A method and system for analyzing and evaluating the site selection of base stations in a broadcast radio positioning system

Abstract

The present invention discloses a method and system for analyzing and evaluating the site selection of base stations in a broadcast radio positioning system, which relates to the field of wireless positioning technology and includes: establishing a sample database of radio ground base station deployments and an elevation model database; selecting the best base station set covering the pre-working area from the databases based on a combined optimization strategy of the greedy algorithm and the genetic algorithm; constructing a three-dimensional evaluation system to comprehensively evaluate the best base station set from three dimensions: deployment cost, system robustness, and positioning accuracy; optimizing the base station set based on digital twin technology, including multi-band signal transmission modeling and machine learning recommendations from a historical case library; and dynamically presenting the deployment effect of the base stations and adjusting parameters through a three-dimensional visualization interface and an interactive sand table system. It can well cover a large working area, avoid signal coverage blind spots, thereby improving work efficiency, and can realize flexible and efficient site selection and evaluation of radio ground base stations in local sudden battlefields.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless positioning, and particularly to a method and system for analyzing and evaluating the site selection of base stations in a broadcast radio positioning system. Background Art

[0002] The radio ground base station is deployed on the ground, and the transmitted radio ranging signal is a high-frequency signal. Due to the curvature of the earth, when the signal is transmitted about 50 km, the signal can no longer "illuminate" the ground, and the radio positioning receiver terminal on the ground can no longer receive the signal of the radio ground base station.

[0003] The broadcast radio positioning system consists of multiple scattered radio ground base stations. The radio positioning receiver terminal needs to receive the signals of at least 4 base stations simultaneously to achieve positioning. Affected by the curvature of the earth, in some areas, it is impossible to receive the signals of multiple radio ground base stations simultaneously, resulting in a decrease in positioning accuracy. Therefore, for the pre-working area where the distance between the radio positioning receiver terminal and the radio ground base station is relatively far (more than 200 kilometers), it is necessary to quickly analyze and evaluate the site selection strategy of the radio ground base station, so that the signals of the broadcast radio positioning system can cover the working area well, and avoid problems such as positioning failure or reduced positioning accuracy of the radio positioning receiver terminal due to the inability to receive the signals of multiple radio ground base stations (at least 4), thereby improving the service quality of the system.

[0004] Global satellite navigation systems such as GPS and Beidou broadcast ranging signals in medium and high orbits through satellites. The satellite navigation receiver receives the signals to complete real-time positioning and speed measurement. However, due to the high orbit of the satellite navigation system, the signal power is extremely weak when it reaches near the ground, and it is easily blocked, or interfered by radar, communication, or malicious interference by criminals, resulting in abnormal operation of the satellite navigation receiver. In high-security fields related to national economy and people's livelihood, or in military applications, it is necessary to consider a navigation and positioning emergency means to meet the navigation and positioning requirements when the satellite signal is interfered or the satellite system is paralyzed. Therefore, research institutions at home and abroad have successively carried out research on related technologies of broadcast radio positioning systems.

[0005] In the broadcast radio positioning system, multiple radio ground base stations are deployed on the ground. The intensity of the signal emitted by a single station can effectively cover a distance of hundreds of kilometers. The radio positioning receiver terminal simultaneously receives the ranging signals of multiple radio ground base stations to complete its own real-time positioning and speed measurement. The radio ground base station emits a strong signal and is difficult to be interfered; the base station is flexibly deployed and not easily damaged, which can effectively solve the problem of the paralysis of the satellite navigation system during special periods. However, due to the influence of the curvature of the earth, when the radio positioning receiver terminal works on the land surface, the actual effective distance of a single base station's signal is less than 50 km. Therefore, it is necessary to solve the effective coverage problem of multiple radio ground base stations in a specific area on the ground to meet the navigation and positioning requirements of the radio positioning receiver terminal.

[0006] The conventional method for evaluating the location of a base station is to select several basic points empirically in the pre-operational area of the radio navigation signal when deploying a radio ground base station, calculate and evaluate the possibility of reaching typical points in the pre-operational area from each base station, and then deploy the base station.

[0007] The conventional method for evaluating the location of a radio ground base station mainly relies on manual temporary calculations and does not have a mature and perfect location software to implement an automated location process.

[0008] In the pre-operational area, in places such as emergency response sites, shooting ranges, and battlefields, there is no mature and perfect location software, and a large amount of basic point data available for station deployment has not been prepared in advance. As a result, when performing an emergency mission, it is only then that the process of selecting points and measuring the basic point information starts based on the possible pre-operational area, and then manually calculating the coverage of limited typical points in the pre-operational area. This is inefficient, and the selected basic points are not necessarily the optimal ones. There is a high probability that re-location, re-measurement, and re-calculation and evaluation will be required during the implementation process. In addition, the evaluation carried out only evaluates the positioning of typical points in the pre-operational area and cannot cover the reliability of the received signals at other points in the pre-operational area.

[0009] In summary, for the pre-operational area of a radio positioning receiver terminal, how to quickly analyze and evaluate the location of a radio ground base station is an urgent problem to be solved by those skilled in the art currently. Summary of the Invention

[0010] The technical solution of the present invention to solve the above technical problems is to provide a method for analyzing and evaluating the location of a base station in a broadcast radio positioning system, including the following steps:

[0011] Step S1: Establish a sample database for radio ground base station deployment and an elevation model database;

[0012] Step S2: Based on a combined optimization strategy of the greedy algorithm and the genetic algorithm, select the best base station set covering the pre-operational area from the database;

[0013] Step S3: Construct a three-dimensional evaluation system to comprehensively evaluate the best base station set from three dimensions: deployment cost, system robustness, and positioning accuracy;

[0014] Step S4: Optimize the base station set based on digital twin technology, including multi-band signal transmission modeling and machine learning recommendation of a historical case library;

[0015] Step S5: Implement dynamic presentation and parameter adjustment of the base station deployment effect through a three-dimensional visualization interface and an interactive sand table system.

[0016] Further, the step S1 includes:

[0017] Divide uniform grid points in the pre-working area at a preset spacing, collect the reference position point information of deployable base stations, and generate a radio ground base station deployment sample database;

[0018] Interpolate the terrain elevation data of the pre-working area to generate a regular grid elevation map, calculate the visible path between the base station and the grid points, and form a terrain occlusion compensation model;

[0019] The judgment formula for the visible path is:

[0020] ;

[0021] Among them, Path(a,g) is the set of straight-line path coordinates from base station a to grid point g, DEM(x,y) is the terrain elevation value at coordinates (x,y), is the absolute height of the base station, : the relative elevation difference of the terrain relative to the base station, is the earth curvature compensation coefficient, and d is other situations.

[0022] Furthermore, the step S2 includes:

[0023] Generate a candidate base station set based on the greedy algorithm: Each time, select the base station that covers the most uncovered areas and compensates for the terrain blind area until the coverage rate reaches 90% or the number of base stations reaches the preset upper limit;

[0024] Globally optimize the candidate base station set through the genetic algorithm, use variable-length coding and fitness function to evaluate the base station combination, and the fitness function is:

[0025] ;

[0026] Among them, β is the base station number penalty coefficient, is and f is and z is and s is and b is .

[0027] Furthermore, the step S2 also includes the optimization of the parallel computing architecture:

[0028] Adopt the GPU acceleration hierarchical strategy. At level 1, each GPU thread block processes the coverage calculation of a base station combination, and at level 2, each thread processes the LOS judgment between a single base station and the grid points.

[0029] Furthermore, in the step S3, the three-dimensional evaluation system includes:

[0030] Deployment cost calculation: Comprehensive cable cost and power supply difficulty cost, and the calculation formula is:

[0031] ,

[0032] where a is the direct distance from base station a to the central node, is the cost per unit length of the cable, n is the central node, and p is the set of base stations;

[0033] System robustness evaluation: By randomly simulating the base station failure scenarios, calculate the effective coverage rate of the remaining base stations;

[0034] Positioning accuracy evaluation: Generate a positioning accuracy score based on the average geometric dilution of precision and the root mean square error;

[0035] The comprehensive scoring formula is:

[0036] ,

[0037] where w1, w2, and w3 are adjustable weight coefficients, and B is the positioning accuracy score.

[0038] Furthermore, the step S4 includes:

[0039] Construct a hierarchical modular digital twin model, separately model the transmitting antenna, power amplifier, and main control module for base stations in different frequency bands, and support dynamic adjustment of transmission parameters;

[0040] Establish a multi-band signal propagation model, introduce quantum noise terms and time series correlation analysis, and use an LSTM network to simulate the dynamic superposition of signals;

[0041] Construct a knowledge graph based on the historical deployment case library, and apply topological data analysis to extract the topological features of the signal coverage area.

[0042] Furthermore, the step S5 includes:

[0043] Adopt physical rendering and ray tracing technologies to generate a three-dimensional visualization interface, and distinguish the color coding of base stations in different frequency bands;

[0044] Integrate the time dimension analysis module to show the impact of day and night and seasonal changes on the performance of base stations;

[0045] Build an interactive deployment sand table system, which supports real-time adjustment of base station parameters and environmental factors through gesture recognition or voice commands.

[0046] To solve the above technical problems, a base station deployment evaluation system for a broadcast radio positioning system is used to execute the base station site selection analysis and evaluation method for the broadcast radio positioning system as described above, and includes:

[0047] A database module for storing base station deployment sample data and elevation models;

[0048] An algorithm optimization module for performing combined optimization of the greedy algorithm and the genetic algorithm;

[0049] An evaluation module for implementing a three-dimensional evaluation system;

[0050] A digital twin module for constructing a multi-band signal model and machine learning recommendations for a historical case library;

[0051] A visualization module providing three-dimensional dynamic rendering and interactive parameter adjustment functions.

[0052] Furthermore, the digital twin module supports a quantum scattering and absorption model to simulate the propagation loss of high-frequency signals at the nanoscale.

[0053] Furthermore, the visualization module integrates global illumination rendering and real-time isosurface dynamic generation technologies to reflect the changing trend of signal coverage over time.

[0054] The technical solution of the present invention is directed to the pre-working area of a radio positioning receiver terminal, quickly analyzes and evaluates the site selection of radio ground base stations, enables the system to well cover a large working area, avoids possible signal coverage blind spots, thereby improving work efficiency, and can achieve flexible and efficient site selection evaluation of radio ground base stations in a local sudden battlefield. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the structures shown in these drawings without creative efforts.

[0056] Figure 1 It is a flowchart of the steps of the method for analyzing and evaluating the site selection of the base station of the broadcast radio positioning system described in the present invention;

[0057] Figure 2 It is a diagram of the grid point division of the working area of the present invention;

[0058] Figure 3 It is a schematic diagram of the base station deployment evaluation of the present invention;

[0059] Figure 4 It is a PDOP value distribution diagram under the typical 6-base station layout of the present invention;

[0060] Figure 5 It is an HDOP value distribution diagram under the typical 6-base station layout of the present invention;

[0061] Figure 6 This is the VDOP value distribution map under the typical 6-base station layout adopted by the present invention. Specific implementation manners

[0062] The present invention provides a method and system for analyzing and evaluating the location selection of base stations in a broadcast radio positioning system, aiming to achieve flexible and efficient location selection and evaluation of radio ground base stations in a local sudden battlefield.

[0063] The method for analyzing and evaluating the location selection of base stations in the broadcast radio positioning system proposed by the present invention will be described in the following specific embodiments:

[0064] Embodiment 1: In the technical solution of this embodiment, as Figure 1 shown, a method for analyzing and evaluating the location selection of base stations in a broadcast radio positioning system includes the following steps:

[0065] Step S1: Establish a sample database for radio ground base station deployment and an elevation model database;

[0066] Step S2: Based on a combined optimization strategy of the greedy algorithm and the genetic algorithm, select the best base station set that covers the pre-working area from the database;

[0067] , the best base station set comprehensively considers multiple dimensions such as the regional coverage range, the positioning accuracy of the positioning system, and the system robustness. Spatially, the coverage area is the widest, the signal strength is uniform and stable, avoiding the situation of too strong or too weak signals; in terms of accuracy, it meets the positioning accuracy requirements of the broadcast radio positioning system, and the positioning error is controlled within a very small range; it has strong system robustness and can effectively cope with various complex environments and emergencies.

[0068] Step S3: Construct a three-dimensional evaluation system to comprehensively evaluate the best base station set from three dimensions: deployment cost, system robustness, and positioning accuracy;

[0069] Step S4: Optimize the base station set based on digital twin technology, including multi-band signal emission modeling and machine learning recommendation of the historical case library;

[0070] Step S5: Implement dynamic presentation and parameter adjustment of the base station deployment effect through a three-dimensional visualization interface and an interactive sand table system.

[0071] Further, the step S1 includes:

[0072] Divide uniform grid points at a preset interval within the pre-working area, collect the reference position point information of deployable base stations, and generate a sample database for radio ground base station deployment;

[0073] Interpolate the topographic elevation data of the pre-working area to generate a regular grid elevation map, calculate the visible paths between the base stations and the grid points, and form a terrain occlusion compensation model;

[0074] The judgment formula for the visible path is: ;

[0075] where Path(a,g) is the set of straight-line path coordinates from base station a to grid point g, and DEM(x,y) is the topographic elevation value at coordinates (x,y), is the absolute height of the base station, : the relative elevation difference of the terrain with respect to the base station, is the earth curvature compensation coefficient, and d is for other cases.

[0076] Further, the step S2 includes:

[0077] Generate a candidate base station set based on the greedy algorithm: Each time, select the base station that covers the most uncovered areas and compensates for the terrain blind area until the coverage rate reaches 90% or the number of base stations reaches the preset upper limit;

[0078] Globally optimize the candidate base station set through the genetic algorithm, and use variable-length coding and fitness function to evaluate the base station combination. The fitness function is: ;

[0079] where β is the base station number penalty coefficient, is for and z is and s is and b is .

[0080] Further, the step S2 also includes the optimization of the parallel computing architecture:

[0081] Adopt the GPU acceleration hierarchical strategy. At level 1, each GPU thread block processes the coverage calculation of a base station combination, and at level 2, each thread processes the LOS judgment between a single base station and the grid points.

[0082] Further, in the step S3, the three-dimensional evaluation system includes:

[0083] Deployment cost calculation: Integrate the cable cost and the power supply difficulty cost. The calculation formula is: ,

[0084] where a is the direct distance from base station a to the central node, is the unit length cable cost, n is the central node, and p is the base station set;

[0085] System robustness evaluation: By randomly simulating the base station failure scenarios, calculating the effective coverage rate of the remaining base stations, and then calculating the system robustness score R;

[0086] ;

[0087] Among them, represents the number of times of simulating the base station failure test;

[0088] i represents the effective coverage rate of the i-th base station failure simulation test;

[0089] Positioning accuracy evaluation: Generating a positioning accuracy score based on the average geometric dilution of precision and the root mean square error;

[0090] The comprehensive scoring formula is: ;

[0091] Among them, w1, w2, and w3 are adjustable weight coefficients, and B is the positioning accuracy score.

[0092] Furthermore, the step S4 includes:

[0093] Constructing a hierarchical modular digital twin model, separately modeling the transmitting antenna, power amplifier, and main control module for base stations in different frequency bands, and supporting dynamic adjustment of transmission parameters;

[0094] Establishing a multi-band signal propagation model, introducing quantum noise terms and time series correlation analysis, and using an LSTM network to simulate the dynamic superposition of signals;

[0095] Constructing a knowledge graph based on the historical deployment case library, and applying topological data analysis to extract the topological features of the signal coverage area.

[0096] Furthermore, the step S5 includes:

[0097] Using physical rendering and ray tracing technologies to generate a three-dimensional visualization interface, distinguishing the color coding of base stations in different frequency bands;

[0098] Integrating a time dimension analysis module to display the impact of day and night and seasonal changes on the performance of base stations;

[0099] Building an interactive deployment sand table system, supporting real-time adjustment of base station parameters and environmental factors through gesture recognition or voice commands.

[0100] Embodiment 2: A method for analyzing and evaluating the location selection of base stations in a broadcast radio positioning system, including the following steps:

[0101] Step S1: Establishing a radio ground base station deployment sample database and an elevation model database;

[0102] Specifically, in a large-scale pre-operation area (or test field environment) of a broadcast radio positioning system, a large number of reference position point information of deployable radio ground base stations are collected in advance to establish a radio ground base station deployment sample database (abbreviated as database), and the number of samples is N. The pre-operation area is divided into uniform grid points, as Figure 2 shown. The dots represent the deployable points of the radio ground base stations, D is the base station spacing; the grid area is the navigation signal coverage area of the radio ground base stations, that is, the pre-operation area, L is the depth length of the evaluation area, and W is the width of the evaluation area. For the broadcast radio positioning system base station deployment evaluation system, the number of base stations can be freely configured, and the blue circles in the figure represent the positions of the planned base stations. Preprocess the terrain elevation data, and align the resolution of the elevation model (DEM) with the coverage grid. Interpolate the DEM data to generate a regular grid elevation map, calculate the line-of-sight (LOS) between the base station and the grid points, and form an elevation database.

[0103] Step S2: Based on a combined optimization strategy of the greedy algorithm and the genetic algorithm, select the best base station set that covers the pre-operation area from the database;

[0104] According to the set number of base stations K (K≥4), traverse all base station sets based on the combination of the greedy algorithm and the genetic algorithm, and select the best base station set. Perform the following operations on each combination:

[0105] (1) The greedy algorithm quickly generates a candidate base station set:

[0106] Search for base stations based on the greedy algorithm to form a base station set that can cover 90% of the pre-operation area.

[0107] Initialize the grid point set.

[0108] Each time, select the base station that covers the most uncovered area and compensates for the terrain blind area.

[0109] Terrain occlusion compensation model (LOS judgment): Determine whether there is a line-of-sight (LOS) between base station a and grid point g: ;

[0110] Parameter description: {Path}(a,g): The set of straight-line path coordinates from base station a to grid point g;

[0111] {DEM}(x,y): The terrain elevation value at coordinates (x,y), unit: meter;

[0112] : The absolute height of base station a (altitude + its own height), unit: meter;

[0113] : Relative elevation difference of the terrain with respect to the base station, unit: meter;

[0114] : Curvature compensation coefficient (empirical value, usually taken as 0.5), used to correct the influence of the earth's curvature and signal diffraction;

[0115] : Other situations.

[0116] The coverage rate reaches 90% or the number of base stations reaches the preset upper limit (such as 8).

[0117] Coverage rate calculation: Define the percentage of the pre-working area that can be covered by the selected radio ground base stations, and terminate the iteration of the greedy algorithm based on this: ,

[0118] where CovGrids: The number of grid points covered by the base stations.

[0119] TotalGrids: The total number of grid points in the pre-working area.

[0120] (2) Genetic algorithm to optimize the base station set:

[0121] Based on the base station set generated by the greedy algorithm, new base stations are preferably selected through the genetic algorithm to achieve the maximum area coverage.

[0122] Base station set = Greedy algorithm result + New base stations added by the genetic algorithm (for example, if the total number of base stations is 8, 6 are preselected by the greedy algorithm, and 2 are optimized by the genetic algorithm). The number of base stations is optimized using variable-length coding. During the optimization process of the genetic algorithm, the fitness function is used to evaluate the quality of each group of base station combinations and guide the evolution of the population. The specific formula is as follows:

[0123] ,

[0124] where, : Base station number penalty coefficient, The larger it is, the more the algorithm tends to reduce the number of base stations, The smaller it is, the more the algorithm tends to improve the coverage rate;

[0125] The larger it is, the more the algorithm emphasizes the coverage of terrain blind areas, The smaller it is, the more the algorithm focuses on the overall coverage rate;

[0126] Terrain blind area compensation score: For each base station in the base station combination, if its coverage area contains the terrain blind areas of other base stations, points are added.

[0127] (3) Parallel architecture to optimize the calculation efficiency:

[0128] Design a GPU-accelerated hierarchical acceleration strategy. At level 1, each GPU thread block processes the coverage calculation of a base station combination. At level 2, each thread processes the LOS determination between a base station and a grid point in the combination.

[0129] Step S3: Construct a three-dimensional evaluation system to comprehensively evaluate the optimal base station set from three dimensions: deployment cost, system robustness, and positioning accuracy.

[0130] To comprehensively evaluate the quality of the selected base station set, construct an evaluation system from three dimensions: deployment cost, system robustness, and positioning accuracy.

[0131] (1) Deployment cost: The deployment cost is used to quantify the economic cost of base station deployment, including cable length, power supply complexity, etc.

[0132] Cable cost: The cable cost is proportional to the distance between the base station and the central node.

[0133] , where a is defined as the direct distance from base station a to the central node, unit: kilometer;

[0134] is defined as the cable cost per unit length, unit: yuan / kilometer.

[0135] Power supply difficulty cost: The power supply difficulty is related to the complexity of the terrain where the base station is located. If mains power cannot be used, battery power supply needs to be considered.

[0136] , where the power supply difficulty coefficient (a) represents the power supply difficulty of the terrain where base station a is located;

[0137] : represents the unit power supply cost, unit: yuan.

[0138] The deployment cost is .

[0139] (2) System robustness, construct a system robustness model to evaluate the robustness of the base station set in the case of sudden base station failures.

[0140] Simulation of sudden base station failures: Randomly simulate the scenario of sudden base station failures and evaluate the coverage ability of the remaining base stations for the pre-working area. Randomly select m base stations to fail and calculate the effective coverage rate of the remaining base stations.

[0141] ,

[0142] Robustness score: Reflects the coverage stability ability of the base station set in the case of partial base station failures for the pre-working area.

[0143] ,

[0144] Among them, represents the number of times of the simulated base station failure test;

[0145] i represents the effective coverage rate of the i-th base station failure simulation test.

[0146] (3) Positioning accuracy evaluation, calculation of the average geometric dilution of precision (AGDOP):

[0147] The positioning accuracy is closely related to the geometric distribution of the base stations and can be evaluated by the average geometric dilution of precision (AGDOP). Calculate the GDOP value of each effective grid point and accumulate it to obtain the total average value AGDOP;

[0148] ,

[0149] The GDOP value reflects the influence of the base station geometric distribution on the navigation accuracy, and the smaller the value, the higher the accuracy.

[0150] ,

[0151] Among them, H: observation matrix, determined by the geometric relationship between the radio ground base station and the radio positioning receiver terminal;

[0152] : trace operation of the matrix.

[0153] Positioning accuracy scoring:

[0154] The positioning accuracy scoring is based on the root mean square error (RMS), and through standardization processing, it is converted into a score from 0 to 100. The higher the score, the better the positioning accuracy.

[0155] Positioning accuracy score ( ,

[0156] Among them, positioning error: root mean square error, that is, RMS;

[0157] : maximum allowable positioning error, such as 10 meters.

[0158] (4) Comprehensive evaluation of the three-dimensional evaluation system, comprehensively evaluate the three indicators of the comprehensive deployment cost, system robustness and positioning accuracy score to conduct a comprehensive score of the base station set;

[0159] ,

[0160] Among them, , 2, 3: weight coefficients, satisfying ( 2 3) = 1,

[0161] Broadcast radio positioning system base station deployment evaluation system, the number of base stations can be freely configured. After a large number of simulation analyses and demonstrations, in a typical area, deploying 4 radio ground base stations can ensure positioning, and deploying 6 radio ground base stations can ensure positioning accuracy in a certain area. By default, the evaluation is based on the principle of deploying 6 base stations, and it is assumed that the radio ground base stations are deployed in the most adverse areas: the base stations can only be deployed in half of the pre-working area. For example Figure 3 , the base station spacing D is 40 km. Starting from the lower left corner of the evaluation area, point analysis is carried out. Distribution diagrams of PDOP, HDOP, and VDOP values at each point within all the action areas of each grid point are as shown in Figure 4 , Figure 5 , Figure 6 , with the focus on analyzing the grid points in the pre-working area.

[0162] Step S4: Optimize the base station set based on digital twin technology, including multi-band signal emission modeling and machine learning recommendation of the historical case library;

[0163] Build a broadcast radio positioning system base station deployment evaluation module based on digital twin technology, verify the site selection feasibility through virtual-real mapping, add a multi-band radio signal emission model, support the mixed deployment of base stations in different frequency bands, build a historical deployment case library module, and develop an intelligent recommendation system based on machine learning to further optimize the base station set.

[0164] Build a digital twin model:

[0165] This patent abandons the traditional single modeling method and adopts a hierarchical modular modeling concept. For base stations in different frequency bands, such as low-frequency band, medium-frequency band, and high-frequency band base stations, build a transmitting antenna module, a power amplifier module, and a main control module respectively, which can realize flexible combination of modules for different usage scenarios, quickly build base station sets with different configurations, and support dynamic adjustment of parameters such as base station transmission power and transmission frequency. Add time dimension parameters, consider the aging of base stations and equipment upgrades, and more realistically simulate the full life cycle state of base stations. Build a radio signal emission model:

[0166] Based on the self-developed adaptive signal propagation model, for signals in different frequency bands, automatically select and switch the most suitable signal emission model according to real-time environmental monitoring data. Introduce the concept in quantum communication theory to improve the high-frequency band signal propagation model. Considering the quantum characteristics of high-frequency band signals, such as the scattering and absorption of photons, introduce a quantum noise term, and more accurately simulate the propagation loss of high-frequency band signals in complex environments, especially the signal changes at the nanoscale, by constructing a quantum scattering and absorption model.

[0167] Considering the time series correlation of signals, when simulating the superposition of multi-band signals, not only the interaction of signals in different frequency bands at the same moment is analyzed, but also the forward and backward correlation of signals on the time axis is considered. For example, the fading situation of the low-frequency band signal at the previous moment may affect the initial phase and amplitude of the high-frequency band signal at the next moment. Based on the principle of the basic ARMA model and applying the long short-term memory network (LSTM) model, a time series correlation model is established to more comprehensively simulate the dynamic superposition process of multi-band signals.

[0168] Create a historical deployment case library and build an intelligent recommendation system for machine learning based on the case library;

[0169] Collect base station deployment case data under different climate and geographical conditions globally to establish a global case library. The climate (such as tropical rainstorms, polar cold) and geographical conditions (such as deserts, rainforests) in different regions have unique impacts on base station deployment. By collecting these diverse cases, more comprehensive references can be provided for base station deployment in different environments. Construct a knowledge graph of case data. Connect various elements (such as base station equipment, environmental factors, technical solutions, performance indicators, etc.) in different cases through semantic relationships to form a knowledge graph. Through the knowledge graph, users can more intuitively understand the associations and similarities between cases. For example, through the graph, multiple cases that adopt the same technical solution to solve similar signal problems can be quickly found.

[0170] Introduce the topological data analysis (TDA) method to extract complex topological features from the historical deployment case library. TDA can analyze the topological structure of data, such as features like the connectivity and hole distribution of the signal coverage area. For example, by calculating the Betti numbers of the signal coverage area to describe the number of its connected components and holes, these topological features can reflect the integrity and stability of signal coverage, providing a new perspective for the evaluation of base station deployment plans.

[0171] Step S5: Dynamically present and adjust parameters of the base station deployment effect through a three-dimensional visualization interface and an interactive sand table system.

[0172] Display the base station deployment effect through a three-dimensional visualization interface, add time dimension analysis to show the performance fluctuations of the radio positioning system after base station deployment at different time periods (day / night / seasons), and create an interactive deployment sand table system to support real-time parameter adjustment and effect preview.

[0173] Construct a three-dimensional visualization interface:

[0174] Using physically based rendering (PBR) technology, based on a high-precision three-dimensional terrain and landform model, ray tracing and global illumination technologies are used for rendering. Ray tracing technology can accurately simulate the reflection, refraction, and shadow effects of light on the surfaces of objects such as terrain, buildings, and vegetation, making the scene more realistic. The base station models constructed in a hierarchical and modular manner are displayed in the three-dimensional scene with high-resolution textures and fine details. For base stations operating in different frequency bands, different color codings are used for differentiation to facilitate quick identification by users.

[0175] Implementation of time dimension analysis:

[0176] The base station operation data and environmental data at different times, including factors such as day and night and seasons, are continuously collected through a sensor network. For day-night changes, focus is placed on the impact of changes in environmental factors such as light intensity, temperature, and electromagnetic interference on the performance of the base station. For seasonal changes, in addition to considering conventional environmental factors such as temperature and humidity, the impact of the vegetation growth state on signal propagation also needs to be considered. A time controller is set in the three-dimensional visualization interface, and users can dynamically switch between scenes at different times by dragging the slider or entering specific time points. When the user switches the time, the environmental factors, the status of the base station equipment, and the signal coverage and performance indicators in the scene will change accordingly.

[0177] Construction of an interactive deployment sand table system:

[0178] In the interface of the interactive deployment sand table system, a detailed parameter adjustment panel is set for each base station model. Users can use interactive elements such as sliders and text input boxes to adjust parameters such as the position, transmit power, antenna direction, and frequency band configuration of the base station in real time. In addition to base station parameters, an environmental parameter adjustment function is also provided. Users can adjust environmental factors such as terrain and landform, the height and position of buildings, and the density of vegetation.

[0179] Embodiment 3: A base station deployment evaluation system for a broadcast radio positioning system, used to execute the base station site selection analysis and evaluation method of the broadcast radio positioning system in Embodiment 1, including:

[0180] A database module for storing base station deployment sample data and elevation models;

[0181] An algorithm optimization module for performing combined optimization of the greedy algorithm and the genetic algorithm;

[0182] An evaluation module for implementing a three-dimensional evaluation system;

[0183] A digital twin module for constructing a multi-band signal model and machine learning recommendations for a historical case library;

[0184] A visualization module that provides three-dimensional dynamic presentation and interactive parameter adjustment functions.

[0185] Furthermore, the digital twin module supports quantum scattering and absorption models to simulate the propagation loss of high-frequency signals at the nanoscale.

[0186] Furthermore, the visualization module integrates global illumination rendering and real-time isosurface dynamic generation technologies to reflect the changing trend of the signal coverage range over time.

[0187] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for analyzing and evaluating the site selection of base stations in a broadcast radio positioning system, characterized in that, It includes the following steps: Step S1: Establish a radio ground base station deployment sample database and an elevation model database; Step S2: Based on a combined optimization strategy of the greedy algorithm and the genetic algorithm, select the best base station set that covers the pre-working area from the database; Step S3: Construct a three-dimensional evaluation system to comprehensively evaluate the best base station set from three dimensions: deployment cost, system robustness, and positioning accuracy; Step S4: Optimize the base station set based on digital twin technology, including multi-band signal transmission modeling and machine learning recommendations from the historical case library; Step S5: Implement dynamic presentation and parameter adjustment of the base station deployment effect through a three-dimensional visualization interface and an interactive sand table system; The said Step S1 includes: Divide uniform grid points at a preset interval within the pre-working area, collect the reference position point information of deployable base stations, and generate a radio ground base station deployment sample database; Perform interpolation processing on the terrain elevation data of the pre-working area, generate a regular grid elevation map, calculate the visible path between the base station and the grid points, and form a terrain occlusion compensation model; The said Step S2 includes: Generate a candidate base station set based on the greedy algorithm: Each time, select the base station that covers the most uncovered area and compensates for the terrain blind area until the coverage rate reaches 90% or the number of base stations reaches the preset upper limit; Globally optimize the candidate base station set through the genetic algorithm, use variable-length coding and a fitness function to evaluate the base station combination, and the fitness function is: ； where β is the base station number penalty coefficient, is , f is , z is , s is , b is .

2. The method for analyzing and evaluating the location selection of the base station of the broadcast radio positioning system according to claim 1, characterized in that The said Step S1 includes: The judgment formula for the said visible path is: ； Among them, Path(a,g) is the set of straight-line path coordinates from base station a to grid point g, and DEM(x,y) is the terrain elevation value at coordinates (x,y). is the absolute height of the base station : the relative elevation difference of the terrain relative to the base station is the earth curvature compensation coefficient, and d is for other cases.

3. The method for analyzing and evaluating the site selection of the base station of the broadcast radio positioning system according to claim 1, wherein, The said Step S2 also includes the optimization of the parallel computing architecture: Adopt a GPU-accelerated hierarchical strategy. At level 1, each GPU thread block processes the coverage calculation of a base station combination, and at level 2, each thread processes the LOS judgment between a single base station and the grid points.

4. The method for analyzing and evaluating the location selection of the base station of the broadcast radio positioning system according to claim 1, characterized in that, In the said Step S3, the three-dimensional evaluation system includes: Deployment cost calculation: Integrate the cable cost and the power supply difficulty cost. The cable cost calculation formula is: ， Among them, is the direct distance from base station a to the central node, is the cost of the cable per unit length, n is the central node, and p is the base station set; System robustness evaluation: By randomly simulating base station failure scenarios, calculate the effective coverage rate of the remaining base stations, and then calculate the system robustness score R; ; ; Among them, represents the number of times of the simulated base station failure test; i represents the effective coverage rate of the i-th base station failure simulation test; Positioning accuracy evaluation: Generate a positioning accuracy score based on the average geometric dilution of precision and the root mean square error; The comprehensive score formula is: ， Among them, , , are adjustable weight coefficients, B is the positioning accuracy score, is the deployment cost.

5. The method for analyzing and evaluating the location selection of the base station of the broadcast radio positioning system according to claim 1, wherein The said Step S4 includes: Construct a hierarchical modular digital twin model, model the transmitting antenna, power amplifier, and main control module for base stations of different frequency bands respectively, and support dynamic adjustment of transmission parameters; Establish a multi-band signal propagation model, introduce a quantum noise term and time series correlation analysis, and use an LSTM network to simulate the dynamic superposition of signals; Construct a knowledge graph based on the historical deployment case library, and apply topological data analysis to extract the topological features of the signal coverage area.

6. The method for analyzing and evaluating the location selection of the base station of the broadcast radio positioning system according to claim 1, wherein, The said Step S5 includes: Generate a three-dimensional visualization interface using physical rendering and ray tracing technology, and distinguish the color coding of base stations of different frequency bands; Integrate a time dimension analysis module to display the impact of day and night and seasonal changes on the base station performance; Build an interactive deployment sand table system, which supports real-time adjustment of base station parameters and environmental factors through gesture recognition or voice commands.

7. A base station deployment evaluation system for a broadcast radio positioning system, which is used to execute the base station site selection analysis and evaluation method for the broadcast radio positioning system described in any one of claims 1-3, characterized in that, It includes: A database module for storing base station deployment sample data and elevation models; An algorithm optimization module for performing combined optimization of the greedy algorithm and the genetic algorithm; An evaluation module for implementing a three-dimensional evaluation system; A digital twin module for constructing a machine learning recommendation of a multi-band signal model and a historical case library; A visualization module providing three-dimensional dynamic presentation and interactive parameter adjustment functions.

8. The base station deployment evaluation system for a broadcast radio positioning system according to claim 7, characterized in that, The digital twin module supports a quantum scattering and absorption model to simulate the propagation loss of high-frequency signals at the nanoscale.

9. The base station deployment evaluation system for a broadcast radio positioning system according to claim 7, wherein The visualization module integrates global illumination rendering and real-time isosurface dynamic generation technologies to reflect the changing trend of the signal coverage range over time.

Images