Communication networking method and system of wireless network card

Abstract

The invention discloses a wireless network card communication networking method and system, and the method comprises the steps: obtaining network signal data of nodes in a communication network through a wireless signal strength tester in a test period, obtaining a signal fluctuation value, taking the signal fluctuation value as a judgment basis, determining a suspected node, and carrying out the detection of the suspected node. Analyzing the historical data of the suspected node in the historical period to obtain a node early warning value, reflecting the fluctuation frequency degree and the fluctuation stability degree of the suspected node in the historical period through the node early warning value, and if the node early warning value is greater than a node early warning threshold value, generating an early warning signal; and marking the suspected node corresponding to the generated early warning signal as an early warning node, and evaluating to obtain the signal strength fluctuation condition of the nodes in the communication network.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a communication networking method and system for a wireless network card. Background Art

[0002] After a complex wireless communication network is established, the signal strength of the network nodes within the wireless communication network may be affected and fluctuate, thereby affecting the communication quality. Therefore, testing is required to determine the network nodes that need to be warned through test results and historical data, and to improve the efficiency of network optimization and adjustment by analyzing the network nodes that need to be warned.

[0003] In the prior art, it is often necessary to rely on regular inspections or user feedback to discover the strength problem of network node signals, and when identifying node signals, it is only through a separate comparison of thresholds, which has low accuracy. Therefore, the present application scheme tests the network signal strength of the node and combines it with historical data analysis to evaluate the signal strength fluctuation of the node in the communication network. At the same time, it analyzes the fluctuation change type of the network node and constructs a corresponding prediction model, which helps network optimization personnel to adjust the network signal in time and improve the efficiency of network optimization and adjustment. Summary of the invention

[0004] The object of the present invention is to provide a communication networking method and system for a wireless network card to solve at least one of the above-mentioned problems in the prior art.

[0005] In a first aspect, a communication networking method of a wireless network card includes: Step 1: During the test period, the network signal data of the nodes in the communication network obtained by the wireless signal strength tester is analyzed to obtain the signal fluctuation value, and the suspected nodes are identified in the communication network according to the comparison result between the signal fluctuation value and the signal fluctuation threshold; Step 2: Analyze the historical data of the suspected node in the historical period, output the node warning value, and obtain the warning signal based on the result of comparing the node warning value with the node warning threshold; Step 3: Based on the warning signal, analyze the fluctuation data of the warning node within the test period to obtain the analysis fluctuation curve, analyze and process the analysis fluctuation curve to obtain the type analysis value, and analyze and process based on the type analysis value to output the fluctuation change type result; The fluctuation change type result includes a linear change signal or a nonlinear change signal; Step 4: Based on the fluctuation change type results, obtain the predicted change slope, and based on the predicted change slope, obtain the prediction model, analyze the prediction model, and determine the shortest over-boundary time.

[0006] In a second aspect, a communication networking system of a wireless network card includes: Node fluctuation test module: During the test period, the network signal data of the nodes in the communication network obtained by the wireless signal strength tester is analyzed to obtain the signal fluctuation value. Based on the comparison result between the signal fluctuation value and the signal fluctuation threshold, the suspected nodes in the communication network are identified; Warning analysis and processing module: Analyze the historical data of suspected nodes in the historical period, output the node warning value, and obtain the warning signal based on the result of comparing the node warning value with the node warning threshold; Fluctuation type prediction module: Based on the warning signal, the fluctuation data of the warning node in the test period is analyzed to obtain the analysis fluctuation curve, the analysis fluctuation curve is analyzed and processed to obtain the type analysis value, and the type analysis value is analyzed and processed to output the fluctuation change type result; The fluctuation change type result includes a linear change signal or a nonlinear change signal; Optimization time determination module: Based on the fluctuation change type results, the predicted change slope is obtained, and based on the predicted change slope, the prediction model is obtained, the prediction model is analyzed, and the shortest over-boundary time is determined.

[0007] Beneficial effects of the present invention: 1. In the test cycle, the present invention obtains the network signal data of the nodes in the communication network through the wireless signal strength tester, obtains the signal fluctuation value, determines the suspected node based on the signal fluctuation value, and then analyzes the historical data of the suspected node in the historical cycle to obtain the node warning value, thereby reflecting the frequency of fluctuations of the suspected node in the historical cycle and the degree of stability between fluctuations through the node warning value. If the node warning value is greater than the node warning threshold, a warning signal is generated, and the suspected node corresponding to the generated warning signal is marked as a warning node, thereby evaluating the signal strength fluctuation of the nodes in the communication network; 2. Based on the early warning signal, the present invention obtains the fluctuation data of the early warning node in the test cycle, and performs analysis and processing to obtain the analysis fluctuation curve. On the basis of the analysis fluctuation curve, fitting is performed to obtain the linear regression model. The difference between the analysis fluctuation curve and the linear regression model is reflected by combining the fitting comparison value and the overlap length value, and the linearity of the analysis fluctuation curve corresponding to the test period in the test cycle is evaluated, which is conducive to early warning of future fluctuation change trends; 3. The present invention analyzes and processes the two types of fluctuation changes in a classified discussion manner according to the results of the fluctuation change types, and outputs a predicted change slope. Based on the predicted change slope, a prediction model is obtained, and analysis and processing are performed through the prediction model to output the shortest over-boundary time, thereby improving the accuracy of predicting trend changes of network nodes, and through the calculated shortest over-boundary time, network optimization measures are taken to solve the problem of continued increase in node signal fluctuations in the communication network, which leads to a decrease in network communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 It is a flow chart of optimizing the network fluctuation of nodes in the communication networking of a wireless network card provided by a communication networking method of a wireless network card of the present invention; Figure 2 It is a structural schematic diagram of a communication networking system of a wireless network card of the present invention; Figure 3 It is a structural schematic diagram of a communication networking method and device of a wireless network card of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0011] Embodiment 1 Figure 1 A flowchart of a communication networking method of a wireless network card provided in the first embodiment of the present invention is applicable to the situation of optimizing the node network fluctuation in the communication networking of the wireless network card. The communication networking method of the wireless network card can be executed by a communication networking system of the wireless network card. The communication networking system of the wireless network card can be implemented by software and / or hardware. The communication networking system of the wireless network card can be configured in a communication networking device of the wireless network card. Optionally, the communication networking device of the wireless network card can be an electronic device, which can be a notebook, a desktop computer, a smart tablet, etc., and the embodiment of the present invention does not limit this.

[0012] like Figure 1 As shown, a communication networking method of a wireless network card provided by an embodiment of the present invention specifically includes the following steps: Step 1: During the test period, the network signal data of the nodes in the communication network is obtained by using a wireless signal strength tester, wherein the network signal data includes signal strength, the signal strength of the nodes in the communication network is analyzed, the signal fluctuation value is obtained, and the suspected node is obtained; In some embodiments, the test cycle is divided into several test periods, the node signal strength in the test period is obtained, and compared with the signal strength warning value, the process is as follows: If the node signal strength in the test period is less than the signal strength warning value, that is, the node signal strength in the analyzed test period is weak, the test period with weak node signal strength is marked as a signal fluctuation period; If the node signal strength in the test period is greater than or equal to the signal strength warning value, that is, the node signal strength in the analyzed test period is normal, the test period with normal node signal strength is marked as a period without signal fluctuation; It should be noted that the test cycle is a test period divided into equal time intervals, and the node signal strength is calculated by adding and summing the unit signal strength corresponding to each test time point in the test period; Count the number of signal fluctuation periods and calculate the ratio with the total number of test periods in the test cycle to get the number of node fluctuations; Subtract the node signal strength corresponding to the signal fluctuation period from the signal strength warning value, take the absolute value, and calculate the ratio with the signal strength warning value to obtain the fluctuation deviation value; Add the fluctuation deviation values ​​corresponding to all node fluctuation periods and take the average to obtain the fluctuation degree value; For example, the signal strength range is -90dbm, the test period is one day, and the node fluctuation period can be 8:05-8:15 in the morning, 11:00-11:10 in the afternoon, and 4:20-4:30 in the afternoon; Among them, the signal strength is -50dbm from 8:05 to 8:15 in the morning, -60dbm from 11:00 to 11:10 in the afternoon, and -55dbm from 4:20 to 4:30 in the afternoon; Therefore, in the morning from 8:05 to 8:15, the signal strength is -50dBm, then |(-50dBm) - (-90dBm)| / (-90dbm)=0.444 (rounded to three decimal places), and the fluctuation deviation value is 0.444. In the afternoon from 11:00 to 11:10, the signal strength is -60dBm, then |(-60dBm) - (-90dBm)| / (-90dbm)=0.333 (rounded to three decimal places), and the fluctuation deviation value is 0.333. In the afternoon from 4:20 to 4:30, the signal strength is -55dBm and -30dBm, then |(-55dBm) - (-30dBm)| / (-90dbm)=0.278 (rounded to three decimal places), and the fluctuation deviation value is 0.278. The fluctuation deviation value of 8:05-8:15 in the morning is 0.222, the fluctuation deviation value of 11:00-11:10 in the afternoon is 0.333, and the fluctuation deviation value of 4:20-4:30 in the afternoon is 0.278. Calculate (0.222+0.333+0.278) / 3=0.278 (rounded to three decimal places); The signal fluctuation value is obtained by multiplying the number of node fluctuations by the fluctuation degree value; The signal fluctuation value is compared with the signal fluctuation threshold value. The process is as follows: If the signal fluctuation value is greater than the signal fluctuation threshold, the node in the communication network has a high fluctuation frequency and a large fluctuation degree during the test period, and is a suspected node; If the signal fluctuation value is less than or equal to the signal fluctuation threshold, the node in the communication network has a low fluctuation frequency and a small fluctuation degree during the test period, and is a non-suspected node; Step 2: Obtain historical data of the suspected node in the historical period, wherein the historical data includes the historical fluctuation period and the historical non-fluctuation period, analyze the historical fluctuation period, obtain the historical duration value and the historical quantity value, process the historical duration value and the historical quantity value, output the node warning value, compare the node warning value with the node warning threshold, if the node warning value is greater than the node warning threshold, generate a warning signal, and obtain the warning node; In some embodiments, within a historical cycle, the historical cycle is divided into a number of historical analysis periods, the nodes within the historical analysis period are analyzed, and the historical fluctuation period is extracted; It should be noted that the historical period division method is consistent with the test period division method, and the historical volatility period is obtained in the same way as the signal volatility period, and the historical non-volatility period is obtained in the same way as the signal non-volatility period; In the historical period, the number of historical fluctuation periods is counted, and the ratio is calculated with the total number of all periods in the historical period to obtain the historical quantity value, which is marked as ; It should be noted that the total number of all periods in the historical cycle is obtained by adding the number of historical volatile periods to the number of historical non-volatile periods; In the historical period, the number of intervals between all historical fluctuation periods and the number of historical non-fluctuation periods within all historical fluctuation period intervals are obtained, which are marked as the number of historical fluctuation period intervals and the number of historical non-fluctuation periods within the intervals, respectively; The ratio of the number of historical non-volatile periods to the number of historical volatile periods is calculated to obtain the mean of the volatile intervals. The ratio of the mean of the volatile intervals to the total number of periods in the historical cycle is calculated to obtain the historical persistence value, which is marked as ; The historical quantity value With historical persistence Substituting into the formula: , calculate the node warning value ,in, , is represented by a preset scaling factor, and The value is 1.3446. The value is 3.689; It can be explained that the meaning of the node warning value is: combined with the number of historical fluctuation periods and the duration of non-fluctuation periods between historical fluctuation periods, it reflects the frequency of fluctuations and the degree of stability between fluctuations in the historical cycle. If this value is larger, the fluctuation period is more frequent in the historical cycle, and the non-fluctuation period between fluctuations is relatively short. On the contrary, if this value is smaller, the fluctuation period is relatively less in the historical cycle, and the non-fluctuation period between fluctuations is relatively long. Compare the node warning value with the node warning threshold. The process is as follows: If the node warning value is greater than the node warning threshold, then in the historical period, the fluctuation period is relatively frequent, and the non-fluctuation period between fluctuations is relatively short, and a warning signal is generated, and the suspected node corresponding to the generated warning signal is marked as a warning node; If the node warning value is less than or equal to the node warning threshold, then in the historical period, the fluctuation period is relatively small, and the non-fluctuation period between fluctuations is relatively long, a non-warning signal is generated, and the suspected node corresponding to the non-warning signal is marked as a non-warning node; The specific implementation plan of the embodiment of the present invention is: within the test period, the network signal data of the nodes in the communication network are obtained through a wireless signal strength tester to obtain the signal fluctuation value, and the suspected node is determined based on the signal fluctuation value. Then, the historical data of the suspected node in the historical period is analyzed to obtain the node warning value, so that the node warning value can reflect the frequency of fluctuations of the suspected node in the historical period and the degree of stability between fluctuations. If the node warning value is greater than the node warning threshold, a warning signal is generated, and the suspected node corresponding to the generated warning signal is marked as a warning node, thereby evaluating the signal strength fluctuation of the nodes in the communication network.

[0013] Embodiment 2 like Figure 1 As shown, based on Example 1, a communication networking method of a wireless network card according to an embodiment of the present invention includes: Step 3: Based on the warning signal, obtain the fluctuation data of the warning node within the test period, wherein the fluctuation data includes the fluctuation time point and the signal fluctuation value, analyze and process the fluctuation time point and the signal fluctuation value to obtain the fluctuation change value, compare the fluctuation change value with the fluctuation change threshold, and obtain the fluctuation change type result; The fluctuation change type result includes generating a linear change signal or generating a nonlinear change signal; In some embodiments, multiple test fluctuation periods within a test cycle are obtained, and one test fluctuation period is arbitrarily selected for analysis. The analysis process is as follows: The test fluctuation period is divided into several fluctuation time points, and the signal fluctuation values ​​corresponding to the fluctuation time points are obtained. A two-dimensional coordinate system is established, with the X-axis as time and the Y-axis as the signal fluctuation value. The signal fluctuation values ​​corresponding to all fluctuation time points in the test fluctuation period are substituted into the two-dimensional coordinate system, and the fluctuation change curve is drawn; In the fluctuation change curve, based on the signal strength warning value, mark points on the Y axis, draw a straight line parallel to the X axis and intersect with the fluctuation change curve to obtain the signal warning line; In the fluctuation change curve, take the signal strength threshold as the benchmark, mark points on the Y axis, draw a straight line parallel to the X axis, and obtain the signal threshold line; It should be noted that the fluctuation change curve does not exceed the range of the signal threshold line; Extract the fluctuation change curve between the signal warning line and the signal threshold line, and mark it as the analysis fluctuation curve; Based on the analysis fluctuation curve, the signal fluctuation value corresponding to the end point of the analysis fluctuation curve is obtained, and the difference is made with the signal strength threshold to obtain the fluctuation proximity value; The fluctuation proximity value is compared with the fluctuation proximity threshold value, and the process is as follows: If the fluctuation proximity value is greater than or equal to the fluctuation proximity threshold, an early warning analysis signal is generated; If the fluctuation proximity value is less than the fluctuation proximity threshold, a continuous monitoring signal is generated until an early warning analysis signal is generated and an early warning analysis operation is performed; Based on the early warning analysis signal, the endpoint where the analysis fluctuation curve intersects with the signal early warning line is obtained as the analysis starting point; Extract the endpoint corresponding to the generated early warning analysis signal as the analysis endpoint; Connect the analysis starting point and the analysis end point to obtain the analysis reference line, and fit it using the least squares method to obtain the linear regression model. , K1 is the slope value, is a constant; Get all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, marked as ( , ), and substitute all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve into The linear analysis coordinates are calculated ( , ); By formula: , calculate the fitted comparison value ,in, It is expressed as all Y values ​​of the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve. Represented as the predicted coordinate Y value of the linear regression model, It is expressed as the mean of all coordinate Y values ​​between the analysis start point and the analysis end point in the analysis fluctuation curve, and n is the total number of all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve; in, , calculate the mean of all Y values ​​between the starting point and the end point of the analysis fluctuation curve , is the Y value of all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, and n is the total number of all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve; It should be noted that The value of is between 0 and 1. The closer it is to 1, the better the fitting effect is, that is, the closer the alignment line is to linearity; Intercept the overlap curve between the analysis fluctuation curve and the analysis reference line, and obtain the length of the overlap curve. Add the lengths of the overlap curves to obtain the overlap length, and calculate the ratio with the analysis reference line to obtain the overlap length value. ; Fitting the comparison value and overlap length Perform addition and summation to obtain the type analysis value; Obtain the type analysis values ​​corresponding to all test fluctuation periods within the test cycle, add them together and take the average to obtain the fluctuation change value; It can be understood that the meaning of the fluctuation change value is: reflecting the linearity of the analysis fluctuation curve corresponding to the test fluctuation period within the test cycle. Specifically, if this value is larger, the linearity of the analysis fluctuation curve corresponding to the test period within the test cycle is higher. On the contrary, if this value is smaller, the linearity of the analysis fluctuation curve corresponding to the test period within the test cycle is lower, which is helpful to provide a reference basis for early warning of future fluctuation change trends; The fluctuation change value is compared with the fluctuation prediction threshold, and the process is as follows: If the fluctuation change value is greater than or equal to the fluctuation prediction threshold, it means that the linearity of the analysis fluctuation curve corresponding to the test period within the test cycle is high, and a linear change signal is generated; If the fluctuation change value is less than the fluctuation prediction threshold, it means that the linearity of the analysis fluctuation curve corresponding to the test period in the test cycle is low, and a nonlinear change signal is generated; The specific implementation method of the embodiment of the present invention is: based on the warning signal, the fluctuation data of the warning node within the test cycle is obtained, and the analysis and processing are performed to obtain the analysis fluctuation curve. On the basis of the analysis fluctuation curve, fitting is performed to obtain a linear regression model. The degree of difference between the analysis fluctuation curve and the linear regression model is reflected by combining the fitting comparison value and the overlap length value, and the linearity of the analysis fluctuation curve corresponding to the test period within the test cycle is evaluated, which is conducive to warning of future fluctuation change trends.

[0014] Embodiment 3 like Figure 1 As shown, based on Embodiment 1 and Embodiment 2, a communication networking method of a wireless network card according to an embodiment of the present invention includes: Step 4: Based on the fluctuation change type results, obtain the predicted change slope, and based on the predicted change slope, obtain the prediction model, analyze the prediction model, determine the shortest over-bound time, and solve the problem that the node signal fluctuation in the communication network continues to increase, resulting in a decrease in network communication quality; In some embodiments, when a nonlinear change signal is generated, the analysis fluctuation curve is divided into a number of analysis sub-curves, the sub-slopes corresponding to the analysis sub-curves are obtained, and the sizes are compared to select the maximum sub-slope as the maximum slope of the time period; During the test period, the maximum slopes of all the analysis fluctuation curves corresponding to the time periods are compared, and the maximum slope of the time period is selected as the predicted change slope K2; When a linear change signal is generated, the analysis fluctuation curve is divided into several analysis sub-curves, the sub-slopes corresponding to the analysis sub-curves are obtained, and the sub-slopes are added and averaged to obtain the average slope of the time period; During the test period, the average values ​​of the slopes of the time periods corresponding to all the analyzed fluctuation curves are added and averaged to obtain the predicted change slope K2; It should be noted that the analytical fluctuation curve is divided in equal time intervals to obtain analytical sub-curves; Substitute the predicted change slope K2 into the linear regression model The prediction model is obtained , where K1 represents the slope value, Expressed as a constant; The shortest optimization time is obtained as follows: In the analysis fluctuation curve, taking the analysis end point as the starting point and based on the prediction model, a straight line is drawn that intersects with the signal threshold line, and the coordinates of the intersection point with the signal threshold line are obtained; Subtract the abscissa of the intersection point from the end point of the analysis fluctuation curve, take the absolute value, and get the shortest optimization time; The specific implementation plan of the embodiment of the present invention is: according to the fluctuation change type results, two fluctuation change types are analyzed and processed in a classified discussion manner, and a predicted change slope is output. Based on the predicted change slope, a prediction model is obtained, and analysis and processing are performed through the prediction model to output the shortest over-boundary time, thereby improving the accuracy of predicting trend changes of network nodes, and through the calculated shortest over-boundary time, network optimization measures are taken to solve the problem of continued increase in node signal fluctuations in the communication network, which leads to a decrease in network communication quality.

[0015] Embodiment 4 like Figure 2 As shown, a communication networking system of a wireless network card according to an embodiment of the present invention includes: Node fluctuation test module: During the test period, the network signal data of the nodes in the communication network obtained by the wireless signal strength tester is analyzed to obtain the signal fluctuation value. Based on the comparison result between the signal fluctuation value and the signal fluctuation threshold, the suspected nodes in the communication network are identified; Warning analysis and processing module: Analyze the historical data of suspected nodes in the historical period, output the node warning value, and obtain the warning signal based on the result of comparing the node warning value with the node warning threshold; Fluctuation type prediction module: Based on the warning signal, the fluctuation data of the warning node in the test period is analyzed to obtain the analysis fluctuation curve, the analysis fluctuation curve is analyzed and processed to obtain the type analysis value, and the type analysis value is analyzed and processed to output the fluctuation change type result; The fluctuation change type result includes a linear change signal or a nonlinear change signal; Optimization time determination module: Based on the fluctuation change type results, the predicted change slope is obtained, and based on the predicted change slope, the prediction model is obtained, the prediction model is analyzed, and the shortest over-boundary time is determined.

[0016] Embodiment 5 Reference Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a communication networking method of a wireless network card as described in any one of the above methods is implemented.

[0017] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0018] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0019] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0020] Embodiment 6 An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a communication networking method for a wireless network card as described in any one of the above methods is implemented.

[0021] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed 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 the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0022] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0023] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0024] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0025] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0026] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0027] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A communication networking method for a wireless network card, characterized in that: The following steps are involved: Step 1: During the test period, the network signal data of the nodes in the communication network obtained by the wireless signal strength tester is analyzed to obtain the signal fluctuation value, and the suspected nodes are identified in the communication network according to the comparison result between the signal fluctuation value and the signal fluctuation threshold; Step 2: Analyze the historical data of the suspected node in the historical period, output the node warning value, and obtain the warning signal based on the result of comparing the node warning value with the node warning threshold; Step 3: Based on the warning signal, analyze the fluctuation data of the warning node within the test period to obtain the analysis fluctuation curve, analyze and process the analysis fluctuation curve to obtain the type analysis value, and analyze and process based on the type analysis value to output the fluctuation change type result; The fluctuation change type result includes a linear change signal or a nonlinear change signal; Step 4: Based on the fluctuation change type results, obtain the predicted change slope, and based on the predicted change slope, obtain the prediction model, analyze the prediction model, and determine the shortest over-boundary time.

2. A communication networking method for a wireless network card according to claim 1, characterized in that: The volatility value is obtained as follows: The test cycle is divided into several test periods, the node signal strength in the test period is obtained, and compared with the signal strength warning value. The process is as follows: If the node signal strength in the test period is less than the signal strength warning value, the test period with weak node signal strength is marked as a signal fluctuation period; If the node signal strength during the test period is greater than or equal to the signal strength warning value, the test period with normal node signal strength is marked as a period without signal fluctuation; Count the number of signal fluctuation periods and calculate the ratio with the total number of test periods in the test cycle to get the number of node fluctuations; Subtract the node signal strength corresponding to the signal fluctuation period from the signal strength warning value, take the absolute value, and calculate the ratio with the signal strength warning value to obtain the fluctuation deviation value; The fluctuation deviation values ​​corresponding to all node fluctuation periods are added together and averaged to obtain the fluctuation degree value.

3. The communication networking method of a wireless network card according to claim 1, characterized in that: The historical data of the suspected nodes in the historical period are analyzed. The analysis process is as follows: In the historical period, the number of historical fluctuation periods is counted, and the ratio is calculated with the total number of all periods in the historical period to obtain the historical quantity value, which is marked as ; In the historical period, the number of intervals between all historical fluctuation periods and the number of historical non-fluctuation periods within all historical fluctuation period intervals are obtained, which are marked as the number of historical fluctuation period intervals and the number of historical non-fluctuation periods within the intervals, respectively; The ratio of the number of historical non-volatile periods to the number of historical volatile periods is calculated to obtain the mean of the volatile intervals. The ratio of the mean of the volatile intervals to the total number of periods in the historical cycle is calculated to obtain the historical persistence value, which is marked as .

4. The communication networking method of a wireless network card according to claim 3, characterized in that: The method of obtaining early warning signals is as follows: The historical quantity value With historical persistence Substituting into the formula: , calculate the node warning value ,in, , Expressed as a preset scaling factor; The node warning value is compared with the node warning threshold. If the node warning value is greater than the node warning threshold, a warning signal is generated, and the suspected node corresponding to the generated warning signal is marked as a warning node.

5. The communication networking method of a wireless network card according to claim 1, characterized in that: Analyze the warning nodes within the test cycle to obtain the analysis fluctuation curve. The process is as follows: Get multiple test fluctuation periods within the test cycle, and arbitrarily select a test fluctuation period for analysis. The analysis process is as follows: The test fluctuation period is divided into several fluctuation time points, and the signal fluctuation values ​​corresponding to the fluctuation time points are obtained. A two-dimensional coordinate system is established, with the X-axis as time and the Y-axis as the signal fluctuation value. The signal fluctuation values ​​corresponding to all fluctuation time points in the test fluctuation period are substituted into the two-dimensional coordinate system, and the fluctuation change curve is drawn; In the fluctuation change curve, based on the signal strength warning value, mark points on the Y axis, draw a straight line parallel to the X axis and intersect with the fluctuation change curve to obtain the signal warning line; In the fluctuation change curve, take the signal strength threshold as the benchmark, mark points on the Y axis, draw a straight line parallel to the X axis, and obtain the signal threshold line; The fluctuation change curve between the signal warning line and the signal threshold line is extracted and marked as the analysis fluctuation curve.

6. The communication networking method of a wireless network card according to claim 5, characterized in that: Based on the analysis of the fluctuation curve, a linear regression model is obtained. The process is as follows: Based on the analysis fluctuation curve, the signal fluctuation value corresponding to the end point of the analysis fluctuation curve is obtained, and the difference is made with the signal strength threshold to obtain the fluctuation proximity value; The fluctuation proximity value is compared with the fluctuation proximity threshold value, and the process is as follows: If the fluctuation proximity value is greater than or equal to the fluctuation proximity threshold, an early warning analysis signal is generated; If the fluctuation proximity value is less than the fluctuation proximity threshold, a continuous monitoring signal is generated until an early warning analysis signal is generated and an early warning analysis operation is performed; Based on the early warning analysis signal, the endpoint where the analysis fluctuation curve intersects with the signal early warning line is obtained as the analysis starting point; Extract the endpoint corresponding to the generated early warning analysis signal as the analysis endpoint; Connect the analysis starting point and the analysis end point to obtain the analysis reference line, and fit it using the least squares method to obtain the linear regression model. , K1 is the slope value, is a constant.

7. A communication networking method for a wireless network card according to claim 6, characterized in that: The linear regression model is analyzed to obtain the fluctuation change type results. The process is as follows: Get all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, marked as ( , ), and substitute all coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve into The linear analysis coordinates are calculated ( , ); By formula: , calculate the fitted comparison value ; Intercept the overlap curve between the analysis fluctuation curve and the analysis reference line, and obtain the length of the overlap curve. Add the lengths of the overlap curves to obtain the overlap length, and calculate the ratio with the analysis reference line to obtain the overlap length value. ; Fitting the comparison value and overlap length Perform addition and summation to obtain the type analysis value; Obtain the type analysis values ​​corresponding to all test fluctuation periods within the test cycle, add them together and take the average to obtain the fluctuation change value; The fluctuation change value is compared with the fluctuation prediction threshold, and the process is as follows: If the fluctuation change value is greater than or equal to the fluctuation prediction threshold, a linear change signal is generated; If the fluctuation change value is less than the fluctuation prediction threshold, a nonlinear change signal is generated.

8. The communication networking method of a wireless network card according to claim 1, characterized in that: Based on the fluctuation change type results, the predicted change slope is obtained, and based on the predicted change slope, the prediction model is obtained. The process is as follows: When a nonlinear change signal is generated, the analysis fluctuation curve is divided into several analysis sub-curves, the sub-slopes corresponding to the analysis sub-curves are obtained, and the sizes are compared to select the maximum sub-slope as the maximum slope of the period; During the test period, the maximum slopes of all the analysis fluctuation curves corresponding to the time periods are compared, and the maximum slope of the time period is selected as the predicted change slope K2; When a linear change signal is generated, the analysis fluctuation curve is divided into several analysis sub-curves, the sub-slopes corresponding to the analysis sub-curves are obtained, and the sub-slopes are added and averaged to obtain the average slope of the time period; During the test period, the average values ​​of the slopes of the time periods corresponding to all the analyzed fluctuation curves are added and averaged to obtain the predicted change slope K2; Substitute the predicted change slope K2 into the linear regression model The prediction model is obtained , where K1 is the slope value, Expressed as a constant.

9. The communication networking method of a wireless network card according to claim 8, characterized in that: The prediction model is analyzed to determine the shortest over-bound time. The process is as follows: In the analysis fluctuation curve, taking the analysis end point as the starting point and based on the prediction model, a straight line is drawn that intersects with the signal threshold line, and the coordinates of the intersection point with the signal threshold line are obtained; Subtract the horizontal coordinate of the intersection point from the end point of the analysis fluctuation curve, take the absolute value, and get the shortest optimization time.

10. A communication networking system for a wireless network card, the system being used to implement the communication networking method for a wireless network card as claimed in any one of claims 1 to 9, characterized in that: include: Node fluctuation test module: During the test period, the network signal data of the nodes in the communication network obtained by the wireless signal strength tester is analyzed to obtain the signal fluctuation value. Based on the comparison result between the signal fluctuation value and the signal fluctuation threshold, the suspected nodes in the communication network are identified; Warning analysis and processing module: Analyze the historical data of suspected nodes in the historical period, output the node warning value, and obtain the warning signal based on the result of comparing the node warning value with the node warning threshold; Fluctuation type prediction module: Based on the warning signal, the fluctuation data of the warning node in the test period is analyzed to obtain the analysis fluctuation curve, the analysis fluctuation curve is analyzed and processed to obtain the type analysis value, and the type analysis value is analyzed and processed to output the fluctuation change type result; The fluctuation change type result includes a linear change signal or a nonlinear change signal; Optimization time determination module: Based on the fluctuation change type results, the predicted change slope is obtained, and based on the predicted change slope, the prediction model is obtained, the prediction model is analyzed, and the shortest over-boundary time is determined.

Images