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饲草料机械设备数据类型较多，设备参数复杂，应用传统关系型数据库构建的数据共享平台缺乏扩展性和通用性，不利于牧草行业信息化发展需求。因此在研究非关系型数据库MongoDB的基础上，针对饲草料机械数据格式复杂的特点，对如何应用MongoDB数据库及数据组织形式进行讨论，并利用Python语言的开发web应用的轻量框架web.py进行饲草料机械设备数据共享平台的开发，解决了饲草料机械数据交互式访问等实际问题，实现了饲草料生产机械设备数据的共享。The forage production machineries and equipments have many different types and the different type owns its special parameters. It is lack of scalability and versatility when building data share platform by relational database, which is not conducive to the information needs of the development of grass industry. Based on non-relational database MongoDB and the characteristics of the forage machinery complex data format, it discussed how to use MongoDB database and data organization form. By using the web.py lightweight framework to develop the web application solve the forage mechanical data interactive access and other practical problems, and realize the sharing of forage production machinery and equipment data.
We use cellular automata for simulating a series of topology control algorithms in Wireless Sensor Networks (WSNs) using various programming environments. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computations using only local information. WSNs are composed of a large number of distributed wireless sensor nodes operating on batteries. The objective of the topology control problem in WSNs is to select an appropriate subset of nodes able to monitor a region at a minimum energy consumption cost and, therefore, extend network lifetime. Herein, we present topology control algorithms based on the selection—in a deterministic or randomized way—of an appropriate subset of sensor nodes that must remain active. We use cellular automata for conducting simulations in order to evaluate the performance of these algorithms and investigate the effect/role of the neighbourhood selection in the efficient application of our algorithms. Furthermore, we implement our simulations in Matlab, Java and Python in order to investigate in which ways the selection of an appropriate programming environment can facilitate experimentation and can result in more efficient application of our algorithms.