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Early warning model building method and system

A technology of early warning model and modeling method, which is applied in the direction of character and pattern recognition, instruments, computer components, etc., can solve the problems of model prediction effect deviation, etc., and achieve the effect of accurate early warning effect, fast early warning speed and high efficiency

Inactive Publication Date: 2017-12-12
国家电网有限公司客户服务中心
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AI Technical Summary

Problems solved by technology

The existing user complaint early warning model uses manual research features to select indicators and uses classification algorithms to predict complaint calls. There may be a problem of overfitting, and the prediction effect is better for small data sets. Once a large amount of data is involved , the prediction effect of the model will be biased, and it is more effective when dealing with traditional small-scale data. However, the huge amount of data and features of the operator's big data make these solutions no longer applicable

Method used

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  • Early warning model building method and system
  • Early warning model building method and system

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Embodiment Construction

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] Such as figure 1 As shown, it is a flow chart of Embodiment 1 of a kind of early warning model modeling method disclosed by the present invention, including:

[0047] S101, calling a random training set;

[0048] The random training set includes multiple random training samples. Taking the State Grid customer service work order as an example, the random training sample set includes multiple customer consultation records.

[0049] S102. Construct an ea...

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Abstract

The invention provides an early warning model building method. The method comprises the steps of calling a random training set; and based on the random training set, building an early warning model. Compared with the prior art, the method has the advantages that behavior paths of a large amount of research objects are collected and comparison is performed to generate the training set; a classifier is trained; and the early warning model is generated. Compared with a conventional mode of performing early warning by using a classification algorithm, a device for performing early warning by using the early warning model is more accurate in early warning effect, higher in early warning speed and higher in efficiency, and is suitable to process data with large data and feature quantities.

Description

technical field [0001] The invention relates to the technical field of risk early warning, in particular to an early warning model modeling method and system. Background technique [0002] At present, the telephone customer service has accumulated a large amount of work order data, among which there are complaint calls and non-complaint calls. In order to save time and cost and better serve customers, we hope to be able to classify the calls as soon as the customer calls. Screening out complaint calls and distinguishing complaint categories will help customer service take effective countermeasures before answering, thereby reducing the number of repeated calls to upgrade customers and improving customer service satisfaction. [0003] The method of forecasting complaint behavior is mainly quantitative forecasting method. Quantitative forecasting is to use historical and current data to establish relevant mathematical models, make quantitative forecasts for the future developm...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/214
Inventor 宫立华陈鹏盛妍张全刘鲲鹏汪莉王宗伟朱龙珠杨菁金鹏
Owner 国家电网有限公司客户服务中心
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