Blockchain Network Based Topic Mining Process for Cognitive Manufacturing

Cognitive manufacturing has brought about an innovative change to the 4th industrial revolution based technology in combination with blockchain distributed ledger, which guarantees reliability, safety, and security, and mining-based intelligence information technology. In addition, artificial intelligence, machine learning, and deep learning technologies are combined in processes for logistics, equipment, distribution, manufacturing, and quality management, so that an optimized intelligent manufacturing system is developed. This study proposes a topic mining process in blockchain-network-based cognitive manufacturing. The proposed method exploits the highly universal Fourier transform algorithm in order to analyze the context information of equipment and human body motion based on a variety of sensor input information in the cognitive manufacturing process. An accelerometer is used to analyze the movement of a worker in the manufacturing process and to measure the state energy of work, movement, rest, and others. Time is split in a certain unit and then a frequency domain is analyzed in real time. For the vulnerable security of smart devices, a side-chain-based distributed consensus blockchain network is utilized. If an event occurs, it is processed according to rules and the blocking of a transaction is saved in a distributed database. In the blockchain network, latent Dirichlet allocation (LDA) based topic encapsulation is used for the mining process. The improved blockchain distributed ledger is applied to the manufacturing process to distribute the ledger of information in a peer-to-peer blockchain network in order to jointly record and manage the information. Further, topic encapsulation, a formatted statistical inference method to analyze a semantic environment, is designed. Through data mining, the time-series-based sequential pattern continuously appearing in the manufacturing process and the correlations between items in the process are found. In the cognitive manufacturing, an equalization-based LDA method is used for associate-clustering the items with high frequency. In the blockchain network, a meaningful item in the manufacturing step is extracted as a representative topic. In a cognitive manufacturing process, through data mining, potential information is extracted and hidden rules are found. Accordingly, in the cognitive manufacturing process, the mining process makes decision-making possible, especially advanced decision-making, such as potential risk, quality prediction, trend prediction, production monitoring, fault diagnosis, and data distortion.