船舶制造业能耗管理服务平台构建及数据挖掘应用研究

发布时间：2018-07-03 04:55

本文选题：船舶制造业 + STM32　；参考：《集美大学》2017年硕士论文

【摘要】：船舶制造业在我国工业体系中,占住非常重要的地位。但是,船舶制造业属于高能耗产业,能耗成本占比高达2%~3%,在能源费用逐年上涨,能源日益紧张的背景下,控制能源消耗是提高船舶制造业竞争力的主要手段。目前我国船舶制造业大都采用手工抄表,粗狂方式管理,能耗数据误差大,时效性差,并且船舶制造业施工现场用电混乱,无法掌握能耗去向。论文以船舶制造业为对象,采用物联网技术,云计算技术和大数据挖掘技术,实现精准、精细、实时在线采集能耗数据,构建了船舶制造业能耗管理服务平台,对能耗数据进行分析和统计,制定能耗标准,实时监管能源消耗,达到节能减排效果;论文还针对船舶制造业施工现场用能散乱设计了智能配电桩系统。本文主要研究内容包括以下几个方面:首先,分析了船舶制造业能耗管理存在的问题,并重点分析了船舶制造业施工现场能耗浪费严重的原因,为能耗数据采集提供理论支撑。结合物联网技术,确定船舶制造业能耗管理服务平台的架构。论文利用ARM Cortex-M4内核的STM32微处理器作为船舶制造业智能配电桩系统的主控器,开发各个硬件模块驱动程序和射频识别、数据信息采集、数据信息无线传输以及存储程序。论文以IntellJ IEDA 14.0.3平台为开发环境,设计BS架构下的船舶制造业能耗管理服务平台,实现了船舶制造业能耗查询、统计和分析等管理功能。论文根据船舶制造业能耗特点,采用小波神经网络预测方法对能耗进行理论分析,在MATLAB中实现建模与能耗数据仿真。本文设计的船舶制造业智能配电桩系统可实现与船舶制造业能耗管理服务平台的信息交互,便于管理者清晰的掌握施工现场能耗细节,杜绝大面积高能耗设备无作业运行的情况。系统可以定时、定点的采集各个支路以及高能耗设备的能耗数据,保证数据的准确性和及时性,在一定程度上可以弥补传统管理上的不足,方便管理者统计能耗成本,极大提高工作效率。同时将大数据挖掘技术应用到船舶制造业能耗数据分析中可以为船舶制造业制定节能方案提供准确、可靠的数据支持,提高船舶制造业的综合效益。
[Abstract]:Shipbuilding industry occupies a very important position in China's industrial system. However, shipbuilding industry belongs to high energy consumption industry, the proportion of energy consumption is as high as 2%. Under the background of energy cost increasing year by year, energy consumption control is the main means to improve the competitiveness of shipbuilding industry. At present, the ship manufacturing industry in China mostly adopts manual meter reading, rough management, large energy consumption data error, poor timeliness, and shipbuilding construction site power chaos, unable to grasp the direction of energy consumption. This paper takes the shipbuilding industry as the object, adopts the technology of Internet of things, cloud computing and big data mining technology, realizes accurate, fine, real-time on-line energy consumption data collection, and constructs the energy consumption management service platform of the shipbuilding industry. Analysis and statistics of energy consumption data, the formulation of energy consumption standards, real-time monitoring of energy consumption to achieve energy conservation and emission reduction effect; the paper also designed an intelligent distribution pile system for shipbuilding construction site energy scattered. The main contents of this paper include the following aspects: firstly, the problems of energy consumption management in shipbuilding industry are analyzed, and the causes of serious energy waste in shipbuilding construction site are analyzed, which provides theoretical support for energy consumption data collection. Combined with Internet of things technology, the structure of energy consumption management service platform in shipbuilding industry is determined. In this paper, the STM32 microprocessor of arm Cortex-M4 core is used as the main controller of the intelligent distribution pile system in the shipbuilding industry, and various hardware module drivers and radio frequency identification (RFID), data information collection, wireless data transmission and storage programs are developed. Based on the platform of tellJ IEDA 14.0.3, this paper designs a service platform for energy consumption management of shipbuilding industry based on BS, and realizes the management functions of energy consumption query, statistics and analysis of shipbuilding industry. According to the characteristics of energy consumption in shipbuilding industry, wavelet neural network prediction method is used to analyze the energy consumption theoretically, and the modeling and simulation of energy consumption data are realized in MATLAB. The intelligent distribution pile system designed in this paper can realize the information interaction with the energy consumption management service platform of the shipbuilding industry, and it is convenient for the manager to grasp the details of the energy consumption in the construction site clearly. Put an end to large areas of high energy consumption equipment without the operation of the situation. The system can collect energy consumption data of each branch and high energy consumption equipment at fixed time and fixed point to ensure the accuracy and timeliness of the data. To some extent, the system can make up the shortcomings of traditional management and facilitate managers to count the energy consumption cost. Greatly improve working efficiency. At the same time, the application of big data mining technology to the energy consumption data analysis of shipbuilding industry can provide accurate and reliable data support for the establishment of energy-saving scheme for shipbuilding industry, and improve the comprehensive benefits of shipbuilding industry.
【学位授予单位】：集美大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13

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