In the industrial internet of things (IIoT), because thousands of pieces of hardware, instruments, and various controllers are involved, the core problem is the sensors. Detection using sensors is the bottom line of the IIoT, directly affecting the detection accuracy and control indicators of the IIoT system. However, when a large number of realtime data generated by IIoT devices are transferred to cloud computing centers, large-scale data will inevitably bring computing load, which will affect the computing speed of cloud computing centers and increase the computing load of cloud computing data centers. These factors directly lead to instability and delay in sensor data collected in real time in the IIoT. In this paper, a sensor outlier detection algorithm based on edge calculation is proposed. Firstly, focusing on the problem of the large amount of data in terminal equipment of the IIoT, the edge technology method of data compression is used to optimize the compression of sensor data, and different thresholds are set according to different industrial process requirements, so as to ensure the real-time aspect and authenticity of the data. Then, using the K-means clustering algorithm, the compressed test data sets are analyzed and the abnormal sensor detection values and labels are obtained. Finally, the effectiveness of such an approach is evaluated through a sample case study involving a temperature control system.

No abstract

No abstract

At present, the Internet of Things (IoT) has attracted more and more researchers' attention. Electromagnetic scattering calculation usually has the characteristics of large-scale calculation, high space-time complexity, and high precision requirement. For the background and objectives of complex environment, it is difficult for a single computer to achieve large-scale electromagnetic scattering calculation and to obtain corresponding large data. Therefore, we use Finite-Difference Time-Domain (FDTD) combined with Internet of Things, cloud computing, and other technologies to solve the above problems. In this paper, we focus on the FDTD method and use it to simulate electromagnetic scattering of electrically large objects. FDTD method has natural parallelism. A computing network cluster based on MPI is constructed. POSIX (Portable Operating System Interface of UNIX) multithreading technology is conducive to enhancing the computing power of multicore CPU and to realize multiprocessor multithreading hybrid parallel FDTD. For two-dimension CPU and memory resources, the Dominant Resource Fairness (DRF) algorithm is used to achieve load balancing scheduling, which guarantees the computing performance. The experimental results show that the hybrid parallel FDTD algorithm combined with load balancing scheduling can solve the problem of low computational efficiency and improve the success rate of task execution.

AKglRACT: At present, China's economy developes very quickly and displays very bright future with the transformation of its development strategy from extensive production and management mode to the intensive bne. There is no doubt that industrial relocation as a long-term task has all round meaning and strategie significance both in capital construction and industrial renewal and r~mke to achieve such a strategic transformation. In this paper, the authors gave a general study on the Chinese industrial relocation in the light of the theory of industrial location and relocation and ~ five types of industrial rd~x:ation concerning factory development in scale, site and organization in China: factory-expanding, factory-converting & renovating, factory-removing & migrating, industry-substituting in a region and trace-reutilization of a factory.