A parameter optimization method, system, device, and medium based on a ray tracing model.

Abstract

This invention belongs to the field of wireless communication technology, and specifically relates to a parameter optimization method, system, device, and medium based on a ray tracing model. The method obtains a triangular element environment model by scanning an indoor scene, simultaneously acquiring the actual power results of the indoor wireless channel. It then uses multipath information from the ray tracing model generated based on the triangular element environment model for field calculations and iteratively adjusts electrical parameters using an error function to optimize the electrical parameters of the indoor scene materials, thereby optimizing the wireless network coverage of the indoor scene. An optimization system includes a starting point cloud data acquisition and processing module, an actual power measurement module, a ray tracing model establishment module, and an electrical parameter optimization module. The device and medium are used to store a computer program, which, when executed, implements the electrical parameter optimization method. This invention improves the prediction accuracy and reliability of the ray tracing model, enhances optimization efficiency, and meets practical application needs.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and specifically relates to a parameter optimization method, system, device and medium based on a ray tracing model. Background Technology

[0002] With the rapid development and widespread application of wireless communication technology, studying the characteristics of wireless channels in indoor environments has become increasingly important. However, the diversity of indoor environments, such as their geometry, object placement, and different object types, presents significant challenges for indoor environment modeling. The differences between various indoor environments, including offices, campuses, industrial settings, and residences, increase the complexity of wireless channel modeling and affect the accurate prediction of channel characteristics.

[0003] In complex indoor scene modeling, obtaining large amounts of surveying data through manual measurement is extremely difficult. Therefore, adopting standardized and automated environmental measurement schemes can effectively improve the accuracy and timeliness of modeling. Laser point cloud scanning technology uses sensors to perceive surrounding objects in unknown environments and matches environmental data acquired at different time points to infer the position of objects within the environment. This technology can provide measurement accuracy at the centimeter or even millimeter level, and its data acquisition efficiency is extremely high. Laser point cloud scanning is widely used in related fields as an effective means of scene reconstruction. However, the extraction and processing of laser scanning data still faces certain challenges.

[0004] In indoor environments, signal propagation is affected by various obstacles, leading to phenomena such as reflection, refraction, and diffraction. These phenomena generate multiple signal paths, forming a multipath effect. The constructive and destructive interference caused by multipath effects disperses the signal over time, resulting in delay spread and inter-symbol interference, which in turn affects the performance of the communication system and the stability of the signal. Due to the complex geometry of indoor environments, signals often encounter obstruction and absorption during propagation, causing the signal strength to gradually weaken with increasing propagation distance. Therefore, signal fading in indoor environments typically exhibits both fast and slow fading characteristics. To accurately evaluate and optimize the performance of wireless communication systems, precise measurement and modeling of indoor wireless channels are essential. Using this measurement data, a channel model describing the signal propagation characteristics in indoor environments can be constructed.

[0005] Ray tracing models are deterministic models that simulate the propagation path of electromagnetic waves, calculating characteristics such as signal attenuation and delay along different paths. The advantage of ray tracing models lies in their ability to consider complex indoor environmental characteristics in detail and provide accurate multipath information. However, applying ray tracing models to simulate electromagnetic wave propagation in indoor environments typically encounters several challenges, such as the level of modeling detail and material electrical parameters. First, the level of modeling detail becomes the primary factor limiting simulation accuracy. Due to the complex and varied nature of indoor spaces, containing numerous irregularly shaped and laid-out obstacles such as walls, furniture, doors, and windows, constructing a 3D model that comprehensively and accurately reflects these physical characteristics is particularly important. The level of modeling detail directly affects whether the multipath information of the ray tracing model matches the actual electromagnetic wave propagation path. Second, the accuracy of material electromagnetic parameters is another major challenge. The interaction characteristics of electromagnetic waves with different materials, such as reflection and transmission, mean that the calculation of the electromagnetic field is highly dependent on the material's electrical parameters, such as dielectric constant and conductivity. However, obtaining and setting these parameters is extremely complex, influenced not only by the type of material and manufacturing process but also by fluctuations with changes in ambient temperature and humidity. Therefore, we need to establish and continuously update a database of material electromagnetic parameters, and at the same time develop effective parameter optimization algorithms to minimize simulation errors caused by inaccurate parameters and improve the prediction accuracy and reliability of ray tracing models.

[0006] In addition, improper selection and placement of base station locations have become a key factor restricting network performance improvement during the actual deployment of mobile communication networks. This inappropriate layout not only significantly increases the construction cost of network infrastructure but also has a direct and far-reaching impact on wireless signal coverage. Due to unreasonable base station distribution, some areas often experience excessively strong signals causing interference, while other areas suffer from weak signals or even lack coverage, creating so-called "signal blind spots." This not only affects call quality but may also lead to decreased data transmission rates and increased dropout rates, greatly reducing the user's communication experience.

[0007] In the prior art, the invention with announcement number "CN 114448531 B" discloses "a channel characteristic analysis method, system, medium, device and processing terminal". Based on the simulation results of a deterministic channel model, it proposes a multi-dimensional wireless channel characteristic analysis method and further improves the channel clustering algorithm. This invention can provide a relatively accurate theoretical basis for the design of wireless communication systems and network planning. However, it fails to further optimize the material-related parameters in the model and cannot determine the reliability of the model in simulating real scenarios. Summary of the Invention

[0008] To address the shortcomings of the existing technologies, the present invention aims to propose a parameter optimization method, system, device, and medium based on a ray tracing model. This method obtains a triangular element environment model by scanning an indoor scene, simultaneously acquiring the actual power results of the indoor wireless channel, generating a ray tracing model based on the triangular element environment model. Multipath information from the ray tracing model is used for field calculations, and electrical parameters are iteratively adjusted using an error function to optimize the electrical parameters of the indoor scene materials. This invention improves the prediction accuracy and reliability of the ray tracing model and significantly enhances optimization efficiency, meeting the needs of practical applications.

[0009] To achieve the above objectives, the technical solution of the present invention includes the following:

[0010] Firstly, a parameter optimization method based on a ray tracing model includes the following steps:

[0011] S1. Use a laser point cloud instrument to scan the indoor scene and generate raw point cloud data. Preprocess the raw point cloud data and generate preprocessed result data. Use 3D modeling software to convert the preprocessed result data into a triangular surface element environment model. Assign electrical parameters of corresponding materials to each surface element in the triangular surface element environment model. The electrical parameters include relative permittivity and conductivity.

[0012] S2. Utilize a channel power measurement device to measure the actual power of the indoor wireless channel in the indoor scene and obtain the actual power result of the indoor wireless channel;

[0013] S3. Based on the triangular surface element environment model described in step S1, determine the multipath information, calculate the power value of each multipath information according to the radio wave propagation mechanism, and generate a ray tracing model. At the same time, configure the parameters of the ray tracing model.

[0014] S4. Perform simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model described in step S3. The specific steps are as follows:

[0015] S4.1 Adjust the Gth (G = 1, 2, ..., G) electrical parameters of the material corresponding to each surface element in the ray tracing model;

[0016] S4.2. Using the multipath information output by the ray tracing model described in step S3, perform field calculations to obtain the simulated power results of the Gth generation (G = 1, 2, ..., G). The multipath information includes the electrical parameters and propagation information of the Gth generation (G = 1, 2, ..., G) described in step S4.1.

[0017] S4.3. Based on the simulated power results of the Gth generation (G = 1, 2, ..., G) described in step S4.2 and the actual power results described in step S2, establish an error function and use the error function as the objective function of the genetic algorithm to calculate the error results of the Gth generation (G = 1, 2, ..., G).

[0018] S4.4 Repeat steps S4.1 to S4.3 until the error result calculation for each generation is completed, and obtain the optimal electrical parameters corresponding to the generation G (G = 1, 2, ..., G) with the smallest error result.

[0019] Furthermore, the channel power measurement device mentioned in step S2 includes a transmitter and a receiver, and the specific steps of step S2 are as follows:

[0020] S2.1 Set the transmission frequency and transmission power of the transmitter, and set the center frequency and resolution bandwidth of the receiver;

[0021] S2.2. Perform actual power measurement of the indoor wireless channel according to the test line to obtain the actual power result of the indoor wireless channel.

[0022] Furthermore, the ray tracing model parameters in step S3 include the number of limits, the number of emitted rays, and the radius and power threshold of the receiving sphere; the position of the omnidirectional receiving antenna in the ray tracing model in step S3 needs to be transformed relative to the coordinate system of the triangular element environment model.

[0023] Furthermore, the algebra G mentioned in step S4.1 is 300-1000.

[0024] Furthermore, the error function formula described in step S4.3 is as follows:

[0025]

[0026] In formula (1), F std Let y represent the error function. i This represents the difference between the simulated power and the actual power at the i-th point. Indicates the y i The mean of the differences, where N represents the number of receiving points.

[0027] Furthermore, a wireless network coverage optimization method based on a ray tracing model, based on the aforementioned parameter optimization method, includes the following steps:

[0028] Q1. Based on the range of omnidirectional transmitting antenna position coordinates of the triangular surface element environment model, adjust the position coordinates of the m-th (m = 1, 2, ..., m) generation of the omnidirectional transmitting antenna.

[0029] Q2. Based on the position coordinates of the m-th (m = 1, 2, ..., m) generation omnidirectional transmitting antenna described in step Q1, the coverage function is used as the objective function of the genetic algorithm, and ray tracing model simulation is performed to obtain the coverage of the m-th (m = 1, 2, ..., m) generation.

[0030] Q3. Repeat steps Q1 and Q2 until the coverage calculation for each generation is completed, and obtain the optimal omnidirectional transmitting antenna position coordinates corresponding to the generation m (m = 1, 2, ..., m) with the largest coverage.

[0031] Secondly, a parameter optimization system based on a ray tracing model, applying the parameter optimization method, the parameter optimization system includes a raw point cloud data acquisition and processing module, an actual power measurement module, a ray tracing model establishment module, and an electrical parameter optimization module:

[0032] Raw point cloud data acquisition and processing module: The indoor scene is scanned using a laser point cloud instrument to generate raw point cloud data. The raw point cloud data is preprocessed and preprocessed result data is generated. The preprocessed result data is converted into a triangular surface element environment model using 3D modeling software. Each surface element in the triangular surface element environment model is assigned electrical parameters of the corresponding material. The electrical parameters include relative permittivity and conductivity.

[0033] Actual power measurement module: Uses channel power measurement equipment to measure the actual power of indoor wireless channels in indoor scenarios and obtains the actual power results of indoor wireless channels;

[0034] Ray tracing model building module: Based on the triangular element environment model, multipath information is determined, the power value of each multipath information is calculated according to the radio wave propagation mechanism, and a ray tracing model is generated. At the same time, the parameters of the ray tracing model are configured.

[0035] Electrical parameter optimization module: Performs simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model.

[0036] Thirdly, an electronic device including a memory and a processor:

[0037] Memory: Used to store the computer program that implements the parameter optimization method based on the ray tracing model;

[0038] Processor: Used to implement the parameter optimization method based on the ray tracing model when executing the computer program.

[0039] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the parameter optimization method based on the ray tracing model.

[0040] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows:

[0041] 1. In step S1 of this invention, laser point cloud is used for scene modeling. The scanning data rate is 320,000 points per second, and the scanning accuracy is ±1.5cm. The established indoor three-dimensional environment model is modeled using triangular facets, which can better match the actual scene and has both universal and detailed environmental data.

[0042] 2. The channel power measurement device established in step S2 of this invention is lightweight, highly stable, and has a wide bandwidth measurement range. It supports demodulation testing (blind scan), frequency sweep measurement (CW measurement), interference analysis and location, and other functions for all frequency bands from 2G to 5G. This measurement platform can effectively and quickly complete the actual power measurement of indoor wireless channels.

[0043] 3. In step S4 of this invention, the actual power result of the indoor wireless channel is used to optimize the material electrical parameters (i.e., relative permittivity and conductivity) of the ray tracing model, thereby improving the prediction accuracy and reliability of the model and ensuring that the wireless channel modeling is more in line with the actual application requirements. At the same time, step S4 uses the multipath information output by the ray tracing model and combines it with the genetic algorithm to optimize the electrical parameters, which eliminates the need to repeatedly call the ray tracing model and can significantly improve the optimization efficiency.

[0044] 4. Based on the optimal electrical parameters in step S4, this invention uses a genetic algorithm to optimize the position of the omnidirectional transmitting antenna, thereby improving signal coverage and quality.

[0045] In summary, compared with existing technologies, this invention optimizes indoor wireless channel modeling and antenna positioning by combining laser point cloud modeling, channel power measurement equipment, ray tracing model, and genetic algorithm. It achieves high-precision and reliable results, effectively improves signal coverage and quality, and enhances optimization efficiency, thus meeting the needs of practical applications. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating a parameter optimization method based on a ray tracing model provided by the present invention.

[0047] Figure 2a This is a schematic diagram of the point cloud data of the exterior wall outline of an indoor scene provided in Embodiment 1 of the present invention.

[0048] Figure 2b This is a schematic diagram of point cloud data for an indoor scene corridor provided in Embodiment 1 of the present invention.

[0049] Figure 2c This is a schematic diagram of point cloud data of interior walls and accessories in an indoor scene provided in Embodiment 1 of the present invention.

[0050] Figure 3This is a schematic diagram of indoor scene modeling provided in Embodiment 1 of the present invention.

[0051] Figure 4 This is a schematic diagram of the test circuit provided in Embodiment 1 of the present invention.

[0052] Figure 5 This is a schematic diagram of the electrical parameter optimization process provided in Embodiment 1 of the present invention.

[0053] Figure 6 This is a comparison chart of the actual power results and the simulated power results before and after electrical parameter optimization provided in Embodiment 1 of the present invention.

[0054] Figure 7 This is a simulated power coverage diagram before antenna position optimization provided in Embodiment 1 of the present invention.

[0055] Figure 8 This is a simulated power coverage diagram with optimized antenna position provided in Embodiment 1 of the present invention. Detailed Implementation

[0056] The following is combined with Figures 1 to 8 The present invention will be further described in detail with reference to embodiments, including a parameter optimization method, system, device and medium based on a ray tracing model. The parameters are the electrical parameters of the material of the objects involved in the indoor scene, including relative permittivity and conductivity. Wireless network coverage optimization is antenna position optimization.

[0057] Firstly, a parameter optimization method based on a ray tracing model, such as... Figure 1 The diagram shown illustrates the process of this method, which includes the following steps:

[0058] S1. Scan the real indoor scene using a laser point cloud instrument to generate raw point cloud data. Preprocess the raw point cloud data to generate preprocessed result data. Use 3D modeling software (Blender, 3D Max, etc.) to convert the generated preprocessed result data into a triangular element environment model. The triangular element environment model can accurately represent the size, relative position, and shape information of objects in the environment. Based on the material characteristics of objects in the real indoor scene (such as walls, floors, windows, doors, tables, chairs, etc.) (materials include concrete, brick, gypsum board, glass, wood, etc.), assign corresponding material electrical parameters (including relative permittivity and conductivity) to each element in the triangular element environment model. The raw point cloud data commonly used in this step S1 is in .las point cloud data format. Preprocessing methods include point cloud filtering, point cloud registration, downsampling, etc.

[0059] S2. Utilize channel power measurement equipment to measure the actual power of indoor wireless channels in indoor scenarios and obtain the actual power results of indoor wireless channels. The specific steps are as follows: 1. Use a transmitter and omnidirectional transmitting antenna as the transmitting end, and a PCTEL sweep frequency analyzer, test tablet computer, and omnidirectional receiving antenna as the receiving end. Set the parameters (frequency, power, bandwidth, etc.) of the transmitting end and the receiving end. Specifically, set the transmitting frequency and transmitting power of the transmitting end, and set the center frequency and resolution bandwidth of the receiving end. 2. Perform actual power measurement of the wireless channel according to the test route (including line-of-sight and non-line-of-sight scenarios) and obtain the actual power results of indoor wireless channels.

[0060] S3. Based on the triangular element environment model in step S1, determine the multipath information, i.e., the radio wave propagation path. Calculate the power value of each multipath based on the radio wave propagation mechanism (involving direct, reflection, transmission, and diffraction) and generate a ray tracing model. The final power value is determined by the superposition of all radio wave propagation paths. Simultaneously, configure the ray tracing model parameters. Specifically, configure the ray tracing model parameters as the limit number of the ray tracing model, the number of emitted rays, the radius of the receiving sphere, and the power threshold. The position of the omnidirectional receiving antenna needs to undergo relative coordinate transformation according to the coordinate system of the triangular element environment model, using the same coordinate system to conform to the actual measurement position.

[0061] S4. Perform simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model described in step S3. The specific steps are as follows:

[0062] S4.1. Based on the optimization range of the electrical parameters (including relative permittivity and conductivity) of the material corresponding to each surface element in the ray tracing model described in step S3, wherein the relative permittivity ranges from 1 to 15 and the conductivity ranges from 0 to 3 S / m, adjust the Gth (G = 1, 2, ..., G) generation electrical parameters of the material corresponding to each surface element, where the value of the generation G ranges from 300 to 1000.

[0063] S4.2. Use the multipath information output by the ray tracing model to perform field calculations and obtain the simulated power results of the Gth generation (G = 1, 2, ..., G). The multipath information includes the electrical parameters of the Gth generation (G = 1, 2, ..., G), as well as the propagation information of direct, reflected, transmitted, and diffracted rays.

[0064] S4.3. Based on the simulated power results of the Gth generation (G = 1, 2, ..., G) described in step S4.2 and the actual power results described in step S2, establish an error function and use the error function as the objective function of the genetic algorithm to obtain the error results of the Gth generation (G = 1, 2, ..., G).

[0065] S4.4 Repeat steps S4.1 to S4.3 until the error results of each generation are calculated iteratively to obtain the optimal electrical parameters corresponding to the generation G (G = 1, 2, ..., G) with the smallest error result; optimize the electrical parameters of the materials involved in the indoor scene, find the minimum error between the simulated power result and the actual power result by continuously changing the value of the electrical parameters and calling the field calculation, and the corresponding electrical parameters of the generation G (G = 1, 2, ..., G) are the optimal electrical parameters.

[0066] Furthermore, a wireless network coverage optimization method based on a ray tracing model, based on the aforementioned parameter optimization method, includes the following steps:

[0067] Q1. Based on the coordinate range of the omnidirectional transmitting antenna position in the triangular element environment model (i.e., the coordinate range of the indoor scene), adjust the coordinates of the omnidirectional transmitting antenna in the m-th generation (m = 1, 2, ..., m).

[0068] Q2. Based on the position coordinates of the m-th (m = 1, 2, ..., m) generation omnidirectional transmitting antenna described in step Q1, the coverage function is used as the objective function of the genetic algorithm. Ray tracing model simulation is performed to obtain the coverage of the m-th (m = 1, 2, ..., m) generation. The coverage is used to evaluate the coverage effect of the wireless network in the indoor environment.

[0069] Q3. Repeat steps Q1 and Q2 until the coverage calculation for each generation is completed. By iterating the genetic algorithm, obtain the optimal omnidirectional transmitting antenna position coordinates corresponding to the generation m (m = 1, 2, ..., m) with the largest coverage. Maximizing the coverage can ensure the uniform distribution of signal strength. This step can achieve the optimal wireless network coverage effect.

[0070] Example 1

[0071] The technical solution of the present invention will be further described in detail below using the indoor environment of a teaching building as an example.

[0072] In step S1, the Lingguang Lixel X1 laser point cloud instrument is used to scan the real indoor scene and generate commonly used .las point cloud data format, such as... Figures 2a to 2c The diagram shows, in sequence, point cloud data illustrations of the exterior wall outline, corridor, interior walls, and accessories of an indoor scene. Preprocessing of the raw point cloud data includes point cloud filtering and point cloud registration. Point cloud filtering primarily aims to eliminate noise points and improve point cloud quality, thereby reducing the amount of point cloud data and optimizing subsequent computation and storage efficiency. Point cloud registration aims to align multiple sets of point cloud data from different perspectives or times, ensuring they have a consistent geometric structure within a unified coordinate system. A detailed 3D indoor environment model is then created using Blender software, followed by triangulation (containing 55,091 scene points and 108,193 polygons) to finally generate the following... Figure 3 The triangular element environment model shown includes the size, relative position, and shape information of objects in the actual environment. The modeled environment contains 8 rooms and 1 corridor, including 40 interior and exterior walls (40 windows and 16 doors), 38 columns, and 178 objects. Corresponding material information is assigned based on ITU electrical parameter reference values, as shown in Table 1.

[0073] Table 1. ITU Reference Values ​​for Electrical Parameters

[0074]

[0075] In step S2, in Example 1, a transmitter with a frequency range of 20–6000 MHz and a transmit power range of -10–33 dBm, along with an omnidirectional transmitting antenna, is used as the transmitting end. A PCTEL frequency sweeper, a test tablet, and an omnidirectional receiving antenna are used as the receiving end. During the test, the transmit frequency and transmit power of the transmitting module are first set, and the center frequency and resolution bandwidth of the receiving module are configured. The test tablet is connected to the PCTEL frequency sweeper via Bluetooth to monitor power changes along the test route in real time, thereby performing actual power measurements of the wireless channel. The test route is as follows: Figure 4 As shown, the red "*" indicates the position of the transmitting antenna, and the blue "." indicates the position of the receiving antenna (a total of 182 receiving points). Due to the high accuracy of the PCTEL sweep frequency analyzer, the receiving points are very densely packed. The relevant measurement and control parameter settings are shown in Table 2.

[0076] Table 2 Measurement Control Parameter Settings

[0077]

[0078] In configuring a measurement for a specific frequency band in the PCTEL sweep analyzer, such as 990MHz to 1010MHz, with a center frequency set to 1000MHz and a resolution bandwidth (RBW) set to 100kHz, the Number ofbin needs to be set according to the center frequency and resolution bandwidth (RBW), i.e., 20MHz / 100kHz = 200. It is important to note that the frequency step set at the receiver must be equal to the resolution bandwidth (RBW). After the test, the test data needs to be exported. Use SeeHawk Collect software on your computer to convert the measured data (LOG) into .csv format.

[0079] In step S3, firstly, a ray is emitted from the omnidirectional transmitting antenna. Then, ray tracing is performed based on the triangular surface element environment model to determine whether the ray intersects with any object or is received by the omnidirectional receiving antenna. If the ray encounters an obstacle, phenomena such as reflection, transmission, diffraction, or scattering will occur. The specific phenomenon or combination of phenomena depends on the geometry and electromagnetic properties of the obstacle. All propagation paths of the radio waves ultimately received by the omnidirectional receiving antenna constitute the multipath information. If the ray is ultimately received by the omnidirectional receiving antenna, the electric field or power distribution of the ray at the receiving point is calculated using field calculation methods.

[0080] In the ray tracing model, the simulation parameters for ray tracing are initialized and optimized according to the indoor scene environment. The initialization parameters are shown in Table 3, configuring the limit number, number of emitted rays, radius of the receiving sphere, and power threshold of the ray tracing model. For indoor scenes, the measured coordinate positions need to be transformed relative to the actual environment to ensure coordinate system consistency.

[0081] Table 3 Simulation parameter settings for ray tracing model

[0082]

[0083] In step S4, the optimization range of electrical parameters for each material is set, such as a relative permittivity range of 1–15 and a conductivity range of 0–3 S / m. Metals, due to their large number of free electrons, high electron mobility, compact crystal structure, and fewer impurities and defects, exhibit high conductivity. The objective function and initialization parameters are set, and the initial values ​​of various electrical parameters (i.e., the first-generation electrical parameters) are set to the reference values ​​corresponding to those of the ITU. A ray tracing model is used to perform ray tracing simulations at each position on the test circuit to obtain the simulated power results before optimization.

[0084] The error function F between the optimized simulated power result and the actual power result of generation G (G = 1, 2, ..., G) is used. std The objective function of the genetic algorithm is defined by the following formula:

[0085]

[0086] In formula (1), F std Let y be the error function. i This represents the difference between the simulated power and the actual power at the i-th point. Indicates the y i The mean of the differences, where N represents the number of receiving points.

[0087] The simulation results are used to optimize the material parameters involved in the indoor scene. Information such as reflection points, transmission points, diffraction points, and the normal directions of corresponding surfaces are stored according to the propagation type. Field calculations are then performed using optimization algorithms. These field calculations involve the reflection field, transmission field, and diffraction field, and the main calculation formulas are as follows:

[0088] Reflectance coefficient (R):

[0089]

[0090] In formulas (2) and (3), R || R is the horizontal component of the reflection coefficient (R). ⊥ Let n be the vertical component of the reflection coefficient (R), n1 be the refractive index of medium 1, n2 be the refractive index of medium 2, and n... i Let θ be the refractive index of the corresponding medium. i Let θ be the angle of incidence. t The angle of transmission is denoted as .

[0091] Transmission coefficient (T):

[0092]

[0093] In formulas (4) and (5), T || T represents the horizontal component of the transmission coefficient (T). ⊥ Let n be the perpendicular component of the transmission coefficient (T), n1 be the refractive index of medium 1, n2 be the refractive index of medium 2, and n... i Let θ be the refractive index of the corresponding medium. i Let θ be the angle of incidence. t The angle of transmission is denoted as .

[0094] Diffraction factor (D):

[0095]

[0096]

[0097] In formulas (6) to (10), To observe the angle between the point and the plane, β0 is the angle between the source point and the surface, n is the wedge factor, F(x) is the transition function, k is the wave number, L is the distance parameter, and j is the imaginary number.

[0098] like Figure 5 The diagram illustrates the specific calculation process for electrical parameter optimization. Following the principle of minimizing the objective function, the optimal electrical parameter values ​​for the material in this indoor environment are obtained. Figure 6The image shows a comparison between the simulated power results before and after optimization, and the actual power results. The black "." represents the measured power result, the blue "." represents the simulated power result before optimization, and the red "▲" represents the simulated power result after optimization. The standard deviation between the simulated power result and the measured power result before optimization was 8.7704 dB, with a mean error of -0.57754 dB. The standard deviation between the simulated power result and the measured power result after optimization was 6.5119 dB, with a mean error of 0.53657 dB. The data shows that the standard deviation improved by 2.2585 dB after optimizing the electrical parameters, indicating a significant improvement in the accuracy of the ray-tracing model. Furthermore, using multipath information output by the ray tracing model and combining it with a genetic algorithm for electrical parameter optimization can significantly improve optimization efficiency. A single simulation takes 1023 seconds, and with 300 iterations of the genetic algorithm, it takes 306,900 seconds. However, after a single simulation, using multipath information and combining it with 300 iterations of the genetic algorithm, it only takes 1653 seconds, which is 185 times more efficient.

[0099] According to the indoor wireless signal edge strength index, the indoor edge coverage strength should be greater than or equal to -95dBm. Coverage rate is defined as the objective function, i.e., the number of receiving points with received power less than -95dBm is recorded and then compared with the total number of receiving points to evaluate the coverage effect of the wireless network in the indoor environment. A genetic algorithm is used to adjust the antenna positions to achieve the best coverage effect. The antenna position information and the effective coverage before and after optimization are shown in Table 4.

[0100] Table 4 Coverage before and after antenna location optimization

[0101]

[0102] Table 4 sets two coverage values ​​for comparison: one is that the coverage strength should be greater than or equal to -95dBm, and the other is that the coverage strength should be greater than or equal to -85dBm. Based on the coverage values, it can be seen that the coverage effect has been improved.

[0103] like Figure 7 The image shown is a simulated power coverage diagram before antenna position optimization. Figure 8 The image shows the simulated power coverage diagram after antenna position optimization. The received power of the line-of-sight path before and after optimization is not much different. However, before optimization, the received power value on the left side of the room was mostly below -90dBm, while after optimization, there was a significant improvement. The coverage rate was improved by 3.14% for coverage strength greater than or equal to -95dBm, and by 12.94% for coverage strength greater than or equal to -85dBm.

[0104] Secondly, a parameter optimization system based on a ray tracing model includes a raw point cloud data acquisition and processing module, an actual power measurement module, a ray tracing model establishment module, and an electrical parameter optimization module.

[0105] Raw point cloud data acquisition and processing module: The indoor scene is scanned using a laser point cloud instrument to generate raw point cloud data. The raw point cloud data is preprocessed and preprocessed result data is generated. The preprocessed result data is converted into a triangular surface element environment model using 3D modeling software. Each surface element in the triangular surface element environment model is assigned electrical parameters of the corresponding material. The electrical parameters include relative permittivity and conductivity.

[0106] Actual power measurement module: Uses channel power measurement equipment to measure the actual power of indoor wireless channels in indoor scenarios and obtains the actual power results of indoor wireless channels;

[0107] Ray tracing model building module: Based on the triangular element environment model, multipath information is determined, the power value of each multipath information is calculated according to the radio wave propagation mechanism, and a ray tracing model is generated. At the same time, the parameters of the ray tracing model are configured.

[0108] Electrical parameter optimization module: Performs simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model.

[0109] Thirdly, an electronic device includes a memory and a processor, wherein:

[0110] Memory: Used to store the computer program that implements the parameter optimization method based on the ray tracing model;

[0111] Processor: Used to implement the parameter optimization method based on the ray tracing model when executing the computer program.

[0112] Fourthly, a computer-readable storage medium stores a computer program that, when executed by a processor, implements the parameter optimization method based on the ray tracing model; the computer-readable storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0113] Working principle of this invention:

[0114] In this invention, the technical flow of the method includes using a laser point cloud instrument to scan a real indoor scene to obtain high-precision point cloud data and appearance information; then, developing a detailed power measurement scheme based on the actual scene and performing actual power measurement; subsequently, considering the complexity of the indoor environment, setting relevant parameters of the ray tracing model, and iteratively adjusting the simulation results using a genetic algorithm. The genetic algorithm optimizes the electrical parameters of the indoor materials through parameter initialization, population initialization, selection operators, crossover operators, and mutation operators to improve the prediction accuracy of the ray tracing model; based on the optimized electrical parameters, the indoor wireless network coverage is optimized to improve signal strength and network performance, ensuring ideal wireless coverage throughout the indoor environment.

Claims

1. A parameter optimization method based on a ray tracing model, characterized in that, Includes the following steps: S1. Use a laser point cloud instrument to scan the indoor scene and generate raw point cloud data. Preprocess the raw point cloud data and generate preprocessed result data. Use 3D modeling software to convert the preprocessed result data into a triangular surface element environment model. Assign electrical parameters of corresponding materials to each surface element in the triangular surface element environment model. The electrical parameters include relative permittivity and conductivity. S2. Utilize a channel power measurement device to measure the actual power of the indoor wireless channel in the indoor scene and obtain the actual power result of the indoor wireless channel; S3. Based on the triangular surface element environment model described in step S1, determine the multipath information, calculate the power value of each multipath information according to the radio wave propagation mechanism, and generate a ray tracing model. At the same time, configure the parameters of the ray tracing model. S4. Perform simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model described in step S3; the specific steps are as follows: S4.1 Adjust the Gth (G = 1, 2, ..., G) electrical parameters of the material corresponding to each surface element in the ray tracing model; S4.

2. Using the multipath information output by the ray tracing model described in step S3, perform field calculations to obtain the simulated power results of the Gth generation (G = 1, 2, ..., G). The multipath information includes the electrical parameters and propagation information of the Gth generation (G = 1, 2, ..., G) described in step S4.

1. S4.

3. Based on the simulated power results of the Gth generation (G = 1, 2, ..., G) described in step S4.2 and the actual power results described in step S2, establish an error function and use the error function as the objective function of the genetic algorithm to calculate the error results of the Gth generation (G = 1, 2, ..., G). S4.4 Repeat steps S4.1 to S4.3 until the error result calculation for each generation is completed, and obtain the optimal electrical parameters corresponding to the generation G (G = 1, 2, ..., G) with the smallest error result.

2. The parameter optimization method as described in claim 1, characterized in that, The channel power measurement device mentioned in step S2 includes a transmitter and a receiver. The specific steps of step S2 are as follows: S2.1 Set the transmission frequency and transmission power of the transmitter, and set the center frequency and resolution bandwidth of the receiver; S2.

2. Perform actual power measurement of the indoor wireless channel according to the test line to obtain the actual power result of the indoor wireless channel.

3. The parameter optimization method as described in claim 1, characterized in that, The ray tracing model parameters in step S3 include the number of limits, the number of emitted rays, and the radius and power threshold of the receiving sphere; the position of the omnidirectional receiving antenna in the ray tracing model in step S3 needs to be transformed relative to the coordinate system of the triangular element environment model.

4. The parameter optimization method as described in claim 1, characterized in that, The algebra G mentioned in step S4.1 is 300-1000.

5. The parameter optimization method as described in claim 1, characterized in that, The error function formula described in step S4.3 is as follows: In formula (1), F std Let y represent the error function. i This represents the difference between the simulated power and the actual power at the i-th point. Indicates y i The mean of the differences, where N represents the number of receiving points.

6. A wireless network coverage optimization method based on a ray tracing model, wherein the parameter optimization method is based on any one of claims 1 to 5, characterized in that, Includes the following steps: Q1. Based on the range of omnidirectional transmitting antenna position coordinates of the triangular surface element environment model, adjust the position coordinates of the m-th (m = 1, 2, ..., m) generation of the omnidirectional transmitting antenna. Q2. Based on the position coordinates of the m-th (m = 1, 2, ..., m) generation omnidirectional transmitting antenna described in step Q1, the coverage function is used as the objective function of the genetic algorithm, and ray tracing model simulation is performed to obtain the coverage of the m-th (m = 1, 2, ..., m) generation. Q3. Repeat steps Q1 and Q2 until the coverage calculation for each generation is completed, and obtain the optimal omnidirectional transmitting antenna position coordinates corresponding to the generation m (m = 1, 2, ..., m) with the largest coverage.

7. A parameter optimization system based on a ray tracing model, employing the parameter optimization method as described in any one of claims 1 to 5, wherein the parameter optimization system comprises a raw point cloud data acquisition and processing module, an actual power measurement module, a ray tracing model establishment module, and an electrical parameter optimization module, characterized in that: Raw point cloud data acquisition and processing module: The indoor scene is scanned using a laser point cloud instrument to generate raw point cloud data. The raw point cloud data is preprocessed and preprocessed result data is generated. The preprocessed result data is converted into a triangular surface element environment model using 3D modeling software. Each surface element in the triangular surface element environment model is assigned electrical parameters of the corresponding material. The electrical parameters include relative permittivity and conductivity. Actual power measurement module: Uses channel power measurement equipment to measure the actual power of indoor wireless channels in indoor scenarios and obtains the actual power results of indoor wireless channels; Ray tracing model building module: Based on the triangular element environment model, multipath information is determined, the power value of each multipath information is calculated according to the radio wave propagation mechanism, and a ray tracing model is generated. At the same time, the parameters of the ray tracing model are configured. Electrical parameter optimization module: Performs simulation optimization on the electrical parameters of the material corresponding to each surface element in the ray tracing model.

8. An electronic device comprising a memory and a processor, characterized in that: Memory: for storing a computer program that implements the parameter optimization method as described in any one of claims 1 to 5; Processor: Used to implement the parameter optimization method as described in any one of claims 1 to 5 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the parameter optimization method as described in any one of claims 1 to 5.

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