The Key Role of Multi-Source Data Fusion in the Construction of Digital Transformation in Manufacturing Industry

The foundation of social and economic growth is seen to be the manufacturing sector. Processing raw materials or components into completed products that are ready for consumer purchase is known as manufacturing. A new technology called the industrial internet of things (IIoT) has the potential to increase manufacturing productivity, reduce costs, and boost industrial intelligence. Digitalization and automation are constrained by political unpredictability, economic volatility, and a shortage of skilled labor. We proposed a completely unique transfer learning-based data fusion machine (TLDF) to deal with these troubles in IIoT. We suggested a multi-source deep Q-networks (MDQN) technique for task classification, task receiver, and private safeguarding for the manufacturing industry. Data fusion, which includes the gathering and analyzing of massive volumes of internet of things (IoT) data produced through industrial packages and gadgets, and the examination of this data is important to the improvement of producing manufacturing applications in IIoT. The outcome demonstrates that the suggested technique carried out low latency, excessive throughput, and accuracy. The focus of the research is on data fusion and its role in the digital transformation of the industrial sector. It emphasizes the need for ongoing innovation in data integration technology and the implementation of a comprehensive data management strategy.