CN110555011B - Application audit failure identification method, device and system and readable storage medium - Google Patents
Application audit failure identification method, device and system and readable storage medium Download PDFInfo
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Abstract
The invention discloses an application audit failure identification method which is applied to a cloud and used for acquiring update log data and audit log quantity data of each application; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the change trend of the log and the updated log data. The method is an identification scheme based on big data statistical analysis, audit log quantity of various applications is collected, change trend of historical data analysis logs is combined, whether audit is invalid or not is judged automatically, the whole process is completed automatically by equipment, manpower cost is avoided, detection real-time performance is good, detection efficiency is improved, and detection effect is guaranteed. In addition, the application also provides an application audit failure recognition device, system and readable storage medium with the technical advantages.
Description
Technical Field
The invention relates to the technical field of computer information, in particular to a method, a device and a system for identifying application audit failure and a computer readable storage medium.
Background
For the internet behavior management industry, the application of the auditing function is very important. With the rapid development of the internet, the updating and upgrading of the application are very rapid, and the problem of how to detect the audit identification failure caused by the updating of the application becomes a pain point of the industry.
The defects of the current application of the audit failure detection scheme are as follows: the application audit failure detection is completely manual, and the failure application detection and the failure time are inaccurate, so that the software updating speed cannot be kept up; the detection method is to judge whether the software is invalid or not by manual operation or automatic simulation of the use behavior of the software and then check the audit result, the software is updated and upgraded, the detection method needs synchronous adaptation and upgrade, the effect of the existing detection method is difficult to guarantee, and the detection effect is very poor.
Disclosure of Invention
The invention aims to provide an application audit failure identification method, device and system and a computer readable storage medium, which aim to solve the problems of low efficiency and poor detection effect in an audit identification failure detection scheme caused by the existing application update.
In order to solve the technical problem, the invention provides an application audit failure identification method, which is applied to a cloud, and the method comprises the following steps:
acquiring update log data and audit log quantity data of each application;
loading historical statistical analysis data through a big data statistical analysis platform;
classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data;
and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
Optionally, the determining whether the application audit is invalid according to the log change trend and the updated log data includes:
judging whether the latest obtained audit log volume data of the current application exceeds a preset control limit or not according to the log change trend;
when the newly acquired audit log volume data exceeds a preset control limit, judging whether the corresponding application is updated or not according to the updated log data;
if yes, judging that the application audit is invalid; if not, it is determined that the application audit may fail.
Optionally, the obtaining update log data of each application includes:
loading an application monitoring list locally stored by each application, wherein the application monitoring list comprises a mobile terminal application list and a PC terminal application list, each application corresponds to a log URL (uniform resource locator) address, and updating logs of each application are obtained through a crawler;
and traversing each application, comparing the latest version update time of the application with the latest recorded update time, and if the time is updated, listing the corresponding application in an update module list.
Optionally, the obtaining audit log volume data of each application includes:
whether an audit strategy is started or not is judged through an AC probe, when the audit strategy is started, whole audit log quantity data and per-capita audit log quantity data are collected respectively, and the collected data are uploaded to the cloud.
Optionally, after the obtaining of the update log data and the audit log volume data of each application, the method further includes:
and performing data statistical processing on the obtained audit log amount data according to each application and each application dimension of each person, and removing data abnormal points in the audit log amount data.
Optionally, after the determining whether the application audit is invalid according to the log change trend and the updated log data, the method further includes:
and outputting the judgment result to a front-end display end for displaying.
The invention also provides an application audit failure recognition device, which is applied to a cloud and comprises the following steps:
the data acquisition module is used for acquiring the update log data and the audit log quantity data of each application;
the historical data loading module is used for loading historical statistical analysis data through the big data statistical analysis platform;
the calculation module is used for performing classification calculation on each application, and calculating the log change trend of each application according to the audit log data and historical statistical analysis data;
and the judging module is used for judging whether the application audit is invalid or not according to the log change trend and the updated log data.
The invention also provides an application audit failure recognition system, which comprises: the system comprises an AC probe acquisition end and a cloud end;
the AC probe acquisition end is used for acquiring audit log quantity data of each application and uploading the acquired data to a cloud end;
the cloud is used for acquiring update log data and audit log volume data of each application; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
Optionally, the cloud is further configured to output a determination result to a front-end display end for display after determining whether application auditing is invalid according to the log change trend and the updated log data.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the above-described application audit failure identification methods.
The application audit failure identification method provided by the invention is applied to a cloud, and updates log data and audit log quantity data of each application are obtained; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the change trend of the log and the updated log data. The method is an identification scheme based on big data statistical analysis, audit log quantity of various applications is collected, change trend of historical data analysis logs is combined, whether audit is invalid or not is judged automatically, the whole process is completed automatically by equipment, manpower cost is avoided, detection real-time performance is good, detection efficiency is improved, and detection effect is guaranteed. The method is very helpful for maintaining the continuously high competitive power of the AC, and the scheme has obvious advantages. In addition, the application audit failure recognition device, system and computer readable storage medium with the technical advantages are further provided.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described 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 that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a method for identifying an audit failure of an application provided by the present invention;
FIG. 2 is a flow chart of a process for determining whether application auditing is stale in an embodiment provided by the present invention;
FIG. 3 is a diagram illustrating operations performed at an application in accordance with another embodiment of the present invention;
FIG. 4 is a diagram illustrating operations performed at the AC side according to another embodiment of the present invention;
FIG. 5 is a diagram illustrating operations performed at the cloud in another embodiment of the present invention;
fig. 6 is a block diagram of a structure of an application audit failure recognition apparatus according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the 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.
A flowchart of a specific implementation of the application audit failure identification method provided by the present invention is shown in fig. 1, and is applied to a cloud, where the method specifically includes:
step S101: acquiring update log data and audit log quantity data of each application;
the obtaining of the update log data of each application specifically includes: loading an application monitoring list locally stored by each application, wherein the application monitoring list comprises a mobile terminal application list and a PC terminal application list, each application corresponds to a log URL (uniform resource locator) address, and updating logs of each application are obtained through a crawler; and traversing each application, comparing the latest version update time of the application with the latest recorded update time, and if the time is updated, listing the corresponding application in an update module list.
The method for acquiring audit log quantity data of each application specifically comprises the following steps: whether an audit strategy is started or not is judged through an AC probe, when the audit strategy is started, whole audit log quantity data and per-capita audit log quantity data are collected respectively, and the collected data are uploaded to the cloud.
Step S102: loading historical statistical analysis data through a big data statistical analysis platform;
step S103: classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data;
step S104: and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
Referring to fig. 2, the process of determining whether application audit is invalid in the embodiment provided by the present invention may specifically be:
step S1041: judging whether the latest obtained audit log volume data of the current application exceeds a preset control limit or not according to the log change trend;
in step S103, classification calculation is performed for each application to obtain a log change trend of each application, and the latest obtained audit log volume data of the current application is the latest updated audit log volume data of the currently analyzed single application in the log change trend.
Step S1042: when the newly acquired audit log volume data exceeds a preset control limit, judging whether the corresponding application is updated or not according to the updated log data;
step S1043: if yes, judging that the application audit is invalid; if not, it is determined that the application audit may fail.
The data are classified according to the year, the week and the year, and then an average value is respectively established according to the application classification and each application classification of each person, so that the preset control limit can be specifically set as-3 sigma standard deviation. And when the latest acquired data exceeds a preset control limit, updating the analysis data by matching with the latest application, if the application is updated and the log quantity data exceeds the preset control limit, judging that the application audit is invalid, and if the application is not updated and the log quantity exceeds the preset control limit, judging that the log quantity is possibly invalid.
For example, if the total audit log quantity and the per-person log quantity of a certain application show a rapid descending trend and exceed a preset control limit, the application audit is determined to be invalid. And if the application is leveled or shows an ascending trend, determining that the application audit is effective.
The application audit failure identification method provided by the invention is applied to a cloud, and updates log data and audit log quantity data of each application are obtained; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the change trend of the log and the updated log data. The method is an identification scheme based on big data statistical analysis, audit log quantity of various applications is collected, change trend of historical data analysis logs is combined, whether audit is invalid or not is judged automatically, the whole process is completed automatically by equipment, manpower cost is avoided, detection real-time performance is good, detection efficiency is improved, and detection effect is guaranteed. The method is very helpful for maintaining the continuously high competitive power of the AC, and the scheme has obvious advantages.
On the basis of the foregoing embodiment, after the obtaining of the update log data and the audit log volume data of each application, the method may further include: and performing data statistical processing on the obtained audit log amount data according to each application and each application dimension of each person, and removing data abnormal points in the audit log amount data. According to the method and the device, data statistics processing is carried out on the obtained audit log quantity data, statistics can be carried out on preset statistics quantity respectively, and the preset statistics quantity can specifically comprise any one or any combination of the following: a minimum value, a first quartile, a median, a third quartile, and a maximum value. And constructing a screening range according to the obtained statistical values, wherein the data beyond the screening range are data abnormal points. After the data abnormal points are identified, the identified abnormal points are removed, so that the purpose of data noise reduction is achieved.
The collected data denoising method is to utilize five statistics in the data aiming at each application and two dimensions of each application of each person: the screening range is constructed by the minimum value, the first quartile, the median, the third quartile and the maximum value, so that abnormal points of the collected data can be rapidly identified, the noise reduction effect is achieved, and the reliability of a data source is ensured.
Optionally, after the determining whether the application audit is invalid according to the log change trend and the updated log data, the method further includes: and outputting the judgment result to a front-end display end for displaying.
The application audit failure identification method provided by the application is further elaborated in detail by combining specific scenes. Referring to fig. 3 to 5, in another embodiment of the schematic diagrams provided by the present invention, the application audit failure identification method can be completed by the cooperation of the application terminal, the AC terminal, and the cloud terminal.
The operations performed at the application side include:
step S201: and the application updating module is started and used for analyzing the updated information of the application and sensing the change of the updated application.
Step S202: and loading a locally stored application monitoring list which comprises a mobile terminal application list and a PC terminal application list, wherein each application corresponds to a log URL (uniform resource locator) address of an official website, and acquiring an update log of the application through a crawler.
Step S203: and traversing each application, comparing the update time of the latest version of the application with the update time of the latest record, and if the time is updated, listing the application in an update module list.
Step S204: and reporting data to the cloud through an interface.
The operations performed at the AC side include:
step S301: and starting a program for scheduling all subcomponents for acquiring actions.
Step S302: and judging whether the audit strategy is started or not, and starting the acquisition action only under the condition that the audit strategy is applied.
Step S303: and starting a parallel process, and respectively collecting the whole audit log volume data and the per-person audit log volume data of the application. Wherein the collected particle size may be days.
Step S304: reporting data to a cloud terminal through an interface; and after the data is reported, performing dormancy, waiting for the arrival of the next data acquisition time, and then entering step S302.
The operation executed in the cloud end comprises the following steps:
step S401: and the cloud end module is started and used for receiving the data collected by the probe and the data collected by the application updating module and then carrying out big data statistical analysis.
Step S402: and loading the data of the historical statistical analysis for completing the trend analysis of the data.
Step S403: and receiving reported audit log quantity data and application updating data.
Step S404: and classifying and calculating each application, calculating the log change trend of each application according to the dimension of the year, the week and the month, and adding application updating data to finish the correction and proofreading of the data.
Step S405: and judging whether the application auditing method fails due to the update of the application, outputting a result and performing front-end display.
According to the application, the audit log quantity of various applications is collected from a product side, the log change trend of each application is analyzed through big data statistics, and whether certain application audit is invalid or not is automatically identified and judged by combining a software list. The detection real-time performance is very good, and the labor cost is avoided.
In the following, the application audit failure recognition apparatus provided by the embodiment of the present invention is introduced, and the application audit failure recognition apparatus described below and the application audit failure recognition method described above may be referred to in correspondence with each other.
Fig. 6 is a block diagram of an application audit failure recognition apparatus according to an embodiment of the present invention, where the application audit failure recognition apparatus according to fig. 6 may include:
a data obtaining module 100, configured to obtain update log data and audit log volume data of each application;
a historical data loading module 200, configured to load historical statistical analysis data through a big data statistical analysis platform;
the calculation module 300 is used for performing classification calculation on each application, and calculating the log change trend of each application according to the audit log data and historical statistical analysis data;
and the judging module 400 is configured to judge whether application auditing is invalid according to the log change trend and the updated log data.
The application audit failure recognition apparatus of this embodiment is used to implement the foregoing application audit failure recognition method, and therefore specific embodiments of the application audit failure recognition apparatus may refer to the foregoing embodiment parts of the application audit failure recognition method, for example, the data obtaining module 100, the historical data loading module 200, the calculating module 300, and the judging module 400, which are respectively used to implement steps S101, S102, S103, and S104 in the foregoing application audit failure recognition method, so that the specific embodiments thereof may refer to descriptions of corresponding respective part embodiments, and are not described herein again.
In addition, the invention also provides an application audit failure recognition system, which comprises: the system comprises an AC probe acquisition end and a cloud end;
the AC probe acquisition end is used for acquiring audit log quantity data of each application and uploading the acquired data to a cloud end;
the cloud is used for acquiring update log data and audit log volume data of each application; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
And further, the cloud is also used for outputting a judgment result to a front-end display end for displaying after judging whether the application audit is invalid according to the log change trend and the updated log data.
Furthermore, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs any of the steps of the above-described method for identifying an application audit failure.
To sum up, this application is based on big data statistics analysis's identification scheme, through the audit log volume of gathering various applications to combine historical data analysis log's trend of change, judge automatically whether the audit is invalid, whole process is accomplished by equipment is automatic, and unmanned cost, and it is very good to detect the real-time, when having improved detection efficiency, has guaranteed the effect that detects. The method is very helpful for maintaining the continuously high competitive power of the AC, and the scheme has obvious advantages.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The application audit failure identification method, device, system and computer readable storage medium provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (10)
1. An application audit failure identification method is applied to a cloud end, and the method comprises the following steps:
acquiring update log data and audit log quantity data of each application;
loading historical statistical analysis data through a big data statistical analysis platform;
classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data;
and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
2. The method for identifying application audit failure according to claim 1, wherein the determining whether application audit is failed according to the log change trend and the update log data includes:
judging whether the latest obtained audit log volume data of the current application exceeds a preset control limit or not according to the log change trend;
when the newly acquired audit log volume data exceeds a preset control limit, judging whether the corresponding application is updated or not according to the updated log data;
if yes, judging that the application audit is invalid; if not, it is determined that the application audit may fail.
3. The application audit failure identification method according to claim 1 or 2, wherein the obtaining update log data of each application includes:
loading an application monitoring list locally stored by each application, wherein the application monitoring list comprises a mobile terminal application list and a PC terminal application list, each application corresponds to a log URL (uniform resource locator) address, and updating logs of each application are obtained through a crawler;
and traversing each application, comparing the latest version update time of the application with the latest recorded update time, and if the time is updated, listing the corresponding application in an update module list.
4. The application audit failure identification method of claim 3, wherein the obtaining audit log volume data for each application includes:
whether an audit strategy is started or not is judged through an AC probe, when the audit strategy is started, whole audit log quantity data and per-capita audit log quantity data are collected respectively, and the collected data are uploaded to the cloud.
5. The application audit failure identification method according to claim 4, further comprising, after said obtaining update log data and audit log volume data for each application:
and performing data statistical processing on the obtained audit log amount data according to each application and each application dimension of each person, and removing data abnormal points in the audit log amount data.
6. The method for identifying application audit failure according to claim 4, wherein after said determining whether application audit is failed according to the log change trend and the updated log data, further comprising:
and outputting the judgment result to a front-end display end for displaying.
7. The utility model provides an use audit inefficacy recognition device which characterized in that is applied to the high in the clouds, includes:
the data acquisition module is used for acquiring the update log data and the audit log quantity data of each application;
the historical data loading module is used for loading historical statistical analysis data through the big data statistical analysis platform;
the calculation module is used for performing classification calculation on each application, and calculating the log change trend of each application according to the audit log data and historical statistical analysis data;
and the judging module is used for judging whether the application audit is invalid or not according to the log change trend and the updated log data.
8. An application audit failure identification system, comprising: the system comprises an AC probe acquisition end and a cloud end;
the AC probe acquisition end is used for acquiring audit log quantity data of each application and uploading the acquired data to a cloud end;
the cloud is used for acquiring update log data and audit log volume data of each application; loading historical statistical analysis data through a big data statistical analysis platform; classifying and calculating each application, and calculating the log change trend of each application according to the audit log quantity data and historical statistical analysis data; and judging whether the application audit is invalid or not according to the log change trend and the updated log data.
9. The application audit failure recognition system of claim 8, wherein the cloud is further configured to output a determination result to a front-end display terminal for display after determining whether the application audit is failed according to the log change trend and the updated log data.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the application audit failure identification method of any one of claims 1 to 6.
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| US20140195554A1 (en) * | 2013-01-08 | 2014-07-10 | International Business Machines Corporation | System and method for case activity monitoring and case data recovery using audit logs in e-discovery |
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