CN110601900A - Network fault early warning method and device - Google Patents
Network fault early warning method and device Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The invention provides a network fault early warning method and a network fault early warning device, wherein the method comprises the following steps: collecting network fault data and network sensing index data; analyzing the correlation of the network fault data and the network sensing index data to determine a relevant index; whether the relevant indexes are abnormal is predicted by using a time series prediction algorithm, and if the relevant indexes are abnormal, the relevant indexes are abnormal indexes; and acquiring abnormal index data for each user-level network device, and determining that the user-level network device has network faults if a preset value of abnormal indexes are abnormal. The invention can accurately position the user-level network equipment with faults, and index data used by network fault early warning of each user-level network equipment is uniform, thereby being capable of covering the whole network.
Description
Technical Field
The invention relates to the field of network fault early warning, in particular to a network fault early warning method and device.
Background
The existing network fault early warning judges the fault by setting a threshold value for index data such as performance and the like, and judges the equipment fault when the threshold value is exceeded. This approach has the following drawbacks: 1. the device can only be effective for equipment capable of acquiring indexes, and equipment faults cannot be judged for the energy-free data equipment such as a light splitter; 2. the performance of the equipment is frequently acquired, so that the performance of the equipment is influenced; 3. the existing network fault judgment can only position a series of devices with faults, and can not judge which devices are the root of the faults; 4. in the existing network fault judgment, different indexes need to be acquired for different equipment, different thresholds need to be set for the different indexes to serve as judgment standards, the setting and adjustment of the thresholds are complex, and the accuracy is low; 5. in the existing equipment classification algorithm, because abnormal equipment occupies a small amount of equipment, and the abnormal equipment is probably not root equipment, equipment classification imbalance exists, and the algorithm accuracy is low.
Disclosure of Invention
In order to solve the defects that a network fault early warning method in the prior art cannot locate specific user-level network equipment with a fault and different standards adopted by different user-level network equipment, a first aspect of the invention provides a network fault early warning method, which comprises the following steps:
collecting network fault data and network sensing index data;
analyzing the correlation of the network fault data and the network sensing index data to determine a relevant index;
whether the relevant indexes are abnormal is predicted by using a time series prediction algorithm, and if the relevant indexes are abnormal, the relevant indexes are abnormal indexes;
and acquiring abnormal index data for each user-level network device, and determining that the user-level network device has network faults if a preset value of abnormal indexes are abnormal.
A second aspect of the present invention provides a network fault early warning apparatus, including:
the data acquisition module is used for acquiring network fault data and network sensing index data;
the relevant index determining module is used for analyzing the relevance of the network fault data and the network sensing index data and determining relevant indexes;
the abnormal index determining module is used for predicting whether the related index is abnormal by using a time series prediction algorithm, and if the related index is abnormal, the related index is an abnormal index;
the first fault determining module is used for acquiring abnormal index data for each user-level network device, and if a preset value of abnormal indexes is abnormal, determining that the user-level network device has a network fault.
A third aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the network fault pre-warning method when executing the computer program.
A fourth aspect of the present invention provides a computer-readable storage medium storing an executable computer program which, when executed by a processor, implements the steps of the network fault early warning method.
The method starts from a user layer, and network fault data and network sensing index data are collected; analyzing the correlation of the network fault data and the network sensing index data to determine a relevant index; whether the relevant indexes are abnormal is predicted by using a time series prediction algorithm, and if the relevant indexes are abnormal, the relevant indexes are abnormal indexes; for each user-level network device, abnormal index data is collected, if a preset value of abnormal indexes is abnormal, the user-level network device is determined to have a network fault, the user-level network device with the fault can be accurately positioned, and index data used for early warning of the network fault of each user-level network device is uniform and can cover the whole network.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 shows a flow chart of a network fault early warning method according to an embodiment of the invention;
FIG. 2 illustrates a flow chart of an embodiment of the present invention for determining a relevant indicator;
3A-3F show schematic diagrams of the perception of metrics with a net according to embodiments of the present invention;
FIGS. 4A to 4B are flowcharts showing whether the prediction related index is abnormal according to the embodiment of the present invention;
FIG. 5 is a schematic diagram showing time series data of an embodiment of the present invention;
FIG. 6 shows another flow chart of a network fault early warning method of an embodiment of the invention;
fig. 7 is a block diagram showing a network failure warning apparatus according to an embodiment of the present invention;
fig. 8 is another structural diagram of a network fault warning apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical features and effects of the invention more obvious, the technical solution of the invention is further described below with reference to the accompanying drawings, and the invention can also be described or implemented by other different specific examples, and any person skilled in the art can do so
In the description herein, references to the description of "an embodiment," "for example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the various embodiments is provided to schematically illustrate the practice of the invention, and the sequence of steps is not limited and can be suitably adjusted as desired.
The existing network fault early warning judges the fault by setting a threshold value for index data such as performance and the like, and judges the equipment fault when the threshold value is exceeded. The method has the defects that the user-level network equipment with specific faults cannot be positioned, and the standards adopted by different user-level network equipment are different.
Based on this, in an embodiment of the present invention, a network fault early warning method is provided, as shown in fig. 1, in this embodiment, a user-level network device that has a fault can be accurately located, and index data used for network fault early warning of each user-level network device is uniform, so that the whole network can be covered. Specifically, the network fault early warning method comprises the following steps:
and step 110, collecting network fault data and network sensing index data.
In detail, the network fault data at least includes: user information, fault description, fault equipment description in the fault receipt, fault occurrence reason and fault occurrence time.
The network perception metric data at least comprises: user information, packet loss, jitter, time delay, pause, video watching switching time delay and playing time length. The frequency of acquiring the data by using the net perception index is, for example, 5 minutes, and the specific acquisition frequency is not limited by the invention. The network sensing index data can cover all network users, and the timeliness and comprehensiveness of fault pre-judgment can be guaranteed.
And step 120, analyzing the correlation of the network fault data and the network sensing index data, and determining a relevant index.
And step 130, predicting whether the relevant indexes are abnormal by using a time series prediction algorithm, and if so, determining that the relevant indexes are abnormal indexes.
Step 140, for each user-level network device, collecting abnormal index data, and if a predetermined number of abnormal indexes are abnormal, determining that the user-level network device has a network fault.
In detail, the predetermined value is determined by the following process: determining the number or proportion distribution of abnormal indexes when the user-level network equipment fails according to historical failure data of the user-level network equipment; and determining the preset value according to the number of abnormal indexes or the proportion distribution.
In some embodiments, as shown in fig. 2, the step 120 analyzes the correlation between the network fault data and the network-aware indicator data, and the process of determining the relevant indicator includes:
and 210, associating network fault data and network sensing index data by using the user information.
And step 220, intercepting the network sensing index data in a preset time period before and after the user breaks down.
Step 230, for each network sensing index, determining whether the intercepted data fluctuation amplitude (sudden increase and sudden decrease) of the network sensing index is greater than a predetermined amplitude, if so, determining that the network sensing index is a related index, as shown in fig. 3A to 3F, the abscissa is time, and the ordinate is an index value, where fig. 3A is packet loss rate, fig. 3B is jitter, fig. 3C is time delay, fig. 3D is video viewing switching time delay, fig. 3E is play duration, fig. 3F is VMOS value, and a corner is a fault occurrence time point.
Research shows that the relevant indexes include playing time, packet loss, jitter and time delay.
In some embodiments, as shown in fig. 4A, the step 130 of predicting whether the relevant index is abnormal by using a time-series prediction algorithm includes:
step 410a, for each relevant index, determining a time window, a period and a prediction point of each period, and constructing time series data by taking historical data before and after the time window of the prediction point in each period.
Specifically, the time window and the predicted point may be set according to the requirement (as shown in fig. 5, the predicted point is 20:00, and the time window is three hours before and after the predicted point). The period can be determined according to the historical change rule of the relevant indexes.
And step 420a, determining a confidence interval of the predicted point by using a time sequence prediction algorithm according to the time sequence data, judging whether the true value of the predicted point of the related index falls in the confidence interval, and if not, judging that the related index is an abnormal index. In detail, the time series prediction algorithm includes, but is not limited to, polynomial regression, ARAMA algorithm, 3sigma algorithm.
In the embodiment, the confidence interval of the predicted point is determined by constructing a plurality of groups of time series data according to the period, so that the error of judging the abnormal index by using a single group of time series data can be reduced.
In another embodiment, in order to improve the accuracy of determining the abnormal index, as shown in fig. 4B, the step 130 of predicting whether the related index is abnormal by using a time-series prediction algorithm includes:
and step 410b, determining a time window, a period and a prediction point of each period for each relevant index, and constructing time sequence data by taking historical data before and after the time window of the prediction point in each period.
And step 420b, respectively determining confidence intervals of the predicted points by utilizing a plurality of time series prediction algorithms according to the time series data. Each time series prediction algorithm may derive a confidence interval for a predicted point.
And step 430b, judging whether the true value of the related index prediction point falls in the confidence interval or not for each confidence interval, and if not, judging that the related index is abnormal.
In step 440b, the number of times that the relevant index is determined to be abnormal is counted, and if the number of times is greater than a predetermined number of times (e.g., twice), the relevant index is determined to be an abnormal index.
In the embodiment, the network sensing index data and the network fault data are subjected to correlation analysis, so that the index highly correlated with the equipment network fault can be obtained, and the fault judgment accuracy is improved.
In an embodiment of the present invention, as shown in fig. 6, the network fault early warning method further includes:
step 150, for each optical splitter, determining a ratio of network failures occurring in the user-level network devices under the optical splitter, and if the ratio falls into a predetermined interval, determining that the optical splitter has a network failure.
Wherein, the process of determining the preset interval comprises the following steps: the method comprises the steps that the fault data of user-level network equipment under a fault optical splitter are utilized, and the proportion of network faults of the user-level network equipment under the fault optical splitter is counted; and determining a predetermined interval according to the ratio of the network fault of the subordinate user-level network equipment of the fault optical splitter.
In detail, the ratio of network failures occurring in the subordinate user-level network devices of the failure optical splitter is as follows: the number of the user-level network devices which are under the fault optical splitter and have network faults accounts for the proportion of the total number of the user-level network devices under the fault optical splitter. The predetermined interval is an interval with high probability of the fault of the optical splitter and can be determined by a probability density distribution mode.
The embodiment can accurately judge the network fault of the optical splitter without the performance index, and can accurately position which optical splitter has the network fault.
In an embodiment of the present invention, in order to cover more fault types, the network fault early warning method further includes: counting the collection times of the relevant indexes every other preset time period, and taking the collection times as new relevant indexes; and calculating the mean and the variance of the correlation indexes, and taking the mean and the variance of the correlation indexes as new correlation indexes.
In detail, the index collection times are mainly used in some cases, for example, the user index cannot be collected due to the fact that the network cannot be accessed, and only the collection times are abnormal at this time, the fault can be reflected, the mean value represents the situation of sudden increase and sudden decrease, and the variance represents the situation of the jitter of the index.
Based on the same inventive concept, the invention also provides a network fault early warning device, as described in the following embodiments. Because the principle of solving the problems of the device is similar to that of the network fault early warning method, the implementation of the device can refer to the implementation of the network fault early warning method, and repeated parts are not described again.
Specifically, as shown in fig. 7, the network fault early warning apparatus includes:
and the data acquisition module 710 is used for acquiring network fault data and network sensing index data. In detail, the network fault data at least includes: user information, fault description, fault equipment description in the fault receipt, fault occurrence reason and fault occurrence time. The network perception metric data at least comprises: user information, packet loss, jitter, time delay, pause, video watching switching time delay and playing time length.
And a correlation index determining module 720, configured to analyze the correlation between the network fault data and the network sensing index data, and determine a correlation index.
The abnormal index determining module 730 is configured to predict whether the related index is abnormal by using a time series prediction algorithm, and if the related index is abnormal, the related index is an abnormal index.
The first failure determining module 740 is configured to collect abnormal indicator data for each user-level network device, and determine that a network failure occurs in the user-level network device if a predetermined number of abnormal indicators are abnormal.
In some embodiments, the process of analyzing the correlation between the network fault data and the network-aware indicator data by the relevant indicator determining module 720 to obtain the relevant indicator includes: associating network fault data and network sensing index data by using user information; intercepting network sensing index data in a preset time period before and after a user breaks down; and for each network use perception index, judging whether the intercepted data fluctuation amplitude of the network use perception index is larger than a preset amplitude, and if so, determining the network use perception index as a related index.
In some embodiments, the process of the abnormal index determination module 730 predicting whether the relevant index is abnormal by using the time series prediction algorithm includes: for each relevant index, determining a time window, a period and a prediction point of each period, and constructing time sequence data by taking historical data before and after the time window of the prediction point in each period; and determining a confidence interval of the predicted point by using a time sequence prediction algorithm according to the time sequence data, judging whether the true value of the predicted point of the related index falls in the confidence interval, and if not, judging that the related index is an abnormal index.
In other embodiments, the process of the abnormal index determination module 730 predicting whether the relevant index is abnormal by using the time series prediction algorithm includes: for each relevant index, determining a time window, a period and a prediction point of each period, and constructing time sequence data by taking historical data before and after the time window of the prediction point in each period; respectively determining confidence intervals of the predicted points by utilizing a plurality of time series prediction algorithms according to the time series data; for each confidence interval, judging whether the true value of the related index prediction point falls in the confidence interval, if not, judging that the related index is abnormal; counting the times of the related index being judged to be abnormal, and if the times is more than the preset times, judging the related index to be an abnormal index.
In an embodiment of the present invention, as shown in fig. 8, the network fault early warning apparatus further includes:
a second fault determining module 750, configured to determine, for each optical splitter, a ratio of network faults occurring in the user-level network devices under the optical splitter, and if the ratio falls into a predetermined interval, determine that a network fault occurs in the optical splitter;
wherein, the process of determining the preset interval comprises the following steps: the method comprises the steps that the fault data of user-level network equipment under a fault optical splitter are utilized, and the proportion of network faults of the user-level network equipment under the fault optical splitter is counted; and determining a preset interval according to the ratio counted by each fault optical splitter.
Further, the network fault early warning device further comprises:
the expansion module 760 is used for counting the collection times of the relevant indexes every other preset time period, and taking the collection times as new relevant indexes; and calculating the mean and the variance of the correlation indexes, and taking the mean and the variance of the correlation indexes as new correlation indexes.
In an embodiment of the present invention, a computer device is further provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the network fault early warning method according to any of the foregoing embodiments are implemented.
In an embodiment of the present invention, a computer-readable storage medium is further provided, where a computer program is stored and executed, and when the computer program is executed by a processor, the steps of the network fault early warning method described in any of the foregoing embodiments are implemented.
In summary, the network fault early warning method, device, computer device and computer readable storage medium provided by the invention can achieve the following technical effects:
1. the network sensing index data and the network fault data are subjected to correlation analysis, so that indexes highly related to equipment network faults can be obtained, and the fault judgment accuracy is improved.
2. The network fault of the optical splitter without the performance index is accurately judged, and the optical splitter which has the network fault can be accurately positioned.
3. The new correlation index is constructed by taking the mean value (the mean value represents the condition of sudden increase and sudden decrease) and the variance (the variance represents the condition of jitter of the index) of the correlation index, the collection times of the correlation index every predetermined time period (corresponding to the condition that the user cannot be on the network) are counted, the collection times are used as the new correlation index, and more fault types can be covered.
4. The confidence interval of the predicted point is determined by constructing a plurality of groups of time series data according to the period, so that the error of judging the abnormal index by using a single group of time series data can be reduced.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the purpose of illustrating the present invention, and any person skilled in the art can modify and change the above embodiments without departing from the spirit and scope of the present invention. Therefore, the scope of the claims should be accorded the full scope of the claims.
Claims (11)
1. A network fault early warning method is characterized by comprising the following steps:
collecting network fault data and network sensing index data;
analyzing the correlation of the network fault data and the network sensing index data to determine a relevant index;
whether the relevant indexes are abnormal is predicted by using a time series prediction algorithm, and if the relevant indexes are abnormal, the relevant indexes are abnormal indexes;
and acquiring abnormal index data for each user-level network device, and determining that the user-level network device has network faults if a preset value of abnormal indexes are abnormal.
2. The method of claim 1, further comprising:
for each optical splitter, judging the proportion of network faults of user-level network equipment under the optical splitter, and if the proportion falls into a preset interval, determining that the optical splitter has the network faults;
wherein, the process of determining the preset interval comprises the following steps: the method comprises the steps that the fault data of user-level network equipment under a fault optical splitter are utilized, and the proportion of network faults of the user-level network equipment under the fault optical splitter is counted; and determining a predetermined interval according to the ratio of network faults of the subordinate user-level network equipment of each fault optical splitter.
3. The method of claim 1, further comprising: counting the collection times of the relevant indexes every other preset time period, and taking the collection times as new relevant indexes;
and calculating the mean and the variance of the correlation indexes, and taking the mean and the variance of the correlation indexes as new correlation indexes.
4. The method of claim 1, wherein analyzing the correlation between the network fault data and the network-aware indicator data to obtain the correlation indicator comprises:
associating network fault data and network sensing index data by using user information;
intercepting network sensing index data in a preset time period before and after a user breaks down;
and for each network use perception index, judging whether the intercepted data fluctuation amplitude of the network use perception index is larger than a preset amplitude, and if so, determining the network use perception index as a related index.
5. The method of claim 1, wherein the process of predicting whether the correlation index is abnormal using a time series prediction algorithm comprises:
for each relevant index, determining a time window, a period and a prediction point of each period, and constructing time sequence data by taking historical data before and after the time window of the prediction point in each period;
and determining a confidence interval of the predicted point by using a time sequence prediction algorithm according to the time sequence data, judging whether the true value of the predicted point of the related index falls in the confidence interval, and if not, judging that the related index is an abnormal index.
6. The method of claim 1, wherein the process of predicting whether the correlation index is abnormal using a time series prediction algorithm comprises:
for each relevant index, determining a time window, a period and a prediction point of each period, and constructing time sequence data by taking historical data before and after the time window of the prediction point in each period;
respectively determining confidence intervals of the predicted points by utilizing a plurality of time series prediction algorithms according to the time series data;
for each confidence interval, judging whether the true value of the related index prediction point falls in the confidence interval, if not, judging that the related index is abnormal;
counting the times of the related index being judged to be abnormal, and if the times is more than the preset times, judging the related index to be an abnormal index.
7. The method of claim 1, wherein the network fault data includes at least: user information, fault description, fault equipment description in the fault receipt, fault occurrence reason and fault occurrence time.
8. The method of claim 1, wherein the web-aware metric data comprises at least: user information, packet loss, jitter, time delay, pause, video watching switching time delay and playing time length.
9. A network fault early warning device, comprising:
the data acquisition module is used for acquiring network fault data and network sensing index data;
the relevant index determining module is used for analyzing the relevance of the network fault data and the network sensing index data and determining relevant indexes;
the abnormal index determining module is used for predicting whether the related index is abnormal by using a time series prediction algorithm, and if the related index is abnormal, the related index is an abnormal index;
the first fault determining module is used for acquiring abnormal index data for each user-level network device, and if a preset value of abnormal indexes is abnormal, determining that the user-level network device has a network fault.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 8 are implemented by the processor when executing the computer program.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores an executable computer program, which when executed by a processor implements the steps of the method of any one of claims 1 to 8.
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