Architecture Design and Application of IIoT Platform in Automobile Manufacturing Based on Microservices and Deep Learning Techniques

An Internet of Things (IoT) platform is a software architecture that enables the connection, management, and analysis of IoT devices, sensors, and data. It provides a centralized system for IoT devices to interact with each other and with the cloud, facilitating the collection, processing, and analysis of data from these devices. However, in the automotive manufacturing industry, traditional Internet of Things (IoT) platforms are facing challenges such as bottleneck issues due to business volume growth and system challenges. To address these challenges, we propose a design methodology for an IoT platform based on microservices. The platform’s modules are divided into front end, database, security, and operation maintenance architecture, all effectively designed. Through practical applications, the platform enables interconnections between different information systems, production status monitoring, efficiency management, performance evaluation, energy consumption analysis, quality detection, and equipment asset evaluation. Finally, a data-driven deep learning algorithm, named Long Short-Term Memory Neural Network (LSTM) is developed for the state recognition of the industrial robot based on the Intelligent data services platform, which validate the effectiveness of the constructed IoT platforms. This platform offers advantages in extendibility, reusability, and provides methods for upgrading, expanding functions, and maintaining industrial IoT platforms in the discrete manufacturing industry.