Prediction model training method, data monitoring method and device, equipment and medium

A data monitoring method and prediction model technology, applied in the field of devices, data monitoring methods, equipment and media, and prediction model training methods, can solve the problems of low accuracy of data prediction results, achieve strong timing, improve accuracy and efficiency fast effect

Inactive Publication Date: 2018-02-23
PING AN TECH (SHENZHEN) CO LTD
View PDF0 Cites 76 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Embodiments of the present invention provide a prediction model training method, data monitoring me

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Prediction model training method, data monitoring method and device, equipment and medium
  • Prediction model training method, data monitoring method and device, equipment and medium
  • Prediction model training method, data monitoring method and device, equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] figure 1 A flow chart of the prediction model training method in this embodiment is shown. The predictive model training method is applied to terminal devices configured by financial institutions such as banks, securities, and insurance, or other institutions, and is used to conduct predictive model training using business data generated by financial institutions or other institutions, so as to predict the future based on the trained predictive model. Business data is predicted to achieve the purpose of artificial intelligence monitoring. Such as figure 1 As shown, the prediction model training method includes the following steps:

[0039] S110: time-mark the original service data and divide it according to a preset time limit, and obtain training service data carrying a time-series state.

[0040] Among them, the original business data is the business data formed in the production and operation process of the financial institution or other institutions. The busines...

Embodiment 2

[0069] Figure 4 A functional block diagram of a predictive model training device corresponding to the predictive model training method in Embodiment 1 is shown. Such as Figure 4 As shown, the prediction model training device includes a training service data acquisition module 110 , a data division module 120 , an original prediction model acquisition module 130 and a target prediction model acquisition module 140 . Wherein, the implementation functions of the training business data acquisition module 110, the data division module 120, the original prediction model acquisition module 130 and the target prediction model acquisition module 140 correspond to the corresponding steps of the prediction model training method in Embodiment 1, in order to avoid repeating, This embodiment does not describe in detail one by one.

[0070] The training service data acquisition module 110 is configured to time stamp the original service data and divide it according to a preset time limit...

Embodiment 3

[0082] Figure 5 The data monitoring method in this embodiment is shown. This data monitoring method can be applied to terminal equipment configured by financial institutions such as banks, securities, insurance, or other institutions that need data monitoring, so as to monitor future business data based on collected original business data and make timely adjustment strategies. As shown in Figure N, the data monitoring method includes the following steps:

[0083] S210: Obtain a data monitoring instruction, where the data monitoring instruction includes a current time, a preset time limit, and a monitoring index.

[0084] Wherein, the data monitoring instruction refers to an instruction for controlling the terminal device to perform service data monitoring. The current time is the system time of the terminal device, and the current time is a specific system day. The monitoring index refers to the category of service data to be monitored, and the monitoring index may corresp...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a prediction model training method, a data monitoring method and device, equipment and a medium. The prediction model training method comprises the steps that time-stamping iscarried out on original service data, and the data are divided according to a preset deadline to acquire training service data carrying a time sequence state; the training service data are divided into a training set and a test set according to a preset ratio; a training set is used to train a long short term memory cycle neural network model to acquire an original prediction model; and the testset is used to test the original prediction model to acquire a target prediction model. The prediction model training method has the advantages of high time sequence and high accuracy in predicting.

Description

technical field [0001] The present invention relates to the field of data monitoring, in particular to a prediction model training method, data monitoring method, device, equipment and medium. Background technique [0002] With the development of the market economy, the competition among enterprises is becoming more and more fierce. In order to improve the competitiveness of enterprises, enterprises conduct big data analysis on historical business data to predict future business development trends and make strategic adjustments. Currently, the SVM (Support Vector Machine, Support Vector Machine) model is used for big data analysis of historical business data. This method lacks consideration of the autocorrelation of its own data and does not have the ability to predict time series data, making the data prediction results accurate. The rate is lower. Contents of the invention [0003] Embodiments of the present invention provide a prediction model training method, data mon...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/06G06Q30/02G06Q40/00G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/084G06Q10/0637G06Q30/0202G06Q40/00G06F18/214G06F18/24
Inventor 张川顾青山金鑫温善安李磊王定鑫李泳章
Owner PING AN TECH (SHENZHEN) CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap