自动在线监测数据质量保证研究

对自动在线监测设备监测数据质量保证系统进行了研究,并在在线监测系统的硬件环境、管理体制和软件系统三个方面进行了具体的分析和研究.     

Using data mining technology to explore causes of inaccurate reliability data and suggestions for maintenance management

Reliability data reflects equipment safety and provides a reference for setting inspection period, thereby serving as crucial information for the implementation of equipment integrity management policies. The calculation foundation of reliability data is maintenance records of adequate data quality. However, maintenance records of doubtful quality are common. Despite excluding poor quality recodes and using only the remaining maintenance recodes to calculate the reliability data, the calculated results generally lack a sufficient degree of confidence. This study applied data mining technology, including quality metrics, the association rule, and clustering, to explore the cause of low-quality maintenance data. The results revealed that the low data quality of maintenance records was due to ineffective maintenance policies, the low integrity of key system columns, nonadherence to the policy, and misunderstanding of column definitions. The proposed method successfully identified the causes of low-quality maintenance records. By incorporating the method into the function module of a CMMS, operators can equip the system with self-diagnosis, self-supervision, and continuous optimization functions.     

基于LM-BP神经网络的Argo数据西北太平洋海水温度模型

以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型。将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验。检验结果为:2008年均方根误差为0.714 0℃,Pearson相关性系数为0.996 8;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965。表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的。     

江苏省地震科学数据共享平台技术研究

本文利用分布式数据库技术着力研究和开发了江苏省地震局的测震数据库、前兆数据库的共享、管理与集中发布技术。主要运用JSP（Java Server Page）技术及PHP（Hypertext Preprocessor）语言对测震的JOPENS系统中MySQL数据库、EQIM数据库、及前兆Oracle数据库等进行进一步地开发。搭建了一个行业内跨计算机操作平台的可视化的地震科技数据共享、交换及管理平台,实现了多种地震科学数据的（例如近、远震地震信息、各学科的运行率等）在线可视化、基于Google Map检索的地震信息服务等。     

通信技术在近海环境监测中的应用

本文针对海洋环境监测覆盖面广、站位分散、数据间断和频繁少量的特点以及海洋环境监测自动化、实时化和连续化的发展趋势,提出一种基于水上移动通信和水声通信技术的海底观测链方式,并以海床基监测数据实时传输系统为例,对系统组成、技术特点、技术原理和应用分别阐述,旨在推广通信技术在海洋环境监测领域中的应用。     

关于建立我国光污染防治体系的思考与建议

本文对建立符合国情的光污染防治体系提出相关建议。首先阐述了光污染的概况;然后对国内外光污染防治的立法现状进行研究和探讨;最后提出具体的立法构想。与欧美和日本等发达国家相比,我国的光污染防治工作才刚起步,关于光污染防治的环境立法还是空白,对光污染防治的系统性研究处于初始阶段。以经济、生态的可持续发展为指导思想,采用符合国情的立法模式,修订《中华人民共和国环境保护法》,制定光污染防治单行法《光污染防治法》,制定光环境质量标准和排放标准,建立光污染防治体系,全面防治光污染。     

玉溪市中心城区大气污染分析及防治对策探讨

利用玉溪市环境监测站2006～2010年监测结果,用在执行标准和新颁布标准对中心城区环境空气质量现状及其变化趋势进行评价,从气象、地形、工业结构和排放量等四个方面对环境空气污染的成因进行分析研究,提出相关的建议。     

中美制药工业水污染物排放标准比较分析

当今医药行业快速发展,是中国国民经济的重要组成部分,而制药工业废水污染控制也已成为影响规模化和可持续发展的关键问题。环境管理是政府管理职能和企事业单位经营管理的一个重要组成部分,对于制药工业,其水污染物排放标准体系更是环境监管和污染控制的基本依据。因此,通过分析比较中美两国环境管理体系和制药工业水污染物排放标准,得出两者各自的特点和异同,从而为进一步完善中国制药工业的污染防治和环境管理体系提供借鉴。     

油气田绿色矿山建设管理要求与实践

针对目前国家对绿色矿山建设的要求和环境保护面临的新形势,结合国内油气勘探与生产业务绿色发展的相关要求,阐述了油气田绿色矿山建设的发展历程、重点内容、建设标准;总结了绿色矿山建设形成的具体成果和经验,这些经验主要包括注重依法合规、提升绿色理念、科技创新、责任落实、多部门协作等,为下一步建设指导方向,进而提升国内油气勘探与生产业务整体绿色发展水平。     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.