Intelligent lightning protection control method, system and medium for an unmanned aerial vehicle

Abstract

Embodiments of the present invention provide a method, system, and medium for intelligent lightning protection management and control of unmanned aerial vehicles. The method includes: obtaining lightning cloud data and cloud development trend data based on cloud field dynamic information and meteorological forecast information in a target route area acquired by the unmanned aerial vehicle in real time; obtaining cloud electric field distribution data based on the lightning cloud data and cloud development trend data to generate a regional cloud electric feature portrait and extract a cloud electric area identifier; generating a target navigation route map based on the cloud electric area identifier and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map; thereby realizing intelligent lightning protection route correction based on intelligent monitoring technology, identifying the cloud electric field distribution of the unmanned aerial vehicle, obtaining the target navigation route map, and performing route correction, thereby improving the intelligence of lightning protection management and control of the unmanned aerial vehicle and the accuracy of lightning protection route prediction and setting.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle management, and in particular to an intelligent lightning protection management and control method, system and medium for unmanned aerial vehicles. Background Art

[0002] Unmanned aerial vehicles (UAS), with their stable flight, hovering capabilities, and minimal technical requirements, are widely used in high-risk, unmanned operations, such as national defense, disaster assessment, agricultural diagnostics, and resource exploration in hazardous areas. With advances in technology and materials, the use of UAVs is becoming increasingly widespread. UAVs will be used in a variety of operating environments, and all-weather operation is an inevitable trend in the future development of UAVs. Operating in harsh natural conditions inevitably increases the risk of UAVs being struck by natural disasters, with lightning strikes being one of the most common.

[0003] At present, in addition to the material and device design of the fuselage, the lightning protection measures for unmanned aerial vehicles usually use preset routes to set preset values or manual control for the flight range or flight mission of the unmanned aerial vehicle. However, there is a lack of intelligent lightning protection and control technology that can intelligently monitor and analyze the real-time monitoring of lightning cloud conditions and meteorological cloud field development to obtain routes that avoid lightning strike areas. At present, the technology and management methods in this area lack systematicity, emergency response and intelligence, and there is a lack of technical means for effective lightning protection of unmanned aerial vehicles.

[0004] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide an intelligent lightning protection management and control method, system and medium for unmanned aerial vehicles, which can predict and judge the route of the unmanned aerial vehicle based on the lightning cloud conditions and meteorological cloud field development conditions of the unmanned aerial vehicle, and perform prediction setting and monitoring adjustment on the route of the unmanned aerial vehicle, thereby improving the accuracy of the route prediction and control setting for the lightning protection and safe flight of the unmanned aerial vehicle.

[0006] The embodiment of the present invention also provides an intelligent lightning protection control method for unmanned aerial vehicles, comprising the following steps:

[0007] Obtain cloud field dynamic information and weather forecast information in the target route area in real time;

[0008] Acquire lightning cloud data based on the cloud field dynamic information, and acquire cloud development trend data based on the meteorological forecast information;

[0009] Obtain cloud electric field distribution data based on the lightning cloud data and cloud development trend data;

[0010] generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field regional identifier;

[0011] A target navigation route map is generated according to the cloud-electric area identifier, and the unmanned aerial vehicle is controlled to perform route correction according to the target navigation route map.

[0012] Optionally, in the intelligent lightning protection control method for an unmanned aerial vehicle according to an embodiment of the present invention, the real-time acquisition of cloud field dynamic information and weather forecast information within the target route area includes:

[0013] Obtain the target route area of the UAV and divide the route area into sub-areas at multiple altitude levels;

[0014] Real-time acquisition of cloud field dynamic information in multiple sub-regions, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information;

[0015] Acquire weather forecast information for the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

[0016] Optionally, in the intelligent lightning protection management and control method for an unmanned aerial vehicle according to an embodiment of the present invention, obtaining lightning cloud data based on the cloud field dynamic information and obtaining cloud development trend data based on the meteorological forecast information include:

[0017] The cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions are input into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and the lightning cloud data of the target route area is obtained by aggregating the lightning cloud data;

[0018] The temperature difference information, flow field information, pressure field information, and longitude and latitude information are input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

[0019] Optionally, in the intelligent lightning protection management and control method for an unmanned aerial vehicle according to an embodiment of the present invention, obtaining cloud electric field distribution data based on the lightning cloud data in combination with cloud development trend data includes:

[0020] The lightning cloud data of the target route area is combined with the cloud development trend data and input into a preset cloud electric field distribution model for processing and calculation to obtain the cloud electric field distribution data in the area;

[0021] The processing formula of the preset cloud electric field distribution model is: where K f is the cloud electric field distribution data, ψ i ,υ is the preset characteristic coefficient, q 0iis the lightning cloud data, n is the number of sub-regions, i is the ith sub-region among n sub-regions, Z c Cloud development trend data.

[0022] Optionally, in the intelligent lightning protection management and control method for an unmanned aerial vehicle according to an embodiment of the present invention, generating a regional cloud electric feature portrait based on the cloud electric field distribution data and extracting a cloud electric area identifier includes:

[0023] Mapping the cloud electric field distribution data in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map;

[0024] generating a regional cloud electric field characteristic image of the target route area according to the cloud electric field distribution characteristic map;

[0025] The cloud-electricity region identification is extracted and marked based on the cloud-electricity feature portrait of the region.

[0026] Optionally, in the intelligent lightning protection control method for an unmanned aerial vehicle according to an embodiment of the present invention, generating a target navigation route map according to the cloud-electricity area identifier and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map includes:

[0027] Perform regional isolation identification according to the cloud power regional identification;

[0028] Acquire a route map by performing a route connection based on the real-time positioning identifier of the unmanned aerial vehicle and the target route endpoint identifier;

[0029] Isolating and marking intersecting marked areas in the voyage route map and connecting the markings;

[0030] Regenerate a target navigation route map based on the voyage route map and the winding mark;

[0031] According to the target navigation route map, the unmanned aerial vehicle is controlled to perform route correction according to the route map route.

[0032] In a second aspect, an embodiment of the present invention provides an intelligent lightning protection management and control system for an unmanned aerial vehicle. The system includes: a memory and a processor. The memory includes a program for an intelligent lightning protection management and control method for an unmanned aerial vehicle. When the program for the intelligent lightning protection management and control method for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:

[0033] Obtain cloud field dynamic information and weather forecast information in the target route area in real time;

[0034] Acquire lightning cloud data based on the cloud field dynamic information, and acquire cloud development trend data based on the meteorological forecast information;

[0035] Obtain cloud electric field distribution data based on the lightning cloud data and cloud development trend data;

[0036] generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field regional identifier;

[0037] A target navigation route map is generated according to the cloud-electric area identifier, and the unmanned aerial vehicle is controlled to perform route correction according to the target navigation route map.

[0038] Optionally, in the intelligent lightning protection management and control system for unmanned aerial vehicles according to an embodiment of the present invention, the real-time acquisition of cloud field dynamic information and weather forecast information within the target route area includes:

[0039] Obtain the target route area of the UAV and divide the route area into sub-areas at multiple altitude levels;

[0040] Real-time acquisition of cloud field dynamic information in multiple sub-regions, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information;

[0041] Acquire weather forecast information for the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

[0042] Optionally, in the intelligent lightning protection management and control system for an unmanned aerial vehicle according to an embodiment of the present invention, obtaining lightning cloud data based on the cloud field dynamic information and obtaining cloud development trend data based on the meteorological forecast information include:

[0043] The cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions are input into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and the lightning cloud data of the target route area is obtained by aggregating the lightning cloud data;

[0044] The temperature difference information, flow field information, pressure field information, and longitude and latitude information are input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

[0045] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, which includes a program for an intelligent lightning protection control method for an unmanned aerial vehicle. When the program for an intelligent lightning protection control method for an unmanned aerial vehicle is executed by a processor, the steps of the intelligent lightning protection control method for an unmanned aerial vehicle as described in any one of the above items are implemented.

[0046] From the above, it can be seen that the embodiment of the present invention provides an intelligent lightning protection management and control method, system and medium for unmanned aerial vehicles, which respectively obtain lightning cloud data and cloud development trend data based on the cloud field dynamic information and meteorological forecast information in the target route area obtained by the unmanned aerial vehicle in real time, obtain cloud electric field distribution data based on the lightning cloud data combined with the cloud development trend data to generate a regional cloud electric feature portrait and extract the cloud electric area identifier, generate a target navigation route map based on the cloud electric area identifier, and control the unmanned aerial vehicle to perform route correction according to the target navigation route map; thereby, based on intelligent monitoring technology, intelligent lightning protection route correction is realized by identifying the cloud electric field distribution of the unmanned aerial vehicle, obtaining the target navigation route map, and performing route correction, thereby improving the intelligence of lightning protection management of unmanned aerial vehicles and the accuracy of lightning protection route prediction setting.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flow chart of the intelligent lightning protection control method for unmanned aerial vehicles provided by an embodiment of the present invention;

[0050] Figure 2 A flow chart of the intelligent lightning protection control method for unmanned aerial vehicles provided in an embodiment of the present invention for obtaining cloud field dynamic information and weather forecast information within a target route area;

[0051] Figure 3 A flowchart of obtaining lightning cloud data and cloud development trend data for the intelligent lightning protection management and control method for unmanned aerial vehicles provided by an embodiment of the present invention;

[0052] Figure 4 A schematic structural diagram of an intelligent lightning protection management and control system for unmanned aerial vehicles provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0054] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0055] Please refer to Figure 1 , Figure 1 The flowchart of the intelligent lightning protection control method for unmanned aerial vehicles in some embodiments of the present invention is shown. The intelligent lightning protection control method for unmanned aerial vehicles is used in terminal devices such as computers and control terminals. The intelligent lightning protection control method for unmanned aerial vehicles includes the following steps:

[0056] S101. Acquire cloud field dynamic information and weather forecast information within the target route area in real time;

[0057] S102, obtaining lightning cloud data based on the cloud field dynamic information, and obtaining cloud development trend data based on the meteorological forecast information;

[0058] S103, obtaining cloud electric field distribution data based on the lightning cloud data and cloud development trend data;

[0059] S104, generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field region identifier;

[0060] S105: Generate a target navigation route map based on the cloud-electric area identifier, and control the unmanned aerial vehicle to perform route correction according to the target navigation route map.

[0061] It should be noted that in order to obtain lightning protection technology for unmanned aerial vehicles that can avoid lightning strike areas, lightning cloud data and cloud development trend data are obtained respectively by obtaining cloud field dynamic information and meteorological forecast information of the aircraft in the target route area in real time, that is, dynamic monitoring is used to obtain cloud field conditions and meteorological development conditions, and then cloud electric field distribution data, that is, cloud electric field distribution information, is obtained based on the lightning cloud data combined with the cloud development trend data. Then, a regional cloud electric feature portrait is generated based on the cloud electric field distribution data and a cloud electric area identifier is extracted, that is, a regional situation image reflecting the cloud electric field distribution and a cloud electric area identifier is extracted. A target navigation route map is generated based on the cloud electric area identifier, and the unmanned aerial vehicle is regulated to perform route correction according to the target navigation route map, that is, a route correction technology for real-time lightning protection is obtained by obtaining, processing and predicting the data on the distribution of clouds that generate lightning, obtaining an information identification map reflecting the distribution of lightning field clouds, and then generating a target navigation route that avoids areas with lightning strike probability, and correcting the route of the unmanned aircraft.

[0062] Please refer to Figure 2 , Figure 2 This is a flow chart of obtaining cloud field dynamic information and weather forecast information within a target route area in a method for intelligent lightning protection control of unmanned aerial vehicles in some embodiments of the present invention. According to an embodiment of the present invention, the real-time acquisition of cloud field dynamic information and weather forecast information within a target route area is specifically as follows:

[0063] S201, obtaining a target route area of the unmanned aerial vehicle, and dividing the route area into sub-areas at multiple altitude levels;

[0064] S202, acquiring cloud field dynamic information of multiple sub-regions in real time, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information;

[0065] S203: Acquire weather forecast information of the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

[0066] It should be noted that in order to obtain the cloud field dynamics and weather forecast conditions in the target route area where unmanned aerial vehicles will navigate in the future, the route area is divided into sub-regions according to different altitudes and different areas. The sub-regions are divided according to the altitude and area where the clouds are located, which can facilitate the collection and processing of cloud field conditions and weather conditions in the area, and then obtain in real time the cloud field dynamics information of each divided sub-region, including cumulus cloud layer information, cloud charge distribution information, cloud temperature difference information, and weather forecast information of the target route area, including temperature difference information, flow field information, pressure field information, and longitude and latitude information, and then further analysis and processing are carried out based on the obtained information.

[0067] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining lightning cloud data and cloud development trend data for an intelligent lightning protection and control method for unmanned aerial vehicles in some embodiments of the present invention. According to an embodiment of the present invention, obtaining lightning cloud data based on the cloud field dynamic information and obtaining cloud development trend data based on the meteorological forecast information are specifically as follows:

[0068] S301, inputting cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and aggregating the lightning cloud data to obtain lightning cloud data of the target route area;

[0069] S302: Input the temperature difference information, flow field information, pressure field information, and longitude and latitude information into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

[0070] It should be noted that in order to monitor and obtain the status of thunderclouds that generate thunderstorms and the impact of meteorology on cloud development, a preset thundercloud monitoring platform is obtained. The platform can obtain thundercloud data by processing the dynamic information of charged cumulus clouds in the cloud field in the region. The dynamic information of the cloud field in each sub-region is input into the platform for processing to obtain thundercloud data corresponding to each region. The meteorological forecast information of the target route area is then input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data reflecting the development trend of the cloud layer. The meteorological cloud prediction model is a data analysis and processing model that analyzes and predicts the impact of temperature difference, flow field, wind field, pressure belt, longitude and latitude on cloud trend. It is obtained through training and processing of a large amount of historical meteorological forecast information including temperature difference information, flow field information, pressure field information, longitude and latitude information and cloud development trend data. The larger the historical data sample, the more accurate the training processing of the model. The cloud development trend data of the target route area is obtained by inputting the obtained meteorological forecast information into the trained model for processing.

[0071] According to an embodiment of the present invention, the cloud electric field distribution data is obtained based on the lightning cloud data in combination with cloud development trend data, specifically:

[0072] The lightning cloud data of the target route area is combined with the cloud development trend data and input into a preset cloud electric field distribution model for processing and calculation to obtain the cloud electric field distribution data in the area;

[0073] The processing formula of the preset cloud electric field distribution model is: where K f is the cloud electric field distribution data, ψ i ,υ is the preset characteristic coefficient, q 0iis the lightning cloud data, n is the number of sub-regions, i is the ith sub-region among n sub-regions, and Z c Cloud development trend data.

[0074] It should be noted that the distribution of cloud electric fields in the region can be inferred based on the acquired lightning cloud data and cloud development trend data, that is, the distribution of clouds and cloud electric fields that generate lightning in the target region. The cloud electric field distribution data in the region is obtained by formula processing the lightning cloud data in the target route area and the collection of cloud development trend data in each sub-region through the program in the cloud electric field distribution model.

[0075] According to an embodiment of the present invention, generating a regional cloud electric field feature portrait based on the cloud electric field distribution data and extracting a cloud electric field region identifier is specifically as follows:

[0076] Mapping the cloud electric field distribution data in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map;

[0077] generating a regional cloud electric field characteristic image of the target route area according to the cloud electric field distribution characteristic map;

[0078] The cloud-electricity region identification is extracted and marked based on the cloud-electricity feature portrait of the region.

[0079] It should be noted that, based on the obtained cloud electric field distribution data, mapping is performed in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map, which reflects the distribution characteristics of the cloud electric field. Then, based on the cloud electric field distribution characteristic map, a regional cloud electric characteristic portrait of the target route area is generated, which maps the cloud electric field distribution characteristics. The data reflects the dynamic distribution characteristics of the cloud electric field. According to the regional cloud electric characteristic portrait, the cloud electric area identification is extracted and marked, that is, the cloud electric area identification of the cloud electric field extracted from the portrait is marked to display the regional identification of the cloud electric field.

[0080] According to an embodiment of the present invention, generating a target navigation route map based on the cloud-electric area identifier and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map specifically includes:

[0081] Perform regional isolation identification according to the cloud power regional identification;

[0082] Acquire a route map by performing a route connection based on the real-time positioning identifier of the unmanned aerial vehicle and the target route endpoint identifier;

[0083] Isolating and marking intersecting marked areas in the voyage route map and connecting the markings;

[0084] Regenerate a target navigation route map based on the voyage route map and the winding mark;

[0085] According to the target navigation route map, the unmanned aerial vehicle is controlled to perform route correction according to the route map route.

[0086] It should be noted that, according to the cloud electric area identification of the marked cloud electric field distribution, the area isolation identification of the lightning generation is divided, and then the intersection, overlap, and interference parts of the route route map of the unmanned aerial vehicle real-time positioning identification and the target route endpoint identification are connected with the isolation identification of each area, and the winding marks are marked, that is, the route route map and the area where the isolation identification is located are wound so that the route route map does not intersect with the identification area, and a route route map that can bypass the isolation identification area is obtained. The target navigation route map is generated based on the route route map reconnected with the winding marks. The route map can bypass the marked cloud electric field area, so that the unmanned aerial vehicle can correct the route according to the route map and bypass the area where the cloud electric field is located, thereby realizing the dynamic intelligent lightning protection technology of the unmanned aerial vehicle to bypass the route according to the dynamic cloud electric field distribution identification area obtained in real time.

[0087] According to an embodiment of the present invention, the further embodiment includes:

[0088] Establish an unmanned aerial vehicle lightning strike risk database based on the lightning defect faults corresponding to the unmanned aerial vehicle lightning strike damage events;

[0089] The UAV lightning strike risk database includes risk data on lightning damage events occurring to various types of UAVs under different meteorological conditions, and performs frequency statistics on the types of lightning damage that cause damage to various types of UAVs, obtaining historical risk data corresponding to the most frequent lightning damage types for various types of UAVs;

[0090] Establishing a lightning strike risk response level based on the frequency of the historical risk data;

[0091] The unmanned aerial vehicle lightning strike risk database collects statistics on the environmental characteristics of meteorological conditions corresponding to the types of high-frequency lightning strike damage of various types of unmanned aerial vehicles based on the historical lightning strike damage risk data of various types of unmanned aerial vehicles, and establishes the lightning meteorological environment characteristic values corresponding to the types of lightning strike damage;

[0092] The unmanned aerial vehicle performs similarity comparison on the meteorological environment characteristics obtained based on dynamic monitoring in the unmanned aerial vehicle lightning strike risk database, and obtains historical risk data corresponding to meteorological environment characteristic values whose similarity to the type of unmanned aerial vehicle meets preset value requirements;

[0093] The mission instruction of the unmanned aerial vehicle is modified according to the lightning strike risk response level corresponding to the obtained historical risk data.

[0094] It should be noted that in order to prevent unmanned aerial vehicles from being damaged by lightning strikes under meteorological conditions, an unmanned aerial vehicle lightning risk database is established based on the historical data of lightning damage to different types of unmanned aerial vehicles under different meteorological conditions. Based on the historical risk data of this type of unmanned aerial vehicle under the corresponding meteorological environment characteristics in the database, the lightning damage risk of the meteorological environment dynamically monitored by the unmanned aerial vehicle in real time can be effectively predicted, and the task instructions can be corrected according to the corresponding lightning risk response level set according to the historical risk data obtained. By comparing and predicting the historical data of dynamic monitoring of the meteorological environment, the risk of lightning damage to unmanned aerial vehicles can be avoided.

[0095] like Figure 4 As shown, the present invention also discloses an intelligent lightning protection control system for unmanned aerial vehicles, including a memory 41 and a processor 42. The memory includes an intelligent lightning protection control method program for the unmanned aerial vehicle. When the intelligent lightning protection control method program for the unmanned aerial vehicle is executed by the processor, the following steps are implemented:

[0096] Obtain cloud field dynamic information and weather forecast information in the target route area in real time;

[0097] Acquire lightning cloud data based on the cloud field dynamic information, and acquire cloud development trend data based on the meteorological forecast information;

[0098] Obtain cloud electric field distribution data based on the lightning cloud data and cloud development trend data;

[0099] generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field regional identifier;

[0100] A target navigation route map is generated according to the cloud-electric area identifier, and the unmanned aerial vehicle is controlled to perform route correction according to the target navigation route map.

[0101] It should be noted that in order to obtain lightning protection technology for unmanned aerial vehicles that can avoid lightning strike areas, lightning cloud data and cloud development trend data are obtained respectively by obtaining cloud field dynamic information and meteorological forecast information of the aircraft in the target route area in real time, that is, dynamic monitoring is used to obtain cloud field conditions and meteorological development conditions, and then cloud electric field distribution data, that is, cloud electric field distribution information, is obtained based on the lightning cloud data combined with the cloud development trend data. Then, a regional cloud electric feature portrait is generated based on the cloud electric field distribution data and a cloud electric area identifier is extracted, that is, a regional situation image reflecting the cloud electric field distribution and a cloud electric area identifier is extracted. A target navigation route map is generated based on the cloud electric area identifier, and the unmanned aerial vehicle is regulated to perform route correction according to the target navigation route map, that is, a route correction technology for real-time lightning protection is obtained by obtaining, processing and predicting the data on the distribution of clouds that generate lightning, obtaining an information identification map reflecting the distribution of lightning field clouds, and then generating a target navigation route that avoids areas with lightning strike probability, and correcting the route of the unmanned aircraft.

[0102] According to an embodiment of the present invention, the real-time acquisition of cloud field dynamic information and weather forecast information within the target route area is specifically as follows:

[0103] Obtain the target route area of the UAV and divide the route area into sub-areas at multiple altitude levels;

[0104] Real-time acquisition of cloud field dynamic information in multiple sub-regions, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information;

[0105] Acquire weather forecast information for the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

[0106] It should be noted that in order to obtain the cloud field dynamics and weather forecast conditions in the target route area where unmanned aerial vehicles will navigate in the future, the route area is divided into sub-regions according to different altitudes and different areas. The sub-regions are divided according to the altitude and area where the clouds are located, which can facilitate the collection and processing of cloud field conditions and weather conditions in the area, and then obtain in real time the cloud field dynamics information of each divided sub-region, including cumulus cloud layer information, cloud charge distribution information, cloud temperature difference information, and weather forecast information of the target route area, including temperature difference information, flow field information, pressure field information, and longitude and latitude information, and then further analysis and processing are carried out based on the obtained information.

[0107] According to an embodiment of the present invention, obtaining lightning cloud data based on the cloud field dynamic information and obtaining cloud development trend data based on the meteorological forecast information are specifically as follows:

[0108] The cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions are input into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and the lightning cloud data of the target route area is obtained by aggregating the lightning cloud data;

[0109] The temperature difference information, flow field information, pressure field information, and longitude and latitude information are input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

[0110] It should be noted that in order to monitor and obtain the status of thunderclouds that generate thunderstorms and the impact of meteorology on cloud development, a preset thundercloud monitoring platform is obtained. The platform can obtain thundercloud data by processing the dynamic information of charged cumulus clouds in the cloud field in the region. The dynamic information of the cloud field in each sub-region is input into the platform for processing to obtain thundercloud data corresponding to each region. The meteorological forecast information of the target route area is then input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data reflecting the development trend of the cloud layer. The meteorological cloud prediction model is a data analysis and processing model that analyzes and predicts the impact of temperature difference, flow field, wind field, pressure belt, longitude and latitude on cloud trend. It is obtained through training and processing of a large amount of historical meteorological forecast information including temperature difference information, flow field information, pressure field information, longitude and latitude information and cloud development trend data. The larger the historical data sample, the more accurate the training processing of the model. The cloud development trend data of the target route area is obtained by inputting the obtained meteorological forecast information into the trained model for processing.

[0111] According to an embodiment of the present invention, the cloud electric field distribution data is obtained based on the lightning cloud data in combination with cloud development trend data, specifically:

[0112] The lightning cloud data of the target route area is combined with the cloud development trend data and input into a preset cloud electric field distribution model for processing and calculation to obtain the cloud electric field distribution data in the area;

[0113] The processing formula of the preset cloud electric field distribution model is: where K f is the cloud electric field distribution data, ψ i ,υ is the preset characteristic coefficient, q 0i is the lightning cloud data, n is the number of sub-regions, i is the ith sub-region among n sub-regions, and Z c Cloud development trend data.

[0114] It should be noted that the distribution of cloud electric fields in the region can be inferred based on the acquired lightning cloud data and cloud development trend data, that is, the distribution of clouds and cloud electric fields that generate lightning in the target region. The cloud electric field distribution data in the region is obtained by formula processing the lightning cloud data in the target route area and the collection of cloud development trend data in each sub-region through the program in the cloud electric field distribution model.

[0115] According to an embodiment of the present invention, generating a regional cloud electric field feature portrait based on the cloud electric field distribution data and extracting a cloud electric field region identifier is specifically as follows:

[0116] Mapping the cloud electric field distribution data in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map;

[0117] generating a regional cloud electric field characteristic image of the target route area according to the cloud electric field distribution characteristic map;

[0118] The cloud-electricity region identification is extracted and marked based on the cloud-electricity feature portrait of the region.

[0119] It should be noted that, based on the obtained cloud electric field distribution data, mapping is performed in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map, which reflects the distribution characteristics of the cloud electric field. Then, based on the cloud electric field distribution characteristic map, a regional cloud electric characteristic portrait of the target route area is generated, which maps the cloud electric field distribution characteristics. The data reflects the dynamic distribution characteristics of the cloud electric field. According to the regional cloud electric characteristic portrait, the cloud electric area identification is extracted and marked, that is, the cloud electric area identification of the cloud electric field extracted from the portrait is marked to display the regional identification of the cloud electric field.

[0120] According to an embodiment of the present invention, generating a target navigation route map based on the cloud-electric area identifier and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map specifically includes:

[0121] Perform regional isolation identification according to the cloud power regional identification;

[0122] Acquire a route map by performing a route connection based on the real-time positioning identifier of the unmanned aerial vehicle and the target route endpoint identifier;

[0123] Isolating and marking intersecting marked areas in the voyage route map and connecting the markings;

[0124] Regenerate a target navigation route map based on the voyage route map and the winding mark;

[0125] According to the target navigation route map, the unmanned aerial vehicle is controlled to perform route correction according to the route map route.

[0126] It should be noted that, according to the cloud electric area identification of the marked cloud electric field distribution, the area isolation identification of the lightning generation is divided, and then the intersection, overlap, and interference parts of the route route map of the unmanned aerial vehicle real-time positioning identification and the target route endpoint identification are connected with the isolation identification of each area, and the winding marks are marked, that is, the route route map and the area where the isolation identification is located are wound so that the route route map does not intersect with the identification area, and a route route map that can bypass the isolation identification area is obtained. The target navigation route map is generated based on the route route map reconnected with the winding marks. The route map can bypass the marked cloud electric field area, so that the unmanned aerial vehicle can correct the route according to the route map and bypass the area where the cloud electric field is located, thereby realizing the dynamic intelligent lightning protection technology of the unmanned aerial vehicle to bypass the route according to the dynamic cloud electric field distribution identification area obtained in real time.

[0127] According to an embodiment of the present invention, the further embodiment includes:

[0128] Establish an unmanned aerial vehicle lightning strike risk database based on the lightning defect faults corresponding to the unmanned aerial vehicle lightning strike damage events;

[0129] The UAV lightning strike risk database includes risk data on lightning damage events occurring to various types of UAVs under different meteorological conditions, and performs frequency statistics on the types of lightning damage that cause damage to various types of UAVs, obtaining historical risk data corresponding to the most frequent lightning damage types for various types of UAVs;

[0130] Establishing a lightning strike risk response level based on the frequency of the historical risk data;

[0131] The unmanned aerial vehicle lightning strike risk database collects statistics on the environmental characteristics of meteorological conditions corresponding to the types of high-frequency lightning strike damage of various types of unmanned aerial vehicles based on the historical lightning strike damage risk data of various types of unmanned aerial vehicles, and establishes the lightning meteorological environment characteristic values corresponding to the types of lightning strike damage;

[0132] The unmanned aerial vehicle performs similarity comparison on the meteorological environment characteristics obtained based on dynamic monitoring in the unmanned aerial vehicle lightning strike risk database, and obtains historical risk data corresponding to meteorological environment characteristic values whose similarity to the type of unmanned aerial vehicle meets preset value requirements;

[0133] The mission instruction of the unmanned aerial vehicle is modified according to the lightning strike risk response level corresponding to the obtained historical risk data.

[0134] It should be noted that in order to prevent unmanned aerial vehicles from being damaged by lightning strikes under meteorological conditions, an unmanned aerial vehicle lightning risk database is established based on the historical data of lightning damage to different types of unmanned aerial vehicles under different meteorological conditions. Based on the historical risk data of this type of unmanned aerial vehicle under the corresponding meteorological environment characteristics in the database, the lightning damage risk of the meteorological environment dynamically monitored by the unmanned aerial vehicle in real time can be effectively predicted, and the task instructions can be corrected according to the corresponding lightning risk response level set according to the historical risk data obtained. By comparing and predicting the historical data of dynamic monitoring of the meteorological environment, the risk of lightning damage to unmanned aerial vehicles can be avoided.

[0135] The third aspect of the present invention provides a readable storage medium, which includes a program for an intelligent lightning protection control method for an unmanned aerial vehicle. When the program for an intelligent lightning protection control method for an unmanned aerial vehicle is executed by a processor, the steps of the intelligent lightning protection control method for an unmanned aerial vehicle as described in any one of the above items are implemented.

[0136] The present invention discloses an intelligent lightning protection control method, system and medium for an unmanned aerial vehicle. The method obtains lightning cloud data and cloud development trend data respectively based on cloud field dynamic information and meteorological forecast information in a target route area obtained by the unmanned aerial vehicle in real time. Cloud electric field distribution data is obtained based on the lightning cloud data combined with cloud development trend data to generate a regional cloud electric feature portrait and extract a cloud electric area identifier. A target navigation route map is generated based on the cloud electric area identifier to control the unmanned aerial vehicle to perform route correction according to the target navigation route map. In this way, intelligent lightning protection route correction is realized based on intelligent monitoring technology, in which the cloud electric field distribution of the unmanned aerial vehicle is identified, the cloud electric area is obtained, and the target navigation route map is obtained for route correction, thereby improving the intelligence of lightning protection control of the unmanned aerial vehicle and the accuracy of lightning protection route prediction setting.

[0137] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0138] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0139] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0140] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0141] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A smart lightning protection control method for unmanned aerial vehicles, characterized in that: The following steps are involved: Real-time acquisition of cloud field dynamic information and weather forecast information within the target route area; Acquire lightning cloud data based on the cloud field dynamic information, and acquire cloud development trend data based on the meteorological forecast information; Obtain cloud electric field distribution data based on the lightning cloud data and cloud development trend data; generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field regional identifier; generating a target navigation route map according to the cloud-electric area identifier, and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map; The step of obtaining cloud electric field distribution data based on the lightning cloud data in combination with cloud development trend data includes: The lightning cloud data of the target route area is combined with the cloud development trend data and input into a preset cloud electric field distribution model for processing and calculation to obtain the cloud electric field distribution data in the area; The processing formula of the preset cloud electric field distribution model is: ,in is the cloud electric field distribution data, is the preset characteristic coefficient, For Thunder Cloud data, is the number of regions, for In the sub-region sub-regions, Cloud development trend data.

2. The intelligent lightning protection control method for unmanned aerial vehicles according to claim 1, characterized in that: The real-time acquisition of cloud field dynamic information and weather forecast information within the target route area includes: Obtain the target route area of the UAV and divide the route area into sub-areas at multiple altitude levels; Real-time acquisition of cloud field dynamic information in multiple sub-regions, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information; Acquire weather forecast information for the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

3. The intelligent lightning protection control method for unmanned aerial vehicles according to claim 2, characterized in that: The obtaining of lightning cloud data according to the cloud field dynamic information and the obtaining of cloud development trend data according to the meteorological forecast information include: The cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions are input into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and the lightning cloud data of the target route area is obtained by aggregating the lightning cloud data; The temperature difference information, flow field information, pressure field information, and longitude and latitude information are input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

4. The intelligent lightning protection control method for unmanned aerial vehicles according to claim 3, characterized in that: Generating a regional cloud electric field feature portrait based on the cloud electric field distribution data and extracting a cloud electric field region identifier includes: Mapping the cloud electric field distribution data in each sub-area of the target route area to generate a corresponding cloud electric field distribution characteristic map; generating a regional cloud electric field characteristic image of the target route area according to the cloud electric field distribution characteristic map; The cloud-electricity region identification is extracted and marked based on the cloud-electricity feature portrait of the region.

5. The intelligent lightning protection control method for unmanned aerial vehicles according to claim 4, characterized in that: Generating a target navigation route map according to the cloud-electric area identifier, and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map, includes: Perform regional isolation identification according to the cloud power regional identification; Acquire a route map by performing a route connection based on the real-time positioning identifier of the unmanned aerial vehicle and the target route endpoint identifier; Isolating and marking intersecting marked areas in the voyage route map and connecting the markings; Regenerate a target navigation route map based on the voyage route map and the winding mark; According to the target navigation route map, the unmanned aerial vehicle is controlled to perform route correction according to the route map route.

6. An intelligent lightning protection control system for unmanned aerial vehicles, characterized by: The system includes: a memory and a processor, wherein the memory includes a program of an intelligent lightning protection control method for an unmanned aerial vehicle, and when the program of the intelligent lightning protection control method for an unmanned aerial vehicle is executed by the processor, the following steps are implemented: Real-time acquisition of cloud field dynamic information and weather forecast information within the target route area; Acquire lightning cloud data based on the cloud field dynamic information, and acquire cloud development trend data based on the meteorological forecast information; Obtain cloud electric field distribution data based on the lightning cloud data and cloud development trend data; generating a regional cloud electric field characteristic portrait based on the cloud electric field distribution data, and extracting a cloud electric field regional identifier; generating a target navigation route map according to the cloud-electric area identifier, and controlling the unmanned aerial vehicle to perform route correction according to the target navigation route map; The step of obtaining cloud electric field distribution data based on the lightning cloud data in combination with cloud development trend data includes: The lightning cloud data of the target route area is combined with the cloud development trend data and input into a preset cloud electric field distribution model for processing and calculation to obtain the cloud electric field distribution data in the area; The processing formula of the preset cloud electric field distribution model is: ,in is the cloud electric field distribution data, 、 is the preset characteristic coefficient, For Thunder Cloud data, is the number of regions, for In the sub-region sub-regions, Cloud development trend data.

7. The intelligent lightning protection control system for unmanned aerial vehicles according to claim 6, characterized in that: The real-time acquisition of cloud field dynamic information and weather forecast information within the target route area includes: Obtain the target route area of the UAV and divide the route area into sub-areas at multiple altitude levels; Real-time acquisition of cloud field dynamic information in multiple sub-regions, including cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information; Acquire weather forecast information for the target route area in real time, including temperature difference information, flow field information, pressure field information, and longitude and latitude information.

8. The intelligent lightning protection control system for unmanned aerial vehicles according to claim 7, characterized in that: The obtaining of lightning cloud data according to the cloud field dynamic information and the obtaining of cloud development trend data according to the meteorological forecast information include: The cumulus cloud layer information, cloud charge distribution information, and cloud temperature difference information of the multiple sub-regions are input into a preset lightning cloud monitoring platform for processing to obtain lightning cloud data of each sub-region, and the lightning cloud data of the target route area is obtained by aggregating the lightning cloud data; The temperature difference information, flow field information, pressure field information, and longitude and latitude information are input into a preset meteorological cloud prediction model for processing to obtain cloud development trend data for the target route area.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes an intelligent lightning protection control method program for an unmanned aerial vehicle. When the intelligent lightning protection control method program for an unmanned aerial vehicle is executed by a processor, the steps of the intelligent lightning protection control method for an unmanned aerial vehicle as described in any one of claims 1 to 5 are implemented.

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