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Smart Token Pre-Recycling Method Based on Time Series Prediction

A technology of time series and recovery method, which is applied in the direction of digital transmission system, data exchange network, electrical components, etc., can solve the problems of waste of token resources, failure to access background requests normally, poor flexibility, etc., and achieve the effect of managing tokens well

Active Publication Date: 2019-08-20
SICHUAN XW BANK CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to: provide a smart token pre-recovery method based on time series prediction, which solves the problem that the latter part of the request may not be able to access the background request normally when there are insufficient tokens in the token pool, or some services continue to There is a lot of waste of token resources if there are tokens but they are not recycled; managers need to manually adjust, and the flexibility is particularly poor

Method used

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  • Smart Token Pre-Recycling Method Based on Time Series Prediction
  • Smart Token Pre-Recycling Method Based on Time Series Prediction
  • Smart Token Pre-Recycling Method Based on Time Series Prediction

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Experimental program
Comparison scheme
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Embodiment 1

[0044] Such as figure 1 , Figure 5 As shown, the smart token pre-recovery method based on time series prediction, the token cache pool, includes the following steps in sequence:

[0045] S1. Establish a time period, divide a time period into several time periods, and establish a time series calculation model;

[0046] S2. Determine whether the token cache pool needs to recycle the token, if it needs to be reclaimed, go to step S3, otherwise loop step S2;

[0047] S3. Bring the tokens in the token cache pool into the time series calculation model established in step S1, and calculate the occurrence probability of the request corresponding to each token within the corresponding time period;

[0048] S4. Reclaim the token corresponding to the request according to the occurrence probability of the request corresponding to each token obtained in step S3 within the corresponding time period.

[0049] This technical solution mainly manages tokens intelligently, removes human oper...

Embodiment 2

[0051] The difference between this embodiment and Embodiment 1 is that, further, the method for establishing a time series calculation model in the step S1 includes the following steps:

[0052] S101: Import historical data of each time period into the time series calculation model;

[0053] S102, calculating the average value and standard deviation of the historical data in step S101;

[0054] S103. When the standard deviation is lower than the preset threshold, go to step S105, otherwise go to step S104;

[0055] S104, remove the data with the largest deviation from the average value in the historical data, and import the remaining data as new historical data into step S102;

[0056] S105. Use the average value of the time period as an occurrence probability of the corresponding request within the corresponding time period.

[0057] average value: It is the average number of calls of a certain interface in a certain period of time in a cycle.

[0058] Standard Deviation...

Embodiment 3

[0073] Such as figure 2 , image 3 As shown, in this embodiment, the main data source is the historical record of the request, and the token for each request is 24 hours by default from the first application to the expiration, so the time interval is one day, and the day is divided into 24 parts by hour , the analysis dimension is to calculate the probability model of this time in the day from a certain time period of a certain interface of a certain application, and predict the recorded request probability distribution of the day;

[0074] When the resources in the token cache pool are tight, the system triggers token recycling, and the token recycling method is as follows;

[0075] S301. The request corresponding to the token has an occurrence probability of 0 within the current time period;

[0076] S302. The request corresponding to the token has the smallest occurrence probability within the current time period;

[0077] S303. The request corresponding to the token ha...

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Abstract

The invention discloses a smart token pre-recovery method based on time series prediction. The technical solution mainly performs intelligent management on tokens, removes manual operations, and recycles tokens and puts them back when the amount of application requests is not large. The pool provides sufficient token request tokens for partners or services with a large number of requests. This design scheme mainly uses the statistical calculation model in machine learning, divides the past flow according to a certain period of time, and then conducts statistical analysis to predict the distribution statistics in the later stage. According to the allocation of resources in the token cache pool, in When the resources of the token buffer pool are insufficient, the statistically predicted usage probability of tokens is used to recycle the minimum usage and the farthest usage to release resources.

Description

technical field [0001] The invention relates to the field of flow control, in particular to a smart token pre-recovery method based on time series prediction. Background technique [0002] The open network platform is a channel for partners to access, and provides a wealth of APIs to open calls to partners. Tokens are a kind of identification for partners to access the internal service system of the network. Without tokens, the services of the open network platform cannot be accessed. , each request will check the time validity of the token. If the time expires, the token will be re-applied, but it will exist for a period of time in the future, but the request may not be accessed during this time, resulting in the token Occupies system resources for a long time; timely recycling of tokens, subsequent call services have great performance optimization, through the request data to partners, statistical analysis from various dimensions, reasonable allocation and recovery of each...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/819H04L47/21
CPCH04L47/215
Inventor 王月超彭剑李秀生毛航陈林江
Owner SICHUAN XW BANK CO LTD