General Industrial Environment and Health Design Software Using a Small Data-Driven Neural Network Model

J Environ Public Health. 2022 Jul 13:2022:1768446. doi: 10.1155/2022/1768446. eCollection 2022.

Abstract

The developed enterprise intellectual brain neural network platform for industrial environment and health design driven by small data converts the wisdom knowledge of enterprise designers into data and helps enterprises retain design experience, accumulate design knowledge results, sort out the design process, and shorten the design cycle. The goal of this project is to combine artificial intelligence, big data, the Internet of things, parameterization, and other computer technologies with the industrial environment and health design and development process of manufacturing enterprises to solve pain points for businesses and designers, as well as to develop and create a large-scale product-level general parameter intelligent design software. The development of this software project can greatly improve the work efficiency of designers, save the time spent by designers on low-end repetitive labor, and in turn promote designers to engage in more valuable creative work and realize the digitization of entire product life cycle. It can also improve the inefficient and redundant work efficiency of designers, and designers can switch between multiple roles. At the same time, a design think tank is formed through the accumulation of enterprise design knowledge through data, and the neural network platform development of big data drives the design brain, that is, an efficient and intelligent industrial environment and health design expert system is generated, just like the enterprise brain, including expert think tanks, internal technical data encryption of enterprises interface, enterprise core technology think tank, enterprise production resources, risk control standard library, etc.

Publication types

  • Research Support, Non-U.S. Gov't
  • Retracted Publication

MeSH terms

  • Artificial Intelligence*
  • Efficiency
  • Environment*
  • Neural Networks, Computer
  • Software