Router Intelligent Layout Method, Device and Medium Based on Environmental Interference Analysis

Abstract

The present invention relates to an intelligent router layout method, device and medium based on environmental interference analysis, and relates to the field of router layout optimization, including: responding to a pre-laying space model, a target signal receiving point group and a router layout constraint area uploaded by a user terminal; performing entity obstacle recognition to obtain an entity obstacle distribution area; performing magnetic field obstacle recognition to obtain a magnetic field obstacle distribution area; obtaining a plurality of first-level router layout schemes; according to the plurality of first-level router layout schemes, performing signal quality analysis to obtain quality coefficients of the plurality of first-level router layout schemes; performing particle swarm optimization for intelligent router layout to obtain a recommended router layout position; and feeding back the recommended router layout position to the user terminal. It solves the technical problem in the prior art that with the increase in the number of electrical appliances and furniture in home and office environments, the traditional subjective method of determining the router layout position has difficulty in ensuring the stable signal coverage quality.

Description

Technical Field

[0001] The present invention relates to the field of router layout optimization, and particularly to an intelligent router layout method, device and medium based on environmental interference analysis. Background Art

[0002] In the deployment and management of wireless networks, the layout of routers is a key factor in ensuring network coverage and performance. Traditional router layout methods mainly rely on manual experience and intuition, and select the installation location of the router by on-site inspection of the indoor environment. However, with the increase in the number of electrical appliances and furniture in home and office environments, the obstacles to wireless signal transmission also increase, resulting in uneven signal coverage and degraded network performance. In complex environments, traditional layout methods often struggle to achieve optimal network performance, and a more scientific and systematic layout strategy is needed to optimize the position of the router. Summary of the Invention

[0003] Aiming at the technical problem that in the prior art, with the increase in the number of electrical appliances and furniture in home and office environments, the traditional subjective method of determining the router layout position makes it difficult to ensure the stable quality of signal coverage, the present invention provides an intelligent router layout method, device and medium based on environmental interference analysis to solve this problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides an intelligent router layout method based on environmental interference analysis, including:

[0006] Responding to a router intelligent layout request uploaded by a user terminal, where the router intelligent layout request includes a pre-layout space model, a target signal receiving point group, and a router layout constraint area;

[0007] Identifying physical obstacles in the pre-layout space model to obtain a physical obstacle distribution area;

[0008] Identifying magnetic field obstacles in the pre-layout space model to obtain a magnetic field obstacle distribution area;

[0009] Evenly distributing routers in the router layout constraint area to obtain a number of first-level router layout plans;

[0010] According to the number of first-level router layout plans, performing signal quality analysis on the target signal receiving point group in the physical obstacle distribution area and the magnetic field obstacle distribution area to obtain a number of quality coefficients of the first-level router layout plans;

[0011] Performing particle swarm optimization for intelligent router layout according to the number of quality coefficients of the first-level router layout plans to obtain a recommended router layout position;

[0012] Identify the recommended position of the router layout in the pre-deployed space model and feedback it to the user terminal.

[0013] In a second aspect, the present invention provides an electronic device, including:

[0014] A memory for storing computer software programs;

[0015] A processor for reading and executing the computer software program, thereby implementing the intelligent router layout method based on environmental interference analysis in the first aspect.

[0016] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, it implements the intelligent router layout method based on environmental interference analysis in the first aspect.

[0017] The beneficial effects of the present invention are as follows: Through the intelligent router layout request uploaded by the user terminal, the request includes a pre-deployed space model, a target signal receiving point group, and a router layout constraint area. The system first identifies the physical obstacles and magnetic field obstacles in the pre-deployed space model to determine the obstacle distribution area. Then, the routers are evenly distributed within the layout constraint area to generate multiple primary layout schemes. Next, signal quality analysis is used to evaluate the performance of these schemes in the obstacle distribution area, and a quality coefficient is calculated for each scheme. Through the particle swarm optimization algorithm, the router layout is optimized according to the quality coefficient, and the best position is recommended. Finally, the recommended position is marked in the pre-deployed space model and feedback to the user terminal.

[0018] First, through the identification of physical and magnetic field obstacles, the propagation of wireless signals in the actual environment can be more accurately simulated, providing a scientific basis for router layout. Second, the application of signal quality analysis and the particle swarm optimization algorithm makes the router layout more intelligent and automated, improving the efficiency and accuracy of the layout. In addition, users can customize the layout scheme according to the specific environment and requirements, enhancing the adaptability and flexibility of the layout scheme. Finally, this method can ensure more uniform and reliable wireless signal coverage in complex environments, improve network performance, while reducing deployment costs and maintenance difficulties, providing users with a more stable and efficient wireless network service. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flow chart of the intelligent router layout method based on environmental interference analysis provided by the present invention;

[0020] Figure 2Schematic diagram of the multi-angle penetration signal attenuation analysis for the router intelligent layout method based on environmental interference analysis provided by the present invention;

[0021] Figure 3 Schematic diagram of the structure of the electronic device provided by the present invention;

[0022] Figure 4 Schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0023] In the drawings, the list of components represented by each reference numeral is as follows:

[0024] Electronic device 500, memory 510, processor 520, computer program 511, computer-readable storage medium 600, computer program 611. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0026] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0027] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order to enable any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0028] Embodiment 1:

[0029] As Figure 1As shown in the figure, an embodiment of the present invention provides a router intelligent layout method based on environmental interference analysis, including the steps of:

[0030] S10: Respond to the router intelligent layout request uploaded by the user terminal, where the router intelligent layout request includes a pre-laying space model, a target signal receiving point group, and a router layout constraint area;

[0031] The router intelligent layout request is a request containing specific parameters and conditions proposed by the user to the system in order to optimize network coverage and performance. It specifically includes a pre-laying space model, a target signal receiving point group, and a router layout constraint area. The pre-laying space model is the digital space information provided by the user according to the actual environment, usually including the size, structure, internal items, household appliances, etc. of the room. The target signal receiving point group refers to the specific areas or positions where the user hopes the network signal covers. The router layout constraint area is the space limit defined by the user for placing the router, which may be based on physical or security considerations.

[0032] The user uploads the router intelligent layout request through the client software or web interface. This request contains the data of the pre-laying space model, and these data may be provided in the form of 3D models, CAD drawings, or files in a specific format. The user also specifies the target signal receiving point group, and these point groups can be manually marked by the user or automatically generated by an algorithm to ensure the uniformity and effectiveness of network coverage. At the same time, the user defines the constraint area for the router layout, which may be based on physical limitations (such as not being able to install the router on load-bearing walls) or other considerations (such as aesthetics or security).

[0033] Through the accurate pre-laying space model and the clear target signal receiving point group, the system can more accurately simulate and optimize the router layout to meet the user's requirements and constraint conditions. At the same time, the setting of the router layout constraint area ensures the feasibility and practicality of the layout scheme.

[0034] S20: Identify the physical obstacles in the pre-laying space model to obtain the physical obstacle distribution area;

[0035] Physical obstacle identification refers to the process of extracting the physical obstacles in the preset space model. The physical obstacle distribution area refers to the area occupied by the physical obstacles identified in the space model. These obstacles may include walls, furniture, large household appliances, glass, etc., which can block or weaken the propagation of wireless signals. The physical obstacle distribution area includes the position, size, and material properties of the obstacles.

[0036] Furthermore, analyze the signal strength attenuation degree when the entity obstacle passes through the WI-FI signal in different directions. Specifically, count the historical sample data with the same position, size, material properties, and passing direction, and calculate the ratio of the difference between the WI-FI signal strength before passing and the WI-FI signal strength after passing in the historical sample data to the WI-FI signal strength before passing, which is set as the WI-FI signal strength attenuation degree, and store it associated with the distribution area of the entity obstacle for facilitating the specific analysis of environmental interference in the subsequent steps.

[0037] S30: Identify magnetic field obstacles in the pre-laid space model to obtain the distribution area of magnetic field obstacles.

[0038] Magnetic field obstacle identification refers to the process of extracting devices that may generate an electromagnetic environment interfering with wireless signal transmission in the pre-laid space model. The distribution area of magnetic field obstacles refers to the set of all magnetic objects or areas that may interfere with wireless signals identified in the space model. These obstacles may include transformers, motors, refrigerators, cordless phones, televisions, etc.

[0039] Furthermore, the magnetic field obstacles in this application, such as transformers, motors, refrigerators, cordless phones, televisions, etc., all have pre-identified magnetic field distribution information during operation. The magnetic field distribution information stores the magnetic field strength data at different positions of the electromagnetic field generated by the magnetic field obstacle. Since the magnetic field distributions generated by the same model of magnetic field obstacles during operation tend to be consistent, the magnetic field distribution of each model of magnetic field obstacle can be counted and stored in a database. When the corresponding model of magnetic field obstacle is identified, the magnetic field distribution information can be distributed in the pre-laid space model according to the specific position of the magnetic field obstacle.

[0040] Furthermore, after determining the magnetic field distribution information of each magnetic field obstacle, collect the historical sample data of WI-FI signal transmission, and calculate the ratio of the difference between the WI-FI signal strength before passing and the WI-FI signal strength after passing in the historical sample data to the WI-FI signal strength before passing, which is set as the WI-FI signal strength attenuation degree. Analyze the WI-FI signal strength attenuation degree under different magnetic field strengths and store it associated with the magnetic field distribution information for facilitating the specific analysis of environmental interference in the subsequent steps.

[0041] In steps S20 and S30, item identification and classification are involved. Preferably, when the user uploads in the pre-deployed space model, the item models and types are pre-calibrated. Then, through the pre-set entity obstacle type database and magnetic field obstacle type database, the pre-calibrated item models and types are matched to achieve rapid identification of entity obstacles and magnetic field obstacles. The embodiment of the present application does not adopt instant image recognition, which has low efficiency and an error probability. Instead, it selects the item models uploaded by the user side and focuses on the efficient layout of routers, improving the processing efficiency.

[0042] S40: Uniformly distribute routers in the router layout constraint area to obtain several first-level router layout schemes;

[0043] In the embodiment of the present application, there is only one router. In the router layout constraint area, according to the preset distribution distance, preferably 3m, the layout positions of multiple routers are uniformly constructed. Each position is regarded as a first-level router layout scheme. The several first-level router layout schemes are all the layout positions of the routers obtained by uniformly distributing routers in the router layout constraint area.

[0044] Furthermore, the first-level router layout scheme can also be subjectively set by the user based on experience. The advantage of subjective setting is that it may be closer to the optimal router layout position. However, the disadvantage is that it is easy to fall into local optimality. The advantage of uniform distribution is that the distribution is relatively discrete, which is convenient for finding the global optimal solution.

[0045] S50: According to the several first-level router layout schemes, perform signal quality analysis on the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area to obtain several first-level router layout scheme quality coefficients;

[0046] Furthermore, according to the several first-level router layout schemes, perform signal quality analysis on the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area to obtain several first-level router layout scheme quality coefficients. Step S50 includes the steps:

[0047] S51: According to the several first-level router layout schemes, extract the first positions of the first-level layout schemes;

[0048] S52: Connect lines from the first positions of the first-level layout schemes to the target signal receiving point group respectively to construct a signal transmission direction set;

[0049] S53: Extract the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information, up to the Y-th transmission direction obstacle distribution information that are located between the first position of the first-level layout scheme and the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area and satisfy the signal transmission direction set;

[0050] S54: Perform quality analysis based on the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information, up to the Y-th transmission direction obstacle distribution information, obtain the quality coefficient of the first position of the first-level layout scheme, and add it to the quality coefficients of the several first-level router layout schemes.

[0051] Further, perform quality analysis based on the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information, up to the Y-th transmission direction obstacle distribution information, obtain the quality coefficient of the first position of the first-level layout scheme, and add it to the quality coefficients of the several first-level router layout schemes. Step S54 includes the steps:

[0052] S541: Obtain the blocking areas of several entity obstacles and the signal attenuation degrees of several entity obstacles in the first transmission direction obstacle distribution information;

[0053] S542: Calculate the sum of the blocking areas of the several entity obstacles to obtain the sum of the entity obstacle blocking areas;

[0054] S543: Calculate the ratio of the blocking area of each entity obstacle to the sum of the entity obstacle blocking areas to obtain the blocking weights of several entity obstacles;

[0055] S544: Sum up the signal attenuation degrees of the several entity obstacles according to the blocking weights of the several entity obstacles to obtain the first quality evaluation parameter;

[0056] S545: Obtain the blocking areas of several magnetic field obstacles and the signal attenuation degrees of several magnetic field obstacles in the first transmission direction obstacle distribution information;

[0057] S546: Calculate the sum of the blocking areas of the several magnetic field obstacles to obtain the sum of the magnetic field obstacle blocking areas;

[0058] S547: Calculate the ratio of the blocking area of each magnetic field obstacle to the sum of the magnetic field obstacle blocking areas to obtain the blocking weights of several magnetic field obstacles;

[0059] S548: Sum up the signal attenuation degrees of the several magnetic field obstacles according to the blocking weights of the several magnetic field obstacles to obtain the second quality evaluation parameter;

[0060] S549: Take the opposite of the sum of the second quality evaluation parameter and the first quality evaluation parameter, and set it as the quality coefficient of the first transmission direction at the first position of the first-level layout scheme.

[0061] S5410: Until the quality coefficient of the Y-th transmission direction at the first position of the first-level layout scheme is obtained, calculate the mean value of the quality coefficient of the first transmission direction at the first position of the first-level layout scheme to the quality coefficient of the Y-th transmission direction at the first position of the first-level layout scheme, and set it as the quality coefficient of the first position of the first-level layout scheme.

[0062] Previously, the distribution area of physical obstacles and the distribution area of magnetic field obstacles have been determined. The distribution area of physical obstacles has size identification affecting signal strength and signal attenuation degree information at corresponding positions, and the distribution area of magnetic field obstacles has magnetic field distribution information affecting signal strength and signal attenuation degree information at corresponding positions. Therefore, according to the target signal receiving point group, the signal transmission quality of several first-level router layout schemes can be evaluated respectively, and several quality coefficients of the first-level router layout schemes representing the WI-FI signal transmission quality can be obtained, which is convenient for guiding the search of the solution space during the subsequent particle swarm optimization.

[0063] Specifically, the signal quality analysis is as follows. Taking any one of the first-level router layout schemes as an example:

[0064] Step 1: Extract any layout position of several first-level router layout schemes and store it as the first position of the first-level layout scheme. Connect the first position of the first-level layout scheme with each point of the target signal receiving point group by a straight line. According to the connection lines between the first position of the first-level layout scheme and each point of the target signal receiving point group, obtain the azimuth vectors from the first position of the first-level layout scheme to each point of the target signal receiving point group, and store them as the signal transmission direction set.

[0065] Step 2: Extract the obstacle set located between the first position of the first-level layout scheme and the target signal receiving point group from the distribution area of physical obstacles and the distribution area of magnetic field obstacles, specifically including the obstacle distribution information of the first transmission direction, the obstacle distribution information of the second transmission direction until the obstacle distribution information of the Y-th transmission direction, where Y represents the total number of transmission directions.

[0066] Step 3: Extract the blocking areas of several physical obstacles and the signal attenuation degrees of several physical obstacles in the obstacle distribution information of the first transmission direction. Among them, according to the first transmission direction of the signal, the azimuth of penetrating the physical obstacle can be determined, and then the signal attenuation degree of the physical obstacle stored above can be determined according to the corresponding azimuth. The blocking area of the physical obstacle refers to the cross-sectional area of the physical obstacle perpendicular to the transmission direction. Since the thickness data of the physical obstacle has been considered when analyzing the signal attenuation degree of the physical obstacle, it is not considered here.

[0067] Step 4: Extract the blocking areas of several magnetic field obstacles and the signal attenuation degrees of several magnetic field obstacles in the first transmission direction obstacle distribution information. The signal attenuation degree of the magnetic field obstacle is the wireless signal attenuation degree data at different magnetic field distribution positions stored above. According to the first transmission direction, the magnetic field intensity at the blocking position can be determined, and then the corresponding signal attenuation degree of the magnetic field obstacle can be extracted. The blocking area of the magnetic field obstacle refers to the area occupied by the magnetic field distributed in the first transmission direction.

[0068] Step 5: Calculate the sum of the blocking areas of the several physical obstacles and store it as the sum of the physical obstacle blocking areas. Calculate the ratio of the blocking areas of the several physical obstacles to the sum of the physical obstacle blocking areas and store it as the blocking weights of the several physical obstacles. Add up the signal attenuation degrees of the several physical obstacles according to the blocking weights of the several physical obstacles, and store the addition result as the first quality evaluation parameter.

[0069] Step 6: Calculate the sum of the blocking areas of the several magnetic field obstacles and store it as the sum of the magnetic field obstacle blocking areas. Calculate the ratio of the blocking areas of the several magnetic field obstacles to the sum of the magnetic field obstacle blocking areas and store it as the blocking weights of the several magnetic field obstacles. Add up the signal attenuation degrees of the several magnetic field obstacles according to the blocking weights of the several magnetic field obstacles and store it as the second quality evaluation parameter.

[0070] Step 7: Take the opposite number of the addition result of the second quality evaluation parameter and the first quality evaluation parameter, and set it as the quality coefficient of the first position in the first transmission direction of the first-level layout scheme. In the same way, until the quality coefficient of the first position in the Y-th transmission direction of the first-level layout scheme is obtained. Calculate the average value of the quality coefficient of the first position in the first transmission direction of the first-level layout scheme until the quality coefficient of the first position in the Y-th transmission direction of the first-level layout scheme, and set it as the quality coefficient of the first position of the first-level layout scheme. Then, obtain the quality coefficients of other layout positions in the same way as obtaining the quality coefficient of the first position of the first-level layout scheme.

[0071] Step S60: Perform particle swarm optimization for intelligent router layout according to the quality coefficients of the several first-level router layout schemes to obtain the recommended router layout positions;

[0072] Among them, performing particle swarm optimization for intelligent router layout according to the quality coefficients of the several first-level router layout schemes to obtain the recommended router layout positions, step S60 includes the steps:

[0073] Step S70: Mark the recommended router layout positions in the pre-laid space model and feedback them to the user terminal.

[0074] S61: Construct a particle swarm distribution space according to the router layout constraint area;

[0075] S62: Distribute the several first-level router layout schemes in the particle swarm distribution space to obtain an initial particle distribution result;

[0076] S63: Based on the quality coefficients of the several first-level router layout schemes, perform particle swarm optimization based on the initial particle distribution result to obtain the recommended router layout position.

[0077] Preferably, the embodiment of the present application realizes the layout optimization of the router through the particle swarm optimization algorithm. The particle swarm distribution space in the embodiment of the present application is an abstract irregular space, and the space coordinate data is the same as the router layout constraint area. Distribute several first-level router layout schemes according to the physical coordinates in the particle swarm distribution space to obtain an initial particle distribution result.

[0078] Then, through the quality coefficients of several first-level router layout schemes, select 3 to 5 distribution positions with larger quality coefficients from the initial particle distribution result, and then select at least 5 distribution positions with smaller quality coefficients from the initial particle distribution result. Then, with a preset step length, preferably 0.5 to 0.7 m, using 3 to 5 distribution positions with larger quality coefficients as the target positions and 5 distribution positions with smaller quality coefficients as the starting positions, search a preset number of times in the particle swarm distribution space to obtain several expanded positions as new solutions. Analyze the quality coefficients of the new solutions to obtain the quality coefficients of the new solutions, and then participate in the second round of iteration of the particle swarm. Iterate repeatedly until at least 15 iterations, and no better solution appears in the quality coefficients of the new solutions. At this time, output the optimal solution, which is set as the recommended router layout position. Finally, mark the recommended router layout position in the pre-laid space model and feedback it to the user terminal to guide the user to layout the router.

[0079] Further, perform entity obstacle recognition on the pre-laid space model to obtain the entity obstacle distribution area. Step S20 includes the steps:

[0080] S21: Retrieve the entity obstacle type library;

[0081] S22: Perform entity extraction on the pre-laid space model according to the entity obstacle type library to obtain a first entity three-dimensional model, where the first entity three-dimensional model has a first entity material feature, a first entity obstacle distribution area, and a first entity deployment attitude parameter;

[0082] S23: Perform multi-angle penetration signal attenuation analysis on the first entity three-dimensional model according to the first entity deployment attitude parameter and the first entity material feature to obtain a first signal attenuation degree list and a first penetration azimuth vector list;

[0083] Furthermore, as Figure 2 shown, based on the first entity deployment attitude parameter and the first entity material feature, perform multi-angle penetration signal attenuation analysis on the first entity three-dimensional model to obtain a first signal attenuation degree list and a first penetration azimuth vector list. Step S23 includes the steps of:

[0084] S231: Randomly configure a first penetration azimuth vector for the first entity three-dimensional model based on the first entity deployment attitude parameter to obtain penetration thickness information;

[0085] S232: According to the penetration thickness information and the first entity material feature, collect mobile hotspot signal attenuation samples, where the mobile hotspot signal attenuation samples include a corresponding number of mobile hotspot signal intensity record data before penetration and a corresponding number of mobile hotspot signal intensity record data after penetration;

[0086] S233: Calculate the central value of the signal intensity attenuation ratio set of the corresponding number of mobile hotspot signal intensity record data before penetration and the corresponding number of mobile hotspot signal intensity record data after penetration, and set it as the first signal attenuation degree;

[0087] S234: Add the first signal attenuation degree to the first signal attenuation degree list, and add the first penetration azimuth vector to the first penetration azimuth vector list.

[0088] S24: Store the first signal attenuation degree list, the first penetration azimuth vector list, and the first entity deployment attitude parameter as the label information of the first entity obstacle distribution area, and add it to the entity obstacle distribution area.

[0089] The entity obstacle type library is a database preset by the administrator, which stores a three-element array of item model - item three-dimensional model - item material type. This database can be dynamically updated by the administrator from the cloud. The pre-deployment space model uploaded by the user includes various item model tags, item distribution attitude tags, and item distribution position tags. By matching with the item model index, the item three-dimensional model - item material type is output, and then the matching item distribution position tag is stored as the entity obstacle distribution area of the item three-dimensional model, the item material type is stored as the entity material feature of the item three-dimensional model, and the item distribution attitude tag is stored as the entity deployment attitude parameter of the item three-dimensional model.

[0090] Furthermore, take any one of the matched item three-dimensional models as an example to elaborate the analysis process of the signal attenuation degree:

[0091] Step 1: Randomly extract one from the matched entity obstacles, and set it as the first entity 3D model. The first entity 3D model has the first entity material feature, the first entity obstacle distribution area, and the first entity deployment attitude parameter.

[0092] Step 2: Randomly configure a penetration direction for the first entity 3D model based on the first entity deployment attitude parameter, and set it as the first penetration azimuth vector; at the same time, count the penetration thickness information of the first entity 3D model in the corresponding penetration direction.

[0093] Step 3: Using the penetration thickness information and the first entity material feature as constraint information, collect the penetration sample data of WI-FI signals with the same penetration thickness and the same entity material, and store it as the mobile hotspot signal attenuation sample. Preferably, the penetration sample data of WI-FI signals specifically includes 2.4GHZ penetration sample data and 5GHZ penetration sample data.

[0094] The 2.4GHZ penetration sample data includes a corresponding number of mobile hotspot signal pre-penetration signal strength record data and a corresponding number of mobile hotspot signal post-penetration signal strength record data. Count the signal strength attenuation amounts of the corresponding number of mobile hotspot signal pre-penetration signal strength record data and the corresponding number of mobile hotspot signal post-penetration signal strength record data, and then calculate the ratio of the corresponding number of signal strength attenuation amounts to the corresponding number of mobile hotspot signal pre-penetration signal strength record data, and set it as the corresponding number of signal attenuation degrees.

[0095] Furthermore, sort the corresponding number of signal attenuation degrees from small to large to obtain the signal attenuation degree sorting result; multiply 0.25 by the number of signal attenuation degrees and round up to obtain the first serial number; multiply 0.75 by the number of signal attenuation degrees and round up to obtain the second serial number; count the first signal attenuation degree where the signal attenuation degree sorting result is equal to the first serial number, and count the second signal attenuation degree where the signal attenuation degree sorting result is equal to the second serial number; delete the data where the corresponding number of signal attenuation degrees is less than the first signal attenuation degree or greater than the second signal attenuation degree to obtain the concentrated signal attenuation degree set, and take the mean of the concentrated signal attenuation degree set and store it as the 2.4GHZ signal attenuation degree.

[0096] Further, calculate the attenuation degree of the 5GHz signal in the same way, and then take the maximum value of the attenuation degree of the 2.4GHz signal and the attenuation of the 5GHz signal, which is stored as the first signal attenuation degree. Add the first signal attenuation degree to the first signal attenuation degree list, add the first penetration azimuth vector to the first penetration azimuth vector list, and in the same way, obtain the signal attenuation degrees in other penetration directions. Store the first signal attenuation degree list, the first penetration azimuth vector list, and the first entity deployment attitude parameters as the label information of the first entity obstacle distribution area, and add them to the entity obstacle distribution area.

[0097] Further, perform magnetic field obstacle recognition on the pre-laid space model to obtain the magnetic field obstacle distribution area. Step S30 includes the steps:

[0098] S31: Retrieve the magnetic field obstacle type library;

[0099] S32: Extract magnetic field obstacles from the pre-laid space model according to the magnetic field obstacle type library to obtain the first magnetic field obstacle model number, where the first magnetic field obstacle model number has the first magnetic field obstacle distribution area;

[0100] S33: Configure the electromagnetic field distribution according to the first magnetic field obstacle model number to obtain the first magnetic field obstacle electromagnetic field distribution parameters, where the first magnetic field obstacle electromagnetic field distribution parameters have the electromagnetic field intensity from the first radius area to the electromagnetic field intensity of the Mth radius area;

[0101] Further, configure the electromagnetic field distribution according to the first magnetic field obstacle model number to obtain the first magnetic field obstacle electromagnetic field distribution parameters. Step S33 includes the steps:

[0102] S331: Collect working magnetic field data according to the first magnetic field obstacle model number to obtain a magnetic field intensity detection list and a magnetic field radius record list, where the magnetic field radius refers to the distance between the detection position and the first magnetic field obstacle;

[0103] S332: Compare the magnetic field radius record list according to the magnetic field radius, cluster the magnetic field intensity detection list, and obtain the first radius magnetic field intensity detection value set, the second radius magnetic field intensity detection value set until the Qth radius magnetic field intensity detection value set;

[0104] S333: Traverse the first radius magnetic field intensity detection value set, the second radius magnetic field intensity detection value set until the Qth radius magnetic field intensity detection value set for mode analysis, and obtain the first radius magnetic field intensity eigenvalue, the second radius magnetic field intensity eigenvalue until the Qth radius magnetic field intensity eigenvalue;

[0105] S334: Sort the first radius magnetic field intensity eigenvalue, the second radius magnetic field intensity eigenvalue, up to the Qth radius magnetic field intensity eigenvalue in ascending order of radius to obtain a magnetic field intensity sorting result, where the magnetic field intensity sorting result has a magnetic field radius label sequence;

[0106] S335: Perform adjacent serial hierarchical clustering analysis on the magnetic field intensity sorting result to obtain an updated magnetic field intensity sorting result;

[0107] S336: Update the magnetic field radius label sequence according to the updated magnetic field intensity sorting result to obtain a radius region label sequence;

[0108] S337: Associatively store the updated magnetic field intensity sorting result and the radius region label sequence to obtain the electromagnetic field distribution parameters of the first magnetic field obstacle.

[0109] S34: Traverse the electromagnetic field intensity from the first radius region to the Mth radius region to perform penetration signal attenuation analysis to obtain a second signal attenuation degree list;

[0110] S35: Store the second signal attenuation degree list and the electromagnetic field distribution parameters of the first magnetic field obstacle as label information of the first magnetic field obstacle distribution region and add them to the magnetic field obstacle distribution region.

[0111] Specifically, the magnetic field obstacle type library stores the item models that generate electromagnetic fields during operation. The pre-deployed space model stores various item models and their distribution positions; according to the magnetic field obstacle type library, the magnetic field obstacles and their distribution positions can be selected from various item models. Taking any magnetic field obstacle as an example, the magnetic field obstacle is extracted from the pre-deployed space model according to the magnetic field obstacle type library and stored as the first magnetic field obstacle model; the distribution position is stored as the first magnetic field obstacle distribution region.

[0112] Furthermore, based on the first magnetic field obstacle model, collect the magnetic field intensity distribution data of the magnetic field obstacles of the same model, and then fuse the magnetic field intensity distribution data. The detailed fusion algorithm is as follows:

[0113] Step 1: Collect working magnetic field data according to the first magnetic field obstacle model to obtain a magnetic field intensity detection list and a magnetic field radius record list. The magnetic field radius refers to the distance between the detection position and the first magnetic field obstacle. According to the magnetic field radius, compare the magnetic field radius record list, cluster the magnetic field intensity detection list, and cluster the magnetic field intensity detection values with the same radius into one category, otherwise, cluster them into two categories. Organize to obtain the first radius magnetic field intensity detection value set, the second radius magnetic field intensity detection value set, up to the Qth radius magnetic field intensity detection value set.

[0114] Step 2: Traverse the first radius magnetic field strength measurement value set, the second radius magnetic field strength measurement value set until the Qth radius magnetic field strength measurement value set for mode analysis, preferably mean analysis, to obtain the first radius magnetic field strength eigenvalue, the second radius magnetic field strength eigenvalue until the Qth radius magnetic field strength eigenvalue.

[0115] Step 4: Sort the first radius magnetic field strength eigenvalue, the second radius magnetic field strength eigenvalue until the Qth radius magnetic field strength eigenvalue in ascending order of radius to obtain the magnetic field strength sorting result, where the magnetic field strength sorting result has a magnetic field radius label sequence. Perform adjacent serial number hierarchical clustering analysis on the magnetic field strength sorting result to obtain the updated magnetic field strength sorting result.

[0116] Specifically, the process of adjacent serial number hierarchical clustering analysis is as follows:

[0117] Extract the magnetic field strengths of any two adjacent serial numbers, calculate the magnetic field strength deviation between the magnetic field strengths of the adjacent serial numbers. If the magnetic field strength deviation is less than or equal to the magnetic field strength deviation threshold preset by the administrator, calculate the mean value of the magnetic field strengths of the two adjacent serial numbers, and at the same time update the magnetic field strength sorting to obtain the updated magnetic field strength sorting result. Until the magnetic field strength deviation between any two adjacent serial numbers is greater than the magnetic field strength deviation threshold, output the final updated magnetic field strength sorting result. Exemplarily, assume that the magnetic field strength serial number 1: 0.18 μT, the magnetic field strength serial number 2: 0.20 μT, and the magnetic field strength deviation threshold is 0.03 μT. Since the magnetic field strength deviation between serial number 1 and serial number 2 is less than the magnetic field strength deviation threshold, the original magnetic field strength sorting result is: (1: 0.18 μT, 2: 0.20 μT), and the updated magnetic field strength sorting result is (1~2: 0.19 μT).

[0118] Step 5: The radius region label sequence refers to the result of updating the magnetic field radius label sequence according to the updated magnetic field strength sorting result. Taking the above case as an example, assume that the original magnetic field strength sorting result is: (1: 0.18 μT, 2: 0.20 μT), and its radius label is (1: 3 m, 2: 4.5 m). Then, according to (1~2: 0.19 μT), the updated radius region label sequence is (1~2: 3 m~4.5 m), and the magnetic field strength in the radius region 3 m~4.5 m is 0.19 μT.

[0119] Step 6: Correlate and store the sorted and updated result of the magnetic field intensity with the radius region label sequence to obtain the electromagnetic field distribution parameters of the first magnetic field obstacle. Traverse the electromagnetic field intensity of the first radius region until the electromagnetic field intensity of the Mth radius region for penetration signal attenuation analysis, that is, for the electromagnetic field intensity of each radius region, collect the signal attenuation degree passing through the corresponding radius region and store it as the second signal attenuation degree list. Among them, the process of penetration signal attenuation analysis is the same as the process of attenuation analysis of physical obstacles. Finally, store the second signal attenuation degree list and the electromagnetic field distribution parameters of the first magnetic field obstacle as the label information of the first magnetic field obstacle distribution region and add it to the magnetic field obstacle distribution region.

[0120] By analyzing the magnetic field obstacle distribution region and carrying the second signal attenuation degree list and the electromagnetic field distribution parameters of the first magnetic field obstacle, it is convenient to perform quality coefficient analysis to facilitate the accurate execution of routing layout optimization.

[0121] The router intelligent layout method based on environmental interference analysis provided by the embodiment of the present invention has at least the following technical effects:

[0122] First of all, through the identification of physical and magnetic field obstacles, the propagation of wireless signals in the actual environment can be more accurately simulated, providing a scientific basis for router layout. Secondly, the application of signal quality analysis and particle swarm optimization algorithm makes the router layout more intelligent and automated, improving the efficiency and accuracy of the layout. In addition, users can customize the layout plan according to specific environments and requirements, enhancing the adaptability and flexibility of the layout plan. Finally, this method can ensure more uniform and reliable wireless signal coverage in complex environments, improve network performance, while reducing deployment costs and maintenance difficulties, and providing users with a more stable and efficient wireless network service.

[0123] Embodiment 2:

[0124] Please refer to Figure 3 , Figure 3 which is the schematic diagram of the embodiment of the electronic device provided by the embodiment of the present invention. As Figure 3As shown in the figure, an embodiment of the present invention provides an electronic device 500, which includes a memory 510, a processor 520, and a computer program 511 stored on the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, the following steps are implemented: in response to a router intelligent layout request uploaded by a user terminal, where the router intelligent layout request includes a pre-layout space model, a target signal receiving point group, and a router layout constraint area; performing entity obstacle recognition on the pre-layout space model to obtain an entity obstacle distribution area; performing magnetic field obstacle recognition on the pre-layout space model to obtain a magnetic field obstacle distribution area; evenly distributing routers in the router layout constraint area to obtain a number of first-level router layout schemes; according to the number of first-level router layout schemes, performing signal quality analysis on the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area to obtain a number of first-level router layout scheme quality coefficients; performing particle swarm optimization for router intelligent layout according to the number of first-level router layout scheme quality coefficients to obtain a recommended router layout position; marking the recommended router layout position in the pre-layout space model and feeding it back to the user terminal.

[0125] Embodiment III:

[0126] Please refer to Figure 4 , Figure 4 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 4 shown, this embodiment provides a computer-readable storage medium 600, on which a computer program 611 is stored. When the computer program 611 is executed by a processor, the following steps are implemented: in response to a router intelligent layout request uploaded by a user terminal, where the router intelligent layout request includes a pre-layout space model, a target signal receiving point group, and a router layout constraint area; performing entity obstacle recognition on the pre-layout space model to obtain an entity obstacle distribution area; performing magnetic field obstacle recognition on the pre-layout space model to obtain a magnetic field obstacle distribution area; evenly distributing routers in the router layout constraint area to obtain a number of first-level router layout schemes; according to the number of first-level router layout schemes, performing signal quality analysis on the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area to obtain a number of first-level router layout scheme quality coefficients; performing particle swarm optimization for router intelligent layout according to the number of first-level router layout scheme quality coefficients to obtain a recommended router layout position; marking the recommended router layout position in the pre-layout space model and feeding it back to the user terminal.

[0127] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0128] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0129] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0130] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0132] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts.

[0133] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. Router intelligent layout method based on environmental interference analysis, characterized in that Including: Responding to a router intelligent layout request uploaded by a client, where the router intelligent layout request includes a pre-deployment space model, a target signal reception point group, and a router layout constraint area; Performing entity obstacle recognition on the pre-deployment space model to obtain an entity obstacle distribution area; Performing magnetic field obstacle recognition on the pre-deployment space model to obtain a magnetic field obstacle distribution area; Performing uniform distribution of routers in the router layout constraint area to obtain a number of first-level router layout schemes; According to the number of first-level router layout schemes, performing signal quality analysis on the target signal reception point group in the entity obstacle distribution area and the magnetic field obstacle distribution area to obtain a number of first-level router layout scheme quality coefficients; Performing particle swarm optimization for router intelligent layout according to the number of first-level router layout scheme quality coefficients to obtain a recommended router layout position; Identifying the recommended router layout position in the pre-deployment space model and feeding it back to the client; Among them, performing entity obstacle recognition on the pre-deployment space model to obtain an entity obstacle distribution area, including: Retrieving an entity obstacle type library; Performing entity extraction on the pre-deployment space model according to the entity obstacle type library to obtain a first entity three-dimensional model, where the first entity three-dimensional model has a first entity material feature, a first entity obstacle distribution area, and a first entity deployment attitude parameter; Performing multi-angle penetration signal attenuation analysis on the first entity three-dimensional model according to the first entity deployment attitude parameter and the first entity material feature to obtain a first signal attenuation degree list and a first penetration azimuth vector list; Storing the first signal attenuation degree list, the first penetration azimuth vector list, and the first entity deployment attitude parameter as label information of the first entity obstacle distribution area and adding them to the entity obstacle distribution area; Among them, performing multi-angle penetration signal attenuation analysis on the first entity three-dimensional model according to the first entity deployment attitude parameter and the first entity material feature to obtain a first signal attenuation degree list and a first penetration azimuth vector list, including: Randomly configuring a first penetration azimuth vector for the first entity three-dimensional model based on the first entity deployment attitude parameter to obtain penetration thickness information; Collecting mobile hotspot signal attenuation samples according to the penetration thickness information and the first entity material feature, where the mobile hotspot signal attenuation samples include a number of mobile hotspot signal pre-penetration signal strength record data and a number of mobile hotspot signal post-penetration signal strength record data that correspond one by one; Calculating the central value of the signal strength attenuation ratio set of the number of mobile hotspot signal pre-penetration signal strength record data and the number of mobile hotspot signal post-penetration signal strength record data, and setting it as the first signal attenuation degree; Adding the first signal attenuation degree to the first signal attenuation degree list and adding the first penetration azimuth vector to the first penetration azimuth vector list.

2. The method according to claim 1, wherein Performing magnetic field obstacle recognition on the pre-deployment space model to obtain a magnetic field obstacle distribution area, including: Retrieve the magnetic field obstacle type library; Extract magnetic field obstacles from the pre-deployed space model according to the magnetic field obstacle type library to obtain the first magnetic field obstacle model number, where the first magnetic field obstacle model number has a first magnetic field obstacle distribution area; Perform electromagnetic field distribution configuration according to the first magnetic field obstacle model number to obtain the first magnetic field obstacle electromagnetic field distribution parameters, where the first magnetic field obstacle electromagnetic field distribution parameters have the electromagnetic field intensity from the first radius area to the electromagnetic field intensity of the Mth radius area; Traverse the electromagnetic field intensity from the first radius area to the electromagnetic field intensity of the Mth radius area to perform penetration signal attenuation analysis to obtain a second signal attenuation degree list; Store the second signal attenuation degree list and the first magnetic field obstacle electromagnetic field distribution parameters as the label information of the first magnetic field obstacle distribution area and add them to the magnetic field obstacle distribution area.

3. The method according to claim 2, wherein Perform electromagnetic field distribution configuration according to the first magnetic field obstacle model number to obtain the first magnetic field obstacle electromagnetic field distribution parameters, including: Collect working magnetic field data according to the first magnetic field obstacle model number to obtain a magnetic field intensity detection list and a magnetic field radius record list, where the magnetic field radius refers to the distance between the detection position and the first magnetic field obstacle; Compare the magnetic field radius record list according to the magnetic field radius, and cluster the magnetic field intensity detection list to obtain the first radius magnetic field intensity detection value set, the second radius magnetic field intensity detection value set until the Qth radius magnetic field intensity detection value set; Traverse the first radius magnetic field intensity detection value set, the second radius magnetic field intensity detection value set until the Qth radius magnetic field intensity detection value set to perform mode analysis to obtain the first radius magnetic field intensity eigenvalue, the second radius magnetic field intensity eigenvalue until the Qth radius magnetic field intensity eigenvalue; Sort the first radius magnetic field intensity eigenvalue, the second radius magnetic field intensity eigenvalue until the Qth radius magnetic field intensity eigenvalue from short to long in terms of radius to obtain a magnetic field intensity sorting result, where the magnetic field intensity sorting result has a magnetic field radius label sequence; Perform adjacent serial number hierarchical clustering analysis on the magnetic field intensity sorting result to obtain a magnetic field intensity sorting update result; Update the magnetic field radius label sequence according to the magnetic field intensity sorting update result to obtain a radius area label sequence; Associate and store the magnetic field intensity sorting update result with the radius area label sequence to obtain the first magnetic field obstacle electromagnetic field distribution parameters.

4. The method according to claim 1, characterized in that, According to the several first-level router layout schemes, perform signal quality analysis on the target signal receiving point group in the physical obstacle distribution area and the magnetic field obstacle distribution area to obtain the quality coefficients of the several first-level router layout schemes, including: Extract the first position of the first-level layout scheme according to the several first-level router layout schemes; Construct a signal transmission direction set by connecting lines from the first position of the first-level layout scheme to the target signal receiving point group respectively; Extract the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information up to the Y-th transmission direction obstacle distribution information that are located between the first position of the first-level layout scheme and the target signal receiving point group in the entity obstacle distribution area and the magnetic field obstacle distribution area, and that satisfy the set of signal transmission directions; Perform quality analysis based on the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information up to the Y-th transmission direction obstacle distribution information to obtain the quality coefficient of the first position of the first-level layout scheme, and add it to the quality coefficients of the several first-level router layout schemes.

5. The method according to claim 4, wherein Performing quality analysis based on the first transmission direction obstacle distribution information, the second transmission direction obstacle distribution information up to the Y-th transmission direction obstacle distribution information to obtain the quality coefficient of the first position of the first-level layout scheme, and adding it to the quality coefficients of the several first-level router layout schemes includes: Obtain the blocking areas of several entity obstacles and the signal attenuation degrees of several entity obstacles in the first transmission direction obstacle distribution information; Calculate the sum of the blocking areas of the several entity obstacles to obtain the sum of the entity obstacle blocking areas; Calculate the ratio of the blocking areas of the several entity obstacles to the sum of the entity obstacle blocking areas to obtain the blocking weights of the several entity obstacles; Sum the signal attenuation degrees of the several entity obstacles according to the blocking weights of the several entity obstacles to obtain the first quality evaluation parameter; Obtain the blocking areas of several magnetic field obstacles and the signal attenuation degrees of several magnetic field obstacles in the first transmission direction obstacle distribution information; Calculate the sum of the blocking areas of the several magnetic field obstacles to obtain the sum of the magnetic field obstacle blocking areas; Calculate the ratio of the blocking areas of the several magnetic field obstacles to the sum of the magnetic field obstacle blocking areas to obtain the blocking weights of the several magnetic field obstacles; Sum the signal attenuation degrees of the several magnetic field obstacles according to the blocking weights of the several magnetic field obstacles to obtain the second quality evaluation parameter; Take the negative value of the sum of the second quality evaluation parameter and the first quality evaluation parameter as the first transmission direction quality coefficient of the first position of the first-level layout scheme; Until the Y-th transmission direction quality coefficient of the first position of the first-level layout scheme is obtained, calculate the average value of the first transmission direction quality coefficient of the first position of the first-level layout scheme until the Y-th transmission direction quality coefficient of the first position of the first-level layout scheme, and set it as the quality coefficient of the first position of the first-level layout scheme.

6. The method according to claim 1, wherein Perform particle swarm optimization for intelligent router layout based on the quality coefficients of the several first-level router layout schemes to obtain the recommended router layout position, including: Construct a particle swarm distribution space according to the router layout constraint area; Distribute the several first-level router layout schemes in the particle swarm distribution space to obtain an initial particle distribution result; Perform particle swarm optimization based on the initial particle distribution result according to the quality coefficients of the several first-level router layout schemes to obtain the recommended router layout position.

7. An electronic device, characterized in that, Includes: A memory for storing computer software programs; A processor, configured to read and execute the computer software program, thereby implementing the router intelligent layout method based on environmental interference analysis according to any one of claims 1 to 6.

8. A non-transitory computer-readable storage medium, characterized in that, The computer software program is stored in the storage medium, and when the computer software program is executed by the processor, it implements the router intelligent layout method based on environmental interference analysis according to any one of claims 1 to 6.

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