Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm.

Haiqiu Li

doi:10.1155/2021/3250062

Abstract

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.

Highlights

People usually use the method of job analysis to understand the requirements of each job position in terms of personnel characteristics, use the method of psychological measurement to understand the psychological characteristics of each personnel member, and place the personnel in the appropriate position through mutual matching [1,2,3]
E multimodal neural network calculation model simultaneously extends the learning model from the vector space to the tensor space. rough the high-order back propagation algorithm oriented to the tensor space, the parameters of the neural network are reversed. e multimodal neural network calculation model can quickly and accurately learn the characteristics of complex data, which has obvious advantages over the conventional multilayer neural network calculation model based on tensor space
Because the numerical range of personnel placement data is not large, in order to ensure the accuracy of prediction results, neural network is not normalized before training

Summary

Introduction

People usually use the method of job analysis to understand the requirements of each job position in terms of personnel characteristics, use the method of psychological measurement to understand the psychological characteristics of each personnel member, and place the personnel in the appropriate position through mutual matching [1,2,3]. With the rapid development of Internet technology and the era of big data, artificial neural network (ANN) has become one of the most popular algorithms in modern times [8]. With the continuous development of artificial intelligence technology and human resources market, a large amount of personnel data with rich information has been produced. In order to place people with different characteristics in their suitable jobs, the constraint satisfaction model of artificial neural network is used to solve this problem. Based on the traditional data processing of multilayer variable neural network, this paper captures the distribution characteristics of data in high-order tensor space, establishes a multimodal neural network calculation model for complex big data feature learning, and realizes the optimization of personnel placement scheme

Related Work

Methodology

Analysis and Discussion

Conclusions