Abstract：Supported intelligence engineering, this article is based on the framework of complex adaptive system, modeling the time series, interactivity, stochastics of power system production simulation to assist the theory research in the area of power system planning, demand side management and so on. The model simulates different links and functions of power system via user-requirements module, power grid scheduling module, electricity market module and electric power production module. The result shows that the production simulation model can play a supporting role in the research of power development planning, demand side management and electricity market modelling.

Abstract：Based on massive equipment test data, using big data mining idea to build equipment big data analysis, processing and intelligent information service，it is one of the key technologies for developing equipment data engineering. In this paper, the characteristics of equipment test data are analyzed, then using big data processing and analysis ideas, presents an equipment big data model, which provides a reference for the construction of equipment data engineering. In the end, an example of navy army is provided.

Abstract：In the traditional grain depot, the indoor location accuracy of the temperature measurement nodes depends on a large number of beacon nodes, which brings great inconvenience to practical engineering applications. To simplify the application model, a differential location model is established according to the distance attenuation curve characteristics of RSSI(Received Signal Strength Indication). Moreover, a positioning method—based on RSSI differential location model of the grain temperature measurement node was proposed. The model adopts dynamic beacon nodes, and determines the nearest unknown node from the beacon node according to the RSSI value, and finally locates all the nodes. Experiments show that the temperature measurement nodes based on the RSSI differential location model do not need additional placement of beacon nodes, which can reduce the random error of the environment and the positioning accuracy is higher than that of the traditional ranging location model.

Abstract：In order to improve the logistics level of fresh prairie fresh products and promote the transformation and upgrading of the logistics of fresh grassland products, a new concept of cloud logistics is put forward. On this basis, an optimal scheduling algorithm for shared cloud logistics resources is designed for fresh prairie fresh products.First, we use DFS, Mapreduce and other big data related technologies to achieve parallel computing and design new logistics resource encapsulation and organization form in this algorithm. We analyze the existing problems of cloud logistics resource scheduling algorithm and propose solutions. A shared cloud logistics resource optimization scheduling algorithm for grassland fresh products is adopted. Dynamic NSGA-II（Multiobjective genetic algorithm） resource planning model is used to find a fast solution for NSGA-II models, and provide a solution for cloud logistics to break through the bottleneck of development. Experiments show that the algorithm is efficient, applicable and stable, and it can effectively improve the current level of fresh grassland logistics, and promote the transformation and upgrading of the current logistics enterprises.

Abstract：With the development of power communication technology, a large number of distributed power communication subsystems and massive power communication data have been generated. It is important to mine important information in the vast amounts of data. Cluster analysis, as an effective means of data processing and information mining, has been widely used in power communication. However, the traditional clustering algorithms can not meet the time performance requirements when dealing with massive power data. To solve this problem, a parallel k-medoids clustering algorithm based on MapReduce model is proposed to support the effective analysis and utilization of power data. The algorithm uses density-based clustering method to optimize the selection strategy of k-medoids initial point, and implements the algorithm parallelization using MapReduce programming framework under Hadoop platform. Experimental results show that compared with other algorithms, the improved parallel algorithm reduces the clustering time and improves the clustering accuracy.