Methods, systems, and media for drone takeoff and hazard avoidance based on GeoHash algorithm

Abstract

This invention discloses a method, system, and medium for UAV takeoff and hazard avoidance based on the GeoHash algorithm. The method involves: dividing the UAV's patrol area into multiple matrix regions; encoding each matrix region using the GeoHash algorithm to obtain a string representing the matrix region; obtaining the latitude and longitude coordinates of each hangar within the UAV's patrol area; encoding the hangar's latitude and longitude coordinates to obtain a string representing the hangar; associating the strings of the matrix regions with the corresponding hangar latitude and longitude coordinates to construct a database and store it in a remote scheduling system; before the UAV performs an inspection mission, the remote scheduling system obtains the latitude and longitude coordinates of the inspection mission's endpoint and meteorological data to determine takeoff conditions; during the UAV's inspection mission, flight conditions are assessed, and the nearest hangar is matched in the database, sending the nearest hangar location and a rapid hazard avoidance command. This invention uses meteorological data to determine takeoff conditions, improving UAV patrol efficiency; and designs an emergency hazard avoidance mechanism to enhance UAV safety.

Description

Technical Field

[0001] This invention belongs to the technical field of drone takeoff and hazard avoidance, specifically relating to a drone takeoff and hazard avoidance method, device and medium based on GeoHash algorithm. Background Technology

[0002] In the field of high-voltage power line inspection, in order to achieve automated inspection and maintenance operations by using drones, fixed drone hangars are being gradually built to solve the problem of drone battery life and charging, as well as to realize functions such as remote dispatching of drones and real-time data transmission.

[0003] A fixed hangar consists of a drone takeoff and landing platform, drone storage device, charging device, meteorological monitoring device, large customized ground station, joint positioning base station, data link module, fixed antenna, communication module, power supply module, and control module. The takeoff process for drones in the hangar is as follows: the drone's main control system creates a new drone inspection task and simultaneously uses data from the meteorological monitoring device to determine if the drone's takeoff conditions are met. However, the automatic drone inspection and maintenance operation range of the hangar is typically a 5-kilometer radius circle centered on the hangar. Within this 5-kilometer range, the weather conditions at the starting and ending points are not identical, especially during periods of regional extreme weather. Because the hangar's meteorological monitoring device can only obtain meteorological data for the current location of the hangar, such as temperature, humidity, and wind speed, it cannot determine whether other locations within the operation range meet the conditions for autonomous drone inspection. If a drone operates in an area with localized severe weather, it is prone to damage or even crash. Furthermore, when a drone encounters a situation that does not meet flight conditions during operation, it needs to choose the nearest available hangar for emergency landing. The remote dispatcher needs to provide the drone with the hangar location for emergency avoidance. The general method in the existing technology is for the drone to send its own latitude and longitude to the remote dispatcher, which then calculates the distance between the drone's latitude and longitude and the hangar's latitude and longitude using a traversal formula, sorts the distances in order, and then combines the drone's own flight range, hangar availability, etc., to provide the optimal solution for emergency avoidance. However, the traversal calculation takes a long time. If multiple drones need emergency avoidance at the same time, some drones may not be able to receive the avoidance plan in time, which also increases the probability of damage to the drones. Summary of the Invention

[0004] The main objective of this invention is to overcome the shortcomings and deficiencies of the prior art and provide a method, device, and medium for UAV takeoff and risk avoidance based on the GeoHash algorithm. This invention judges the takeoff conditions of UAVs based on meteorological data, thereby improving the patrol efficiency of UAVs. At the same time, it designs an emergency risk avoidance mechanism when the flight conditions are not met during the patrol mission, thereby improving the safety of UAVs in performing missions and reducing the damage rate of UAVs.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] On the one hand, the present invention provides a method for drone takeoff and hazard avoidance based on the GeoHash algorithm, the method comprising the following steps:

[0007] The drone's patrol range is divided into multiple matrix regions, and the GeoHash algorithm is used to encode the matrix regions to obtain strings for the matrix regions;

[0008] Obtain the latitude and longitude coordinates of each hangar within the drone's patrol range, and encode the hangar's latitude and longitude coordinates using the GeoHash algorithm to obtain the hangar's string;

[0009] Associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, build a database, and store it in the remote dispatch system;

[0010] Before a drone performs an inspection mission, the remote dispatcher obtains the latitude and longitude coordinates and meteorological data of the inspection mission's endpoint to determine whether the conditions for takeoff are met. If they are met, the drone takes off to perform the inspection mission; otherwise, the dispatcher makes the decision.

[0011] When a drone is performing an inspection mission, the remote dispatcher judges the flight conditions based on the meteorological data fed back by the drone. If the flight conditions are not met, the dispatcher matches the nearest hangar in the database based on the drone's current latitude and longitude coordinates and sends the location of the nearest hangar and a rapid evacuation command.

[0012] As a preferred technical solution, each of the multiple matrix regions corresponds to a Base32 string of fixed encoding length; all latitude and longitude coordinates in the matrix region share the string of that matrix region;

[0013] The latitude and longitude coordinates of each hangar are encoded into binary using the GeoHash algorithm to obtain a Base32 string of fixed encoding length;

[0014] Link the string of the matrix region where the hangar is located with the latitude and longitude coordinates of the hangar, build a database, and store it in the remote scheduling.

[0015] As a preferred technical solution, the remote scheduling acquires the latitude and longitude coordinates and meteorological data of the inspection mission endpoint to determine whether the takeoff conditions are met, specifically as follows:

[0016] Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task's endpoint, and encode them using the GeoHash algorithm to obtain the endpoint string;

[0017] The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint.

[0018] Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, it is determined whether the drone takes off. If the takeoff conditions are met, the drone takes off normally to perform the inspection mission; if the takeoff conditions are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection mission.

[0019] As a preferred technical solution, the step of determining whether the conditions for drone takeoff are met based on meteorological data from the hangar where the drone is located and meteorological data from the hangar in the matrix area where the destination is located or meteorological data near the destination specifically involves:

[0020] Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions.

[0021] If any condition is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and relevant data will be sent. The dispatcher will then decide whether the drone can take off.

[0022] As a preferred technical solution, the step of matching the nearest hangar in the database based on the current latitude and longitude coordinates of the UAV specifically involves:

[0023] When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone.

[0024] The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone.

[0025] Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database;

[0026] By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out.

[0027] The remote dispatcher retrieves the latitude and longitude coordinates of the hangars corresponding to the current matrix region where the drone is located and the eight surrounding matrix regions from the database, and calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates.

[0028] Sort the distances of the drones to each hangar and select the hangar closest to the drone;

[0029] Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance.

[0030] If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

[0031] On the other hand, the present invention provides a drone take-off and avoidance system based on the GeoHash algorithm, the system including a matrix partitioning module, a hangar coding module, a database construction module, a take-off judgment module and a rapid avoidance module;

[0032] The matrix partitioning module is used to divide the UAV's patrol range into multiple matrix regions, and uses the GeoHash algorithm to encode the matrix regions to obtain the strings of the matrix regions;

[0033] The hangar encoding module is used to obtain the latitude and longitude coordinates of each hangar within the patrol range of the UAV, and uses the GeoHash algorithm to encode the latitude and longitude coordinates of the hangar to obtain the string of hangar;

[0034] The database construction module is used to associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, construct the database, and store it in the remote dispatch.

[0035] The takeoff judgment module is used to remotely schedule and obtain the latitude and longitude coordinates and meteorological data of the end point of the inspection mission before the UAV performs the inspection mission, and to judge the takeoff conditions.

[0036] The rapid avoidance module is used to remotely schedule the drone to judge the flight conditions based on the meteorological data fed back by the drone when the drone is performing an inspection mission. If the flight conditions are not met, the module will match the nearest hangar in the database based on the drone's current latitude and longitude coordinates and send the location of the nearest hangar and a rapid avoidance command.

[0037] As a preferred technical solution, the takeoff determination module specifically comprises:

[0038] Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task's endpoint, and encode them using the GeoHash algorithm to obtain the endpoint string;

[0039] The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint.

[0040] Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, it is determined whether the drone takes off. If the takeoff conditions are met, the drone takes off normally to perform the inspection mission; if the takeoff conditions are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection mission.

[0041] As a preferred technical solution, the step of determining whether the conditions for drone takeoff are met based on meteorological data from the hangar where the drone is located and meteorological data from the hangar in the matrix area where the destination is located or meteorological data near the destination specifically involves:

[0042] Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions.

[0043] If any condition is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and relevant data will be sent. The dispatcher will then decide whether the drone can take off.

[0044] As a preferred technical solution, the rapid risk avoidance module specifically comprises:

[0045] When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone.

[0046] The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone.

[0047] Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database;

[0048] By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out.

[0049] The remote dispatcher retrieves the latitude and longitude coordinates of the hangars corresponding to the current matrix region where the drone is located and the eight surrounding matrix regions from the database, and calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates.

[0050] Sort the distances of the drones to each hangar and select the hangar closest to the drone;

[0051] Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance.

[0052] If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

[0053] In another aspect, the present invention provides a computer-readable storage medium storing a program, characterized in that, when the program is executed by a processor, it implements the above-mentioned method for UAV takeoff and risk avoidance based on the GeoHash algorithm.

[0054] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0055] 1. This invention improves the safety of UAVs performing inspection missions by acquiring meteorological data at the end of the UAV inspection mission and making a comprehensive judgment on takeoff conditions.

[0056] 2. This invention divides the matrix into regions, encodes them into strings using the GeoHash algorithm, and then establishes a connection with the latitude and longitude coordinates of the hangars. This allows for the rapid elimination of hangars that are far away, while only performing traversal calculations on some of the closer hangars. This solves the problems of long traversal time and low accuracy, and reduces the damage rate of drones.

[0057] 3. This method queries the nearest hangar by matching strings, which consumes less computing resources, is fast, and has a short processing time, greatly improving its universality and applicability. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a flowchart of the drone takeoff and hazard avoidance method based on the GeoHash algorithm in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart of the takeoff determination process in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of a prior art method for calculating the shortest distance from a drone to a hangar, as described in an embodiment of the present invention.

[0062] Figure 4 This is a flowchart illustrating rapid risk avoidance via remote scheduling in an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram illustrating the method for matching a drone to the nearest hangar in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the structure of the UAV takeoff and avoidance system based on the GeoHash algorithm in an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram of the structure of a computer-readable storage medium in an embodiment of the present invention. Detailed Implementation

[0066] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0067] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0068] The GeoHash algorithm is an address encoding method that encodes two-dimensional spatial latitude and longitude data into a string. It consists of three steps: 1. Converting longitude and latitude into binary; 2. Merging the binary longitude and binary latitude; 3. Encoding the merged binary longitude and latitude according to Base32 to obtain a string that indicates the location without exposing the precise coordinates, thus helping to protect privacy.

[0069] like Figure 1 As shown, this embodiment provides a method for drone takeoff and hazard avoidance based on the GeoHash algorithm, including the following steps:

[0070] S1. Divide the UAV's patrol area into multiple matrix regions, and use the GeoHash algorithm to encode the matrix regions to obtain a string for each matrix region. Each matrix region corresponds to a string of fixed encoding length. All points (i.e., latitude and longitude coordinates) in the entire matrix region share the string of that matrix region. Generally, the longer the encoding length of the string, the better the accuracy. The encoding length can be determined according to the actual situation. For example, a 5-bit encoding string can represent a matrix region of 10 square kilometers.

[0071] S2. Obtain the latitude and longitude coordinates of each hangar within the drone's patrol range, and encode the hangar's latitude and longitude coordinates using the GeoHash algorithm to obtain the hangar's string;

[0072] The latitude and longitude coordinates of each hangar are encoded into binary using the GeoHash algorithm, resulting in a Base32 string of fixed encoding length.

[0073] Associate the matrix region string with the corresponding hangar latitude and longitude coordinates, construct a database and store it in the remote scheduling system;

[0074] S3. Before the drone performs an inspection mission, the remote dispatcher obtains the latitude and longitude coordinates and meteorological data of the inspection mission endpoint to determine whether the takeoff conditions are met. If they are met, the drone takes off to perform the inspection mission; if they are not met, the dispatcher will make the decision.

[0075] like Figure 2 As shown, the specific process for determining drone takeoff is as follows:

[0076] Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task endpoint, and use the GeoHash algorithm to encode them into binary to obtain a Base32 endpoint string of fixed encoding length; this string represents the matrix area traversed by the drone inspection trajectory.

[0077] The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint.

[0078] Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, it is determined whether the drone takes off. If the takeoff conditions are met, the drone takes off normally to perform the inspection mission; if the takeoff conditions are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection mission.

[0079] In determining the takeoff conditions for drones, a sequential judgment is made based on meteorological data such as humidity, wind force, and temperature. Specifically:

[0080] Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions.

[0081] If any one of the conditions is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and the relevant data for the unmet condition will be sent. The dispatcher will then decide whether the drone can take off.

[0082] S4. When the drone is performing an inspection mission, the remote dispatcher judges the flight conditions based on the meteorological data fed back by the drone. If the flight conditions are not met, the dispatcher matches the nearest hangar in the database based on the drone's current latitude and longitude coordinates and sends the location of the nearest hangar and a rapid avoidance command.

[0083] like Figure 3 As shown, existing technologies for rapid drone hazard avoidance involve remotely scheduling the drone to determine its latitude and longitude position, then using a formula to traverse all hangars within a certain range to calculate the distance. This is combined with the drone's own range, hangar availability, and other factors to return the drone to the latitude and longitude position of a hangar for landing. Essentially, the drone needs to measure the distance to each hangar and prioritize the nearest one.

[0084] In this embodiment, such as Figure 4 , 5 As shown, the nearest hangar is matched against the database based on the drone's current latitude and longitude coordinates. The specific steps are as follows:

[0085] When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone.

[0086] The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone.

[0087] Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database;

[0088] By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out.

[0089] The remote dispatcher retrieves the latitude and longitude coordinates of the current drone's matrix region and the eight surrounding matrix regions from the database, effectively filtering out distant hangars without needing to calculate the distance using a formula. Then, it calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates.

[0090] Sort the distances of the drones to each hangar and select the hangar closest to the drone;

[0091] Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance.

[0092] If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

[0093] Assume the current latitude and longitude coordinates of the drone are The coordinates of a certain hangar are in λ represents latitude, λ represents longitude, and R represents the Earth's radius (6371 km).

[0094] Considering both accuracy and practicality, the distance L between the UAV and the hangar is calculated using the Haversine Formula combined with a planar vector calculation method. The formula is as follows:

[0095]

[0096] According to the existing technology, it is necessary to traverse all hangars within the range to calculate the distance, and then sort them according to the distance. If there are i hangars within the range of the drone's inspection, i distance results are obtained, and the number of calculations N = i. As the distance result i increases, the time complexity increases, but the execution efficiency gradually decreases, and the sorting difficulty also increases.

[0097] The present invention uses the Geohash algorithm to filter out matrix regions other than the matrix region where the current drone is located and the surrounding 8 matrix regions. It only performs formula traversal calculations on the accurate latitude and longitude of the hangar and the drone in the matrix region where the drone is located and the surrounding 8 matrix regions, and then sorts them by distance. Although the number of calculations N = i, since i ≤ 9, the number of calculations will not continue to increase, and the time complexity will not increase indefinitely.

[0098] This demonstrates that the drone takeoff and avoidance method based on the GeoHash algorithm requires fewer computational resources and has a faster computation time. At the same time, the number of formula traversal calculations is finite, and the sorting time complexity is low. When the number of hangars is large enough, it can effectively reduce the number of computer traversal calculations, improve the algorithm's execution efficiency, and help provide multiple drones with the coordinates of the nearest landing hangar in a timely manner, thus reducing the probability of damage.

[0099] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously.

[0100] Based on the same idea as the drone takeoff and avoidance method based on the GeoHash algorithm in the above embodiments, this invention also provides a drone takeoff and avoidance system based on the GeoHash algorithm. This system can be used to execute the aforementioned drone takeoff and avoidance method based on the GeoHash algorithm. For ease of explanation, the structural diagram of the embodiment of the drone takeoff and avoidance system based on the GeoHash algorithm only shows the parts related to the embodiments of this invention. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0101] like Figure 6 As shown, another embodiment of the present invention provides a drone takeoff and avoidance system based on the GeoHash algorithm, including a matrix partitioning module, a hangar coding module, a database construction module, a takeoff judgment module, and a rapid avoidance module;

[0102] The matrix partitioning module is used to divide the UAV's patrol range into multiple matrix regions, and uses the GeoHash algorithm to encode the matrix regions to obtain the strings of the matrix regions;

[0103] The hangar encoding module is used to obtain the latitude and longitude coordinates of each hangar within the drone's patrol range, and uses the GeoHash algorithm to encode the hangar's latitude and longitude coordinates to obtain the hangar string;

[0104] The database construction module is used to associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, build the database, and store it in the remote dispatch.

[0105] The takeoff judgment module is used to remotely schedule and obtain the latitude and longitude coordinates and meteorological data of the end point of the inspection mission before the UAV performs the inspection mission, and to determine whether the takeoff conditions are met. If they are met, the UAV will take off and perform the inspection mission; if they are not met, the decision will be made by the dispatcher.

[0106] The rapid avoidance module is used to remotely schedule the drone to judge the flight conditions based on the meteorological data fed back by the drone when the drone is performing inspection tasks. If the flight conditions are not met, the module will match the nearest hangar in the database based on the drone's current latitude and longitude coordinates and send the location of the nearest hangar and the rapid avoidance command.

[0107] Specifically, the takeoff determination module is as follows:

[0108] Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task's endpoint, and encode them using the GeoHash algorithm to obtain the endpoint string;

[0109] The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint.

[0110] Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, it is determined whether the drone takes off. If the takeoff conditions are met, the drone takes off normally to perform the inspection mission; if the takeoff conditions are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection mission.

[0111] Specifically, the weather conditions for drone takeoff are determined based on meteorological data from the hangar where the drone is located and meteorological data from hangars in the matrix area where the destination is located, or meteorological data near the destination.

[0112] Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions.

[0113] If any condition is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and relevant data will be sent. The dispatcher will then decide whether the drone can take off.

[0114] Specifically, the rapid risk avoidance module is as follows:

[0115] When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone.

[0116] The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone.

[0117] Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database;

[0118] By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out.

[0119] The remote dispatcher retrieves the latitude and longitude coordinates of the hangars corresponding to the current matrix region where the drone is located and the eight surrounding matrix regions from the database, and calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates.

[0120] Sort the distances of the drones to each hangar and select the hangar closest to the drone;

[0121] Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance.

[0122] If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

[0123] It should be noted that the drone takeoff and avoidance system based on the GeoHash algorithm of the present invention corresponds one-to-one with the drone takeoff and avoidance method based on the GeoHash algorithm of the present invention. The technical features and beneficial effects described in the embodiments of the drone takeoff and avoidance method based on the GeoHash algorithm are applicable to the embodiments of the drone takeoff and avoidance system based on the GeoHash algorithm. For details, please refer to the description in the embodiments of the method of the present invention, which will not be repeated here.

[0124] Furthermore, in the above embodiments of the UAV takeoff and avoidance system based on the GeoHash algorithm, the logical division of each program module is only an example. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or software implementation convenience. That is, the internal structure of the UAV takeoff and avoidance system based on the GeoHash algorithm is divided into different program modules to complete all or part of the functions described above.

[0125] like Figure 7 As shown, in one embodiment, a computer-readable storage medium is provided, storing a program in a memory. When the program is executed by a processor, it implements the aforementioned drone takeoff and obstacle avoidance method based on the GeoHash algorithm, specifically as follows:

[0126] The drone's patrol range is divided into multiple matrix regions, and the GeoHash algorithm is used to encode the matrix regions to obtain strings for the matrix regions;

[0127] Obtain the latitude and longitude coordinates of each hangar within the drone's patrol range, and encode the hangar's latitude and longitude coordinates using the GeoHash algorithm to obtain the hangar's string;

[0128] Associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, build a database, and store it in the remote dispatch system;

[0129] Before a drone performs an inspection mission, the remote dispatcher obtains the latitude and longitude coordinates and meteorological data of the inspection mission's endpoint to determine whether the conditions for takeoff are met. If they are met, the drone takes off to perform the inspection mission; otherwise, the dispatcher makes the decision.

[0130] When a drone is performing an inspection mission, the remote dispatcher judges the flight conditions based on the meteorological data fed back by the drone. If the flight conditions are not met, the dispatcher matches the nearest hangar in the database based on the drone's current latitude and longitude coordinates and sends the location of the nearest hangar and a rapid evacuation command.

[0131] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0132] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for UAV takeoff and hazard avoidance based on the GeoHash algorithm, characterized in that, The method includes the following steps: The drone's patrol range is divided into multiple matrix regions, and the GeoHash algorithm is used to encode the matrix regions to obtain strings for the matrix regions; Obtain the latitude and longitude coordinates of each hangar within the drone's patrol range, and encode the hangar's latitude and longitude coordinates using the GeoHash algorithm to obtain the hangar's string; Associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, build a database, and store it in the remote dispatch system; Before a drone performs an inspection mission, the remote dispatcher obtains the latitude and longitude coordinates and meteorological data of the inspection mission's endpoint to determine whether the conditions for takeoff are met. If they are met, the drone takes off to perform the inspection mission; otherwise, the dispatcher makes the decision. The remote dispatching process acquires the latitude and longitude coordinates and meteorological data of the inspection mission's endpoint to determine whether takeoff conditions are met. Specifically: Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task's endpoint, and encode them using the GeoHash algorithm to obtain the endpoint string; The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint. Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, determine whether the conditions for drone takeoff are met. If the conditions for takeoff are met, the drone will take off normally and perform the inspection mission. If the conditions for takeoff are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection task. When a drone is performing an inspection mission, the remote dispatcher judges the flight conditions based on the meteorological data fed back by the drone. If the flight conditions are not met, the dispatcher matches the nearest hangar in the database based on the drone's current latitude and longitude coordinates and sends the location of the nearest hangar and a rapid evacuation command.

2. The UAV takeoff and hazard avoidance method based on GeoHash algorithm according to claim 1, characterized in that, Each of the multiple matrix regions corresponds to a Base32 string of fixed encoding length; all latitude and longitude coordinates in the matrix region share the string of that matrix region; The latitude and longitude coordinates of each hangar are encoded into binary using the GeoHash algorithm to obtain a Base32 string of fixed encoding length; Link the string of the matrix region where the hangar is located with the latitude and longitude coordinates of the hangar, build a database, and store it in the remote scheduling.

3. The UAV takeoff and hazard avoidance method based on GeoHash algorithm according to claim 1, characterized in that, The determination of whether the drone's takeoff conditions are met, based on meteorological data from the hangar where the drone is located and meteorological data from hangars in the matrix area where the destination is located or near the destination, is specifically as follows: Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions. If any condition is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and relevant data will be sent. The dispatcher will then decide whether the drone can take off.

4. The UAV takeoff and hazard avoidance method based on GeoHash algorithm according to claim 1, characterized in that, The process of matching the nearest hangar in the database based on the drone's current latitude and longitude coordinates specifically involves: When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone. The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone. Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database; By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out. The remote dispatcher retrieves the latitude and longitude coordinates of the hangars corresponding to the current matrix region where the drone is located and the eight surrounding matrix regions from the database, and calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates. Sort the distances of the drones to each hangar and select the hangar closest to the drone; Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance. If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

5. A drone takeoff and hazard avoidance system based on the GeoHash algorithm, characterized in that, The system includes a matrix partitioning module, a hangar coding module, a database construction module, a takeoff judgment module, and a rapid risk avoidance module; The matrix partitioning module is used to divide the UAV's patrol range into multiple matrix regions, and uses the GeoHash algorithm to encode the matrix regions to obtain the strings of the matrix regions; The hangar encoding module is used to obtain the latitude and longitude coordinates of each hangar within the patrol range of the UAV, and uses the GeoHash algorithm to encode the latitude and longitude coordinates of the hangar to obtain the string of hangar; The database construction module is used to associate the strings in the matrix region with the corresponding hangar latitude and longitude coordinates, construct the database, and store it in the remote dispatch. The takeoff determination module is used to remotely schedule and obtain the latitude and longitude coordinates and meteorological data of the inspection mission endpoint before the UAV performs the inspection mission, and to determine the takeoff conditions; the takeoff determination module specifically includes: Remotely schedule and create drone inspection tasks, obtain the latitude and longitude coordinates of the inspection task's endpoint, and encode them using the GeoHash algorithm to obtain the endpoint string; The system determines the matrix region based on the endpoint string, queries the database to see if there is a hangar in the matrix region where the endpoint is located, and if there is a hangar, it directly obtains the meteorological data of the hangar in the matrix region where the endpoint is located; if there is no hangar, it accesses the external meteorological data interface based on the latitude and longitude coordinates of the endpoint, and obtains the latitude and longitude information of the vicinity of the endpoint by decoding the endpoint string, and obtains the meteorological data of the vicinity of the endpoint. Based on the meteorological data of the hangar where the drone is located and the meteorological data of the hangar in the matrix area where the destination is located or the meteorological data near the destination, determine whether the conditions for drone takeoff are met. If the conditions for takeoff are met, the drone will take off normally and perform the inspection mission. If the conditions for takeoff are not met, the remote dispatcher will remind the dispatcher to decide whether the drone should take off to perform the inspection task. The rapid avoidance module is used to remotely schedule the drone to judge the flight conditions based on the meteorological data fed back by the drone when the drone is performing an inspection mission. If the flight conditions are not met, the module will match the nearest hangar in the database based on the drone's current latitude and longitude coordinates and send the location of the nearest hangar and a rapid avoidance command.

6. The UAV takeoff and danger avoidance system based on GeoHash algorithm according to claim 5, characterized in that, The determination of whether the drone's takeoff conditions are met, based on meteorological data from the hangar where the drone is located and meteorological data from hangars in the matrix area where the destination is located or near the destination, is specifically as follows: Based on the meteorological data of the hangar where the drone is located, determine whether the temperature of the hangar where the drone is located, the hangar in the matrix area where the destination is located, or the area near the destination is less than or equal to 45℃, whether the humidity is less than or equal to 80℃, and whether the wind force is less than or equal to 10.8m / s; if all of these conditions are met, the drone meets the takeoff conditions. If any condition is not met, the dispatcher will be notified that the drone does not meet the takeoff requirements and relevant data will be sent. The dispatcher will then decide whether the drone can take off.

7. The UAV takeoff and danger avoidance system based on GeoHash algorithm according to claim 5, characterized in that, The rapid risk avoidance module is specifically as follows: When the remote dispatcher determines that the current flight conditions of the drone are not suitable based on the meteorological data fed back by the drone, it obtains the current latitude and longitude coordinates of the drone. The GeoHash algorithm is used to encode the current latitude and longitude coordinates of the drone into binary to obtain the current string of the drone. Use the current string of the drone to match the string of the matrix region where the drone is currently located in the database; By matching strings, the matrix regions other than the current drone's matrix region and the eight surrounding matrix regions are filtered out. The remote dispatcher retrieves the latitude and longitude coordinates of the hangars corresponding to the current matrix region where the drone is located and the eight surrounding matrix regions from the database, and calculates the distance from the drone to each hangar by comparing these coordinates with the drone's current latitude and longitude coordinates. Sort the distances of the drones to each hangar and select the hangar closest to the drone; Determine if the hangar closest to the drone meets other conditions for hazard avoidance. If not, select the next hangar closest to the drone and re-evaluate whether it meets other conditions for hazard avoidance. If the conditions are met, the nearest hangar location and a rapid evacuation command will be sent to the drone.

8. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the drone takeoff and risk avoidance method based on the GeoHash algorithm as described in any one of claims 1-4.

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