Ray tracking model-based parameter optimization method, system, equipment and medium

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a parameter optimization method and system based on a ray tracing model, equipment and a medium. According to the method, an indoor scene is scanned to obtain a triangular surface element environment model, meanwhile, an actual power result of an indoor wireless channel is obtained, multi-path information in a ray tracking model generated based on the triangular surface element environment model is used for field calculation, an error function is combined for electrical parameter iteration adjustment, and the indoor wireless channel is obtained. The optimization of the material electrical parameters of the indoor scene is realized, and the wireless network coverage optimization of the indoor scene is further realized; the optimization system comprises an initial point cloud data acquisition and processing module, an actual power measurement module, a ray tracking model establishment module and an electrical parameter optimization module. The equipment and the medium are used for storing a computer program, and an electrical parameter optimization method is realized when the program is executed; the method can improve the prediction precision and reliability of the ray tracking model, improves the optimization efficiency, and meets the actual application demands.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and in particular relates to a parameter optimization method, system, device and medium based on a ray tracing model. Background Art

[0002] With the rapid development and widespread application of wireless communication technology, it is becoming increasingly important to study the characteristics of wireless channels in indoor environments. However, due to the diversity of indoor environments, such as geometric structures, object placement, and different types of objects, indoor environment modeling faces great challenges. The differences between various indoor environments such as offices, campuses, industrial scenes, and residences increase the complexity of wireless channel modeling and affect the accurate prediction of channel characteristics.

[0003] In complex indoor scene modeling, it is very difficult to obtain a large amount of mapping data by manual measurement. Therefore, the adoption of standardized and automated environmental measurement solutions can effectively improve the accuracy and timeliness of modeling. Laser point cloud scanning technology uses sensors to perceive surrounding objects in an unknown environment, and matches the environmental data obtained at different time points to infer the position of objects in the environment. This technology can provide centimeter-level or even millimeter-level measurement accuracy, and the data acquisition efficiency is extremely high. Laser point cloud scanning is widely used in related fields as an effective means of scene restoration. However, the extraction and processing of laser scanning data still faces certain challenges.

[0004] In indoor environments, signal propagation is affected by various obstacles, resulting in reflection, refraction, and diffraction. These phenomena will generate multiple signal paths, forming a multipath effect. The constructive and destructive interference caused by the multipath effect will cause the signal to disperse in time, resulting in delay spread, which will cause inter-symbol interference, and thus affect the performance of the communication system and the stability of the signal. Due to the complex geometric structure of the indoor environment, the signal propagation process is often blocked and absorbed by obstacles, which makes the signal strength gradually weaken as the propagation distance increases. Therefore, the signal fading in the indoor environment usually exhibits two characteristics: fast fading and slow fading. In order to accurately evaluate and optimize the performance of wireless communication systems, it is necessary to accurately measure and model the indoor wireless channel. Through these measurement data, a channel model that can describe the propagation characteristics of the signal in the indoor environment can be constructed.

[0005] The ray tracing model is a deterministic model that simulates the propagation path of electromagnetic waves and calculates the characteristics of the signal such as attenuation and delay on different paths. The advantage of the ray tracing model is that it can consider the complex indoor environment characteristics in detail and provide accurate multipath information. However, when using the ray tracing model to simulate the propagation of electromagnetic waves in indoor environments, many challenges are usually encountered, such as the precision of modeling and the electrical parameters of materials. First of all, the precision of modeling becomes the primary factor restricting the simulation accuracy. Due to the complexity and changeability of indoor spaces, including many irregular shapes and layouts of walls, furniture, doors and windows and other obstacles, it is particularly important to build a three-dimensional model that can fully and accurately reflect these physical characteristics. The precision of modeling directly affects whether the multipath information of the ray tracing model is consistent with the actual electromagnetic wave propagation path. Secondly, the accuracy of the electromagnetic parameters of the material is another major challenge. The interaction characteristics of electromagnetic waves with different materials, such as reflection and transmission, and the calculation of electromagnetic fields are highly dependent on the electrical parameters of the material, such as dielectric constant and conductivity. However, the acquisition and setting of these parameters are extremely complex. They are not only affected by factors such as material type and manufacturing process, but may also fluctuate with changes in ambient temperature and humidity. Therefore, we need to establish and continuously update the material electromagnetic parameter database, and develop an effective parameter optimization algorithm to minimize the simulation error caused by inaccurate parameters and improve the prediction accuracy and reliability of the ray tracing model.

[0006] In addition, in the actual deployment of mobile communication networks, improper selection and setting of base station locations has become one of the key factors restricting the improvement of network performance. This improper layout not only significantly increases the construction cost of network infrastructure, but also has a direct and far-reaching impact on the coverage of wireless signals. Due to the unreasonable distribution of base station locations, some areas often have too strong signals that cause interference, while other areas have weak signals or even no coverage, forming the so-called "signal blind spots". This not only affects the user's call quality, but may also lead to a decrease in data transmission rate and an increase in drop rate, greatly reducing the user's communication experience.

[0007] In the prior art, the invention with the announcement number "CN 114448531 B" discloses "a channel characteristic analysis method, system, medium, device and processing terminal". Based on the simulation results of the deterministic channel model, it proposes a multi-dimensional wireless channel characteristic analysis method and further improves the channel clustering algorithm. The invention can provide a more accurate theoretical basis for wireless communication system design and network planning, but 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] In view of the deficiencies in the above-mentioned prior art, the purpose of the present invention is to propose a parameter optimization method, system, device and medium based on a ray tracing model. The method obtains a triangular surface element environment model by scanning and processing the indoor scene, and simultaneously obtains the actual power result of the indoor wireless channel, generates a ray tracing model based on the triangular surface element environment model, uses the multipath information of the ray tracing model to perform field calculations, and combines the error function to iteratively adjust the electrical parameters to achieve the optimization of the electrical parameters of the indoor scene materials. The present invention can improve the prediction accuracy and reliability of the ray tracing model, and significantly improve the optimization efficiency to meet the needs of actual applications.

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

[0010] In a first aspect, a parameter optimization method based on a ray tracing model comprises the following steps:

[0011] S1. Scanning an indoor scene using a laser point cloud instrument to generate raw point cloud data, preprocessing the raw point cloud data and generating preprocessing result data, converting the preprocessing result data into a triangular surface element environment model using 3D modeling software, and assigning electrical parameters of corresponding materials to each surface element in the triangular surface element environment model, wherein the electrical parameters include relative dielectric constant and conductivity;

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

[0013] S3, determining multipath information based on the triangular facet environment model in step S1, calculating the power value of each multipath information according to the radio wave propagation mechanism and generating a ray tracing model, and configuring the ray tracing model parameters;

[0014] S4, performing simulation optimization on the electrical parameters of the material corresponding to each facet in the ray tracing model described in step S3, the specific steps are as follows:

[0015] S4.1, adjusting the Gth (G=1, 2..., G)th ​​generation electrical parameters of the material corresponding to each facet in the ray tracing model;

[0016] S4.2, using the multipath information output by the ray tracing model in step S3 to perform field calculations to obtain G-th (G=1, 2..., G) generation simulation power results, wherein the multipath information includes the G-th (G=1, 2..., G) generation electrical parameters and propagation information in step S4.1;

[0017] S4.3, establishing an error function based on the Gth (G=1, 2..., G)th ​​generation simulation power result described in step S4.2 and the actual power result described in step S2, and using the error function as the objective function of the genetic algorithm to calculate the Gth (G=1, 2..., G)th ​​generation error result;

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

[0019] Furthermore, the channel power measurement device in step S2 includes a transmitting end and a receiving end, and the specific steps of step S2 are as follows:

[0020] S2.1, set the transmitting frequency and transmitting power of the transmitting end, and set the center frequency and resolution bandwidth of the receiving end;

[0021] S2.2. Measure the actual power 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 restrictions, 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 into relative coordinates according to the coordinate system of the triangular surface element environment model.

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

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

[0025]

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

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

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

[0029] Q2. Based on the position coordinates of the mth (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 a ray tracing model simulation is performed to obtain the mth (m=1, 2..., m) generation coverage;

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

[0031] In the second aspect, a parameter optimization system based on a ray tracing model is provided, and the parameter optimization method is applied. The parameter optimization system includes an original 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] Original point cloud data acquisition and processing module: use a laser point cloud instrument to scan the indoor scene to generate original point cloud data, pre-process the original point cloud data and generate pre-processing result data, use 3D modeling software to convert the pre-processing result data into a triangular surface element environment model, and assign electrical parameters of corresponding materials to each surface element in the triangular surface element environment model, wherein the electrical parameters include relative dielectric constant and conductivity;

[0033] Actual power measurement module: Use the 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;

[0034] A ray tracing model building module: determines multipath information based on the triangular surface element environment model, calculates the power value of each multipath information according to the radio wave propagation mechanism and generates a ray tracing model, and configures the ray tracing model parameters;

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

[0036] In a third aspect, an electronic device includes a memory and a processor:

[0037] Memory: used for storing a computer program for implementing 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] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parameter optimization method based on the ray tracing model is implemented.

[0040] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0041] 1. In step S1 of the present invention, laser point cloud is used for scene modeling, the scanning data rate is 320,000 points per second, the scanning accuracy is plus or minus 1.5 cm, and the established indoor three-dimensional environment model is modeled by triangular surface elements, which can be more in line with the actual scene and has universal and refined environmental data.

[0042] 2. The channel power measurement equipment established in step S2 of the present invention has the characteristics of lightweight, high stability and wide bandwidth measurement range. It supports demodulation tests of all frequency bands from 2G to 5G (supporting blind scanning), frequency scanning measurement (CW measurement), interference analysis and positioning, etc. The measurement platform can effectively and quickly complete the actual power measurement of indoor wireless channels.

[0043] 3. In step S4 of the present invention, the actual power result of the indoor wireless channel is used to optimize the material electrical parameters (i.e., relative dielectric constant 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 actual application requirements; at the same time, step S4 uses the multipath information output by the ray tracing model and combines it with a genetic algorithm to optimize the electrical parameters, so that the ray tracing model does not need to be repeatedly called, which can significantly improve the optimization efficiency.

[0044] 4. Based on the optimal electrical parameters in step S4, the present 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 the prior art, the present invention optimizes indoor wireless channel modeling and antenna position through the combination of laser point cloud modeling, channel power measurement equipment, ray tracing model and genetic algorithm, with high-precision and reliable results, effectively improving signal coverage and quality, while improving optimization efficiency to meet practical application needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a parameter optimization method based on a ray tracing model provided by the present invention.

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

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

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

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

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

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

[0053] Figure 6 It is a comparison chart of the actual power result provided by the first embodiment of the present invention and the simulated power results before and after the electrical parameter optimization.

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

[0055] Figure 8 This is a simulated power coverage diagram after the antenna position is optimized according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0056] Combine the following Figures 1 to 8 The present invention is further described in detail in the embodiments of the present invention, which include a parameter optimization method, system, device and medium based on a ray tracing model, wherein the parameters are electrical parameters of the material of objects involved in indoor scenes, and the electrical parameters include relative dielectric constant and electrical conductivity, and wireless network coverage optimization is antenna position optimization.

[0057] In the first aspect, a parameter optimization method based on a ray tracing model, such as Figure 1 The flowchart of the method is shown, which specifically comprises the following steps:

[0058] S1. Use a laser point cloud instrument to scan a real indoor scene to generate raw point cloud data, pre-process the raw point cloud data to generate pre-processed result data, and use 3D modeling software (Blender software, 3D Max software, etc.) to convert the generated pre-processed result data into a triangular surface element environment model. The triangular surface element environment model can accurately represent the size, relative position and shape information of objects in the environment. According to the material characteristics of objects (such as walls, floors, windows, doors, tables and chairs, etc.) in the real indoor scene (materials involve concrete, bricks, gypsum boards, glass, wood, etc.), each surface element in the triangular surface element environment model is assigned the electrical parameters of the corresponding material (electrical parameters include relative dielectric constant and conductivity); the raw point cloud data commonly used in this step S1 is in .las point cloud data format, and the pre-processing methods include point cloud filtering, point cloud registration, downsampling and other methods;

[0059] S2. Use the channel power measurement equipment 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. The specific steps are as follows: 1. The transmitter and the omnidirectional transmitting antenna are used as the transmitting end, and the PCTEL frequency scanner, the test tablet computer and the omnidirectional receiving antenna are used as the receiving end. Set the parameters of the transmitting end and the receiving end (frequency, power, bandwidth, etc.), 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. Measure the actual power of the wireless channel according to the test line (including line-of-sight and non-line-of-sight scenarios), and obtain the actual power result of the indoor wireless channel;

[0060] S3, based on the triangular surface element environment model in step S1, determine the multipath information, i.e., the radio wave propagation path, calculate the power value of each multipath information according to the radio wave propagation mechanism (involving direct radiation, reflection, transmission, and diffraction), and generate a ray tracing model, wherein the final power value is determined by the superposition result of all radio wave propagation paths, and configure the ray tracing model parameters at the same time; the specific configuration of the ray tracing model parameters is the limit number of the ray tracing model, the number of transmitted rays, the radius size of the receiving sphere, and the power threshold; the position of the omnidirectional receiving antenna needs to be relatively converted according to the coordinate system of the triangular surface element environment model, and the same coordinate system is used to conform to the actual measurement position;

[0061] S4, performing simulation optimization on the electrical parameters of the material corresponding to each facet in the ray tracing model described in step S3, the specific steps are as follows:

[0062] S4.1, according to the optimization range of the electrical parameters (including relative dielectric constant and conductivity) of the material corresponding to each face element in the ray tracing model described in step S3, wherein the relative dielectric constant ranges from 1 to 15, and the conductivity ranges from 0 to 3 S / m, adjust the Gth (G=1, 2..., G)th ​​generation electrical parameters of the material corresponding to each face element, and the value range of the algebra G is 300-1000;

[0063] S4.2. Perform field calculation using the multipath information output by the ray tracing model to obtain G (G=1, 2…, G) generation simulation power results, where the multipath information includes G (G=1, 2…, G) generation electrical parameters, as well as direct, reflected, transmitted, and diffraction propagation information;

[0064] S4.3, establishing an error function based on the Gth (G=1, 2..., G) generation simulation power results described in step S4.2 and the actual power results described in step S2, and using the error function as the objective function of the genetic algorithm to obtain the Gth (G=1, 2..., G) generation error results;

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

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

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

[0068] Q2. Based on the position coordinates of the mth (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 a ray tracing model simulation is performed to obtain the mth (m=1, 2..., m) generation coverage, which is used to evaluate the coverage effect of the wireless network in the indoor environment;

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

[0070] Embodiment 1

[0071] The technical solution of the present invention is further described in detail below by taking the indoor environment of a certain teaching building as an example.

[0072] In step S1, a real indoor scene is scanned using a Lixel X1 laser point cloud instrument to generate a commonly used .las point cloud data format, such as Figure 2a to Figure 2c The figure shows the schematic diagram of the point cloud data of the exterior wall contour, corridor, interior wall and accessories of the indoor scene. The original point cloud data is preprocessed, including point cloud filtering, point cloud registration and other processing methods. The main purpose of point cloud filtering is to eliminate noise points and improve the quality of point cloud. By reducing the amount of point cloud data, the subsequent calculation and storage efficiency are optimized. The main purpose of point cloud registration is to align multiple groups of point cloud data from different perspectives or at different times so that they have a consistent geometric structure in a unified coordinate system. The Blender software is used to establish a refined indoor 3D environment model, and then the triangular face element division (including 55091 scene points and 108193 face elements) is finally generated as shown below. Figure 3 The triangular surface element environment model shown in the figure includes the size, relative position and shape information of objects in the actual environment. The modeling environment includes 8 rooms and 1 corridor, which includes 40 internal and external walls (40 windows and 16 doors on the internal and external walls), 38 columns and 178 objects. At the same time, the corresponding material information is assigned according to the ITU electrical parameter reference values. The ITU electrical parameter reference values ​​are shown in Table 1:

[0073] Table 1 ITU electrical parameter reference value table

[0074]

[0075] In the step S2, the embodiment 1 uses a transmitter with a frequency range of 20 to 6000 MHz and a transmission power range of -10 to 33 dBm and an omnidirectional transmitting antenna as a transmitting end, and a PCTEL frequency sweeper, a test tablet computer and an omnidirectional receiving antenna as a receiving end; during the test process, the transmitting frequency and the transmitting power of the transmitting end module are first set, and the center frequency and the resolution bandwidth of the receiving end module are configured. The test tablet computer is connected to the PCTEL frequency sweeper via Bluetooth to realize real-time monitoring of the power changes on the test route, and then the actual power measurement of the wireless channel is performed; the test line is such as Figure 4 As shown in the figure, 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 accuracy of the PCTEL sweeper, the receiving points are very dense; the relevant measurement control parameter settings are shown in Table 2:

[0076] Table 2 Measurement control parameter settings

[0077]

[0078] Among them, in the measurement of a certain frequency band configured in the PCTEL sweeper, such as 990MHz~1010MHz, the center frequency is set to 1000MHz, and the resolution bandwidth RBW is set to 100KHz, then the Number ofbin needs to be set according to the center frequency and the resolution bandwidth RBW, that is, 20MHz / 100KHz=200. It is worth noting that the frequency interval (FrequencyStep) set at the receiving end must be equal to the resolution bandwidth RBW. After the test is completed, the test data needs to be exported, and the measured data LOG is converted into .csv format using the SeeHawk Collect software on the computer.

[0079] In step S3, first, a ray is emitted from the omnidirectional transmitting antenna, and 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, reflection, transmission, diffraction or scattering will occur, and the specific phenomenon or combination of multiple phenomena that occurs depends on the geometric shape and electromagnetic characteristics of the obstacle; all radio wave propagation paths ultimately received by the omnidirectional receiving antenna are multipath information, and if the ray is ultimately received by the omnidirectional receiving antenna, the field calculation method is used to calculate the electric field or power distribution of the ray at the receiving point.

[0080] In the ray tracing model, the simulation parameter configuration of ray tracing is initialized, and the simulation parameters are optimized according to the indoor scene environment. The initialization parameters are shown in Table 3. The number of limits, number of emitted rays, radius of the receiving sphere, and power threshold of the ray tracing model are configured. For indoor scenes, the measured coordinate position needs to be converted to relative coordinates according to the actual environment to ensure the uniformity of the coordinate system.

[0081] Table 3 Ray tracing model simulation parameter settings

[0082]

[0083] In step S4, the optimization range of the electrical parameters of each material is set, such as the relative dielectric constant range of 1 to 15, and the conductivity range of 0 to 3S / m. Metals have the characteristic of high conductivity due to a large number of free electrons, high electron mobility, compact crystal structure, and fewer impurities and defects. The optimization 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 ITU. Ray tracing simulation is performed on each position on the test line using a ray tracing model to obtain the simulation power results before optimization.

[0084] The error function F between the optimized Gth (G = 1, 2, ..., G) generation simulation power results and the actual power results std As the objective function of the genetic algorithm, the specific formula is as follows:

[0085]

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

[0087] The material parameters involved in the indoor scene are optimized using the simulation result information. The reflection point, transmission point, diffraction point and the normal direction of the corresponding surface are stored separately according to the propagation type, and the field calculation is performed in combination with the optimization algorithm. Among them, the field calculation involves the reflection field, transmission field and diffraction field, and the corresponding main calculation formulas are as follows:

[0088] Reflection coefficient (R):

[0089]

[0090] In formula (2) and (3), R || is the horizontal component of the reflection coefficient (R), R ⊥ is the vertical component of the reflection coefficient (R), n 1 is the refractive index of medium 1, n 2 is the refractive index of medium 2, n i is the refractive index of the corresponding medium, θ i is the incident angle, θ t is the transmission angle.

[0091] Transmission coefficient (T):

[0092]

[0093] In formula (4) and (5), T || is the horizontal component of the transmission coefficient (T), T ⊥ is the vertical component of the transmission coefficient (T), n 1 is the refractive index of medium 1, n 2 is the refractive index of medium 2, n i is the refractive index of the corresponding medium, θ i is the incident angle, θ t is the transmission angle.

[0094] Diffraction coefficient (D):

[0095]

[0096]

[0097] In formulas (6) to (10), is the angle between the observation point and the surface, is the angle between the source point and the surface, β 0 is the angle between the incident ray and the diffraction edge, n is the wedge factor, F(x) is the transition function, k is the wave number, L is the distance parameter, and j is an imaginary number.

[0098] like Figure 5 The figure shows the specific calculation process of electrical parameter optimization. Following the principle of minimizing the objective function, the optimal electrical parameter value of the material under the indoor environment is obtained. Figure 6 The figure shows a comparison between the simulated power results and the actual power results before and after optimization. The black "." indicates the measured power result, the blue "." indicates the simulated power result before optimization, and the red "▲" indicates the simulated power result after optimization. The standard deviation between the simulated power results before optimization and the measured power results is 8.7704dB, with an average error of -0.57754dB. The standard deviation between the simulated power results after optimization and the measured power results is 6.5119dB, with an average error of 0.53657dB. It can be seen from the data that the standard deviation after optimizing the electrical parameters has increased by 2.2585dB, and the accuracy of the ray tracing model has been significantly improved. In addition, using the multipath information output by the ray tracing model and combining it with a genetic algorithm for electrical parameter optimization can significantly improve the optimization efficiency. A single simulation takes 1023 seconds, and when the number of iterations of the genetic algorithm G is 300, it takes 306900 seconds. After a single simulation, using the multipath information and combining it with the number of iterations of the genetic algorithm G is 300, 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. Define coverage as the objective function, that is, record the number of receiving points with receiving power less than -95dBm, and then compare it with the total number of receiving points to evaluate the coverage effect of the wireless network in the indoor environment. Through the genetic algorithm, adjust the position of the antenna 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 position optimization

[0101]

[0102] Table 4 sets two coverage values ​​for comparison. One is that the coverage intensity should be greater than or equal to -95dBm, and the other is that the coverage intensity should be greater than or equal to -85dBm. According to the coverage value, it can be found that the coverage effect is improved.

[0103] like Figure 7 The figure shows the simulated power coverage diagram before the antenna position is optimized. Figure 8 The figure shows the simulated power coverage diagram after the antenna position is optimized. The received power of the line-of-sight path is not much different before and after the optimization. However, the received power value before the optimization is mostly below -90dBm on the left side of the room. After the optimization, it has been significantly improved. The coverage rate for coverage strength greater than or equal to -95dBm has increased by 3.14%, and the coverage rate for coverage strength greater than or equal to -85dBm has increased by 12.94%.

[0104] In the second aspect, a parameter optimization system based on a ray tracing model includes an original 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] Original point cloud data acquisition and processing module: use a laser point cloud instrument to scan the indoor scene to generate original point cloud data, pre-process the original point cloud data and generate pre-processing result data, use 3D modeling software to convert the pre-processing result data into a triangular surface element environment model, and assign electrical parameters of corresponding materials to each surface element in the triangular surface element environment model, wherein the electrical parameters include relative dielectric constant and conductivity;

[0106] Actual power measurement module: Use the 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;

[0107] A ray tracing model building module: determines multipath information based on the triangular surface element environment model, calculates the power value of each multipath information according to the radio wave propagation mechanism and generates a ray tracing model, and configures the ray tracing model parameters;

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

[0109] According to a third aspect, an electronic device includes a memory and a processor, wherein:

[0110] Memory: used for storing a computer program for implementing 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] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parameter optimization method based on the ray tracing model is implemented; the computer-readable storage medium includes: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0113] Working principle of the present invention:

[0114] In the present 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 formulating a detailed power measurement plan according to the actual scene and performing actual power measurement; then considering the complexity of the indoor environment, setting the relevant parameters of the ray tracing model, and iteratively adjusting the simulation results in combination with a genetic algorithm, wherein the genetic algorithm initializes the parameters, and optimizes the electrical parameters of the indoor materials by initializing the population, selecting an operator, a crossover operator, and a mutation operator to improve the prediction accuracy of the ray tracing model; based on the electrical parameter optimization results, the indoor wireless network is covered and optimized to improve the signal strength and network performance, thereby ensuring an ideal wireless coverage effect in the entire indoor environment.

Claims

1. A parameter optimization method based on a ray tracing model, characterized in that: The following steps are involved: S1. Scanning an indoor scene using a laser point cloud instrument to generate raw point cloud data, preprocessing the raw point cloud data and generating preprocessing result data, converting the preprocessing result data into a triangular surface element environment model using 3D modeling software, and assigning electrical parameters of corresponding materials to each surface element in the triangular surface element environment model, wherein the electrical parameters include relative dielectric constant and conductivity; S2. Use a channel power measurement device to measure the actual power of an indoor wireless channel in an indoor scene, and obtain the actual power result of the indoor wireless channel; S3, determining multipath information based on the triangular facet environment model in step S1, calculating the power value of each multipath information according to the radio wave propagation mechanism and generating a ray tracing model, and configuring the ray tracing model parameters; S4, performing simulation optimization on the electrical parameters of the material corresponding to each facet in the ray tracing model described in step S3; the specific steps are as follows: S4.1, adjusting the Gth (G=1, 2..., G)th ​​generation electrical parameters of the material corresponding to each facet in the ray tracing model; S4.2, using the multipath information output by the ray tracing model in step S3 to perform field calculations to obtain G-th (G=1, 2..., G) generation simulation power results, wherein the multipath information includes the G-th (G=1, 2..., G) generation electrical parameters and propagation information in step S4.1; S4.3, establishing an error function based on the Gth (G=1, 2..., G)th ​​generation simulation power result described in step S4.2 and the actual power result described in step S2, and using the error function as the objective function of the genetic algorithm to calculate the Gth (G=1, 2..., G)th ​​generation error result; S4.4, repeat steps S4.1 to S4.3 until the calculation of the error results of each generation is completed, and the optimal electrical parameters corresponding to the generation G (G=1, 2..., G) with the smallest error result are obtained.

2. The parameter optimization method according to claim 1, characterized in that: The channel power measurement device in step S2 includes a transmitting end and a receiving end. The specific steps of step S2 are as follows: S2.1, set the transmitting frequency and transmitting power of the transmitting end, and set the center frequency and resolution bandwidth of the receiving end; S2.

2. Measure the actual power 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 according to claim 1, characterized in that: The ray tracing model parameters in step S3 include the number of restrictions, 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 into relative coordinates according to the coordinate system of the triangular surface element environment model.

4. The parameter optimization method according to claim 1, characterized in that: In step S4.1, the algebra G is 300-1000.

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

6. A wireless network coverage optimization method based on a ray tracing model, based on the parameter optimization method according to any one of claims 1 to 5, characterized in that: The following steps are involved: Q1. Based on the omnidirectional transmitting antenna position coordinate range of the triangular surface element environment model, adjust the mth (m=1, 2..., m) generation omnidirectional transmitting antenna position coordinates; Q2. Based on the position coordinates of the mth (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 a ray tracing model simulation is performed to obtain the mth (m=1, 2..., m) generation coverage; Q3. Repeat steps Q1 and Q2 until each generation of coverage calculation is completed, and the optimal omnidirectional transmitting antenna position coordinates corresponding to the generation m (m=1, 2..., m) with the largest coverage are obtained.

7. A parameter optimization system based on a ray tracing model, applying the parameter optimization method according to any one of claims 1 to 5, the parameter optimization system comprising an original 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: Original point cloud data acquisition and processing module: use a laser point cloud instrument to scan the indoor scene to generate original point cloud data, pre-process the original point cloud data and generate pre-processing result data, use 3D modeling software to convert the pre-processing result data into a triangular surface element environment model, and assign electrical parameters of corresponding materials to each surface element in the triangular surface element environment model, wherein the electrical parameters include relative dielectric constant and conductivity; Actual power measurement module: Use the 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; A ray tracing model building module: determines multipath information based on the triangular surface element environment model, calculates the power value of each multipath information according to the radio wave propagation mechanism and generates a ray tracing model, and configures the ray tracing model parameters; Electrical parameter optimization module: performs simulation optimization on the electrical parameters of the material corresponding to each face element in the ray tracing model.

8. An electronic device, comprising a memory and a processor, characterized in that: Memory: used to store a computer program for implementing the parameter optimization method according to any one of claims 1 to 5; Processor: used to implement the parameter optimization method as claimed 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, and when the computer program is executed by a processor, the parameter optimization method according to any one of claims 1 to 5 is implemented.

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