Mountainous city network planning system based on GIS and ray tracing

Abstract

The present application relates to a kind of mountain wisdom city network planning system based on GIS and ray tracking, belong to communication technical field.The system includes three-dimensional GIS geographic information module, ray tracking simulation module and GIS and ray tracking integrated interface, wherein GIS and ray tracking integrated interface includes map conversion interface, initialization global data interface and global data simulation interface.The system combines GIS technology with space-time big data technology, realizes spatial data visualization, query, analysis and transmission etc.Operation, while using high-performance ray tracking technology, realizes high-quality topography and geophysical simulation effect, better service in the field such as city planning and design.Combined with 5G technology, the precise docking of city data and wisdom city application scene is realized.

Description

Technical Field

[0001] This invention belongs to the field of communication technology and relates to a smart city network planning system for mountainous areas based on the integration of GIS and ray tracing. Background Technology

[0002] Urban network planning systems are urban planning and design tools that combine GIS and ray tracing technologies. Their background technologies involve knowledge of Geographic Information System (GIS) technology, urban planning and design theory, terrain analysis technology, ray tracing technology, and other related fields.

[0003] GIS technology is a technology that integrates data management, processing, analysis, and visualization within a geographic information system. It enables spatial analysis and visualization of geographic data. In urban planning and design theory, it requires knowledge of urban development planning, urban layout, and urban transportation planning. Topographic analysis techniques necessitate understanding of digital elevation models (DEMs), terrain analysis, and data visualization. Key issues with GIS data include data integrity, accuracy, and representativeness. Data integrity refers to the completeness of the data, which may be missing or incomplete. Accuracy refers to the precision of the data, which may contain measurement errors or model errors. Representativeness refers to the sufficiency of the data's representation, which may be subject to sample bias. Furthermore, GIS technology requires processing and managing large amounts of spatial and attribute data, demanding significant computing and storage capabilities.

[0004] Ray tracing is a technique in computer graphics that uses ray tracing to calculate and visualize parameters such as the position, size, orientation, and movement of objects in a scene. In mountainous smart city network planning systems, ray tracing can be used to determine the occlusion relationships of buildings and plan traffic network paths, thus providing reliable data support for urban planning and design. The main challenges of ray tracing include computational speed and accuracy. Due to the large amount of computation required, it necessitates high computational power and algorithm optimization techniques.

[0005] However, existing urban network planning systems have the following problems:

[0006] (1) Accuracy issues

[0007] GIS and ray tracing technologies require high-precision processing and analysis of large amounts of geographic data, and the accuracy of this data directly impacts the accuracy of the results. Insufficient geographic data accuracy can lead to errors in data analysis and prediction; therefore, improving geographic data accuracy is one of the key technical challenges that needs to be addressed. For example, in GIS applications, users need professional map-making and data processing skills, which may be difficult for the average user. Furthermore, the high precision and high resolution requirements of GIS technology necessitate substantial computing resources and storage space, which also limits its application.

[0008] (2) Data processing issues

[0009] GIS and ray tracing technologies require substantial geographic data support, such as map data, meteorological data, and remote sensing data. The acquisition, processing, and analysis of this data demand significant computing resources and algorithmic support. Large data volumes and inconsistent data formats can negatively impact processing efficiency and accuracy. Furthermore, integrating geographic data from different types and sources, such as remote sensing and sensor data, presents integration challenges due to their varying origins and formats. Improving data consistency and reliability through integration is a critical issue. Since geographic data acquisition and processing involve multiple departments and organizations, data sharing and exchange are also essential. Ensuring data security and confidentiality, and addressing the efficiency of data sharing and utilization, are equally important.

[0010] (3) Algorithm problem

[0011] GIS and ray tracing technologies involve high algorithmic complexity, requiring extensive computation and analysis. However, current algorithms still suffer from limitations, such as insufficient accuracy and low efficiency. For instance, in GIS, the extensive map creation and data processing necessitate algorithm optimization to improve computational efficiency and accuracy. Similarly, ray tracing technology, with its complex 3D reconstruction and rendering processes, demands efficient algorithms and optimization methods to enhance both rendering quality and computational efficiency.

[0012] (4) Visualization rendering issues

[0013] GIS and ray tracing technologies can process and analyze massive amounts of geographic data, but how to visualize this data remains a crucial issue. Data visualization can intuitively show the distribution and trends of data, but how to visually represent processed data is one of the problems that needs to be solved. In GIS, due to the diverse sources of data, the accuracy and consistency of the data are difficult to guarantee. For example, map data may contain misalignments and deviations, all of which can affect GIS applications. In ray tracing technology, because 3D modeling and rendering are required, a large amount of model and texture data is needed.

[0014] Therefore, a new urban network planning system is urgently needed to adapt to mountainous cities. Summary of the Invention

[0015] In view of this, the purpose of this invention is to provide a mountain smart city network planning system based on GIS and ray tracing. This system combines GIS technology with spatiotemporal big data technology to enable spatial data visualization, querying, analysis, and transmission. Simultaneously, it utilizes high-performance ray tracing technology to achieve high-quality terrain and geophysical simulation effects, better serving urban planning and design. Furthermore, it integrates 5G technology to achieve precise integration of urban data with smart city application scenarios, such as traffic navigation, environmental monitoring, and public safety, providing technological support and guarantees for urban management and development.

[0016] To achieve the above objectives, the present invention provides the following technical solution:

[0017] A smart city network planning system for mountainous areas based on GIS and ray tracing includes a 3D GIS geographic information module, a ray tracing simulation module, and a GIS and ray tracing integration interface; the GIS and ray tracing integration interface includes multiple interfaces such as a 3D map conversion interface, a global data initialization interface, and a global data simulation interface.

[0018] The 3D GIS geographic information module is used to accurately simulate the effects of solar altitude angle, shadow and reflective topographic factors on network construction analysis. It analyzes the impact of topography and landforms on network planning, that is, to accurately simulate the effects of light in three-dimensional space, thereby helping planners to determine the best network layout and construction plan.

[0019] The ray tracing simulation module obtains information from the 3D GIS geographic information module and calculates the light intensity and reflection of each area, realizing the visualization of the 3D real-scene base station network coverage effect and the output of specific road loss data, thereby estimating the cost of building optical cables and base stations;

[0020] The GIS and ray tracing integration interface converts TIFF format map data to planet format via a 3D map conversion interface; it initializes the global data interface by calling the material library path, building height file path, feature type file path, feature index file path, simulation point and moving scatterer file path, transmitter and receiver antenna file path from the 3D GIS geographic information module, and defines the propagation mechanism to complete the global initialization before simulation calculation; and it completes the simulation calculation after global data loading by calling the propagation mechanism and simulation calculation function of the ray tracing simulation module through the global data simulation interface.

[0021] Furthermore, the distributed computing technology in the ray tracing simulation module adopts a ray tracing simulation support algorithm, which is divided into two categories according to the direction of the tracing ray: the emission-bounce ray method based on the forward algorithm and the mirror method based on the reverse algorithm.

[0022] Furthermore, the channel modeling method based on the forward algorithm-based transmit-reflect ray method is as follows: a set of test rays are emitted from the source in any direction that can be expected to propagate from the source, and each ray is expected to capture the behavior of a certain solid radiation angle from the transmitting antenna; then, these test rays are tracked in the scene, and in most cases, modeled as a triangular mesh of interfaces between electrically homogeneous materials; in the post-processing step, the intersection points of the rays with objects in the scene are recorded, and the electric field of a given transmitting antenna at a certain observation point is calculated; from the perspective of power attenuation, rays are usually regarded as spherical waves; while from the perspective of matter interaction, rays are usually regarded as plane waves, whose magnitude decays with the radius and interacts with the plane.

[0023] Furthermore, the specific steps of the mirroring method based on the reverse algorithm are as follows:

[0024] 1) Build the scene module, which mainly completes the simulation environment modeling;

[0025] 2) Build the material properties module. This module mainly determines the building material properties parameters of the simulation environment and completes the environment initialization.

[0026] 3) Build the antenna module. This module mainly includes determining the type, location, height, gain, and transmit power of the transmit and receive antennas, and completing the antenna initialization setup for the simulation environment.

[0027] 4) Build a path search module. This module mainly includes the path search process and specific mathematical methods for direct, reflected, and diffracted rays, and completes the ray tracing process.

[0028] 5) Build a diffuse scattering module. The main contents of this module include determining the diffuse scattering propagation model and calculating the effective diffuse scattering region, completing the combination of diffuse scattering and ray tracing algorithms, and improving the accuracy of the algorithm.

[0029] 6) Build a combined field strength module. This module is mainly used to calculate the combined field strength of multipath signals arriving at the receiver and output the algorithm results.

[0030] Furthermore, the construction method of the system of the present invention is as follows:

[0031] 1) Import base station, antenna, material, and building scene information, and you can finally see if all the required files have been successfully imported;

[0032] 2) Configure simulation parameters;

[0033] The simulation propagation mechanism is set according to the document: direct light, Fresnel reflection, Deygout diffraction, transmission, ground feature loss, and self-ground feature loss; the simulation area and other parameters necessary for simulation are set according to the actual area and the program.

[0034] 3) Scene preprocessing;

[0035] Based on the imported material and building information, a basic geometric structure is constructed, including the bounding box of triangles and the corresponding material information, etc.; and an acceleration structure (uniform mesh acceleration structure) is constructed based on this geometric information to reduce the number of intersection detections between rays and the scene.

[0036] 4) Start the simulation;

[0037] First, the type of simulation scene (mountainous or urban) is determined based on whether the building file contains a building vector. For urban scenes, it is determined whether to use an adaptive algorithm. If an adaptive algorithm is used, the program will divide the urban scene into multiple sub-scenes, calculate statistical data for the sub-scenes, and determine the propagation mechanism of the sub-scenes based on the statistical results. If the adaptive switch is not turned on, the simulation will be performed according to the user-preset propagation mechanism.

[0038] 5) Determine the receiver type;

[0039] There are different multipath calculation methods for indoor and outdoor receivers. For outdoor receivers, path loss is calculated based on propagation mechanisms such as direct light, O2O transmission, Fresnel reflection, and Degyout diffraction, as well as ground object loss and self-ground object loss.

[0040] 6) Indoor receiver;

[0041] The indoor receiver calculation is divided into two parts. The first part is the virtual direct path between the outdoor transmitter and the indoor receiver, and the intersection point (i.e., the boundary point) of the building surface where the indoor receiver is located. This virtual direct path uses propagation mechanisms such as direct path, O2O transmission, Fresnel reflection, and Degyout diffraction, as well as ground object loss and self-ground object loss, just like the outdoor path. The second part is the path between the boundary point and the receiver. This requires calculating the path loss based on the O2I outdoor-to-indoor transmission mechanism. The sum of the path losses of the two parts is the path loss from the outdoor transmitter to the indoor receiver.

[0042] 7) Scene judgment;

[0043] When the scene is identified as a mountainous area, the path loss between the receiver and the transmitter will be calculated by considering direct beam, Deygout diffraction, self-ground object loss, and ground object loss.

[0044] 8) File output;

[0045] Calculate the path loss based on the propagation model and output the .loss file;

[0046] 9) File merging;

[0047] Merge the road damage files into the Data file.

[0048] Furthermore, the system has a calibration function, specifically including:

[0049] 1) Parse the path name based on the file path input from the interface and initialize the propagation mechanism. The files input from the interface include: map elevation, map building height, map feature information, antenna pattern, base station operating parameters, road loss data, and material parameters, etc.

[0050] 2) Import map data and process drive test data and base station information. Run the drive test data of each base station to obtain RSRP values ​​and calculate the maximum and minimum measured RSRP values.

[0051] 3) Randomly select nmax non-repeating samples to participate in the correction;

[0052] 4) Prepare material parameters and store the data before calibration, which will be used to identify the parameters to be calibrated;

[0053] 5) Prepare an initial calibration material for each CPU, prefixed with "materialProcess";

[0054] 6) Perform geometric calculations and run the Cost Function to initialize the error;

[0055] 7) Perform calibration, update RT parameters, and output a report.

[0056] The beneficial effects of this invention are as follows:

[0057] 1) This invention combines a GIS geographic information system with a ray tracing simulation module. Specifically, the network planning system based on a high-performance ray tracing module and a 3D GIS spatiotemporal engine offers the following advantages in network planning:

[0058] (1) Line projection and collision detection

[0059] This invention can accurately simulate the impact of topographical factors such as solar altitude angle, shadows, and reflections on network construction analysis and network planning. Ray tracing can accurately simulate the effects of light in three-dimensional space, thereby helping planners determine the optimal network layout and construction scheme.

[0060] (2) Cost estimation

[0061] This invention, combined with GIS, can generate accurate topographic and geomorphological maps. By using ray tracing, it can calculate the light intensity and reflection of each area, thereby predicting the cost of constructing optical cables and base stations.

[0062] (3) Signal prediction

[0063] This invention utilizes line tracking to simulate the propagation and attenuation process of wireless signals, and combines it with GIS to obtain information such as terrain and landforms, thereby predicting the coverage and signal strength of network signals, thus providing a scientific basis for network planning.

[0064] (4) Network optimization

[0065] This invention combines ray tracing and GIS technology to generate high-precision 3D maps. By utilizing data mining and machine learning techniques, it can analyze and optimize factors such as network quality and user needs, thereby improving network performance and user experience.

[0066] 2) The intelligent sensing twin CIST system of this invention innovatively utilizes a smart computing platform based on mobile edge computing (MEC), enabling it to provide efficient, low-latency computing and data processing services for scenarios such as smart cities. It pushes computing and storage resources to the network edge, i.e., user terminals and edge servers, to achieve lower latency and higher bandwidth utilization. Based on the MEC-based smart computing platform, CIST distributes computing and data processing tasks as far as possible to the network edge, reducing data transmission and processing time and costs, and improving the utilization efficiency of computing resources, i.e., simulation accuracy and response rate.

[0067] In summary, this invention adopts an efficient urban network layout planning approach, solving the problems of low efficiency and high cost in on-site road testing; it also solves the problems of inconsistent geographic information formats and the conversion and acquisition of three-dimensional spatiotemporal data in the GIS industry; and it addresses the issues of existing smart city 5G+ network layout solutions.

[0068] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0069] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0070] Figure 1 This is a schematic diagram of the overall framework of the intelligent sensing twin CIST system of the present invention;

[0071] Figure 2 Implementation process for ray tracing;

[0072] Figure 3 The shadowed area under the obstruction;

[0073] Figure 4 This is a schematic diagram of the forward algorithm;

[0074] Figure 5 This is a schematic diagram of ray tracing based on the inverse algorithm;

[0075] Figure 6 Here is a flowchart of the ray tracing process;

[0076] Figure 7 A flowchart illustrating the overall design of the ray tracing simulation algorithm. Detailed Implementation

[0077] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0078] Please see Figures 1 to 7This invention provides a mountain smart city network planning system based on GIS and ray tracing (i.e., a smart sensing twin CIST system), including a 3D GIS geographic information module, a ray tracing simulation module, and a GIS and ray tracing integration interface; the GIS and ray tracing integration interface includes multiple interfaces such as a 3D map conversion interface, a global data initialization interface, and a global data simulation interface.

[0079] The 3D GIS geographic information module is used to accurately simulate the effects of solar altitude angle, shadow and reflection topographic factors on network construction analysis. It analyzes the impact of topography and landforms on network planning, that is, to accurately measure the effects of light in three-dimensional space, thereby helping planners determine the optimal network layout and construction scheme.

[0080] The ray tracing simulation module obtains information from the 3D GIS geographic information module and calculates the light intensity and reflection of each area, realizing the visualization of the 3D real-scene base station network coverage effect and the output of specific road loss data, thereby estimating the cost of building optical cables and base stations.

[0081] The GIS and ray tracing integration interface converts TIFF format map data to planet format via a 3D map conversion interface. It initializes the global data interface by calling the material library path, building height file path, feature type file path, feature index file path, simulation point and moving scatterer file path, and transmitter and receiver antenna file paths from the 3D GIS geographic information module, and defines the propagation mechanism to complete global initialization before simulation calculation. Finally, it uses the global data simulation interface to call the propagation mechanism and simulation calculation functions of the ray tracing simulation module to complete the simulation calculation after global data loading.

[0082] The design methodology for the ray tracing simulation module specifically includes:

[0083] 1) Fundamental Theory of Ray Tracing

[0084] Ray tracing, a leading method for deterministic channel modeling, is an electromagnetic environment prediction technique based on Geometrical Optics (GO) and Uniform Theory of Diffraction (UTD). Combining these two theories allows for accurate prediction of all signal propagation paths given specific environmental information. After discarding rays that do not meet realistic propagation requirements, all possible propagation paths are predicted, and the field strength at the receiver point is calculated using electromagnetic calculations. Then, by analyzing other locations within the environment, the channel information for the entire environment can be obtained. The specific implementation flowchart of ray tracing is as follows: Figure 2 As shown.

[0085] Geometric optics theory:

[0086] As the frequency increases (wavelength decreases), especially in the millimeter-wave band where the wavelength is much smaller than the size of scattering objects in the environment, the propagation process approximates the path of light, allowing for geometrical optics approximation using Gog's theory. Gog's theory is an approximation for calculating electric field strength, capable of predicting suitable signal propagation paths and processes. However, it's important to note that Gog's theory only considers direct, reflection, and transmission propagation mechanisms, and cannot accurately calculate diffraction. Gog obeys Fermat's four laws of optics: direct, reflection, and refraction. It's clear that, except for diffraction, which is outside the scope of Gog's calculations, Gog can calculate and predict direct, reflection, and transmission paths, and can also provide guidance for calculating transmission losses under other propagation mechanisms.

[0087] Uniform diffraction theory:

[0088] Diffraction is a mechanism of electromagnetic wave propagation where, when an electromagnetic wave encounters a scattering object during its propagation, the wave can still be observed within the shadow region of the scattering object. The shadow region under the scattering object is like... Figure 3 As shown.

[0089] When using Go theory to calculate electromagnetic fields, the electric field within the shadow region of a scatterer is considered zero. However, diffraction does exist, and the electric field within the shadow region is not zero. Therefore, as diffraction is a major propagation mechanism, the presence of occlusion effects must be considered in electric field calculations under all conditions. This is especially true when using ray tracing techniques for channel modeling, where the impact of diffraction on prediction accuracy must be taken into account.

[0090] 2) Ray Tracing Simulation Support Algorithm

[0091] Ray tracing technology describes the propagation path between base stations and mobile stations in a wireless channel by converting transmitted signals into optical rays, thereby obtaining the multipath parameter information needed to calculate the electric field strength. Ray tracing technology can be divided into two categories according to the direction of the tracing ray: the shooting and bouncing ray (SBR) method based on the forward algorithm and the imaging method (IM) method based on the reverse algorithm.

[0092] Forward algorithm:

[0093] The core idea of ​​the forward algorithm is to set up a transmitting antenna as the source point to emit rays, track all rays, record the propagation path of the rays in the environment, and then set an appropriate threshold according to the required model accuracy to determine whether the ray intersects with the scattering object or reaches the receiver. A schematic diagram of the forward algorithm is shown below. Figure 4 As shown.

[0094] Several aspects need to be considered when using the forward algorithm: First, it is necessary to ensure that the rays emitted from the source point encompass the directions of all possible paths. Second, during the tracing process, it is necessary to determine whether the ray intersects with scatterers in the environment, and to identify and record the intersection order. Third, it is necessary to determine whether the ray can reach the receiver, i.e., whether the ray is received by the receiver. However, since this method must traverse and trace all emitted rays, the computation time and workload will increase exponentially with the number of predicted reflections.

[0095] Reverse algorithm:

[0096] The mirror method based on reverse tracing starts at the receiver and uses the mirror principle to trace the ray reaching the receiver back to the transmitter, thus determining the ray propagation path. When multiple scatterers exist in the propagation scenario, simply repeating the process of calculating the mirror point and connecting the mirror point to the transmitter and receiver is sufficient to calculate the entire propagation path. A schematic diagram of ray tracing based on the reverse algorithm is shown below. Figure 5 As shown.

[0097] Because the reverse algorithm requires determining whether a ray travels from the transmitting antenna to the receiving antenna, each ray must actually exist during its propagation. Unlike the transmit-reflect ray method, which traverses every single ray, this significantly reduces computational workload. The image method is more suitable for predicting signal propagation paths in environments where the positions of the transmitting and receiving antennas are known. However, the reverse algorithm is not suitable for complex indoor environments because the presence of numerous scattering objects creates many image points, increasing the computational load.

[0098] 3) Channel modeling based on the emission-reflection ray method for ray tracing

[0099] The emit-reflect ray method requires emitting a set of test rays from a source in any expected direction of propagation from the source. Each ray is intended to capture the behavior of some solid-state radiation angle from the transmitting antenna. These test rays are then traced in the scene, in most cases modeled as a triangular mesh of interfaces between electrically homogeneous materials. In a post-processing step, the intersections of the rays with objects in the scene are recorded, and the electric field of a given transmitting antenna at a given observation point is calculated. From the perspective of power attenuation, rays are generally considered spherical waves; while from the perspective of matter-matter interaction, rays are generally considered plane waves, their magnitude attenuating with the radius as they interact with the plane. The ray tracing process is as follows: Figure 6 As shown.

[0100] The signal ultimately received by the receiving antenna is obtained by coherently superimposing the complex electric fields of each ray, and the final received power is the superposition of the powers of all rays. Therefore, in ray-tracing-based channel modeling, the channel impulse response is synthesized from multipath components, as shown in the following equation:

[0101]

[0102] Among them, P l t is the received power of the l-th path. l N represents the arrival time of the l-th path. p This represents the total number of paths. Let l be the relative phase of the l-th path. The path loss of ray tracing is determined by the antenna's transmit power, gain, and the sum of the power of each path, as calculated by the following formula:

[0103]

[0104] The electric field strength at the receiver determines the received power along each path, and the sum of the power of all paths reaching the receiver is the final received power. Therefore, the received power at each measurement point during ray tracing simulation can be calculated, and P... l It can be represented as:

[0105]

[0106] In the formula, λ is the wavelength, η0 is the impedance in free space, typically taken as 377Ω, and E θ,1 This represents the electric field strength in the vertical direction when the l-th ray is received by the receiver. Similarly, It is the electric field strength in the horizontal direction, θ l and These are the angles of arrival of the ray.

[0107] The ray tracing method of this invention only considers the LOS (Low-In-Sight) scenario. The K-factor represents the ratio of the direct path to other multipath components in the LOS scenario, and its expression is:

[0108]

[0109] In the formula, P represents the sum of the received power of the multipath components, and PLOS is the power of the direct path. The signal is emitted from the antenna and converted into rays in all directions. These rays then propagate in free space and finally reach the receiving antenna. Therefore, it is necessary to calculate the angle of arrival for each ray. The angle of arrival of a ray at the receiving point, expressed as a unit direction vector, can be expressed as:

[0110] a=sin(θ)cos(φ)x+sin(θ)sin(φ)y+cos(θ)z

[0111] Where θ and φ represent the angles of arrival of the ray, and x, y, and z represent the lengths in the given directions.

[0112] 4) Ray Tracing Simulation Model

[0113] The ray tracing simulation algorithm mainly adopts the ray tracing technology based on the image method, and combines it with the diffuse scattering propagation model of rough surfaces. The algorithm design mainly uses MATLAB software.

[0114] like Figure 7 As shown, the design of the ray tracing algorithm based on the mirror method mainly includes six parts, namely:

[0115] ①Scene Module: This module is mainly responsible for modeling the simulation environment.

[0116] ② Material Properties Module: This module mainly determines the building material properties parameters of the simulation environment and completes the initial setup of the environment.

[0117] ③ Antenna module: This module mainly includes determining the type, location, height, gain, and transmit power of the transmitting and receiving antennas, and completing the antenna initialization setup for the simulation environment.

[0118] ④ Path search module: This module mainly includes the path search process and specific mathematical methods for direct, reflected, and diffracted rays, and completes the ray tracing process.

[0119] ⑤ Diffuse scattering module: This module mainly includes determining the diffuse scattering propagation model and calculating the effective diffuse scattering region, completing the combination of diffuse scattering and ray tracing algorithms, and improving the accuracy of the algorithm.

[0120] ⑥ Combined Field Strength Module: This module mainly calculates the combined field strength of multipath signals arriving at the receiver and outputs the algorithm results.

[0121] 5) High-performance ray tracing simulation module framework.

[0122] 6) The overall simulation process of the system of this invention, specifically the following steps:

[0123] (1) Import information on base stations, antennas, materials, and building scenes.

[0124] Finally, you can check whether all the required files have been successfully imported.

[0125] (2) Configure simulation parameters

[0126] The simulation propagation mechanism is set according to the document: direct light, Fresnel reflection, Deygout diffraction, transmission, ground feature loss, and self-ground feature loss; the simulation area and other parameters necessary for simulation are set according to the actual area.

[0127] (3) Scene preprocessing

[0128] Based on imported information such as materials and buildings, a basic geometric structure is constructed, including the bounding box of triangles and corresponding material information; and based on this geometric information, an acceleration structure (uniform mesh acceleration structure) is constructed to reduce the number of intersection detections between rays and the scene.

[0129] (4) Start simulation

[0130] First, the type of simulation scene (mountainous or urban) is determined based on whether the building file contains a building vector. For urban scenes, it is determined whether to use an adaptive algorithm. If an adaptive algorithm is used, the program will divide the urban scene into multiple sub-scenes, calculate statistical data for the sub-scenes, and determine the propagation mechanism of the sub-scenes based on the statistical results. If the adaptive switch is not turned on, the simulation will be performed according to the user-preset propagation mechanism.

[0131] (5) Determine the receiver type

[0132] Different multipath calculation methods exist for indoor and outdoor receivers. For outdoor receivers, path loss is calculated based on propagation mechanisms such as direct light, O2O transmission, Fresnel reflection, and Degyout diffraction, as well as ground object loss and self-ground object loss.

[0133] (6) Indoor receiver

[0134] The calculation is divided into two parts. The first part is the virtual direct path between the outdoor transmitter and the indoor receiver, and the intersection point of the direct path between the outdoor transmitter and the indoor receiver's building surface (hereinafter referred to as the boundary point) and the transmitter. It uses the same propagation mechanisms as the outdoor path, such as direct path, O2O transmission, Fresnel reflection, and Degyout diffraction, as well as ground object loss and self-ground object loss to calculate the path loss. The second part is the path between the boundary point and the receiver. It needs to calculate the path loss based on the O2I outdoor to indoor transmission mechanism. The sum of the path losses of the two is the path loss from the outdoor transmitter to the indoor receiver. The following are the calculation formulas and corresponding parameter models for the received power of indoor and outdoor receivers in urban scenarios.

[0135] The formula for calculating outdoor received power is expressed as follows:

[0136] P O2O (i)=G sys +20log 10 H(f)-L Diff -L c -L sc (1)

[0137] Where G is the system gain that includes the effects of transmission power, line loss, etc., H(f)=∑ai- is the frequency response of direct, reflected, and O2O transmission superposition, ai is the amplitude of the i-th path (considering the antenna directional gain), is the phase, is the Deygout diffraction loss, is the cumulative loss of ground objects passed through during the propagation process, and is the loss of the ground object type itself at the current receiver location.

[0138] Formula for calculating outdoor to indoor received power:

[0139] P O2I (i)=P O2O (j)-L IN (i)

[0140] Among them, L IN This refers to the penetration loss from the outside to the inside, caused by the penetration loss through the exterior wall (L). O2I ), Horizontal loss per meter inside the building (L) 02I_hor ) and vertical loss per meter (L O2I_ver Jointly decided:

[0141] L IN (i)=L O2I +Δd hor *L O2I_hor +Δd ver *L O2I_ver

[0142] The parameters of each model and their physical meanings are shown in Table 1.

[0143] Table 1. Model parameters and their physical meanings

[0144]

[0145]

[0146] (7) Scene judgment

[0147] When the scene is determined to be in a mountainous area, the path loss between the receiver and transmitter will be calculated, including direct beam, Deygout diffraction, self-ground object loss, and ground object loss.

[0148] (8) File output

[0149] Calculate the path loss based on the propagation model and output a .loss file.

[0150] (9) File Merging

[0151] Merge the road damage files into the Data file.

[0152] 7) The system of the present invention has a calibration process, which specifically includes the following steps:

[0153] ① Based on the file path input from the interface, parse the path name and initialize the propagation mechanism. The files input from the interface include: map altitude, map building height, map feature information, antenna pattern, base station operating parameters, road loss data, and material parameters, etc.

[0154] ② Import map data and process drive test data and base station information. Run the drive test data of each base station to obtain RSRP values ​​and calculate the maximum and minimum measured RSRP values.

[0155] ③ Randomly select nmax non-repeating samples to participate in the correction.

[0156] ④ Prepare material parameters and store the data before calibration, which will be used to identify the parameters to be calibrated.

[0157] ⑤ Prepare an initial calibration material for each CPU, prefixed with materialProcess.

[0158] ⑥ Perform geometric calculations and run the Cost Function to initialize the error.

[0159] ⑦ Perform calibration, update RT parameters, and output a report.

[0160] 8) Parallel computing process, specifically including the following steps:

[0161] ① Use the CQUPT_Set_Pathloss_Path function to specify the path loss file path to use. This function ensures that the simulation calculation uses the correct path loss file.

[0162] Furthermore, the `CQUPT_Prepare_Parallel` function automatically generates the `.cell` and `.BSmerge` files required for simulation calculations, eliminating the need for manual creation. Simultaneously, the `CQUPT_Set_Thread` function specifies the number of threads for concurrent computation. This improves the speed and efficiency of simulation calculations. In summary, these functions utilize multithreading techniques to enhance computational speed.

[0163] ② Monitor each thread, and when all threads have completed, call BS_Combine to merge the path loss files and generate the Data file.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart city network planning system for mountainous areas based on GIS and ray tracing, characterized in that, The system includes a 3D GIS geographic information module, a ray tracing simulation module, and a GIS-ray tracing integration interface; the GIS-ray tracing integration interface includes a 3D map conversion interface, a global data initialization interface, and a global data simulation interface; The 3D GIS geographic information module is used to accurately simulate the effects of solar altitude angle, shadow and reflective topographic factors on network construction analysis. It analyzes the impact of topography and geomorphology on network planning, that is, to accurately simulate the effects of light in three-dimensional space, thereby helping planners to determine network layout and construction scheme. The ray tracing simulation module obtains information from the 3D GIS geographic information module and calculates the light intensity and reflection of each area, realizing the visualization of the 3D real-scene base station network coverage effect and the output of specific road loss data, thereby estimating the cost of building optical cables and base stations; The GIS and ray tracing integration interface converts TIFF format map data to planet format via a 3D map conversion interface; it initializes the global data interface by calling the material library path, building height file path, feature type file path, feature index file path, simulation point and moving scatterer file path, transmitter and receiver antenna file path from the 3D GIS geographic information module, and defines the propagation mechanism to complete the global initialization before simulation calculation; and it completes the simulation calculation after global data loading by calling the propagation mechanism and simulation calculation function of the ray tracing simulation module through the global data simulation interface.

2. The mountain smart city network planning system according to claim 1, characterized in that, The distributed computing technology in the ray tracing simulation module uses a ray tracing simulation support algorithm, which is divided into two categories according to the direction of the tracing ray: the emission-bounce ray method based on the forward algorithm and the mirror method based on the reverse algorithm.

3. The mountain smart city network planning system according to claim 2, characterized in that, The channel modeling method based on the forward algorithm-based transmit-reflect ray method is as follows: a set of test rays are emitted from the source in any direction that can be expected to propagate from the source, and each ray is expected to capture the behavior of a certain solid radiation angle from the transmitting antenna; These test rays are then traced in the scene and modeled as a triangular mesh of interfaces between electrically homogeneous materials; In the post-processing step, the intersection points of the rays with objects in the scene are recorded, and the electric field of a given transmitting antenna at a certain observation point is calculated. From the perspective of power attenuation, rays are usually regarded as spherical waves; while from the perspective of matter interaction, rays are usually regarded as plane waves, whose magnitude decreases with the radius and interacts with the plane.

4. The mountain smart city network planning system according to claim 2, characterized in that, The specific steps of the mirroring method based on the reverse algorithm are as follows: 1) Build the scene module and complete the simulation environment modeling; 2) Build the material properties module, determine the building material properties parameters of the simulation environment, and complete the environment initialization setup; 3) Build the antenna module, including determining the type, location, height, gain, and transmit power of the transmit and receive antennas, and complete the antenna initialization setup for the simulation environment; 4) Build a path search module, including the path search process and specific mathematical methods for direct, reflected, and diffracted paths, and complete the ray tracing process; 5) Build a diffuse scattering module, including determining the diffuse scattering propagation model and calculating the effective diffuse scattering region, and completing the combination of diffuse scattering and ray tracing algorithms; 6) Build a combined field strength module to calculate the combined field strength of the multipath signal arriving at the receiver and output the algorithm results.

5. The mountain smart city network planning system according to any one of claims 1 to 4, characterized in that, The specific construction method of this system is as follows: 1) Import base station, antenna, material, and building scene information, and finally check whether all the required files have been successfully imported; 2) Configure simulation parameters; The simulation propagation mechanism is set according to the document: direct light, Fresnel reflection, Deygout diffraction, transmission, ground feature loss, and self-ground feature loss; the parameters necessary for simulation are set according to the actual area. 3) Scene preprocessing; Based on the imported materials and buildings, construct the basic geometric structure, the bounding box of the triangle, and the corresponding material information; and based on this geometric information, construct the acceleration structure to reduce the number of intersection detections between rays and the scene; 4) Start the simulation; First, the simulation scene type is determined based on whether the building file contains a building vector. For urban scenes, it is determined whether an adaptive algorithm is used. If an adaptive algorithm is used, the program will divide the urban scene into multiple sub-scenes, calculate statistical data for the sub-scenes, and determine the propagation mechanism of the sub-scenes based on the statistical results. If the adaptive switch is not turned on, the simulation will be performed according to the user-preset propagation mechanism. 5) Determine the receiver type; There are different multipath calculation methods for indoor and outdoor receivers; For outdoor receivers, road loss is calculated based on direct sunlight, O2O transmission, Fresnel reflection, Degyout diffraction, ground object loss, and self-ground object loss. 6) Indoor receiver; The indoor receiver calculation is divided into two parts. The first part is the virtual direct path between the outdoor transmitter and the indoor receiver, and the intersection point (i.e., the boundary point) of the building surface where the indoor receiver is located. This virtual direct path is calculated using the same methods as the outdoor path, including direct path, O2O transmission, Fresnel reflection, Degyout diffraction, ground object loss, and self-ground object loss. The second part is the path between the boundary point and the receiver. This path loss needs to be calculated based on the O2I outdoor-to-indoor transmission mechanism. The sum of the path losses of the two parts is the path loss from the outdoor transmitter to the indoor receiver. 7) Scene judgment; When the scene is identified as a mountainous area, the path loss between the receiver and the transmitter will be calculated by considering direct beam, Deygout diffraction, self-ground object loss, and ground object loss. 8) File output; Calculate the path loss based on the propagation model and output the .loss file; 9) File merging; Merge the road damage files into the Data file.

6. The mountain smart city network planning system according to claim 5, characterized in that, The system has a calibration function, specifically including: 1) Parse the path name based on the file path input from the interface and initialize the propagation mechanism. The files input from the interface include: map elevation, map building height, map feature information, antenna pattern, base station operating parameters data, path loss data, and material parameters; 2) Import map data and process drive test data and base station information. Run the drive test data of each base station to obtain RSRP values ​​and calculate the maximum and minimum measured RSRP values. 3) Randomly select nmax non-repeating samples to participate in the correction; 4) Prepare material parameters and store the data before calibration to identify the parameters to be calibrated; 5) Prepare an initial calibration material for each CPU, prefixed with "materialProcess"; 6) Perform geometric calculations and run the Cost Function to initialize the error; 7) Perform calibration, update RT parameters, and output a report.

Images