A communication networking method and system for a wireless network card

Abstract

The present invention discloses a communication networking method and system for a wireless network card. During the test period, network signal data of nodes in the communication network are obtained through a wireless signal strength tester to obtain a signal fluctuation value. Based on the signal fluctuation value as a judgment basis, suspected nodes are determined. Then, the historical data of the suspected nodes in the historical period are analyzed to obtain a node warning value, so as to reflect the frequency of fluctuations and the smoothness between fluctuations of the suspected nodes in the historical period 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 situation of the nodes in the communication network.

Description

Technical Field

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

[0002] After a complex wireless communication network is networked, the signal strength of network nodes within the wireless communication network may be affected and fluctuate, thus affecting communication quality. Therefore, testing is required. Based on the test results and historical data, the network nodes that need to be warned are determined, and by analyzing the network nodes that need to be warned, the efficiency of network optimization and adjustment is improved.

[0003] In the prior art, it often relies on regular inspections or user feedback to discover the signal strength problems of network nodes, and when identifying node signals, it only compares them separately through thresholds, with relatively low accuracy. Therefore, the solution of this application tests the network signal strength of nodes and combines historical data analysis to evaluate the signal strength fluctuation of nodes within the communication network. At the same time, it analyzes the fluctuation change types of network nodes and correspondingly constructs a prediction model, which helps network optimization personnel adjust network signals in a timely manner and improve the efficiency of network optimization and adjustment. Summary of the Invention

[0004] The purpose 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 prior art problems.

[0005] In a first aspect, a communication networking method for a wireless network card includes:

[0006] Step 1: During a test period, analyze the network signal data of nodes within the communication network obtained by a wireless signal strength tester to obtain a signal fluctuation value. Based on the comparison result between the signal fluctuation value and a signal fluctuation threshold, identify suspected nodes within the communication network;

[0007] Step 2: Analyze the historical data of the suspected nodes within a historical period, output a node warning value, and obtain a warning signal based on the result after comparing the node warning value with a node warning threshold;

[0008] Step 3: Based on the warning signal, analyze the fluctuation data of the warning nodes during the test period to obtain an analysis fluctuation curve, perform analysis processing on the analysis fluctuation curve to obtain a type analysis value, and perform analysis processing based on the type analysis value to output a fluctuation change type result;

[0009] Among them, the fluctuation change type result includes a linear change signal or a non-linear change signal;

[0010] Step 4: Based on the result of the fluctuation change type, obtain the predicted change slope, and based on the predicted change slope, obtain a prediction model, analyze the prediction model, and determine the shortest overrun time.

[0011] In a second aspect, a communication networking system for a wireless network card includes:

[0012] Node fluctuation test module: During the test period, analyze the network signal data of the nodes in the communication network obtained by the wireless signal strength tester to obtain a signal fluctuation value, and identify suspected nodes in the communication network according to the comparison result between the signal fluctuation value and the signal fluctuation threshold;

[0013] Early warning analysis and processing module: Analyze the historical data of the suspected nodes in the historical period, output a node early warning value, and obtain an early warning signal based on the result of comparing the node early warning value with the node early warning threshold;

[0014] Fluctuation type prediction module: Based on the early warning signal, analyze the fluctuation data of the early warning nodes during the test period to obtain an analyzed fluctuation curve, analyze and process the analyzed fluctuation curve to obtain a type analysis value, and perform analysis and processing based on the type analysis value to output a fluctuation change type result;

[0015] Among them, the fluctuation change type result includes a linear change signal or a non-linear change signal;

[0016] Optimization time determination module: Based on the result of the fluctuation change type, obtain the predicted change slope, and based on the predicted change slope, obtain a prediction model, analyze the prediction model, and determine the shortest overrun time.

[0017] Advantages of the present invention:

[0018] 1. During the test period of the present invention, the network signal data of the nodes in the communication network is obtained through a wireless signal strength tester to obtain a signal fluctuation value. Based on the signal fluctuation value as a judgment basis, suspected nodes are determined, and then the historical data of the suspected nodes in the historical period is analyzed to obtain a node early warning value, so as to reflect the frequency of fluctuations and the smoothness between fluctuations of the suspected nodes in the historical period through the node early warning value. If the node early warning value is greater than the node early warning threshold, an early warning signal is generated, and the suspected node corresponding to the generated early warning signal is marked as an early warning node, thereby evaluating the signal strength fluctuation situation of the nodes in the communication network;

[0019] 2. Based on the warning signal, the present invention obtains the fluctuation data of the warning node within the test period, analyzes and processes it to obtain an analysis fluctuation curve. On the basis of the analysis fluctuation curve, fitting is performed to obtain a linear regression model. By combining the fitting comparison value and the coincidence length value, the difference degree between the analysis fluctuation curve and the linear regression model is reflected, and the linear degree of the analysis fluctuation curve corresponding to the test period is evaluated, which is beneficial to predicting the future fluctuation change trend;

[0020] 3. According to the result of the fluctuation change type, the present invention analyzes and processes the two fluctuation change types in a classified discussion manner, outputs and obtains a predicted change slope. Based on the predicted change slope, a prediction model is obtained. Through the analysis and processing of the prediction model, the shortest overrun time is output, thereby improving the accuracy of predicting the trend change of network nodes. And through the calculated shortest overrun time, network optimization measures are taken to solve the problem that the continuous increase of node signal fluctuation in the communication network leads to the decline of network communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of optimizing the network fluctuation of nodes in the communication network of a wireless network card provided by a communication networking method of a wireless network card of the present invention;

[0023] Figure 2 It is a schematic structural diagram of a communication networking system of a wireless network card of the present invention;

[0024] Figure 3 It is a schematic structural diagram of a device for a communication networking method of a wireless network card of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] Embodiment 1

[0027] Figure 1The flowchart of a communication networking method for a wireless network card provided in Embodiment 1 of the present invention. The embodiments of the present invention are applicable to the situation of optimizing network fluctuations in the node network of the communication networking of the wireless network card. This communication networking method for a wireless network card can be executed by a communication networking system for a wireless network card. This communication networking system for a wireless network card can be implemented by software and / or hardware, and this communication networking system for a wireless network card can be configured in a communication networking device for a wireless network card. Optionally, the communication networking device for a wireless network card can be an electronic device, and the electronic device can be a notebook, a desktop computer, a smart tablet, etc. The embodiments of the present invention do not limit this.

[0028] As Figure 1 shown, a communication networking method for a wireless network card provided in the embodiments of the present invention specifically includes the following steps:

[0029] Step 1: During the test period, obtain the network signal data of the nodes in the communication network through a wireless signal strength tester. Among them, the network signal data includes signal strength. Analyze the signal strength of the nodes in the communication network to obtain a signal fluctuation value and obtain suspected nodes.

[0030] In some embodiments, divide the test period into several test time periods, obtain the node signal strength during the test time period, and compare it with the signal strength warning value. The process is as follows:

[0031] If the node signal strength during the test time period is less than the signal strength warning value, that is, the node signal strength is weak during the analyzed test time period, mark the test time period with weak node signal strength as a signal fluctuation time period;

[0032] If the node signal strength during the test time period is greater than or equal to the signal strength warning value, that is, the node signal strength is normal during the analyzed test time period, mark the test time period with normal node signal strength as a signal non-fluctuation time period;

[0033] It should be noted that the test period is divided into test time periods in the way of equal time intervals, and the node signal strength is obtained by adding up the unit signal strengths corresponding to each test time point during the test time period;

[0034] Count the number of signal fluctuation time periods and calculate the ratio with the total number of test time periods in the test period to obtain the node fluctuation times;

[0035] Subtract the node signal strength corresponding to the signal fluctuation time 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;

[0036] Sum up the fluctuation deviation values corresponding to all node fluctuation periods and take the average to obtain the fluctuation degree value;

[0037] Exemplarily, if the signal strength range is -90 dbm and the test period is one day, the node fluctuation periods can be from 8:05 to 8:15 in the morning, from 11:00 to 11:10 at noon, and from 4:20 to 4:30 in the afternoon;

[0038] Among them, the signal strength from 8:05 to 8:15 in the morning is -50 dbm, the signal strength from 11:00 to 11:10 at noon is -60 dbm, and the signal strength from 4:20 to 4:30 in the afternoon is -55 dbm;

[0039] Therefore, for the signal strength of -50 dbm from 8:05 to 8:15 in the morning, |(-50 dBm) - (-90 dBm)| / (-90 dbm) = 0.444 (rounded to three decimal places), obtaining the fluctuation deviation value of 0.444. For the signal strength of -60 dbm from 11:00 to 11:10 at noon, |(-60 dBm) - (-90 dBm)| / (-90 dbm) = 0.333 (rounded to three decimal places), obtaining the fluctuation deviation value of 0.333. For the signal strength of -55 dbm from 4:20 to 4:30 in the afternoon, |(-55 dBm) - (-30 dBm)| / (-90 dbm) = 0.278 (rounded to three decimal places), obtaining the fluctuation deviation value of 0.278;

[0040] Calculate the values with the fluctuation deviation value of 0.222 from 8:05 to 8:15 in the morning, the fluctuation deviation value of 0.333 from 11:00 to 11:10 at noon, and the fluctuation deviation value of 0.278 from 4:20 to 4:30 in the afternoon, (0.222 + 0.333 + 0.278) / 3 = 0.278 (rounded to three decimal places);

[0041] Multiply the number of node fluctuations by the fluctuation degree value to obtain the signal fluctuation value;

[0042] Compare the signal fluctuation value with the signal fluctuation threshold, and the process is as follows:

[0043] If the signal fluctuation value is greater than the signal fluctuation threshold, the nodes in the communication network have a relatively high fluctuation frequency and a relatively large fluctuation degree during the test period, and are suspected nodes;

[0044] If the signal fluctuation value is less than or equal to the signal fluctuation threshold, the nodes in the communication network have a relatively low fluctuation frequency and a relatively small fluctuation degree during the test period, and are non-suspected nodes;

[0045] Step 2: Obtain the historical data of the suspected node within the historical period. The historical data includes historical fluctuation periods and historical non - fluctuation periods. Analyze the historical fluctuation periods to obtain the historical duration value and the historical quantity value. Process the historical duration value and the historical quantity value, and 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 to obtain the warning node;

[0046] In some embodiments, within the historical period, divide the historical period into several historical analysis periods, analyze the nodes within the historical analysis periods, and extract the historical fluctuation periods;

[0047] It should be noted that the way of dividing the historical period is the same as the way of dividing the test period, and the way of obtaining the historical fluctuation period is the same as the way of obtaining the signal fluctuation period, and the way of obtaining the historical non - fluctuation period is the same as the way of obtaining the signal non - fluctuation period;

[0048] Within the historical period, count the number of historical fluctuation periods, calculate the ratio with the total number of all periods within the historical period to obtain the historical quantity value, and mark it as ;

[0049] It should be noted that the total number of all periods within the historical period is obtained by summing up the number of historical fluctuation periods and the number of historical non - fluctuation periods;

[0050] Within the historical period, obtain the number of intervals between all historical fluctuation periods and the number of historical non - fluctuation periods within the intervals between all historical fluctuation periods, and mark them as the number of intervals of historical fluctuation periods and the number of interval historical non - fluctuation periods respectively;

[0051] Calculate the ratio of the number of interval historical non - fluctuation periods to the number of intervals of historical fluctuation periods to obtain the average fluctuation interval. Calculate the ratio of the average fluctuation interval to the total number of all periods within the historical period to obtain the historical duration value, and mark it as ;

[0052] Substitute the historical quantity value and the historical duration value into the formula: , calculate to obtain the node warning value , where 、 represent preset proportionality coefficients, and takes the value of 1.3446, takes the value of 3.689;

[0053] It can be explained that the meaning represented by the node warning value is as follows: By combining the proportion of the number of historical fluctuation periods and the duration of the non-fluctuation periods between historical fluctuation periods, it reflects the frequency of fluctuations and the smoothness between fluctuations within the historical cycle. If this value is larger, then within the historical cycle, the fluctuation periods are relatively frequent and the non-fluctuation periods between fluctuations are relatively short. On the contrary, if this value is smaller, then within the historical cycle, the fluctuation periods are relatively few and the non-fluctuation periods between fluctuations are relatively long;

[0054] Compare the node warning value with the node warning threshold, and the process is as follows:

[0055] If the node warning value is greater than the node warning threshold, then within the historical cycle, the fluctuation periods are relatively frequent and the non-fluctuation periods between fluctuations are relatively short, generate a warning signal, and mark the suspected node corresponding to the generated warning signal as a warning node;

[0056] If the node warning value is less than or equal to the node warning threshold, then within the historical cycle, the fluctuation periods are relatively few and the non-fluctuation periods between fluctuations are relatively long, generate a non-warning signal, and mark the suspected node corresponding to the generated non-warning signal as a non-warning node;

[0057] The specific implementation scheme of the embodiment of the present invention is: within the test cycle, obtain the network signal data of the nodes in the communication network through a wireless signal strength tester, obtain the signal fluctuation value, use the signal fluctuation value as the judgment basis to determine the suspected nodes, and then analyze the historical data of the suspected nodes within the historical cycle to obtain the node warning value, so as to reflect the frequency of fluctuations and the smoothness between fluctuations of the suspected nodes within the historical cycle through the node warning value. If the node warning value is greater than the node warning threshold, generate a warning signal, mark the suspected node corresponding to the generated warning signal as a warning node, and further evaluate the signal strength fluctuation situation of the nodes in the communication network.

[0058] Embodiment 2

[0059] As Figure 1 shown, on the basis of Embodiment 1, a communication networking method for a wireless network card described in the embodiment of the present invention includes:

[0060] Step 3: Based on the warning signal, obtain the fluctuation data of the warning nodes within the test cycle. Among them, the fluctuation data includes the fluctuation time points and the signal fluctuation values. Analyze and process the fluctuation time points and the signal fluctuation values to obtain the fluctuation change value, and compare the fluctuation change value with the fluctuation change threshold to obtain the fluctuation change type result;

[0061] Among them, the fluctuation change type result includes generating a linear change signal or generating a non-linear change signal;

[0062] In some embodiments, multiple test fluctuation periods within a test cycle are obtained, and any one of the test fluctuation periods is selected for analysis. The analysis process is as follows:

[0063] 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 representing time and the Y-axis representing the signal fluctuation value. The signal fluctuation values corresponding to all the fluctuation time points within the test fluctuation period are substituted into the two-dimensional coordinate system, and a fluctuation change curve is plotted;

[0064] Within the fluctuation change curve, based on the signal intensity warning value, punctuation is made on the Y-axis, and a straight line parallel to the X-axis is drawn, which intersects the fluctuation change curve to obtain a signal warning line;

[0065] Within the fluctuation change curve, based on the signal intensity threshold, punctuation is made on the Y-axis respectively, and a straight line parallel to the X-axis is drawn to obtain a signal threshold line;

[0066] It should be noted that the range of the fluctuation change curve does not exceed the signal threshold line;

[0067] The fluctuation change curve between the signal warning line and the signal threshold line is extracted and marked as the analysis fluctuation curve;

[0068] 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 taken with the signal intensity threshold to obtain a fluctuation proximity value;

[0069] The fluctuation proximity value is compared with the fluctuation proximity threshold. The process is as follows:

[0070] If the fluctuation proximity value is greater than or equal to the fluctuation proximity threshold, a warning analysis signal is generated;

[0071] If the fluctuation proximity value is less than the fluctuation proximity threshold, a continuous monitoring signal is generated until a warning analysis signal is generated for warning analysis operations;

[0072] Based on the warning analysis signal, the end point where the analysis fluctuation curve intersects the signal warning line is obtained as the analysis starting point;

[0073] The end point corresponding to the generated warning analysis signal is extracted as the analysis end point;

[0074] The analysis starting point and the analysis end point are connected to obtain an analysis reference line, which is fitted by the least squares method to obtain a linear regression model , where K1 is the slope value, is a constant;

[0075] All the coordinates between the analysis starting point and the analysis end point within the analysis fluctuation curve are obtained and marked as ( , ), and substitute all the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve into to calculate the linear analysis coordinates ( , );

[0076] Through the formula: , calculate the fitting comparison value , where represents all the Y values of the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, represents the predicted coordinate Y value of the linear regression model, represents the mean value of all the Y values of the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, and n is the total number of all the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve;

[0077] Among them, , calculate the mean value of all the Y values of the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, is all the Y values of the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve, and n is the total number of all the coordinates between the analysis start point and the analysis end point in the analysis fluctuation curve;

[0078] It should be noted that the value of

[0079] is between 0 and 1. The closer it is to 1, the better the fitting effect, that is, the comparison line is closer to linearity; ;

[0080] Intercept the overlapping curve between the analysis fluctuation curve and the analysis reference line, and obtain the length of the overlapping curve. Add up the lengths of the overlapping curves to get the overlapping length, and calculate the ratio with the analysis reference line to get the overlapping length value and the overlapping length to get the type analysis value;

[0081] Obtain the sum of the type analysis values corresponding to all the test fluctuation periods in the test cycle and take the mean value to get the fluctuation change value;

[0082] It can be understood that the meaning represented by the fluctuation change value is: it reflects the linear degree of the analysis fluctuation curve corresponding to the test fluctuation period in the test cycle. Specifically, if this value is larger, the linear degree of the analysis fluctuation curve corresponding to the test period in the test cycle is higher. On the contrary, if this value is smaller, the linear degree of the analysis fluctuation curve corresponding to the test period in the test cycle is lower, which is conducive to providing a reference basis for warning future fluctuation change trends;

[0083] Compare the fluctuation change value with the fluctuation prediction threshold, and the process is as follows:

[0084] If the fluctuation change value is greater than or equal to the fluctuation prediction threshold, it indicates that the linearity of the analyzed fluctuation curve corresponding to the test period in the test time period is relatively high, and a linear change signal is generated;

[0085] If the fluctuation change value is less than the fluctuation prediction threshold, it indicates that the linearity of the analyzed fluctuation curve corresponding to the test period in the test time period is relatively low, and a non-linear change signal is generated;

[0086] The specific implementation manner of the embodiment of the present invention is as follows: Based on the warning signal, obtain the fluctuation data of the warning node in the test period, and perform analysis and processing to obtain the analyzed fluctuation curve. On the basis of the analyzed fluctuation curve, perform fitting to obtain a linear regression model. The difference degree between the analyzed fluctuation curve and the linear regression model is reflected by combining the fitting comparison value and the coincidence length value, and the linearity of the analyzed fluctuation curve corresponding to the test period in the test time period is evaluated, so as to facilitate warning of future fluctuation change trends.

[0087] Embodiment III

[0088] As Figure 1 shown, on the basis of Embodiment 1 and Embodiment 2, a communication networking method for a wireless network card according to an embodiment of the present invention includes:

[0089] Step 4: Based on the result of the fluctuation change type, obtain the predicted change slope, and based on the predicted change slope, obtain a prediction model, and analyze the prediction model to determine the shortest overrun time, so as to solve the problem that the signal fluctuation of the nodes in the communication network continues to increase, resulting in a decline in network communication quality;

[0090] In some embodiments, when a non-linear change signal is generated, the analyzed fluctuation curve is divided into several analyzed sub-curves, the sub-slopes corresponding to the analyzed sub-curves are obtained, and size comparison is performed to screen out the maximum sub-slope as the maximum slope of the time period;

[0091] In the test period, perform size comparison on the maximum slopes of the time periods corresponding to all the analyzed fluctuation curves, and screen out the maximum maximum slope of the time period as the predicted change slope K2;

[0092] When a linear change signal is generated, the analyzed fluctuation curve is divided into several analyzed sub-curves, the sub-slopes corresponding to the analyzed sub-curves are obtained, and the sum is calculated and the mean value is taken to obtain the mean slope of the time period;

[0093] In the test period, sum up and take the mean value of the mean slopes of the time periods corresponding to all the analyzed fluctuation curves to obtain the predicted change slope K2;

[0094] It should be noted that the analysis of the fluctuation curve is divided in the way of equal time intervals to obtain the analysis sub-curves;

[0095] Substitute the predicted change slope K2 into the linear regression model to obtain the prediction model , where K1 represents the slope value, which is expressed as a constant;

[0096] The method for obtaining the shortest optimization time is as follows:

[0097] Within the analysis fluctuation curve, starting from the analysis end point and based on the prediction model, draw a straight line that intersects the signal threshold line, and obtain the intersection coordinates of the intersection with the signal threshold line;

[0098] Subtract the abscissa of the intersection point from the analysis end point of the analysis fluctuation curve, take the absolute value, and obtain the shortest optimization time;

[0099] The specific implementation scheme of the embodiment of the present invention is as follows: According to the result of the fluctuation change type, analyze and process the two types of fluctuation change types in the way of classification discussion, output the predicted change slope, based on the predicted change slope, obtain the prediction model, analyze and process through the prediction model, output the shortest overrun time, thereby improving the accuracy of the trend change of the predicted network node, and taking network optimization measures through the calculated shortest overrun time to solve the problem that the signal fluctuation of the node in the communication network continues to increase, resulting in the decline of the network communication quality.

[0100] Embodiment 4

[0101] As Figure 2 shown, a communication networking system of a wireless network card described in the embodiment of the present invention includes:

[0102] Node fluctuation test module: During the test period, analyze the network signal data of the nodes in the communication network obtained by the wireless signal strength tester to obtain the signal fluctuation value, and identify suspected nodes in the communication network according to the comparison result between the signal fluctuation value and the signal fluctuation threshold;

[0103] Early warning analysis and processing module: Analyze the historical data of the suspected nodes in the historical period, output the node early warning value, and obtain the early warning signal based on the result after comparing the node early warning value with the node early warning threshold;

[0104] Fluctuation type prediction module: Based on the early warning signal, analyze the fluctuation data of the early warning nodes during 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 result of the fluctuation change type;

[0105] Among them, the fluctuation change type results include linear change signals or non-linear change signals;

[0106] Optimized time determination module: Based on the fluctuation change type results, obtain the predicted change slope, and based on the predicted change slope, obtain a prediction model, analyze the prediction model, and determine the shortest overrun time.

[0107] Embodiment Five

[0108] Refer to Figure 3 , the embodiment of the present invention also provides a computer device 3, including: a memory 302, a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements a communication networking method for a wireless network card as described in any one of the above methods.

[0109] The computer device 3 may be a computing device such as a desktop computer, a notebook, a palm computer, and 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 can understand that

[0110] Figure 3 merely examples of the computer device 3, which do not constitute a limitation on the computer device 3, may include more or fewer components than shown in the figure, or combine some components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0111] The so-called processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0112] 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 Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 3. Further, the memory 302 may also include both the internal storage unit and the external storage device of the computer device 3. The memory 302 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as program codes of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0113] Embodiment Six

[0114] 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 run by a processor, it implements a communication networking method of a wireless network card as described in any one of the above methods.

[0115] 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 such an understanding, to implement all or part of the processes in the above embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. 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 the above various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium may not be an electrical carrier signal and a telecommunication signal.

[0116] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0117] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0118] In the embodiments disclosed in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to get the closest real situation. The preset parameters in the formula are set by technicians in this field according to the actual situation.

[0121] The above has described a detailed description of an embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered to be used to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent 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-bound time; The specific steps of obtaining the prediction model include: 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.

2. A communication networking method for a wireless network card according to claim 1, characterized in that: The signal fluctuation 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; Add the fluctuation deviation values ​​corresponding to all node fluctuation periods and take the average to obtain the fluctuation degree value; The signal fluctuation value is obtained by multiplying the number of node fluctuations by 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 ,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; 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 value 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: 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.

9. 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 8, 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.

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