CN113965487B - Fault diagnosis system based on network flow data - Google Patents
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Abstract
The invention discloses a fault diagnosis system based on network traffic data, which relates to the technical field of network traffic data diagnosis, and solves the technical problem that the stability of traffic data transmission is reduced because network traffic data cannot be detected in the prior art, the traffic information of a network is analyzed in real time through a data diagnosis unit, so that data diagnosis is carried out on the network traffic, a diagnosis coefficient Xi of the traffic data is obtained through a formula, if the diagnosis coefficient Xi of the traffic data is more than or equal to a diagnosis coefficient threshold value of the traffic data, the corresponding traffic data is judged to be abnormal, a traffic data abnormal signal is generated, the traffic data is marked as abnormal traffic data, and then the traffic data abnormal signal and the corresponding abnormal traffic data are sent to a data management platform; the network flow data is detected, so that the stability of flow data transmission is improved, the frequency of network faults is reduced, and the use quality of users is improved.
Description
Technical Field
The invention relates to the technical field of network traffic data diagnosis, in particular to a fault diagnosis system based on network traffic data.
Background
With the continuous acceleration of the social development process, the computer information technology is rapidly developed, and the number of broadband users of the private network of the communication company is also continuously increased. In order to ensure higher speed and reliability of broadband connection of users, the reasonable application of the existing network resources is realized aiming at different broadband access modes developed by different types of users.
The research on computer network flow anomaly detection and prediction comprises timely finding out network flow anomaly condition, analyzing network flow anomaly detection technology based on summary computer network flow anomaly, predicting anomaly frame in large-scale network, timely analyzing route information and network flow by means of high-performance measurement and online analysis, and predicting network flow anomaly real-time alarm.
However, in the prior art, network traffic data cannot be detected, resulting in a decrease in stability of traffic data transmission.
Disclosure of Invention
The invention aims to provide a fault diagnosis system based on network flow data, which is used for carrying out real-time analysis on the flow information of a network through a data diagnosis unit so as to carry out data diagnosis on the network flow, obtaining a diagnosis coefficient Xi of the flow data through a formula, and comparing the diagnosis coefficient Xi of the flow data with a diagnosis coefficient threshold value of the flow data: if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform; if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform; the network flow data is detected, so that the stability of flow data transmission is improved, the frequency of network faults is reduced, and the use quality of users is improved.
The aim of the invention can be achieved by the following technical scheme:
a fault diagnosis system based on network flow data comprises a device detection unit, a network detection unit, a data diagnosis unit, a maintenance distribution unit, a data management platform, a registration login unit and a database;
the data diagnosis unit is used for analyzing the flow information of the network in real time so as to perform data diagnosis on the network flow, the flow information of the network comprises time data, network speed data and character data, the time data is terminal response time length in the network flow data transmission process, the network speed data is the maximum fluctuation value of the network flow data transmission speed, the character data is the number of characters compressed in the network flow data transmission process, the detection time is marked as i, i=1, 2, … …, n and n are positive integers, and the specific analysis diagnosis process is as follows:
step S1: acquiring a terminal response time length in the network flow data transmission process, and marking the terminal response time length in the network flow data transmission process as SCi;
step S2: obtaining the maximum fluctuation value of the network flow data transmission speed, and marking the maximum fluctuation value of the network flow data transmission speed as BDi;
step S3: acquiring the number of characters compressed in the network traffic data transmission process, and marking the number of characters compressed in the network traffic data transmission process as ZFi;
step S4: obtaining a diagnosis coefficient Xi of the flow data through a formula xi=beta (scixa1+bdixa2+ ZFi xa3), wherein a1, a2 and a3 are all proportional coefficients, a1 > a2 > a3 > 0, beta is an error correction factor, and the value is 2.032125;
step S5: comparing the diagnostic factor Xi of the flow data with a diagnostic factor threshold of the flow data:
if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform;
if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform.
Further, after receiving the abnormal traffic data signal and the corresponding abnormal traffic data, the data management platform generates a network detection signal and sends the network detection signal to a network detection unit, after receiving the network detection signal, the network detection unit analyzes network information corresponding to the abnormal traffic data, so as to detect the corresponding network, wherein the network information comprises bandwidth data, concurrent data and time delay data, the bandwidth data is the average bandwidth of the network when the abnormal traffic data is transmitted, the concurrent data is the maximum concurrent number of the network when the abnormal traffic data is transmitted, the time delay data is the average time delay of the network when the abnormal traffic data is transmitted, the abnormal traffic data is marked as o, o=1, 2, … …, m and m are positive integers, and the specific analysis and detection process is as follows:
step SS1: acquiring the network average bandwidth during abnormal traffic data transmission, and marking the network average bandwidth during abnormal traffic data transmission as DKo;
step SS2: acquiring the maximum concurrency number of the network during abnormal traffic data transmission, and marking the maximum concurrency number of the network during abnormal traffic data transmission as BFo;
step SS3: acquiring the average time delay of the network during abnormal traffic data transmission, and marking the average time delay of the network during abnormal traffic data transmission as SYo;
step SS4: acquiring a network detection coefficient FXo of abnormal flow data through a formula, wherein b1, b2 and b3 are proportionality coefficients, and b1 is more than b2 is more than b3 is more than 0;
step SS5: comparing the network detection coefficient FXo of the abnormal traffic data with a network detection coefficient threshold:
if the network detection coefficient FXo of the abnormal flow data is more than or equal to the network detection coefficient threshold, judging that the network corresponding to the abnormal flow data is normal, generating a network normal signal and sending the network normal signal to a data management platform, and after the data management platform receives the network normal signal, generating an equipment detection signal and sending the equipment detection signal to an equipment detection unit;
if the network detection coefficient FXo of the abnormal flow data is smaller than the network detection coefficient threshold, judging that the network corresponding to the abnormal flow data is abnormal, generating a network abnormal signal and sending the network abnormal signal to the data management platform, and after the data management platform receives the network abnormal signal, generating a network maintenance signal and sending the network maintenance signal to the maintenance distribution unit.
Further, after receiving the device detection signal, the device detection unit analyzes the terminal device information corresponding to the abnormal traffic data, so as to detect the terminal device, where the terminal device includes a router and a modem, and the terminal device information includes a maximum number of interfaces used simultaneously when the modem operates, an average duration of the router for transmitting the traffic data throughout the day, and a maximum temperature of the router for transmitting the traffic data throughout the day, and a specific analysis and detection process is as follows:
step T1: obtaining the maximum simultaneous use number of the interfaces of the modem in operation, and marking the maximum simultaneous use number of the interfaces of the modem in operation as JK;
step T2: acquiring the average time length of the router in all days for traffic data transmission, and marking the average time length of the router in all days for traffic data transmission as SC;
step T3: obtaining the highest temperature of the router for transmitting the flow data all the day, and marking the highest temperature of the router for transmitting the flow data all the day as WD;
step T4: obtaining a terminal equipment detection coefficient JC corresponding to abnormal flow data through a formula, wherein s1, s2 and s3 are proportionality coefficients, s1 is more than s2 is more than s3 is more than 0, alpha is an error correction factor, and the value is 2.3698745;
step T5: comparing a detection coefficient JC of the terminal equipment corresponding to the abnormal flow data with a detection coefficient threshold of the terminal equipment:
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is more than or equal to the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is abnormal, generating a terminal equipment abnormal signal, marking the corresponding terminal equipment as abnormal equipment, then sending the terminal equipment abnormal signal and the abnormal equipment to a data management platform, and after receiving the terminal equipment abnormal signal, generating an equipment maintenance signal and sending the equipment maintenance signal to a maintenance distribution unit by the data management platform;
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is smaller than the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is normal, generating a terminal equipment normal signal, marking the corresponding terminal equipment as normal equipment, and then sending the terminal equipment normal signal and the normal equipment to a mobile phone terminal of a manager.
Further, after the maintenance distribution unit receives the network maintenance signal and the equipment maintenance signal, maintenance personnel are reasonably distributed, and the specific distribution process is as follows:
step TT1: acquiring idle maintenance personnel, marking the idle maintenance personnel as preselected maintenance personnel, setting the marks k, k=1, 2, … …, p and p as positive integers, then acquiring the time for entering the preset maintenance personnel, comparing the time for entering the preset maintenance personnel with the current time of the system to acquire the time for entering the preset maintenance personnel, and marking the time for entering the preset maintenance personnel as Sk;
step TT2: acquiring the total number of times of maintenance performed by a preset maintainer in the time of job entry, and marking the total number of times of maintenance performed by the preset maintainer in the time of job entry as Ck;
step TT3: acquiring the complaint times of preset maintenance personnel in the time of job entry, and marking the complaint times of the preset maintenance personnel in the time of job entry as Tk;
step TT4: obtaining a distribution coefficient Xk of a preset maintainer through a formula, wherein v1, v2 and v3 are proportionality coefficients, v1 is more than v2 is more than v3 is more than 0, and e is a natural constant;
step TT5: and sorting the preset maintenance personnel according to the sequence from the larger value to the smaller value of the corresponding distribution coefficient Xk, marking the preset maintenance personnel with the first sorting as the selected personnel, and marking the preset maintenance personnel with the second sorting as the candidate personnel.
Further, the registration login unit is used for submitting manager information and maintainer information through the mobile phone terminal, and sending the manager information and maintainer information which are successfully registered to the database for storage, wherein the manager information comprises names, ages, job entering time of the manager and mobile phone numbers of the real-name authentication of the maintainer, and the maintainer information comprises names, ages, job entering time of the maintainer and the mobile phone numbers of the real-name authentication of the maintainer.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, the flow information of the network is analyzed in real time through the data diagnosis unit, so that the network flow is subjected to data diagnosis, the diagnosis coefficient Xi of the flow data is obtained through a formula, and the diagnosis coefficient Xi of the flow data is compared with the diagnosis coefficient threshold value of the flow data: if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform; if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform; the network flow data is detected, so that the stability of flow data transmission is improved, the frequency of network faults is reduced, and the use quality of users is improved;
2. in the invention, after a network maintenance signal and an equipment maintenance signal are received through a maintenance distribution unit, reasonable distribution is carried out on maintenance personnel, idle maintenance personnel are obtained and marked as preselected maintenance personnel, then the time for entering the preset maintenance personnel is obtained, the time for entering the preset maintenance personnel is compared with the current time of the system to obtain the time for entering the preset maintenance personnel, the distribution coefficient Xk of the preset maintenance personnel is obtained through a formula, the preset maintenance personnel are ordered according to the sequence of the corresponding distribution coefficient Xk value from large to small, the preset maintenance personnel with the first order is marked as selected personnel, and the preset maintenance personnel with the second order is marked as alternative personnel; maintenance personnel are reasonably distributed to faults of the network and the equipment, so that the working efficiency of maintenance is improved, and the possibility of reworking is reduced.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a fault diagnosis system based on network traffic data includes a device detection unit, a network detection unit, a data diagnosis unit, a maintenance allocation unit, a data management platform, a registration login unit and a database;
the registration login unit is used for submitting manager information and maintainer information through the mobile phone terminal, and sending the manager information and maintainer information which are successfully registered to the database for storage, wherein the manager information comprises the name, age, job entering time of the manager and the mobile phone number of the real name authentication of the maintainer, and the maintainer information comprises the name, age, job entering time of the maintainer and the mobile phone number of the real name authentication of the maintainer;
the data diagnosis unit is used for analyzing the flow information of the network in real time so as to perform data diagnosis on the network flow, the flow information of the network comprises time data, network speed data and character data, the time data is the terminal response time length in the network flow data transmission process, the network speed data is the maximum fluctuation value of the network flow data transmission speed, the character data is the number of characters compressed in the network flow data transmission process, the detection time is marked as i, i=1, 2, … …, n and n are positive integers, and the specific analysis diagnosis process is as follows:
step S1: acquiring a terminal response time length in the network flow data transmission process, and marking the terminal response time length in the network flow data transmission process as SCi;
step S2: obtaining the maximum fluctuation value of the network flow data transmission speed, and marking the maximum fluctuation value of the network flow data transmission speed as BDi;
step S3: acquiring the number of characters compressed in the network traffic data transmission process, and marking the number of characters compressed in the network traffic data transmission process as ZFi;
step S4: obtaining a diagnosis coefficient Xi of the flow data through a formula xi=beta (scixa1+bdixa2+ ZFi xa3), wherein a1, a2 and a3 are all proportional coefficients, a1 > a2 > a3 > 0, beta is an error correction factor, and the value is 2.032125;
step S5: comparing the diagnostic factor Xi of the flow data with a diagnostic factor threshold of the flow data:
if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform;
if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform;
after receiving the abnormal flow data signals and the corresponding abnormal flow data, the data management platform generates network detection signals and sends the network detection signals to the network detection unit, the network detection unit analyzes network information corresponding to the abnormal flow data after receiving the network detection signals, so that the corresponding network is detected, the network information comprises bandwidth data and concurrency data, the bandwidth data is the average bandwidth of the network when the abnormal flow data is transmitted, the concurrency data is the maximum concurrency number of the network when the abnormal flow data is transmitted, the time delay data is the average time delay of the network when the abnormal flow data is transmitted, the abnormal flow data is marked as o, o=1, 2, … …, m and m are positive integers, and the specific analysis and detection processes are as follows:
step SS1: acquiring the network average bandwidth during abnormal traffic data transmission, and marking the network average bandwidth during abnormal traffic data transmission as DKo;
step SS2: acquiring the maximum concurrency number of the network during abnormal traffic data transmission, and marking the maximum concurrency number of the network during abnormal traffic data transmission as BFo;
step SS3: acquiring the average time delay of the network during abnormal traffic data transmission, and marking the average time delay of the network during abnormal traffic data transmission as SYo;
step SS4: acquiring a network detection coefficient FXo of abnormal flow data through a formula, wherein b1, b2 and b3 are proportionality coefficients, and b1 is more than b2 is more than b3 is more than 0;
step SS5: comparing the network detection coefficient FXo of the abnormal traffic data with a network detection coefficient threshold:
if the network detection coefficient FXo of the abnormal flow data is more than or equal to the network detection coefficient threshold, judging that the network corresponding to the abnormal flow data is normal, generating a network normal signal and sending the network normal signal to a data management platform, and after the data management platform receives the network normal signal, generating an equipment detection signal and sending the equipment detection signal to an equipment detection unit;
if the network detection coefficient FXo of the abnormal flow data is smaller than the network detection coefficient threshold value, judging that the network corresponding to the abnormal flow data is abnormal, generating a network abnormal signal and sending the network abnormal signal to a data management platform, and after the data management platform receives the network abnormal signal, generating a network maintenance signal and sending the network maintenance signal to a maintenance distribution unit;
after receiving the device detection signal, the device detection unit analyzes the terminal device information corresponding to the abnormal flow data so as to detect the terminal device, wherein the terminal device comprises a router and a modem, the terminal device information comprises the maximum number of interfaces used simultaneously when the modem operates, the average duration of the router for carrying out flow data transmission on the whole day and the highest temperature of the router for carrying out flow data transmission on the whole day, and the specific analysis and detection processes are as follows:
step T1: obtaining the maximum simultaneous use number of the interfaces of the modem in operation, and marking the maximum simultaneous use number of the interfaces of the modem in operation as JK;
step T2: acquiring the average time length of the router in all days for traffic data transmission, and marking the average time length of the router in all days for traffic data transmission as SC;
step T3: obtaining the highest temperature of the router for transmitting the flow data all the day, and marking the highest temperature of the router for transmitting the flow data all the day as WD;
step T4: obtaining a terminal equipment detection coefficient JC corresponding to abnormal flow data through a formula, wherein s1, s2 and s3 are proportionality coefficients, s1 is more than s2 is more than s3 is more than 0, alpha is an error correction factor, and the value is 2.3698745;
step T5: comparing a detection coefficient JC of the terminal equipment corresponding to the abnormal flow data with a detection coefficient threshold of the terminal equipment:
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is more than or equal to the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is abnormal, generating a terminal equipment abnormal signal, marking the corresponding terminal equipment as abnormal equipment, then sending the terminal equipment abnormal signal and the abnormal equipment to a data management platform, and after receiving the terminal equipment abnormal signal, generating an equipment maintenance signal and sending the equipment maintenance signal to a maintenance distribution unit by the data management platform;
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is smaller than the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is normal, generating a terminal equipment normal signal, marking the corresponding terminal equipment as normal equipment, and then sending the terminal equipment normal signal and the normal equipment to a mobile phone terminal of a manager;
after the maintenance distribution unit receives the network maintenance signal and the equipment maintenance signal, maintenance personnel are reasonably distributed, and the specific distribution process is as follows:
step TT1: acquiring idle maintenance personnel, marking the idle maintenance personnel as preselected maintenance personnel, setting the marks k, k=1, 2, … …, p and p as positive integers, then acquiring the time for entering the preset maintenance personnel, comparing the time for entering the preset maintenance personnel with the current time of the system to acquire the time for entering the preset maintenance personnel, and marking the time for entering the preset maintenance personnel as Sk;
step TT2: acquiring the total number of times of maintenance performed by a preset maintainer in the time of job entry, and marking the total number of times of maintenance performed by the preset maintainer in the time of job entry as Ck;
step TT3: acquiring the complaint times of preset maintenance personnel in the time of job entry, and marking the complaint times of the preset maintenance personnel in the time of job entry as Tk;
step TT4: obtaining a distribution coefficient Xk of a preset maintainer through a formula, wherein v1, v2 and v3 are proportionality coefficients, v1 is more than v2 is more than v3 is more than 0, and e is a natural constant;
step TT5: and sorting the preset maintenance personnel according to the sequence from the larger value to the smaller value of the corresponding distribution coefficient Xk, marking the preset maintenance personnel with the first sorting as the selected personnel, and marking the preset maintenance personnel with the second sorting as the candidate personnel.
The working principle of the invention is as follows:
in operation, the network flow information is analyzed in real time through the data diagnosis unit, so that the network flow is subjected to data diagnosis, the diagnosis coefficient Xi of the flow data is obtained through a formula, and the diagnosis coefficient Xi of the flow data is compared with the diagnosis coefficient threshold value of the flow data: if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform; if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (5)
1. The fault diagnosis system based on the network flow data is characterized by comprising a device detection unit, a network detection unit, a data diagnosis unit, a maintenance allocation unit, a data management platform, a registration login unit and a database;
the data diagnosis unit is used for analyzing the flow information of the network in real time so as to perform data diagnosis on the network flow, the flow information of the network comprises time data, network speed data and character data, the time data is terminal response time length in the network flow data transmission process, the network speed data is the maximum fluctuation value of the network flow data transmission speed, the character data is the number of characters compressed in the network flow data transmission process, the detection time is marked as i, i=1, 2, … …, n and n are positive integers, and the specific analysis diagnosis process is as follows:
step S1: acquiring a terminal response time length in the network flow data transmission process, and marking the terminal response time length in the network flow data transmission process as SCi;
step S2: obtaining the maximum fluctuation value of the network flow data transmission speed, and marking the maximum fluctuation value of the network flow data transmission speed as BDi;
step S3: acquiring the number of characters compressed in the network traffic data transmission process, and marking the number of characters compressed in the network traffic data transmission process as ZFi;
step S4: obtaining a diagnosis coefficient Xi of the flow data through a formula xi=beta (scixa1+bdixa2+ ZFi xa3), wherein a1, a2 and a3 are all proportional coefficients, a1 > a2 > a3 > 0, beta is an error correction factor, and the value is 2.032125;
step S5: comparing the diagnostic factor Xi of the flow data with a diagnostic factor threshold of the flow data:
if the diagnostic coefficient Xi of the flow data is more than or equal to the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is abnormal, generating a flow data abnormal signal, marking the flow data as abnormal flow data, and then sending the flow data abnormal signal and the corresponding abnormal flow data to a data management platform;
if the diagnostic coefficient Xi of the flow data is smaller than the diagnostic coefficient threshold value of the flow data, judging that the corresponding flow data is normal, generating a flow data normal signal, marking the flow data as normal flow data, and then sending the flow data normal signal and the corresponding normal flow data to a data management platform.
2. The fault diagnosis system based on network traffic data according to claim 1, wherein the data management platform generates a network detection signal and sends the network detection signal to the network detection unit after receiving the traffic data anomaly signal and the corresponding anomaly traffic data, the network detection unit analyzes network information corresponding to the anomaly traffic data after receiving the network detection signal, so as to detect the corresponding network, the network information comprises bandwidth data and concurrency data, the bandwidth data is an average bandwidth of the network when the anomaly traffic data is transmitted, the concurrency data is a maximum concurrency number of the network when the anomaly traffic data is transmitted, the time delay data is an average time delay of the network when the anomaly traffic data is transmitted, the anomaly traffic data is marked as o, o=1, 2, … …, m, m is a positive integer, and the specific analysis detection process is as follows:
step SS1: acquiring the network average bandwidth during abnormal traffic data transmission, and marking the network average bandwidth during abnormal traffic data transmission as DKo;
step SS2: acquiring the maximum concurrency number of the network during abnormal traffic data transmission, and marking the maximum concurrency number of the network during abnormal traffic data transmission as BFo;
step SS3: acquiring the average time delay of the network during abnormal traffic data transmission, and marking the average time delay of the network during abnormal traffic data transmission as SYo;
step SS4: by the formulaAcquiring a network detection coefficient FXo of abnormal flow data, wherein b1, b2 and b3 are proportionality coefficients, and b1 is more than b2 is more than b3 is more than 0;
step SS5: comparing the network detection coefficient FXo of the abnormal traffic data with a network detection coefficient threshold:
if the network detection coefficient FXo of the abnormal flow data is more than or equal to the network detection coefficient threshold, judging that the network corresponding to the abnormal flow data is normal, generating a network normal signal and sending the network normal signal to a data management platform, and after the data management platform receives the network normal signal, generating an equipment detection signal and sending the equipment detection signal to an equipment detection unit;
if the network detection coefficient FXo of the abnormal flow data is smaller than the network detection coefficient threshold, judging that the network corresponding to the abnormal flow data is abnormal, generating a network abnormal signal and sending the network abnormal signal to the data management platform, and after the data management platform receives the network abnormal signal, generating a network maintenance signal and sending the network maintenance signal to the maintenance distribution unit.
3. The system of claim 1, wherein the device detecting unit analyzes terminal device information corresponding to the abnormal traffic data after receiving the device detecting signal, so as to detect the terminal device, the terminal device includes a router and a modem, the terminal device information includes a maximum number of simultaneous use of interfaces of the modem in operation, an average duration of the router for traffic data transmission throughout the day, and a maximum temperature of the router for traffic data transmission throughout the day, and the specific analysis and detection process is as follows:
step T1: obtaining the maximum simultaneous use number of the interfaces of the modem in operation, and marking the maximum simultaneous use number of the interfaces of the modem in operation as JK;
step T2: acquiring the average time length of the router in all days for traffic data transmission, and marking the average time length of the router in all days for traffic data transmission as SC;
step T3: obtaining the highest temperature of the router for transmitting the flow data all the day, and marking the highest temperature of the router for transmitting the flow data all the day as WD;
step T4: by the formulaAcquiring a terminal equipment detection coefficient JC corresponding to abnormal flow data, wherein s1, s2 and s3 are proportionality coefficients, s1 is more than s2 is more than s3 is more than 0, alpha is an error correction factor, and the value is 2.3698745;
step T5: comparing a detection coefficient JC of the terminal equipment corresponding to the abnormal flow data with a detection coefficient threshold of the terminal equipment:
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is more than or equal to the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is abnormal, generating a terminal equipment abnormal signal, marking the corresponding terminal equipment as abnormal equipment, then sending the terminal equipment abnormal signal and the abnormal equipment to a data management platform, and after receiving the terminal equipment abnormal signal, generating an equipment maintenance signal and sending the equipment maintenance signal to a maintenance distribution unit by the data management platform;
if the detection coefficient JC of the terminal equipment corresponding to the abnormal flow data is smaller than the detection coefficient threshold of the terminal equipment, judging that the terminal equipment corresponding to the abnormal flow data is normal, generating a terminal equipment normal signal, marking the corresponding terminal equipment as normal equipment, and then sending the terminal equipment normal signal and the normal equipment to a mobile phone terminal of a manager.
4. The fault diagnosis system based on network traffic data according to claim 1, wherein after the maintenance allocation unit receives the network maintenance signal and the equipment maintenance signal, the maintenance personnel are reasonably allocated, and the specific allocation process is as follows:
step TT1: acquiring idle maintenance personnel, marking the idle maintenance personnel as preselected maintenance personnel, setting the marks k, k=1, 2, … …, p and p as positive integers, then acquiring the time for entering the preset maintenance personnel, comparing the time for entering the preset maintenance personnel with the current time of the system to acquire the time for entering the preset maintenance personnel, and marking the time for entering the preset maintenance personnel as Sk;
step TT2: acquiring the total number of times of maintenance performed by a preset maintainer in the time of job entry, and marking the total number of times of maintenance performed by the preset maintainer in the time of job entry as Ck;
step TT3: acquiring the complaint times of preset maintenance personnel in the time of job entry, and marking the complaint times of the preset maintenance personnel in the time of job entry as Tk;
step TT4: by the formulaObtaining a distribution coefficient Xk of a preset maintainer, wherein v1, v2 and v3 are proportionality coefficients, v1 is more than v2 is more than v3 is more than 0, and e is a natural constant;
step TT5: and sorting the preset maintenance personnel according to the sequence from the larger value to the smaller value of the corresponding distribution coefficient Xk, marking the preset maintenance personnel with the first sorting as the selected personnel, and marking the preset maintenance personnel with the second sorting as the candidate personnel.
5. The system of claim 1, wherein the registration unit is configured to submit manager information and maintainer information through a mobile phone terminal, and send the manager information and maintainer information that are successfully registered to the database for storage, where the manager information includes a name, an age, an time of entering a job, and a mobile phone number of a real name authentication of the principal, and the maintainer information includes a name, an age, a time of entering a job, and a mobile phone number of a real name authentication of the principal.
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