The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step.

Smart campus is based on the Internet of Things (IoT) technology, and its purpose is to achieve the intelligent management and service on campus. Through the IoT, the mutual blending of teachers and students, learning facilities, and information generated by facilities is realized. This paper introduces and analyzes the current research status of smart campus and the difficulties in integrating various kinds of service data in smart campus. After analyzing various kinds of situational data, this paper analyzes and designs smart campus service discovery algorithm and perceptual data fusion algorithm on the basis of previous research on the situational awareness system framework for smart campus. Compared with the traditional campus platform, smart campus can manage perceptual data more efficiently, which brings convenience to campus service and management.

With the development of information technology, the Internet of Things (IoT) has the characteristics of strong permeability, large use of action, and good comprehensive benefits. It promotes the development of the IoT technology in the detection of structural engineering. It is conducive to the development of intelligent, refined, and networked structures. Crack is the most common threat to the safety of bridges. Historical data show that the safety accidents caused by cracks account for more than 90% of the total bridge disasters. After a long period of engineering practice and rigorous theoretical analysis, it was found that 0.3 mm is the maximum allowable for bridge cracks. If the width exceeds the limit, the integrity of the bridge will be destroyed, and even a collapse accident will occur. Therefore, it is very important to identify cracks in bridge structure effectively and provide effective information for structural disaster reduction projects in time. Based on the structure of the IoT and the structural characteristics of the bridge engineering, this paper analyzed the practical application value of the IoT technology in the crack identification of bridge structures and established a bridge structure health monitoring system based on the IoT technology. On this basis, this paper also studied a digital and intelligent bridge crack detection method to improve the efficiency of bridge safety diagnosis and reduced the risk factor. First, the collected bridge crack photographs were preprocessed, the bridge crack convolution neural network classification model was established, and the model was simulated and trained using MATLAB. The bridge crack classification was obtained. The simulation results showed that the overall accuracy rate was greater than 90%.

Based on the fact that the converter mouth flame is the comprehensive external appearance of the physical and chemical reactions in the converter during the steelmaking process, the continuous spectrum information of the converter mouth flame was obtained by USB4000 spectrometer. In the framework of the Internet of Things (IoT), a bidirectional recursive multi-scale convolution depth neural network algorithm, which can take into account the characteristics of frequency domain structure and time domain dynamic sequence. It is applied to the deep-learning of the converter mouth flame spectrum information. The dynamic prediction model of carbon content and temperature value in molten steel at the later stage of steelmaking is constructed. The static control system and dynamic prediction model of automatic steelmaking are intelligently fused to realize one-key steelmaking control. The results show that the average hit rate of carbon content, temperature and carbon temperature at the end of steelmaking is 94.78%, 98.41% and 93.43%, which makes the end-point control of steelmaking more stable and the blowing rate less than 1%.

Based on the advantages of Internet of things, this paper focuses on the research of intelligent recommendation model for cancer patients' rehabilitation, and designs a user-friendly intelligent recommendation system of cancer rehabilitation scheme. In view of the uncertainty of the cause and time of recurrence of cancer patients, the convolutional neural network algorithm was used to predict both of them. The prediction results of the model showed that the prediction accuracy was high, reaching 92%. To solve the problem of the optimal nutrition program for the rehabilitation of cancer patients, we took the recurrence time as the objective function, and established the recommendation model of the optimal nutrition support program for the rehabilitation by using BAS algorithm. Finally, under the framework of Internet of things technology, the intelligent recommendation model of cancer rehabilitation prediction model and nutrition support program was integrated to realize the recommendation system of intelligent recommendation of rehabilitation nutrition support program for cancer rehabilitation patients according to their different characteristics. After the system simulation experiment, it was found that under the condition that the predicted recurrence location was almost unchanged (49% of simulation results and 50% of actual results), the nutritional support scheme recommended by the intelligent recommendation system could extend the postoperative recurrence time of patients by more than 95%. This recommendation system can help doctors select personalized nutrition and rehabilitation programs suitable for patients in the later stage of rehabilitation treatment according to different cancer patients, and has certain guiding significance for the field of cancer rehabilitation.