Due to the competitive relationship among different smart factories, equipment manufacturers cannot integrate the private information of all smart factories to train the intelligent manufacturing equipment fault prediction model and improve the accuracy of intelligent manufacturing equipment fault detection. The use of a low fault recognition rate model for smart factories will cause additional losses for them. In this work, we propose a blockchain-based privacy information security sharing scheme in Industrial Internet of Things (IIoT) to solve the sharing problem of private information in smart factories. Firstly, we abstract smart factories as edge nodes and build decentralized, distributed trusted blockchain networks based on Ethereum clients on simulated edge devices and propose an Intelligent Elliptic Curve Digital Signature Algorithm (IECDSA) to guarantee the ownership of shared information by edge nodes. Secondly, we propose the Reputation-based Delegated Proof of Stake (RDPoS) consensus algorithm to improve the security and reliability of the Delegated Proof of Stake (DPoS) consensus algorithm. Furthermore, we design and implement an incentive mechanism based on information attributes to increase the motivation of edge nodes to share information. Finally, the proposed solution is simulated. Through theoretical and simulation experiments, it is proved that the blockchain-based privacy information security sharing scheme in IIoT can improve the enthusiasm of edge nodes to share information on the premise of ensuring the security of information sharing.

Meta-learning aims to teach the machine how to learn. Embedding model-based meta-learning performs well in solving the few-shot problem. The methods use an embedding model, usually a convolutional neural network, to extract features from samples and use a classifier to measure the features extracted from a particular stage of the embedding model. However, the feature of the embedding model at the low stage contains richer visual information, while the feature at the high stage contains richer semantic information. Existing methods fail to consider the impact of the information carried by the features at different stages on the performance of the classifier. Therefore, we propose a meta-learning method based on adaptive feature fusion and weight optimization. The main innovations of the method are as follows: firstly, a feature fusion strategy is used to fuse the feature of each stage of the embedding model based on certain weights, effectively utilizing the information carried by different stage features. Secondly, the particle swarm optimization algorithm was used to optimize the weight of feature fusion, and determine each stage feature’s weight in the process of feature fusion. Compared to current mainstream baseline methods on multiple few-shot image recognition benchmarks, the method performs better.