With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social development. To ensure the quality and safety of rice and reduce the flow of problematic rice, a multi-layer blockchain-based rice supply chain refinement supervision model MBRRSM (Multi-blockchain Rice Refined Supervision Model) is proposed from the information level. First, the characteristics of information flow in the rice supply chain are analyzed, and a classification table of key information is constructed. Second, the MBRRSM framework is designed. Based on a multi-party hybrid encryption algorithm, secure multi-party computing algorithm, multi-mode storage mechanism, and SPOP (Supervision Proof of Peers) consensus algorithm, a set of mechanisms is designed for the transmission, use, storage, and consensus of rice data in MBRRSM. Subsequently, the security and performance capabilities of MBRRSM are analyzed. Meanwhile, the SPOP consensus algorithm is analyzed. Finally, a prototype system is built based on MBRRSM and verified through exemplary scenarios in different usage situations. The results show that research on the refined supervision of the rice supply chain based on multi-blockchain can finely supervise all types of data in the rice supply chain, and provide a guarantee for enterprise users to safely transmit and use data with different privacy levels. This study presents a unique research paradigm that introduces the theories and methods of the new information field generation into the field of agricultural research, and thus assists in the strategy implementation of “holding grain in the land and storing grain in technology”.

Gain and oil are important compounds in global food supplies, and ensuring the quality and safety of grains and oil is a critical issue in the food supply chain security. Data traceability is the key factor in quality and safety management. Currently, it is a big challenge to ensure the reliability of data and guarantee the efficient exchange of data in various highly heterogeneous systems. To address this challenge, we develop a reliable traceability model applied to the grain and oil industry. In this paper, we first analyze the characteristics of the whole chain traceability information flow, and then we propose the concept that the connector for blockchain and industrial internet is suitable for data traceability in the grain and oil industry. Based on this concept, a reliable traceability model of grain and oil quality and safety is constructed. Finally, a reliable traceability prototype system for wheat quality and safety was designed, and the system implementation of the model was validated. The overall advantage of the proposed model is that the traceability information is safe and credible, the interaction is concise and efficient, and the security and full-process traceability of cross-chain information interaction are guaranteed. This paper fills the gap in the application of research chain network in the field of grain and oil traceability. Reference to this model can also be used to implement and adjust the traceability system, which is adaptable to stakeholders in the grain and oil industry. The model and techniques in this paper not only demonstrate value in real-world applications but also inspire further research in the field.

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

As an important part of the industrial internet, identity analysis data are growing with the expansion of the field involved in the industrial internet. The management of industrial internet identity analysis data faces many problems, such as complex types, a wide range of information, rapid growth, reduced security, etc. In view of the above problems, a trusted management model of industrial internet identity analysis data based on blockchain is first designed. Meanwhile, the identity analysis data information is analyzed and classified, and industrial data are divided into three levels according to the degree of privacy for hierarchical encryption. Secondly, the “on-chain + off-chain” storage model combining the blockchain main-slave chain and the off-chain database is designed to improve the efficiency of the whole model. Then, a collaborative consensus mechanism suitable for the main-slave multi-chain of the industrial internet is also designed, including slave-chain CIPBFT consensus, inter-chain cross-chain transmission protocol and main chain KZKP consensus. Finally, a prototype system is built to analyze the correctness, security, scalability and consensus efficiency of the model proposed in this study. The results show that the model proposed in this study can be applied to trusted management of data information for industrial internet identity analysis, and also provides an optimized solution for the same problem in fields of the industrial internet.