CN117472079A - Unmanned aerial vehicle route planning and control method based on GIS and BIM integration - Google Patents

Abstract

The invention relates to an unmanned aerial vehicle route planning and control method based on GIS and BIM integration, which comprises the following steps: and respectively acquiring the topography and landform information of the target area and the building information of the target area based on the GIS and BIM technologies. And creating an optimal flight path of the unmanned aerial vehicle according to the topography and the landform information of the target area, the building information of the target area and the actual influencing factors. And importing the unmanned aerial vehicle parameters and the optimal flight path into control software of the unmanned aerial vehicle to simulate flight, and optimizing the simulated flight process to obtain the adjusted optimal flight path of the unmanned aerial vehicle. And taking the adjusted optimal flight path as an actual flight path of the unmanned aerial vehicle, and monitoring and adjusting the actual flight process of the unmanned aerial vehicle in real time by utilizing GIS and BIM technologies. And the two-dimensional map limitation in the traditional planning method is avoided. And the route planning of the unmanned aerial vehicle can be dynamically adjusted according to the information such as the terrain, the ground feature and the like acquired in real time, so that the safe and efficient flight of the unmanned aerial vehicle is realized.

Description

Unmanned aerial vehicle route planning and control method based on GIS and BIM integration

Technical Field

The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle route planning and control method based on GIS and BIM integration.

Background

At present, the unmanned aerial vehicle route planning and control method mainly comprises a rule-based method, a genetic algorithm, an ant colony algorithm, a particle swarm algorithm and the like. Among them, the rule-based method is one of the earliest methods applied to unmanned aerial vehicle route planning, and has the advantages of simple implementation and easy understanding, but has the disadvantage of being unable to handle complex environments and varying task demands. In contrast, intelligent optimization algorithms such as genetic algorithm, ant colony algorithm and particle swarm algorithm have stronger adaptability and global searching capability, but also have the problems of high computational complexity, low convergence speed and the like.

The conventional PID control method, although simple and easy to use, performs poorly in the face of complex nonlinear systems. Although unmanned aerial vehicle control techniques are continually evolving and perfecting, the following disadvantages still exist:

1. instability and error of sensor technology: the sensors used by the unmanned aerial vehicle, such as a GPS (global positioning system) and an inertial measurement unit, may generate errors due to mechanical abrasion, temperature change and other reasons, and influence the positioning and control precision of the unmanned aerial vehicle.

2. Delay and reliability problems of communication technology: unmanned aerial vehicle communication technology is the key of realizing remote control and real-time supervision, but still has delay and reliability problem at present, for example radio communication can receive the interference, and microwave communication can receive factors such as weather influence, influence real-time and the accuracy of control.

3. Application scope and limitations of artificial intelligence techniques: although artificial intelligence techniques may provide better support for unmanned aerial vehicle control, limitations and application scope issues remain. For example, machine learning algorithms may require a lot of data and computing resources, while deep learning algorithms require a lot of training and adjustment, and applications of artificial intelligence techniques are also faced with security, privacy protection, etc.

Disclosure of Invention

Based on the above, it is necessary to provide an unmanned aerial vehicle route planning and control method based on GIS and BIM integration, which avoids the limitation of a two-dimensional map in the traditional planning method, and can dynamically adjust the route planning of the unmanned aerial vehicle according to the information of the terrain, the ground features and the like acquired in real time, thereby realizing the safe and efficient flight of the unmanned aerial vehicle.

The invention provides an unmanned aerial vehicle route planning and control method based on GIS and BIM integration, which comprises the following steps:

respectively acquiring topography and geomorphic information of a target area and building information of the target area based on GIS and BIM technologies;

creating an optimal flight path of the unmanned aerial vehicle according to the topography and the landform information of the target area, the building information of the target area and the actual influencing factors;

the unmanned aerial vehicle parameters and the optimal flight path are imported into unmanned aerial vehicle control software to simulate flight, and the simulated flight process is optimized to obtain an optimal flight path after unmanned aerial vehicle adjustment;

and taking the adjusted optimal flight path as an actual flight path of the unmanned aerial vehicle, and monitoring and adjusting the actual flight process of the unmanned aerial vehicle in real time by utilizing GIS and BIM technologies.

In one embodiment, the obtaining, based on GIS and BIM technologies, the topography and geomorphic information of the target area and the building information of the target area includes:

using an unmanned aerial vehicle to carry a high-resolution camera and a sensor to acquire an image and related data of a target area;

the method comprises the steps of obtaining terrain and landform information of a target area by utilizing a GIS technology, and processing the terrain and landform information of the target area to obtain a map and a model required by unmanned aerial vehicle flight;

building information of a target area is obtained by using a BIM technology, and the building information of the target area is processed to obtain a three-dimensional model and attribute information of the building of the target area.

In one embodiment, the creating the optimal flight path of the unmanned aerial vehicle according to the topography and the geomorphic information of the target area, the building information of the target area and the actual influencing factors includes:

creating an unmanned aerial vehicle flight path based on a map and a model required by the unmanned aerial vehicle flight and the three-dimensional model and attribute information of the target area building;

and determining the optimal flight path of the unmanned aerial vehicle based on the flight path of the unmanned aerial vehicle, flight safety, flight efficiency and flight economy factors.

In one embodiment, the creating the optimal flight path of the unmanned aerial vehicle according to the topography and the geomorphic information of the target area, the building information of the target area and the actual influencing factors further includes:

and automatically planning the unmanned aerial vehicle flight path by using a point cloud data related algorithm of a building.

In one embodiment, the point cloud data correlation algorithm of the building comprises a Hough transformation method and a RANSAN method based on model fitting, a DBSCAN method and an LSH method based on region growing, and an OPTICS method and a FLANN method based on clustering features.

In one embodiment, the step of importing the unmanned aerial vehicle parameters and the optimal flight path into control software of the unmanned aerial vehicle to perform simulated flight and optimizing a simulated flight process to obtain an adjusted optimal flight path of the unmanned aerial vehicle includes:

setting parameters of a flight chess on a GIS platform, and importing the optimal flight path into control software of the unmanned aerial vehicle to simulate flight;

and adjusting the most-available flight path based on the information of the detected flight attitude, the flight age and the like in the simulated flight process, and finally obtaining the adjusted optimal flight path.

In one embodiment, the taking the adjusted optimal flight path as the actual flight path of the unmanned aerial vehicle and performing real-time monitoring and adjustment on the actual flight process of the unmanned aerial vehicle by using GIS and BIM technologies includes:

performing actual flight based on the adjusted optimal flight path, and positioning and navigating by using a GPS and an inertial navigation system carried by the unmanned aerial vehicle;

and carrying out real-time monitoring and adjustment on the unmanned aerial vehicle by using GIS and BIM technologies so as to ensure that the unmanned aerial vehicle can fly according to the adjusted optimal flight path.

In one embodiment, the method further comprises:

and analyzing and evaluating the flight result by using a GIS technology, and simulating and optimizing the building by using a BIM technology, so as to further optimize the adjusted optimal flight path.

In one embodiment, the method further comprises:

and integrating the GIS, the BIM and the unmanned aerial vehicle control system to realize data sharing and cooperative work.

In one embodiment, the integrating the GIS, BIM and the unmanned aerial vehicle control system to realize data sharing and cooperative work includes:

through development interfaces and data exchange standards, data interaction and function coordination among the systems are realized;

through the functions of collaborative design, collaborative planning and the like, the working efficiency and the collaborative efficiency are improved.

According to the unmanned aerial vehicle route planning and control method based on GIS and BIM integration, two technologies of a Geographic Information System (GIS) and a Building Information Model (BIM) are combined, and information such as terrain, ground features and the like is subjected to visual processing through the GIS, so that basic data support is provided for unmanned aerial vehicle route planning; meanwhile, information such as buildings, infrastructure and the like is accurately acquired by using BIM, and basic support is provided for unmanned aerial vehicle route planning. In the course of unmanned aerial vehicle route planning, GIS is mainly used for processing and analyzing geospatial data, providing environmental information such as topography, ground object, climate, etc., and potential risks and challenges of unmanned aerial vehicle flight path, confirm the optimal flight path of unmanned aerial vehicle; the BIM is mainly used for constructing and managing three-dimensional models of buildings and other infrastructures so as to provide accurate space positioning information and help the unmanned aerial vehicle to avoid the buildings and other obstacles, and the three-dimensional models of the BIM can provide more accurate flight paths and avoid two-dimensional map limitation in the traditional planning method. And finally, dynamically adjusting the route planning of the unmanned aerial vehicle according to the information such as the terrain, the ground object and the like acquired in real time, and realizing the safe and efficient flight of the unmanned aerial vehicle.

Drawings

In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a method for planning and controlling a route of an unmanned aerial vehicle according to the present invention;

FIG. 2 is a second flowchart of the unmanned aerial vehicle route planning and control method provided by the invention;

FIG. 3 is a third flowchart of the unmanned aerial vehicle route planning and control method provided by the present invention;

FIG. 4 is a flowchart of a method for planning and controlling a route of an unmanned aerial vehicle according to the present invention;

FIG. 5 is a flowchart of a method for planning and controlling a route of an unmanned aerial vehicle according to the present invention;

fig. 6 is a flow chart of a route planning and control method of an unmanned aerial vehicle provided by the invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The unmanned aerial vehicle route planning and control method based on GIS and BIM integration of the present invention is described below with reference to FIGS. 1 to 6.

As shown in fig. 1, in one embodiment, a method for planning and controlling a route of an unmanned aerial vehicle based on GIS and BIM integration includes the following steps:

step S110, the topography and the geomorphic information of the target area and the building information of the target area are respectively obtained based on the GIS and BIM technologies.

And step S120, creating an optimal flight path of the unmanned aerial vehicle according to the topography and the geomorphic information of the target area, the building information of the target area and the actual influencing factors.

And step S130, the unmanned aerial vehicle parameters and the optimal flight path are led into control software of the unmanned aerial vehicle to carry out simulated flight, and the simulated flight process is optimized to obtain the optimal flight path after the unmanned aerial vehicle is regulated.

And step S140, taking the adjusted optimal flight path as an actual flight path of the unmanned aerial vehicle, and monitoring and adjusting the actual flight process of the unmanned aerial vehicle in real time by utilizing GIS and BIM technologies.

According to the unmanned aerial vehicle route planning and control method based on GIS and BIM integration, two technologies of a Geographic Information System (GIS) and a Building Information Model (BIM) are combined, and information such as terrain, ground features and the like is subjected to visual processing through the GIS, so that basic data support is provided for unmanned aerial vehicle route planning; meanwhile, information such as buildings, infrastructure and the like is accurately acquired by using BIM, and basic support is provided for unmanned aerial vehicle route planning. In the course of unmanned aerial vehicle route planning, GIS is mainly used for processing and analyzing geospatial data, providing environmental information such as topography, ground object, climate, etc., and potential risks and challenges of unmanned aerial vehicle flight path, confirm the optimal flight path of unmanned aerial vehicle; the BIM is mainly used for constructing and managing three-dimensional models of buildings and other infrastructures so as to provide accurate space positioning information and help the unmanned aerial vehicle to avoid the buildings and other obstacles, and the three-dimensional models of the BIM can provide more accurate flight paths and avoid two-dimensional map limitation in the traditional planning method. And finally, dynamically adjusting the route planning of the unmanned aerial vehicle according to the information such as the terrain, the ground object and the like acquired in real time, and realizing the safe and efficient flight of the unmanned aerial vehicle.

As shown in fig. 2, in one embodiment, the unmanned aerial vehicle route planning and control method based on GIS and BIM integration provided by the present invention obtains the topography and landform information of a target area and the building information of the target area based on GIS and BIM technologies, respectively, and includes the following steps:

in step S111, an image of the target area and related data are acquired using the unmanned aerial vehicle-mounted high-resolution camera and sensor.

And step S112, the geographic and geomorphic information of the target area is obtained by utilizing a GIS technology, and the geographic and geomorphic information of the target area is processed to obtain a map and a model required by unmanned aerial vehicle flight.

Specifically, the data is processed, including preprocessing, analyzing and integrating the acquired data, to extract useful information, and to form maps and models required for flight. Meanwhile, geographic information is processed and analyzed by GIS software, and terrain elevation, calculated distance, area and the like are extracted.

Step S113, building information of the target area is obtained by using BIM technology, and the building information of the target area is processed to obtain a three-dimensional model and attribute information of the building of the target area.

As shown in fig. 3, in one embodiment, the unmanned aerial vehicle route planning and controlling method based on GIS and BIM integration provided by the present invention creates an optimal flight path of an unmanned aerial vehicle according to the topography and landform information of a target area, building information of the target area and actual influencing factors, and includes the following steps:

step S121, creating a flight path of the unmanned aerial vehicle based on the map and model required for the unmanned aerial vehicle to fly and the three-dimensional model and attribute information of the target area building.

Step S122, determining an optimal flight path of the unmanned aerial vehicle based on the unmanned aerial vehicle flight path and flight safety, flight efficiency and flight economy factors.

Specifically, GIS software is used for planning and optimizing the flight path, and factors such as flight safety, flight time and flight cost are considered. Building location and simulation is performed using BIM software to better understand the environment surrounding the flight path. Thereby determining the optimal route and ensuring the safety and stability of the flight.

In one embodiment, the unmanned aerial vehicle route planning and controlling method based on GIS and BIM integration provided by the invention creates an optimal flight path of the unmanned aerial vehicle according to the topography and landform information of a target area, the building information of the target area and actual influencing factors, and further comprises the steps of: and automatically planning the flight path of the unmanned aerial vehicle by using a point cloud data related algorithm of the building.

Specifically, the point cloud data correlation algorithm of the building comprises a Hough transformation method and a RANSAN method based on model fitting, a DBSCAN method and an LSH method based on region growing, and an OPTICS method and a FLANN method based on clustering features.

RANSAC (Random Sample Consensus) is an uncertainty algorithm, and is mainly used for processing outlier problems in data. RANSAC achieves this by iteratively selecting a random subset of the data, one model being applied to the hypothesized local points, i.e. all unknown parameters can be deduced from the hypothesis, and the other model being applied to all points not selected to the subset, i.e. all unknown parameters take arbitrary values. Finally, RANSAC will choose a model that minimizes the sum of squares of residuals as the final result.

DBSCAN (Density-Based Spatial Clustering ofApplications withNoise) is a Density-based clustering algorithm that defines a maximum set of clusters as Density-connected points, is capable of dividing regions with a sufficiently high Density into clusters, and can find clusters of arbitrary shape in noisy data. The principle of the DBSCAN algorithm is as follows:

for each sample, calculating the number of core points in epsilon neighborhood;

if the number of the core points is greater than or equal to minPts, marking the sample as the core point;

if one sample is not a core point, marking it as a boundary point;

if one sample is both a core point and a boundary point, it is marked as a dense point;

the above steps are repeated until all samples have been marked.

OPTICS (Ordering Points to Identify the Clustering Structure) is a density-based clustering algorithm that is an improved version of DBSCAN, and thus the OPTICS algorithm is also a density-based clustering algorithm. The OPTICS algorithm has many similarities to the DBSCAN algorithm, which can be seen as a generalization of DBSCAN, relaxing eps requirements from a single value to a range. The OPTICS algorithm builds a potential reachability graph, assigns a reach_distance to each sample, and a point in the cluster ordering_attribute; these two attributes are assigned when fitting (fit) models and are used to determine the members of the cluster.

As shown in fig. 4, in one embodiment, the unmanned aerial vehicle route planning and controlling method based on GIS and BIM integration provided by the present invention, the unmanned aerial vehicle parameters and the optimal flight path are imported into the unmanned aerial vehicle control software to perform simulated flight, and the simulated flight process is optimized to obtain the optimal flight path after unmanned aerial vehicle adjustment, including the following steps:

and S131, setting parameters of the flight chess on the GIS platform, and guiding the optimal flight path into control software of the unmanned aerial vehicle to simulate flight.

And step S132, adjusting the most-available flight path based on the information of the flight attitude, the flight age and the like detected in the simulated flight process, and finally obtaining the adjusted optimal flight path.

As shown in fig. 5, in one embodiment, the method for planning and controlling a route of an unmanned aerial vehicle based on GIS and BIM integration provided by the present invention takes an adjusted optimal flight path as an actual flight path of the unmanned aerial vehicle, and monitors and adjusts the actual flight process of the unmanned aerial vehicle in real time by using GIS and BIM technologies, including the following steps:

and step S141, performing actual flight based on the adjusted optimal flight path, and positioning and navigating by using a GPS and inertial navigation system carried by the unmanned aerial vehicle.

And step S142, monitoring and adjusting the unmanned aerial vehicle in real time by using GIS and BIM technologies so as to ensure that the unmanned aerial vehicle can fly according to the adjusted optimal flight path.

Specifically, flight control needs to consider control requirements of various stages of take-off, cruising, landing and the like of the unmanned aerial vehicle, and the capability of coping with sudden events. The flight control method based on the GIS and the BIM can provide more accurate and reliable flight control, and is suitable for flight requirements in complex environments.

In one embodiment, the unmanned aerial vehicle route planning and control method based on GIS and BIM integration provided by the invention further comprises the following steps: and analyzing and evaluating the flight result by using a GIS technology, and simulating and optimizing the building by using a BIM technology, so that the adjusted optimal flight path is further optimized.

Specifically, GIS software is used to analyze and evaluate flight results, such as calculating track length, analyzing flight errors, etc. Building simulation and optimization was performed using BIM software to better understand the impact of flight results on the building. Through data analysis, the route planning and control method can be further optimized, and the flight efficiency and accuracy of the unmanned aerial vehicle are improved. Meanwhile, the flight result is evaluated and analyzed to know the advantages and disadvantages of the route planning and control method, and a reference is provided for future flights.

In one embodiment, the unmanned aerial vehicle route planning and control method based on GIS and BIM integration provided by the invention further comprises the following steps: and integrating the GIS, the BIM and the unmanned aerial vehicle control system to realize data sharing and cooperative work.

Specifically, through developing interfaces and data exchange standards, data interaction and function coordination among the systems are realized. For example, the map data of the GIS and the building information of the BIM can be integrated together, so that more comprehensive and accurate information support is provided, and the accuracy and reliability of the unmanned aerial vehicle route are improved. Through the functions of collaborative design, collaborative planning and the like, the working efficiency and the collaborative efficiency are improved. By integrating the independent systems, data sharing and cooperative work are realized, and the working efficiency and the cooperative efficiency are improved.

According to the unmanned aerial vehicle route planning and control method based on GIS and BIM integration, the flow frame is shown in FIG. 6, the advantages of the GIS and BIM are fully utilized by researching, the microscopic data of the building model are integrated into the surrounding macroscopic geographic environment, the surrounding building system and the surrounding environment are combined to form the three-dimensional integrated space of BIM and GIS, and macroscopic and microscopic information integration is achieved. The route planning and control of the unmanned aerial vehicle are closer to the real scene, the planarization short plates for the prevention and control of the unmanned aerial vehicle are eliminated, the flight control method of the unmanned aerial vehicle is improved, and the accuracy and the targeting of the unmanned aerial vehicle route flight are improved.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.

Claims (10)

1. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration is characterized by comprising the following steps:

respectively acquiring topography and geomorphic information of a target area and building information of the target area based on GIS and BIM technologies;

creating an optimal flight path of the unmanned aerial vehicle according to the topography and the landform information of the target area, the building information of the target area and the actual influencing factors;

the unmanned aerial vehicle parameters and the optimal flight path are imported into unmanned aerial vehicle control software to simulate flight, and the simulated flight process is optimized to obtain an optimal flight path after unmanned aerial vehicle adjustment;

and taking the adjusted optimal flight path as an actual flight path of the unmanned aerial vehicle, and monitoring and adjusting the actual flight process of the unmanned aerial vehicle in real time by utilizing GIS and BIM technologies.

2. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 1, wherein the GIS and BIM technology based respectively acquires the topography and landform information of the target area and the building information of the target area, comprising:

using an unmanned aerial vehicle to carry a high-resolution camera and a sensor to acquire an image and related data of a target area;

the method comprises the steps of obtaining terrain and landform information of a target area by utilizing a GIS technology, and processing the terrain and landform information of the target area to obtain a map and a model required by unmanned aerial vehicle flight;

building information of a target area is obtained by using a BIM technology, and the building information of the target area is processed to obtain a three-dimensional model and attribute information of the building of the target area.

3. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 2, wherein the creating the optimal flight path of the unmanned aerial vehicle according to the topography and the landform information of the target area, the building information of the target area and the actual influencing factors includes:

creating an unmanned aerial vehicle flight path based on a map and a model required by the unmanned aerial vehicle flight and the three-dimensional model and attribute information of the target area building;

and determining the optimal flight path of the unmanned aerial vehicle based on the flight path of the unmanned aerial vehicle, flight safety, flight efficiency and flight economy factors.

4. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 3, wherein the creating an optimal flight path of the unmanned aerial vehicle according to the topography and the landform information of the target area, the building information of the target area, and the actual influencing factors further comprises:

and automatically planning the unmanned aerial vehicle flight path by using a point cloud data related algorithm of a building.

5. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 4, wherein the point cloud data correlation algorithm of the building comprises a Hough transformation method and a RANSAN method based on model fitting, a DBSCAN method and an LSH method based on region growing, and an OPTICS method and a FLANN method based on clustering features.

6. The unmanned aerial vehicle route planning and controlling method based on GIS and BIM integration according to claim 5, wherein the steps of introducing the unmanned aerial vehicle parameters and the optimal flight path into the unmanned aerial vehicle control software to perform simulated flight and optimizing the simulated flight process to obtain the adjusted optimal flight path of the unmanned aerial vehicle comprise:

setting parameters of a flight chess on a GIS platform, and importing the optimal flight path into control software of the unmanned aerial vehicle to simulate flight;

and adjusting the most-available flight path based on the information of the detected flight attitude, the flight age and the like in the simulated flight process, and finally obtaining the adjusted optimal flight path.

7. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 6, wherein the adjusting the optimal flight path is used as an actual flight path of the unmanned aerial vehicle, and the real-time monitoring and adjusting are performed on the actual flight process of the unmanned aerial vehicle by utilizing GIS and BIM technology, and the method comprises the following steps:

performing actual flight based on the adjusted optimal flight path, and positioning and navigating by using a GPS and an inertial navigation system carried by the unmanned aerial vehicle;

and carrying out real-time monitoring and adjustment on the unmanned aerial vehicle by using GIS and BIM technologies so as to ensure that the unmanned aerial vehicle can fly according to the adjusted optimal flight path.

8. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration of claim 7, wherein the method further comprises:

and analyzing and evaluating the flight result by using a GIS technology, and simulating and optimizing the building by using a BIM technology, so as to further optimize the adjusted optimal flight path.

9. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration of claim 8, wherein the method further comprises:

and integrating the GIS, the BIM and the unmanned aerial vehicle control system to realize data sharing and cooperative work.

10. The unmanned aerial vehicle route planning and control method based on GIS and BIM integration according to claim 9, wherein the integration of GIS, BIM and unmanned aerial vehicle control system realizes data sharing and collaborative work, comprising:

through development interfaces and data exchange standards, data interaction and function coordination among the systems are realized;

through the functions of collaborative design, collaborative planning and the like, the working efficiency and the collaborative efficiency are improved.