Wireless signal simulation method and system based on house type diagram bearing wall

Abstract

The invention discloses a wireless signal simulation method and system based on a house type diagram bearing wall. The wireless signal simulation method based on the house type diagram bearing wall comprises the steps that a, position information of the bearing wall in a target area is obtained; b, randomly generating a plurality of signal points as candidate router positions in the credible position range; c, taking a central coordinate of a signal point Gaussian kernel function as a corresponding signal point position, and dynamically setting a standard deviation sigma according to a signal frequency band; d, calculating the signal contribution of each corresponding signal point to each pixel point in the space according to a Gaussian kernel function, carrying out signal superposition, and dynamically adjusting an attenuation coefficient according to the position of the wall body; e, distributing the total signal intensity of each pixel point after normalization superposition to a range of 0-255, and generating a visual signal intensity distribution diagram; and f, based on the signal intensity distribution diagram, reversely deducing the positions of the optimal router and the repeater. Based on the method, the simulation efficiency is relatively high.

Description

Technical Field

[0001] The present invention relates to a wireless signal simulation method and system. Background Art

[0002] In order to obtain a better user experience, for the construction of a wireless local area network currently, especially for the same type of buildings, simulation can be performed first to facilitate finding a suitable deployment location for a wireless router. Currently, for the deployment of wireless signals in a wireless local area network, usually only one signal source is set, and then the signal strength at other positions is obtained through simulation. The disadvantages of the simulation for a single signal source are very obvious. Mainly, many factors need to be considered in the corresponding simulation model, there are many input parameters, and the model is huge, which will lead to a relatively slow simulation speed, violating the original intention of fast simulation for such simulation projects. Moreover, limited by time and scale in a single simulation, factors such as diffraction and absorption are often not fully considered, resulting in serious distortion of the simulation results.

[0003] Since households generally use a single wireless router to build a home network, but with the development of technology, it has gradually become normal to deploy wireless APs or other relays in the home network. However, currently, the simulation for a single wireless router in the home network is still the mainstream. In some WiFi (Wireless Fidelity, abbreviated as wireless) signal simulation models based on a single signal source, a fixed wall attenuation coefficient is adopted (such as a 10 dB attenuation for a reinforced concrete wall). Since the wall not only has a penetration problem but also involves a diffraction problem, this will generate a multipath effect and the corresponding signal superposition problem, and naturally there is also a problem that the cooperative effect of multiple devices cannot be simulated.

[0004] Relatively speaking, using a Gaussian kernel function to simulate wireless signal attenuation can more clearly map the distribution of indoor wireless signals, but currently, there is still a lack of dynamic attenuation adjustment in scenarios such as floor plan and load-bearing walls that have a greater impact on signal transmission.

[0005] More generally, for wireless signal simulation, the location of the router is mostly recommended through machine learning algorithms, but this method overly relies on historical data training and does not combine real-time signal simulation. In other words, it is not based on the reverse derivation of the physical signal model but is purely data-driven. Summary of the Invention

[0006] The purpose of the present invention is to provide a wireless signal simulation method based on a floor plan and load-bearing walls with relatively high simulation efficiency and relatively good simulation accuracy by defining a number of signal points, and the present invention also provides a wireless signal simulation system based on a floor plan and load-bearing walls.

[0007] According to the first aspect of the embodiments of the present invention, a wireless signal simulation method based on load-bearing walls in a house floor plan is provided, including: a. Obtain the house floor plan of the target area and the position information of the load-bearing walls; b. Randomly generate a plurality of signal points within the range of credible positions as candidate router positions according to the size of the house floor plan and the distribution of the load-bearing walls; c. Generate a Gaussian kernel function for each signal point, where the center coordinates of the Gaussian kernel function are the positions of the corresponding signal points, and the standard deviation σ is dynamically set according to the signal frequency band, and the σ value corresponding to the 2.4GHz frequency band is greater than that of the 5GHz frequency band; d. Calculate the signal contribution of each signal point to each pixel point in the space according to the Gaussian kernel function, and then perform signal superposition. If there is a load-bearing wall in the signal propagation path during the superposition process, the attenuation coefficient is dynamically adjusted according to the wall position; e. Normalize the total signal intensity distribution of each pixel point after superposition to the range of 0 to 255 to generate a visualized signal intensity distribution map; f. Based on the signal intensity distribution map, inversely deduce the optimal router and repeater positions.

[0008] Optionally, the random generation method of the signal points in step b includes: First, divide the area determined by the house floor plan into uniform grids, and the grid density is positively correlated with the signal simulation accuracy; Exclude the grid areas covered by the load-bearing walls from the determined area, and only allow signal points to be generated in the non-load-bearing wall areas.

[0009] Optionally, the dynamic setting rule of the standard deviation σ in step c is: The value range of the σ value of the 2.4GHz frequency band signal is [3, 5], unit: meter; the value range of the σ value of the 5GHz frequency band signal is [1, 3], unit: meter.

[0010] Optionally, the specific steps of dynamically adjusting the attenuation coefficient in step d include: 1) Determine whether the pixel point (x, y) is located in the area near the load-bearing wall, and the determination formula is: isInVicinity=(x≥x1-T)∧(x≤x2+T)∧(y≥y1-T)∧(y≤y2+T) where (x1, y1), (x2, y2) are the boundary coordinates of the load-bearing wall, and T is a preset threshold; 2) If it is located in the area near the load-bearing wall, then use the adjusted standard deviation σ adj to calculate the signal contribution, and σ adj =σ base ×α, where α is an attenuation factor, 0 < α < 1); otherwise, use the basic standard deviation σ baseCalculate signal contribution.

[0011] Optionally, the router recommendation method in step f is as follows: Overlay the signal strength distribution map with the user-defined required area; if the signal strength in a certain required area is lower than a given threshold β , then it is recommended to add a router in this area.

[0012] Optionally, the repeater recommendation method in step f is as follows: Calculate the average signal strength of each pixel point; select the center point coordinates P 1 and P 2 of the two areas with the highest average intensity; the position of the repeater is taken as the arithmetic mean of the center point coordinates of the two areas.

[0013] Optionally, it further includes a signal simulation speed optimization step: Allocate the Gaussian kernel contributions of different signal points to multi-threaded processing; Perform pre-computation caching on the load-bearing wall area to reduce the overhead of repeated determination.

[0014] According to the second aspect of the embodiments of the present invention, there is provided a wireless signal simulation system based on load-bearing walls of a house plan for implementing the signal simulation method described in the first aspect of the embodiments of the present invention, including: A data input module for receiving the house plan and load-bearing wall information; A signal point generation module for generating candidate router positions based on the house plan and load-bearing wall information according to the grid division and area exclusion rules; A Gaussian kernel calculation module for dynamically generating Gaussian kernel functions of different frequency bands for each signal point, where the standard deviation σ of the Gaussian kernel function is dynamically set according to the signal frequency band, and the σ value corresponding to the 2.4 GHz frequency band is greater than that of the 5 GHz frequency band; An attenuation adjustment module for calculating the signal contribution of each signal point to each pixel point in space according to the Gaussian kernel function, and then performing signal superposition. If there is a load-bearing wall in the signal propagation path during the superposition process, the attenuation coefficient is dynamically adjusted according to the wall position; An intensity distribution map generation module for generating a visual signal intensity distribution map by normalizing the total signal intensity distribution of each pixel point after superposition to the range of 0 - 255 A position determination module for inversely deriving the optimal router and repeater positions based on the signal intensity distribution map.

[0015] Optionally, the intensity distribution map generation module includes: A color mapping sub-module for using warm colors to represent high signal strength and cold colors to represent low signal strength; An interaction sub-module for interacting with the user to manually adjust the signal point position and update the simulation result in real time.

[0016] Optionally, the system further includes a cloud deployment module, which includes: An allocation module, configured to allocate signal simulation calculation tasks to cloud servers; A transceiver module, configured to allow users to upload floor plans through a mobile terminal and receive recommended results.

[0017] Different from the traditional method for simulating wireless signals in a home network that only forward-simulates a single signal source, the wireless signal simulation method based on load-bearing walls in floor plans according to the embodiments of the present invention significantly improves the simulation efficiency and accuracy through reverse superposition of multiple signal points. Furthermore, in the embodiments of the present invention, the σ value is dynamically adjusted in combination with the position of the load-bearing wall, making it more in line with the actual scenario. Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the signal strength distribution obtained by simulation considering load-bearing walls in an embodiment.

[0019] Figure 2 It is a schematic diagram of the average signal strength distribution and device recommendation results obtained by simulation in an embodiment.

[0020] Figure 3 It is a flowchart of Gaussian kernel generation and superposition in an embodiment. Detailed Embodiments

[0021] Aiming at the problems of the accuracy and efficiency of signal attenuation prediction (through simulation) in indoor wireless network deployment, in the embodiments of the present invention, an efficient and relatively accurate simulation method that integrates the structural characteristics of buildings is provided. The structural characteristics of the building are directly taken from the floor plan of the building. The typical feature of the floor plan is that load-bearing walls can be distinguished. Since load-bearing walls contain metal cages made of steel bars and the like, they have a certain shielding effect on wireless signals, thus having a greater impact on the propagation of wireless signals. Therefore, it is an important factor that needs to be considered in signal simulation.

[0022] Furthermore, in the embodiments of the present invention, by establishing a signal propagation model based on Gaussian kernel functions and combining a dynamic correction mechanism for load-bearing wall attenuation, intelligent recommendations for the positions of routers and repeaters are realized. Experiments show that this method significantly improves the simulation speed and coverage accuracy compared with the traditional ray tracing method.

[0023] Currently, wireless routers mainly support two frequency bands, namely the 2.4GHz frequency band and the 5GHz frequency band. The wall-penetrating capabilities of the two frequency bands are different, and each has its own advantages and disadvantages. And in some wireless routers, both of these two frequency bands are available at the same time. This is common knowledge in the art and will not be elaborated here.

[0024] In addition, the propagation of indoor wireless signals is relatively greatly affected by building structures. When wireless signals pass through certain load-bearing walls, the signal attenuation can reach more than 20 dB. The attenuation of the load-bearing walls for 2.4 / 5 GHz band signals is generally between 10 and 20 dB.

[0025] The wireless signal simulation method based on the embodiments of the present invention is mainly used to solve the non-linear relationship modeling between building structure characteristics and signal attenuation, the fast calculation of the superposition of multiple signal points (from the perspective of efficiency), and the reverse position recommendation.

[0026] See the attached Figure 3 In some embodiments, the provided wireless signal simulation method based on the load-bearing walls of the house type plan includes the following steps: a. Obtain the house type plan of the target area and the position information of the load-bearing walls; b. According to the dimensions of the house type plan and the distribution of the load-bearing walls, randomly generate multiple signal points within the credible position range as the candidate router positions; c. Generate a Gaussian kernel function for each signal point. The center coordinates of the Gaussian kernel function are the corresponding signal point positions, and the standard deviation σ is dynamically set according to the signal frequency band, where the σ value corresponding to the 2.4 GHz band is greater than that of the 5 GHz band; d. Calculate the signal contribution of each corresponding signal point to each pixel point in the space according to the Gaussian kernel function, and then perform signal superposition. If there is a load-bearing wall in the signal propagation path during the superposition process, the attenuation coefficient is dynamically adjusted according to the wall position; e. Normalize the total signal intensity distribution of each pixel point after superposition to the range of 0 to 255 to generate a visual signal intensity distribution map; f. Based on the signal intensity distribution map, inversely deduce the optimal router and repeater positions.

[0027] Among them, the standard deviation σ is expressed as an attenuation factor in the Gaussian kernel function. In the embodiments of the present invention, it is mainly used to characterize the weakening intensity of the load-bearing walls on wireless signals. As mentioned above, the shielding effects of the load-bearing walls on 2.4 GHz and 5 GHz band wireless signals are different, and different values are adopted for the attenuation factor σ.

[0028] Furthermore, for the 2.4 GHz band, due to its strong wall penetration ability, a relatively large attenuation factor σ can be selected, such as [3, 5], unit: meter. For the 5 GHz band with relatively poor wall penetration ability, a relatively small attenuation factor σ can be selected, and its value range is [1, 3], unit: meter.

[0029] In addition, the attenuation factor σ is positively correlated with the wall penetration thickness and negatively correlated with the skin depth, and the skin depth is related to the relative permittivity (about 6.5 for concrete) and the incident angle of the signal. This is general knowledge in the art and will not be elaborated here.

[0030] Regarding the dynamic adjustment of the attenuation coefficient, specifically: 1) Determine whether the pixel point ( x , y ) is located in the area near the load-bearing wall. The determination formula is: isInVicinity=(x≥x 1 -T)∧(x≤x 2 +T)∧(y≥y 1 -T)∧(y≤y 2 +T) where ([[]] x 1 , y 1 ) and ([[]] x 2 , y 2 ) are the boundary coordinates of the load-bearing wall, T is a preset threshold; 2) If it is located in the area near the load-bearing wall, then use the adjusted standard deviation σ adj to calculate the signal contribution, σ adj =σ base × α , where α is the attenuation factor, 0 < α < 1); otherwise, use the basic standard deviation σ base to calculate the signal contribution.

[0031] The mathematical model of the Gaussian kernel function for describing the attenuation of signal intensity with distance is: where, ( x 0 , y 0 ) represents the coordinates of the signal point, ( x , y ) is the current position coordinate for calculation, e is the base of the natural logarithm, a constant with a value of 2.71828.

[0032] To reduce the computational amount, only a part of it can be taken, that is to represent the signal intensity of the current position coordinate. Among them, ( mx , my ) represents the signal point, m is a natural number used to define different signal points.

[0033] Furthermore, in the embodiments of the present invention, the gradient descent method is adopted with the help of the Gaussian kernel function. The core idea is to use the reverse thinking. First, a series of signal points are defined. Obviously, based on the customization, the positions of this series of signal points are predetermined, their coordinates in the given coordinate system are known, and in the non-superimposed state, the signal intensities of these signal points are also known.

[0034] Furthermore, other signal intensities are superimposed at the positions of each signal point to obtain the signal strength at this point. The signal strength at this point synthesizes the signals at other positions, and to a certain extent, factors such as diffraction and refraction are simulated. At the same time, the multi-point synthesis is equivalent to multiple simulations, which can reduce the occurrence of extreme simulation cases. Through the signal strength at this point, the average signal strength at this point after placing a router at other positions in the room can be deduced inversely.

[0035] The position with the strongest signal superposition often does not require a router to be set, while the position with the weakest signal superposition but with signal strength requirements often requires an additional router or repeater to be set.

[0036] When independently calculating the contribution of each signal point, the Gaussian kernel function needs to be used. The following are the steps to simulate the WiFi signal strength distribution through the Gaussian kernel function in the embodiments of the present invention: Signal point position: First, a series of signal points (i.e., the positions of WiFi routers or repeaters, such as APs) are defined, and they are randomly obtained within the credible position range after obtaining the size of the house floor plan and the positions of load-bearing walls.

[0037] The load-bearing walls can be directly obtained from the electronic data of the house floor plan or the load-bearing wall identification of the scanned paper house floor plan.

[0038] It should be known that load-bearing walls are usually cast-in-place walls embedded with, for example, steel mesh cages. In some implementations, such as in prefabricated buildings, precast wall panels generally also contain steel cages.

[0039] If the load-bearing walls are from a paper house floor plan or other pictures rather than electronic data (such as vector drawings in dwg format, etc.), image recognition can be used to obtain the position information of the load-bearing walls. It should be known that the standardized load-bearing wall elements in the house floor plan have obvious features, generally filled with pure black or dense cross-hatching, which are significantly different from other elements. Simply using the color principle of single-channel or multi-channel can generally make a distinction, and the color can be identified and extracted through color values.

[0040] The standardized load-bearing wall elements in the house floor plan have obvious features, generally filled with pure black or dense cross-hatching, which are significantly different from other elements. Simply using the color principle can generally make a distinction, and the color can be identified and extracted through color values.

[0041] Based on the foregoing description, whether setting one signal point or multiple signal points, each signal point has a specific signal strength. Based on this signal strength, the following processing can be further performed.

[0042] Gaussian kernel generation: For each signal point, a Gaussian kernel function is generated. The center of the kernel function is located at the position of the signal point, and the standard deviation σ depends on the frequency of the signal (for example, 2.4 GHz and 5 GHz have different attenuation factor σ values, which are represented as the standard deviation here). The larger the standard deviation σ, the slower the signal decays and the wider the coverage range; the smaller the standard deviation, the faster the signal decays and the smaller the coverage range, thereby simulating the propagation differences of these two signals of 2.4 GHz and 5 GHz.

[0043] Superposition contribution: The Gaussian kernel function of each signal point represents the signal strength contribution of this point at each point in space. By superimposing the Gaussian kernel functions of all signal points in space, the signal strength distribution of the entire given area can be obtained.

[0044] During the superposition process, if there is a load-bearing wall on the superposition path in the given coordinate system, the attenuation coefficient of the load-bearing wall is inserted to simulate the absorption effect of the load-bearing wall on the signal.

[0045] Mathematical operation of the kernel function: For each pixel point (x, y) in the image, calculate the distance between this pixel point and each signal point (mx, my).

[0046] Use the Gaussian kernel function to calculate the contribution of the signal strength at this pixel point, that is, call the foregoing formula for calculation: 。

[0047] As described above, adopting this simplified formula does not affect the simulation effect under the condition of reducing the calculation amount. Based on the concept of the present invention, it can be inferred that based on the simulation of the embodiments of the present invention, the signal strength differences in different regions are emphasized. These differences are distinguished by images, and there is no substantial requirement for calculating the actual size of the strength. It only needs to be able to distinguish the difference degree between each other, similar to omitting the same factors in mathematical calculations, thereby reducing the calculation amount.

[0048] Normalization: Normalize the obtained signal strength distribution matrix to the range of 0 to 255 for the convenience of visualization. The values corresponding to 0 to 255 are color values. For example, in a black and white image, the color value of pure black is 0, and the color value of pure white is 255. If it is in the RGB color mode, the color value of pure black is 0, 0, 0. The foregoing "0 to 255" is only used to represent the normalization method, rather than only used to describe black and white images.

[0049] The aforementioned signal strength distribution matrix is a distribution matrix of each pixel point in a plane direct coordinate system, and matrixization is conducive to calculation.

[0050] Through the normalization process, the Gaussian kernel function simulates the process of signal attenuation from a signal point source (such as a WiFi router) to the surroundings. The signal strength is the strongest at the signal point, and gradually weakens as the distance from the signal point increases. The larger the standard deviation σ, the slower the signal attenuation, and the wider the coverage range of the wireless local area network; the smaller the standard deviation, the faster the signal attenuation, and the smaller the coverage range of the wireless local area network. By calculating the contribution of each signal point to each pixel point in the entire image, considering the attenuation coefficient of the load-bearing wall, and superimposing these contributions, the signal strength distribution map of the WiFi signal on the entire plane is finally obtained.

[0051] According to this strength distribution map, it is possible to further inversely deduce where the best location of the WiFi router is. The location with the strongest signal superposition often does not require a router to be set, while the location with the weakest signal superposition but with signal strength requirements often requires an additional router or repeater to be set. The subsequent recommendation function will make recommendations based on this concept.

[0052] If a single router cannot meet the requirements, the location of the repeater needs to calculate the average strength of each signal point, and select the locations of the two optimal strength points to calculate the average distance as the location of the repeater.

[0053] Regarding visualization, to improve the user experience, the signal strength distribution is displayed in the form of numbers and colors, and the image processing toolbox is also used. The specific steps are as follows: Step 1, draw the original picture: • Use the imshow function to display the read planar house type map.

[0054] • Use the title function to add a title to the image.

[0055] Step 2, draw signal points: • Use the scatter function to draw signal points on the original picture. The size and color of these signal points represent the strength of the WiFi signal, and the hue from warm to cold represents the signal strength from strong to weak.

[0056] • Add a color bar through the colorbar function to facilitate observing the size of the signal strength.

[0057] Step 3, calculate and display the signal strength distribution map: • Through loops and mathematical operations, use the Gaussian kernel function to calculate the contribution of each signal point to the signal strength distribution of the entire floor plan.

[0058] • Superimpose the contribution of each signal point on the strength distribution matrix.

[0059] • Normalize the intensity distribution matrix to the range of 0 - 255 and convert it to the uint8 type for easy visualization.

[0060] Step 4, display the normalized intensity distribution map: • Use the imagesc function to display the intensity distribution map. imagesc maps colors according to the values in the matrix to show the intensity distribution of the signal.

[0061] • Use the colormap function to set the color mapping scheme. Here, the jet color mapping is used.

[0062] • Use the colorbar function again to add a color bar to facilitate the interpretation of the relationship between color and signal intensity.

[0063] • Use the subplot function to display the intensity distribution maps of 2.4GHz and 5GHz signals respectively in the same figure window.

[0064] The final effect is as shown in Figure 1 and Figure 2 where, Figure 1 is the signal intensity distribution map after considering load-bearing walls, and Figure 2 is a schematic diagram of the average signal intensity distribution and device recommendation results obtained by simulation in an embodiment. Based on Figure 2 , it can be more accurately determined that the signal intensity at locations such as the restaurant and kitchen is weaker than that at other locations. For example, if there is a signal intensity requirement in the restaurant, a repeater can be added at or near this location.

[0065] In addition, regarding the aforementioned matrixization, the finer the matrixization, the greater the computational amount, and the higher the accuracy of signal simulation. On the contrary, while the computational amount decreases, the simulation accuracy also decreases. In addition, considering that the coverage range of the house floor plan often has certain boundaries and there are various types of house floor plans, the simulation is only carried out for the area within the house floor plan, and the boundaries of the house floor plan can be determined in advance to reduce unnecessary operations.

[0066] Matrixization is first carried out in a uniform grid manner after the house floor plan is determined. The grid density is positively correlated with the signal simulation accuracy.

[0067] Furthermore, the grid areas covered by load-bearing walls are excluded from the determined area. In other words, signal points cannot be deployed at the locations of load-bearing walls, and these areas are not areas where users use the wireless network, so there is no need to simulate them, thus further reducing the computational amount.

[0068] In the above description, the computational complexity was emphasized. In further consideration, it also includes steps for optimizing the signal simulation speed: Allocate the Gaussian kernel contributions of different signal points to multi-threaded processing; Perform pre-computation caching for the load-bearing wall area to reduce the overhead of repeated determination.

[0069] A wireless signal simulation system based on the load-bearing walls of a house floor plan corresponding to the aforementioned signal simulation method, including: A data input module for receiving the house floor plan and load-bearing wall information; A signal point generation module for generating candidate router positions based on the house floor plan and load-bearing wall information, according to the grid division and area exclusion rules; A Gaussian kernel calculation module for dynamically generating Gaussian kernel functions of different frequency bands for each signal point. The standard deviation σ of the Gaussian kernel function is dynamically set according to the signal frequency band, where the σ value corresponding to the 2.4GHz frequency band is greater than that of the 5GHz frequency band; An attenuation adjustment module for calculating the signal contribution of each signal point to each pixel point in space according to the Gaussian kernel function, and then performing signal superposition. If there is a load-bearing wall in the signal propagation path during the superposition process, the attenuation coefficient is dynamically adjusted according to the wall position; An intensity distribution map generation module for generating a visual signal intensity distribution map by normalizing the total signal intensity distribution of each pixel point after superposition to the range of 0~255 A position determination module for inversely deriving the optimal router and repeater positions based on the signal intensity distribution map.

[0070] The intensity distribution map generation module includes: A color mapping sub-module for using warm colors to represent high signal intensity and cold colors to represent low signal intensity; An interaction sub-module for interacting with the user to manually adjust the signal point positions and update the simulation results in real time.

[0071] The system also includes a cloud deployment module, which includes: An allocation module for allocating signal simulation calculation tasks to cloud servers; A transceiver module for the user to upload the house floor plan through a mobile terminal and receive the recommended results.

Claims

1. A wireless signal simulation method based on a load-bearing wall of a house plan, characterized in that: include: a. Obtain the floor plan and load-bearing wall location information of the target area; b. Based on the size of the floor plan and the distribution of load-bearing walls, multiple signal points are randomly generated within the trusted location range as candidate router locations; c. Generate a Gaussian kernel function for each signal point. The center coordinate of the Gaussian kernel function is the position of the corresponding signal point. The standard deviation σ is dynamically set according to the signal frequency band. The σ value corresponding to the 2.4 GHz frequency band is greater than that of the 5 GHz frequency band. d. Calculate the signal contribution of each corresponding signal point to each pixel point in space according to the Gaussian kernel function, and then perform signal superposition. During the superposition process, if there is a load-bearing wall in the signal propagation path, the attenuation coefficient is dynamically adjusted according to the position of the wall; e. The total signal intensity of each pixel after normalization and superposition is distributed to the range of 0~255, and a visual signal intensity distribution map is generated; f. Based on the signal strength distribution map, reversely deduce the optimal router and repeater locations.

2. The signal simulation method according to claim 1, characterized in that: The random generation method of the signal points in step b includes: Firstly, the area determined by the floor plan is divided into uniform grids, and the grid density is positively correlated with the signal simulation accuracy; The determined area excludes the grid area covered by the load-bearing walls, and only allows signal points to be generated in the non-load-bearing wall area.

3. The signal simulation method according to claim 1, characterized in that: The dynamic setting rule of standard deviation σ in step c is: The σ value range for 2.4 GHz band signals is [3, 5], in meters. The σ value range for 5 GHz band signals is [1, 3], in meters.

4. The signal simulation method according to claim 1, characterized in that: The specific steps of dynamically adjusting the attenuation coefficient in step d include: 1) Determine the pixel point ( x , y ) is located near the load-bearing wall. The determination formula is: isInVicinity=(x≥x 1 -T)∧(x≤x 2 +T)∧(y≥y 1 -T)∧(y≤y 2 +T) in( x 1 , y 1 )、( x 2 , y 2 ) are the boundary coordinates of the load-bearing wall, T is the preset threshold; 2) If it is located near the load-bearing wall, the adjusted standard deviation is used σ adj Calculate the signal contribution, σ adj =σ base ×α ,in α is the attenuation factor, 0<α<1); otherwise, the basic standard deviation is used σ base Calculate signal contribution.

5. The signal simulation method according to claim 1, characterized in that: The recommended method for the router in step f is: The signal strength distribution map is superimposed on the user's preset demand area; if the signal strength of a certain demand area is lower than a given threshold β, it is recommended to add a router in this area.

6. The signal simulation method according to claim 1 or 5, characterized in that: The recommended method for the repeater in step f is: Calculate the average signal strength of each pixel; select the center coordinates of the two areas with the highest average strength P 1 and P 2; The repeater position takes the arithmetic mean of the coordinates of the center points of the two areas.

7. The signal simulation method according to claim 1, characterized in that: Also included are steps to optimize signal simulation speed: Distribute the Gaussian kernel contributions of different signal points to multi-threaded processing; Pre-calculate and cache the load-bearing wall area to reduce the overhead of repeated determination.

8. A wireless signal simulation system based on the load-bearing wall of a house plan for implementing the signal simulation method of claims 1 to 7, characterized in that: include: A data input module, used to receive floor plan and load-bearing wall information; The signal point generation module is used to generate candidate router locations based on the floor plan and load-bearing wall information, grid division and area exclusion rules; Gaussian kernel calculation module, used to dynamically generate Gaussian kernel functions of different frequency bands for each signal point. The standard deviation σ of the Gaussian kernel function is dynamically set according to the signal frequency band, where the σ value corresponding to the 2.4 GHz frequency band is greater than that of the 5 GHz frequency band; The attenuation adjustment module calculates the signal contribution of each corresponding signal point to each pixel point in the space according to the Gaussian kernel function, and then performs signal superposition. During the superposition process, if there is a load-bearing wall in the signal propagation path, the attenuation coefficient is dynamically adjusted according to the position of the wall. The intensity distribution map generation module generates a visual signal intensity distribution map by normalizing the total signal intensity of each pixel after superposition to a range of 0 to 255. The location determination module reversely derives the optimal router and repeater locations based on the signal strength distribution map.

9. The signal simulation system according to claim 8, characterized in that: The intensity distribution map generating module comprises: A color mapping submodule for using warm colors to represent high signal strength and cool colors to represent low signal strength; The interactive submodule is used to interact with the user to manually adjust the signal point position and update the simulation results in real time.

10. The signal simulation system according to claim 8, characterized in that: The system further includes a cloud deployment module, which includes: A distribution module, used for distributing signal simulation calculation tasks to a cloud server; The transceiver module is used for users to upload floor plans through mobile terminals and receive recommendation results.

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