Design and implementation scheme on Laboratory Intelligent Management System Based on Neural Network

Nowadays, laboratory scale is becoming larger and research needs are becoming increasingly complex, posing many challenges to traditional laboratory management models. Traditional management methods often rely on manual experience, which leads to problems such as poor information flow, unreasonable resource allocation, and low efficiency. With the rise of big data and artificial intelligence technologies, neural networks have provided new ideas and methods for solving these problems due to their ability to automatically learn complex patterns and nonlinear relationships in data. This article proposes a design and implementation scheme for a laboratory intelligent management system based on neural networks, detailing the overall architecture of the system, data acquisition and preprocessing modules, resource scheduling model based on neural networks, security and permission management modules, as well as the implementation and testing results of the system, providing an effective solution for intelligent management of laboratories.