Automatic network planning method for unmanned aerial vehicle base station in emergency scene

Abstract

The invention relates to the technical field of unmanned aerial vehicle communication, in particular to a method for automatic network planning of an unmanned aerial vehicle base station in an emergency scene, and the method specifically comprises the following steps: 1, collecting and preprocessing DEM data: collecting the DEM data of an emergency target field area, importing the DEM data into a geographic information system (GIS), and preprocessing the DEM data; 2, collecting and processing parameters of the unmanned aerial vehicle base station: collecting the parameters of the unmanned aerial vehicle base station, and structuring the parameters of the unmanned aerial vehicle base station by using an LLM (Language Language Model); and step 3, loading DEM data through a geographic information system (GIS) and designing an unmanned aerial vehicle base station position scheme. According to the method, the DEM data is used for carrying out topographic feature analysis on the emergency target field area, potential obstacles of signal propagation are identified, and the influence of the topography on the signal propagation is evaluated more accurately. A signal propagation model is perfected by combining unmanned aerial vehicle base station technical parameters and topographic features. The unmanned aerial vehicle base station adapts to a complex terrain environment, and the signal coverage capability and flexibility of the unmanned aerial vehicle base station are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle communication technology, and in particular to a method for automatic network planning of an unmanned aerial vehicle base station in an emergency scenario. Background Art

[0002] With the rapid development of wireless communication technology, the construction and optimization of communication infrastructure has become an indispensable part of modern society. In specific scenarios such as emergency communications, remote area coverage, and large-scale events, traditional fixed base stations are difficult to deploy quickly and flexibly due to their fixedness, long construction period, and high cost. These scenarios often require rapid response and temporary communication solutions to ensure the smooth flow of information and effective command in emergency situations. The emergence of drone-mounted base station technology provides an innovative solution to the above problems. Drone base stations have the advantages of rapid deployment, high flexibility, and relatively low cost. They can quickly reach hard-to-reach areas such as disaster areas, remote mountainous areas, or large-scale event sites, quickly establish communication networks, and provide necessary communication services. In addition, the flight altitude and mobility of drone base stations enable them to avoid ground obstacles and provide a wider signal coverage range.

[0003] However, drone base stations also face a series of challenges in practical applications. Complex terrain environments, such as mountains, hills, and urban buildings, will have a significant impact on the signal propagation of drone base stations. The undulations of the terrain and the obstruction of buildings will lead to signal attenuation, coverage blind spots, and decreased communication quality. In addition, the flight parameters of the drone base station, such as the flight altitude, hovering radius, and flight center point, also directly affect the signal coverage effect and communication quality. In order to overcome these challenges, researchers are exploring how to optimize the layout and flight parameters of drone base stations to achieve the best signal coverage effect. This involves accurate analysis of terrain data, establishment of signal propagation models, development of optimization algorithms, and simulation verification. At present, research methods based on digital elevation models (DEMs) have gradually become an important means to optimize the layout of drone base stations. DEM data can provide three-dimensional information of the terrain, helping researchers to more accurately evaluate the impact of terrain on signal propagation and optimize the deployment strategy of drone base stations accordingly.

[0004] In the prior art, the deployment of drone base stations is designed manually using ArcGIS software, which is inefficient and difficult to find the best solution. Drone base station technology has huge application potential, but its signal coverage problem in complex terrain environments needs to be solved urgently. The purpose of this invention is to optimize the layout and flight parameters of drone base stations through innovative technical means to improve their communication performance in complex terrain environments and meet the needs of modern society for efficient and flexible communication solutions. Summary of the invention

[0005] The present invention provides a method for automatic network planning of a UAV base station in an emergency scenario to achieve the purpose of improving the communication performance of the UAV base station in a complex terrain environment.

[0006] To achieve the above object, the technical solution of the present invention is to provide a method for automatic network planning of a UAV base station in an emergency scenario, and its innovation lies in: specifically comprising the following steps:

[0007] Step 1. Collect DEM data and pre-process it: collect DEM data of the emergency target site area and import it into the geographic information system (GIS), and pre-process the DEM data through the geographic information system (GIS);

[0008] Step 2. Collect and process the parameters of the drone base station: Collect the parameters of the drone base station and structure the parameters of the drone base station using the LLM large language model;

[0009] Step 3. Load DEM data through the Geographic Information System (GIS) and design the drone base station location plan:

[0010] S1. Terrain feature analysis: After DEM data is imported into the geographic information system (GIS), the terrain features of the emergency target site area are obtained through the three-dimensional model in the GIS, and the terrain obstacles that affect signal propagation are identified;

[0011] S2. Analysis of UAV base station characteristics: Considering the flight characteristics and signal transmission characteristics of the UAV, the signal conditions of each grid in the emergency target site area are calculated using a grid method to obtain the signal coverage model of the UAV base station;

[0012] S3. Signal propagation model construction: Select the signal propagation model of the UAV base station, combine DEM data and terrain features, introduce the terrain occlusion model to evaluate the signal propagation characteristics of the UAV at different flight altitudes, and consider the influencing factors including atmospheric conditions and building occlusion. According to different terrains, the AutoGen framework automatically selects one or more signal propagation models for calculation, and then automatically improves the signal propagation model;

[0013] S4. UAV base station layout and flight parameter optimization: Based on DEM data and signal propagation model, linear programming and genetic algorithm are used to determine the optimal number and location of UAV base stations;

[0014] S5. Construct an optimization objective function and complete network planning: Construct an optimization objective function and solve it through a linear programming solver to complete automatic network planning in the emergency scenario of the drone base station.

[0015] Furthermore, the sources of the DEM data of the emergency target site area collected in the step 1 include satellite remote sensing, aerial photogrammetry, and ground measurement; the collected DEM data include transmission power, antenna gain, frequency range, flight altitude, and flight time.

[0016] Furthermore, the preprocessing of DEM data in step 1 includes data cleaning, data denoising, and data interpolation.

[0017] Furthermore, the terrain features in S1 in step three include slope, slope aspect, and altitude; and the terrain obstacles include mountains and buildings.

[0018] Furthermore, the signal propagation model in S3 in step 3 includes

[0019] Hata model, Okumura model, COST 231 model, Walfisch-Ikegami model, Lee model, Kuo model, ITU-R P.526-13 model, SUI model, Kunz model, Hata-C model.

[0020] Furthermore, the signal propagation characteristics in S3 in step three include signal coverage, signal attenuation rate, signal strength and visibility between the drone base station and the receiving point.

[0021] Furthermore, the optimal position determined in S4 in step three includes the optimal flight altitude, circling radius and flight center point.

[0022] Furthermore, in step 3, S5 is used to complete the construction of the target optimization function, and the specific process of network planning is as follows:

[0023] A. Determine the optimization target, construct the optimization objective function, and set the threshold of the optimization target in the emergency target site area based on the signal coverage quality, number of drone base stations, energy consumption and deployment cost;

[0024] B. Setting constraints in the optimization objective function, including the maximum number of base stations or the maximum coverage or signal strength of each base station;

[0025] C. Use the linear programming library in Python as a solver to solve the optimization objective function;

[0026] D. Evaluate the effectiveness of the solution based on the solver output. When the solver outputs the optimal solution, the network planning of the drone base station is completed.

[0027] Furthermore, in the step D, scipy.optimize.linprog is specifically used as a solver to solve the optimization objective function.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] (1) The present invention uses DEM data to analyze the terrain characteristics of the emergency target site area, identify potential obstacles to signal propagation, and more accurately evaluate the impact of terrain on signal propagation. The signal propagation model is improved by combining the technical parameters of the drone base station and the terrain characteristics. The drone base station can adapt to complex terrain environments and enhance the signal coverage capability and flexibility of the drone base station.

[0030] (2) A terrain occlusion model is introduced to evaluate the signal propagation characteristics, including signal attenuation, and optimize the layout and flight parameters of the UAV base stations. This improves the signal coverage quality of the UAV base stations, reduces the number of base stations, and reduces deployment costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 The present invention is a flow chart of a method for automatic network planning of a UAV base station in an emergency scenario.

[0033] Figure 2-4 This is a diagram of the implementation results of Example 6 of the present invention (viewed from different sides).

[0034] Figure 5 This is a diagram (top view) of the implementation results in Example 6 of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Geographic Information System (GIS) is also known as "Geo-Science Information System". It is a specific and very important spatial information system. It is a technical system that collects, stores, manages, calculates, analyzes, displays and describes the geographical distribution data in the entire or part of the earth's surface (including the atmosphere) space with the support of computer hardware and software systems.

[0037] Drone base stations are 4G and 5G mobile signal base stations carried by drones, which can achieve mobile signal network coverage. Drones can be equipped with different types of mobile signal base stations, which are integrated together by hoisting or installation, referred to as drone base stations.

[0038] Example 1

[0039] This embodiment provides a method for automatic network planning of a drone base station in an emergency scenario, and the specific process is as follows: Figure 1 As shown, the specific steps include:

[0040] Step 1. Collect DEM data and pre-process it: Collect DEM data of the emergency target site area and import it into the Geographic Information System (GIS), and pre-process the DEM data through the Geographic Information System (GIS).

[0041] Among them, the sources of DEM data of the emergency target site area collected in step one include satellite remote sensing, aerial photogrammetry, and ground measurement; the collected DEM data include transmission power, antenna gain, frequency range, flight altitude, and flight time.

[0042] Step 2. Collect and process the parameters of the drone base station: Collect the parameters of the drone base station and use the LLM large language model to structure the parameters of the drone base station. Large language models including ChatGPT, Llama, Gemini, etc. all provide APIs for structured processing. The parameters of the drone base station will be described in the manual of the drone base station.

[0043] Step 3. Load DEM data through the Geographic Information System (GIS) and design the drone base station location plan:

[0044] S1. Terrain feature analysis: After the DEM data is imported into the geographic information system (GIS), the terrain features of the emergency target site area are obtained through the three-dimensional model in the geographic information system (GIS), and the terrain obstacles that may affect signal propagation are identified.

[0045] Among them, the terrain features in S1 include slope, aspect, and altitude; terrain obstacles include mountains and buildings.

[0046] S2. Analysis of UAV base station characteristics: Considering the flight characteristics and signal transmission characteristics of the UAV, a grid method is used to calculate the signal conditions of each grid in the emergency target site area to obtain the signal coverage model of the UAV base station; the flight characteristics and signal transmission characteristics of the UAV will be described in the UAV manual.

[0047] S3. Signal propagation model construction: Select the signal propagation model of the UAV base station, combine DEM data and terrain features, introduce the terrain occlusion model to evaluate the signal propagation characteristics of the UAV at different flight altitudes, and consider the influencing factors including atmospheric conditions and building occlusion. According to different terrains, the AutoGen framework automatically selects one or more signal propagation models for calculation, and then automatically improves the signal propagation model.

[0048] Among them, the signal propagation characteristics in S3 include signal coverage, signal attenuation rate, signal strength, and visibility between the drone base station and the receiving point.

[0049] S4. UAV base station layout and flight parameter optimization: Based on DEM data and signal propagation model, linear programming and genetic algorithm are used to determine the optimal number and location of UAV base stations. The optimal location determined in S4 includes the optimal flight altitude, hovering radius and flight center point.

[0050] S5. Construct an optimization objective function and complete network planning: Construct an optimization objective function and solve it through a linear programming solver to complete automatic network planning in the emergency scenario of the drone base station.

[0051] Example 2

[0052] In order to improve the data quality, the preprocessing of DEM data in step 1 of this embodiment includes data cleaning, data denoising, and data interpolation. The rest is the same as in embodiment 1.

[0053] Example 3

[0054] The signal propagation models in this embodiment include Hata model, Okumura model, COST 231 model (used to predict mobile communication signal propagation in cities and suburbs), Walfisch-Ikegami model (applicable to urban environments, especially signal propagation between high-rise buildings), Lee model (mainly used to predict mobile communication signal propagation in urban environments, especially in dense urban areas), Kuo model (applicable to predict mobile communication signal propagation in cities and suburbs, especially under different terrain and building density conditions), ITU-R P.526-13 model (used to predict mobile communication signal propagation in different environments (such as cities, suburbs, and villages)), SUI model (applicable to mobile communication signal propagation prediction in mountainous areas and complex terrains), Kunz model (used to predict indoor mobile communication signal propagation), Hata-C model (a variant of Hata model, used to more accurately predict mobile communication signal propagation in urban environments). When using, select a suitable signal propagation model according to the actual emergency target site.

[0055] The rest is the same as in Example 1.

[0056] Example 4

[0057] In step S5 of step three of this embodiment, the target optimization function is constructed, and the specific process of network planning is as follows:

[0058] A. Determine the optimization target, i.e., signal range, and construct the optimization objective function. Set the threshold of the optimization target within the emergency target site area based on the signal coverage quality, number of drone base stations, energy consumption, and deployment cost;

[0059] B. Setting constraints in the optimization objective function, including the maximum number of base stations or the maximum coverage or signal strength of each base station;

[0060] C. Use the linear programming library in Python as a solver to solve the optimization objective function;

[0061] D. Evaluate the effectiveness of the solution based on the solver output. When the solver outputs the optimal solution, that is, the optimal flight altitude, hovering radius and flight center point are obtained, the network planning of the UAV base station is completed.

[0062] The rest is the same as in Example 1.

[0063] Example 5

[0064] In this embodiment, scipy.optimize.linprog is used as a solver to solve the optimization objective function. The rest is the same as in Embodiment 4.

[0065] Example 6

[0066] This embodiment implements the automatic network planning simulation in a specific emergency scenario based on the above-mentioned embodiments 1-5. The emergency target site of this embodiment is a natural disaster site. In the case of power outage, circuit breaker and network outage (three outages), the specific terrain range of the simulation implementation in this embodiment is x_range=(0,1000), y_range=(0,1000).

[0067] Step 1. Collect DEM data and preprocess it: The UAV flies over the emergency target site to collect DEM data of the emergency target site area, and imports it into the geographic information system (GIS) for modeling. The DEM data is preprocessed through the geographic information system (GIS) for data cleaning, data denoising, and data interpolation.

[0068] Step 2. Collect and process the parameters of the drone base station: Collect the parameters of the drone base station and use the LLM large language model to structure the parameters of the drone base station. Large language models including ChatGPT, Llama, Gemini, etc. all provide APIs for structured processing. The parameters of the drone base station will be described in the manual of the drone base station.

[0069] Step 3. Load DEM data through the Geographic Information System (GIS) and design the drone base station location plan:

[0070] S1. Terrain feature analysis: After the DEM data is imported into the geographic information system (GIS), the terrain features of the emergency target site area are obtained through the three-dimensional model in the geographic information system (GIS), and terrain obstacles that may affect signal propagation are identified, including mountains, hills or buildings.

[0071] S2. Analysis of UAV base station characteristics: Considering the flight characteristics and signal transmission characteristics of the UAV, a grid method is used to calculate the signal conditions of each grid in the emergency target site area to obtain the signal coverage model of the UAV base station; the flight characteristics and signal transmission characteristics of the UAV will be described in the UAV manual.

[0072] Among them, the position of the drone base station in the emergency target site area is (500, 500, 100) (unit: m), and the maximum distance of signal propagation of the drone base station is 500m.

[0073] S3. Construction of signal propagation model: In this simulation implementation, the signal propagation model of the UAV base station is selected as the SUI model. Combined with DEM data and terrain features, the terrain occlusion model is introduced to evaluate the signal propagation characteristics of the UAV at different flight altitudes, and the influencing factors including atmospheric conditions and building occlusion are considered. The signal propagation model is calculated through the AutoGen framework according to different terrains, and then the signal propagation model is automatically improved.

[0074] Among them, the signal propagation characteristics in S3 include signal coverage, signal attenuation rate, signal strength, and visibility between the drone base station and the receiving point.

[0075] S4. UAV base station layout and flight parameter optimization: Based on DEM data and signal propagation model, linear programming and genetic algorithm are used to determine the optimal number and location of UAV base stations. The optimal location determined in S4 includes the optimal flight altitude, hovering radius and flight center point.

[0076] S5. Construct an optimization objective function and complete network planning: Construct an optimization objective function and solve it through a linear programming solver to complete automatic network planning in the emergency scenario of the drone base station.

[0077] A. Determine the optimization target, i.e., signal strength, and construct an optimization target function. According to the signal coverage quality, the number of drone base stations, energy consumption, and deployment cost, set the threshold of the optimization target within the emergency target site area, i.e., the range of signal strength. In this embodiment, the setting range of signal strength is 10db-100db;

[0078] B. Setting constraints in the optimization objective function, including the maximum number of base stations or the maximum coverage or signal strength of each base station; the constraint set in this embodiment is to set the signal strength to the minimum value, i.e., 10db;

[0079] C. In this simulation implementation, scipy.optimize.linprog in the linear programming library in Python is used as a solver to solve the optimization objective function;

[0080] D. Evaluate the effectiveness of the solution based on the output of the solver. When the solver outputs the optimal solution, that is, the optimal flight altitude, hovering radius and flight center point are obtained, the network planning of the UAV base station is completed. In this embodiment, the optimal flight altitude is 75.36m, the optimal hovering radius is 700.04m, and the position of the optimal flight center point is (495, 495, 75.36) (unit: m). The optimal flight center point refers to the center point of the base station when it runs at the optimal hovering radius.

[0081] The above simulation implementation results are presented in charts, such as Figure 2-5 As shown, Figure 2-4 For side views from different directions, Figure 5 The result is a bird's-eye view. The dotted circle in the figure is the optimal trajectory of the planned UAV base station. The base station can get the optimal signal strength distribution at the emergency target site when it runs on this trajectory. Figure 2-5 The base station is based on the flight operation of drones and is in constant operation. Figure 2-5 The drone base station is not shown.

[0082] The embodiments described above are merely descriptions of preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering technicians in the field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the technical requirements.

Claims

1. A method for automatic network planning of drone base stations in emergency scenarios, characterized in that: The specific steps include: Step 1. Collect DEM data and pre-process it: collect DEM data of the emergency target site area and import it into the geographic information system (GIS), and pre-process the DEM data through the geographic information system (GIS); Step 2. Collect and process the parameters of the drone base station: Collect the parameters of the drone base station and structure the parameters of the drone base station using the LLM large language model; Step 3. Load DEM data through the Geographic Information System (GIS) and design the drone base station location plan: S1. Terrain feature analysis: After DEM data is imported into the geographic information system (GIS), the terrain features of the emergency target site area are obtained through the three-dimensional model in the GIS, and the terrain obstacles that affect signal propagation are identified; S2. Analysis of UAV base station characteristics: Considering the flight characteristics and signal transmission characteristics of the UAV, the signal conditions of each grid in the emergency target site area are calculated using a grid method to obtain the signal coverage model of the UAV base station; S3. Signal propagation model construction: Select the signal propagation model of the UAV base station, combine DEM data and terrain features, introduce the terrain occlusion model to evaluate the signal propagation characteristics of the UAV at different flight altitudes, and consider the influencing factors including atmospheric conditions and building occlusion. According to different terrains, the AutoGen framework automatically selects one or more signal propagation models for calculation, and then automatically improves the signal propagation model; S4. UAV base station layout and flight parameter optimization: Based on DEM data and signal propagation model, linear programming and genetic algorithm are used to determine the optimal number and location of UAV base stations; S5. Construct an optimization objective function and complete network planning: Construct an optimization objective function and solve it through a linear programming solver to complete automatic network planning in the emergency scenario of the drone base station.

2. According to claim 1, a method for automatic network planning of a drone base station in an emergency scenario is characterized by: The sources of the DEM data of the emergency target site area collected in the step 1 include satellite remote sensing, aerial photogrammetry, and ground measurement; the collected DEM data include transmission power, antenna gain, frequency range, flight altitude, and flight time.

3. The method for automatic network planning of a drone base station in an emergency scenario according to claim 2 is characterized in that: The preprocessing of DEM data in step 1 includes data cleaning, data denoising and data interpolation.

4. The method for automatic network planning of a drone base station in an emergency scenario according to claim 1 is characterized in that: The terrain features in S1 in step 3 include slope, slope direction, and altitude; terrain obstacles include mountains and buildings.

5. The method for automatic network planning of a drone base station in an emergency scenario according to claim 1 is characterized in that: The signal propagation models in S3 in the step three include Hata model, Okumura model, COST 231 model, Walfisch-Ikegami model, Lee model, Kuo model, ITU-R P.526-13 model, SUI model, Kunz model, and Hata-C model.

6. The method for automatic network planning of a drone base station in an emergency scenario according to claim 1 is characterized in that: The signal propagation characteristics in S3 of step three include signal coverage, signal attenuation rate, signal strength, and visibility between the drone base station and the receiving point.

7. The method for automatic network planning of a drone base station in an emergency scenario according to claim 1 is characterized in that: The optimal position determined in S4 in step three includes the optimal flight altitude, circling radius and flight center point.

8. The method for automatic network planning of a drone base station in an emergency scenario according to claim 1 is characterized in that: In step 3, S5 is used to complete the construction of the target optimization function. The specific process of network planning is as follows: A. Determine the optimization target, construct the optimization objective function, and set the threshold of the optimization target in the emergency target site area based on the signal coverage quality, number of drone base stations, energy consumption and deployment cost; B. Setting constraints in the optimization objective function, including the maximum number of base stations or the maximum coverage or signal strength of each base station; C. Use the linear programming library in Python as a solver to solve the optimization objective function; D. Evaluate the effectiveness of the solution based on the solver output. When the solver outputs the optimal solution, the network planning of the drone base station is completed.

9. The method for automatic network planning of a drone base station in an emergency scenario according to claim 8, characterized in that: In the step D, scipy.optimize.linprog is specifically used as a solver to solve the optimization objective function.

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