CN114153224A - Micro-meteorology-based unmanned aerial vehicle flight path intelligent planning control system and method - Google Patents
Micro-meteorology-based unmanned aerial vehicle flight path intelligent planning control system and method Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle flight path intelligent planning control system and method based on microclimate, and relates to the technical field of unmanned aerial vehicles. By the intelligent unmanned aerial vehicle flight path planning control system based on microclimate, the defects that the existing unmanned aerial vehicle routing planning does not consider the state of the unmanned aerial vehicle and is unreasonable in planning are overcome, and the routing inspection efficiency of the unmanned aerial vehicle on the power distribution network is improved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle flight path intelligent planning control system and method based on microclimate.
Background
The power distribution network is a link in power generation, power transmission, power distribution and power utilization of a power system, which directly faces to users for power supply, and is used as a conversion junction of the power transmission link and the power utilization link, and plays a vital role in the whole power grid.
At present, there is the circuit length in suburb, rural and mountain forest area distribution overhead line, and the road conditions of locating are complicated, basic unit team patrols the dimension inconvenient, and needs more basic team and a large amount of time to maintain the circuit, and unmanned aerial vehicle then can be fine solution needs a large amount of manpower problems, and unmanned aerial vehicle patrols and examines transmission line and have high efficiency, swift, reliable, with low costs, do not receive advantages such as region influence, unmanned aerial vehicle patrols and examines and has become one of the important mode that transmission line patrolled and examined.
However, when the corresponding travel is long or the endurance mileage of the unmanned aerial vehicle is not high, and the unmanned aerial vehicle routing inspection path does not consider endurance and is unreasonable in path planning, so that the unmanned aerial vehicle cannot exert the advantages of the unmanned aerial vehicle, the unmanned aerial vehicle flight path intelligent planning control method and the unmanned aerial vehicle flight path intelligent planning control system based on microclimate are provided, and the problems are solved.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle flight path intelligent planning control system and method based on microclimate, so that the defect that the conventional unmanned aerial vehicle inspection route is unreasonable in planning is overcome.
In order to achieve the aim, the invention provides an unmanned aerial vehicle flight path intelligent planning control method based on microclimate, which comprises the following steps:
acquiring predicted meteorological data through meteorological sensor data and meteorological department data of a patrol place;
constructing a map model of the inspection ground according to the map of the inspection ground and the data of the unmanned aerial vehicle charging platform;
the path intelligent planning module generates a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
when the unmanned aerial vehicle flies according to the preset routing inspection path, the unmanned aerial vehicle does not fly according to the preset routing inspection path completely, and an actual routing inspection path is generated according to actual flight records in routing inspection records of the unmanned aerial vehicle;
and optimizing the preset routing inspection path according to the actual routing inspection path data to obtain an optimized routing inspection path.
Preferably, the patrol ground map model is a three-dimensional model.
Preferably, the construction of the map model of the patrol inspection place according to the map of the patrol inspection place and the unmanned aerial vehicle charging platform data comprises the following steps:
acquiring a map of a routing inspection area, and constructing a basic three-dimensional map according to the map of the routing inspection area;
establishing all unmanned aerial vehicle charging platform models on the basic three-dimensional map according to the unmanned aerial vehicle charging platform data;
marking an emergency stop place on the basic three-dimensional map;
and marking the main power distribution network facilities on the basic three-dimensional map so as to obtain a routing inspection area map model.
Preferably, the generating of the preset routing inspection path includes the following steps:
determining the position coordinates of a starting point and a terminal point of the unmanned aerial vehicle according to the routing inspection task, and initializing the constraint conditions of the preset routing inspection path according to the flight parameters of the unmanned aerial vehicle;
determining a basic flight route according to the routing inspection content of the option task in the routing inspection map model;
correcting and setting the basic flight route according to constraint conditions to obtain a preset routing inspection path;
presetting time suitable for the flight of the unmanned aerial vehicle according to the predicted meteorological data; calculating preset inspection time by combining a preset inspection path according to the flight speed of the unmanned aerial vehicle;
according to unmanned aerial vehicle's power consumption is expected unmanned aerial vehicle's power consumption, according to unmanned aerial vehicle's power consumption is judged whether the unmanned aerial vehicle's that needs to patrol and examine battery state can accomplish and is patrolled and examined the route, when can't accomplish the round trip journey, then according to unmanned aerial vehicle's battery state set in patrolling and examining the ground problem model unmanned aerial vehicle charging platform and charge to the route is patrolled and examined in the correspondence modification is predetermine.
Preferably, the basic flight path is calculated using the Dubins curves.
Preferably, the unmanned aerial vehicle's patrol and examine record includes: speed during flight, data detected by an airborne sensor of the unmanned aerial vehicle, shot pictures, real-time data of the unmanned aerial vehicle, a battery real-time state of the unmanned aerial vehicle and unmanned flight height.
Preferably, the unmanned aerial vehicle according to when predetermineeing the route of patrolling and examining the flight, still judge according to unmanned aerial vehicle real-time supervision's microclimate and whether continue the flight, when needs suspend the flight, then look for nearest scram place or unmanned aerial vehicle charging platform and descend.
Unmanned aerial vehicle flight path intelligent planning control system based on microclimate, unmanned aerial vehicle flight path intelligent planning control system uses foretell unmanned aerial vehicle flight path intelligent planning control method, its characterized in that includes:
the meteorological data module is used for acquiring predicted meteorological data through meteorological sensor data and meteorological department data of a patrol place;
the unmanned aerial vehicle parameter module is used for acquiring flight parameters, battery states and physical parameters of the unmanned aerial vehicle;
the inspection ground map model module is used for constructing an inspection ground map model according to an inspection ground map and unmanned aerial vehicle charging platform data;
the intelligent path planning module is used for generating a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
the actual path module is used for generating an actual inspection path according to actual flight records in the inspection records of the unmanned aerial vehicle; and
and the path optimization module is used for optimizing the preset routing inspection path according to the actual routing inspection path data to obtain an optimized routing inspection path.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an unmanned aerial vehicle flight path intelligent planning control system and method based on microclimate, which are characterized in that predicted meteorological data are obtained according to meteorological sensor data and meteorological department data passing through a patrol site; constructing a map model of the inspection ground according to the map of the inspection ground and the data of the unmanned aerial vehicle charging platform; the path intelligent planning module generates a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle; when the unmanned aerial vehicle flies according to the preset routing inspection path, the unmanned aerial vehicle does not fly according to the preset routing inspection path completely, and an actual routing inspection path is generated according to actual flight records in routing inspection records of the unmanned aerial vehicle; according to it is right actually to patrol and examine the path data predetermine and patrol and examine the path and optimize, obtain the route of patrolling and examining of optimizing, combine through path intelligent planning module prediction meteorological data, patrol and examine ground map model, patrol and examine the task, unmanned aerial vehicle's flight parameter and the preset route of patrolling and examining that unmanned aerial vehicle's battery state generated for unmanned aerial vehicle flight path is more perfect, patrol and examine for unmanned aerial vehicle and provide better flight route, reduce the task volume that the manual work was patrolled and examined, through also having reduced the condition that unmanned aerial vehicle flight half way does not have electricity etc. and cause unmanned aerial vehicle to damage, thereby reduce manpower and materials, conveniently patrol and examine the distribution network. By the method and the system, the defects that the existing unmanned aerial vehicle routing inspection route planning does not consider the state of the unmanned aerial vehicle and is unreasonable in planning are overcome, and the routing inspection efficiency of the unmanned aerial vehicle on the power distribution network is improved.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flow chart of an unmanned aerial vehicle flight path intelligent planning control method based on microclimate.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows an unmanned aerial vehicle flight path intelligent planning control method based on microclimate, which comprises the following steps:
s1, acquiring forecast meteorological data through meteorological sensor data and meteorological department data of the inspection area;
s2, constructing an inspection ground map model according to the map of the inspection ground and the unmanned aerial vehicle charging platform data;
s3, the path intelligent planning module generates a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
s4, when the unmanned aerial vehicle flies according to the preset routing inspection path and the unmanned aerial vehicle does not fly according to the preset routing inspection path completely, generating an actual routing inspection path according to actual flight records in routing inspection records of the unmanned aerial vehicle;
and S5, optimizing the preset inspection path according to the actual inspection path data to obtain an optimized inspection path.
According to the intelligent planning control method for the flight path of the unmanned aerial vehicle based on microclimate, predicted meteorological data are obtained according to meteorological sensor data and meteorological department data passing through a patrol area; constructing a map model of the inspection ground according to the map of the inspection ground and the data of the unmanned aerial vehicle charging platform; the path intelligent planning module generates a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle; when the unmanned aerial vehicle flies according to the preset routing inspection path, the unmanned aerial vehicle does not fly according to the preset routing inspection path completely, and an actual routing inspection path is generated according to actual flight records in routing inspection records of the unmanned aerial vehicle; according to it is right actually to patrol and examine the path data predetermine and patrol and examine the path and optimize, obtain the route of patrolling and examining of optimizing, combine through path intelligent planning module prediction meteorological data, patrol and examine ground map model, patrol and examine the task, unmanned aerial vehicle's flight parameter and the preset route of patrolling and examining that unmanned aerial vehicle's battery state generated for unmanned aerial vehicle flight path is more perfect, patrol and examine for unmanned aerial vehicle and provide better flight route, reduce the task volume that the manual work was patrolled and examined, through also having reduced the condition that unmanned aerial vehicle flight half way does not have electricity etc. and cause unmanned aerial vehicle to damage, thereby reduce manpower and materials, conveniently patrol and examine the distribution network.
In one embodiment, in step S1, the meteorological sensor data of the patrol location includes: the temperature, the humidity, the wind speed, the wind direction, the rainfall condition and the like of the environment of the inspection place are detected.
The meteorological department data includes: the current and predicted temperature, humidity, wind speed, wind direction, rainfall condition and the like of the inspection area.
In one embodiment, the unmanned aerial vehicle charging platform data includes the position of unmanned aerial vehicle platform and the state of unmanned aerial vehicle platform, wherein, the state of unmanned aerial vehicle platform includes: occupied, idle, and out of use.
In one embodiment, in step S2, the patrol ground map model is a three-dimensional model.
Specifically, in step S2, the method for constructing the patrol inspection area map model according to the patrol inspection area map and the unmanned aerial vehicle charging platform data includes the following steps:
s21, acquiring a map of the inspection area, and constructing a basic three-dimensional map according to the map of the inspection area;
s22, establishing all unmanned aerial vehicle charging platform models on the basic three-dimensional map according to the unmanned aerial vehicle charging platform data;
s23, marking an emergency stop place on the basic three-dimensional map;
and S24, marking main power distribution network facilities on the basic three-dimensional map so as to obtain a routing inspection land map model. Wherein, main join in marriage single net facility includes: the facilities such as shaft tower, overhead line, distribution transformer, etc.
One of them embodiment, still include S6, according to the unmanned aerial vehicle patrol and examine the image pair in the record and optimize the map model of patrolling and examining for the map model of patrolling and examining is more accurate, improves the accuracy of predetermineeing and examining the route, and is concrete, including following step:
acquiring image data belonging to environmental information from the unmanned aerial vehicle inspection record;
denoising and enhancing the image data, and extracting the characteristics of the image, wherein the characteristics of the image comprise position coordinate information and environment data of the image;
and correcting the characteristics of the corresponding position in the routing inspection ground map model according to the processed picture characteristics, thereby realizing the real-time updating of the routing inspection ground map model and further realizing the optimization of the routing inspection ground map model.
Wherein the correcting comprises: and (4) correcting parameters of the tree, mountain and house heights, ground surface environments such as unmanned aerial vehicle platforms and the like.
In one embodiment, in step S3, the generating the preset routing inspection path includes the following steps:
s31, determining the position coordinates of the starting point and the end point of the unmanned aerial vehicle according to the inspection task, and initializing the constraint conditions of the preset inspection path according to the flight parameters of the unmanned aerial vehicle;
wherein the constraint condition comprises: maximum turning angle, maximum climbing angle, minimum flying height, maximum flying height, etc. The polling task comprises the following steps: starting point and destination in inspection, inspection content and the like.
S32, determining a basic flight route according to the inspection content of the option task in the inspection ground map model;
specifically, when the routing inspection content is routing inspection of the power distribution network equipment, the power distribution equipment in the routing inspection map model is taken as a waypoint, and the waypoints are sequentially connected from the waypoint closest to the starting point along the direction of the end point, so that a basic flight route is formed; when the relief position exists between two adjacent waypoints, the relief position is increased as the waypoint;
when the routing inspection content is a certain destination, the positions of relief are taken as waypoints, and the waypoints are sequentially connected from the waypoint closest to the starting point along the direction of the end point, so that a basic flight route is formed.
Specifically, the basic flight path adopts a Dubins curve to calculate parameters.
S33, correcting and setting the basic flight path according to constraint conditions to obtain a preset inspection path;
specifically, the correction in step S3 includes the following steps:
acquiring a corresponding altitude according to the geographical position between two adjacent waypoints, and flying the unmanned aerial vehicle at a normal flying height which is corresponding to the distance between the unmanned aerial vehicle and is also high when the altitude between the two adjacent waypoints is not higher than the set normal flying height of the unmanned aerial vehicle; when the altitude between two adjacent waypoints is higher than the set normal flying height of the unmanned aerial vehicle and is lowest than the highest flying height, the unmanned aerial vehicle flies, namely ascends or descends according to the ridge ground-imitating flying track and in the range smaller than the maximum climbing angle; and when one of the altitudes between two adjacent waypoints is higher than the highest flying altitude, performing detour flight according to the maximum turning angle and the terrain.
S34, presetting time suitable for the flight of the unmanned aerial vehicle according to the predicted meteorological data; calculating preset inspection time by combining a preset inspection path according to the flight speed of the unmanned aerial vehicle; the staff can conveniently arrange other matters according to the time of patrolling and examining, also can be according to the time of patrolling and examining to the modification of unmanned aerial vehicle flight path, charge unmanned aerial vehicle etc..
Wherein, predetermine the time that is fit for unmanned aerial vehicle flight and include: the start time, the time of flight, the time of arrival, and a threshold for each time period may be used to evaluate the flight path of the drone.
S35, according to unmanned aerial vehicle 'S power consumption is expected to unmanned aerial vehicle' S power consumption, according to unmanned aerial vehicle 'S power consumption judgement needs unmanned aerial vehicle' S that patrols and examines whether battery state can accomplish and patrols and examines the route, when can't accomplish the round trip journey, then set up according to unmanned aerial vehicle' S battery state and charge in patrolling and examining the ground subject model unmanned aerial vehicle charging platform to the route of patrolling and examining is predetermine in the corresponding modification.
Specifically, the energy consumption calculation of the unmanned aerial vehicle is optimized by adopting a DQN algorithm.
In one embodiment, the battery status of the drone includes: battery capacity, remaining charge, last charge time, etc.
In one embodiment, in step S4, the situation that the unmanned aerial vehicle does not fly according to the preset inspection path completely includes: the unmanned aerial vehicle temporarily breaks down, a planned route has obstacles, and the weather condition does not allow the unmanned aerial vehicle to continuously fly forwards or manually perform micro-regulation and control.
In one embodiment, in step S5, the polling record of the unmanned aerial vehicle includes: speed during flight, data detected by an airborne sensor of the unmanned aerial vehicle, shot pictures, real-time data of the unmanned aerial vehicle, a battery real-time state of the unmanned aerial vehicle, unmanned flying height and the like.
In one embodiment, in step S4, the unmanned aerial vehicle determines whether to continue flying according to microclimate monitored in real time by the unmanned aerial vehicle when flying according to the preset routing inspection path, and when the flying needs to be suspended, the nearest emergency stop place or the unmanned aerial vehicle charging platform is searched for landing.
According to one embodiment, the preset flight path of the unmanned aerial vehicle makes a round trip to adopt a consistent route, the power distribution network which is patrolled and examined can be patrolled and examined again through the consistent route, and certainly, the return route can also adopt a shortest planning flight path.
Unmanned aerial vehicle flight path intelligent planning control system based on microclimate, unmanned aerial vehicle flight path intelligent planning control system use foretell unmanned aerial vehicle flight path intelligent planning control method, include: a meteorological data module, an unmanned aerial vehicle parameter module, a patrol ground map model module, a path intelligent planning module, an actual path module and a path optimization module,
the meteorological data module is used for acquiring predicted meteorological data through meteorological sensor data and meteorological department data of a patrol place;
the unmanned aerial vehicle parameter module is used for acquiring unmanned aerial vehicle parameters such as flight parameters, battery states and physical parameters of the unmanned aerial vehicle;
the inspection ground map model module is used for constructing an inspection ground map model according to an inspection ground map and unmanned aerial vehicle charging platform data;
the intelligent path planning module is used for generating a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
the actual path module is used for generating an actual inspection path according to actual flight records in the inspection records of the unmanned aerial vehicle;
and the path optimization module is used for optimizing the preset routing inspection path according to the actual routing inspection path data to obtain an optimized routing inspection path.
According to the unmanned aerial vehicle flight path intelligent planning control system based on microclimate, the meteorological data module is used for obtaining and predicting meteorological data according to meteorological sensor data and meteorological department data passing through the inspection area; the inspection ground map model module constructs an inspection ground map model according to an inspection ground map and unmanned aerial vehicle charging platform data; the route intelligent planning module generates a preset routing inspection route according to the prediction meteorological data obtained by the meteorological data module, the routing inspection map model obtained by the meteorological data module, the routing inspection task, the flight parameters of the unmanned aerial vehicle obtained by the unmanned aerial vehicle parameter module and the battery state of the unmanned aerial vehicle; the actual path module generates an actual inspection path according to actual flight records in the inspection records of the unmanned aerial vehicle; and the path optimization module optimizes the preset routing inspection path according to the actual routing inspection path data obtained by the actual path module to obtain the optimized routing inspection path.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (8)
1. An unmanned aerial vehicle flight path intelligent planning control method based on microclimate is characterized by comprising the following steps:
acquiring predicted meteorological data through meteorological sensor data and meteorological department data of a patrol place;
constructing a map model of the inspection ground according to the map of the inspection ground and the data of the unmanned aerial vehicle charging platform;
the path intelligent planning module generates a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
when the unmanned aerial vehicle flies according to the preset routing inspection path, the unmanned aerial vehicle does not fly according to the preset routing inspection path completely, and an actual routing inspection path is generated according to actual flight records in routing inspection records of the unmanned aerial vehicle;
and optimizing the preset routing inspection path according to the actual routing inspection path data to obtain an optimized routing inspection path.
2. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 1, wherein the patrol ground map model is a three-dimensional model.
3. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 1, wherein the construction of the patrol ground map model according to the patrol ground map and the unmanned aerial vehicle charging platform data comprises the following steps:
acquiring a map of a routing inspection area, and constructing a basic three-dimensional map according to the map of the routing inspection area;
establishing all unmanned aerial vehicle charging platform models on the basic three-dimensional map according to the unmanned aerial vehicle charging platform data;
marking an emergency stop place on the basic three-dimensional map;
and marking the main power distribution network facilities on the basic three-dimensional map so as to obtain a routing inspection area map model.
4. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 1, wherein the generation of the preset patrol route comprises the following steps:
determining the position coordinates of a starting point and a terminal point of the unmanned aerial vehicle according to the routing inspection task, and initializing the constraint conditions of the preset routing inspection path according to the flight parameters of the unmanned aerial vehicle;
determining a basic flight route according to the routing inspection content of the option task in the routing inspection map model;
correcting and setting the basic flight route according to constraint conditions to obtain a preset routing inspection path;
presetting time suitable for the flight of the unmanned aerial vehicle according to the predicted meteorological data; calculating preset inspection time by combining a preset inspection path according to the flight speed of the unmanned aerial vehicle;
according to unmanned aerial vehicle's power consumption is expected unmanned aerial vehicle's power consumption, according to unmanned aerial vehicle's power consumption is judged whether the unmanned aerial vehicle's that needs to patrol and examine battery state can accomplish and is patrolled and examined the route, when can't accomplish the round trip journey, then according to unmanned aerial vehicle's battery state set in patrolling and examining the ground problem model unmanned aerial vehicle charging platform and charge to the route is patrolled and examined in the correspondence modification is predetermine.
5. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 4, wherein the basic flight path adopts a Dubins curve for parameter calculation.
6. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 1, wherein the patrol record of the unmanned aerial vehicle comprises: speed during flight, data detected by an airborne sensor of the unmanned aerial vehicle, shot pictures, real-time data of the unmanned aerial vehicle, a battery real-time state of the unmanned aerial vehicle and unmanned flight height.
7. The microclimate-based unmanned aerial vehicle flight path intelligent planning control method according to claim 1, characterized in that when the unmanned aerial vehicle flies according to the preset routing inspection path, whether the unmanned aerial vehicle continues to fly is judged according to microclimate monitored by the unmanned aerial vehicle in real time, and when the flying needs to be suspended, the nearest emergency stop place or unmanned aerial vehicle charging platform is searched for landing.
8. An intelligent unmanned aerial vehicle flight path planning control system based on microclimate, which applies the intelligent unmanned aerial vehicle flight path planning control method of claims 1-7, and is characterized by comprising the following steps:
the meteorological data module is used for acquiring predicted meteorological data through meteorological sensor data and meteorological department data of a patrol place;
the unmanned aerial vehicle parameter module is used for acquiring flight parameters, battery states and physical parameters of the unmanned aerial vehicle;
the inspection ground map model module is used for constructing an inspection ground map model according to an inspection ground map and unmanned aerial vehicle charging platform data;
the intelligent path planning module is used for generating a preset inspection path according to the predicted meteorological data, the inspection ground map model, the inspection task, the flight parameters of the unmanned aerial vehicle and the battery state of the unmanned aerial vehicle;
the actual path module is used for generating an actual inspection path according to actual flight records in the inspection records of the unmanned aerial vehicle; and
and the path optimization module is used for optimizing the preset routing inspection path according to the actual routing inspection path data to obtain an optimized routing inspection path.
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| CN118363932A (en) * | 2024-06-19 | 2024-07-19 | 德阳经开智航科技有限公司 | Unmanned aerial vehicle-based intelligent patrol method and system |
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| CN116820141B (en) * | 2023-08-31 | 2023-12-08 | 深圳市金泰谊电子有限公司 | Security inspection method and device based on 5G communication, unmanned aerial vehicle and storage medium |
| CN117217739A (en) * | 2023-11-07 | 2023-12-12 | 厦门闽投科技服务有限公司 | Intelligent electric power inspection system |
| CN117217739B (en) * | 2023-11-07 | 2024-02-23 | 厦门闽投科技服务有限公司 | Intelligent electric power inspection system |
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