In this article, an event-triggered state estimation problem for wireless sensor network systems affected by random packet losses and correlated noises is considered. A set of independent Bernoulli variables are used to describe the random packet losses in the measurement transmission. An event-triggered transmission strategy is introduced to decrease limited network bandwidth consumption, and the measurement noise is correlated with the process noises of the same moment and the previous moment. Event-triggered estimator of process noises under the linear minimum variance criterion is derived. Then, an event-triggered state estimation algorithm related to the packet loss rate, noise correlation coefficient and triggering threshold is designed. Sufficient conditions are provided to guarantee convergence of the estimation error covariances of the proposed estimator. Finally, comparative simulation verifies the effectiveness of our algorithm.

Under the framework of event‐triggered transmission mechanism, the problem of attack detection and state estimation of multi‐sensor linear time‐invariant systems under static attacks is considered. First, for each transmission channel, the sensor collects measurement information according to an event‐triggered mechanism to reduce unnecessary energy consumption. Then, inspired by the clustering algorithm in machine learning, a detection mechanism based on Gaussian mixture model, which can set a confidence level for the measurement of each sensor is proposed. Finally, centralised data fusion is performed according to the results of attack detection and event‐triggered judgement to realise remote state estimation. A numerical example proves that the proposed algorithm can locate the damaged sensor, save the network transmission bandwidth under the premise of ensuring accuracy and efficiency of sensor estimation.

China's customized clothing industry has been developing rapidly in recent years. China has entered the industrial Internet era from the consumer Internet era, and the original brand and trade enterprise-oriented design of scale transformed into a consumer-oriented personalized design stage. However, due to the long process of the clothing industry chain and the many steps involved, it is difficult to achieve data interconnection and resource sharing between the various steps, and the information silo phenomenon between consumers and clothing enterprises and raw material suppliers is serious, and the equipment maturity and model algorithm of the clothing virtual fitting system have not been widely verified by the market. In view of the above problems, this paper, based on the identification analysis technology of the industrial Internet, improves the simulation model algorithm of clothing fabric and designs the clothing virtual fitting system driven by the industrial Internet. This system combines the identification analysis system with computer graphics technology to create the virtual fitting tool by using 3D modeling and physical simulation technology, which provides solutions for a personalized, reliable, effective, and immersive fitting experience under industrial Internet scenarios.