Intelligent networking method of mesh network, electronic device and storage medium

Abstract

The invention relates to an intelligent networking method of a mesh network, which comprises the following steps of: when a wireless mesh network (Wi-Fi Mesh) is constructed, converting other communication pointers into signal strength, and calculating an appropriate value function of a performance pointer for constructing the Wi-Fi mesh network by using the signal strength; the optimal networking method of the Wi-Fi mesh network is worked out by using artificial intelligence (AI) on the basis of a self-defined value function, and the optimal networking method of the Wi-Fi mesh network is worked out by using the AI. The invention further provides an electronic device and a storage medium for implementing the method.

Description

Technical Field

[0001] The present invention relates to an intelligent networking method, and in particular to an intelligent networking method, an electronic device and a storage medium for a mesh network. Background Art

[0002] With the development of digital network life, the demand for wireless mesh networks (Wi-Fi Mesh) is increasing, and building a wireless mesh network with excellent performance has become a very important issue. Summary of the Invention

[0003] In view of the above, it is necessary to provide an intelligent mesh network networking method, electronic device, and storage medium to calculate the optimal mesh network networking combination with the best performance.

[0004] An embodiment of the present invention provides an intelligent networking method for a mesh network, which is applied to an electronic device, comprising: dividing a designated target area evenly to generate a networking model; initializing multiple communication parameters of the networking model; randomly initializing the initial positions of multiple wireless access points (APs) within the designated target area; calculating the maximum value of the fitness function of the adjacent nodes of the initial position combination of the APs based on the communication parameters to obtain the multiple position combinations of the APs within the target area; determining whether the initial position combination of the APs within the target area is the optimal position combination among the multiple position combinations; if the initial position combination of the APs is the optimal position combination, storing the optimal position combination in an optimal position combination set; determining whether the number of times the initial positions of the APs within the target area are randomly reset is greater than a default value; and if the number of times the initial positions of the APs within the target area are randomly reset is greater than the default value, selecting a target position combination from the optimal position combination set.

[0005] An embodiment of the present invention also provides an electronic device, comprising a memory, a processor, and a mesh network intelligent networking program stored in the memory and executable on the processor. The electronic device further comprises an initialization module and a calculation and judgment module. When the mesh network intelligent networking program is executed by the processor, the following steps are implemented: dividing the designated target area evenly to generate a networking model; initializing the multiple communication parameters of the networking model; randomly initializing the initial positions of the multiple wireless access points (APs) within the designated target area; calculating the maximum value of the fitness function of the adjacent nodes of the initial position combination of the APs based on the communication parameters to obtain the multiple position combination of the APs within the target area; determining whether the initial position combination of the APs within the target area is the optimal position combination among the multiple position combinations; if the initial position combination of the APs is the optimal position combination, storing the optimal position combination in an optimal position combination set; determining whether the number of times the initial positions of the APs within the target area are randomly reset is greater than a default value; and if the number of times the initial positions of the APs within the target area are randomly reset is greater than the default value, selecting a target position combination from the optimal position combination set.

[0006] An embodiment of the present invention further provides a storage medium having a computer program stored thereon. When the computer program is executed, the steps of the aforementioned intelligent networking method for a mesh network are implemented.

[0007] The intelligent mesh network networking method, electronic device, and storage medium of the embodiments of the present invention use an artificial intelligence (AI) algorithm (e.g., a randomized reset hill climbing algorithm) based on a custom fitness function to calculate the optimal networking method for the Wi-Fi mesh network. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flowchart of the steps of the intelligent networking method of the mesh network according to an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of an application of the intelligent networking method of a mesh network according to an embodiment of the present invention.

[0010] Figure 3 Schematic diagram of randomly generating device positions according to an embodiment of the present invention.

[0011] Figure 4 Schematic diagram of fitness function modeling according to an embodiment of the present invention.

[0012] Figure 5A and 5B FIG. 4 is a schematic diagram of estimating RSSI signal strength between devices during barrier-free operation according to an embodiment of the present invention.

[0013] Figure 6 FIG. 4 is an application diagram of the relationship between RSSI signal strength and transmission rate according to an embodiment of the present invention.

[0014] Figure 7 Schematic diagram of determining the obstacle factor according to an embodiment of the present invention.

[0015] Figure 8 This is a schematic diagram of determining the number of devices in an embodiment of the present invention.

[0016] Figure 9 FIG. 4 is a schematic diagram of the hardware architecture of an electronic device according to an embodiment of the present invention.

[0017] Figure 10 FIG. 4 is a functional block diagram of an electronic device according to an embodiment of the present invention.

[0018] Description of main component symbols

[0019]

[0020] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0021] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0022] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. The embodiments described are merely some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0024] It should be noted that the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the said features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0025] The intelligent networking method for a wireless mesh (Wi-Fi Mesh) network according to an embodiment of the present invention converts other communication indicators into signal strength when constructing a Wi-Fi Mesh network. This signal strength is then used to calculate a fitness function for the Wi-Fi Mesh network's performance indicators. These performance indicators include the Received Signal Strength Indication (RSSI), the obstacle factor between wireless access points (APs), the number of APs, and AP radiation coverage. Based on this custom fitness function, an artificial intelligence (AI) algorithm (e.g., a randomized hill climbing algorithm) is used to calculate the optimal Wi-Fi Mesh network configuration.

[0026] Figure 1 The flowchart of the intelligent networking method for a mesh network according to an embodiment of the present invention is applied to a processor of an electronic device. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0027] Step S11, dividing the target area, dividing the designated target area (for example, a large intelligent storage warehouse) evenly, thereby converting a networking model. Figure 2 As shown, the target area can be divided into 8*8=64 locations. By calculating the maximum value of the fitness function of each device (eg, access point (AP)), the location of the corresponding AP during networking is obtained, for example, A1, A2 and A3.

[0028] Step S12, initializing multiple communication parameters of the networking model, which include RSSI, obstacle factor between wireless APs, number of APs, AP radiation coverage, etc.

[0029] Step S13: randomly initialize the initial positions of the plurality of APs in the target area.

[0030] refer to Figure 3,AP A’s neighboring nodes are 1 to 8, and APB’s neighboring nodes are a to h. When random reset occurs, the AP’s position will be randomly generated among the 64 positions.

[0031] In step S14 , the maximum value of the fitness function of the neighboring nodes of the initial position combinations of the APs is calculated according to the communication parameters to obtain a plurality of position combinations of the APs in the target area.

[0032] The formula for calculating the fitness function F is as follows:

[0033]

[0034] R xy Indicates the RSSI signal strength of AP Ax receiving AP Ay in the absence of obstacles. N represents the number of APs, Oxy represents the obstacle factor (Obstacle) between AP Ax and AP Ay, A represents the area of ​​the building, and C represents the area of ​​the building covered by the AP radiation. xy The weight is The weight of N is I N , O XY The weight is In this embodiment, priority is given to the received signal strength R when there is no obstacle. xy , the obstacle factor Oxy is second, and the number of APs N is the last, so set R xy The weight is 45%, O XY The weight of is 35%, and the weight of N is 20%. In other embodiments, R xy The weight of O XY The weight of and the weight of N can be set according to the actual application scenario requirements.

[0035] Obstacle factors include concrete walls, hollow brick walls, glass doors, wooden doors, etc. In actual calculations, the obstacle factor is substituted into the calculation based on the actual material type and the number of layers of penetrating material.

[0036] like Figure 4 As shown, F={((R 12 +R 13 +R 23 …+R (N-1)N )×45%+((R 12 +R 13 +R 23 …+R (N-1)N )-(O 12 +O 13 +O 23 …+O (N-1)N )) / N)×20%-(O 12 +O13 +O 23 …+O (N-1)N )×35%}×(C / A).

[0037] When there are no obstacles, the formula for calculating the RSSI signal strength between APs is as follows:

[0038] RSSI = -1.5 × -30D (dBm), where D is the communication distance between APs.

[0039] Taking three APs as an example, according to the calculation results, the relationship between the distance between APs and RSSI (RSSI1, RSSI2, RSSI3) is as follows: Figure 5A and 5B shown.

[0040] refer to Figure 6 In the absence of obstacles and with the attenuation adjusted, the average throughput of the first left block (0dB, 10dB, 20dB) is measured to be 904Mbps, the average throughput of the second left block (30dB, 40dB) is 565Mbps, the average throughput of the second right block (0dB, 60dB) is 94Mbps, and the average throughput of the second right block (0dB, 80dB) is 0Mbps.

[0041] The data shows that when the RSSI is around -50dBm, the transmission throughput will drop significantly. To achieve a stable transmission rate, when selecting the number of APs N in the fitness function, try to ensure that the RSSI between two APs is greater than -50dBm.

[0042] Regarding the barrier factor, see Figure 7 , according to the scene mode, the physical walls and doors of the specific scene are fitted to their nearest grid edges. Arrays in the X and Y directions are established to store the parameters of the obstacle factor of each grid. The attenuation parameters are filled in according to the actual objects in the locations with physical objects, and "0" is filled in for the locations without obstacles. Figure 7 The array created is as follows:

[0043] and

[0044]

[0045] See Figure 8 The distance between each AP is less than 14 meters (M), and the RSSI between two APs is calculated to be greater than -50dBm using the following formula: RSSI = -1.5 × -30D-K (dBm). The AP radiation coverage radius is calculated as r = 14M to ensure that the radiation surface of all APs covers the desired deployment area.

[0046] Step S15 , determining whether the initial position combination of the APs in the target area is the optimal position combination among the plurality of position combinations.

[0047] In step S16 , if the initial position combination of the APs is not the optimal position combination, the optimal position combination is updated to the initial position combination, and then the process returns to step S14 .

[0048] In step S17 , if the initial position combination of the APs is the optimal position combination, the optimal position combination is stored in the optimal position combination set.

[0049] Step S18: Determine whether the number of times the initial positions of the APs in the target area are randomly reset is greater than a default value, for example, 50. If not, return to step S13 to continue randomly initializing the initial positions of the APs in the target area.

[0050] Step S19: If the number of times of randomly resetting the initial positions of the APs in the target area is greater than the default value, a target position combination is selected from the optimal position combination set, that is, the optimal position combination among the optimal position combinations.

[0051] It should be noted that the present invention uses a custom fitness function and a custom formula to calculate the optimal networking combination of Wi-Fi networks. The calculation process is a common technical means used by those skilled in the art and will not be described in detail herein.

[0052] Figure 9 Schematic diagram of the hardware architecture of an electronic device according to an embodiment of the present invention. The electronic device 200, for example, but not limited to, an intelligent networking server, can be interconnected via a system bus to connect a processor 210, a memory 220, and an intelligent networking system 230 of a mesh network. Figure 9 The electronic device 200 is shown only with components 210 - 230 , but it is understood that implementing all of the illustrated components is not a requirement, and greater or fewer components may alternatively be implemented.

[0053] The memory 220 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 220 can be an internal storage unit of the electronic device 200, such as a hard disk or memory of the electronic device 200. In other embodiments, the memory can also be an external storage device of the electronic device 200, such as a plug-in hard disk equipped on the electronic device 200, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. Of course, the memory 220 can also include both the internal storage unit of the electronic device 200 and its external storage device. In this embodiment, the memory 220 is generally used to store the operating system and various application software installed on the electronic device 200, such as the program code of the mesh network intelligent networking system 230. In addition, the memory 220 can also be used to temporarily store various data that has been output or will be output.

[0054] In some embodiments, the processor 210 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 210 is generally used to control the overall operation of the electronic device 200. In this embodiment, the processor 210 is used to execute program code stored in the memory 220 or process data, for example, to operate the intelligent networking system 230 of the mesh network.

[0055] It should be noted that Figure 9 The electronic device 200 is merely illustrated as an example. In other embodiments, the electronic device 200 may include more or fewer components, or have a different component configuration.

[0056] If the modules / units integrated in the electronic device 200 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0057] Figure 10 2 is a functional block diagram of an electronic device according to an embodiment of the present invention, which is configured to execute a method for intelligently establishing a mesh network. The method for intelligently establishing a mesh network according to an embodiment of the present invention can be implemented by a computer program stored in a storage medium, such as memory 220 of electronic device 200. When the computer program implementing the method is loaded into memory 220 by processor 210, it drives processor 210 of device 200 to execute the method for intelligently establishing a mesh network according to an embodiment of the present invention.

[0058] The electronic device 200 according to the embodiment of the present invention includes an initialization module 310 and a calculation and determination module 320 .

[0059] The initialization module 310 divides the target area and evenly divides the designated target area (for example, a large intelligent storage warehouse) to convert the network model. Figure 2 As shown, the target area can be divided into 8*8=64 locations. By calculating the maximum value of the fitness function of each AP, the location of the corresponding access point (AAP) during networking is obtained, for example, A1, A2 and A3.

[0060] The initialization module 310 initializes multiple communication parameters of the networking model, including RSSI, obstacle factors between wireless APs, number of APs, AP radiation coverage, etc.

[0061] The initialization module 310 randomly initializes the initial positions of a plurality of APs within the target area.

[0062] refer to Figure 3 , the adjacent nodes of APA are 1 to 8, and the adjacent nodes of APB are a to h. When random reset occurs, the position of AP will be randomly generated among the 64 positions.

[0063] The calculation and determination module 320 calculates the maximum value of the fitness function of the neighboring nodes of the initial position combinations of the APs according to the communication parameters to obtain the multiple position combinations of the APs in the target area.

[0064] The formula for calculating the fitness function F is as follows:

[0065]

[0066] R xy Indicates the RSSI signal strength of AP Ax receiving AP Ay in the absence of obstacles. N represents the number of APs, Oxy represents the obstacle factor (Obstacle) between AP Ax and AP Ay, A represents the area of ​​the building, and C represents the area of ​​the building covered by the AP radiation. xy The weight of The weight of N is I N , O XY The weight of In this embodiment, priority is given to the received signal strength R when there is no obstacle. xy , the obstacle factor Oxy is second, and the number of APs N is the last, so set R xy The weight is 45%, O XY The weight of is 35%, and the weight of N is 20%. In other embodiments, R xy The weight of O XY The weight of and the weight of N can be set according to the actual application scenario requirements.

[0067] Obstacle factors include concrete walls, hollow brick walls, glass doors, wooden doors, etc. In actual calculations, the obstacle factor is substituted into the calculation based on the actual material type and the number of layers of penetrating material.

[0068] The calculation and determination module 320 determines whether the initial position combination of the APs in the target area is the optimal position combination among the plurality of position combinations.

[0069] If the initial position combination of the APs is not the optimal position combination, the calculation and determination module 320 updates the optimal position combination to the initial position combination.

[0070] If the initial position combination of the APs is the optimal position combination, the calculation and determination module 320 stores the optimal position combination in the optimal position combination set.

[0071] The calculation and determination module 320 determines whether the number of times the initial positions of the APs in the target area are randomly reset is greater than a default value, for example, 50.

[0072] If the number of times of randomly resetting the initial positions of the APs in the target area is greater than the default value, the calculation and judgment module 320 selects a target position combination from the optimal position combination set, ie, the optimal position combination among the optimal position combinations.

[0073] It is understood that the module division described above is only a logical functional division, and other division methods may be used in actual implementation. In addition, the functional modules in the various embodiments of the present application can be integrated into the same processing unit, or each module can exist physically separately, or two or more modules can be integrated into the same unit. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0074] For ordinary technicians in this field, they can make other corresponding changes or adjustments based on actual needs generated by combining the technical solutions and technical concepts provided by the embodiments of the present invention, and these changes and adjustments should fall within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligently forming a mesh network, applied to an electronic device, characterized in that: The method comprises: Divide the designated target area equally to generate a networking model; Initialize the complex communication parameters of the networking model; Randomly initializing the initial positions of a plurality of wireless access points (APs) within the designated target area; Calculating the maximum value of the fitness function of the neighboring nodes of the initial position combination of the APs according to the communication parameters to obtain a plurality of position combinations of the APs in the target area; Determining whether the initial position combination of the APs in the target area is an optimal position combination among the plurality of position combinations; If the initial position combination of the APs is the optimal position combination, storing the optimal position combination in the optimal position combination set; Determining whether the number of times the initial positions of the APs in the target area are randomly reset is greater than a default value; and If the number of times the initial positions of the APs in the target area are randomly reset is greater than the default value, a target position combination is selected from the optimal position combination set.

2. The intelligent networking method of a mesh network according to claim 1, wherein: Also includes: The formula for calculating the maximum value of the fitness function is as follows: Among them, R xy Indicates the received signal strength indication (RSSI) signal strength measured at AP Ax receiving AP Ay in the absence of obstacles. N represents the number of APs, Oxy represents the obstacle factor (Obstacle) between AP Ax and AP Ay, A represents the area of ​​the building, C represents the area of ​​the building covered by the AP radiation, and R xy The weight of The weight of N is I N , and O XY The weight of 3. The intelligent networking method of a mesh network according to claim 1, wherein: Also includes: If the initial position combination of the APs is not the optimal position combination, the optimal position combination is updated to the initial position combination.

4. The intelligent networking method of a mesh network according to claim 1, wherein: Also includes: The formula for calculating the RSSI signal strength between APs is as follows: RSSI = -1.5 × -30D (dBm), where D is the communication distance between APs.

5. The intelligent networking method of a mesh network according to claim 1, wherein: The default value is 50.

6. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a mesh network intelligent networking program stored in the memory and executable on the processor. The electronic device also includes an initialization module and a calculation and judgment module. When the mesh network intelligent networking program is executed by the processor, the following steps are implemented: Divide the designated target area equally to generate a networking model; Initialize the complex communication parameters of the networking model; Randomly initializing initial positions of a plurality of wireless access points (APs) within the designated target area; Calculating the maximum value of the fitness function of the neighboring nodes of the initial position combination of the APs according to the communication parameters to obtain a plurality of position combinations of the APs in the target area; Determining whether the initial position combination of the APs in the target area is an optimal position combination among the plurality of position combinations; If the initial position combination of the APs is the optimal position combination, storing the optimal position combination in the optimal position combination set; Determine whether the number of times the initial positions of the APs in the target area are randomly reset is greater than a default value; as well as If the number of times the initial positions of the APs in the target area are randomly reset is greater than the default value, a target position combination is selected from the optimal position combination set.

7. The electronic device according to claim 6, wherein: The formula for calculating the maximum value of the fitness function is as follows: Among them, R xy Indicates the RSSI signal strength of AP Ax receiving AP Ay in the absence of obstacles. N represents the number of APs, Oxy represents the obstacle factor between AP Ax and AP Ay, A represents the area of ​​the building, C represents the area of ​​the building covered by AP radiation, and R xy The weight of The weight of N is I N , and O XY The weight of 8. The electronic device according to claim 6, wherein: When the processor executes the intelligent networking program for the mesh network, the following steps are further implemented: If the initial position combination of the APs is not the optimal position combination, the optimal position combination is updated to the initial position combination.

9. The electronic device according to claim 6, wherein: The formula for calculating the RSSI signal strength between APs is as follows: RSSI = -1.5 × -30D (dBm), where D is the communication distance between APs.

10. A storage medium having at least one computer instruction stored thereon, characterized in that: The instructions are loaded by the processor and executed by the intelligent networking method of the mesh network according to any one of claims 1 to 5.

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