CN111555895A - Method, device, storage medium and computer equipment for analyzing website faults - Google Patents
Method, device, storage medium and computer equipment for analyzing website faults Download PDFInfo
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Abstract
The invention discloses a method, a device, a storage medium and computer equipment for analyzing website faults. The disclosed method comprises: performing first numerical value statistics on numerical value fields and/or non-numerical value fields in log data by adopting at least one first statistical method to obtain a first numerical value statistical sequence; performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence; rejecting second numerical statistic sequences, which are specific to each first numerical statistic sequence and have statistical errors exceeding preset errors, in the second numerical statistic sequences to obtain third numerical statistic sequences, which are specific to each first numerical statistic sequence and are used for analyzing website faults; and determining the abnormality which can cause the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence. The disclosed solution enables automatic monitoring of website anomalies.
Description
Technical Field
The present invention relates to the field of computer technology and network technology, and in particular, to a method, an apparatus, a storage medium, and a computer device for analyzing a website failure.
Background
The system log of the website is important data for analyzing, positioning and solving website faults. In the prior art, when a website fault is processed, usually, an operation and maintenance engineer manually checks a system log to analyze, locate and solve the fault. Therefore, the prior art solutions have the following drawbacks:
1. a great deal of labor cost is needed to screen massive logs, and the analysis and processing of faults completely depend on the personal experience of an operation and maintenance engineer and the knowledge of the whole system architecture.
2. A lot of repeated labor is also required for the same failure.
3. The manual troubleshooting and repairing time of the problems is long, and the duration and the cost of the service failure of the website are increased.
Therefore, a technical solution capable of automatically analyzing the website failure needs to be provided.
Disclosure of Invention
The method for analyzing the website fault comprises the following steps:
performing first numerical value statistics on numerical value fields and/or non-numerical value fields in log data by adopting at least one first statistical method to obtain a first numerical value statistical sequence;
performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence;
rejecting second numerical statistic sequences, which are specific to each first numerical statistic sequence and have statistical errors exceeding preset errors, in the second numerical statistic sequences to obtain third numerical statistic sequences, which are specific to each first numerical statistic sequence and are used for analyzing website faults;
and determining the abnormality which can cause the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence.
The method for analyzing the website fault further comprises the following steps:
performing third numerical statistics on each first numerical statistical sequence by adopting at least one third statistical method to obtain a fourth numerical statistical sequence;
performing fourth numerical statistics on the abnormal time sequence in the specified time period by adopting at least one fourth statistical method to obtain a fifth numerical statistical sequence;
sorting the statistical results of the fifth numerical statistical sequence, associating the exception and the exception time before the sorting result with the user-defined event in the specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical value statistical sequence and the second numerical value statistical sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
The method for analyzing the website fault further comprises the following steps:
and determining the condition simultaneously containing the exception and the custom event related to the exception as the website fault.
According to the method for analyzing the website fault, the first statistical method comprises the following steps: the second statistical method comprises the following steps of information entropy, accumulated and average values in unit time and difference values of adjacent values: the third statistical method comprises the following steps: the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein log data comprise: non-secure product log data and secure product log data.
The device for analyzing the website fault comprises the following components:
the first statistical module is used for carrying out first numerical statistics on numerical fields and/or non-numerical fields in the log data by adopting at least one first statistical method to obtain a first numerical statistical sequence;
the second statistical module is used for performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence;
the selection module is used for eliminating second numerical value statistical sequences, which are specific to each first numerical value statistical sequence and have statistical errors exceeding preset errors, in the second numerical value statistical sequences to obtain third numerical value statistical sequences, which are used for analyzing website faults and are specific to each first numerical value statistical sequence;
and the abnormality detection module is used for determining the abnormality possibly causing the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence.
The device for analyzing the website fault further comprises the following steps:
the third statistical module is used for carrying out third numerical statistics on each first numerical statistical sequence by adopting at least one third statistical method to obtain a fourth numerical statistical sequence;
the fourth statistical module is used for carrying out fourth numerical statistics on the abnormal time sequence in the specified time period by adopting at least one fourth statistical method to obtain a fifth numerical statistical sequence;
the association module is used for sequencing the statistical results of the fifth numerical statistical sequence, associating the exception and the exception time at the front of the sequencing result with the user-defined event in the specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical value statistical sequence and the second numerical value statistical sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
The device for analyzing the website fault further comprises the following steps:
and the fault determining module is used for determining the condition that the abnormal condition and the custom event related to the abnormal condition are simultaneously contained as the website fault.
According to the device for analyzing the website failure, the first statistical method comprises the following steps: the second statistical method comprises the following steps of information entropy, accumulated and average values in unit time and difference values of adjacent values: the third statistical method comprises the following steps: the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein log data comprise: non-secure product log data and secure product log data.
A storage medium according to the invention, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
The computer device according to the invention comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method described above when executing the program.
According to the technical scheme of the invention, the website abnormity can be automatically monitored.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention. In the drawings, like reference numerals are used to indicate like elements. The drawings in the following description are directed to some, but not all embodiments of the invention. For a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 schematically shows a flow chart of a method of analyzing a website failure according to the present invention.
Fig. 2 schematically shows a block schematic of an apparatus for analyzing website failures according to the present invention.
Fig. 3 shows schematically an embodiment in which the above-described solution according to the invention can be implemented.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 schematically shows a flow chart of a method of analyzing a website failure according to the present invention.
As shown in the solid line box of fig. 1, the method for analyzing website failure according to the present invention includes:
step S102: performing first numerical value statistics on numerical value fields and/or non-numerical value fields in log data by adopting at least one first statistical method to obtain a first numerical value statistical sequence;
step S104: performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence;
step S106: rejecting second numerical statistic sequences, which are specific to each first numerical statistic sequence and have statistical errors exceeding preset errors, in the second numerical statistic sequences to obtain third numerical statistic sequences, which are specific to each first numerical statistic sequence and are used for analyzing website faults;
step S108: and determining the abnormality which can cause the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence.
Optionally, as shown in the dashed box of fig. 1, the method for analyzing website failure according to the present invention further includes:
step S110: performing third numerical statistics on each first numerical statistical sequence by adopting at least one third statistical method to obtain a fourth numerical statistical sequence;
step S112: performing fourth numerical statistics on the abnormal time sequence in the specified time period by adopting at least one fourth statistical method to obtain a fifth numerical statistical sequence;
step S114: sorting the statistical results of the fifth numerical statistical sequence, associating the exception and the exception time before the sorting result with the user-defined event in the specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical value statistical sequence and the second numerical value statistical sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
Optionally, as shown in the dashed box of fig. 1, the method for analyzing website failure according to the present invention further includes:
step S116: and determining the condition simultaneously containing the exception and the custom event related to the exception as the website fault.
Optionally, the first statistical method comprises: the information entropy, the accumulated and average value in unit time and the difference value of adjacent values, and the second statistical method comprises the following steps: exponential smoothing, multi-layer perception, linear regression, quantile, standard deviation, the third statistical method includes: the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein the log data comprises: non-secure product log data and secure product log data.
For example, security products may include: anti-attack products, log analysis type attack recognition products, intelligent interception products, firewall server products, virtual firewall products and the like.
Fig. 2 schematically shows a block schematic of an apparatus for analyzing website failures according to the present invention.
As shown in the solid line box of fig. 2, the apparatus 200 for analyzing website failure according to the present invention includes:
a first statistical module 201, configured to perform first numerical statistics on a numerical field and/or a non-numerical field in log data by using at least one first statistical method, so as to obtain a first numerical statistical sequence;
the second statistical module 203 is configured to perform second numerical statistics on the first numerical statistical sequence by using at least four different second statistical methods, so as to obtain a second numerical statistical sequence;
the selecting module 205 is configured to eliminate a second numerical statistic sequence, which is in the second numerical statistic sequence and has a statistic error exceeding a predetermined error, from each first numerical statistic sequence, to obtain a third numerical statistic sequence, which is used for analyzing a website fault and is in the first numerical statistic sequence;
the anomaly detection module 207 is configured to determine an anomaly that may cause a website failure and an anomaly time based on a voting result of a statistical result of a third numerical statistical sequence of the at least one first numerical statistical sequence.
Optionally, as shown in the dashed line box of fig. 2, the apparatus 200 for analyzing website failure according to the present invention further includes:
a third statistical module 209, configured to perform third numerical statistics on each first numerical statistical sequence by using at least one third statistical method to obtain a fourth numerical statistical sequence;
a fourth statistical module 211, configured to perform fourth numerical statistics on the abnormal time sequence within the specified time period by using at least one fourth statistical method to obtain a fifth numerical statistical sequence;
an association module 213, configured to order the statistical results of the fifth numerical statistical sequence, associate the exception before the ordering result and the exception time with a user-defined event in a specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical value statistical sequence and the second numerical value statistical sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
Optionally, as shown in the dashed line box of fig. 2, the apparatus 200 for analyzing website failure according to the present invention further includes:
and a failure determining module 215, configured to determine that the case that includes both the exception and the custom event associated with the exception is a website failure.
Optionally, the first statistical method comprises: the information entropy, the accumulated and average value in unit time and the difference value of adjacent values, and the second statistical method comprises the following steps: exponential smoothing, multi-layer perception, linear regression, quantile, standard deviation, the third statistical method includes: the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein the log data comprises: non-secure product log data and secure product log data.
In order to make the technical solutions according to the present invention more clearly understood by those skilled in the art, the following description will be given with reference to specific embodiments.
Fig. 3 shows schematically an embodiment in which the above-described solution according to the invention can be implemented.
As shown in fig. 3, this embodiment includes the following modules (operations): "log" (corresponding to the above-mentioned log data), "security product" (corresponding to the above-mentioned security product), "monitoring index statistics" (corresponding to the above-mentioned first numerical statistical sequence, second numerical statistical sequence, etc. statistical indexes), "association coefficient" (corresponding to the association degree statistics in the third statistical method), "information entropy" (corresponding to the information entropy in the first statistical method), "timing prediction" (corresponding to the vote detection in the above-mentioned step S108), "anomaly point detection" (corresponding to the above-mentioned anomaly and anomaly time), "attribute analysis" (corresponding to the above-mentioned step S116), "failure event" (corresponding to the above-mentioned website failure), "trigger custom (repair) action," and "custom event" (corresponding to the above-mentioned custom event).
The above method according to the present invention may comprise the following exemplary steps:
s1, monitoring index statistics (corresponding to the above step S102)
S1.1, calculating a chaos degree sequence of numerical value sequence distribution of each numerical value field in the log in unit time by using an information entropy algorithm.
S1.2, counting the numerical characteristics of each field of the log (including the original log and the safety product identification log) in unit time, and calculating the accumulated and average numerical value in unit time. Several numerical sequences of successive units of time are obtained.
And S1.3, taking the difference value of adjacent numerical values of the sequence to form a difference value sequence.
S2, statistical characteristic analysis
S2.1, predicting time sequence.
Taking each output sequence of S1, calculating a sequence of predicted numerical value confidence intervals (corresponding to step S104) of sequences such as several consecutive unit times of index values, several days of the same time period, and the like, respectively, using a plurality of prediction methods such as exponential smoothing, multilayer perception, linear regression, quantiles, standard deviations, and the like, and rejecting the prediction method with a large error (corresponding to step S106) for each sequence, respectively.
S2.2, the correlation coefficient (corresponding to step S110 described above).
And (5) calculating the spearman correlation coefficient of the historical data between every two output sequences of S1 to obtain the correlation relations such as the direct ratio, the inverse ratio and the correlation degree between the sequences of the indexes.
S3, self-defining event
And customizing a query window, and setting judgment conditions such as change rate, ratio, upper limit, lower limit and the like of each monitoring index or customized calculation expression. And executing the query at regular time, and generating a self-defined event when the condition is met.
Or simply specify a point in time and a window.
S4, abnormal point detection (corresponding to the step S108)
And (4) detecting abnormal points of monitoring index values in a voting mode through the prediction method and parameters obtained in the S2.1, wherein the abnormal points are abnormal in the time sequence rule.
S5, attribution analysis
The abnormal points near the starting and ending time points of the custom event are sorted according to the absolute value of the correlation coefficient, the change rate, the data source correlation degree and the time difference (corresponding to the step S112), and N abnormal points in the top of the sorting are taken to be correlated to the custom event (corresponding to the step S114).
S6, failure event (corresponding to step S116 above)
And archiving the custom event and the associated monitoring index abnormal point in the S5 into a fault event.
In connection with the above method and apparatus according to the present invention, a storage medium is also proposed, on which a computer program is stored, which when executed by a processor implements the steps of the method as described above.
In combination with the above method and apparatus according to the present invention, there is also provided 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 method as described above when executing the program.
According to the technical scheme of the invention, the method has the following advantages:
1. the abnormality of the website can be automatically monitored.
2. The method can further automatically perform data analysis operations such as access trend analysis, fault reason analysis, fault influence range analysis, fault duration analysis and the like on the website based on abnormal monitoring of the website.
3. The abnormal points of the website monitoring indexes can be detected by combining methods such as single-index time sequence prediction, single-index information entropy and multi-index correlation analysis.
4. Website failure can be determined in conjunction with detected website anomalies and custom (monitoring) events (e.g., defined by query statements).
5. The cause and process of the fault can be further described for the anomaly grouping based on various statistical indicators such as the anomaly, the timing of the custom event, the interval, the historical correlation coefficient (e.g., the cause, the extent of influence of the event, and optionally the entire change process that is ultimately recovered can be described by grouping the anomaly on a timeline).
6. The website abnormity and/or fault can be automatically notified to the operation and maintenance engineer, and a user-defined (repairing) action is triggered, so that the operation and maintenance engineer is helped to quickly locate and process problems, the service fault duration of the website is shortened, and the cost is saved.
The above-described aspects may be implemented individually or in various combinations, and such variations are within the scope of the present invention.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Finally, it should be noted that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for analyzing website failures, comprising:
performing first numerical value statistics on numerical value fields and/or non-numerical value fields in log data by adopting at least one first statistical method to obtain a first numerical value statistical sequence;
performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence;
rejecting second numerical statistic sequences, which are specific to each first numerical statistic sequence and have statistical errors exceeding preset errors, in the second numerical statistic sequences to obtain third numerical statistic sequences, which are specific to each first numerical statistic sequence and are used for analyzing website faults;
and determining the abnormality which can cause the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence.
2. The method of analyzing website failures of claim 1, further comprising:
performing third numerical statistics on each first numerical statistical sequence by adopting at least one third statistical method to obtain a fourth numerical statistical sequence;
performing fourth numerical statistics on the abnormal time sequence in the specified time period by adopting at least one fourth statistical method to obtain a fifth numerical statistical sequence;
sorting the statistical results of the fifth numerical statistical sequence, associating the exception and the exception moment at the front of the sorting result with the user-defined event in the specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical statistic sequence and the second numerical statistic sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
3. The method of analyzing website failures of claim 2, further comprising:
and determining the condition simultaneously containing the exception and the custom event related to the exception as the website fault.
4. The method of analyzing website failures of claim 2, wherein said first statistical method comprises: the information entropy, the accumulated and average value in unit time, and the difference between adjacent values, wherein the second statistical method comprises the following steps: exponential smoothing, multi-level perception, linear regression, quantile, standard deviation, the third statistical method comprising: proportional ratio, inverse ratio and correlation degree, and the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein the log data comprises: non-secure product log data and secure product log data.
5. An apparatus for analyzing website failures, comprising:
the first statistical module is used for carrying out first numerical statistics on numerical fields and/or non-numerical fields in the log data by adopting at least one first statistical method to obtain a first numerical statistical sequence;
the second statistical module is used for performing second numerical statistics on the first numerical statistical sequence by adopting at least four different second statistical methods to obtain a second numerical statistical sequence;
the selection module is used for eliminating second numerical value statistical sequences, which are specific to each first numerical value statistical sequence and have statistical errors exceeding preset errors, in the second numerical value statistical sequences to obtain third numerical value statistical sequences, which are used for analyzing website faults and are specific to each first numerical value statistical sequence;
and the abnormality detection module is used for determining the abnormality possibly causing the website fault and the abnormal time based on the voting result of the statistical result of the third numerical statistical sequence aiming at the at least one first numerical statistical sequence.
6. The apparatus for analyzing website failure according to claim 5, further comprising:
the third statistical module is used for carrying out third numerical statistics on each first numerical statistical sequence by adopting at least one third statistical method to obtain a fourth numerical statistical sequence;
the fourth statistical module is used for carrying out fourth numerical statistics on the abnormal time sequence in the specified time period by adopting at least one fourth statistical method to obtain a fifth numerical statistical sequence;
the association module is used for sequencing the statistical results of the fifth numerical statistical sequence, associating the exception and the exception moment at the front of the sequencing result with the user-defined event in the specified time period,
wherein the custom event comprises at least one of: events of the change rate, the ratio, the upper limit and the lower limit of the first numerical statistic sequence and the second numerical statistic sequence are respectively larger than a preset change rate threshold, a ratio threshold, an upper limit threshold and a lower limit threshold, and the specified time period is a specified time period containing the self-defined events.
7. The apparatus for analyzing website failure according to claim 6, further comprising:
and the fault determining module is used for determining the condition that the abnormal condition and the custom event related to the abnormal condition are simultaneously contained as the website fault.
8. The apparatus for analyzing website failure according to claim 6, wherein the first statistical method comprises: the information entropy, the accumulated and average value in unit time, and the difference between adjacent values, wherein the second statistical method comprises the following steps: exponential smoothing, multi-level perception, linear regression, quantile, standard deviation, the third statistical method comprising: proportional ratio, inverse ratio and correlation degree, and the fourth statistical method comprises the following steps: absolute value direct ratio, change rate direct ratio, data source association degree inverse ratio and time difference inverse ratio, wherein the log data comprises: non-secure product log data and secure product log data.
9. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
10. 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 method of any one of claims 1 to 4 when executing the program.
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