High dimensional reliability analysis is unavoidable if a structural system involving stochastic process because Karhunen–Loève (K‐L) expansion is commonly used. Reliability analysis for structural systems with computationally intensive numerical models and high dimensions is challenging. In this study, an effective high dimensional reliability analysis method is proposed based on principal components and active subspace (PCAS) and active Kriging, and is termed as PCAS‐AK. The proposed method can address two shortcomings existing in PCAS: the latter select training samples randomly and cannot deal with parallel processing. The proposed method, however, selects training samples adaptively, subsequently, the training samples that have high contribution to probability of failure are selected, which can improve computational efficiency. To reduce overall computational time and allow parallelization, K‐weighted‐means combined with learning function are used to select multiple samples at each iteration. After the dimension reduction of both input and output spaces, the samples selected by adaptive method are used to construct Kriging model in the latent low‐dimensional space. Three examples are used to demonstrate the applicability and accuracy of the proposed method. Results show that the proposed method can deal with parallel processing and is, generally, more effective than PCAS for high dimensional reliability problems.

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In order to realize the fault tolerance of real-time tasks in scientific research data platform information platform, based on adaptive feedback equalization and symbol modulation technology, a fault-tolerant scheduling model of real-time tasks in scientific research data platform information is proposed in the paper. What is more, the platform transmission channel model of the scientific research data platform information under routing conflicts is first constructed to optimize the information transmission protocol of the scientific research data platform. Secondly, the fuzzy C-means clustering method is applied to perform the inspection robot information fusion. Meanwhile, the symbol modulation method is used for fault-tolerant scheduling of real-time tasks in the information platform. Finally, a simulation experiment is carried out. The results show that the method proposed in the paper has better fault tolerance for platform real-time task scheduling of scientific research data platform information, and the channel balance of the information platform is stronger, which improves the platform real-time task scheduling capability of scientific research data platform information.

Aiming at the large deviation of the traditional Internet of things (IoT) communication consistency algorithm, this paper designs the IoT information communication consistency algorithm. According to the obtained eigenvectors, the communication consensus protocol is generated, and the truncation factor is introduced to solve the problem that the cooperative variable deviation between the new node or restart node and the eigenvector is too large in the process of consistency, so as to realize the information communication consistency of the Internet of things. The experimental results show that the deviation value of this method is lower than that of the traditional algorithm, and is close to the standard value. This design ensures the consistency of the information communication of the Internet of things, and has certain application significance.