculated using the following steps:

[0105] The angle between the drone's target heading in the first adjustment command and the no-fly zone boundary in the geographic information data is compared. This angle reflects the relationship between the drone's current heading and the no-fly zone. If the angle is small, it means that the drone may be approaching the no-fly zone and there is a risk.

[0106] The heading deviation is multiplied by a preset heading ratio factor; the heading ratio factor is used to adjust the degree of influence of the heading deviation on the speed limit; a large heading deviation may cause the drone to slow down to prevent it from accidentally entering a no-fly zone; that is, when the heading deviation is large, the drone will automatically reduce its speed to ensure that it can adjust its heading in time and avoid entering potentially dangerous areas.

[0107] Calculate the actual distance between the drone and the boundary of the no-fly zone, and compare it with a preset safe distance threshold; the safe distance threshold refers to the minimum safe distance that the drone should maintain to avoid entering the no-fly zone;

[0108] The difference between the distance between the drone and the no-fly zone boundary and the safe distance threshold is multiplied by an altitude restriction factor; this factor determines the degree to which the difference affects altitude control; if the drone is too close to the no-fly zone boundary, the altitude restriction factor will prompt the drone to rise to ensure safety.

[0109] The sum of the speed and altitude limits is the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle. This instruction provides specific guidance for the UAV to adjust its speed and altitude to ensure that it does not enter the no-fly zone during flight, while maintaining a safe and stable flight state.

[0110] In an optional embodiment, after controlling the drone to operate according to the first adjustment instruction and the second adjustment instruction, before returning to the step of determining whether the initial task set meets the preset distribution conditions, the method further includes:

[0111] If the drone's real-time location coordinates fall within the no-fly zone boundary in the geographic information data, or if the drone's remaining battery power is less than the second battery threshold for a duration longer than the second preset duration, then the target mission package for controlling the drone is an emergency return-to-home mission.

[0112] It should be noted that the drone's location is monitored in real time; if the drone's real-time location coordinates fall within the boundary of a no-fly zone defined in the geographic information data, it means that the drone is approaching or entering an area where flying is not permitted.

[0113] Once this situation is detected, the drone's target mission package will be immediately changed to "emergency return mission"; the purpose of this measure is to prevent the drone from entering the no-fly zone, thereby reducing the risk and ensuring its safe return.

[0114] Check the drone's remaining battery power; if the remaining battery power is below a preset battery threshold (second battery threshold), it means that the drone may not be able to complete the current task and there is a risk of insufficient battery power.

[0115] If the drone remains below the battery threshold for more than the second preset duration, it indicates that the drone's battery status is critical and it may not be able to safely complete subsequent tasks. In this case, the drone will change its target mission package to "emergency return mission" to ensure that it can return to charge or land in time to avoid flight accidents caused by running out of power.

[0116] Whether due to entering a no-fly zone or insufficient battery power, the drone will change its current objective to an emergency return-to-home mission; this decision aims to prioritize the safety and effective operation of the drone; once it is confirmed that an emergency return-to-home mission is required, the drone will automatically return to the starting point or a safe location according to the preset return-to-home path to ensure a safe landing.

[0117] In an optional embodiment, the sum of the speed limit and the altitude limit is used as the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle, including:

[0118] Use the sum of the speed limit and the altitude limit as the initial adjustment command;

[0119] If the initial adjustment instruction is greater than or equal to the first instruction threshold, then the initial adjustment instruction is set to the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle.

[0120] If the initial adjustment instruction is less than the first instruction threshold, then the first instruction threshold is set to the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle.

[0121] It should be noted that the speed limit and altitude limit are added together to obtain a value, which is called the "initial adjustment instruction". This initial adjustment instruction reflects the comprehensive flight control requirements that the UAV should follow during the current scheduling cycle.

[0122] The initial adjustment command is compared with a preset first command threshold to determine the final second adjustment command for the flight control strategy;

[0123] If the initial adjustment command is greater than or equal to the first command threshold, it indicates that the current flight control strategy is sufficient or more stringent; in this case, the system directly regards the initial adjustment command as the second adjustment command of the flight control strategy corresponding to the current scheduling cycle.

[0124] If the initial adjustment command is less than the first command threshold, it indicates that the current flight control requirements are not strict enough and there may be safety hazards. In this case, the system will set the first command threshold to the second adjustment command of the flight control strategy corresponding to the current scheduling cycle. In other words, the system chooses to use a higher safety standard (the first command threshold) to ensure the safety of the UAV.

[0125] In an optional embodiment, after determining the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, the method further includes:

[0126] If the duration of the second adjustment command being less than the third command threshold is greater than the third preset duration, then the output of the coordinated adjustment strategy and the flight control strategy will be turned off.

[0127] After controlling the operation of the drone according to the first adjustment command and the second adjustment command, the method further includes:

[0128] If a new inspection task instruction is received, the conflict avoidance item of the control coordination adjustment strategy will be gradually reduced to zero according to the preset number of times.

[0129] It should be noted that after determining the second adjustment instruction of the current flight control strategy based on the first adjustment instruction of the current scheduling cycle and the no-fly zone boundary in the geographic information data, the following steps involve several condition judgments.

[0130] If the second adjustment command is less than the third command threshold, and this state is maintained for a duration exceeding the third preset duration, it indicates that the UAV's flight control strategy has become insufficiently strict and may pose a safety risk. In this case, the system will disable the output of the collaborative adjustment strategy and the flight control strategy. The purpose of this is to protect the safety of the UAV and prevent it from continuing to fly under an inappropriate control strategy. This operation can prevent potential accidents and ensure that the UAV is within a safe operating range.

[0131] After controlling the drone to operate according to the first and second adjustment instructions, the system also needs to process the reception of new mission instructions:

[0132] If a new inspection task instruction is received, it means that the UAV needs to start executing the new task. The conflict avoidance item of the control coordination adjustment strategy will be gradually reduced to zero according to the preset number of times. This means that after receiving a new task instruction, the system will gradually reduce the restrictions or interventions on the existing coordination adjustment strategy so that the UAV can respond to the new task requirements more flexibly. This step ensures that the UAV can adapt to the new task instruction, while minimizing the conflicts caused by the adjustment strategy, thereby improving the efficiency of task execution.

[0133] In an optional embodiment, the geographic information data includes the distribution of terrain obstacles and the boundaries of no-fly zones;

[0134] After acquiring geographic information data of the target inspection area and real-time status data of multiple drones, the method further includes:

[0135] Identify no-fly zones and restricted-fly zones within the target inspection area based on the distribution of terrain obstacles in geographic information data;

[0136] Determine whether each drone is located in a no-fly zone or a restricted-fly zone based on the position coordinates in the real-time status data of multiple drones;

[0137] If a drone is located in a no-fly zone, a forced landing command is generated and sent to the drone; if a drone is located in a restricted-fly zone, the drone's flight altitude limit parameters are adjusted.

[0138] It should be noted that terrain obstacle distribution refers to various terrain features and obstacles within the target inspection area, such as buildings, mountains, and trees; this information is crucial for the flight path planning of drones; no-fly zone boundaries refer to specific areas where drones are prohibited from entering (such as around airports, military bases, etc.).

[0139] Acquire geographic information data of the target inspection area and real-time status data of multiple drones; real-time status data includes information such as the drone's position, speed, and altitude;

[0140] Based on the distribution of terrain obstacles in the geographic information data, the system can identify no-fly zones and restricted-fly zones in the target inspection area; no-fly zones are areas where any aircraft are prohibited from entering, while restricted-fly zones may have restrictions, such as altitude or speed restrictions.

[0141] Based on the location coordinates in the real-time status data of multiple drones, determine whether each drone is in the identified no-fly zone or restricted flight zone;

[0142] If a drone is detected in a no-fly zone, the system will generate an emergency landing command and send it to the drone; this command requires the drone to land immediately to ensure its safety and comply with relevant regulations.

[0143] If a drone is located in a restricted flight area, the system will adjust the drone's flight altitude limit parameters; this means that the system will set a new altitude limit according to the regulations of the restricted flight area to prevent the drone from flying at an unsafe altitude.

[0144] In one alternative embodiment, the path planning data includes path nodes and obstacle avoidance height;

[0145] Based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data, the path planning data corresponding to the current scheduling cycle is determined, including:

[0146] Extract the node coordinates of each inspection point in the coordinate grid from the target task package;

[0147] Based on the distribution of terrain obstacles in the node coordinates and geographic information data, a path search algorithm is used to generate a preliminary path connecting each node.

[0148] The vertical height of the initial path is corrected based on the height data of terrain obstacles to obtain path planning data including path nodes and obstacle avoidance heights.

[0149] It should be noted that path nodes refer to the key coordinate points that the drone needs to pass through during flight; these points are usually important locations in inspection work; obstacle avoidance altitude refers to the minimum altitude that the drone must maintain during flight to avoid collisions with terrain obstacles.

[0150] Based on the coordinate grid and terrain obstacle distribution in the geographic information data contained in the target task package, the path planning data for the current scheduling cycle is determined; the specific steps are as follows:

[0151] Extract the node coordinates of each inspection point in the coordinate grid from the target task package; these node coordinates are the specific locations that the UAV needs to reach, defining the inspection targets.

[0152] Based on the extracted node coordinates and the distribution of terrain obstacles in the geographic information data, the system uses path search algorithms (such as A* algorithm, Dijkstra algorithm, etc.) to calculate the preliminary path connecting each node; the purpose of this algorithm is to find a feasible path from the starting point to the ending point, taking into account terrain obstacles.

[0153] On the initial path generated, the system will correct the vertical height of the path based on the height data of terrain obstacles. This step is to ensure that the drone will not collide with obstacles on the ground during flight, including buildings, trees, etc. The corrected path will include the coordinates of each path node and the corresponding obstacle avoidance height to ensure that the drone stays at a safe altitude during flight.

[0154] Example 2, please refer to Figure 2. This invention provides a technical solution: a GIS-based UAV collaborative inspection and scheduling system, which is applicable to the above-mentioned GIS-based UAV collaborative inspection and scheduling method, including:

[0155] Data acquisition unit 1 is used to acquire geographic information data of the target inspection area and real-time status data of multiple drones; it generates an initial task set based on the geographic information data and preset inspection tasks, and maps each inspection point in the initial task set to the coordinate grid corresponding to the geographic information data.

[0156] The distribution judgment unit 2 is used to determine whether the initial task set meets the preset distribution conditions; wherein, the initial task set meets the preset distribution conditions when the total number of tasks in the initial task set is greater than the first quantity threshold and the average remaining power of multiple drones is greater than the power threshold and the duration of maintenance is greater than the first preset duration.

[0157] Task determination unit 3 is used to determine the target task package corresponding to each UAV based on the initial task set and the real-time status data of multiple UAVs when the initial task set meets the preset distribution conditions; otherwise, it sorts the initial task set according to the preset priority rules and controls the UAVs to perform inspections in sequence according to the sorting results.

[0158] The adjustment strategy unit 4 is used to determine the path planning data corresponding to the current scheduling cycle based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data; and to determine the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle based on the path planning data and the real-time position coordinates of multiple UAVs.

[0159] The collaborative inspection unit 5 is used to determine the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction and the no-fly zone boundary in the geographic information data, and to control the operation of the UAV according to the first adjustment instruction and the second adjustment instruction, and return to the step of judging whether the initial task set meets the preset distribution conditions.

[0160] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A GIS-based UAV collaborative inspection and scheduling method, characterized in that, include: Acquire geographic information data of the target inspection area and real-time status data of multiple drones; An initial task set is generated based on geographic information data and preset inspection tasks, and each inspection point in the initial task set is mapped to the coordinate grid corresponding to the geographic information data. It is then determined whether the initial task set meets preset distribution conditions. These conditions are met if the total number of tasks in the initial task set is greater than a first quantity threshold, the average remaining battery power of multiple drones is greater than the battery threshold, and the duration of this duration is greater than a first preset duration. If the initial task set meets the preset distribution conditions, the target task package corresponding to each drone is determined based on the initial task set and the real-time status data of multiple drones. Otherwise, the initial task set is sorted according to preset priority rules, and the drones are controlled to perform inspections sequentially according to the sorting results. Path planning data corresponding to the current scheduling cycle is determined based on the coordinate grid corresponding to the target task package and the terrain obstacle distribution in the geographic information data. A first adjustment instruction for the collaborative adjustment strategy corresponding to the current scheduling cycle is determined based on the path planning data and the real-time position coordinates of multiple drones. A second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle is determined based on the first adjustment instruction and the no-fly zone boundary in the geographic information data. The drones are then controlled to operate according to the first and second adjustment instructions, and the process returns to the step of determining whether the initial task set meets the preset distribution conditions.

2. The UAV collaborative inspection and scheduling method based on GIS according to claim 1, characterized in that, Real-time status data includes the drone's location coordinates and remaining battery power. Based on the initial task set and the real-time status data of multiple drones, the target task package corresponding to each drone is determined, including: using the quotient of the number of inspection points corresponding to the initial task set and the number of multiple drones as the basic task load for each drone; using the product of the difference between the remaining battery power of multiple drones and the average battery power and a preset weighting coefficient as the battery power correction amount for each drone; using the sum of the basic task load and the battery power correction amount as the task limit for the target task package corresponding to each drone; and assigning the inspection points in the initial task set to the corresponding drones based on the task limit of the target task package and the coordinate distance of the inspection points in the initial task set.

3. The UAV collaborative inspection and scheduling method based on GIS according to claim 2, characterized in that, Coordinated adjustment strategies include spacing maintenance items and conflict avoidance items; The first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle is determined based on the path planning data and the real-time position coordinates of multiple UAVs, including: taking the difference between the path node corresponding to the path planning data and the real-time position coordinates of multiple UAVs as the position deviation of the current scheduling cycle. The product of the difference between the position deviation and the preset spacing threshold and the spacing maintenance coefficient is used as the spacing maintenance term of the collaborative adjustment strategy; the product of the difference between the time difference of multiple UAVs arriving at the path node and the preset time threshold and the conflict avoidance coefficient is used as the conflict avoidance term of the collaborative adjustment strategy; the sum of the spacing maintenance term and the conflict avoidance term is used as the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle.

4. The GIS-based UAV collaborative inspection and scheduling method according to claim 3, characterized in that, The flight control strategy includes speed and altitude restrictions. Based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, a second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle is determined. This includes: using the angle between the target heading corresponding to the first adjustment instruction and the no-fly zone boundary in the geographic information data as the heading deviation for the current scheduling cycle; using the product of the heading deviation and the heading proportionality coefficient as the speed restriction of the flight control strategy; using the product of the difference between the distance between the UAV and the no-fly zone boundary and the safe distance threshold and the altitude restriction coefficient as the altitude restriction of the flight control strategy; and using the sum of the speed and altitude restrictions as the second adjustment instruction for the flight control strategy corresponding to the current scheduling cycle.

5. The UAV collaborative inspection and scheduling method based on GIS according to claim 4, characterized in that, After controlling the drone to operate according to the first adjustment instruction and the second adjustment instruction, before returning to the step of determining whether the initial task set meets the preset distribution conditions, the method further includes: if the drone's real-time position coordinates fall into the no-fly zone boundary in the geographic information data, or if the drone's remaining battery power is less than the duration of the second battery threshold and is greater than the second preset duration, then the target task package for controlling the drone is an emergency return mission.

6. The GIS-based UAV collaborative inspection and scheduling method according to claim 5, characterized in that, Using the sum of the speed limit and the altitude limit as the second adjustment instruction for the flight control strategy corresponding to the current scheduling period includes: using the sum of the speed limit and the altitude limit as the initial adjustment instruction; if the initial adjustment instruction is greater than or equal to a first instruction threshold, then setting the initial adjustment instruction as the second adjustment instruction for the flight control strategy corresponding to the current scheduling period; if the initial adjustment instruction is less than the first instruction threshold, then setting the first instruction threshold as the second adjustment instruction for the flight control strategy corresponding to the current scheduling period.

7. The GIS-based UAV collaborative inspection and scheduling method according to claim 6, characterized in that, After determining the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction corresponding to the current scheduling cycle and the no-fly zone boundary in the geographic information data, the method further includes: if the duration of the second adjustment instruction being less than the third instruction threshold is greater than the third preset duration, then the output of the collaborative adjustment strategy and the flight control strategy is turned off; after controlling the operation of the UAV according to the first adjustment instruction and the second adjustment instruction, the method further includes: if a new inspection task instruction is received, then the conflict avoidance item of the collaborative adjustment strategy is gradually reduced to zero according to a preset number of times.

8. The GIS-based UAV collaborative inspection and scheduling method according to claim 7, characterized in that, Geographic information data includes the distribution of terrain obstacles and the boundaries of no-fly zones; After acquiring geographic information data of the target inspection area and real-time status data of multiple drones, the method further includes: identifying no-fly zones and restricted-fly zones in the target inspection area based on the distribution of terrain obstacles in the geographic information data; Based on the position coordinates of multiple drones in real-time status data, determine whether each drone is in a no-fly zone or a restricted-fly zone; if a drone is in a no-fly zone, generate a forced landing command and send it to the drone; if a drone is in a restricted-fly zone, adjust the drone's flight altitude limit parameters.

9. A GIS-based UAV collaborative inspection and scheduling method according to claim 8, characterized in that, Path planning data includes path nodes and obstacle avoidance heights. Based on the coordinate grid corresponding to the target task package and the distribution of terrain obstacles in the geographic information data, the path planning data for the current scheduling cycle is determined. This includes: extracting the node coordinates of each inspection point in the target task package within the coordinate grid; generating a preliminary path connecting each node using a path search algorithm based on the node coordinates and the distribution of terrain obstacles in the geographic information data; and correcting the vertical height of the preliminary path based on the height data of the terrain obstacles to obtain path planning data including path nodes and obstacle avoidance heights.

10. A GIS-based UAV collaborative inspection and scheduling system, applicable to the GIS-based UAV collaborative inspection and scheduling method described in any one of claims 1-9, characterized in that, include: The data acquisition unit is used to acquire geographic information data of the target inspection area and real-time status data of multiple drones; An initial task set is generated based on geographic information data and preset inspection tasks, and each inspection point in the initial task set is mapped to the coordinate grid corresponding to the geographic information data. The system includes a distribution judgment unit for determining whether the initial task set meets preset distribution conditions. The initial task set meets the preset distribution conditions if the total number of tasks in the initial task set is greater than a first quantity threshold and the average remaining battery power of multiple drones is greater than the battery threshold for a duration greater than a first preset duration. A task determination unit, in response to the initial task set meeting the preset distribution conditions, determines the target task package corresponding to each drone based on the initial task set and the real-time status data of multiple drones. Otherwise, it sorts the initial task set according to preset priority rules and controls the drones to perform inspections sequentially according to the sorting results. An adjustment strategy unit determines the path planning data corresponding to the current scheduling cycle based on the coordinate grid corresponding to the target task package and the terrain obstacle distribution in the geographic information data. It determines the first adjustment instruction of the collaborative adjustment strategy corresponding to the current scheduling cycle based on the path planning data and the real-time position coordinates of multiple drones. A collaborative inspection unit determines the second adjustment instruction of the flight control strategy corresponding to the current scheduling cycle based on the first adjustment instruction and the no-fly zone boundary in the geographic information data. It controls the drones to operate according to the first and second adjustment instructions and returns to the step of determining whether the initial task set meets the preset distribution conditions.

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