Multi-angle failure risk early warning device and method

Abstract

The invention provides a multi-angle loss risk early warning device and method, and the method comprises the steps: obtaining the related data of loss rate, national economic development level, labor resources, trade, financial currency and living consumption level according to business demands; screening and storing the obtained data, constructing a failure index system at different time angles, and performing statistics and monitoring; counting the current failure rate according to different time angles, and monitoring the failure rate at the next time point; comparing a monitoring result with a preset miss warning line, conducting corresponding early warning, and generating an evaluation report; judging whether a problem miss index exists or not, and if yes, adjusting and updating a miss index system. According to the invention, an accurate information feedback mechanism can be formed, the development situation of the human resource market can be grasped timely, and the steady development of the human resource market is promoted.

Description

technical field [0001] The invention belongs to the technical field of information risk early warning, and in particular relates to a multi-angle unemployment risk early warning device and method. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Unemployment risk early warning is an important means to effectively prevent unemployment risk. In general, unemployment early warning calculates a relatively accurate unemployment / warning line through the introduction of relevant parameter variables and the establishment of mathematical models, or it is based on the modeling of macro-social and economic early warning systems. There are also scholars who use hierarchical diagonal neural network, BP neural network or multiple regression techniques to realize the construction of early warning models. [0004] However, as far as the inventor knows, t...

AI Technical Summary

Problems solved by technology

The amount of data related to the unemployment rate is small, and the distribution of the unemployment rate cannot be fully displayed, and the relationship between the unemployment rate and different indicators cannot be fully reflected, resulting in a large error in the forecast of the unemployment rate in units of years

Unemployment rate forecasting from a single time perspective cannot simultaneously meet the dual requirements of seasonal changes in unemployment rate and excessive increase in short-term unemployment risk

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