CN112579408A - Classification method of embedded point information - Google Patents
Classification method of embedded point information Download PDFInfo
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- CN112579408A CN112579408A CN202011177759.9A CN202011177759A CN112579408A CN 112579408 A CN112579408 A CN 112579408A CN 202011177759 A CN202011177759 A CN 202011177759A CN 112579408 A CN112579408 A CN 112579408A
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000005540 biological transmission Effects 0.000 claims description 19
- 230000003111 delayed effect Effects 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000009933 burial Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
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- 238000012216 screening Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3438—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment monitoring of user actions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Abstract
The invention discloses a classification method of embedded point information, which is characterized by comprising the following steps of; s10, acquiring the buried point information of the user; s20, the embedded point information is immediately sent to a log record; s30, accumulating the buried point information of the same user, and classifying the customers according to the buried point information of the customers, wherein S30 further comprises; s310, the single-user APP is used for more than 15 minutes once, or enters a specific page for more than 15 times, or a certain button is clicked for more than 15 times, and the user is marked as a class A user; the single-user APP is used for more than 10 minutes once, or enters a specific page for more than 10 times, or clicks a certain button for more than 10 times, and the user is marked as a B-class user; a single user APP may be used more than 5 minutes a single time, or enter a particular page more than 5 times, or click a button more than 5 times, and the user will be tagged as a class B user. And classifying the users according to the embedded point information acquisition of the users so as to provide different services for different users and improve the experience of the customers.
Description
Technical Field
The invention relates to the field of financial data processing, in particular to a classification method of buried point information.
Background
The existing financial information system can realize APP end buried point information acquisition and capture and analysis of abnormal/breakdown information in the card swiping process to optimize code implementation in the card swiping process. The financial information processing system includes: the main process and each sub-process of the log system realize the content and the service process; constructing a format and sending configuration of the log message; exception capture implementation and exception code table; buried point information/normal log records; the main technical point specification and application configuration. The service objects comprise card swiping service operators, card swiping service testers, card swiping service technology developers and card swiping service project managers. The APP terminal embedded point information acquisition cannot effectively classify the client at present, so that relevant background personnel of the card swiping service cannot well provide card swiping service experience for the client.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to classify users according to the acquisition of the burial point information of the users, so as to provide different services for different users, and improve the experience of the customers.
In order to achieve the above object, the present invention provides a classification method of embedded point information, comprising the following steps;
s10, acquiring the buried point information of the user;
s20, the embedded point information is immediately sent to a log record;
s30, the information of the same user is accumulated, and the customers are classified according to the information of the customer' S embedded points.
Further, the S30 further includes;
s310, the single-user APP is used for more than 15 minutes once, or enters a specific page for more than 15 times, or a certain button is clicked for more than 15 times, and the user is marked as a class A user;
s320, the single-user APP is used for more than 10 minutes once, or enters a specific page for more than 10 times, or clicks a certain button for more than 10 times, and the user is marked as a B-class user;
s330, the single-user APP is used for more than 5 minutes once, or enters a specific page for more than 5 times, or clicks a certain button for more than 5 times, and the user is marked as a B-class user;
s340, the single-user APP is used for less than 5 minutes once, the specific page is entered for less than 5 times, a certain button is clicked for less than 5 times, and the user marks the page as a B-class user.
Further, the log records include normal logs, abnormal logs, buried point logs and crash records.
Further, the normal log is sent, whether the maximum configuration byte is exceeded is checked, and if not, new log information is added to the file; if the value exceeds the preset value, the message is sent in time.
Further, the embedded point log and the abnormal log are immediately sent; the crash record and the normal log are stored in a file form, and the transmission is delayed.
Further, the log records sent immediately are executed by one thread at one time so as to deal with high concurrency; and the log files sent in a delayed mode are executed in the same thread.
Further, the priority of the immediate transmission is higher than that of the delayed transmission, and the immediate transmission needs to be sent to the server log processing center in time, and the immediate transmission is discarded if the transmission fails.
Further, the sending operation is executed asynchronously, and the normal operation of the main thread is not influenced.
The method classifies the users through the acquisition of the embedded point information of the users, provides different services for different users, and accordingly improves the experience of the users.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a principal business flow diagram of a preferred embodiment of the present invention;
FIG. 2 is a flow chart of an initialization system in accordance with a preferred embodiment of the present invention;
fig. 3 is a main business flow diagram of a preferred embodiment of the present invention.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
As shown in fig. 1, the main business process description:
1. initializing log system parameters
2. Starting a log system and monitoring APP operations
3. The APP operation logs are output to an APP log service center module, processed respectively according to different service types by the center module, and then uploaded to a log server processing center
4. The log server processing center analyzes the APP log and stores the APP log in the data center
5. The log server processing center informs the APP log service center module: if the data reception is OK, the APP journal service center checks whether the data is sent, if so, the data is continuously sent, and if not, the operation is stopped to wait for the next call; if the communication fails, the log processing center module stops the follow-up operation and waits for the next call
6. Kibana reads the log information of the data center and displays the log information item by item on the interface. At the same time, the screening and statistical functions are provided for operators to use
7. The user normally quits the APP, the log system cleans resources and closes the current operation
8. And the user abnormally exits the APP, for example, if the page operation is crashed, the crash log information is recorded to the file system, and the log information is timely sent to the server after the log system is restarted.
As shown in fig. 2, the initialization system flow illustrates:
1. obtaining a public parameter: mobile phone system version, APP brand and application version information, etc
2. Configuring sending address, setting multiple groups of addresses corresponding to internal test and formal release request addresses respectively
3. And configuring the maximum transmission byte of the normal log, such as setting 2M. The log file is centrally sent to the server log processing center module after the normal log file is stored for more than 2M
4. Configuring whether log operation turns on a switch or not, and turning off a log service system if the log operation is not turned on
5. Configuring whether normal log is printed or not, if not, not sending normal log information
6. Checking whether the file operation authority is granted, if not, the log service system can not normally run, and prompting the application that the related authority needs to be granted
7. And preparing a file operation directory under which the related log files are temporarily stored. If not, create
8. If the normal log sending switch is turned on, the normal log record file of the last operation is loaded, and if not, the normal log record file is automatically created. Subsequent log files may be appended within this file. If the file exceeds the set maximum transmission byte, a new file is additionally established to store a normal log
9. Can be initialized as many times as required, and the latest initialization data is reserved
10. And a print information output switch is configured, and the print information output can be closed by the formal version.
As shown in fig. 3, the operation flow of the log processing center module is illustrated as follows:
1. receiving APP end logs and adopting different processing strategies according to different log types
2. The log types include: normal log, abnormal log, buried point log, crash record
3. Sending a normal log, checking whether the maximum configuration byte is exceeded, and if not, adding new log information to the file; if so, timely sending
4. The log records sent immediately are executed by one thread at one time so as to deal with high concurrency; and the log files sent in a delayed mode are executed in the same thread. The priority of immediate transmission is higher than that of delayed transmission, and the immediate transmission needs to be sent to a server log processing center in time, and if the transmission fails, the immediate transmission is discarded
5. If the log is a buried point log and an abnormal log, the log is immediately sent; the crash record and normal log are stored in file form and delayed to be sent
6. The sending operation is executed asynchronously, and the normal operation of the main thread is not influenced
7. The normal log file will contain a plurality of log records; although the crash record is also stored as a file, the crash record only comprises one log record; the abnormal log and the embedded point log are only one record and are not stored in a file form
8. After the APP is started, a log file system is checked by selecting a machine on a main interface, if files exist in the log file system, all normal log files and abnormal log files to be sent are taken out, and the log files are ready to be sent
9. Reading public configuration parameters, taking out file contents, assembling into a request message according to an agreed format, and sending the request message to a server log processing center
10. And after each log file is sent, waiting for the server to confirm, and if the data is received normally and the httpstatus is returned to be 200, determining that the sending is successful. Then deleting the successfully sent files to ensure that the same files cannot be sent to the server again; if the server returns an error, the file is retained and another attempt to send is made to wait for the next call
11. After the file is successfully sent, continuously checking whether the next file is to be sent, if so, repeatedly executing: the method comprises the steps of configuring parameters, sending a message, confirming a message, deleting a sent file until all files are sent completely, and waiting for next calling.
The invention provides a classification method of embedded point information, which comprises the following steps;
s10, acquiring the buried point information of the user;
s20, the embedded point information is immediately sent to a log record;
s30, the information of the same user is accumulated, and the customers are classified according to the information of the customer' S embedded points.
S30 further includes;
s310, the single-user APP is used for more than 15 minutes once, or enters a specific page for more than 15 times, or a certain button is clicked for more than 15 times, and the user is marked as a class A user;
s320, the single-user APP is used for more than 10 minutes once, or enters a specific page for more than 10 times, or clicks a certain button for more than 10 times, and the user is marked as a B-class user;
s330, the single-user APP is used for more than 5 minutes once, or enters a specific page for more than 5 times, or clicks a certain button for more than 5 times, and the user is marked as a B-class user;
s340, the single-user APP is used for less than 5 minutes once, the specific page is entered for less than 5 times, a certain button is clicked for less than 5 times, and the user marks the page as a B-class user.
The log records comprise normal logs, abnormal logs, buried point logs and crash records. Sending a normal log, checking whether the maximum configuration byte is exceeded, and if not, adding new log information to the file; if the value exceeds the preset value, the message is sent in time. The embedded point log and the abnormal log are immediately sent; the crash record and the normal log are stored in a file form, and the transmission is delayed. The log records sent immediately are executed by one thread at one time so as to deal with high concurrency; and the log files sent in a delayed mode are executed in the same thread. The priority of immediate sending is higher than that of delayed sending, and the immediate sending needs to be sent to the server log processing center in time, and the immediate sending is discarded if the sending fails. The sending operation is executed asynchronously, and the normal operation of the main thread is not influenced.
The method classifies the users through the acquisition of the embedded point information of the users, provides different services for different users, and accordingly improves the experience of the users.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A classification method of buried point information is characterized by comprising the following steps;
s10, acquiring the buried point information of the user;
s20, the embedded point information is immediately sent to a log record;
s30, the information of the same user is accumulated, and the customers are classified according to the information of the customer' S embedded points.
2. The method for classifying buried point information according to claim 1, wherein said S30 further includes; s310, the single-user APP is used for more than 15 minutes once, or enters a specific page for more than 15 times, or a certain button is clicked for more than 15 times, and the user is marked as a class A user;
s320, the single-user APP is used for more than 10 minutes once, or enters a specific page for more than 10 times, or clicks a certain button for more than 10 times, and the user is marked as a B-class user;
s330, the single-user APP is used for more than 5 minutes once, or enters a specific page for more than 5 times, or clicks a certain button for more than 5 times, and the user is marked as a B-class user;
s340, the single-user APP is used for less than 5 minutes once, the specific page is entered for less than 5 times, a certain button is clicked for less than 5 times, and the user marks the page as a B-class user.
3. The method for classifying buried point information according to claim 1, wherein the log records include normal logs, abnormal logs, buried point logs, and crash records.
4. The method of classifying buried point information according to claim 3, wherein said normal log transmission checks whether a maximum configuration byte is exceeded, and if not, adds new log information to the file; if the value exceeds the preset value, the message is sent in time.
5. The method for classifying buried point information according to claim 3, wherein the buried point log and the abnormal log are immediately transmitted; the crash record and the normal log are stored in a file form, and the transmission is delayed.
6. The method of classifying the embedded point information according to claim 1, wherein the immediately sent log records are all executed by one thread at a time to cope with high concurrency; and the log files sent in a delayed mode are executed in the same thread.
7. The method of claim 6, wherein the immediate transmission is higher priority than the delayed transmission, and is sent to the server log processing center in time, and is discarded if the transmission fails.
8. The method for classifying buried point information according to any one of claims 4 to 7, wherein said sending operation is performed asynchronously without affecting the normal operation of the main thread.
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