CN109885417B - Anomaly analysis method, electronic device and readable storage medium - Google Patents
Anomaly analysis method, electronic device and readable storage medium Download PDFInfo
- Publication number
- CN109885417B CN109885417B CN201811625627.0A CN201811625627A CN109885417B CN 109885417 B CN109885417 B CN 109885417B CN 201811625627 A CN201811625627 A CN 201811625627A CN 109885417 B CN109885417 B CN 109885417B
- Authority
- CN
- China
- Prior art keywords
- time period
- abnormal
- dimension
- period file
- file
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Debugging And Monitoring (AREA)
Abstract
The invention discloses an anomaly analysis method, which comprises the steps of selecting an anomaly dimension from dimension indexes preset by a system and setting an anomaly time period; starting analysis, extracting dimension index data of abnormal dimensions from the dimension indexes, acquiring a normal time period file and an abnormal time period file according to an abnormal time period, and judging whether the data volume of the normal time period file and the data volume of the abnormal time period file are in the same order; and calculating an abnormal dimension influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, and analyzing the abnormal reason of the abnormal dimension. The method can reduce the positioning time of the abnormal reason, simplify the analysis steps of the abnormal reason, reduce the cost of the abnormal analysis and reduce the economic loss caused by the abnormal reason.
Description
Technical Field
The present invention relates to the field of game technologies, and in particular, to an anomaly analysis method, an electronic device, and a readable storage medium.
Background
The game industry often has the condition of abnormal key data, such as abnormal registration rate, abnormal corner creation rate, abnormal retention rate and the like. The abnormal registration rate refers to the situation that the number of game registration rates is increased sharply or normal user registration is limited within a certain time due to cheating programs special for online games such as plug-ins; the corner creation rate abnormity is the condition that a certain character of the game has excessive registration quantity in a certain time, the occurrence rate of the game character is higher than the normal frequency or a new game character is created and the like due to cheating programs special for the online game, such as plug-in application and the like; the retention rate abnormality refers to a situation where the retention of a game character in a certain game process is abnormal or the game character cannot exit. The game abnormity easily caused by excessive plug-in and abnormity appearing in the game running process easily influence the game progress and bring economic loss to game operators, and the problem can be solved pertinently only by finding out the reason of the abnormity.
At present, the practice of the industry for the occurrence of these data anomalies is to extract data from a database by a programmer or a data analyst after the anomalies are found, and then the data are analyzed by a professional data analyst, so that the cause of the anomalies can be located finally. The whole process needs at least half a day, a plurality of departments are matched, the processing time is long, the flow is complex, and the anomaly analysis method has a higher threshold for game operators without a certain data analysis basis.
Therefore, it is an urgent technical problem to provide an anomaly analysis method capable of reducing the time for locating the anomaly cause, simplifying the analysis steps of the anomaly cause, reducing the cost of the anomaly analysis, and reducing the economic loss caused by the anomaly cause.
Disclosure of Invention
In order to overcome the defects of the prior art, an object of the present invention is to provide an abnormality analysis method, which can reduce the time for locating an abnormality cause, simplify the abnormality cause analysis steps, reduce the abnormality analysis cost, and reduce the economic loss caused by the abnormality cause.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an abnormality analysis method includes the steps of,
s1, selecting abnormal dimensions from the preset dimension indexes of the system, and setting an abnormal time period;
step S2, starting analysis, extracting dimension index data of abnormal dimensions from the dimension indexes, acquiring a normal time period file and an abnormal time period file according to the abnormal time period, and judging whether the data volume of the normal time period file and the data volume of the abnormal time period file are in the same order of magnitude;
and step S3, calculating an abnormal dimension influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, and analyzing the abnormal reason of the abnormal dimension.
Preferably, the following steps are further included between step S2 and step S3:
if the data volume of the abnormal time period file and the data volume of the normal time period file are in the same order of magnitude, executing step S3;
and if the data volume of the abnormal time period file and the data volume of the normal time period file are not in the same order, normalizing the dimension index data of the normal time period file and the dimension index data of the abnormal time period file.
Preferably, step S3 specifically includes the following steps,
step S31, obtaining dimension index data of the normal time period file and dimension index data of the abnormal time period file, wherein the dimension index data of the normal time period file comprises the number of people ratio R of the dimension index under the dimension of the normal time period i i And an index value r i The dimension index data of the abnormal time period file comprises the number of people of the dimension index under the dimension of the abnormal time period i to R' i And an index value r' i ；
Step S32, calculating a dimension influence factor and a dimension subdivision cross influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file;
and step S33, analyzing the abnormal reason of the abnormal dimension according to the dimension influence factor and the dimension subdivision cross influence factor, and generating a report.
Preferably, the dimension influence factor is
The cross-influence factor of dimension subdivision is
Wherein i is 1,2,3 … … n; j is a subdivision of the i dimension, j is 1,2,3 … m; r ij 、r ij Respectively, the ratio of the number of subdivided intersecting persons in the normal time period
Index value, R' ij 、r′ ij Respectively are the proportion of the number of the subdivided intersected persons in the abnormal time period and the index value.
Preferably, the dimension indexes comprise registration rate, wound angle rate and retention rate.
Preferably, the preset dimension index includes game version number, system version number, player region, player mobile phone model, player mobile phone brand, and dimension index data of player source channel.
It is another object of the present invention to provide an electronic device that can reduce the time for locating an abnormal cause, simplify the step of analyzing the abnormal cause, reduce the cost of analyzing the abnormal cause, and reduce the economic loss due to the abnormal cause.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which when executing the program performs an anomaly analysis method as one of the objects of the present invention.
It is another object of the present invention to provide a readable storage medium, which can reduce the time for locating an abnormal cause, simplify the step of analyzing the abnormal cause, reduce the cost of analyzing the abnormal cause, and reduce the economic loss due to the abnormal cause.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
on which a computer program is stored which, when being executed by a processor, carries out an anomaly analysis method as one of the objects of the invention.
Compared with the prior art, the invention has the beneficial effects that:
the abnormal dimension influence factor is calculated according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, so that the abnormal reason of the abnormal dimension is further analyzed, the abnormal reason analysis step is simplified, the abnormal reason positioning time is shortened, the abnormal analysis cost is reduced, and the economic loss caused by the abnormal reason is reduced.
Drawings
FIG. 1 is a flow chart illustrating an anomaly analysis method according to a preferred embodiment of the present invention;
FIG. 2 is a schematic step diagram of an anomaly analysis method according to a preferred embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and the detailed description below:
as shown in fig. 1-2, the anomaly analysis method, in turn, comprises the following steps,
and step S1, selecting abnormal dimensions from the dimension indexes preset by the system, and setting an abnormal time period.
Preferably, the dimension index includes common game abnormal conditions such as registration rate, corner creation rate, retention rate and the like. The preset dimension index includes, but is not limited to, the game version number, the system version number, the player region, the player mobile phone model, the player mobile phone brand, the player source channel and other dimensions of dimension index data.
Step S2, starting analysis, the system automatically extracts the dimension index data of the abnormal dimension such as game version number, system version number, player area, player mobile phone model, player mobile phone brand, player source channel, etc. from the dimension index of the database, acquires the normal time period file and the abnormal time period file according to the set abnormal time period, the system automatically compares the data volume of the normal time period file and the data volume of the abnormal time period file, and judges whether the data volumes of the two are in the same order.
Preferably, if the data amount of the abnormal time period file and the data amount of the normal time period file are in the same order, step S3 is directly executed; if the data volume of the abnormal time period file and the data volume of the normal time period file are not in the same order, the system normalizes the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, and the processing method is random layered sampling.
And step S3, calculating an abnormal dimension influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, and analyzing the abnormal reason of the abnormal dimension.
Step S3 specifically includes the following steps, in order:
step S31, obtaining the dimension index data of the normal time period file and the dimension index data of the abnormal time period file from the database, wherein the dimension index data of the normal time period file comprises the number of people ratio R of the dimension index under the dimension of the normal time period i i And an index value r i The dimension index data of the abnormal time period file comprises the number of people of the dimension index under the dimension of the abnormal time period i to R' i And an index value r' i Wherein i is 1,2,3 … … n;
step S32, calculating dimension influence factor and dimension subdivision cross influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file,
specifically, the calculation process is as follows:
according to the obtained dimension index data of the normal time period file and the dimension index data of the abnormal time period file, the influence of each abnormal dimension is as follows: e i ＝R i (1-r i )-R i '(1-r i ')；
The overall effect of the dimension index is:
the total influence factor of each anomaly dimension is:
correspondingly, when the dimensions are expanded, the influence of each abnormal dimension is as follows:
E ij ＝R ij (1-r ij )-R ij '(1-r ij ')；
the total subdivision cross-influence of the dimension index is
The total dimension subdivision cross-influence factor of each anomaly dimension is
Where j is a subdivision of the i dimension, j ═ 1,2,3 … m; r ij 、r ij Respectively are the subdivided cross people number ratio and the subdivided cross index value R 'in the normal time period' ij 、r′ ij The number of the subdivision cross people in the abnormal time period and the subdivision cross index value are respectively.
And step S33, analyzing the reason of the abnormal dimension to generate an abnormal analysis report according to the dimension influence factor and the dimension subdivision cross influence factor by the system.
The invention selects the corner creation rate index abnormal dimension of the game A as an example, and the specific implementation process is as follows:
the starting angle rate of the game A is found to be abnormal in a certain time period, which is far lower than the normal time period, and the reason for the abnormal starting angle rate needs to be analyzed.
The 'corner-making rate' index is input, because the index exists in the system, the dimensionality which can cause the 'corner-making rate' to be reduced is directly selected, the dimensionality such as a game version number, a system version number, a player region, a player mobile phone model, a player mobile phone brand, a player source channel and the like is selected, and meanwhile, an abnormal time period is set.
After clicking analysis, the system automatically extracts the corner rate data of the game A with dimensions including the game version number, the system version number, the player region, the player mobile phone model, the player mobile phone brand, the player source channel and the like from the database, and divides the game files into files in abnormal time periods and files in normal time periods according to the abnormal time periods. The system automatically compares whether the data volume of the two files is in the same magnitude, aiming at the fact that the starting angle rate index of the game A is abnormal, because the starting angle rate is just abnormal and the data volume is small, the system automatically screens a batch of data of which the data volume is different from that of the abnormal data in the same period of time, calculates the influence factor of each dimension and the dimension subdivision cross influence factor according to the formula according to the two data, and generates an abnormal analysis report.
The anomaly analysis method greatly reduces the time for anomaly positioning, and the anomaly analysis method can shorten the time for anomaly positioning of anomaly analysis from half a day to several minutes; the operation is simple and convenient, and the learning cost of the abnormity analysis staff is reduced; and because the reason for the abnormity is determined, the economic loss caused by the abnormal condition is greatly reduced.
The present invention also provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned anomaly analysis method when executing the program, and the electronic device includes, but is not limited to, an anomaly analysis tool that can implement the anomaly analysis method.
The present invention also provides a readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the aforementioned anomaly analysis method of the present invention.
Various other modifications and changes may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such modifications and changes should fall within the scope of the claims of the present invention.
Claims (6)
1. An abnormality analysis method characterized by comprising the steps of,
s1, selecting abnormal dimensions from the preset dimension indexes of the system, and setting an abnormal time period;
step S2, starting analysis, extracting dimension index data of the abnormal dimension from the dimension index, acquiring a normal time period file and an abnormal time period file according to the abnormal time period, and judging whether the data volume of the normal time period file and the data volume of the abnormal time period file are in the same order of magnitude;
step S3, calculating an abnormal dimension influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file, and analyzing the abnormal reason of the abnormal dimension;
the step S3 specifically includes the following steps:
step S31, obtaining dimension index data of the normal time period file and dimension index data of the abnormal time period file, wherein the dimension index data of the normal time period file comprises the number of people ratio R of the dimension index under the dimension of the normal time period i i And an index value r i The dimension index data of the abnormal time period file comprises the number of people of the dimension index under the dimension of the abnormal time period i to R' i And an index value r' i ；
Step S32, calculating a dimension influence factor and a dimension subdivision cross influence factor according to the dimension index data of the normal time period file and the dimension index data of the abnormal time period file;
step S33, analyzing the abnormal reason of the abnormal dimensionality according to the dimensionality influence factor and the dimensionality subdivision cross influence factor to generate a report;
the dimension influence factor is
The dimensionality subdivision cross-influence factor is
Wherein i is 1,2,3 … … n; j is a subdivision of the i dimension, j is 1,2,3 … m; r ij 、r ij Respectively are the subdivided cross people number ratio and the subdivided cross index value R 'in the normal time period' ij 、r′ ij The proportion of the number of the subdivision cross people in the abnormal time period and the subdivision cross index value are respectively.
2. The abnormality analysis method according to claim 1, further comprising, between said step S2 and said step S3, the steps of:
if the data volume of the abnormal time period file and the data volume of the normal time period file are in the same order of magnitude, executing step S3;
and if the data volume of the abnormal time period file and the data volume of the normal time period file are not in the same order, normalizing the dimension index data of the normal time period file and the dimension index data of the abnormal time period file.
3. The anomaly analysis method according to any one of claims 1-2, wherein the dimension index includes a registration rate, a corner-creation rate, and a retention rate.
4. The anomaly analysis method according to any one of claims 1-2, wherein the preset dimension index comprises dimension index data of a game version number, a system version number, a player region, a player mobile phone model, a player mobile phone brand, and a player source channel.
5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein: the processor, when executing the program, implements the anomaly analysis method of any one of claims 1-4.
6. A readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements an anomaly analysis method as claimed in any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811625627.0A CN109885417B (en) | 2018-12-28 | 2018-12-28 | Anomaly analysis method, electronic device and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811625627.0A CN109885417B (en) | 2018-12-28 | 2018-12-28 | Anomaly analysis method, electronic device and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109885417A CN109885417A (en) | 2019-06-14 |
| CN109885417B true CN109885417B (en) | 2022-08-02 |
Family
ID=66925382
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811625627.0A Active CN109885417B (en) | 2018-12-28 | 2018-12-28 | Anomaly analysis method, electronic device and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109885417B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007267876A (en) * | 2006-03-30 | 2007-10-18 | Namco Bandai Games Inc | Program, information storage medium, game device and game device management mechanism |
| CN102163251A (en) * | 2010-02-22 | 2011-08-24 | 深圳市腾讯计算机系统有限公司 | Method and device for recognizing game cheating |
| CN107080944A (en) * | 2017-05-26 | 2017-08-22 | 合肥泽诺信息科技有限公司 | A kind of method for detecting network game external store |
| CN107158707A (en) * | 2017-04-27 | 2017-09-15 | 浙江大学 | A kind of method for detecting abnormality and device played for MMORPGs |
| CN108347355A (en) * | 2017-01-22 | 2018-07-31 | 腾讯科技（深圳）有限公司 | A kind of detection method and its equipment of application state |
| CN108563548A (en) * | 2018-03-19 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Method for detecting abnormality and device |
| CN108854075A (en) * | 2018-05-30 | 2018-11-23 | 腾讯科技（深圳）有限公司 | Game role anomaly detection method and device |
-
2018
- 2018-12-28 CN CN201811625627.0A patent/CN109885417B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007267876A (en) * | 2006-03-30 | 2007-10-18 | Namco Bandai Games Inc | Program, information storage medium, game device and game device management mechanism |
| CN102163251A (en) * | 2010-02-22 | 2011-08-24 | 深圳市腾讯计算机系统有限公司 | Method and device for recognizing game cheating |
| CN108347355A (en) * | 2017-01-22 | 2018-07-31 | 腾讯科技（深圳）有限公司 | A kind of detection method and its equipment of application state |
| CN107158707A (en) * | 2017-04-27 | 2017-09-15 | 浙江大学 | A kind of method for detecting abnormality and device played for MMORPGs |
| CN107080944A (en) * | 2017-05-26 | 2017-08-22 | 合肥泽诺信息科技有限公司 | A kind of method for detecting network game external store |
| CN108563548A (en) * | 2018-03-19 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Method for detecting abnormality and device |
| CN108854075A (en) * | 2018-05-30 | 2018-11-23 | 腾讯科技（深圳）有限公司 | Game role anomaly detection method and device |
Non-Patent Citations (1)
| Title |
|---|
| 网络游戏案例研究：用户行为分析和流失预测;过岩巍，吴悦昕，赵鑫，闫宏飞，黄建兴;《中文信息学报》;20160131;第30卷(第1期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109885417A (en) | 2019-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102315497B1 (en) | Method and device for building a scoring model and evaluating user credit | |
| WO2020062660A1 (en) | Enterprise credit risk evaluation method, apparatus and device, and storage medium | |
| US7730023B2 (en) | Apparatus and method for strategy map validation and visualization | |
| US20180374098A1 (en) | Modeling method and device for machine learning model | |
| CN108876636B (en) | Intelligent air control method, system, computer equipment and storage medium for claim settlement | |
| CN108446621A (en) | Bank slip recognition method, server and computer readable storage medium | |
| WO2019061994A1 (en) | Electronic device, insurance product recommendation method and system, and computer readable storage medium | |
| WO2018090641A1 (en) | Method, apparatus and device for identifying insurance policy number, and computer-readable storage medium | |
| CN109635007B (en) | Behavior evaluation method and device and related equipment | |
| WO2019071906A1 (en) | Financial product recommendation device and method, and computer-readable storage medium | |
| EP2648152A1 (en) | Data solutions system | |
| CN108960269B (en) | Feature acquisition method and device for data set and computing equipment | |
| WO2020228283A1 (en) | Feature extraction method and apparatus, and computer readable storage medium | |
| CN109033220B (en) | Automatic selection method, system, equipment and storage medium of labeled data | |
| US11562262B2 (en) | Model variable candidate generation device and method | |
| WO2021135104A1 (en) | Multi-source data-based object pushing method and apparatus, device, and storage medium | |
| CN108509561B (en) | Post recruitment data screening method and system based on machine learning and storage medium | |
| CN112819611A (en) | Fraud identification method, device, electronic equipment and computer-readable storage medium | |
| US20220067136A1 (en) | Verification method and apparatus, and computer readable storage medium | |
| CN105405051B (en) | Financial event prediction method and device | |
| CN109241075B (en) | Index basic data processing method and equipment and computer readable storage medium | |
| CN107679862B (en) | Method and device for determining characteristic value of fraud transaction model | |
| US11467943B2 (en) | System and method for struggle identification | |
| CN106874286B (en) | Method and device for screening user characteristics | |
| CN109885417B (en) | Anomaly analysis method, electronic device and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |