Method for distributing wireless intercom relay devices for railway yards

Abstract

The application discloses a wireless intercom relay device distribution method for a railway station yard, which solves the problem that a traditional static design cannot simulate train dynamic shielding to cause a signal blind area by constructing a three-dimensional electromagnetic scene model containing static environment information and a dynamic shielding factor; signal coverage prediction in different time periods is carried out based on a dynamic ray tracing algorithm, and dynamic weak field areas and signal penetration difficult areas are accurately identified; a multi-objective optimization model is established by fusing signal quality, engineering economy and interference avoidance indexes, and coverage performance and construction cost are balanced; and an improved multi-objective evolutionary algorithm is used to optimize and generate a Pareto optimal distribution scheme set. The application improves the fitting degree of the distribution scheme and actual operation conditions, avoids overdesign, and effectively solves the wireless signal coverage problem of a railway station yard with a covered property.

Description

Technical Field

[0001] This application relates to the field of railway communication and intelligent optimization algorithms, specifically to a method for deploying wireless intercom relay equipment in railway stations, which is particularly suitable for complex electromagnetic environments such as railway stations with covered properties and EMU depots. Background Technology

[0002] Various railway stations, including railway parking lots and EMU depots, occupy large areas and are often located in central urban areas, thus impacting the city's land planning and utilization. Some railway stations, whose daily operations rarely involve high-rise, multi-level space utilization, are ideally suited for development and construction in conjunction with elevated property development, effectively improving land utilization, optimizing the urban landscape, and enhancing both economic and social benefits. Therefore, elevated property development schemes are receiving increasing attention and application in railway station construction design, as seen in several new and renovated elevated property development projects in recent years, such as the Hangzhou West EMU Depot and the Wenzhou South EMU Depot.

[0003] However, the special building structure of the overpass property has a shielding effect on the wireless signal coverage, which greatly reduces the coverage effect of the wireless system and can also easily lead to weak fields or blind spots in the site due to network coverage defects. This places higher demands on the design of wireless communication systems in such environments.

[0004] Railway intercom systems, as crucial wireless communication systems in railway stations, are primarily used for communication between station staff, ground personnel, and onboard staff. They are indispensable for vehicle dispatching and daily maintenance. In typical small, open working environments, walkie-talkies typically communicate by directly transmitting and receiving wireless signals that travel freely in space. However, in the complex environment of railway stations, due to obstructions from structures and the vast size of the station area, handheld walkie-talkies only allow for short-range communication, making it impossible to conduct intercoms while personnel move extensively within the facilities.

[0005] Currently, the design of wireless intercom systems in railway stations (especially EMU depots with overpasses) mainly relies on manual experience or simulation software based on static environments, which has the following two limitations:

[0006] Limitations of Static Simulation: Existing ray tracing techniques typically model only the static building structures (walls, roofs) of the station. However, railway stations contain numerous trains and maintenance vehicles made of metal. When trains stop or pass, their massive metal components strongly reflect and block radio signals (Faraday cage effect). Existing static design methods cannot predict the dynamic blind zone where signals disappear after a train enters the station, leading to significant deviations between design results and actual operating conditions.

[0007] Single optimization objective: Existing optimization algorithms typically only have "maximizing coverage area" or "maximizing signal strength" as their single objective. This often results in a design scheme that, while providing good signal strength, requires the installation of a large number of devices in high-altitude, narrow, or power-free areas, leading to high construction difficulty, high construction costs, and maintenance difficulties. Summary of the Invention

[0008] This application aims to address the problems of "design not matching actual operating conditions" and "difficulty in balancing cost and performance" in existing technologies. Specific technical problems include: 1. How to incorporate the dynamic running / parking states of trains into a 3D simulation model to simulate signal obstruction and fading under real electromagnetic conditions. 2. How to establish a multi-objective optimization model that includes signal quality indicators and engineering economic indicators (installation difficulty, cable cost). To achieve the above objectives, this application adopts the following technical solutions.

[0009] In a first aspect, embodiments of this application provide a method for deploying wireless intercom relay equipment in railway stations, including:

[0010] Construct a three-dimensional electromagnetic scene model that includes static environmental information and dynamic occlusion factors;

[0011] Based on the aforementioned three-dimensional electromagnetic scene model, a dynamic ray tracing algorithm is used to predict wireless signal coverage at different times, identifying dynamic weak field areas and areas where signals are difficult to penetrate.

[0012] Establish a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators;

[0013] An improved multi-objective evolutionary algorithm is used to optimize the multi-objective optimization mathematical model and generate a Pareto optimal point placement scheme set.

[0014] Select and output a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set.

[0015] Furthermore, the step of constructing a three-dimensional electromagnetic scene model that includes static environmental information and dynamic occlusion factors specifically includes:

[0016] Construct a three-dimensional static BIM model of the railway station and surrounding buildings, and assign corresponding electromagnetic parameters to each component in the model;

[0017] Obtain train operation plan data, and determine the position and status of the train in the station at different time points based on the operation plan data;

[0018] A dynamic occlusion matrix is ​​established, the elements of which correspond to the signal occlusion states at different times and spatial locations. The dynamic occlusion matrix is ​​then spatiotemporally fused with a 3D static BIM model to obtain a 3D electromagnetic scene model.

[0019] Furthermore, the step of using the dynamic ray tracing algorithm to predict wireless signal coverage at different time periods specifically includes:

[0020] Set the operating frequency band and antenna parameters for the wireless intercom system;

[0021] For three-dimensional electromagnetic scene models at different times, the mirror ray tracing method is used to obtain the direct path, reflection path and diffraction path of wireless signals;

[0022] Obtain the Fresnel diffraction loss in the train obstruction edge region and correct the signal field strength calculation results;

[0023] Generate signal coverage heatmaps for different time periods, and identify dynamic weak field areas and areas where signals have difficulty penetrating by comparing the signal coverage heatmaps for different time periods.

[0024] Furthermore, the steps for establishing a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators specifically include:

[0025] A signal quality function is constructed, which is used to quantify the coverage probability of wireless signals and the degree of elimination of dynamic weak field areas;

[0026] Construct an engineering cost function, which is used to quantify the installation difficulty, cable cost and power acquisition cost of the relay equipment;

[0027] Construct an interference avoidance function, which is used to quantify the risk of co-channel interference between multiple relay devices;

[0028] Based on the actual engineering requirements, weight coefficients are assigned to each function, and the signal quality function, engineering cost function, and interference avoidance function are weighted and combined to form a fitness function for multi-objective optimization.

[0029] Furthermore, the step of using an improved multi-objective evolutionary algorithm to optimize the multi-objective mathematical model specifically includes:

[0030] The location coordinates, antenna height, azimuth angle, and downtilt angle of the relay device are encoded into the chromosome of the algorithm;

[0031] An initial population is generated within a predetermined candidate area of ​​the station; the predetermined candidate area includes at least one of the following: the top of the lighthouse, the eaves of the maintenance depot, and the edge of the upper podium building, which are physically feasible and easy to install and maintain;

[0032] The fitness function of the multi-objective optimization mathematical model is used to evaluate individuals in the population;

[0033] A new generation of population is generated through selection, crossover, and mutation operations, and the process iterates until the algorithm converges.

[0034] Furthermore, the steps for generating a new generation population through selection, crossover, and mutation operations specifically include:

[0035] A tournament selection method is used to select outstanding individuals from the current population as parents.

[0036] An adaptive crossover operator is used for crossover operations. The crossover probability is dynamically adjusted according to the fitness distribution of the population. When the fitness of the population tends to be consistent, the crossover probability is increased; when the fitness difference of the population exceeds a preset threshold, the crossover probability is decreased.

[0037] An adaptive mutation operator is used for mutation operations. The mutation probability is dynamically adjusted according to the fitness value of the individual. Individuals with higher fitness values ​​use a lower mutation probability.

[0038] Parent and offspring individuals are merged, and a new generation of population is selected by non-dominated sorting and crowding calculation.

[0039] Furthermore, the iterative evolution process also includes the following steps:

[0040] Identify atriums, openwork areas, and multi-column structures in superstructure properties;

[0041] The guidance algorithm prioritizes searching for antenna locations that can use the aforementioned building structure for signal propagation, directs the antenna beam towards the atrium or openwork area, and uses the waveguide effect formed by the multi-column structure to introduce the signal into the core operating layer of the station.

[0042] For high-cost locations located inside the upper cover, evaluate the coverage effect after relocating them to the lower site and adjusting the antenna parameters. If the coverage effect is comparable, then the locations inside the upper cover will be eliminated.

[0043] Furthermore, the step of selecting and outputting a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set specifically includes:

[0044] Each scheme in the Pareto optimal deployment scheme set is evaluated in multiple dimensions, including at least one of the following: signal coverage, dynamic weak field area, total number of devices, total construction cost, and interference risk level.

[0045] Based on the project requirements, the priority and threshold of each evaluation dimension are set, and the layout scheme that meets the requirements is selected.

[0046] The selected deployment scheme is output, including at least one of the following: the specific installation location of the equipment, antenna parameters, predicted coverage heat map, and simulation video of the dynamic changes in signal when trains enter and leave the station.

[0047] Secondly, embodiments of this application provide a wireless intercom relay equipment deployment system for railway stations, comprising:

[0048] The 3D scene modeling module is used to construct a 3D electromagnetic scene model that includes static environmental information and dynamic occlusion factors.

[0049] The dynamic coverage prediction module is connected to the three-dimensional scene modeling module and is used to predict wireless signal coverage at different time periods based on the three-dimensional electromagnetic scene model using a dynamic ray tracing algorithm, and to identify dynamic weak field areas and areas where signals are difficult to penetrate.

[0050] A multi-objective optimization module, connected to the dynamic coverage prediction module, is used to establish a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators.

[0051] The intelligent optimization module, connected to the multi-objective optimization module, is used to optimize the multi-objective optimization mathematical model using an improved multi-objective evolutionary algorithm to generate a Pareto optimal point placement scheme set.

[0052] The solution output module, connected to the intelligent optimization module, is used to select and output a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set.

[0053] Thirdly, embodiments of this application provide an electronic device, including: one or more processors;

[0054] A memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are able to implement the steps in any of the preceding placement methods.

[0055] Fourthly, embodiments of this application provide a computer-readable medium storing a computer program, which, when executed by a processor, can implement the steps in the placement method described in any of the preceding claims.

[0056] This application discloses a method for deploying wireless intercom relay equipment in railway stations. This method solves the problem of signal blind spots caused by dynamic train obstruction caused by traditional static design by constructing a three-dimensional electromagnetic scene model that includes static environmental information and dynamic obstruction factors. It predicts signal coverage at different times based on a dynamic ray tracing algorithm, accurately identifying dynamic weak signal areas and areas with difficult signal penetration. A multi-objective optimization model integrating signal quality, engineering economy, and interference avoidance indicators is established to balance coverage performance and construction costs. An improved multi-objective evolutionary algorithm is used to generate a Pareto-optimal deployment scheme set. This application improves the fit between the deployment scheme and actual operating conditions, avoids over-design, and effectively solves the problem of wireless signal coverage in railway stations with covered properties. Attached Figure Description

[0057] Figure 1 A core flowchart illustrating a method for deploying wireless intercom relay equipment in railway stations, as provided in this application embodiment;

[0058] Figure 2 A schematic diagram of the module structure of a wireless intercom relay equipment deployment system for railway stations, provided as an embodiment of this application;

[0059] Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] To enable those skilled in the art to better understand the technical solutions of this application, exemplary embodiments of this application are described below with reference to the accompanying drawings, including various details of the embodiments of this application to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. Unless otherwise specified, the various embodiments of this application and the features within those embodiments can be combined with each other.

[0061] As used herein, the term "and / or" includes any and all combinations of one or more of the associated enumerated entries. The terminology used herein is for describing particular embodiments only and is not intended to limit the application. As used herein, the singular forms "a" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that when the terms "comprising" and / or "made of" are used herein, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0062] Unless otherwise specified, all terms used in this application (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It should also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this application.

[0063] refer to Figure 1 One embodiment of this application takes a high-speed train depot with a covered property as the application scenario, and describes in detail a method for deploying wireless intercom relay equipment in railway stations. The deployment method may specifically include the following steps.

[0064] Step S1: Construct a three-dimensional electromagnetic scene model that includes static environmental information and dynamic occlusion factors.

[0065] This step constructs a high-precision 3D electromagnetic scene using a combination of static and dynamic methods, providing an accurate environmental foundation for subsequent simulations. Specifically, it includes:

[0066] S1.1 Static Environment Modeling

[0067] Using 3D modeling software such as Revit or SketchUp, a high-precision BIM model is constructed based on the construction drawings of the railway station and the property above it, accurately reproducing the track layout, maintenance warehouse, catenary columns, and the beam and column structure and floor slab open areas (atrium, skylight, etc.) of the property above it.

[0068] Assign corresponding electromagnetic parameters to each component in the model:

[0069] Concrete walls: their relative permittivity ε r =5.8, conductivity σ=0.01 S / m.

[0070] Metal components (contact wire columns, steel beams): their relative permittivity ε r =∞, conductivity σ=5.8×10 7 S / m.

[0071] Glass curtain wall: Its relative permittivity εr=7.0, conductivity σ=0.001 S / m.

[0072] S1.2, Dynamic Occlusion Factor Injection

[0073] Import the station's train timetable or vehicle parking schedule, and set the simulation timeline (e.g., 08:00-20:00, covering the main operating periods of the day). Based on the timetable, dynamically overlay standardized train entity models (high-loss metal bodies) onto the track model. Set the train model as a high-loss dynamic obstacle; when the simulation time is during train stopping periods, the ray path in this area is forcibly determined as occlusion or high attenuation.

[0074] S1.3, Dynamic Occlusion Matrix Construction and Spatiotemporal Fusion

[0075] Establish a dynamic occlusion matrix M(t,x,y,z) in the spatiotemporal dimension, where t is a time slice (e.g., every 15 minutes is a time slice); (x,y,z) are the coordinates of the spatial grid cell; a matrix element value of 1 indicates that the spatial cell is blocked by the train in that time slice, and the signal propagation path is blocked; a value of 0 indicates that it is not blocked.

[0076] By spatiotemporally linking the dynamic occlusion matrix with the 3D static BIM model, the electromagnetic characteristics of the scene at different points in time can be automatically switched to form a complete 3D electromagnetic scene model.

[0077] Step S2: Based on the three-dimensional electromagnetic scene model, a dynamic ray tracing algorithm is used to predict wireless signal coverage at different time periods, and to identify dynamic weak field areas and areas where signals are difficult to penetrate.

[0078] This step uses dynamic simulation to calculate signal coverage at different times to accurately identify problem areas, specifically including:

[0079] S2.1 Ray Tracing Parameter Settings

[0080] Configure the operating parameters of the wireless intercom system:

[0081] Operating frequency band: 400-470 MHz (UHF band, railway industry standard frequency band).

[0082] Transmitting power: 5 W.

[0083] Antenna gain: 3 dBi.

[0084] Maximum number of reflections: 4.

[0085] Energy threshold: -80 dB (rays below this threshold will no longer be tracked).

[0086] S2.2 Dynamic Simulation Calculation

[0087] The mirror ray tracing method is used to calculate the signal propagation under the conditions of "no trains" (all trains have left the depot) and "full trains" (all tracks are full of trains during peak hours), and to calculate the direct path, reflection path and diffraction path of the signal.

[0088] S2.3, Fresnel diffraction loss correction

[0089] For the region at the edge of the train body, Fresnel diffraction theory is used to calculate the signal loss around the train body, thus correcting the field strength calculation results of traditional ray tracing. The correction formula is:

[0090] L d =20lg(F1 / d)+10lg(λ / 2π)

[0091] Among them, L d λ is the Fresnel diffraction loss (dB); F1 is the radius of the first Fresnel zone (m); d is the distance from the diffraction point to the receiving point (m); λ is the signal wavelength (m).

[0092] S2.4 Problem Area Identification

[0093] Generate signal coverage heatmaps for both no-vehicle and full-vehicle states, and identify the following by comparing the two heatmaps:

[0094] Dynamic weak signal area: The area with signal strength ≥ -85 dBm when there are no vehicles and signal strength ≤ -100 dBm when there are vehicles.

[0095] Areas with poor signal penetration: Areas located below the above-ground property, continuously blocked by multiple floors, where the signal strength is ≤-100 dBm regardless of whether there are trains or not.

[0096] Step S3: Establish a multi-objective optimization mathematical model for fused signal quality indicators, engineering economic indicators, and interference avoidance indicators.

[0097] This step establishes a comprehensive multi-objective optimization model to balance signal quality, engineering costs, and interference risks, specifically including:

[0098] S3.1 Constructing the signal quality function

[0099] The signal quality function S(x) is used to quantify the coverage performance of the deployment scheme, and the formula is:

[0100]

[0101] in, The signal coverage probability of the key operational area of ​​the station (value range 0-1). The ratio of the dynamic weak field area to the total working area (value range 0-1); These are weighting coefficients, which can be adjusted according to project requirements (usually). =0.7, =0.3).

[0102] S3.2 Constructing the Engineering Cost Function

[0103] The engineering cost function C(x) is used to quantify the engineering implementation difficulty and cost of the site selection plan. The formula is as follows:

[0104]

[0105] in, The distance (m) from the equipment installation location to the nearest power source; To estimate the feeder length (m); Installation difficulty coefficient (1.0 for ground operations, 1.5 for high-altitude operations, and 2.0 for operations in confined spaces). , , This is a weighting factor, which can be adjusted based on local labor and material costs.

[0106] S3.3 Constructing the interference avoidance function

[0107] The interference avoidance function I(x) is used to quantify the risk of co-channel interference between multiple relay devices, and the formula is:

[0108]

[0109] Where n is the total number of relay devices; P i , P j denoted as the received signal level (dBm) of the i-th and j-th relay devices in the overlapping coverage area. This is the threshold for isolation from co-channel interference (usually taken as 70 dB). = For the received signal level difference in the overlapping area of ​​multiple repeaters, if it is less than the required isolation threshold... If so, it is determined to be a risk of interference.

[0110] S3.4 Constructing a multi-objective fitness function

[0111] The three functions mentioned above are weighted and combined to form the fitness function F(x) for multi-objective optimization, as shown in the formula:

[0112]

[0113] Among them, w1, w2, and w3 are weighting coefficients, which can be adjusted according to project needs (w1=0.5, w2=0.3, w3=0.2 for newly built stations; w1=0.3, w2=0.5, w3=0.2 for renovated stations); k1 is an adjustment coefficient (usually taken as 10). The fitness function represents the target signal coverage satisfaction (usually taken as 0.95). A higher fitness function value indicates a better distribution scheme.

[0114] Step S4: Use an improved multi-objective evolutionary algorithm to optimize the multi-objective optimization mathematical model and generate a Pareto optimal point placement scheme set.

[0115] This step uses an improved genetic algorithm to solve the multi-objective optimization model and generate a set of Pareto optimal point placement schemes, specifically including:

[0116] S4.1 Chromosome Coding

[0117] Using real-number encoding, the parameters of each relay device are encoded as a gene segment of a chromosome. Each gene segment contains: three-dimensional position coordinates (x, y, z); antenna height h; antenna azimuth angle θ; and antenna downtilt angle φ.

[0118] S4.2 Initial Population Generation

[0119] Candidate areas are pre-defined on the station map, excluding areas within vehicle clearance limits, high-altitude hazardous areas (height exceeding 30 m and without maintenance access), and areas without power supply. Candidate areas include: the top of the lighthouse, the eaves of the maintenance depot, the edge of the upper podium, and the columns around the atrium.

[0120] 100 deployment schemes are randomly generated within the above candidate areas as the initial population, with each scheme containing 3-8 relay devices.

[0121] S4.3 Fitness Evaluation

[0122] The fitness function F(x) constructed in step S3 is used to evaluate each individual in the population and calculate its fitness value.

[0123] S4.4 Adaptive Selection Crossover Mutation Operation

[0124] Selection operation: The tournament selection method is adopted. Each time, 3 individuals are randomly selected from the population, and the individual with the highest fitness value is selected as the parent. A total of 70 parent individuals are selected.

[0125] Crossover operation: An adaptive crossover operator is used, with a crossover probability p. c Dynamically adjusted based on population fitness variance:

[0126] When the population fitness variance < σ threshold, p c =0.9;

[0127] When the population fitness variance is ≥ σ threshold , p c =0.5.

[0128] Where, σ threshold This is the variance threshold (usually set to 0.01). During crossover, partial gene segments are exchanged between the two parent individuals.

[0129] Mutation operation: An adaptive mutation operator is used, with a mutation probability p. m Dynamically adjusted based on individual fitness values:

[0130] When an individual's fitness ranks in the top 30%, p m =0.01;

[0131] When the individual fitness ranking is in the bottom 30%, p m =0.1;

[0132] In other cases, p m =0.05.

[0133] During mutation, the value of a certain gene in an individual is randomly adjusted.

[0134] Population update: The parent and offspring individuals are merged, and the top 100 individuals are selected as the new generation population through non-dominated sorting and crowding calculation.

[0135] S4.5 Optimization Guidance for Architectural Structure Perception

[0136] During the iterative evolution process, a building structure perception mechanism is incorporated:

[0137] 1. Identify the atrium, openwork areas, and multi-column structures in the above-ground property.

[0138] 2. The guidance algorithm prioritizes searching for antenna points around the above-mentioned areas, directing the main beam of the antenna towards the atrium or hollowed-out area, and using vertical penetration to introduce the signal into the core operating layer of the station.

[0139] 3. Utilize the natural waveguide effect formed by the multi-column structure to optimize the antenna azimuth angle, enabling the signal to propagate horizontally within the station.

[0140] 4. For high-cost locations located inside the top cover (C(x)>C threshold The coverage effect after relocating the antenna to the lower site and adjusting its height and downtilt angle will be evaluated. If the coverage effect is comparable (signal coverage difference <2%) and the cost is reduced by >30%, the internal points of the upper cover will be eliminated, and the vertical coverage points below will be retained.

[0141] S4.6 Algorithm Convergence Judgment

[0142] After 50 iterations, the algorithm converges and outputs multiple sets of Pareto optimal placement schemes.

[0143] Step S5: Select and output a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set.

[0144] This step filters the Pareto optimal solution set and outputs a point layout scheme that meets the engineering requirements, specifically including:

[0145] S5.1 Multi-dimensional evaluation

[0146] Each scheme in the Pareto optimal solution set is evaluated from multiple dimensions. The evaluation indicators include: signal coverage in key operating areas, the proportion of dynamic weak field areas, the total number of relay devices, the estimated total construction cost, and the risk level of co-channel interference.

[0147] S5.2 Solution Selection

[0148] Thresholds for each evaluation indicator are set according to project requirements, such as: signal coverage rate in critical operating areas ≥ 98%; dynamic weak signal area ratio ≤ 1%; total construction cost ≤ budget ceiling. Solutions that simultaneously meet all thresholds are selected. If multiple solutions meet the requirements, they are chosen based on preset priorities (e.g., cost priority or coverage priority). Two typical solutions are typically output:

[0149] Option A (Ultimate Coverage): This option involves a large number of devices, has a higher cost, and eliminates any signal blind spots. It is suitable for core sites with extremely high requirements for communication reliability.

[0150] Option B (Economic Balanced): The number of equipment is moderate, it tolerates a very small number of weak fields in non-critical areas, the cost is low, and it is suitable for most ordinary stations.

[0151] S5.3 Output of Results

[0152] Output detailed results for the selected scheme, including: a coordinate table of relay equipment installation locations; an antenna parameter table (mount height, azimuth, downtilt angle); a signal coverage heat map (no train status and full train status); construction drawings (including equipment installation locations and feeder routes); and a simulation video of dynamic signal changes when trains enter and leave the station.

[0153] refer to Figure 2 An embodiment of this application also provides a system for implementing the above-described placement method, comprising:

[0154] 1. 3D Scene Modeling Module: Used to import BIM models and train operation plan data, construct a 3D electromagnetic scene model containing static environmental information and dynamic occlusion factors, and output it to the dynamic coverage prediction module.

[0155] 2. Dynamic Coverage Prediction Module: Receives a 3D electromagnetic scene model, runs dynamic ray tracing simulation, generates signal coverage heatmaps for different time periods, identifies dynamic weak field areas and areas where signal penetration is difficult, and outputs the results to the multi-target optimization module.

[0156] 3. Multi-objective optimization module: Receives problem area information and engineering parameters, establishes a multi-objective optimization mathematical model that integrates signal quality, engineering economy and interference avoidance indicators, and outputs it to the intelligent optimization module.

[0157] 4. Intelligent Optimization Module: Runs an improved genetic algorithm to iteratively optimize the point placement scheme, generate a Pareto optimal point placement scheme set, and output it to the scheme output module.

[0158] 5. Solution Output Module: Based on the engineering requirements and thresholds set by the user, the module selects the optimal site layout scheme and generates construction drawings, coverage heat maps, and dynamic simulation videos.

[0159] The embodiments of the aforementioned placement method and the embodiments of the aforementioned placement system are identical or related in technical concept, and can be referenced and learned from each other in terms of technical details and technical effects, which will not be repeated here.

[0160] Overall, the advantages of this application compared to the prior art include:

[0161] 1. Existing technologies rely solely on ray tracing simulations based on the static building structures of railway stations, completely ignoring the dynamic blocking effect of numerous metal trains within the station. This makes it impossible to predict dynamic blind spots where signals suddenly interrupt after a train enters the station, leading to significant discrepancies between design results and actual operating conditions. This often results in the problem of "full coverage in design, multiple blind spots in operation." This application constructs a three-dimensional electromagnetic scene model incorporating static environmental information and dynamic blocking factors. It transforms train operation plan data into a spatiotemporal dynamic blocking matrix and combines this with a dynamic ray tracing algorithm to simulate signal propagation characteristics at different times. This allows for accurate identification of dynamic weak signal areas where signals are present during normal times but interrupted when trains pass through, significantly improving the accuracy of signal coverage prediction and making the signal distribution scheme more closely aligned with the actual operating conditions of railway stations.

[0162] 2. Existing deployment optimization algorithms typically focus solely on maximizing coverage area or signal strength. In pursuit of theoretically perfect coverage, they often deploy numerous repeater devices in high-altitude, narrow, or power-deprived areas, significantly increasing construction costs and creating problems such as complex construction and difficult maintenance. This application establishes a multi-objective optimization mathematical model that integrates signal quality, engineering economics, and interference avoidance indicators. It quantifies practical engineering factors such as equipment installation difficulty, cable costs, and power supply acquisition difficulty, while also considering co-channel interference risks. This model automatically avoids high-cost, high-difficulty installation locations while ensuring signal coverage in critical operational areas, effectively preventing over-design that prioritizes signal strength above all else.

[0163] 3. Existing technologies for antenna placement in railway stations with covered properties typically employ a uniform placement method within the covered area. This approach fails to fully utilize the structural characteristics of the covered property, resulting in low signal penetration efficiency. A large number of devices are required to cover the station's operational level below, and construction within the covered area is often constrained by property operations. This application introduces a building structure perception mechanism into the intelligent optimization process. This guides the algorithm to prioritize searching for antenna locations around the covered atrium and openwork areas, directing the antenna beams towards the vertical penetration channel. Simultaneously, the natural waveguide effect formed by the multi-column structure enables horizontal signal propagation within the station. This reduces the number of devices required while maintaining coverage quality, and avoids construction in high-cost areas within the covered area, significantly reducing the complexity of the project and its impact on property operations.

[0164] 4. Existing genetic algorithms typically use fixed crossover and mutation probabilities. When the fitness of the population tends to be consistent, they are prone to getting trapped in local optima. Conversely, when the fitness of the population varies greatly, they may destroy superior individuals, resulting in low optimization accuracy and slow convergence speed. This application employs an adaptive crossover and mutation operator, dynamically adjusting the crossover probability based on the fitness distribution of the population and the mutation probability based on the fitness value of the individual. This not only increases population diversity when the population tends to converge, avoiding getting trapped in local optima, but also protects superior individuals when the population varies greatly, accelerating the convergence speed and effectively improving the optimization efficiency and quality of the placement scheme.

[0165] 5. Existing technologies typically only output a single deployment scheme, failing to address the diverse needs of different projects. Furthermore, the output is mostly static coverage heatmaps, which are insufficient to visually represent signal changes during dynamic train operation, potentially leading to insufficient prediction by construction teams during on-site implementation. This application generates a Pareto-optimal deployment scheme set, providing engineers with various options such as extreme coverage and economical balance schemes, while also outputting simulation videos of dynamic signal changes during train entry and exit from stations. This allows construction teams to intuitively understand signal coverage at different times, promptly identify and resolve potential problems, and improve construction efficiency and system reliability after completion.

[0166] Based on the same inventive concept, embodiments of this application also provide an electronic device. Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of this application. Figure 3 As shown in the embodiments of this application, an electronic device includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the placement methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0167] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0168] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0169] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0170] This application also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the placement methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0171] This application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described placement method.

[0172] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0173] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0174] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0175] The computer program instructions used to perform the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing the status information of the computer-readable program instructions. These electronic circuits can execute the computer-readable program instructions to implement various aspects of this application.

[0176] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0177] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0178] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0179] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0180] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0181] Exemplary embodiments have been disclosed in this application, and while specific terminology has been used, it is used only and should be interpreted in a general illustrative sense and is not intended to be limiting. In some embodiments, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this application as set forth by the appended claims.

Claims

1. A method for deploying wireless intercom relay equipment in railway stations, characterized in that, include: Construct a three-dimensional electromagnetic scene model that includes static environmental information and dynamic occlusion factors; Based on the aforementioned three-dimensional electromagnetic scene model, a dynamic ray tracing algorithm is used to predict wireless signal coverage at different times, identifying dynamic weak field areas and areas where signals are difficult to penetrate. Establish a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators; An improved multi-objective evolutionary algorithm is used to optimize the multi-objective optimization mathematical model and generate a Pareto optimal point placement scheme set. Select and output a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set.

2. The point-distribution method according to claim 1, characterized in that, The steps for constructing a three-dimensional electromagnetic scene model that includes static environmental information and dynamic occlusion factors specifically include: Construct a three-dimensional static BIM model of the railway station and surrounding buildings, and assign corresponding electromagnetic parameters to each component in the model; Obtain train operation plan data, and determine the position and status of the train in the station at different time points based on the operation plan data; A dynamic occlusion matrix is ​​established, the elements of which correspond to the signal occlusion states at different times and spatial locations. The dynamic occlusion matrix is ​​then spatiotemporally fused with a 3D static BIM model to obtain a 3D electromagnetic scene model.

3. The point-distribution method according to claim 1, characterized in that, The steps for predicting wireless signal coverage at different time periods using the dynamic ray tracing algorithm specifically include: Set the operating frequency band and antenna parameters for the wireless intercom system; For three-dimensional electromagnetic scene models at different times, the mirror ray tracing method is used to obtain the direct path, reflection path and diffraction path of wireless signals; Obtain the Fresnel diffraction loss in the train obstruction edge region and correct the signal field strength calculation results; Generate signal coverage heatmaps for different time periods, and identify dynamic weak field areas and areas where signals have difficulty penetrating by comparing the signal coverage heatmaps for different time periods.

4. The point-distribution method according to claim 1, characterized in that, The steps for establishing a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators specifically include: A signal quality function is constructed, which is used to quantify the coverage probability of wireless signals and the degree of elimination of dynamic weak field areas; Construct an engineering cost function, which is used to quantify the installation difficulty, cable cost and power acquisition cost of the relay equipment; Construct an interference avoidance function, which is used to quantify the risk of co-channel interference between multiple relay devices; Based on the actual engineering requirements, weight coefficients are assigned to each function, and the signal quality function, engineering cost function, and interference avoidance function are weighted and combined to form a fitness function for multi-objective optimization.

5. The point-distribution method according to claim 1, characterized in that, The steps of using an improved multi-objective evolutionary algorithm to optimize the multi-objective mathematical model specifically include: The location coordinates, antenna height, azimuth angle, and downtilt angle of the relay device are encoded into the chromosome of the algorithm; An initial population is generated within a predetermined candidate area of ​​the station; the predetermined candidate area includes at least one of the following: the top of the lighthouse, the eaves of the maintenance depot, and the edge of the upper podium building, which are physically feasible and easy to install and maintain; The fitness function of the multi-objective optimization mathematical model is used to evaluate individuals in the population; A new generation of population is generated through selection, crossover, and mutation operations, and the process iterates until the algorithm converges.

6. The point-distribution method according to claim 5, characterized in that, The steps for generating a new generation population through selection, crossover, and mutation operations specifically include: A tournament selection method is used to select outstanding individuals from the current population as parents. An adaptive crossover operator is used for crossover operations. The crossover probability is dynamically adjusted according to the fitness distribution of the population. When the fitness of the population tends to be consistent, the crossover probability is increased; when the fitness difference of the population exceeds a preset threshold, the crossover probability is decreased. An adaptive mutation operator is used for mutation operations. The mutation probability is dynamically adjusted according to the fitness value of the individual. Individuals with higher fitness values ​​use a lower mutation probability. Parent and offspring individuals are merged, and a new generation of population is selected by non-dominated sorting and crowding calculation.

7. The point-distribution method according to claim 5, characterized in that, The iterative evolution process also includes the following steps: Identify atriums, openwork areas, and multi-column structures in superstructure properties; The guidance algorithm prioritizes searching for antenna locations that can use the aforementioned building structure for signal propagation, directs the antenna beam towards the atrium or openwork area, and uses the waveguide effect formed by the multi-column structure to introduce the signal into the core operating layer of the station. For high-cost locations located inside the upper cover, evaluate the coverage effect after relocating them to the lower site and adjusting the antenna parameters. If the coverage effect is comparable, then the locations inside the upper cover will be eliminated.

8. The point-distribution method according to claim 1, characterized in that, The step of selecting and outputting a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set specifically includes: Each scheme in the Pareto optimal deployment scheme set is evaluated in multiple dimensions, including at least one of the following: signal coverage, dynamic weak field area, total number of devices, total construction cost, and interference risk level. Based on the project requirements, the priority and threshold of each evaluation dimension are set, and the layout scheme that meets the requirements is selected. The selected deployment scheme is output, including at least one of the following: the specific installation location of the equipment, antenna parameters, predicted coverage heat map, and simulation video of the dynamic changes in signal when trains enter and leave the station.

9. A deployment system for wireless intercom relay equipment in railway stations, characterized in that, include: The 3D scene modeling module is used to construct a 3D electromagnetic scene model that includes static environmental information and dynamic occlusion factors. The dynamic coverage prediction module is connected to the three-dimensional scene modeling module and is used to predict wireless signal coverage at different time periods based on the three-dimensional electromagnetic scene model using a dynamic ray tracing algorithm, and to identify dynamic weak field areas and areas where signals are difficult to penetrate. A multi-objective optimization module, connected to the dynamic coverage prediction module, is used to establish a multi-objective optimization mathematical model that integrates signal quality indicators, engineering economic indicators, and interference avoidance indicators. The intelligent optimization module, connected to the multi-objective optimization module, is used to optimize the multi-objective optimization mathematical model using an improved multi-objective evolutionary algorithm to generate a Pareto optimal point placement scheme set. The solution output module, connected to the intelligent optimization module, is used to select and output a layout scheme that meets the engineering requirements from the Pareto optimal layout scheme set.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it can implement the steps in the placement method as described in any one of claims 1 to 8.

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