A method and system for presetting and correcting electrical parameters

Abstract

The present invention belongs to the technical field of radio wave propagation model analysis and optimization, and discloses a method and system for presetting and correcting electrical parameters, which include: importing the 3D digital information format of the complex indoor environment required by the ray tracing model, fixing the initial input parameters of the ray tracing model; examining the influence of the change of the electrical parameters of each material on the radio wave propagation, and setting the priority of the electrical parameters of specific materials according to the actual prediction point of interest; formulating a measurement plan for the actual indoor scene; combining the ray tracing model with the measurement plan, and setting appropriate initial input parameters for the ray tracing model according to the measured conditions; optimizing the electrical parameters of the ray tracing model by using the error function between simulation and measurement as the objective function of the genetic algorithm, and comparing the difference between the simulated power and the measured power before and after the optimization of the electrical parameters. The corrected ray model of the present invention can better meet the standard deviation requirements in the industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of radio wave propagation model analysis and optimization, and in particular relates to an electrical parameter preset and parameter correction method and system. Background Art

[0002] At present, the ray tracing model is the application of optical ray technology in the field of electromagnetic computing. Compared with the traditional empirical model, the ray tracing model is a deterministic computing model that can accurately predict the receiving power and multipath information of indoor scenes. The model needs to input the actual environment model data, the transmitting antenna power, the transmitting electromagnetic wave frequency, the location of the transceiver point in the environment and other information, and then output the receiving point receiving power and multipath information through simulated ray tracing calculation. Through this information, the real radio wave propagation process can be simulated.

[0003] In the application of actual indoor scenes, the electromagnetic wave signal is regarded as an idealized light through the ray tracing algorithm, so that the propagation of the electromagnetic wave signal in the indoor medium can be calculated by tracing rays. Starting from a certain initial source point, the electromagnetic wave reaches a certain prediction point after experiencing direct radiation, reflection, transmission, and diffraction in the form of rays. The size of the electromagnetic wave signal at the prediction point is determined by the size of the electromagnetic wave signal emitted by the source point and the attenuation generated during the propagation process. The precision of the indoor environment modeling and the accuracy of the electrical parameters of each medium (i.e., the main objects such as air and indoor walls) directly affect the direct radiation, reflection, transmission, and diffraction mechanisms in the electromagnetic wave propagation process. Generally speaking, the more precise the environmental modeling is and the more realistic the electrical parameter settings of each medium are, the better the electromagnetic wave signal predicted by the ray tracing model is in line with the actual measurement. Therefore, without considering the influence of environmental modeling, it is an effective method to improve the model accuracy by optimizing the preset electrical parameters.

[0004] Genetic algorithm is a meta-heuristic algorithm inspired by the natural selection process. Genetic algorithm usually relies on biologically inspired operators, such as mutation, crossover and selection, to generate high-quality solutions to optimization and search problems. In terms of actual engineering problems, the purpose of genetic algorithm is to find the optimal solution that best suits the engineering problem. For example, genetic algorithm can be used to find the solution corresponding to the minimum value of a bounded binary function. The parameter correction of ray tracing model aims to obtain the optimal parameter set in a specific scenario, thereby improving the accuracy of ray tracing model. The target results of genetic algorithm optimization and parameter correction of ray tracing model are the same, that is, both are to find the optimal solution of multivariate function. The use of genetic algorithm can be well used to correct the parameters of ray tracing model, thereby improving the prediction accuracy of model.

[0005] Through the above analysis, the problems and defects of the existing technology are: the prediction accuracy of the ray tracing model is particularly affected by the accuracy of modeling the actual environment, and the preset parameters required by the model also have a great influence on its accuracy.

[0006] The difficulty in solving the above problems and defects is: limited by the 3D digital information format stored in the environment modeling, it is difficult to describe the irregular objects or room building structures in the actual environment with digital accuracy. Instead of pursuing high-precision modeling that fits the actual environment, it is more reliable to solve the optimal electrical parameters that are most suitable for the environment. The selection of the optimal electrical parameters is determined by the minimum error between the simulated results and the measured results. Therefore, without traversing the simulation results corresponding to each set of electrical parameters, it is impossible to truly know the optimal electrical parameters. However, traversing all electrical parameters is extremely time-consuming, and the electrical parameters are real numbers, and there will be problems in the selection of traversal intervals. Therefore, how to combine other optimization algorithms to solve the optimal electrical parameters of the ray tracing model while considering timeliness and accuracy is the difficulty of the above problems.

[0007] The significance of solving the above problems and defects is that compared with the original electrical parameters, the ray tracing model under the optimized electrical parameters can more accurately predict the propagation of electromagnetic wave signals, and the simulated results are more consistent with the measured results, that is, the applicability of the ray tracing model is improved, so that according to the simulation results, the radio wave resources can be planned more reasonably. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a method and system for presetting and correcting electrical parameters, and more particularly, relates to a method and system for presetting and correcting electrical parameters based on an indoor ray tracing model.

[0009] The present invention is implemented as follows: an electrical parameter preset and parameter correction method, the electrical parameter preset and parameter correction method comprising the following steps:

[0010] Step 1: Import the 3D digital information format of the complex indoor environment required by the ray tracing model, and fix the initial input parameters of the ray tracing model. This step is mainly to provide the operating environment and conditions of the ray tracing model, and the results obtained by running with the initial parameters are used as the original simulation results.

[0011] Step 2: By changing the values ​​of the electrical parameters of different materials and running the ray tracing model to calculate the field strength, power, and multipath information of the prediction point, the impact of the changes in the electrical parameters of each material on the propagation of radio waves is examined, and the electrical parameters of specific materials are prioritized according to the actual prediction points of interest. This step considers the electrical parameters of each material in a graded manner, simplifying the problem of too many variables in the objective function optimization in the subsequent electrical parameter correction.

[0012] Step 3: Develop a measurement plan for the actual indoor scene and conduct relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measured conditions. The purpose of this step is to obtain the corresponding simulation results based on the measured position and conditions, so that the simulation results can be compared with the measured results.

[0013] Step 4: According to the influence of the electrical parameters of various materials in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm from large to small. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found. After obtaining the optimal electrical parameters in this step, they are fed back to the ray tracing model to obtain an optimized ray tracing model, so that the optimized model can more accurately predict the propagation of radio waves in this environment.

[0014] Furthermore, in step 1, the initial input parameters include frequency, transmission power and transceiver antenna positions, but do not include electrical parameters.

[0015] In step 2, the effect of changes in electrical parameters of various materials on radio wave propagation is investigated, including:

[0016] Import the initial electrical parameters of each material and call the model to simulate the initial power prediction results. Then change the electrical parameters of one material while keeping the electrical parameters of other materials unchanged, obtain the power prediction results after the parameters are changed, and compare them with the initial results. Repeat the previous operation continuously, keeping the value of each electrical parameter change consistent, and examine the impact of the changes in the electrical parameters of each material on the power prediction results one by one.

[0017] Further, in step 3, the setting of appropriate initial input parameters includes:

[0018] For the position of the transmitting and receiving antennas, the simulation and the actual measurement should correspond to each other in terms of position parameters. For some measurement position points selected in the actual measurement, reasonable prediction points should also be set in the simulation to correspond to them.

[0019] The transmission frequency and simulation frequency shall be based on the actual measured frequency;

[0020] Transmit power: The preset transmit power during simulation is determined by the actual measured power.

[0021] Further, in step 4, the indoor environmental electrical parameters include

[0022] The electrical parameter information of common indoor materials includes: the relative dielectric constant of concrete is 5.31 and the conductivity is 0.1; the relative dielectric constant of glass is 6.27 and the conductivity is 0.022; the relative dielectric constant of wood is 1.99 and the conductivity is 0.021; the relative dielectric constant of metal is 1 and the conductivity is 107.

[0023] When selecting environmental electrical parameters as adaptive variables to be imported into the genetic algorithm, only the electrical parameters of materials that account for a large proportion in the actual environment are selected as variables to be imported.

[0024] Further, in step 4, the influence of the electrical parameters of various materials in the indoor environment on the simulation results is introduced in order from large to small as adaptive variables of the genetic algorithm, and the electrical parameters with the smallest error between the simulation results and the measured results are found by continuously changing the values ​​of the electrical parameters and calling the ray tracing model, including:

[0025] The error function between simulation and measurement is used as the objective function of the genetic algorithm to optimize the electrical parameters of the ray tracing model, and the difference between the simulated power and the measured power before and after the optimization of the electrical parameters is compared.

[0026] Furthermore, the error function, i.e. the objective function, includes two error factors:

[0027]

[0028]

[0029] Among them, y i It represents the difference between the predicted value and the measured value of the i-th point position. It represents the mean of the point difference values; the first formula is the standard deviation formula of simulation and measurement, and the error value is used to measure the consistency of the change trend of simulation and measurement; the second formula is the average error formula of simulation and measurement, and the error value is used to measure the overall error between simulation and measurement.

[0030] The error function of the genetic algorithm, i.e., the objective function, is defined as the linear sum of the two error factors, namely:

[0031] F object =λError std +(1-λ)Error avg ;

[0032] Among them, λ is a real number between 0 and 1, and λ is set to 0.8; the corresponding objective function is:

[0033]

[0034] Another object of the present invention is to provide an electrical parameter preset and parameter correction system using the electrical parameter preset and parameter correction method, the electrical parameter preset and parameter correction system comprising:

[0035] An initial input parameter fixing module is used to import the 3D digital information format of the complex indoor environment required by the ray tracing model and fix the initial input parameters of the ray tracing model;

[0036] The electrical parameter priority setting module is used to calculate the field strength, power and multipath information of the prediction point by running the ray tracing model by changing the electrical parameter values ​​of different materials, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of specific materials according to the actual prediction points of interest;

[0037] The initial input parameter setting module is used to formulate a measurement plan for the actual indoor scene and conduct relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measurement conditions;

[0038] The optimal electrical parameter acquisition module is used to import the electrical parameters of various materials in the indoor environment as adaptive variables of the genetic algorithm from large to small according to the influence of the electrical parameters on the simulation results. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

[0039] Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0040] Import the 3D digital information format of the complex indoor environment required by the ray tracing model, and fix the initial input parameters of the ray tracing model; by changing the values ​​of the electrical parameters of different materials and running the ray tracing model to calculate the field strength, power and multipath information of the prediction point, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of specific materials according to the actual prediction points of interest;

[0041] A measurement plan is formulated for actual indoor scenes and relevant power tests are carried out. The ray tracing model is combined with the measurement plan, and appropriate initial input parameters are set for the ray tracing model according to the measured conditions. According to the influence of the electrical parameters of each material in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm in descending order. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0043] Import the 3D digital information format of the complex indoor environment required by the ray tracing model, and fix the initial input parameters of the ray tracing model; by changing the values ​​of the electrical parameters of different materials and running the ray tracing model to calculate the field strength, power and multipath information of the prediction point, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of specific materials according to the actual prediction points of interest;

[0044] A measurement plan is formulated for actual indoor scenes and relevant power tests are carried out. The ray tracing model is combined with the measurement plan, and appropriate initial input parameters are set for the ray tracing model according to the measured conditions. According to the influence of the electrical parameters of each material in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm in descending order. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

[0045] Another object of the present invention is to provide an information data processing terminal, which is used to implement the electrical parameter preset and parameter correction system.

[0046] Combining all the above-mentioned technical solutions, the advantages and positive effects of the present invention are as follows: in this specific complex indoor environment, by changing the electrical parameters and continuously using the ray tracing model for simulation, it is analyzed which material electrical parameters in this environment are the main material electrical parameters affecting the propagation of radio waves, and the priority of correction can be set according to the size of the impact.

[0047] According to the priority of the electrical parameters of each material, resources are planned in sequence for correction, which simplifies the electrical parameter correction problem to a certain extent. The electrical parameters of the model are verified using a genetic algorithm based on the measured results and the ray tracing model. The genetic algorithm iterates by taking the inverse of the objective function value as the fitness, and finds the solution corresponding to the maximum fitness, that is, the minimum objective function value. The solution obtained at this time is fed back to the ray tracing model as the optimized electrical parameter value, and then a simulation comparison is performed for verification. The feasibility of the correction method is verified in the embodiment of the present invention, and the output result of the ray tracing model after correction can better match the measured value than the output result before correction, and the corrected ray model can better meet the standard deviation requirements in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1It is a flow chart of the electrical parameter preset and parameter correction method provided by an embodiment of the present invention.

[0050] Figure 2 It is a structural block diagram of an electrical parameter preset and parameter correction system provided by an embodiment of the present invention;

[0051] In the figure: 1. Initial input parameter fixing module; 2. Electrical parameter priority setting module; 3. Initial input parameter setting module; 4. Optimal electrical parameter acquisition module.

[0052] Figure 3 This is a digital three-dimensional environment modeling diagram that needs to be imported into the ray tracing model provided in the embodiment of the present invention.

[0053] Figure 4 It is a diagram of power coverage of initial material electrical parameter simulation provided by an embodiment of the present invention.

[0054] Figure 5 This is a diagram of simulated power coverage after changing concrete electrical parameters provided by an embodiment of the present invention.

[0055] Figure 6 This is a diagram of simulated power coverage after changing the electrical parameters of marble provided by an embodiment of the present invention.

[0056] Figure 7 This is a diagram of simulated power coverage after changing the electrical parameters of wood provided by an embodiment of the present invention.

[0057] Figure 8 It is a flow chart of correcting model electrical parameters using a genetic algorithm provided by an embodiment of the present invention.

[0058] Fig. 9 It is a point-taking diagram of a measured path provided by an embodiment of the present invention.

[0059] Fig.10 It is a comparison diagram between simulation and actual measurement before parameter correction provided by an embodiment of the present invention.

[0060] Fig.11 It is a comparison diagram between simulation and actual measurement after parameter correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] In view of the problems existing in the prior art, the present invention provides an electrical parameter preset and parameter correction method and system, and the present invention is described in detail below in conjunction with the accompanying drawings.

[0063] like Figure 1 As shown, the electrical parameter preset and parameter correction method provided by the embodiment of the present invention includes the following steps:

[0064] S101, importing the 3D digital information format of the complex indoor environment required by the ray tracing model, and fixing the initial input parameters of the ray tracing model;

[0065] S102, by changing the values ​​of electrical parameters of different materials and running a ray tracing model to calculate the field strength, power and multipath information of the prediction point, the influence of the change of electrical parameters of each material on the propagation of radio waves is investigated, and the electrical parameters of specific materials are prioritized according to the actual prediction point of interest;

[0066] S103, formulate a measurement plan for the actual indoor scene and perform relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measurement conditions;

[0067] S104, according to the influence of the electrical parameters of various materials in the indoor environment on the simulation results, they are imported in order from large to small as adaptive variables of the genetic algorithm, and the electrical parameters with the smallest error between the simulation results and the measured results are found by continuously changing the values ​​of the electrical parameters and calling the ray tracing model.

[0068] like Figure 2 As shown, the electrical parameter preset and parameter correction system provided by the embodiment of the present invention includes:

[0069] An initial input parameter fixing module 1 is used to import the 3D digital information format of the complex indoor environment required by the ray tracing model and fix the initial input parameters of the ray tracing model;

[0070] The electrical parameter priority setting module 2 is used to calculate the field strength, power and multipath information of the prediction point by running the ray tracing model by changing the electrical parameter values ​​of different materials, examine the impact of the change of electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of specific materials according to the actual prediction points of interest;

[0071] The initial input parameter setting module 3 is used to formulate a measurement plan for the actual indoor scene and perform relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measurement conditions;

[0072] The optimal electrical parameter acquisition module 4 is used to import the influence of the electrical parameters of various materials in the indoor environment on the simulation results in order from large to small as the adaptive variables of the genetic algorithm, and find the electrical parameters with the smallest error between the simulation results and the measured results by continuously changing the values ​​of the electrical parameters and calling the ray tracing model.

[0073] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0074] Example 1

[0075] The present invention provides an electrical parameter priority presetting method and a parameter correction method for an indoor ray tracing model in order to reasonably analyze the channel parameters output by the ray tracing model and reasonably optimize its electrical parameters.

[0076] The present invention provides a method for presetting electrical parameters of a ray tracing model: the output of the ray tracing model usually includes power, path loss, three-dimensional ray path, channel power impulse response, wave arrival and wave launch angle, etc. By rationally planning simulation conditions and comparing simulation results under different conditions, the factors affecting radio wave propagation can be more clearly understood. When using a ray tracing model to predict radio waves at expected environmental locations, the prediction results are usually directly related to the electrical parameters of the environmental materials used in the model, but the changes in the electrical parameters of different materials have different degrees of influence on the prediction results of the prediction points. Here, the changes in the predicted output power are observed by changing the electrical parameters of each material separately, so as to determine the influence of the changes in the electrical parameters of each material on the prediction results. Among them, the electrical parameters of materials with large influence are given priority to preset precise values, and the electrical parameters of materials with small influence are given secondary consideration to set approximate values ​​or even be ignored, thereby reducing the calculation time and complexity when the ray tracing model is calculated.

[0077] Another object of the present invention is to provide a model correction method for a ray tracing model: the ray tracing model is regarded as a multivariate function, the electrical parameters of the material that accounts for a large proportion in the environment are selected as independent variables, and the material electrical parameters are changed while keeping other initial input parameters unchanged. It is considered that the predicted power output of the ray tracing model at this time is the value of this multivariate function. By controlling the processing method of the variables in this way, the ray tracing model can be regarded as a complete function mapping relationship, so that the present invention can use a genetic algorithm to optimize the electrical parameters of the ray tracing model.

[0078] Among them, the use of genetic algorithms requires relevant calculations such as objective functions and fitness, that is, the genetic algorithm is to find the optimal solution of the original multivariate function relative to a certain situation, and this certain situation is reflected by the objective function. In the present invention, the standard deviation between the predicted power value of the ray tracing model and the measured power value is used as the objective function value of the genetic algorithm. The genetic algorithm obtains the optimal solution through internal iterative processing according to the mapping relationship between the multivariate function to be sought and the objective function.

[0079] The calculation formula of the objective function in the genetic algorithm of the present invention is:

[0080]

[0081] Among them, yi It represents the difference between the predicted value and the measured value of the i-th point position. represents the mean of these point differences, and N represents the number of measured points in the population. 1i is the predicted value of a test point, x 2i is the measured value of the point, then:

[0082] y i =x 1i -x 2i

[0083]

[0084] The genetic algorithm in the present invention iterates by taking the inverse of the above objective function value as the fitness to find the solution corresponding to the maximum fitness, that is, the minimum objective function value. The solution obtained at this time is fed back to the ray tracing model as the optimized electrical parameter value, and then a simulation comparison is performed for verification. The feasibility of the correction method will be verified in the following embodiments of the present invention, and the output result of the ray tracing model after correction can better match the measured value than the output result before correction.

[0085] The present invention predicts the power results based on the comparative simulation output of the ray tracing model, and considers the preset priority of the electrical parameters of various materials in the room according to the magnitude of their influence on the prediction results, thereby simplifying the parameter preset problem. Based on the mapping relationship between the output of the ray tracing model and the preset parameters, the present invention uses a genetic algorithm to optimize the electrical parameters of the ray tracing model, thereby achieving the purpose of model parameter correction.

[0086] Example 2

[0087] The present invention mainly analyzes and optimizes the indoor ray tracing model. Whether it is the analysis of the results or the correction of the model parameters, the model imports the environmental information unchanged. Figure 3 The scene model diagram of the present invention is shown. The modeling scene includes 34 rooms, 34 floors, 34 ceilings, 44 exterior walls (19 doors and windows on the exterior walls), 332 interior walls (82 doors and windows on the interior walls), 64 indoor objects (including tables, cabinets and tables) and 10 beams. The red objects in the figure are wooden products, generally tables and chairs, and the green objects are iron products (iron doors).

[0088] The electrical parameter preset scheme for indoor ray tracing model simulation in this embodiment includes the following steps:

[0089] (1) Configure the initial parameters of the ray tracing model (excluding electrical parameters). Here, the center frequency of the transmitting antenna is set to 4 GHz, the transmitting power is 0.001 w (0 dBm), the gain of the transmitting and receiving antennas is 1, that is, no amplification, and the transmitting antenna is located at Figure 3The coordinates in are (11.485, 1.952, 1.52). The transmitting antenna position is as follows from a bird's-eye view: Fig.10 As shown in T1, the receiving antenna position is located on a plane 1.5m above the ground in the entire simulation environment, and the point interval is 0.3m. The initial values ​​of the material electrical parameters are shown in Table 1.

[0090] Table 1 Electrical parameters of indoor environment materials

[0091]

[0092] (2) Considering Figure 3 In the environment shown, the space occupied by all materials is arranged from large to small (material number): 0, 2, 4, 3, 5. The environment does not contain materials with other numbers. Here, the electrical parameters of materials 0, 2 and 4 are changed respectively, and other electrical parameters are kept unchanged. The influence of the changes of these three parameters on the power coverage prediction results is investigated, so as to consider whether to give them an accurate value.

[0093] (3) First, the electrical parameters in Table 1 are used as the original parameters for simulation. The resulting power coverage diagram is shown in Figure 4 As shown. Secondly, the electrical parameters of the concrete material are changed to: relative dielectric constant of 10.31, conductivity of 5.1. Then the simulation is performed to obtain the received power coverage diagram after changing the electrical parameters of the concrete (see Figure 5 ). Similarly, based on the parameters in Table 1, only the electrical parameters of marble and wood are changed and simulation is performed. The simulation results are shown in the figure below. Figure 6 and Figure 7 As shown, the electrical parameters of marble and wood after the change are: the relative dielectric constant of marble is 12 and the electrical conductivity is 5; the relative dielectric constant of wood is 6.99 and the electrical conductivity is 5.02.

[0094] (4) Figure 4 The simulation results are for reference. Table 2 lists the Figures 5 to 7 Simulation results are relatively Figure 4 The power RMS error value. It can be seen from the figure and table that the material with the greatest impact on the simulation results in this environment is concrete, followed by wood and marble. Therefore, when considering the preset material electrical parameters, firstly, a preset value with the highest model accuracy should be set for the material with the greatest impact (concrete) as much as possible, and then the electrical parameters of other materials with less impact should be given, or a fuzzy value should be given. Therefore, the processing can be simplified to a certain extent when presetting electrical parameters, which brings convenience to the presetting of electrical parameters, and the same idea is adopted when verifying the electrical parameters later.

[0095] Table 2 Effect of electrical parameter changes on simulation power

[0096] Materials that need to change electrical parameters RMS error with original simulation power / dBm Concrete 11.3862 marble 2.9186 wood 3.8144

[0097] The parameter correction scheme for indoor ray tracing model simulation in this embodiment includes the following steps:

[0098] (1) Acquisition of measured data.

[0099] like Fig. 9 As shown, two transmitting antenna positions and 41 receiving antenna positions were selected for the actual measurement. The transmitting antenna frequency is 4GHz, the transmitting power is -15dBm, the transmitting and receiving antennas are of the same type, the transmitting and receiving antennas are both 1.52m high, and are placed vertically. They are all considered to be horizontal omnidirectional antennas. During the test, the position of the transmitting antenna T1 is first fixed, and then the receiving antenna is moved from R1 to R41 to measure a total of 41 power values. After changing the transmitting antenna position to T2, the data from R1 to R41 are repeatedly measured. In the present invention, the variables of the genetic algorithm are selected as the input electrical parameters of the ray tracing model, and the change of the transmitting antenna position is not considered for the time being. Therefore, the data measured at the transmitting antenna position T1 is taken as an example for model validation.

[0100] (2) Comparison between initial simulation data and measured data.

[0101] In the ray tracing simulation program, the position of the transmitting and receiving antennas is consistent with the measured settings (T1 position), the transmit power is set to -15dBm, the transmit frequency is 4GHz, and the initial imported electrical parameters are shown in Table 1. The initial simulation result curve is calculated using the ray tracing model and compared with the measured power curve. Fig.10 As shown, the horizontal axis represents the receiving antenna number, the vertical axis represents the predicted power value, and the error between the two curves is calculated. The calculation formula for the standard deviation is:

[0102]

[0103] The average error calculation formula is:

[0104]

[0105] The present invention uses the standard deviation and average error between the predicted received power and the measured received power to measure the applicability of the model. The standard error value is used to measure the consistency of the change trend between the simulation and the actual measurement, and the average error value is used to measure the overall error between the simulation and the actual measurement. What is considered more here is to keep the overall error within a certain range so that the trends of the simulation and the actual measurement are as consistent as possible, that is, to use the standard deviation as the main optimization indicator.

[0106] (3) Construct a genetic algorithm iterative framework.

[0107] The ray tracing model is regarded as a multivariate function, and the electrical parameters of the material that accounts for a large proportion in the environment are selected as independent variables. The genetic algorithm is used to change the material electrical parameters while keeping other initial input parameters unchanged, and the corresponding objective function and fitness are calculated. The optimal solution is found through continuous iteration of the algorithm.

[0108] Among them, the use of genetic algorithms requires relevant calculations such as objective functions and fitness, that is, the genetic algorithm is to find the optimal solution of the original multivariate function relative to a certain situation, and this certain situation is reflected by the objective function. In the present invention, the standard deviation between the predicted power value of the ray tracing model and the measured power value is used as the objective function value of the genetic algorithm, that is:

[0109]

[0110] The genetic algorithm in the present invention iterates by taking the inverse of the above objective function value as the fitness, namely:

[0111]

[0112] Find the solution corresponding to the maximum fitness, that is, the minimum objective function value. The solution obtained at this time is fed back to the ray tracing model as the optimized electrical parameter value, and then a simulation comparison is performed.

[0113] In the design of the genetic algorithm, the electrical parameters numbered 0 (concrete) and 4 (wood) in Table 1 are selected as adaptive variables, and their value ranges are: 1-16 (relative dielectric constant of concrete), 0.01-8 (concrete conductivity), 1-16 (relative dielectric constant of wood), 0.01-8 (conductivity of wood). First, the initial population is initialized, and the population size is set to 40. Each individual corresponds to a set of random electrical parameters within a limited range, which are brought into the ray tracing model to calculate the simulation value, and the standard deviation and fitness are calculated from the simulation and measured values. According to the fitness of each individual, relevant selection, crossover, mutation and other optimization operations are then performed to generate the next generation of individuals. The above fitness calculation, selection, crossover, mutation and other operations are continuously performed until the number of iterations reaches a certain number and the iteration is stopped (the number of iterations in the present invention is 300, and the program calculation takes about 10 hours), and the fitness and corresponding electrical parameters of the best individuals in all generations are output. The electrical parameters obtained at this time are substituted into the ray tracing model as the optimized results (the electrical parameters of the concrete after optimization in the present invention are: 7.518620, 0.511400; the electrical parameters of the wood are: 6.034137, 0.318417), and the optimized simulation results are output and compared with the measured results. Fig.11 shown.

[0114] Comparison of the output results before and after electrical parameter correction is made by Fig.10 and Fig.11It can be seen that the standard deviation of the simulation curve and the measured curve has dropped from the original 7.2863dB to 6.5892dB, and the average error has changed from the original -1.3844dB to 1.5633dB. The main consideration here is to reduce the standard error as much as possible while keeping the average error within a certain range, that is, in order to make the trend of the simulation curve as consistent as possible with the trend of the measured curve, the output result of the ray tracing model after correcting the electrical parameters is lower than the standard error of the output result before correction, that is, the prediction results of the corrected model can better match the measured values, which can better meet the industry's requirement of less than 8dB standard deviation.

[0115] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0116] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for presetting and correcting electrical parameters, characterized in that: The electrical parameter preset and parameter correction method comprises the following steps: Step 1, importing the 3D digital information format of the complex indoor environment required by the ray tracing model, and fixing the initial input parameters of the ray tracing model; Step 2: By changing the values ​​of electrical parameters of different materials and running the ray tracing model to calculate the field strength, power and multipath information of the prediction point, the influence of the change of electrical parameters of each material on the propagation of radio waves is investigated, and the electrical parameters of each material are prioritized according to the actual prediction point of interest; Step 3: formulate a measurement plan for the actual indoor scene and conduct relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measurement conditions; Step 4: According to the influence of the electrical parameters of various materials in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm from large to small, and the electrical parameters with the smallest error between the simulation results and the measured results are found by continuously changing the values ​​of the electrical parameters and calling the ray tracing model; In step 4, the influence of the electrical parameters of various materials in the indoor environment on the simulation results is introduced in order from large to small as adaptive variables of the genetic algorithm, and the electrical parameters with the smallest error between the simulation results and the measured results are found by continuously changing the values ​​of the electrical parameters and calling the ray tracing model, including: The error function between simulation and actual measurement is used as the objective function of the genetic algorithm to optimize the electrical parameters of the ray tracing model, and the difference between the simulated power and the measured power before and after the optimization of the electrical parameters is compared. The error function, i.e. the objective function, contains two error factors: Among them, yi represents the difference between the predicted value and the measured value of the i-th point position, represents the mean of the point difference value; the first formula is the standard deviation formula of simulation and actual measurement, and the error value is used to measure the consistency of the change trend of simulation and actual measurement; the second formula is the average error formula of simulation and actual measurement, and the error value is used to measure the overall error between simulation and actual measurement; The error function of the genetic algorithm, i.e. the objective function, is defined as the linear sum of the two error factors: F object =λError std +(1-λ)Error avg ; Among them, λ is a real number between 0 and 1, and λ is set to 0.8; the corresponding objective function is:

2. The method for presetting and correcting electrical parameters according to claim 1, characterized in that: In step 1, the initial input parameters include frequency, transmission power and transceiver antenna positions, but do not include electrical parameters; In step two, the influence of the change of electrical parameters of each material on the propagation of radio waves is examined, including: after importing the initial electrical parameters of each material and calling the model to simulate the initial power prediction result, the electrical parameters of one material are changed while keeping the electrical parameters of other materials unchanged, the power prediction result after the parameter change is obtained, and compared with the initial result; the previous operation is repeated continuously, wherein the value of each electrical parameter change remains consistent, and the influence of the change of electrical parameters of each material on the power prediction result is examined one by one.

3. The method for presetting and correcting electrical parameters according to claim 1, characterized in that: In step 3, setting appropriate initial input parameters includes: For the position of the transmitting and receiving antennas, the simulation and the actual measurement should correspond to each other in terms of position parameters. For some measurement position points selected in the actual measurement, reasonable prediction points should also be set in the simulation to correspond to them. The transmission frequency and simulation frequency shall be based on the actual measured frequency; Transmit power: The transmit power preset during simulation is determined by the actual measured power.

4. The method for presetting and correcting electrical parameters according to claim 1, characterized in that: In step 4, the indoor environmental electrical parameters include electrical parameter information of common indoor materials, including: the relative dielectric constant of concrete is 5.31, and the electrical conductivity is 0.1; the relative dielectric constant of glass is 6.27, and the electrical conductivity is 0.022; the relative dielectric constant of wood is 1.99, and the electrical conductivity is 0.021; the relative dielectric constant of metal is 1, and the electrical conductivity is 107; When selecting environmental electrical parameters as adaptive variables to be imported into the genetic algorithm, only the electrical parameters of materials that account for a large proportion in the actual environment are selected as variables to be imported.

5. An electrical parameter preset and parameter correction system for implementing the electrical parameter preset and parameter correction method according to any one of claims 1 to 4, characterized in that: The electrical parameter preset and parameter correction system comprises: An initial input parameter fixing module is used to import the 3D digital information format of the complex indoor environment required by the ray tracing model and fix the initial input parameters of the ray tracing model; The electrical parameter priority setting module is used to calculate the field strength, power and multipath information of the prediction point by running the ray tracing model by changing the electrical parameter values ​​of different materials, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and set the priority of the electrical parameters of each material according to the actual prediction point of interest; The initial input parameter setting module is used to formulate a measurement plan for the actual indoor scene and conduct relevant power tests; combine the ray tracing model with the measurement plan, and set appropriate initial input parameters for the ray tracing model according to the measurement conditions; The optimal electrical parameter acquisition module is used to import the electrical parameters of various materials in the indoor environment as adaptive variables of the genetic algorithm from large to small according to the influence of the electrical parameters on the simulation results. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

6. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the electrical parameter preset and parameter correction method according to any one of claims 1 to 4, including the following steps: Import the 3D digital information format of the complex indoor environment required by the ray tracing model, and fix the initial input parameters of the ray tracing model; by changing the values ​​of the electrical parameters of different materials and running the ray tracing model to calculate the field strength, power and multipath information of the prediction point, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of each material according to the actual prediction point of interest; A measurement plan is formulated for actual indoor scenes and relevant power tests are carried out. The ray tracing model is combined with the measurement plan, and appropriate initial input parameters are set for the ray tracing model according to the measured conditions. According to the influence of the electrical parameters of each material in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm in descending order. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

7. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the electrical parameter preset and parameter correction method according to any one of claims 1 to 4, comprising the following steps: Import the 3D digital information format of the complex indoor environment required by the ray tracing model, and fix the initial input parameters of the ray tracing model; by changing the values ​​of the electrical parameters of different materials and running the ray tracing model to calculate the field strength, power and multipath information of the prediction point, examine the impact of the changes in the electrical parameters of each material on the propagation of radio waves, and prioritize the electrical parameters of each material according to the actual prediction point of interest; A measurement plan is formulated for actual indoor scenes and relevant power tests are carried out. The ray tracing model is combined with the measurement plan, and appropriate initial input parameters are set for the ray tracing model according to the measured conditions. According to the influence of the electrical parameters of each material in the indoor environment on the simulation results, they are imported as adaptive variables of the genetic algorithm in descending order. By continuously changing the values ​​of the electrical parameters and calling the ray tracing model, the electrical parameters with the smallest error between the simulation results and the measured results are found.

8. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the electrical parameter preset and parameter correction system as described in claim 5.

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