CN114679738B - Network communication signal abnormity diagnosis and analysis method, equipment and storage medium - Google Patents

Network communication signal abnormity diagnosis and analysis method, equipment and storage medium Download PDF

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CN114679738B
CN114679738B CN202210298280.3A CN202210298280A CN114679738B CN 114679738 B CN114679738 B CN 114679738B CN 202210298280 A CN202210298280 A CN 202210298280A CN 114679738 B CN114679738 B CN 114679738B
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CN114679738A (en
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杨志
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China Highway Engineering Consultants Corp
CHECC Data Co Ltd
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China Highway Engineering Consultants Corp
CHECC Data Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H04W12/121—Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H04W12/121—Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]
    • H04W12/122—Counter-measures against attacks; Protection against rogue devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H04W12/128—Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic

Abstract

The invention discloses a network communication signal abnormity diagnosis and analysis method, equipment and a storage medium, wherein a network abnormity diagnosis platform is constructed and installed on a computer, when a user uses the computer to have wireless network communication signal abnormity, the user starts the network abnormity diagnosis platform to automatically diagnose the wireless network communication signal abnormity of the computer, and a diagnosis result is automatically displayed on a main interface of the network abnormity diagnosis platform after the diagnosis is finished, so that the intelligent automatic diagnosis of the wireless network communication signal abnormity of the computer is realized.

Description

Network communication signal abnormity diagnosis and analysis method, equipment and storage medium
Technical Field
The invention relates to the technical field of network signal abnormity diagnosis, in particular to a network communication signal abnormity diagnosis and analysis method, equipment and a storage medium.
Background
At present, the computer network technology is rapidly developing, and it is changing the study, life and work of people with irreplaceable trend, bringing more efficient and faster life style to people, however, with the continuous development of communication technology, the immobility of the traditional wired broadband network can not meet the desire of people to surf the internet anytime and anywhere, and more enterprises and personal users use wireless networks, and surfing the internet by using wireless networks becomes a trend.
Although the wireless network solves the defect that the limited broadband network cannot overcome, compared with the limited broadband network, the wireless network still has the defects, such as unstable network transmission performance quality, easy interference, poor safety and the like, and due to the defects, when a user uses a computer through the wireless network, the problem of network communication signal abnormality often occurs, wherein the most important problem is that a webpage cannot be accessed, and under the condition, the diagnosis of the computer wireless network communication signal abnormality becomes necessary work of the user.
At present, the method for computer users to diagnose the abnormality of the computer wireless network communication signals is basically manual diagnosis, such as checking the wireless network connection state, detecting a wireless network connection port, detecting the transmission speed of a wireless network and the like, on one hand, the diagnosis method requires that the users have certain computer network knowledge, but not all the users have certain computer network knowledge in the practical situation, for the small computer network Bai Lai, when the computer has the abnormality of the wireless network communication signals, blind diagnosis is easy to carry out, a large amount of diagnosis time is wasted, and the cause of the abnormality cannot be accurately diagnosed; on the other hand, the manual diagnosis mode can only diagnose the primary reasons causing the wireless network communication signal abnormality, such as the wireless network connection abnormality, the wireless network connection is not authorized, the wireless network transmission speed is too low, but because the wireless network has the problem of poor security, when the wireless network is used for downloading data, the wireless network communication signal abnormality is easily attacked by viruses, under the condition, the causes causing the wireless network communication signal abnormality are not only the primary reasons, but also can be caused by the poor security of the downloaded network data, and the manual diagnosis mode often cannot diagnose the primary reasons, so that the manual diagnosis mode has the defect of incomplete diagnosis, and the diagnosis failure rate is high.
Disclosure of Invention
In order to overcome the defects, the invention provides a network communication signal abnormity diagnosis and analysis method, equipment and a storage medium.
The purpose of the invention can be realized by the following technical scheme:
the first aspect of the present invention provides a method for diagnosing and analyzing network communication signal abnormality, including the following steps:
a, constructing a network abnormity diagnosis platform and installing the platform on a computer;
when the wireless network communication signal abnormality occurs when a user uses a computer, starting a network abnormality diagnosis platform by the user, and importing a wireless network communication signal abnormality interface on the network abnormality diagnosis platform;
when the network abnormity diagnosis platform receives a wireless network communication signal abnormity interface led in by a user, the network abnormity diagnosis platform carries out initial diagnosis of wireless network communication signal abnormity, wherein the flow of the initial diagnosis is as follows:
c-1, diagnosing the wireless network connection state according to the imported wireless network communication signal abnormal interface, identifying whether the wireless network connection state of the local machine is normal, if the wireless network connection state of the local machine is identified to be abnormal, taking the abnormal wireless network connection state as a preliminary diagnosis result, and if the wireless network connection state of the local machine is identified to be normal, executing the step C-2;
c-2, acquiring a mac address and a router management address configured by a local wireless network, performing wireless network authorization diagnosis, identifying whether the mac address configured by the local wireless network is authorized, if not, taking a wireless network authorization failure as a preliminary diagnosis result, and if the mac address is authorized, executing the step C-3;
c-3, acquiring signal transmission parameters of a local wireless network and network state parameters of a router, performing wireless network performance quality diagnosis, identifying whether the local wireless network has performance quality problems, if so, taking the poor wireless network performance quality as a preliminary diagnosis result, and if not, taking the no problem of the preliminary diagnosis as a preliminary diagnosis result;
obtaining a preliminary diagnosis result, further judging whether wireless network communication signal abnormity deep diagnosis is needed, and when the wireless network communication signal abnormity deep diagnosis is needed, carrying out wireless network communication signal abnormity deep diagnosis and obtaining a deep diagnosis result;
and E, when judging that the wireless network communication signal abnormity deep diagnosis is not needed, displaying the preliminary diagnosis result on a main interface of the network abnormity diagnosis platform, and when judging that the wireless network communication signal abnormity deep diagnosis is needed, displaying the deep diagnosis result on the main interface of the network abnormity diagnosis platform.
According to one implementation manner of the first aspect of the present invention, the diagnosis method for diagnosing the wireless network connection status in C-1 refers to the following steps:
c-1-1: extracting network connection subscript patterns from a wireless network communication signal abnormal interface;
c-1-2: comparing the extracted network connection subscript pattern with a corresponding network connection subscript pattern when the wireless network is connected, if the extracted network connection subscript pattern is not consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is not connected, identifying that the wireless network connection state of the local machine is abnormal, if the extracted network connection subscript pattern is consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is connected, and executing the step C-1-3:
c-1-3: and checking a network port interface of the local computer from the background, extracting state data of each network port from the network port interface, matching the state data with the state data corresponding to the network port in the normal connection state of the wireless network, identifying that the wireless network connection state of the local computer is abnormal if the matching fails, and identifying that the wireless network connection state of the local computer is abnormal if the matching succeeds.
According to a manner that can be realized by the first aspect of the present invention, the operation steps of the diagnosis method corresponding to the wireless network authorization diagnosis in C-2 are as follows:
c-2-1, accessing the router management interface from the background according to the router management address, and extracting the mac address which the router allows to access from the router management interface;
c-2-2, matching the mac address configured by the local wireless network with the mac address allowed to be accessed by the router, if the matching is successful, identifying that the mac address configured by the local wireless network is authorized, and if the matching is failed, identifying that the mac address configured by the local wireless network is unauthorized.
According to a manner that can be realized by the first aspect of the present invention, the local wireless network signal transmission parameters include a current network signal transmission rate and a current network signal strength, and the router network state parameters include a current device online number, a current input/output traffic difference, and a current ratio of multithreading software connection failures.
According to one implementation manner of the first aspect of the present invention, the diagnosis method for diagnosing the transmission performance quality of the wireless network in C-3 performs the following steps:
c-3-1, accessing a router management interface from a background according to the router management address, and extracting the current fastest download rate from the router management interface;
c-3-2, extracting the current network signal transmission rate from the local wireless network signal transmission parameters, comparing the current network signal transmission rate with the current fastest download rate, and calculating the local wireless network signal transmission rate quality index, wherein the calculation formula is
Figure BDA0003562515330000051
Eta is expressed as the quality index, V, of the signal transmission speed of the local wireless network max The current fastest download rate is shown, v is the current network signal transmission rate, and e is a natural constant;
c-3-3, extracting the current network signal strength from the signal transmission parameters of the local wireless network, comparing the current network signal strength with the preset network signal strength ranges corresponding to various network signal strength levels, and screening out the signal strength level of the local wireless network from the network signal strength ranges, wherein the signal strength level is marked as K;
c-3-4, importing the router network state parameters into a router network quality index calculation formula to obtain the router network quality index, wherein the router network quality index calculation formula is
Figure BDA0003562515330000052
Figure BDA0003562515330000053
Expressed as the network quality index of the router, lambda 1, lambda 2 and lambda 3 are respectively expressed as the current online quantity of equipment, the current input/output flow difference and the current ratio of the multithreading software connection failure occupation, lambda 1' is expressed as the online optimal quantity of the equipment, and an e tableShown as natural constants;
c-3-5, comparing the network quality index of the router with the preset network quality index ranges corresponding to the network transmission quality grades of various routers, and screening out the network transmission quality grade corresponding to the wireless network signal of the router from the router network quality index ranges, and marking the router network transmission quality grade as M;
c-3-6, counting the performance quality coefficient of the local wireless network based on the high quality index of the signal transmission speed of the local wireless network, the signal intensity level of the local wireless network and the network transmission quality level of the router corresponding to the local wireless network signal, wherein the calculation formula is
Figure BDA0003562515330000061
Q is expressed as the local wireless network performance quality coefficient, f 0 Expressed as a set constant;
and C-3-7, comparing the performance quality coefficient of the local wireless network with a predefined standard wireless network performance quality coefficient, if the performance quality coefficient of the local wireless network is greater than or equal to the standard wireless network performance quality coefficient, identifying that no wireless network performance quality problem exists, otherwise, identifying that the wireless network performance quality problem exists.
According to a manner that can be implemented in the first aspect of the present invention, the determination method for determining whether to perform the deep diagnosis of the wireless network communication signal abnormality corresponds to determining that the deep diagnosis of the wireless network communication signal abnormality is not required when the preliminary diagnosis result indicates that the wireless network connection state is abnormal or the wireless network authorization fails or the quality of the wireless network transmission performance is not good, and determining that the deep diagnosis of the wireless network communication signal abnormality is required when the preliminary diagnosis result indicates that there is no problem in the preliminary diagnosis.
According to an implementable manner of the first aspect of the present invention, the diagnostic method for diagnosing the abnormality of the wireless network communication signal in depth includes:
the first step is as follows: recording the time point of leading a user into the wireless network communication signal abnormal interface, and taking the time point as the time point of finishing the extraction of the network data;
the second step is that: acquiring a network data extraction starting time point according to a set extraction time interval and a network data extraction finishing time point;
the third step: extracting various historical network data downloaded from a background machine based on a network data extraction starting time point and a network data extraction finishing time point, numbering the various historical network data according to the sequence of the downloading time points, and marking the various historical network data as 1,2, a.
The fourth step: analyzing the data type of each extracted historical network data to obtain the data type corresponding to each historical network data, comparing the data type with the set influence weight factors corresponding to each data type, and obtaining the influence weight factor corresponding to each historical network data from the data type, and marking the influence weight factor as omega i ；
The fifth step: based on the data type corresponding to each historical network data, importing the data type into a download risk coefficient calculation model corresponding to the corresponding data type network data, calculating the download risk coefficient corresponding to each historical network data and marking as mu i ；
And a sixth step: according to the download risk coefficient and the influence weight factor corresponding to each historical network data, the comprehensive risk coefficient corresponding to the downloaded network data of the local computer is counted, and the calculation formula is
Figure BDA0003562515330000071
Chi is expressed as the comprehensive danger coefficient, mu, corresponding to the local download network data i 、ω i Respectively representing the downloading risk coefficient and the influence weight factor corresponding to the ith historical network data;
the seventh step: and comparing the comprehensive risk coefficient corresponding to the downloaded network data of the local machine with the maximum allowable value of the comprehensive risk coefficient for downloading the network data, if the comprehensive risk coefficient corresponding to the downloaded network data of the local machine is greater than the maximum allowable value of the comprehensive risk coefficient for downloading the network data, judging that the downloaded network data of the local machine has potential safety hazard and taking the potential safety hazard as a deep diagnosis result, otherwise, judging that the downloaded network data of the local machine does not have the potential safety hazard and taking the downloaded network data as the deep diagnosis result.
According to one possible implementation of the first aspect of the invention, the data types include a program type and a file type.
A second aspect of the present invention provides an apparatus, including a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; when the processor runs, the processor calls the computer program from the nonvolatile memory through the network interface and runs the computer program through the memory so as to execute the network communication signal abnormity diagnosis and analysis method.
A third aspect of the present invention provides a storage medium, where a computer program is recorded in the storage medium, and when the computer program runs in a memory of a server, the method for diagnosing and analyzing an abnormality of a network communication signal according to the present invention is implemented.
Based on the above, the invention has the advantages that:
1. the invention constructs the network abnormity diagnosis platform, installs the network abnormity diagnosis platform on the computer, when the user uses the computer to generate wireless network communication signal abnormity, the user starts the network abnormity diagnosis platform to automatically diagnose the computer wireless network communication signal abnormity, and automatically displays the diagnosis result on the main interface of the network abnormity diagnosis platform after the diagnosis is finished, thereby realizing the intelligent automatic diagnosis of the computer wireless network communication signal abnormity.
2. In the process of diagnosing the computer wireless network communication signal abnormality through the network abnormality diagnosis platform, the invention firstly carries out preliminary diagnosis and carries out deep diagnosis when no problem exists in the preliminary diagnosis, so that diagnosis of various reasons of the computer wireless network communication signal abnormality is included, the comprehensive diagnosis of the computer wireless network communication signal abnormality is realized, the defect of incomplete diagnosis in a manual diagnosis mode is effectively avoided, and the diagnosis success rate is further improved.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1, a first aspect of the present invention provides a method for diagnosing and analyzing network communication signal abnormality, including the following steps:
a, constructing a network abnormity diagnosis platform and installing the platform on a computer;
in the embodiment, the network abnormity diagnosis platform is constructed and installed on the computer, when the wireless network communication signal abnormity occurs when a user uses the computer, the network abnormity diagnosis platform is started by the user to automatically diagnose the wireless network communication signal abnormity of the computer, and the diagnosis result is automatically displayed on the main interface of the network abnormity diagnosis platform after the diagnosis is finished, so that the intelligent automatic diagnosis of the wireless network communication signal abnormity of the computer is realized, on one hand, the diagnosis efficiency and the diagnosis accuracy are greatly improved, on the other hand, the method is suitable for all computer users, particularly, the computer network is small and white, and has the characteristic of strong practicability;
when the wireless network communication signal abnormality occurs when a user uses a computer, starting a network abnormality diagnosis platform by the user, and importing a wireless network communication signal abnormality interface on the network abnormality diagnosis platform;
c, when the network abnormity diagnosis platform receives a wireless network communication signal abnormity interface led in by a user, performing initial diagnosis on the wireless network communication signal abnormity, identifying whether the connection state of the wireless network of the local machine is normal, if the connection state of the wireless network of the local machine is identified to be abnormal, taking the abnormal connection state of the wireless network as an initial diagnosis result, and if the connection state of the wireless network of the local machine is identified to be normal, executing the step C-2;
the flow of the preliminary diagnosis is as follows:
c-1, diagnosing the wireless network connection state according to the imported wireless network communication signal abnormal interface, wherein the diagnosis method comprises the following steps:
c-1-1: extracting network connection subscript patterns from a wireless network communication signal abnormal interface;
c-1-2: comparing the extracted network connection subscript pattern with a corresponding network connection subscript pattern when the wireless network is connected, if the extracted network connection subscript pattern is not consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is not connected, identifying that the wireless network connection state of the local machine is abnormal, if the extracted network connection subscript pattern is consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is connected, and executing the step C-1-3:
c-1-3: checking a network port interface of the local computer from a background, extracting state data of each network port from the network port interface, matching the state data with the state data corresponding to the network port in the normal connection state of the wireless network, if the matching fails, identifying that the wireless network connection state of the local computer is abnormal, and if the matching succeeds, identifying that the wireless network connection state of the local computer is abnormal;
illustratively, the state data corresponding to the network port in the normal connection state of the wireless network is ESTABLISHED to indicate that the connection is ESTABLISHED, and the state data corresponding to the network port in the abnormal connection state of the wireless network is CLOSE _ WAIT to indicate that the network is abnormal;
c-2, acquiring a mac address and a router management address configured by a local wireless network, performing wireless network authorization diagnosis, identifying whether the mac address configured by the local wireless network is authorized, if not, taking a wireless network authorization failure as a preliminary diagnosis result, and if the identification is authorized, executing the step C-3, wherein the wireless network authorization diagnosis method comprises the following corresponding operation steps:
c-2-1, accessing the router management interface from the background according to the router management address, and extracting the mac address which the router allows to access from the router management interface;
c-2-2, matching the mac address configured by the local wireless network with the mac address allowed to be accessed by the router, if the matching is successful, identifying that the mac address configured by the local wireless network is authorized, and if the matching is failed, identifying that the mac address configured by the local wireless network is not authorized;
c-3, acquiring local wireless network signal transmission parameters and router network state parameters, wherein the local wireless network signal transmission parameters comprise the current network signal transmission rate and the current network signal strength, the router network state parameters comprise the current equipment on-line number, the current input/output flow difference and the current multithreading software connection failure ratio, performing wireless network performance quality diagnosis, identifying whether the local wireless network has a performance quality problem, if so, taking the wireless network performance quality problem as a preliminary diagnosis result, and if not, taking the preliminary diagnosis problem as a preliminary diagnosis result;
it should be noted that the source of acquiring the network state parameter of the router is acquired from a router management interface, and the specific acquiring method is as follows:
counting the current online number of the equipment from a router management interface;
extracting current input flow and current output flow from a router management interface, comparing the current input flow with the current output flow, and calculating the current input and output flow difference degree, wherein the calculation formula is
Figure BDA0003562515330000111
The larger the difference between the current input flow and the current output flow is, the larger the difference degree of the current input flow and the current output flow is;
extracting the total connection frequency of the current multithreading software and the connection failure frequency of the current multithreading software from the router management interface so as to calculate the connection failure occupation ratio of the current multithreading software, wherein the calculation formula is
Figure BDA0003562515330000121
The more frequent the connection failure of the current multi-thread software is, the larger the ratio of the connection failure of the current multi-thread software is;
the diagnosis method corresponding to the wireless network transmission performance quality diagnosis executes the following steps:
c-3-1, accessing a router management interface from a background according to the router management address, and extracting the current fastest download rate from the router management interface;
c-3-2, extracting the current network signal transmission rate from the local wireless network signal transmission parameters, comparing the current network signal transmission rate with the current fastest download rate, and calculating the local wireless network signal transmission rate quality index, wherein the calculation formula is
Figure BDA0003562515330000122
Eta is expressed as quality index of signal transmission speed of local wireless network, V max The method comprises the steps of expressing the current fastest download rate, expressing v as the current network signal transmission rate, and expressing e as a natural constant, wherein the closer the current network signal transmission rate is to the current fastest download rate, the larger the quality index of the local wireless network signal transmission speed is;
c-3-3, extracting the current network signal strength from the local wireless network signal transmission parameters, comparing the current network signal strength with the preset network signal strength ranges corresponding to various network signal strength levels, screening the local wireless network signal strength level from the network signal strength ranges, and marking the local wireless network signal strength level as K, wherein the higher the level is, the stronger the wireless network signal is;
c-3-4, importing the router network state parameters into a router network quality index calculation formula to obtain the router network quality index, wherein the router network quality index calculation formula is
Figure BDA0003562515330000131
Figure BDA0003562515330000132
Expressed as the router network quality index, λ 1, λ 2, λ 3Respectively representing the current online quantity of equipment, the current input/output flow difference and the current ratio of the multithreading software connection failure, wherein lambda 1' represents the online optimal quantity of the equipment, and e represents a natural constant;
preferably, in the above calculation formula of the network quality index of the router, the smaller the number of online devices, the smaller the difference between the current input and output flows, the smaller the ratio of the current multi-thread software connection failure occupation, and the larger the network quality index of the router;
c-3-5, comparing the network quality index of the router with the network quality index ranges of the routers corresponding to the preset network transmission quality grades of various routers, screening out the network transmission quality grade of the router corresponding to the local wireless network signal from the network quality index ranges of the routers, and marking the network transmission quality grade as M, wherein the larger the network quality index of the router is, the higher the network transmission quality grade of the router corresponding to the local wireless network signal is;
c-3-6, counting the performance quality coefficient of the local wireless network based on the high quality index of the signal transmission speed of the local wireless network, the signal intensity level of the local wireless network and the network transmission quality level of the router corresponding to the local wireless network signal, wherein the calculation formula is
Figure BDA0003562515330000133
Q is expressed as the local wireless network performance quality coefficient, f 0 Expressed as a set constant;
preferably, in the local wireless network performance quality coefficient calculation formula, the larger the quality index of the local wireless network signal transmission speed is, the higher the local wireless network signal strength level is, the higher the router network transmission quality level corresponding to the local wireless network signal is, and the larger the local wireless network performance quality coefficient is;
in a specific embodiment, the local wireless network signal transmission parameter and the router network state parameter are comprehensively considered in the calculation of the local wireless network performance quality coefficient, because the local wireless network signal is transmitted through the router, the router network state parameter determines the network quality condition corresponding to the router, and the network quality condition of the router can indirectly influence the local wireless network performance quality, under the condition, if the local wireless network signal transmission parameter is simply used as the basis for calculating the local wireless network performance quality coefficient, the calculation result is one-sided, and further, an accurate and reliable diagnosis basis is difficult to provide for the local wireless network performance quality diagnosis;
c-3-7, comparing the performance quality coefficient of the local wireless network with a predefined standard wireless network performance quality coefficient, if the performance quality coefficient of the local wireless network is greater than or equal to the standard wireless network performance quality coefficient, identifying that no wireless network performance quality problem exists, otherwise, identifying that the wireless network performance quality problem exists;
obtaining a preliminary diagnosis result, and further judging whether wireless network communication signal abnormity deep diagnosis needs to be carried out, wherein the judging mode is that when the preliminary diagnosis result is that the wireless network connection state is abnormal or the wireless network authorization fails or the wireless network transmission performance quality is not good, the wireless network communication signal abnormity deep diagnosis does not need to be carried out, when the preliminary diagnosis result is that the preliminary diagnosis is not problematic, the wireless network communication signal abnormity deep diagnosis needs to be carried out, and at the moment, the wireless network communication signal abnormity deep diagnosis is carried out, and a deep diagnosis result is obtained;
the diagnosis method corresponding to the wireless network communication signal abnormity deep diagnosis comprises the following steps:
the first step is as follows: recording a time point when a user imports the wireless network communication signal abnormal interface, and taking the time point as a network data extraction finishing time point;
the second step is that: subtracting the set extraction time interval from the network data extraction ending time point to obtain a network data extraction starting time point;
the third step: extracting each historical network data downloaded from the background based on the network data extraction starting time point and the network data extraction ending time point, numbering each historical network data according to the sequence of the downloading time points, and sequentially marking the historical network data as 1,2, a.
The fourth step: performing data type analysis on each extracted historical network data, and analyzing party thereofThe formula is a storage form in a computer according to each historical network data, wherein the data types comprise program types and file types, the data types corresponding to the historical network data are obtained, the data types are compared with the set influence weight factors corresponding to various data types, and the influence weight factors corresponding to the historical network data are obtained from the data types and are marked as omega i Wherein the influence weight factor corresponding to the program type is greater than the influence weight factor corresponding to the file type;
the fifth step: based on the data type corresponding to each historical network data, importing the data type into a download risk coefficient calculation model corresponding to the corresponding data type network data, calculating the download risk coefficient corresponding to each historical network data and marking as mu i ；
The operation steps of the download risk coefficient calculation model corresponding to the intermediate program type network data are as follows:
reading a downloading source link address corresponding to the program type network data from a background, and extracting website domain name characteristics corresponding to the downloading source link address;
matching the extracted website domain name features with preset dangerous website domain name features, recording a downloading danger coefficient corresponding to program type network data as epsilon if the website domain name features corresponding to certain program type network data are successfully matched with the dangerous website domain name features, and otherwise recording a downloading danger coefficient corresponding to the program type network data as epsilon ', wherein epsilon is more than epsilon';
the operation steps of the download risk coefficient calculation model corresponding to the file type network data are as follows:
extracting an extension name corresponding to the file type network data from a background, judging whether the extension name is a double extension name, recording a downloading risk coefficient corresponding to the file type network data as sigma if the extension name corresponding to the certain file type network data is judged to be the double extension name, otherwise recording the downloading risk coefficient corresponding to the file type network data as sigma ', wherein the sigma is more than sigma';
and a sixth step: according to the download danger coefficient and the influence weight factor corresponding to each historical network data, statistics is carried out on the statistics of the download network data corresponding to the local computerThe comprehensive risk coefficient is calculated by the formula
Figure BDA0003562515330000161
Chi is expressed as the comprehensive danger coefficient, mu, corresponding to the local download network data i 、ω i Respectively representing a downloading risk coefficient and an influence weight factor corresponding to ith historical network data, wherein when the data type corresponding to certain historical network data is a program type, the downloading risk coefficient value corresponding to the historical network data is epsilon or epsilon ', and when the data type corresponding to certain historical network data is a file type, the downloading risk coefficient value corresponding to the historical network data is sigma or sigma';
the seventh step: comparing the comprehensive risk coefficient corresponding to the downloaded network data of the local machine with the maximum allowable value of the comprehensive risk coefficient for downloading the network data, if the comprehensive risk coefficient corresponding to the downloaded network data of the local machine is greater than the maximum allowable value of the comprehensive risk coefficient for downloading the network data, judging that the downloaded network data of the local machine has potential safety hazard and using the potential safety hazard as a deep diagnosis result, otherwise, judging that the downloaded network data of the local machine does not have the potential safety hazard and using the potential safety hazard as the deep diagnosis result;
and E, when judging that the wireless network communication signal abnormity deep diagnosis is not needed, displaying the preliminary diagnosis result on a main interface of the network abnormity diagnosis platform, and when judging that the wireless network communication signal abnormity deep diagnosis is needed, displaying the deep diagnosis result on the main interface of the network abnormity diagnosis platform.
In the embodiment of the invention, the preliminary diagnosis is firstly carried out in the process of carrying out the computer wireless network communication signal abnormality diagnosis through the network abnormality diagnosis platform, and the deep diagnosis is carried out when no problem exists in the preliminary diagnosis, so that the diagnosis of various reasons of the computer wireless network communication signal abnormality is included, the comprehensive diagnosis of the computer wireless network communication signal abnormality is realized, the defect of incomplete diagnosis in a manual diagnosis mode is effectively avoided, and the diagnosis success rate is further improved.
A second aspect of the present invention provides an apparatus, including a processor, and a memory and a network interface connected to the processor; the network interface is connected with a nonvolatile memory in the server; when the processor runs, the processor calls the computer program from the nonvolatile memory through the network interface and runs the computer program through the memory so as to execute the network communication signal abnormity diagnosis and analysis method.
A third aspect of the present invention provides a storage medium, where a computer program is burned in the storage medium, and when the computer program runs in a memory of a server, the method for diagnosing and analyzing an abnormality of a network communication signal according to the present invention is implemented.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (9)

1. A network communication signal abnormity diagnosis and analysis method is characterized by comprising the following steps:
a, constructing a network abnormity diagnosis platform and installing the platform on a computer;
when the wireless network communication signal abnormality occurs when a user uses a computer, starting a network abnormality diagnosis platform by the user, and importing a wireless network communication signal abnormality interface on the network abnormality diagnosis platform;
when the network abnormity diagnosis platform receives a wireless network communication signal abnormity interface led in by a user, the network abnormity diagnosis platform carries out initial diagnosis of wireless network communication signal abnormity, wherein the flow of the initial diagnosis is as follows:
c-1, diagnosing the wireless network connection state according to the imported wireless network communication signal abnormal interface, identifying whether the wireless network connection state of the local machine is normal, if the wireless network connection state of the local machine is identified to be abnormal, taking the abnormal wireless network connection state as a preliminary diagnosis result, and if the wireless network connection state of the local machine is identified to be normal, executing the step C-2;
c-2, acquiring a mac address and a router management address configured by a local wireless network, performing wireless network authorization diagnosis, identifying whether the mac address configured by the local wireless network is authorized, if not, taking a wireless network authorization failure as a preliminary diagnosis result, and if the mac address is authorized, executing the step C-3;
c-3, acquiring signal transmission parameters of a local wireless network and network state parameters of a router, performing wireless network performance quality diagnosis, identifying whether the local wireless network has performance quality problems, if so, taking the poor wireless network performance quality as a preliminary diagnosis result, and if not, taking the no problem of the preliminary diagnosis as a preliminary diagnosis result;
obtaining a preliminary diagnosis result, further judging whether the wireless network communication signal abnormity deep diagnosis is needed, and when the wireless network communication signal abnormity deep diagnosis is judged to be needed, carrying out the wireless network communication signal abnormity deep diagnosis and obtaining a deep diagnosis result;
when the wireless network communication signal abnormity deep diagnosis is not needed, displaying the preliminary diagnosis result on a main interface of a network abnormity diagnosis platform, and when the wireless network communication signal abnormity deep diagnosis is needed, displaying the deep diagnosis result on the main interface of the network abnormity diagnosis platform;
the diagnosis method corresponding to the wireless network authorization diagnosis in the C-2 comprises the following operation steps:
c-2-1, accessing the router management interface from the background according to the router management address, and extracting the mac address which the router allows to access from the router management interface;
c-2-2, matching the mac address configured by the local wireless network with the mac address which the router allows to access, if the matching is successful, identifying that the mac address configured by the local wireless network is authorized, and if the matching is failed, identifying that the mac address configured by the local wireless network is unauthorized.
2. The method according to claim 1, wherein the method comprises the following steps: the diagnosis method corresponding to the diagnosis of the wireless network connection state in the C-1 is as follows:
c-1-1: extracting network connection subscript patterns from a wireless network communication signal abnormal interface;
c-1-2: comparing the extracted network connection subscript pattern with a corresponding network connection subscript pattern when the wireless network is connected, if the extracted network connection subscript pattern is not consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is not connected, identifying that the wireless network connection state of the local machine is abnormal, if the extracted network connection subscript pattern is consistent with the corresponding network connection subscript pattern when the wireless network is connected, indicating that the wireless network is connected, and executing the step C-1-3:
c-1-3: and checking a network port interface of the local computer from the background, extracting state data of each network port from the network port interface, matching the state data with the state data corresponding to the network port in the normal connection state of the wireless network, identifying that the wireless network connection state of the local computer is abnormal if the matching fails, and identifying that the wireless network connection state of the local computer is abnormal if the matching succeeds.
3. The method according to claim 1, wherein the method comprises the following steps: the local wireless network signal transmission parameters comprise the current network signal transmission rate and the current network signal strength, and the router network state parameters comprise the current equipment online number, the current input/output flow difference and the current multithreading software connection failure ratio.
4. The method according to claim 1, wherein the method comprises the following steps: the diagnosis method corresponding to the diagnosis of the transmission performance quality of the wireless network in the C-3 comprises the following steps:
c-3-1, accessing a router management interface from a background according to the router management address, and extracting the current fastest download rate from the router management interface;
c-3-2, extracting the current network signal transmission rate from the local wireless network signal transmission parameters, comparing the current network signal transmission rate with the current fastest download rate, and calculating the local wireless network signal transmission rate quality index, wherein the calculation formula is
Figure FDA0003890305520000031
Eta is expressed as the quality index, V, of the signal transmission speed of the local wireless network max The current fastest download rate is shown, v is the current network signal transmission rate, and e is a natural constant;
c-3-3, extracting the current network signal strength from the signal transmission parameters of the local wireless network, comparing the current network signal strength with the preset network signal strength ranges corresponding to various network signal strength levels, and screening out the signal strength level of the local wireless network from the network signal strength ranges, wherein the signal strength level is marked as K;
c-3-4, importing the router network state parameters into a router network quality index calculation formula to obtain the router network quality index, wherein the router network quality index calculation formula is
Figure FDA0003890305520000041
Figure FDA0003890305520000042
The method comprises the following steps of (1) representing the network quality index of the router, wherein lambda 1, lambda 2 and lambda 3 respectively represent the online quantity of current equipment, the current input and output flow difference and the current ratio of the multithreading software connection failure, lambda 1' represents the online optimal quantity of the equipment, and e represents a natural constant;
c-3-5, comparing the network quality index of the router with the preset network quality index ranges corresponding to the network transmission quality grades of various routers, and screening out the network transmission quality grade corresponding to the wireless network signal of the router from the router network quality index ranges, and marking the router network transmission quality grade as M;
c-3-6, counting the performance quality coefficient of the local wireless network based on the high quality index of the signal transmission speed of the local wireless network, the signal intensity level of the local wireless network and the network transmission quality level of the router corresponding to the local wireless network signal, wherein the calculation formula is
Figure FDA0003890305520000043
Q is expressed as the local wireless network performance quality coefficient，f 0 Expressed as a set constant;
and C-3-7, comparing the performance quality coefficient of the local wireless network with a predefined standard wireless network performance quality coefficient, if the performance quality coefficient of the local wireless network is greater than or equal to the standard wireless network performance quality coefficient, identifying that no wireless network performance quality problem exists, otherwise, identifying that the wireless network performance quality problem exists.
5. The method for diagnosing and analyzing the abnormality of the network communication signal according to claim 1, wherein: the judgment mode corresponding to the judgment of whether the wireless network communication signal abnormity deep diagnosis needs to be carried out is to judge that the wireless network communication signal abnormity deep diagnosis is not needed when the initial diagnosis result is that the wireless network connection state is abnormal or the wireless network authorization fails or the wireless network transmission performance quality is not good, and judge that the wireless network communication signal abnormity deep diagnosis needs to be carried out when the initial diagnosis result is that the initial diagnosis is not problematic.
6. The method according to claim 1, wherein the method comprises the following steps: the diagnosis method corresponding to the wireless network communication signal abnormity deep diagnosis comprises the following steps:
the first step is as follows: recording a time point when a user imports the wireless network communication signal abnormal interface, and taking the time point as a network data extraction finishing time point;
the second step is that: acquiring a network data extraction starting time point according to a set extraction time interval and a network data extraction finishing time point;
the third step: extracting each historical network data downloaded from the background based on the network data extraction starting time point and the network data extraction ending time point, numbering each historical network data according to the sequence of the downloading time points, and sequentially marking the historical network data as 1,2, a.
The fourth step: analyzing the data type of each extracted historical network data to obtain the data type corresponding to each historical network data, and comparing the data type with the set various data typesComparing the corresponding influence weight factors, and acquiring the influence weight factors corresponding to the historical network data from the corresponding influence weight factors, which are marked as omega i ；
The fifth step: based on the data type corresponding to each historical network data, importing the data type into a download risk coefficient calculation model corresponding to the corresponding data type network data, calculating the download risk coefficient corresponding to each historical network data and marking as mu i ；
And a sixth step: according to the download risk coefficient and the influence weight factor corresponding to each historical network data, the comprehensive risk coefficient corresponding to the downloaded network data of the local computer is counted, and the calculation formula is
Figure FDA0003890305520000051
Chi is expressed as the comprehensive danger coefficient, mu, corresponding to the local download network data i 、ω i Respectively representing the downloading risk coefficient and the influence weight factor corresponding to the ith historical network data;
the seventh step: and comparing the comprehensive risk coefficient corresponding to the downloaded network data of the local machine with the maximum allowable value of the comprehensive risk coefficient for downloading the network data, if the comprehensive risk coefficient corresponding to the downloaded network data of the local machine is greater than the maximum allowable value of the comprehensive risk coefficient for downloading the network data, judging that the downloaded network data of the local machine has potential safety hazard and taking the potential safety hazard as a deep diagnosis result, otherwise, judging that the downloaded network data of the local machine does not have the potential safety hazard and taking the downloaded network data as the deep diagnosis result.
7. The method according to claim 6, wherein the method comprises the following steps: the data types include a program type and a file type.
8. A communication device, characterized by: the device comprises a processor, a memory and a network interface, wherein the memory and the network interface are connected with the processor; the network interface is connected with a nonvolatile memory in the server; the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-7.
9. A storage medium, characterized by: the storage medium is burned with a computer program, which when run in the memory of the server implements the method of any of the above claims 1-7.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107567025A (en) * 2017-09-14 2018-01-09 宁波大红鹰学院 WLAN accesses management system and method based on stored value card consumption mode
CN108092970A (en) * 2017-12-13 2018-05-29 腾讯科技（深圳）有限公司 A kind of wireless network maintaining method and its equipment, storage medium, terminal
CN108900351A (en) * 2018-07-13 2018-11-27 中国科学院信息工程研究所 The recognition methods of Intranet device type and device
CN111526529A (en) * 2020-04-30 2020-08-11 维沃移动通信有限公司 Network prompting method and device and electronic equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107567025A (en) * 2017-09-14 2018-01-09 宁波大红鹰学院 WLAN accesses management system and method based on stored value card consumption mode
CN108092970A (en) * 2017-12-13 2018-05-29 腾讯科技（深圳）有限公司 A kind of wireless network maintaining method and its equipment, storage medium, terminal
CN108900351A (en) * 2018-07-13 2018-11-27 中国科学院信息工程研究所 The recognition methods of Intranet device type and device
CN111526529A (en) * 2020-04-30 2020-08-11 维沃移动通信有限公司 Network prompting method and device and electronic equipment

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