CN-121981351-A - Unmanned aerial vehicle nest position planning method, system and medium based on communication iron tower

CN121981351ACN 121981351 ACN121981351 ACN 121981351ACN-121981351-A

Abstract

A method, a system and a medium for planning the nest position of an unmanned aerial vehicle based on a communication iron tower relate to the field of unmanned aerial vehicles. The application realizes the system integration and the optimized utilization of urban infrastructure resources, reduces the deployment cost of the aircraft nest, improves the scientificity and the feasibility of the site selection scheme, ensures the coverage efficiency and the operation reliability of the unmanned aerial vehicle nest network through multi-objective optimization and simulation verification, and provides reliable infrastructure support for the application of the urban unmanned aerial vehicle.

Inventors

ZHONG ZHICHENG
Wu Zhuyi
LU JUNTONG
CHEN WENXIONG
PAN SHIHUI
XU BAIHONG
GUO SHUAI
LIN GUANGQI

Assignees

中通服中睿科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The unmanned aerial vehicle nest position planning method based on the communication iron tower is characterized by comprising the following steps of: Constructing a comprehensive database, integrating the spatial positions, structural parameters, power supply capacity, communication interfaces and maintenance accessibility information corresponding to four facilities of a communication tower, a power tower, a street lamp pole and a building roof, and screening candidate facility nodes meeting preset conditions; Based on the candidate facility nodes, constructing a multi-objective optimization model by combining the flight radius of the unmanned aerial vehicle, the coverage requirement of a task area and the distribution of obstacles; solving the multi-objective optimization model by adopting a self-adaptive genetic algorithm, and outputting the optimal location scheme; And carrying out three-dimensional simulation verification on the site selection scheme, simulating the taking-off and landing processes of the unmanned aerial vehicle, the flight of the route and the communication relay process, and carrying out dynamic optimization on the position of the machine nest, the number of the machine nest and the task path according to simulation results.
2. The method of claim 1, wherein the multi-objective optimization model comprises an objective function that minimizes overall cost of nest deployment, maximizes task area coverage and redundancy, maximizes network service reliability, and is constrained by the number of nests, radius of service, and budget.
3. The method according to claim 1, wherein the multi-objective optimization model is solved by adopting an adaptive genetic algorithm, and a most preferred address scheme is output, and the method specifically comprises the following steps: Randomly generating an initial machine nest site selection scheme to obtain an initial population, and calculating the fitness value of the initial population to obtain an evaluation result; Genetic operation is carried out on the evaluation result, an optimized candidate scheme is generated, and dynamic adjustment is carried out on the candidate scheme, so that a new generation population is obtained; Judging whether the new generation population meets the convergence condition, and if so, outputting the optimal address scheme.
4. A method according to claim 3, wherein the adaptive genetic algorithm incorporates an anti-interference assessment mechanism in the solution process, the dynamic constraints of which include electromagnetic interference, meteorological conditions, airspace regulations and structural safety factors.
5. The method of claim 1, wherein the site selection scheme is subjected to three-dimensional simulation verification, unmanned aerial vehicle take-off and landing, route flight and communication relay processes are simulated, and the position of the aircraft nest, the number of the aircraft nest and the task path are dynamically optimized according to simulation results, and the method specifically comprises the following steps: constructing a scene environment and an obstacle model according to the geographical information of the task area and the building distribution to obtain a three-dimensional scene model; Planning the unmanned aerial vehicle route track according to the three-dimensional scene model and the airspace control requirement, and obtaining an initial flight path; configuring communication link parameters and monitoring indexes according to the initial flight path and network coverage requirements to generate a simulation test scheme; performing state evaluation and performance analysis on the simulation test scheme, and calculating to obtain a system operation index; And according to the system operation index, adjusting the position layout of the aircraft nest, the flight airspace, the communication parameters and the task path, and determining a final deployment scheme.
6. The method according to claim 1, characterized in that the building of the integrated database comprises in particular the following steps: collecting communication tower position and structure data in a geographic information system, and obtaining a communication facility layer; reading power tower distribution and power supply parameters in a power system to obtain a power supply resource layer; space coordinates of urban street lamp poles and building roofs are obtained, and municipal facility layers are formed; and integrating the communication facility layer, the power supply resource layer and the municipal facility layer to generate a candidate facility information base.
7. The method according to claim 1, characterized in that the method further comprises the steps of: Constructing a machine nest network digital twin system and accessing real-time monitoring equipment to obtain running state data and environmental parameters of a physical entity; according to the running state data and the environmental parameters, a multi-dimensional evaluation index system comprising network coverage rate, service quality, energy efficiency and safety risk is established, and a network performance evaluation result is obtained; identifying performance degradation nodes and potential risk areas based on the network performance evaluation result, and determining a network optimization target and an adjustment suggestion; And calculating a resource reallocation scheme and a deployment adjustment strategy according to the network optimization target and the adjustment proposal to obtain an updated machine nest network deployment scheme.
8. Unmanned aerial vehicle nest position planning system based on communication tower, its characterized in that, the system includes: One or more processors and memory coupled with the one or more processors, the memory to store computer program code, the computer program code comprising computer instructions that the one or more processors invoke to cause the system to perform the method of any of claims 1-7.
9. A computer readable storage medium comprising instructions which, when run on a system, cause the system to perform the method of any of claims 1-7.
10. A computer program product, characterized in that the computer program product, when run on a system, causes the system to perform the method according to any of claims 1-7.

Description

Unmanned aerial vehicle nest position planning method, system and medium based on communication iron tower Technical Field The application belongs to the field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle nest position planning method, system and medium based on a communication tower. Background With the rapid development of unmanned aerial vehicle technology, the application scenes such as express delivery, emergency rescue, city inspection and the like based on unmanned aerial vehicles are increasing. In order to ensure the continuous operation capability of the unmanned aerial vehicle, an unmanned aerial vehicle nest needs to be arranged in a task area to serve as a take-off, landing and supply base station. However, in the urban complex environment, the newly built special machine nest facility is high in cost, and the problems of high site selection difficulty, long approval period and the like are faced. In the related art, a scheme for deploying unmanned aerial vehicle nests on the roof of an existing building is proposed, such as taking off, landing and charging supply of unmanned aerial vehicles are realized by installing a standardized nest module on the roof, or movable nest units are adopted and flexibly arranged on the tops of different buildings according to task requirements. The technical scheme also provides that a small-sized machine nest is arranged on the urban street lamp pole, and an unmanned aerial vehicle supply network with low cost is constructed by modifying the street lamp pole structure and accessing into the urban power system. However, the prior art scheme lacks system integration and comprehensive evaluation of infrastructure resources, the problems of scattered property rights and great transformation difficulty exist in the building roof scheme, a unified networked management system is difficult to form, the lamp pole scheme is limited by bearing capacity and power supply conditions, the use requirement of a medium-sized and large-sized unmanned aerial vehicle is difficult to meet, and the situation needs to be further improved. Disclosure of Invention The application provides a communication iron tower-based unmanned aerial vehicle nest position planning method, a communication iron tower-based unmanned aerial vehicle nest position planning system and a communication iron tower-based unmanned aerial vehicle nest position planning medium, which are used for solving the technical problems of high unmanned aerial vehicle nest deployment cost and high site selection difficulty in urban environments. According to the method, the existing infrastructure resources are integrated by constructing a comprehensive database, the aircraft nest position planning is performed by adopting a multi-objective optimization model, and the feasibility and the optimization effect of a scheme are ensured by combining a self-adaptive genetic algorithm and three-dimensional simulation verification, so that the efficient deployment and the networked management of the unmanned aerial vehicle aircraft nest are realized. In a first aspect, the application provides a method for planning the position of an unmanned aerial vehicle nest based on a communication tower, which comprises the following steps: Constructing a comprehensive database, integrating the spatial positions, structural parameters, power supply capacity, communication interfaces and maintenance accessibility information corresponding to four facilities of a communication tower, a power tower, a street lamp pole and a building roof, and screening candidate facility nodes meeting preset conditions; Based on the candidate facility nodes, constructing a multi-objective optimization model by combining the flight radius of the unmanned aerial vehicle, the coverage requirement of a task area and the distribution of obstacles; solving the multi-objective optimization model by adopting a self-adaptive genetic algorithm, and outputting the optimal location scheme; And carrying out three-dimensional simulation verification on the site selection scheme, simulating the taking-off and landing processes of the unmanned aerial vehicle, the flight of the route and the communication relay process, and carrying out dynamic optimization on the position of the machine nest, the number of the machine nest and the task path according to simulation results. By adopting the technical scheme, the application integrates the existing infrastructure resources by constructing the comprehensive database, performs site selection planning based on the multi-objective optimization and the self-adaptive genetic algorithm, and performs dynamic tuning by combining with three-dimensional simulation verification, thereby realizing system integration and optimization utilization of the urban infrastructure resources, reducing the deployment cost of the aircraft nest, improving the scientificity and feasibility of the site selection scheme,