Random Matrix-Based Multivariate Statistical Analysis of Enterprises in a Distributed Environment Human Resource Management

Abstract

This paper addresses the design of an enterprise human resource management system due to multivariate statistical analysis computation in a random matrix recommendation algorithm in a distributed scenario. This paper defines multivariate statistical analysis human resource practice (DI-RP). It determines the composition of DI-HRP based on the nature of multivariate statistical analysis and enterprise human resource practice. In addition, the role of DI-HRP in influencing employees' innovative behaviors is explored based on the theoretical basis of resource conservation theory. This paper mainly develops according to the software development process typical to software engineering, organizes the current business logic of the company, understands the relevant content of the company's human resource management, conducts requirement research on the human resource department, analyzes the feasibility of system implementation from different perspectives, and finally designs a human resource management system based on B/S architecture on the result of requirement analysis. The technology and tools used in the system were decided on the existing technical architecture of the company. The system design was divided into five modules: Personal information management module, work management module, attendance management module, reimbursement management module, and entry/exit management module according to the requirements and finishing process, and each functional module of the system was coded and implemented, respectively. The development tool is PyCharm, and some front-end pages are edited and modified by Visual Studio Code. The Permission model of Django is used to add corresponding permissions for various types of users to ensure the security of the system when running.