Mobile network rapid deployment method applied to network-free environment

Abstract

The invention discloses a mobile network rapid deployment method applied to a network-free environment, and relates to the technical field of network deployment, and the method comprises the steps: collecting a position which can be set by an unmanned plane when a mobile network is deployed in a target region; obtaining the maximum coverage range of the unmanned aerial vehicle at each position, taking the total coverage range of the unmanned aerial vehicle as a target function, and obtaining an unmanned aerial vehicle position deployment scheme by adopting a particle swarm optimization algorithm; calculating a signal coverage rate by combining the total coverage range of the unmanned aerial vehicle with the target area range, obtaining a signal strength indication coefficient according to the unmanned aerial vehicle deployment position and the unmanned aerial vehicle parameters, and calculating a signal evaluation coefficient by combining the signal coverage rate; virtual force between the unmanned aerial vehicles is obtained, a deployment evaluation coefficient is calculated according to the virtual force between the unmanned aerial vehicles and the signal evaluation coefficient, the deployment evaluation coefficient and a preset evaluation threshold value are compared and analyzed, and the deployment positions of the unmanned aerial vehicles are updated according to a comparison and analysis result; and rapid deployment of the mobile network based on the unmanned aerial vehicle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of network deployment, and more specifically, to a method for rapid deployment of a mobile network in a networkless environment. Background Art

[0002] In industrial environments, a network-free environment brings many disadvantages and challenges, affecting production efficiency, data management and overall operations. A network-free environment makes it impossible to collect and access production data in real time, limiting the monitoring of equipment performance and production processes. Data between various production links cannot be shared, resulting in insufficient decision-making basis, which may affect production efficiency and quality. Many modern industrial equipment rely on the Internet for remote monitoring and management. The lack of a network will make these functions unusable. In a network-free environment, it is difficult to quickly adjust production plans and configurations, affecting the flexibility and responsiveness of the production line. There is a lack of effective communication channels between the production team and management, and the information transmission efficiency is low, affecting the decision-making process. When problems arise during the production process, it is impossible to quickly contact technical support or other departments, delaying the time to solve the problem. When equipment fails, it is impossible to obtain remote technical support through the network, which may lead to extended downtime. The maintenance team cannot monitor the equipment status in real time, making it difficult to detect potential faults in time, increasing the risk of equipment maintenance. Slow information transmission and untimely data updates may cause the production plan to fail to execute smoothly, resulting in delays. Lack of automation and intelligent support, more reliance on manual intervention, increased labor costs and the probability of error.

[0003] Deficiencies of existing technologies: The networkless environment will bring many challenges in the industry, from data management to production efficiency, which may be restricted. In order to improve production efficiency and safety, industrial enterprises usually invest in network infrastructure to ensure a higher level of automation and intelligent operation. Using drones to carry base stations or communication equipment to quickly provide network coverage in emergency situations can solve the networkless problem in industrial production. The present invention provides a method for rapid deployment of mobile networks in a networkless environment to achieve rapid deployment of mobile networks based on drones.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for rapid deployment of a mobile network in a networkless environment, which solves the problems raised in the above-mentioned background technology by rapid deployment of a mobile network based on drones.

[0006] To achieve the above object, the present invention provides the following technical solutions: The invention discloses a method for rapid deployment of a mobile network in a network-free environment, comprising the following steps: collecting positions that can be set by drones when deploying a mobile network in a target area; obtaining the maximum coverage of drones at each position, taking the total coverage of the drones as an objective function, and adopting a particle swarm optimization algorithm to obtain a deployment plan for the drone positions; calculating the signal coverage rate by combining the total coverage of the drones with the range of the target area, obtaining a signal strength indication coefficient according to the deployment position of the drone and the parameters of the drone, and calculating a signal evaluation coefficient in combination with the signal coverage rate; obtaining a virtual force between the drones, calculating a deployment evaluation coefficient according to the virtual force between the drones and the signal evaluation coefficient, performing a comparative analysis between the deployment evaluation coefficient and a preset evaluation threshold, and updating the deployment position of the drone according to the comparative analysis result.

[0007] In a preferred embodiment, the method for obtaining the position that the drone can set is as follows: obtain the coordinates of the target area, grid the target area, and obtain each grid center in the target area, and the grid center is the position that the drone can set.

[0008] In a preferred embodiment, the total coverage range acquisition process of the drone is as follows: according to the defined coverage area and grid size, a two-dimensional array is generated to represent the grid; each grid cell is represented by a Boolean value (0 or 1), 1 indicates that the cell is covered by at least one drone, and 0 indicates that it is not covered; the position of each drone is traversed to calculate the grid cells within the coverage range of the drone; it is checked whether the center point of each grid cell is within the coverage range of the drone, if so, the grid cell is marked as covered and recorded as 1; the two-dimensional array representing the grid is traversed, and the number of cells with a statistical value of 1, that is, the number of grid cells covered by at least one drone; the total coverage area is calculated according to the grid size and the number of covered grid cells.

[0009] In a preferred embodiment, the specific process of using the particle swarm optimization algorithm to obtain the drone position deployment plan is as follows: randomly generate a group of particle positions and initial speeds, and calculate the initial fitness value, the fitness value is the total coverage range of the drone, set the initial position of the particle as the individual extreme position of each particle; traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position; for each particle, update its own position according to its current speed and position, calculate the fitness value of the new position, and update the individual optimal position and global optimal position of the particle based on the fitness value; when the preset number of iterations is reached, stop the iteration, and the particle updated position is the drone deployment position.

[0010] In a preferred embodiment, the specific method for obtaining the signal strength indication coefficient is as follows: The UAV deployment height is obtained according to the UAV deployment position, and the path loss data is obtained by combining the path loss index and the reference loss. The UAV signal transmission power, transmitting antenna gain and receiving antenna gain are obtained according to the UAV parameters. The UAV signal transmission power, transmitting antenna gain and receiving antenna gain are weighted and summed to obtain the UAV signal index. The UAV signal index is subtracted from the path loss data to obtain the signal strength indication coefficient.

[0011] In a preferred embodiment, the method for obtaining the virtual force between the drones is as follows: the virtual force between the drones includes gravity and repulsion. According to Newton's law of universal gravitation, the gravity between the two drones is calculated, and then the total gravity is summed up; the distance between the two drones is calculated, and the unit distance vector between the drones is obtained according to the coordinates of the two drones combined with the distance between the drones. The distance between the drones and the unit distance vector are combined with the repulsion coefficient according to a preset repulsion formula to calculate the repulsion between each pair of drones, and the repulsion between each pair of drones is weighted and summed up to obtain the total repulsion; the total repulsion is subtracted from the total gravity to obtain the virtual force between the drones.

[0012] In a preferred embodiment, the specific process of performing comparative analysis based on the deployment evaluation coefficient and the preset evaluation threshold and updating the deployment position of the drone based on the comparative analysis result is as follows: The deployment evaluation coefficient is compared with the preset evaluation threshold. When the deployment evaluation coefficient is greater than the preset evaluation threshold, the UAV position deployment plan is executed; when the deployment evaluation coefficient is less than the preset evaluation threshold, the particle swarm optimization algorithm is used again to obtain the UAV position deployment plan, and the deployment evaluation coefficient is calculated, and a secondary comparative analysis is performed with the preset evaluation threshold until the deployment evaluation coefficient is greater than the preset evaluation threshold and the iteration is stopped. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a structural schematic diagram of the method for rapid deployment of a mobile network in a networkless environment according to the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] Embodiment 1, Figure 1 The invention provides a method for rapid deployment of a mobile network in a network-free environment.

[0016] S10, collecting positions that the drone can set when deploying the mobile network in the target area; The method for obtaining the position where the drone can be deployed is as follows: First, it is necessary to determine the geographic location of the target area and obtain the geographic coordinates of the area. The coordinates are usually expressed in the form of longitude and latitude, which can be obtained through GPS equipment, geographic information system (GIS) or other positioning technologies; Grid the acquired target area. Gridding means dividing the target area into several small, uniform grid cells. Select a suitable grid size. The smaller the grid size, the higher the calculation accuracy, but the amount of calculation will also increase. Usually, it is reasonable to select a scale related to the coverage of the drone as the grid size. Set the drones to the same height. For each grid cell, the coordinates of its center point are calculated using a simple geometric formula. For a square grid, for example, if the coordinates of the lower left corner of a grid cell are , the coordinates of the upper right corner are , The grid center coordinates are: ; All calculated grid center points are the locations where drones can be deployed. These locations should meet the conditions for drones to take off, land, operate and return safely. Factors to consider include: Spatial obstacles: Make sure there are no buildings, trees or other obstacles around the center point; Flight safety: Choose the appropriate altitude and area to ensure flight safety and avoid interference or risks; Task adaptability: Consider the task type and choose the best location for monitoring, shooting, delivery, etc.

[0017] When deploying mobile networks via drones, meshing the target range has several significant benefits. This approach not only improves the efficiency and effectiveness of network deployment, but also helps optimize resource utilization. The following are the main benefits of meshing: 1. Accurate positioning and coverage Uniform coverage: By dividing the target area into small grids, it can ensure that each grid can be fully covered, which helps to avoid blind spots and improve overall signal quality and network coverage; Precise deployment: Each grid center can serve as an ideal deployment point for drones to conduct signal testing and tuning, ensuring that the network can operate normally in every key area; 2. Resource optimization Efficient resource allocation: Gridding allows resource allocation to be adjusted based on demand. By analyzing network traffic and usage of each grid, more drones can be deployed in high-demand areas. Save time and cost: Proper planning and organization of the grid can reduce deployment and maintenance time and reduce operating costs; 3. Flexibility and adaptability Rapid response: When mobile network demand changes or network failures occur, the grid in the affected area can be quickly assessed and drone deployment can be quickly adjusted to improve emergency response capabilities; Adapt to environmental changes: As the environment changes (such as weather and terrain), the grid approach can help quickly identify and adapt to new deployment requirements; 4. Data Collection and Analysis Systematic data collection: After gridding, the network performance of each grid (such as signal strength, bandwidth, etc.) can be systematically collected and analyzed, which is helpful for network optimization; Fault location: By monitoring the performance of each grid, the problem area can be quickly located for troubleshooting and repair; 5. Planning and simulation Visual planning: After the area is gridded, the network coverage can be visualized in the Geographic Information System (GIS), providing an intuitive reference for network planning; Simulation and testing: In the grid area, different network configurations can be simulated to evaluate the best deployment plan and improve the scientific nature of decision-making; 6. Improve user experience Optimize network performance: Through refined management and rapid response, users' network experience in specific areas can be significantly improved; Meeting different needs: Customized services can be provided based on the user needs and traffic characteristics of each grid to improve customer satisfaction; 7. Simplify the operation process Clear responsibilities: Gridding can clearly divide the operation area, facilitate the formulation of specific work plans and division of responsibilities, and improve the efficiency of team collaboration; Standardized operations: Based on the characteristics of the grid, unified operation standards and processes are formulated to improve the efficiency of drone deployment and management; Meshing the target range has significant advantages in the deployment of drone mobile networks. It not only improves network coverage and performance, but also optimizes resource allocation, enhances flexibility and emergency response capabilities. This systematic approach can help operators better respond to dynamically changing network needs and ultimately improve users' network experience.

[0018] S20, obtaining the maximum coverage of the drones at each position, taking the total coverage of the drones as the objective function, and using the particle swarm optimization algorithm to obtain the drone position deployment plan; The coverage of the drone is expressed as a circular area, and the area calculation formula of the maximum coverage of the drone is as follows: ; In the formula, S is the area of ​​the maximum coverage of the drone, h is the flight altitude of the drone, and FOV is the field of view of the known drone sensor; Based on the defined coverage area and grid size, a two-dimensional array is generated to represent the grid; each grid cell can be represented by a Boolean value (0 or 1), 1 means that the cell is covered by at least one drone, and 0 means it is not covered; Traverse the position of each drone and calculate the grid cells within the coverage range of the drone; check whether the center point of each grid cell is within the coverage range of the drone. If the distance is less than or equal to the radius of the coverage range, the grid cell is marked as covered and recorded as 1; Traverse the grid and count the number of cells with a value of 1, that is, the number of grid cells covered by at least one drone; calculate the total coverage area based on the grid size and the number of covered grid cells; the calculation formula for the total coverage area is as follows: ; Where Z is the total coverage area, N is the number of grid cells covered by at least one drone, and L is the grid side length; The objective function is the total coverage of the drone; The particle swarm optimization algorithm steps are as follows: First, a set of particle positions and initial velocities are randomly generated, and the initial fitness value is calculated. The fitness value is the total coverage of the drone, and the initial position of the particle is set to the individual extreme position of each particle. Traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position; For each particle, update its position according to its current speed and position, calculate the fitness value of the new position, and update the individual best position and global best position of the particle based on the fitness value; The specific formula for updating the particle speed and position is as follows: ; ; In the formula, and Represents particles exist In the The velocity and position at the iteration, is the inertia weight, and is the learning factor, and is a random number between 0 and 1, Represents particles In the The individual extreme value position at the iteration, Indicates that the entire particle group is The global extremum position at the iteration; When the preset number of iterations is reached, the iteration is stopped, and the particle update position is the UAV deployment position; The core idea of ​​the particle swarm optimization algorithm is to represent the potential solutions of the problem as "particles". These particles search for the optimal solution by updating their speed and position in the search space. Each particle has two important properties: Position: represents the current solution; Speed: controls the direction and amplitude of particle movement in the search space; Each particle adjusts its speed and position based on two things: Individual optimal solution: the best position found by the particle itself in history; Global optimal solution: the best position found in the entire particle swarm; The global optimal solution is the particle update position; Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. Inspired by the foraging behavior of bird flocks, the use of PSO in the UAV location deployment solution has the following significant benefits: 1. Global optimization capability The PSO algorithm can effectively avoid falling into the local optimal solution by simulating particles moving in the search space and updating their positions, which enables it to find a better global solution in complex UAV location deployment problems; 2. Simple and easy to implement The PSO algorithm has a simple mathematical model and an easy-to-understand algorithm framework, which is suitable for rapid implementation and debugging, which is very important for the rapid development and deployment of UAV applications, especially in time-sensitive situations; 3. Strong adaptability The particle swarm optimization algorithm is highly adaptable to the problem and can flexibly adjust the algorithm parameters to meet different UAV deployment requirements, such as coverage, energy consumption, and task priority. 4. Parallel processing capabilities PSO particles can work in parallel during the search process, which is suitable for implementation in multi-core processors or distributed computing environments. This parallelism can accelerate the optimization process and improve computational efficiency, especially in large-scale UAV networks; 5. A few parameter adjustments Compared with other optimization algorithms, PSO requires fewer adjustment parameters, usually only a few simple parameters (such as the number of particles, maximum number of iterations, etc.), which makes it more convenient in practical applications and reduces dependence on expert knowledge; 6. Dynamic Environmental Adaptation UAV applications usually need to operate in a dynamic environment. The PSO algorithm can adapt to changes in the environment and adjust the deployment plan in time to cope with changing needs and conditions by updating the position and velocity of particles in real time. 7. Multi-objective optimization In the deployment of drones, multiple objectives may need to be considered simultaneously (such as coverage, energy efficiency, and mission success rate). PSO can achieve multi-objective optimization and find the best deployment plan by designing appropriate fitness functions. 8. Combining with other algorithms PSO can be mixed with other optimization algorithms (such as genetic algorithm, ant colony algorithm, etc.) to form a hybrid optimization algorithm to further improve the optimization effect and efficiency. This flexibility enables it to better cope with complex UAV deployment problems; 9. Visualization and Analysis The operation process of the PSO algorithm is easy to visualize, and the particle search process and optimization process can be displayed through a graphical interface, which provides developers and decision makers with an intuitive analysis tool to facilitate understanding and improving deployment strategies; In summary, the application of particle swarm optimization algorithm in the UAV location deployment scheme provides an effective solution to improve the overall performance and efficiency of the UAV system due to its global optimization ability, easy implementation characteristics and adaptability to complex environments.

[0019] S30, calculating the signal coverage rate by combining the total coverage range of the drone with the range of the target area, obtaining the signal strength indication coefficient according to the deployment position of the drone and the parameters of the drone, and calculating the signal evaluation coefficient by combining the signal coverage rate; The signal coverage rate is obtained as follows: the total coverage area of ​​the drone is divided by the target area to obtain the signal coverage rate F. If the division result is greater than 1, the signal coverage rate is also recorded as 1. The specific steps of obtaining the signal strength indication coefficient are as follows: The deployment height of the UAV is obtained according to the deployment position of the UAV, and the path loss data is obtained by combining the path loss index and the reference loss; the UAV signal transmission power, the transmitting antenna gain and the receiving antenna gain are obtained according to the UAV parameters, and the UAV signal transmission power, the transmitting antenna gain and the receiving antenna gain are weighted and summed to obtain the UAV signal index, and the UAV signal index is subtracted from the path loss data to obtain the signal strength indication coefficient; The specific calculation formula of the signal strength indication coefficient is as follows: ; Where R is the signal strength indicator coefficient, P is the UAV signal transmission power, T is the transmitting antenna gain, J is the receiving antenna gain, h is the UAV deployment height, n is the path loss index, and k is the reference loss; The signal evaluation coefficient is calculated based on the signal strength indication coefficient and the signal coverage rate. The specific calculation formula of the signal evaluation coefficient is as follows: ; Where Z is the signal evaluation coefficient, R is the signal strength indication coefficient, and F is the signal coverage rate; The benefits of calculating signal evaluation coefficients in the deployment of mobile networks by unmanned aerial vehicles (UAVs) are mainly reflected in optimizing network performance, improving user experience, and supporting decision making. The following are some specific benefits and their explanations: 1. Optimize signal coverage Signal strength and quality assessment: By calculating signal assessment coefficients such as signal-to-noise ratio (SNR), received signal strength indication (RSSI), etc., the effectiveness of network coverage after drone deployment can be evaluated; Area selection: Signal evaluation results can help operators determine the best drone flight path and deployment altitude to ensure maximum coverage and minimum signal blind spots; 2. Dynamic network adjustment Real-time signal monitoring: UAVs can monitor network signal strength and quality in real time, collect data in a timely manner, and dynamically adjust network parameters (such as power, frequency, etc.) to adapt to environmental changes and user needs; Intelligent decision-making: Based on the signal evaluation coefficient, the system can perform intelligent analysis, achieve adaptive optimization, and improve network performance; 3. Improve user experience User access optimization: By analyzing the signal evaluation coefficient, wireless resources can be better allocated, user access speed and service quality can be improved, and user experience can be enhanced; Reduced latency: In drone mobile networks, signal evaluation can help reduce latency and ensure faster response times, which is especially important in real-time applications (such as autonomous driving, telemedicine, etc.); 4. Effective resource management Spectrum utilization: Signal evaluation can help evaluate and manage spectrum resources, optimize frequency allocation, reduce interference, and improve spectrum utilization; Load balancing: By monitoring the signal evaluation coefficient, the load in the drone network can be reasonably distributed to avoid overload in certain areas and ensure the smooth operation of the entire network; 5. Improve network security Signal strength monitoring: By analyzing the signal evaluation coefficient, abnormal signal strength changes can be identified, potential security threats (such as signal interference or attacks) can be discovered in a timely manner, and network security can be improved; 6. Cost-Benefit Analysis Reduce construction and maintenance costs: The signal evaluation coefficient can provide accurate data support for the deployment of drones, help optimize network design, and reduce infrastructure construction and maintenance costs; Improve return on investment: By improving network performance and user satisfaction, you can increase return on investment and ensure business sustainability; 7. Support emergency response Disaster recovery: When a disaster occurs, drones can be quickly deployed and use signal evaluation coefficients to assess the status of the communication network, providing the communication support needed for emergency rescue; Rapid deployment: drone networks can be deployed quickly in situations where rapid response is required, providing essential support to emergency services; Calculating the signal evaluation coefficient provides key technical support for the deployment of mobile networks using drones, making network coverage more optimized, resource allocation more reasonable, user experience significantly improved, and network security and reliability enhanced. These benefits make drones an indispensable part of modern mobile networks, especially in a rapidly developing and complex environment.

[0020] S40, obtaining the virtual force between the drones, calculating the deployment evaluation coefficient according to the virtual force between the drones and the signal evaluation coefficient, performing a comparative analysis between the deployment evaluation coefficient and a preset evaluation threshold, and updating the drone deployment position according to the comparative analysis result.

[0021] The virtual force between drones includes gravity and repulsion. According to Newton's law of universal gravitation, there is gravity between drones. First, calculate the gravity between the two drones, and then sum them up to get the total gravity. Calculating the repulsion between multiple drones can also be achieved by extending the repulsion formula between two drones. It is necessary to consider the repulsion between each pair of drones and add them together. The calculation process between each pair of drones is as follows: First, the distance between the two drones is calculated. The unit distance vector between the drones is obtained based on the coordinates of the two drones and the distance between the drones. The distance between the drones and the unit distance vector are combined with the repulsion coefficient to calculate the repulsion between each pair of drones according to the preset repulsion formula. The weighted sum of the repulsion between each pair of drones is obtained to obtain the total repulsion. The total repulsion is subtracted from the total gravitational force to obtain the virtual force between the drones. The calculation formula of the virtual force is as follows: ; In the formula, is the virtual force between drones, k is the repulsion coefficient, and are the coordinates of the two drones, G is the gravitational constant, and They are the masses of the two drones; The specific calculation formula for the deployment evaluation coefficient is calculated based on the virtual force and signal evaluation coefficient between drones as follows: ; Where PG is the deployment evaluation coefficient, is the virtual force between UAVs, Z is the signal evaluation coefficient; The deployment evaluation coefficient is compared with the preset evaluation threshold, and the deployment position of the drone is updated according to the comparison and analysis results. The specific process is as follows: Compare the deployment evaluation coefficient with a preset evaluation threshold, and when the deployment evaluation coefficient is greater than the preset evaluation threshold, execute the drone position deployment plan; When the deployment evaluation coefficient is less than the preset evaluation threshold, the particle swarm optimization algorithm is used again to obtain the UAV position deployment plan, and the deployment evaluation coefficient is calculated, and a secondary comparative analysis is performed with the preset evaluation threshold until the deployment evaluation coefficient is greater than the preset evaluation threshold and the iteration is stopped.

[0022] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0023] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0024] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0025] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0026] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0027] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for rapid deployment of a mobile network in a networkless environment, characterized in that: The following steps are involved: Collecting locations that drones can set when deploying mobile networks in target areas; Obtain the maximum coverage of drones at each location, take the total coverage of drones as the objective function, and use the particle swarm optimization algorithm to obtain the drone location deployment plan; The signal coverage rate is calculated by combining the total coverage range of the drone with the target area range, and the signal strength indication coefficient is obtained according to the drone deployment position and drone parameters, and the signal evaluation coefficient is calculated by combining the signal coverage rate; The virtual force between the UAVs is obtained, and the deployment evaluation coefficient is calculated according to the virtual force between the UAVs and the signal evaluation coefficient. A comparative analysis is performed between the deployment evaluation coefficient and the preset evaluation threshold, and the UAV deployment position is updated according to the comparative analysis result.

2. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The method for obtaining the position that the drone can set is as follows: The coordinates of the target area are obtained, and the target area is gridded, and each grid center in the target area is obtained, and the grid center is the position that the drone can set.

3. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The process of obtaining the total coverage of the drone is as follows: Generate a two-dimensional array to represent the grid according to the defined coverage area and grid size; Each grid cell is represented by a Boolean value (0 or 1), where 1 means the cell is covered by at least one drone and 0 means it is not covered; Traverse the position of each drone and calculate the grid cells within the drone's coverage area; Check whether the center point of each grid cell is within the coverage range of the drone. If so, the grid cell is marked as covered and recorded as 1; Traverse the two-dimensional array representing the grid, count the number of cells with a value of 1, that is, the number of grid cells covered by at least one drone; calculate the total coverage area based on the grid size and the number of covered grid cells.

4. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The specific process of using the particle swarm optimization algorithm to obtain the UAV location deployment plan is as follows: Randomly generate a set of particle positions and initial velocities, and calculate the initial fitness value, which is the total coverage of the drone, and set the initial position of the particle to the individual extreme position of each particle; Traverse the individual extreme fitness values ​​of all particles, find the maximum fitness value, and set the individual extreme position of the corresponding particle as the global extreme position; For each particle, update its position according to its current speed and position, calculate the fitness value of the new position, and update the individual best position and global best position of the particle based on the fitness value; The specific formula for updating the particle speed and position is as follows: ； ； In the formula, and Represents particles exist In the The velocity and position at the iteration, is the inertia weight, and is the learning factor, and is a random number between 0 and 1, Represents particles In the The individual extreme value position at the iteration, Indicates that the entire particle group is The global extremum position at the iteration; When the preset number of iterations is reached, the iteration is stopped, and the particle update position is the UAV deployment position.

5. The method for rapid deployment of a mobile network in a networkless environment according to claim 4, characterized in that: The specific method for obtaining the signal strength indication coefficient is as follows: The UAV deployment height is obtained according to the UAV deployment position, and the path loss data is obtained by combining the path loss index and the reference loss; According to the drone parameters, the drone signal transmission power, the transmitting antenna gain and the receiving antenna gain are obtained, and the drone signal index is obtained by weighted summing the drone signal transmission power, the transmitting antenna gain and the receiving antenna gain; Subtract the drone signal index from the path loss data to obtain the signal strength indicator coefficient.

6. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The method for obtaining the virtual force between the drones is as follows: The virtual force between drones includes gravitational force and repulsive force. According to Newton's law of universal gravitation, the gravitational force between two drones is calculated and then summed to obtain the total gravitational force. Calculate the distance between the two drones, obtain the unit distance vector between the drones according to the coordinates of the two drones combined with the distance between the drones, combine the distance between the drones and the unit distance vector with the repulsion coefficient according to the preset repulsion formula to calculate the repulsion between each pair of drones, and obtain the total repulsion by weighted summation of the repulsion between each pair of drones; Subtract the total repulsive force from the total attractive force to obtain the virtual force between the drones.

7. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The specific process of performing comparative analysis based on the deployment evaluation coefficient and the preset evaluation threshold and updating the deployment position of the drone based on the comparative analysis result is as follows: Compare the deployment evaluation coefficient with a preset evaluation threshold, and when the deployment evaluation coefficient is greater than the preset evaluation threshold, execute the drone position deployment plan; When the deployment evaluation coefficient is less than the preset evaluation threshold, the particle swarm optimization algorithm is used again to obtain the UAV position deployment plan, and the deployment evaluation coefficient is calculated, and a secondary comparative analysis is performed with the preset evaluation threshold until the deployment evaluation coefficient is greater than the preset evaluation threshold and the iteration is stopped.

8. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The specific calculation formula of the signal strength indication coefficient is as follows: ； Where R is the signal strength indicator coefficient, P is the UAV signal transmission power, T is the transmitting antenna gain, J is the receiving antenna gain, h is the UAV deployment height, n is the path loss index, and k is the reference loss.

9. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The calculation formula of the virtual force is as follows: ； In the formula, is the virtual force between drones, k is the repulsion coefficient, and are the coordinates of the two drones, G is the gravitational constant, and These are the masses of the two drones respectively.

10. The method for rapid deployment of a mobile network in a networkless environment according to claim 1, characterized in that: The specific calculation formula for calculating the deployment evaluation coefficient based on the virtual force and signal evaluation coefficient between drones is as follows: ； Where PG is the deployment evaluation coefficient, is the virtual force between UAVs, and Z is the signal evaluation coefficient.

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