Intelligent networking planning system based on multi-target dynamic optimization

Abstract

The invention relates to an intelligent networking planning system based on multi-objective dynamic optimization, which comprises a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module and a scheme output module, and is characterized in that the data input module is used for receiving geographic information, environmental conditions, equipment models and performance parameters input by a user; the data preprocessing module is used for carrying out standardization processing on the input geographic information and equipment parameters; the system modeling module is used for establishing a terrain and terrain model; the multi-target optimization module is used for carrying out multi-target dynamic optimization and generating an optimal deployment scheme; and the scheme output module is used for generating an equipment deployment scheme report. According to the invention, complementary coverage between devices is realized, the system cost is reduced, and the deployment efficiency is improved. The system has good expansibility and adaptability, can be flexibly adjusted according to different application scenes and requirements, improves the overall performance and adaptability of a low-altitude security system, and meets diversified low-altitude security requirements.

Description

Technical Field

[0001] This invention belongs to the field of Internet technology, especially Industrial Internet, Industrial Internet of Things services, Industrial Internet technology system services or Industrial Artificial Intelligence Network System services. It proposes an intelligent networking planning system based on multi-objective dynamic optimization, which can be applied to the field of low-altitude security. Background Technology

[0002] With the rapid development of drone technology, the safety of low-altitude airspace has received increasing attention. The low-altitude security field faces numerous challenges, such as complex and varied terrain, diverse weather conditions, and the ever-increasing demand for low-altitude activities. To address these challenges, there is an urgent need for an intelligent networking planning system that can comprehensively consider the performance of various equipment and environmental factors, dynamically optimize deployment schemes, and achieve efficient and reliable low-altitude security monitoring.

[0003] Existing low-altitude security equipment networking planning systems have the following drawbacks: 1) Limitations of manual deployment: Traditional networking relies on human experience and cannot dynamically adjust the equipment layout according to complex terrain, resulting in coverage blind spots (such as radar signal attenuation between urban buildings). 2) Single-objective optimization: Existing systems only focus on a single indicator such as coverage or cost, lacking the ability to optimize multiple objectives collaboratively; 3) Insufficient collaboration among heterogeneous devices: Radar, optoelectronic, and wireless equipment are networked independently, failing to fully utilize the advantages of multi-sensor data fusion. Summary of the Invention

[0004] This invention aims to solve the problems existing in current low-altitude security equipment network planning systems, and proposes an intelligent network planning system based on multi-objective dynamic optimization, including a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module, and a scheme output module. The data input module is used to receive geographic information, environmental conditions, equipment model and performance parameters input by the user; The data preprocessing module is used to standardize the input geographic information and equipment parameters and convert them into a format that the system can recognize. The system modeling module is used to create terrain and feature models using geographic information; The multi-objective optimization module is used to perform dynamic optimization for multiple objectives and generate the optimal deployment plan; The solution output module is used to output the Pareto optimal solution set and generate a device deployment solution report.

[0005] Furthermore, in the data input module, the geographic information adopts a standard geographic information system data format; the device performance parameters include detection range, accuracy, power, and cost.

[0006] Furthermore, in the data preprocessing module, the standardization process includes: Coordinate transformation: Using the WGS-84 coordinate system, the input geographic information is converted into a unified coordinate format; Normalization: The equipment performance parameters are normalized to a range of [0,1]. Data cleaning: removing outliers and filling in missing values.

[0007] Furthermore, in the system modeling module, the step of establishing a terrain and feature model using geographic information includes: Terrain and feature model construction: Using geographic information system data, generate a three-dimensional terrain model; Construct a multi-objective optimization function: , in, For coverage, For the number of deployment points, For deployment costs; Define constraints: Establish constraints on detection efficiency, synergy between devices, environmental impact, and budget limitations; Establish a quantitative model of the relationship between terrain obstruction, meteorological conditions, and equipment performance: , in, For equipment performance, For equipment i Theoretical coverage radius, This is the meteorological attenuation coefficient. For terrain occlusion correction factor, Occlusion rate: .

[0008] Furthermore, in the multi-objective optimization module, the step of performing multi-objective dynamic optimization to generate the optimal deployment scheme includes: Define the objective function: Clearly define the objectives that need to be optimized, including coverage, cost, and number of deployment points; Establish constraints: Define the constraints that need to be met during the optimization process, including budget constraints and equipment performance constraints; Optimization algorithm selection: Select the improved NSGA-III genetic algorithm; Initialize the population: Generate an initial set of solutions as the starting point for the optimization algorithm; Iterative optimization: Through an iterative process, the objective function is gradually optimized to find the optimal solution; Dynamic adjustment: Based on real-time feedback data, dynamically adjust the weights and optimization strategies of the objective function; Evaluation Results: Evaluate the optimization results and select the optimal solution or the Pareto optimal solution set.

[0009] Furthermore, in the solution output module, the device deployment solution report includes recommended locations, device types and quantities, and expected coverage effects.

[0010] This invention achieves complementary coverage between devices, reducing system costs and improving deployment efficiency while meeting performance requirements. It also possesses excellent scalability and adaptability, allowing for flexible adjustments based on different application scenarios and needs, thereby enhancing the overall performance and adaptability of low-altitude security systems and meeting diverse low-altitude security requirements. Attached Figure Description

[0011] Figure 1 This is a structural diagram of the present invention; Figure 2 This is a flowchart of the multi-objective dynamic optimization steps in this invention. Detailed Implementation

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described below with reference to specific embodiments and accompanying drawings.

[0013] The system of this invention can comprehensively consider factors such as coverage, number of deployment points, and cost to dynamically generate the optimal deployment scheme. It also designs collaborative rules for heterogeneous devices to achieve complementary coverage between devices, improve the overall performance and adaptability of the low-altitude security system, and solve the problems of coverage efficiency, cost control, and device collaborative optimization in the deployment of low-altitude monitoring equipment in complex environments.

[0014] like Figure 1 As shown in a specific embodiment of the present invention, the intelligent networking planning system includes a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module, and a scheme output module.

[0015] Specifically, the data input module is responsible for receiving user-inputted geographic information, environmental conditions, equipment models, and performance parameters of the low-altitude security area. Users provide the latitude and longitude range of the coverage area through a graphical interface or text input, loading the geographic information data of the selected area; and inputting the available detection equipment models, detection range, accuracy, power, and other performance parameters. The input data undergoes preliminary verification to ensure its validity and completeness. Specific requirements are as follows: 1) Geographic information: Use standard Geographic Information System (GIS) data formats, such as Shapefile or GeoJSON.

[0016] 2) Equipment performance parameters: including detection range, accuracy, power, cost, etc., should be entered in tabular form.

[0017] Specifically, the data preprocessing module standardizes the input geographic information and equipment parameters, converting them into a system-recognizable format. Geographic information is transformed to conform to the coordinate system's required format. Equipment performance parameters are normalized to eliminate differences in parameter dimensions between different devices, ensuring the accuracy of subsequent analysis. Furthermore, the module performs data cleaning, removing invalid or abnormal data and filling in missing values ​​to guarantee data quality. The specific methods are as follows: 1) Coordinate transformation: The WGS-84 coordinate system is used to convert the input geographic information into a unified coordinate format.

[0018] 2) Normalization: The equipment performance parameters are normalized to a range of [0,1].

[0019] 3) Data cleaning: Remove outliers and fill in missing values.

[0020] Specifically, the system modeling module utilizes geographic information to establish a terrain and feature model of the covered area. By analyzing factors such as terrain undulation, building distribution, and vegetation cover, a three-dimensional terrain model is constructed to identify potential obstructions and interference sources, providing a foundation for subsequent detection effect evaluation and optimization. The specific steps are as follows: 1) Terrain and feature model construction: Using GIS data, generate a three-dimensional terrain model of the covered area.

[0021] 2) Construct a multi-objective optimization function: .

[0022] in, Coverage Number of deployment points Deployment costs (equipment procurement + operation and maintenance energy consumption) Define constraints: Establish constraints such as detection efficiency, synergy between equipment, environmental impact, and budget limitations.

[0023] 3) Establish an environmental coupling response mechanism, namely, establish a quantitative relationship model between terrain obstruction, meteorological conditions, and equipment performance: in, Equipment performance; Theoretical coverage radius of device i; Meteorological attenuation coefficient (such as the effect of rain and fog on photoelectric power) ); Terrain occlusion correction factor; Occlusion rate, is: .

[0024] Specifically, the multi-objective optimization module uses an improved NSGA-III genetic algorithm (introducing a dynamic weight mechanism) and combines the following innovative designs to perform multi-objective dynamic optimization and generate the optimal deployment scheme.

[0025] 1) Dynamic adjustment of coverage: Set coverage indicators for different types of areas based on their value. 2) Heterogeneous device collaboration rules: Optoelectronic devices are prioritized for deployment in high-value target areas to provide high-precision identification; radar ensures comprehensive coverage; radio coverage requires protected areas.

[0026] 3) Real-time feedback mechanism: Dynamically adjust the weight of photoelectric equipment by accessing the meteorological API (e.g., reduce the photoelectric coverage coefficient in rainy or foggy weather).

[0027] like Figure 2 As shown, the implementation steps of multi-objective dynamic optimization are as follows: 1) Define the objective function: Identify the multiple objectives that need to be optimized, such as coverage, cost, number of deployment points, etc.

[0028] 2) Establish constraints: Define the constraints that need to be met during the optimization process, such as budget limits, equipment performance limits, etc.

[0029] 3) Select optimization algorithm: Select the improved NSGA-III genetic algorithm.

[0030] 4) Initialize the population: Generate an initial set of solutions as the starting point for the optimization algorithm.

[0031] 5) Iterative optimization: Through an iterative process, the objective function is gradually optimized to find the optimal solution.

[0032] 6) Dynamic adjustment: Based on real-time feedback data, dynamically adjust the weights and optimization strategies of the objective function.

[0033] 7) Evaluation results: Evaluate the optimization results and select the optimal solution or Pareto optimal solution set.

[0034] Genetic algorithms explore optimal solutions for equipment deployment by simulating natural selection and genetic mechanisms. During the optimization process, constraints such as synergistic effects between devices, environmental impact, and budget limitations are considered to ensure that the generated solutions are cost-effective while meeting performance requirements.

[0035] Specifically, the solution output module outputs the Pareto optimal solution set, allowing users to interactively select solutions (focusing on coverage / cost / balanced mode) and generate a device deployment plan report. This report includes recommended locations, device types and quantities, expected coverage effects, and is visualized through a 3D map. Users can intuitively view the deployment locations, coverage areas, and overlapping regions of each device to evaluate the feasibility and effectiveness of the solution. Furthermore, the module provides detailed parameters and performance indicators for user reference and decision-making.

[0036] This invention employs an improved NSGA-III genetic algorithm (introducing a dynamic weighting mechanism) for multi-objective optimization, comprehensively considering factors such as coverage, number of deployment points, and cost to dynamically generate the optimal deployment scheme. Specifically designed for the collaborative rules of heterogeneous devices, it achieves complementary coverage between devices, reducing system costs and improving deployment efficiency while meeting performance requirements. The modular design of this system gives it excellent scalability and adaptability, allowing for flexible adjustments based on different application scenarios and needs, enhancing the overall performance and adaptability of the low-altitude security system, and meeting diverse low-altitude security requirements.

[0037] This invention demonstrates higher efficiency and robustness in handling multi-objective optimization problems, and by combining heterogeneous device collaboration rules and real-time feedback mechanisms, it can better adapt to the complex needs of the low-altitude security field.

[0038] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An intelligent network planning system based on multi-objective dynamic optimization, characterized in that, It includes a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module, and a solution output module. The data input module is used to receive geographic information, environmental conditions, equipment model and performance parameters input by the user; The data preprocessing module is used to standardize the input geographic information and equipment parameters and convert them into a format that the system can recognize. The system modeling module is used to create terrain and feature models using geographic information; The multi-objective optimization module is used to perform dynamic multi-objective optimization and generate the optimal deployment plan; The solution output module is used to output the Pareto optimal solution set and generate a device deployment solution report.

2. The intelligent networking planning system based on multi-objective dynamic optimization according to claim 1, characterized in that, In the data input module, the geographic information adopts a standard geographic information system data format; the equipment performance parameters include detection range, accuracy, power, and cost.

3. The intelligent networking planning system based on multi-objective dynamic optimization according to claim 1, characterized in that, In the data preprocessing module, the standardization process includes: Coordinate transformation: Using the WGS-84 coordinate system, the input geographic information is converted into a unified coordinate format; Normalization: The equipment performance parameters are normalized to a range of [0,1]. Data cleaning: removing outliers and filling in missing values.

4. The intelligent networking planning system based on multi-objective dynamic optimization according to claim 1, characterized in that, In the system modeling module, the step of establishing a terrain and feature model using geographic information includes: Terrain and feature model construction: Using geographic information system data, generate a three-dimensional terrain model; Construct a multi-objective optimization function: ， in, For coverage, For the number of deployment points, For deployment costs; Define constraints: Establish constraints on detection efficiency, synergy between devices, environmental impact, and budget limitations; Establish a quantitative model of the relationship between terrain obstruction, meteorological conditions, and equipment performance: ， in, For equipment performance, For equipment i Theoretical coverage radius, This is the meteorological attenuation coefficient. For terrain occlusion correction factor, Occlusion rate: 。 5. The intelligent networking planning system based on multi-objective dynamic optimization according to claim 1, characterized in that, In the multi-objective optimization module, the step of performing multi-objective dynamic optimization to generate the optimal deployment scheme includes: Define the objective function: Clearly define the objectives that need to be optimized, including coverage, cost, and number of deployment points; Establish constraints: Define the constraints that need to be met during the optimization process, including budget constraints and equipment performance constraints; Optimization algorithm selection: Select the improved NSGA-III genetic algorithm; Initialize the population: Generate an initial set of solutions as the starting point for the optimization algorithm; Iterative optimization: Through an iterative process, the objective function is gradually optimized to find the optimal solution; Dynamic adjustment: Based on real-time feedback data, dynamically adjust the weights and optimization strategies of the objective function; Evaluation Results: Evaluate the optimization results and select the optimal solution or the Pareto optimal solution set.

6. The intelligent networking planning system based on multi-objective dynamic optimization according to claim 1, characterized in that, The device deployment plan report in the solution output module includes recommended locations, device types and quantities, and expected coverage effects.

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