CN104268064A

CN104268064A - Abnormity diagnosis method and device of product logs

Info

Publication number: CN104268064A
Application number: CN201410461063.7A
Authority: CN
Inventors: 杜鹏
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Shenzhen Taile Culture Technology Co ltd
Priority date: 2014-09-11
Filing date: 2014-09-11
Publication date: 2015-01-07
Anticipated expiration: 2034-09-11
Also published as: CN104268064B

Abstract

An embodiment of the invention discloses an abnormity diagnosis method and device of product logs. The abnormity diagnosis method comprises obtaining logs after an abnormity test is performed on the product logs, wherein the abnormity diagnosis is to be performed on the logs; extracting logs to be positioned in the logs, wherein the logs meet the consistency rule; performing dimension division statistics on the logs to be positioned based on the set statistical dimension and positioning abnormal logs in the logs to be positioned according to a statistical result so as to achieve the abnormity diagnosis on the product logs. According to the abnormity diagnosis method and device of the product logs, the existing product log diagnosis technology is optimized, the growing efficient and convenient product log abnormity diagnosis requirements of people are met, the working efficiency of the abnormity diagnosis personnel is greatly improved, and the input of the labor cost is reduced.

Description

The abnormality diagnostic method of product daily record and device

Technical field

The embodiment of the present invention relates to computer technology, particularly relates to a kind of abnormality diagnostic method and device of product daily record.

Background technology

Along with the development of Internet technology and the information processing technology, increasing terminal user has accessed internet, and uses various internet product in daily work, studying and living.Such as: Baidu's search, Baidu's music and Baidu's map etc.When terminal user operates above-mentioned internet product, system can produce corresponding product daily record with recording user operation behavior.Wherein, product daily record can reflect the ruuning situation of product each side, also contributes to the internet behavior that user understands in service of goods provider, and therefore, it can be internet product and provides technical operational support and improve foundation.

In existing product log analysis process, if find that product daily record exists extremely, abnormal investigation personnel mainly rely on personal experience, are obtained the abnormal log in product daily record by manual analysis, the mode of manually searching and are completed corresponding abnormity diagnosis accordingly.But, along with enriching constantly of internet product function, improving constantly of product complexity, product occurs that abnormal probability is also continuing to increase, and complete in abnormality diagnostic process in use prior art, the time and efforts needing investigation personnel to drop into also can be more, testing efficiency is low, testing procedure is loaded down with trivial details, and human cost drops into comparatively large, cannot meet the abnormity diagnosis demand of product daily record of the growing high efficiency of people, facilitation.

Summary of the invention

In view of this, the embodiment of the present invention provides a kind of abnormality diagnostic method and device of product daily record, to optimize existing product daily record diagnostic techniques, meets the abnormity diagnosis demand of product daily record of the growing high efficiency of people, facilitation.

In first aspect, embodiments provide a kind of abnormality diagnostic method of product daily record, comprising:

Obtain abnormal test is carried out to product daily record after treat abnormity diagnosis daily record;

The daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is treated described in extraction;

Based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

In second aspect, embodiments provide a kind of apparatus for diagnosis of abnormality of product daily record, comprising:

Treat abnormity diagnosis log acquisition unit, for obtain abnormal test is carried out to product daily record after treat abnormity diagnosis daily record;

Daily record extraction unit to be positioned, for treating to meet in abnormity diagnosis daily record the daily record to be positioned of consistance rule described in extracting;

Abnormal log positioning unit, for carrying out fractional dimension statistics based on setting statistics dimension to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

Treating after the embodiment of the present invention carries out abnormal test by acquisition to product log information diagnoses abnormity diagnosis daily record data; Wait described in extraction to diagnose the daily record data to be positioned meeting consistance rule in the daily record data of abnormal location; Based on setting statistics dimension, fractional dimension statistics is carried out to described daily record data to be positioned, and according to the abnormal log data in the described daily record data to be positioned in statistics location, to complete the abnormality diagnostic technological means to product daily record, optimize existing product daily record diagnostic techniques, meet the abnormity diagnosis demand of product daily record of the growing high efficiency of people, facilitation, greatly improve the work efficiency of abnormity diagnosis personnel, decrease the input of human cost.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of first embodiment of the invention;

Fig. 2 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of second embodiment of the invention;

Fig. 3 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of third embodiment of the invention;

Fig. 4 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of fourth embodiment of the invention;

Fig. 5 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of fifth embodiment of the invention;

Fig. 6 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of sixth embodiment of the invention;

Fig. 7 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of seventh embodiment of the invention;

Fig. 8 is the structural drawing of the apparatus for diagnosis of abnormality of a kind of product daily record of eighth embodiment of the invention.

Embodiment

In order to make the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the specific embodiment of the invention is described in further detail.Be understandable that, specific embodiment described herein is only for explaining the present invention, but not limitation of the invention.It also should be noted that, for convenience of description, illustrate only part related to the present invention in accompanying drawing but not full content.

First the application scenarios of various embodiments of the present invention and layout are summarized as follows:

In various embodiments of the present invention, in order to finally complete the abnormity diagnosis to product daily record, needing first to obtain and treating abnormity diagnosis daily record after abnormal test is carried out to product daily record.Abnormal test is generally the operation tentatively noted abnormalities, and abnormity diagnosis is having in abnormal daily record the diagnostic operation carrying out abnormal location and identify.

Wherein, the abnormal test that abnormal test mainly comprises following two kinds of scenes is carried out to product daily record:

1, the abnormal test of differentiation data: for the abnormal test between completing the product daily record deriving from different pieces of information platform.

In a concrete application scenarios, an existing log statistic platform of internet product is the first data platform, Data Source in this data platform is each bottom daily record corresponding with this internet product, first data platform is by being undertaken after certain filtration treatment (such as by bottom daily record, by reptile daily record, static resource Request Log, and PV (Page View in a period of time in bottom daily record data, page browsing amount or click volume) or UV (Unique Visitor, independent visitor) exceed predetermined threshold value after daily record filter out), store as product daily record, along with the continuous progress of technology, product operator wishes each bottom daily record to carry out more standardized management, such as, managed by data warehouse, can by each bottom daily record by ETL (Extract-Transform-Load, extraction-transposition-loading) data warehouse technology process after, be stored in the second data platform (data warehouse) as product daily record.In order to verify the accuracy of each product daily record stored in the second data platform, by carrying out abnormal test to the product daily record stored in the first data platform and the second data platform, the abnormity diagnosis to product daily record can be completed.

2, the abnormal test of non-differentiation data: for the abnormal test between completing the product daily record in the different time interval deriving from same data platform.

In a concrete application scenarios, product operator can by each product daily record in same data platform, calculate the PV value of this internet product in different time interval or UV value, and then abnormal test can be carried out to the data burst of different time sections is abnormal, and then complete the abnormity diagnosis to product daily record.

Be described in detail at the second embodiment herein and the 3rd embodiment abnormal test mainly for differentiation data; 4th embodiment-the 7th embodiment is described in detail mainly for the abnormal test of non-differentiation data.

First embodiment

Fig. 1 is the abnormality diagnostic method process flow diagram of a kind of product daily record of first embodiment of the invention, the method of the present embodiment can be performed by the apparatus for diagnosis of abnormality of product daily record, this device realizes by the mode of hardware and/or software, and general accessible site is in for completing in the abnormality diagnostic server of product daily record.The method of the present embodiment specifically comprises following operation:

110, obtain abnormal test is carried out to product daily record after treat abnormity diagnosis daily record.

In the present embodiment, abnormity diagnosis server obtain abnormal test is carried out to product daily record after treat abnormity diagnosis daily record.

In the present embodiment, exist abnormal after abnormity diagnosis daily record is specifically by abnormal test, and need to carry out follow-up abnormality diagnostic product daily record.

Wherein, described in treat that abnormity diagnosis daily record can for deriving from least two daily record data groups of at least two data platforms, also can treat abnormity diagnosis daily record for what derive from same target data platform, this is not limited.

In an object lesson, obtain and treat that abnormity diagnosis daily record can specifically comprise after abnormal test is carried out to product daily record:

Obtain the first data platform and the second data platform in the product daily record of setting-up time interval (such as, 2014.8.87:00:00-8:00:00), form the first daily record data group and the second daily record data group;

Calculate and the first daily record data group and the not corresponding verification desired value (such as, PV value or UV value etc.) of the second daily record data component;

If there are differences (such as between the verification desired value corresponding with the first daily record data group and the verification desired value corresponding with the second daily record data group, difference value is greater than predetermined threshold), determine to exist abnormal (needing to carry out follow-up abnormity diagnosis) in the first daily record data group and the second daily record data group, and using the first daily record data group and the second daily record data group as treating abnormity diagnosis daily record.

In another object lesson, obtain and treat that abnormity diagnosis daily record can specifically comprise after abnormal test is carried out to product daily record:

Obtain at least three the daily record data groups corresponding with at least three time intervals of same target data platform;

Calculate the verification desired value corresponding respectively with these at least three daily record data groups;

If there are differences between the verification desired value of target journaling data group and all the other each daily record data groups, determine to exist extremely in this target journaling data group, and using this target journaling data group as treating abnormity diagnosis daily record.

Concrete, for same target data platform, obtain the three daily record data group corresponding with 2014.8.87:00:00-8:00:00, the four daily record data group corresponding with 2014.8.88:00:00-9:00:00, and the five daily record data group corresponding with 2014.8.89:00:00-10:00:00; The PV value of the PV value calculating the 3rd daily record data group to be the PV value of the 520, four daily record data group be the 525, five daily record data group is 1850; If prespecified when two difference value are greater than 100, determine to there are differences between two verification desired values, then the 5th daily record data group and all the other two daily record data groups all there are differences, using the 5th daily record data group as treating abnormity diagnosis daily record.

120, the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is treated described in extraction.

In the present embodiment, to described, abnormity diagnosis server treats that consistance rule test is carried out in abnormity diagnosis daily record, and treats the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record described in extracting.

In the present embodiment, after abnormity diagnosis daily record is treated in acquisition, can not directly determine to treat necessarily to include abnormal log in abnormity diagnosis daily record.

For example, when to two the daily record data groups deriving from two data platforms, carry out abnormal test and obtain the first daily record data group and the second daily record data group as after abnormity diagnosis daily record, only when determining that there is between the first daily record array with the second daily record array identical comparison condition, just can determine to treat to include abnormal log in abnormity diagnosis daily record.And at some in particular cases, such as, first daily record array and the second daily record array be not for identical product (Baidu's music or Baidu's music in a certain song) or same type operation (such as, search class, download class or audition class) the product daily record that produces, because the two does not have identical comparison condition, then can not determine and treat necessarily to include abnormal log in abnormity diagnosis daily record, only have and re-start abnormal test after being set to identical comparison condition between the first daily record array with the second daily record array, and then could determine to treat whether include abnormal log in abnormity diagnosis daily record.

According to different actual conditions, treat not comprise abnormal log in abnormal location daily record, or be only treat to include abnormal log in the partial log in abnormal location daily record, in order to improve location efficiency and the Position location accuracy of abnormal log, in the present embodiment, abnormity diagnosis server needs described in extraction after meeting the daily record to be positioned of consistance rule in abnormity diagnosis daily record, just can locate abnormal log wherein further.

Wherein, daily record to be positioned is specially the product daily record treating to include in abnormity diagnosis daily record abnormal log.Abnormity diagnosis server, by carrying out abnormal location to daily record to be positioned, namely can obtain abnormal log wherein.

Wherein, consistance rule is specially: treat whether abnormity diagnosis daily record includes daily record to be positioned and the comparison condition set for determining, such as, the consistance of the consistance comparison of filtering rule, the consistance comparison of data source identification or product daily record build environment, than equity, does not limit this.Wherein, if described in treat that abnormity diagnosis daily record is at least two the daily record data groups deriving from least two data platforms, described consistance rule test specifically can comprise: the consistency check of filtering rule and the consistency check etc. of data source identification, do not limit this.

More specifically, the consistency check of filtering rule specifically can comprise: determine whether these at least two daily record data groups all filter reptile daily record or static resource Request Log, or determine PV (the Page View of these at least two daily record data groups, page browsing amount or click volume) or UV (Unique Visitor, independent visitor) filtering rule whether consistent etc.; The consistency check of data source identification specifically can comprise: determine that whether the Data Source of these at least two daily record data groups is identical, such as, whether be click logs or travel log etc.

Wherein, if described in treat abnormity diagnosis daily record be derive from same target data platform treat abnormity diagnosis daily record, described consistance rule test specifically can comprise: determine treating in the determined time interval of abnormity diagnosis daily record, whether the filtering rule of this target data platform changes and determines treating in the determined time interval of abnormity diagnosis daily record, whether system has reached the standard grade the product function etc. be associated with this abnormity diagnosis daily record, and contrast does not limit.

130, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

In general, a lot of valuable field parameter is included in product daily record, such as, User IP (Internet Protocol, Internet protocol), the field parameter such as PID (Product IDentity, product identification), channel and time.

In the present embodiment, one or more field parameter in product daily record is carried out fractional dimension statistics as statistics dimension to described daily record to be positioned by abnormity diagnosis server, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

Wherein, abnormal log is specially and is carrying out in abnormality diagnosis process to product daily record, causes abnormal product daily record, and product development personnel or tester, by analyzing abnormal log, can complete the abnormity diagnosis to product daily record.

Wherein, if described in treat that abnormity diagnosis daily record is at least two the daily record data groups deriving from least two data platforms, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and can specifically comprise according to the abnormal log in the statistics described daily record to be positioned in location:

At least one setting statistics dimension is used to carry out fractional dimension statistics to described at least two daily record data groups, to add up in described daily record data group, setting desired value corresponding with statistics dimension values under described setting statistics dimension; For same statistics dimension values, if there are differences between the setting desired value of at least two daily record data groups, obtain product daily record corresponding with this statistics dimension values in the data platform corresponding with this daily record data group as abnormal log.

Wherein, if described in treat abnormity diagnosis daily record be derive from same target data platform treat abnormity diagnosis daily record, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and can specifically comprise according to the abnormal log in the statistics described daily record to be positioned in location:

Use at least one setting statistics dimension, fractional dimension statistics carried out to described daily record to be positioned, with add up under described setting statistics dimension with setting desired value corresponding to statistics dimension values; Daily record to be positioned after statistics is sorted by described setting desired value order from big to small, and obtains the statistics dimension values of predetermined number according to ranking results; The product daily record corresponding with the statistics dimension values of described predetermined number is obtained, as abnormal log in target journaling data platform.

Second embodiment

Fig. 2 is the abnormality diagnostic method process flow diagram of a kind of product daily record of second embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, preferably operation is obtained being optimized for until abnormity diagnosis daily record after abnormal test is carried out to product daily record: obtain at least two the daily record data groups deriving from least two data platforms after abnormal test is carried out to product daily record, as treating abnormity diagnosis daily record;

Preferably operation is carried out fractional dimension statistics based on setting statistics dimension to described daily record to be positioned, and be optimized for according to the abnormal log in the statistics described daily record to be positioned in location: use at least one setting statistics dimension to carry out fractional dimension statistics to described at least two daily record data groups, to add up in described daily record data group, setting desired value corresponding with statistics dimension values under described setting statistics dimension; For same statistics dimension values, if there are differences between the setting desired value of at least two daily record data groups, obtain product daily record corresponding with this statistics dimension values in the data platform corresponding with this daily record data group as abnormal log;

Treat that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is optimized for described in preferably operation being extracted: obtain the filtering rule corresponding with described at least two daily record data groups; If the filtering rule corresponding with daily record data group is identical, using daily record data group identical for basic filtering rule as described daily record to be positioned; Otherwise, from data platform, again obtain daily record data group according to supplementary filtering rule, and re-start abnormal test: if the assay re-starting abnormal test disappears, using inconsistent for the filtering rule abnormity diagnosis result as product daily record for abnormal; Otherwise, by the daily record data group again obtained, as described daily record to be positioned.

Accordingly, the method for the present embodiment comprises following operation:

210, obtain abnormal test is carried out to product daily record after derive from least two daily record data groups of at least two data platforms, as treating abnormity diagnosis daily record.

220, the filtering rule corresponding with described at least two daily record data groups is obtained.

In the present embodiment, can by artificially judging that the mode of the code logic corresponding with described at least two daily record data groups obtains corresponding filtering rule, also abnormity diagnosis Servers-SQL (Structured Query Language can be passed through, structuralized query voice) mode of automatically carrying out code analysis obtains corresponding filtering rule, this do not limited.

230, judge that whether the filtering rule corresponding with daily record data group be identical: if so, perform 240; Otherwise, perform 250.

In the present embodiment, abnormity diagnosis server carries out the consistency check of filtering rule by treating abnormity diagnosis daily record, obtains daily record to be positioned wherein.

In the present embodiment, the different the judged results whether filtering rule corresponding from daily record data group be identical are judged, corresponding to following Different treatments for abnormity diagnosis server:

If abnormity diagnosis server judges that the filtering rule corresponding from least two daily record data groups is all different, the filtering rule corresponding with wherein any one daily record data group filtering rule as a supplement can be selected, after data platform corresponding to other daily record data group uses this supplementary filtering rule again to obtain product daily record, again these at least two daily record data groups are carried out abnormal test;

If there is the identical daily record data group different with filtering rule of filtering rule (such as at least two daily record data groups described in abnormity diagnosis server judges simultaneously, treat that abnormity diagnosis daily record comprises three daily record data groups, wherein the filtering rule of two daily record data groups is identical, and the 3rd daily record data group is different from the filtering rule of these two daily record data groups), can using this identical filtering rule as basic filtering rule, and directly using daily record data group identical for basic filtering rule as daily record to be positioned.Afterwards by this identical filtering rule filtering rule as a supplement, after data platform corresponding to the different daily record data group of filtering rule uses this supplementary rule again to obtain product daily record, again carry out abnormal test with above-mentioned daily record to be positioned;

If include at least two daily record data groups described in abnormity diagnosis server judges and distinguish at least two daily record data groups of correspondent equal (such as from different filtering rule, treat that abnormity diagnosis daily record comprises four daily record data groups, wherein the filtering rule of two daily record data groups is identical, wherein the filtering rule of another two daily record data groups is identical), can be regular using above-mentioned different filtering rule as basic filtering, and obtain the identical daily record data group of basic filtering rule as daily record to be positioned.

240, using the identical daily record data group of basic filtering rule as daily record to be positioned, perform 290.

250, from data platform, again obtain daily record data group according to supplementary filtering rule, and re-start abnormal test, perform 260.

260, judge whether the assay re-starting abnormal test is abnormal disappearance: if so, perform 270; Otherwise, perform 280.

270, using inconsistent for the filtering rule abnormity diagnosis result as product daily record, process ends.

For example, after product daily record abnormal test is carried out to the first data platform and the second data platform, find to exist extremely (such as, by the product daily record in comparison two data platforms, obtain existing abnormal in this day of 2014.8.8 for the playback volume of this first song of ordinary road), obtain all over products daily record of the first data platform in this day of 2014.8.8 as the first daily record data group, obtain all over products daily record of the second data platform in this day of 2014.8.8 as the second daily record data group, the filtering rule of comparison first daily record data group and the second daily record data group, find when calculating playback volume, the filtering rule that first data platform adopts is " type=playstart ", and the filtering rule that the second data platform adopts is " type=play100ms ", both are different, after the filtering rule of product daily record in the second data platform is revised as " type=playstart ", again abnormal test is carried out with the product daily record of the first data platform, disappear if abnormal, then " filtering rule calculating playback volume is inconsistent " is carried out record as abnormal test result, and terminate abnormity diagnosis flow process.

280, the daily record data group will again obtained, as described daily record to be positioned, performs 290.

290, at least one setting statistics dimension is used to carry out fractional dimension statistics to described at least two daily record data groups, to add up in described daily record data group, setting desired value corresponding with statistics dimension values under described setting statistics dimension.

In the present embodiment, setting statistics dimension can be User IP (Internet Protocol, Internet protocol), parameter field in the product daily record such as PID (Product IDentity, product identification), channel and time, this is not limited.

For example, abnormity diagnosis server uses User IP to carry out fractional dimension statistics as setting statistics dimension to two daily record data groups (the first daily record data group and the second daily record data group), with the PV value that statistics is corresponding with different user IP, statistics is as shown in table 1.

Table 1

2100, for same statistics dimension values, whether there are differences between the setting desired value judging at least two daily record data groups: if so, perform 2110; Otherwise, return 2100.

As shown in table 1, be the statistics dimension values of 132.11.43.2 for User IP, the PV value of the first daily record data group is the PV value of the 121, second daily record data group is 104, there are differences between the two.

2110, product daily record corresponding with this statistics dimension values in the data platform corresponding with this daily record data group is obtained as abnormal log, to complete the abnormity diagnosis to product daily record.

As previously mentioned, abnormity diagnosis server can in the data platform that the first daily record data and the second daily record data are corresponding respectively, obtain and User IP be product daily record that 132.11.43.2 is corresponding as abnormal log, to complete the abnormity diagnosis to product daily record.

For example, by analyze obtain the first data platform and the second data platform in, find after all over products daily record corresponding with 132.11.43.2, producing abnormal reason is because have special separator " t " in the abnormal log of the second data platform, affect the storage of product daily record in the second data platform, cause PV value to reduce, and then the abnormity diagnosis to product daily record can be completed.

On the basis of the various embodiments described above, obtain the filtering rule corresponding with described at least two daily record data groups preferably can comprise: in described at least two data platforms, obtain the SQL program code of the filtering rule corresponding with described at least two daily record data groups; Code analysis is carried out to the described SQL program code obtained, based on setting keyword search filtercondition statement; Using the filtercondition statement that searches as the filtering rule corresponding with described at least two daily record data groups.

Wherein, in the preferred embodiment, set keyword specifically can comprise: where and/or case when.

The benefit of such setting is: solve the filtering rule brought when artificially judging code logic to obtain filtering rule and obtain comprehensive and have the technical matters of omission, greatly reduce the artificial participation in the abnormality diagnosis process of product daily record, further improve abnormality diagnostic efficiency.

On the basis of the various embodiments described above, based on setting statistics dimension, fractional dimension statistics is being carried out to described daily record to be positioned, and according to after the abnormal log in the statistics described daily record to be positioned in location, is also comprising:

By the abnormal log in described daily record to be positioned, be supplied to user according to the statistics dimension of setting.

In the preferred embodiment, abnormity diagnosis server, by the abnormal log in described daily record to be positioned, is supplied to user according to the statistics dimension of setting.

The benefit of such setting is: user can based on the statistics dimension abnormal log that provide of abnormity diagnosis server according to setting, convenient, effectively carry out abnormity diagnosis.

3rd embodiment

Fig. 3 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of third embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, preferably described in operation extraction, treat that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is optimized for: obtain the data source identification corresponding with described at least two daily record data groups; If the data source identification corresponding with daily record data group is identical, master data source is identified identical daily record data group as the first daily record; Otherwise, again from data platform, daily record data group is obtained according to supplementary data source mark, and re-start abnormal test: if the assay re-starting abnormal test disappears, then using inconsistent for the data source identification abnormity diagnosis result as product daily record for abnormal; Otherwise, by the daily record data group again obtained, as described first daily record; Extract in described first daily record and meet the conforming daily record to be positioned of filtering rule.

310, obtain abnormal test is carried out to product daily record after derive from least two daily record data groups of at least two data platforms, as treating abnormity diagnosis daily record.

320, the data source identification corresponding with described at least two daily record data groups is obtained.

In the present embodiment, first abnormity diagnosis server extracts and waits to diagnose the first daily record data meeting data source identification consistency on messaging in the daily record data of abnormal location;

Extract again afterwards in described first daily record data and meet the conforming daily record data to be positioned of filtering rule.

Wherein, in each product daily record, record the data source identification type corresponding with product daily record, such as: click logs, travel log, download log or inquiry log etc.Abnormity diagnosis server by extracting specific field contents in product daily record, can obtain described data source identification type.

330, judge that whether the data source identification corresponding with daily record data group be identical: if so, perform 340; Otherwise, perform 350.

In the present embodiment, the different the judged results whether data source identification corresponding from daily record data group be identical are judged, corresponding to following Different treatments for abnormity diagnosis server:

If abnormity diagnosis server judges that the data source identification corresponding from least two daily record data groups is all different, the data source identification corresponding with wherein any one daily record data group data source identification as a supplement can be selected, after data platform corresponding to other daily record data group uses this supplementary data source to identify again to obtain product daily record, again these at least two daily record data groups are carried out abnormal test;

If there is the identical daily record data group different with data source identification of data source identification (such as at least two daily record data groups described in abnormity diagnosis server judges simultaneously, treat that abnormity diagnosis daily record comprises three daily record data groups, wherein the data source identification of two daily record data groups is identical, and the 3rd daily record data group is different from the data source identification of these two daily record data groups), using this same data source mark as master data source mark, and directly master data source can be identified identical daily record data group as the first daily record.Afterwards by this identical data source identification data source identification as a supplement, after data platform corresponding to the daily record data group that data source identification is different uses this supplementary data source to identify again to obtain product daily record, again carry out abnormal test with above-mentioned first daily record;

Include at least two daily record data groups described in if abnormity diagnosis server judges identify with different pieces of information source distinguish correspondent equal at least two daily record data groups (such as, treat that abnormity diagnosis daily record comprises four daily record data groups, wherein the data source identification of two daily record data groups is identical, wherein the data source identification of another two daily record data groups is identical), can identify above-mentioned different data source identification as master data source, and obtain master data source and identify identical daily record data group as the first daily record.

340, master data source is identified identical daily record data group as the first daily record, perform 390.

350, again from data platform, obtain daily record data group according to supplementary data source mark, and re-start abnormal test.

360, judge whether the assay re-starting abnormal test is abnormal disappearance: if so, perform 370; Otherwise, perform 380.

370, using inconsistent for the data source identification abnormity diagnosis result as product daily record, process ends.

380, the daily record data group will again obtained, as described first daily record.

390, the filtering rule corresponding with described at least two the first daily records is obtained.

3100, judge that whether the filtering rule corresponding with the first daily record be identical: if so, perform 3110; Otherwise, perform 3120.

3110, using identical the first daily record of basic filtering rule as daily record to be positioned, perform 3160.

3120, from data platform, again obtain daily record data group according to supplementary filtering rule, and re-start abnormal test, perform 3130.

3130, judge whether the assay re-starting abnormal test is abnormal disappearance: if so, perform 3140; Otherwise, perform 3150.

3140, using inconsistent for the filtering rule abnormity diagnosis result as product daily record, process ends.

3150, the daily record data group will again obtained, as described daily record to be positioned, performs 3160.

3160, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

4th embodiment

Fig. 4 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of fourth embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, preferably operation is obtained abnormal test is carried out to product daily record after being optimized for until abnormity diagnosis daily record: obtain to derive from target data platform after abnormal test is carried out to product daily record treat abnormity diagnosis daily record;

Preferably operation is carried out fractional dimension statistics based on setting statistics dimension to described daily record to be positioned, and be optimized for according to the abnormal log in the statistics described daily record to be positioned in location: use at least one setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, with add up under described setting statistics dimension with setting desired value corresponding to statistics dimension values; Daily record to be positioned after statistics is sorted by described setting desired value order from big to small, and obtains the statistics dimension values of predetermined number according to ranking results; The product daily record corresponding with the statistics dimension values of described predetermined number is obtained, as abnormal log in target journaling data platform.

410, obtain abnormal test is carried out to product daily record after derive from target data platform treat abnormity diagnosis daily record.

420, the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is treated described in extraction.

430, use at least one setting statistics dimension, fractional dimension statistics carried out to described daily record to be positioned, with add up under described setting statistics dimension with setting desired value corresponding to statistics dimension values.

440, the daily record to be positioned after statistics is sorted by described setting desired value order from big to small, and obtain the statistics dimension values of predetermined number according to ranking results.

450, in target journaling data platform, the product daily record corresponding with the statistics dimension values of described predetermined number is obtained, as abnormal log, to complete the abnormity diagnosis to product daily record.

For example, abnormity diagnosis server uses User IP to carry out fractional dimension statistics as setting statistics dimension to daily record to be positioned, with the PV value that statistics is corresponding with different user IP, and statistics sorted according to order from big to small, ranking results is as shown in table 2.

Table 2

In general, abnormal data can appear at very large probability in the product daily record at the statistics dimension values place corresponding with larger setting desired value.In the present embodiment, after statistics sorts according to order from big to small by abnormity diagnosis server, obtain the product daily record corresponding with the statistics dimension values of predetermined number (such as, 3,4 or 5 etc.), as abnormal log.

For example, abnormity diagnosis server obtains in target data platform, and the product daily record corresponding with the User IP of front 5 the PV values after sequence, as abnormal log.

In the present embodiment, if when setting grouping dimension is User IP, can judge whether that same IP carries out the behavior (same IP repeatedly clicks or downloads the behavior of same song) of brush list according to abnormal log; If setting grouping dimension is (the directly input network address access of access approach, or the access etc. from other web portal water conservancy diversion carry out), can judge that customer group is carried out source distribution and whether had certain source abnormal according to abnormal log, and then complete the abnormity diagnosis to product daily record.

5th embodiment

Fig. 5 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of fifth embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, preferably operation is carried out fractional dimension statistics based on setting statistics dimension to described daily record to be positioned, and be optimized for according to the abnormal log in the statistics described daily record to be positioned in location: in the product daily record of reference data platform, obtain the reference daily record corresponding with daily record to be positioned; Calculate the verification desired value corresponding with described reference daily record and described daily record difference to be positioned; If there are differences with between described daily record to be positioned and the described verification desired value corresponding respectively with reference to daily record, use at least one setting statistics dimension to described daily record to be positioned and describedly carry out fractional dimension statistics with reference to daily record, with setting desired value corresponding with statistics dimension values under adding up described setting statistics dimension; For same statistics dimension values, if there are differences between the daily record to be positioned after statistics and the setting desired value with reference to daily record, obtain product daily record corresponding with described statistics dimension values in target data platform and reference data platform respectively, as abnormal log.

510, obtain abnormal test is carried out to product daily record after derive from target data platform treat abnormity diagnosis daily record.

520, the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is treated described in extraction.

530, in the product daily record of reference data platform, the reference daily record corresponding with daily record to be positioned is obtained.

In the present embodiment, abnormity diagnosis server is after acquisition derives from the daily record to be positioned of same target data platform, before carrying out exception location, not such to the 4th embodiment, consider that abnormal cause has the abnormal log of this target data platform to cause, but consider that abnormal cause is caused by the data variance of different pieces of information platform.

In the present embodiment, abnormity diagnosis server treats abnormity diagnosis daily record according to what derive from target data platform, obtains in the product daily record of reference data platform, obtains the reference daily record corresponding with daily record to be positioned.

Such as, if treat that abnormity diagnosis daily record is the product daily record of the first data platform in this time period of 2014.8.88:00:00-9:00:00, the second data platform can be selected in the product daily record of this time period of 2014.8.88:00:00-9:00:00 as with reference to daily record.

540, the verification desired value corresponding with described reference daily record and described daily record difference to be positioned is calculated.

550, judge whether there are differences with between described daily record to be positioned and the described verification desired value corresponding respectively with reference to daily record: if so, perform 560; Otherwise, perform 570.

560, use at least one setting statistics dimension to described daily record to be positioned and describedly carry out fractional dimension statistics with reference to daily record, with setting desired value corresponding with statistics dimension values under adding up described setting statistics dimension, performing 580.

570, process ends.

580, for same statistics dimension values, judge whether there are differences between the daily record to be positioned after adding up and the setting desired value with reference to daily record: if so, perform 590; Otherwise, return 580.

590, product daily record corresponding with described statistics dimension values in target data platform and reference data platform is obtained respectively, as abnormal log.

6th embodiment

Fig. 6 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of sixth embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, treat that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record is optimized for described in preferably operation being extracted: if treat in the determined time interval of abnormity diagnosis daily record described, the filtering rule of target data platform is not modified, and treats that abnormity diagnosis daily record is as described daily record to be positioned using what derive from target data platform; Otherwise, recover filtering rule and again from target data platform, obtain product daily record, and re-start abnormal test; If the assay re-starting abnormal test disappears for abnormal, then using the abnormity diagnosis result of amendment filtering rule as product daily record; Otherwise, by the product daily record again obtained, as described daily record to be positioned.

610, obtain abnormal test is carried out to product daily record after derive from target data platform treat abnormity diagnosis daily record.

620, judge to treat, in the determined time interval of abnormity diagnosis daily record, whether the filtering rule of target data platform is modified described: if so, perform 630; Otherwise, perform 640.

630, recover filtering rule and again from target data platform, obtain product daily record, and re-start abnormal test, perform 650;

640, treat that abnormity diagnosis daily record is as described daily record to be positioned using what derive from target data platform, perform 680.

650, judge whether the assay re-starting abnormal test is abnormal disappearance: if so, perform 660; Otherwise, perform 670.

660, using the abnormity diagnosis result of amendment filtering rule as product daily record, process ends.

670, the product daily record will again obtained, as described daily record to be positioned, performs 680.

680, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

7th embodiment

Fig. 7 is the process flow diagram of the abnormality diagnostic method of a kind of product daily record of seventh embodiment of the invention.The present embodiment is optimized based on above-described embodiment, in the present embodiment, preferably described in operation extraction, treat that the daily record data to be positioned meeting consistance rule in abnormity diagnosis daily record is optimized for: treat described in extraction in abnormity diagnosis daily record, to meet conforming first daily record of filtering rule; If in the determined time interval of the first daily record, the online implementing product function be associated with described first daily record, using the abnormity diagnosis result of new product function as product daily record of reaching the standard grade; Otherwise, using described first daily record as described daily record to be positioned.

710, obtain abnormal test is carried out to product daily record after derive from target data platform treat abnormity diagnosis daily record.

720, judge to treat, in the determined time interval of abnormity diagnosis daily record, whether the filtering rule of target data platform is modified described: if so, perform 630; Otherwise, perform 640.

730, recover filtering rule and again from target data platform, obtain product daily record, and re-start abnormal test, perform 750;

740, treat that abnormity diagnosis daily record is as the first daily record using what derive from target data platform, perform 780.

750, judge whether the assay re-starting abnormal test is abnormal disappearance: if so, perform 760; Otherwise, perform 770.

760, using the abnormity diagnosis result of amendment filtering rule as product daily record, process ends.

770, the product daily record will again obtained, as described first daily record, performs 780.

780, judge in the determined time interval of the first daily record, whether system has reached the standard grade the product function be associated with described first daily record: if so, perform 790; Otherwise, perform 7100.

790, using the abnormity diagnosis result of new product function as product daily record of reaching the standard grade, process ends.

7100, using described first daily record as described daily record to be positioned, perform 7110.

7110, based on setting statistics dimension, fractional dimension statistics is carried out to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

8th embodiment

Figure 8 illustrates the structural drawing of the apparatus for diagnosis of abnormality of a kind of product daily record of eighth embodiment of the invention.As shown in Figure 8, described device comprises:

Treat abnormity diagnosis log acquisition unit 81, for obtain abnormal test is carried out to product daily record after treat abnormity diagnosis daily record.

Daily record extraction unit 82 to be positioned, for treating to meet in abnormity diagnosis daily record the daily record to be positioned of consistance rule described in extracting.

Abnormal log positioning unit 83, for carrying out fractional dimension statistics based on setting statistics dimension to described daily record to be positioned, and according to the abnormal log in the statistics described daily record to be positioned in location, to complete the abnormity diagnosis to product daily record.

On the basis of the various embodiments described above, described in treat that abnormity diagnosis log acquisition unit may be used for:

Obtain at least two the daily record data groups deriving from least two data platforms after abnormal test is carried out to product daily record, as treating abnormity diagnosis daily record.

On the basis of the various embodiments described above, described abnormal log positioning unit specifically may be used for:

On the basis of the various embodiments described above, daily record extraction unit to be positioned can comprise:

Filtering rule obtains subelement, for obtaining the filtering rule corresponding with described at least two daily record data groups; Subelement is extracted in first location daily record, if identical for the filtering rule corresponding with daily record data group, using daily record data group identical for basic filtering rule as described daily record to be positioned; Otherwise, from data platform, daily record data group is again obtained according to supplementary filtering rule, and re-starting abnormal test: subelement is extracted in the second location daily record, if disappeared, using inconsistent for the filtering rule abnormity diagnosis result as product daily record for abnormal for the assay re-starting abnormal test; Otherwise, by the daily record data group again obtained, as described daily record to be positioned.

On the basis of the various embodiments described above, described filtering rule obtains subelement and specifically may be used for:

In described at least two data platforms, obtain the SQL program code of the filtering rule corresponding with described at least two daily record data groups; Code analysis is carried out to the described SQL program code obtained, based on setting keyword search filtercondition statement; Using the filtercondition statement that searches as the filtering rule corresponding with described at least two daily record data groups.

On the basis of the various embodiments described above, described daily record extraction unit to be positioned may be used for:

Obtain the data source identification corresponding with described at least two daily record data groups; If the data source identification corresponding with daily record data group is identical, master data source is identified identical daily record data group as the first daily record; Otherwise, again from data platform, daily record data group is obtained according to supplementary data source mark, and re-start abnormal test: if the assay re-starting abnormal test disappears, then using inconsistent for the data source identification abnormity diagnosis result as product daily record for abnormal; Otherwise, by the daily record data group again obtained, as described first daily record; Extract in described first daily record and meet the conforming daily record to be positioned of filtering rule.

What derive from target data platform after acquisition carries out abnormal test to product daily record treats abnormity diagnosis daily record.

In the product daily record of reference data platform, obtain the reference daily record corresponding with daily record to be positioned; Calculate the verification desired value corresponding with described reference daily record and described daily record difference to be positioned; If there are differences with between described daily record to be positioned and the described verification desired value corresponding respectively with reference to daily record, use at least one setting statistics dimension to described daily record to be positioned and describedly carry out fractional dimension statistics with reference to daily record, with setting desired value corresponding with statistics dimension values under adding up described setting statistics dimension; For same object statistics dimension values, if there are differences between the setting desired value of the daily record to be positioned after statistics and reference daily record, obtain product daily record corresponding with described object statistics dimension values in target data platform and reference data platform respectively, as abnormal log.

If treat that in the determined time interval of abnormity diagnosis daily record, the filtering rule of target data platform is not modified described, treat that abnormity diagnosis daily record is as described daily record to be positioned using what derive from target data platform; Otherwise, recover filtering rule and again from target data platform, obtain product daily record, and re-start abnormal test; If the assay re-starting abnormal test disappears for abnormal, then using the abnormity diagnosis result of amendment filtering rule as product daily record; Otherwise, by the product daily record again obtained, as described daily record to be positioned.

Treat described in extraction in abnormity diagnosis daily record, to meet conforming first daily record of filtering rule; If in the determined time interval of the first daily record, the online implementing product function be associated with described first daily record, using the abnormity diagnosis result of new product function as product daily record of reaching the standard grade; Otherwise, using described first daily record as described daily record to be positioned.

The apparatus for diagnosis of abnormality of the product daily record that the embodiment of the present invention provides can be used for the abnormality diagnostic method performing the product daily record that any embodiment of the present invention provides, and possesses corresponding functional module, realizes identical beneficial effect.

Obviously, it will be understood by those skilled in the art that above-mentioned of the present invention each module or each step can by server implementations as above.Alternatively, the embodiment of the present invention can realize by the executable program of computer installation, thus they storages can be performed by processor in the storage device, described program can be stored in a kind of computer-readable recording medium, the above-mentioned storage medium mentioned can be ROM (read-only memory), disk or CD etc.; Or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the present invention is not restricted to the combination of any specific hardware and software.

The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, to those skilled in the art, the present invention can have various change and change.All do within spirit of the present invention and principle any amendment, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims

1. an abnormality diagnostic method for product daily record, is characterized in that, comprising:

2. method according to claim 1, is characterized in that, obtains to treat that abnormity diagnosis daily record comprises after carrying out abnormal test to product daily record:

3. method according to claim 2, is characterized in that, carries out fractional dimension statistics, and specifically comprise according to the abnormal log in the statistics described daily record to be positioned in location based on setting statistics dimension to described daily record to be positioned:

At least one setting statistics dimension is used to carry out fractional dimension statistics to described at least two daily record data groups, to add up in described daily record data group, setting desired value corresponding with statistics dimension values under described setting statistics dimension;

For same statistics dimension values, if there are differences between the setting desired value of at least two daily record data groups, obtain product daily record corresponding with this statistics dimension values in the data platform corresponding with this daily record data group as abnormal log.

4. method according to claim 2, is characterized in that, treats that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record comprises described in extraction:

Obtain the filtering rule corresponding with described at least two daily record data groups;

If the filtering rule corresponding with daily record data group is identical, using daily record data group identical for basic filtering rule as described daily record to be positioned; Otherwise, from data platform, again obtain daily record data group according to supplementary filtering rule, and re-start abnormal test:

If the assay re-starting abnormal test disappears, using inconsistent for the filtering rule abnormity diagnosis result as product daily record for abnormal; Otherwise, by the daily record data group again obtained, as described daily record to be positioned.

5. method according to claim 4, is characterized in that, obtains the filtering rule corresponding with described at least two daily record data groups and specifically comprises:

In described at least two data platforms, obtain the Structured Query Language (SQL) SQL program code of the filtering rule corresponding with described at least two daily record data groups;

Code analysis is carried out to the described SQL program code obtained, based on setting keyword search filtercondition statement;

Using the filtercondition statement that searches as the filtering rule corresponding with described at least two daily record data groups.

6. method according to claim 2, is characterized in that, treats that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record comprises described in extraction:

Obtain the data source identification corresponding with described at least two daily record data groups;

If the data source identification corresponding with daily record data group is identical, master data source is identified identical daily record data group as the first daily record; Otherwise, again from data platform, obtain daily record data group according to supplementary data source mark, and re-start abnormal test:

If the assay re-starting abnormal test disappears, then using inconsistent for the data source identification abnormity diagnosis result as product daily record for abnormal; Otherwise, by the daily record data group again obtained, as described first daily record;

Extract in described first daily record and meet the conforming daily record to be positioned of filtering rule.

7. method according to claim 1, is characterized in that, obtains to treat that abnormity diagnosis daily record comprises after carrying out abnormal test to product daily record:

8. method according to claim 7, is characterized in that, carries out fractional dimension statistics, and specifically comprise according to the abnormal log in the statistics described daily record to be positioned in location based on setting statistics dimension to described daily record to be positioned:

Use at least one setting statistics dimension, fractional dimension statistics carried out to described daily record to be positioned, with add up under described setting statistics dimension with setting desired value corresponding to statistics dimension values;

Daily record to be positioned after statistics is sorted by described setting desired value order from big to small, and obtains the statistics dimension values of predetermined number according to ranking results;

The product daily record corresponding with the statistics dimension values of described predetermined number is obtained, as abnormal log in target journaling data platform.

9. method according to claim 7, is characterized in that, carries out fractional dimension statistics, and specifically comprise according to the abnormal log in the statistics described daily record to be positioned in location based on setting statistics dimension to described daily record to be positioned:

In the product daily record of reference data platform, obtain the reference daily record corresponding with daily record to be positioned;

Calculate the verification desired value corresponding with described reference daily record and described daily record difference to be positioned;

If there are differences with between described daily record to be positioned and the described verification desired value corresponding respectively with reference to daily record, use at least one setting statistics dimension to described daily record to be positioned and describedly carry out fractional dimension statistics with reference to daily record, with setting desired value corresponding with statistics dimension values under adding up described setting statistics dimension;

For same statistics dimension values, if there are differences between the daily record to be positioned after statistics and the setting desired value with reference to daily record, obtain product daily record corresponding with described statistics dimension values in target data platform and reference data platform respectively, as abnormal log.

10. method according to claim 7, is characterized in that, treats that the daily record to be positioned meeting consistance rule in abnormity diagnosis daily record comprises described in extraction:

If treat that in the determined time interval of abnormity diagnosis daily record, the filtering rule of target data platform is not modified described, treat that abnormity diagnosis daily record is as described daily record to be positioned using what derive from target data platform; Otherwise, recover filtering rule and again from target data platform, obtain product daily record, and re-start abnormal test;

If the assay re-starting abnormal test disappears for abnormal, then using the abnormity diagnosis result of amendment filtering rule as product daily record; Otherwise, by the product daily record again obtained, as described daily record to be positioned.

11. methods according to claim 7, is characterized in that, treat that the daily record data to be positioned meeting consistance rule in abnormity diagnosis daily record comprises described in extraction:

Treat described in extraction in abnormity diagnosis daily record, to meet conforming first daily record of filtering rule;

If in the determined time interval of the first daily record, the online implementing product function be associated with described first daily record, using the abnormity diagnosis result of new product function as product daily record of reaching the standard grade; Otherwise, using described first daily record as described daily record to be positioned.

The apparatus for diagnosis of abnormality of 12. 1 kinds of product daily records, is characterized in that, comprising:

13. devices according to claim 12, is characterized in that, described in treat abnormity diagnosis log acquisition unit for:

14. devices according to claim 13, is characterized in that, described abnormal log positioning unit specifically for:

15. devices according to claim 13, is characterized in that, daily record extraction unit to be positioned comprises:

Filtering rule obtains subelement, for obtaining the filtering rule corresponding with described at least two daily record data groups;

Subelement is extracted in first location daily record, if identical for the filtering rule corresponding with daily record data group, using daily record data group identical for basic filtering rule as described daily record to be positioned; Otherwise, from data platform, again obtain daily record data group according to supplementary filtering rule, and re-start abnormal test:

Subelement is extracted in second location daily record, if disappeared, using inconsistent for the filtering rule abnormity diagnosis result as product daily record for abnormal for the assay re-starting abnormal test; Otherwise, by the daily record data group again obtained, as described daily record to be positioned.

16. devices according to claim 13, is characterized in that, described daily record extraction unit to be positioned is used for:

17. devices according to claim 12, is characterized in that, described in treat abnormity diagnosis log acquisition unit for:

18. devices according to claim 17, is characterized in that, described abnormal log positioning unit specifically for:

19. devices according to claim 17, is characterized in that, described abnormal log positioning unit specifically for:

For same object statistics dimension values, if there are differences between the setting desired value of the daily record to be positioned after statistics and reference daily record, obtain product daily record corresponding with described object statistics dimension values in target data platform and reference data platform respectively, as abnormal log.

20. devices according to claim 17, is characterized in that, described daily record extraction unit to be positioned is used for: