Analysis of Common Problems and Technical Points in Quality Management of City Water Supply and Drainage Construction Quality

In recent years,the quality of people’s lifestyle has significantly increased when the speed of social and economic development is accelerating.Among them,the management of city water supply and drainage construction quality can provide the necessary guarantee for the improvement of people’s quality of life,so the scientific of construction quality management will also become the focus of urban modernization.However,there are many problems in the management of water supply and drainage construction quality in cities after considering the real situation.If the problems are not solved as soon as possible,it will affect people’s daily life and works badly and also restrict the development of urbanization.Therefore,the implementation and practical of city water supply and drainage construction quality management are very important.Based on this,the main research content of this article is about the city water supply and drainage construction quality management and to elaborate the existed common problems and raises up some technical points and hopes that this article content can help in the management of drainage construction quality.

Systematic review of data-centric approaches in artificial intelligence and machine learning

Artificial intelligence(AI)relies on data and algorithms.State-of-the-art(SOTA)AI smart algorithms have been developed to improve the performance of AI-oriented structures.However,model-centric approaches are limited by the absence of high-quality data.Data-centric AI is an emerging approach for solving machine learning(ML)problems.It is a collection of various data manipulation techniques that allow ML practitioners to systematically improve the quality of the data used in an ML pipeline.However,data-centric AI approaches are not well documented.Researchers have conducted various experiments without a clear set of guidelines.This survey highlights six major data-centric AI aspects that researchers are already using to intentionally or unintentionally improve the quality of AI systems.These include big data quality assessment,data preprocessing,transfer learning,semi-supervised learning,machine learning operations(MLOps),and the effect of adding more data.In addition,it highlights recent data-centric techniques adopted by ML practitioners.We addressed how adding data might harm datasets and how HoloClean can be used to restore and clean them.Finally,we discuss the causes of technical debt in AI.Technical debt builds up when software design and implementation decisions run into“or outright collide with”business goals and timelines.This survey lays the groundwork for future data-centric AI discussions by summarizing various data-centric approaches.

Research on Welding Quality Traceability Model of Offshore Platform Block Construction Process

Quality traceability plays an essential role in assembling and welding offshore platform blocks.The improvement of the welding quality traceability system is conducive to improving the durability of the offshore platform and the process level of the offshore industry.Currently,qualitymanagement remains in the era of primary information,and there is a lack of effective tracking and recording of welding quality data.When welding defects are encountered,it is difficult to rapidly and accurately determine the root cause of the problem from various complexities and scattered quality data.In this paper,a composite welding quality traceability model for offshore platform block construction process is proposed,it contains the quality early-warning method based on long short-term memory and quality data backtracking query optimization algorithm.By fulfilling the training of the early-warning model and the implementation of the query optimization algorithm,the quality traceability model has the ability to assist enterprises in realizing the rapid identification and positioning of quality problems.Furthermore,the model and the quality traceability algorithm are checked by cases in actual working conditions.Verification analyses suggest that the proposed early-warningmodel for welding quality and the algorithmfor optimizing backtracking requests are effective and can be applied to the actual construction process.

Construction management and technical innovation of the main project of Hong Kong–Zhuhai–Macao Bridge

Design Units： CCCC Highway Consultants Co., Ltd.; China Zhongtie Major Bridge Reconnaissance ＆ Design Institute Co., Ltd. Construction Units： China Communications Construction Company Limited Consortium; China Railway Shanhaiguan Bridge Group Co., Ltd.; Wuhan Heavy Engineering Co., Ltd.; CCCC First Harbor Engineering Company Ltd. Consortium; Guangdong Changda High- way Engineering Co., Ltd.; China ZhongTie Major Bridge Engineering Group Co., Ltd. Consortium; Chongqing Zhixiang Paving Technology Engineering Co., Ltd.; Hunan Construction Engineering Group; China Railway Electrification Bureau Group Co., Ltd. Consortium

Data Integrity and Risk

Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.

Knowledge Discovery in Data: A Case Study

It is common in industrial construction projects for data to be collected and discarded without being analyzed to extract useful knowledge. A proposed integrated methodology based on a five-step Knowledge Discovery in Data (KDD) model was developed to address this issue. The framework transfers existing multidimensional historical data from completed projects into useful knowledge for future projects. The model starts by understanding the problem domain, industrial construction projects. The second step is analyzing the problem data and its multiple dimensions. The target dataset is the labour resources data generated while managing industrial construction projects. The next step is developing the data collection model and prototype data ware-house. The data warehouse stores collected data in a ready-for-mining format and produces dynamic On Line Analytical Processing (OLAP) reports and graphs. Data was collected from a large western-Canadian structural steel fabricator to prove the applicability of the developed methodology. The proposed framework was applied to three different case studies to validate the applicability of the developed framework to real projects data.

信息资源管理领域数字人文研究特征、研究体系及构建路径

[目的/意义]文章旨在厘清信息资源管理领域数字人文研究现状、研究特征,构建信息资源管理领域数字人文研究体系及发展路径,提高信息资源管理学科在数字人文领域的学术话语权,实现数字人文研究的高质量发展。[方法/过程]以CSSCI数据库中收录的357篇信息资源管理领域期刊论文为研究对象,借鉴扎根理论质性研究范式对文献数据进行整理加工和编码分析,最终得出信息资源管理领域数字人文研究现状、研究特征并尝试分析信息资源管理领域数字人文研究体系构建路径。[结果/结论]研究结果显示,信息资源管理领域数字人文在研究内容、研究技术和数据分析三个方面形成典型的学科特征与研究特色。以数字人文研究特征为基础,结合守正创新的学科发展理念,详细论述了信息资源管理领域数字人文研究体系构建路径,具体包括学科深度融合研究、数据基础设施研究以及数字人文技术研究三个方面。

档案数据治理概念研究进展与演进逻辑

深入开展档案数据治理研究,成为近几年档案学术研究的热点话题之一。对这一学术概念的产生和演进过程进行梳理,有利于厘清该领域学术研究的路径和逻辑,为今后在档案数据治理的理论研究和实践开展提供思路与方向。文章梳理了学界对档案数据治理概念的认知,并在此基础上重新思考,认为档案数据治理概念是档案学研究管理与技术取向双向建构产生的新概念,今后除了现有的档案数据治理研究内容以外,还要增强档案数据技术治理相关研究。

数智技术时代的中国档案学学科发展

数智技术,直接导致我国千行百业的原始数据形成,其管理正发生前所未有的变化。为适应从人人相连走向万物智联的大数据安全管理,需要对我国高等教育相关学科专业进行系统研究。梳理数字技术对我国组织的业务活动及其原始记录形成和管理的作用影响之后,提出传统的“中国档案学”学科将演进为“档案大数据工程”学科。并且指出,档案大数据工程学科高等教育包括“管理文秘”“数据资源管理”“大数据技术”等专业,尝试提出以上几个专业建设的发展方向。

大数据时代高校学生管理信息化建设的战略研究

大数据背景下互联网、云计算以及人工智能等技术开始应用各个领域与行业中。从高校层面看,大数据技术为学生管理信息化建设提供技术支撑,同时也对高校学生管理工作提出新的要求。高校作为国内发展人才兴国战略的重要资源储备地,其管理水平将直接决定人才培育的质量。在这种情况下,高校应积极利用大数据技术不断强化教育管理质量,将管理工作的针对性、全面性与精确性提升新的水准,以此夯实高校育人管理工作基础,提升高校人才培养质量。

大数据时代高校资产管理信息化建设研究

随着大数据技术的快速发展和广泛应用,高校资产管理正面临着信息化建设的新机遇与挑战。利用大数据技术可以使高校资产管理更加精确、高效和透明。文章深入探讨大数据时代下开展高校资产管理信息化建设的意义、问题和策略,旨在帮助高校更好地实施资产管理信息化建设,提高其资产管理的质量和效率。

大数据时代医院档案管理信息化建设的思考

本文以大数据时代医院档案管理信息化建设的积极价值为切入点,在此基础上探讨当前该工作的不足以及应对策略,就确保覆盖效应、保证系统延伸性、重视安全隐患控制等内容做具体论述,以改善大数据时代医院档案管理信息化建设水平,为其医疗服务、区域卫生管理等工作提供参考。

智能建造模式下建筑企业数据管理成熟度研判

本文立足于建筑业新型生产要素数据,探究智能建造模式下建筑企业数据管理高效集成利用的理论框架,以赋能建筑业高质量发展。基于数据管理能力成熟度模型(DCMM),从数据战略、数据标准、数据信息平台、数据应用、组织建设、数据治理、数据安全、数据质量8个维度构建数据管理成熟度模型,以实现建筑企业数据管理成熟度研判。

校园数据治理体系的设计与实践

阐述智慧校园中的数据质量问题和数据治理策略,包括管理体系、技术体系中的数据中台的功能、总体架构、数据架构。分析数据治理服务与应用,实现数据接口服务、数据报表服务、数据填报服务、开展综合应用。

Design of 1000-kV AC Single-Circuit Overhead Transmission Line

The UHVAC 1 000-kV transmission system is so far the one with the most advanced transmission technique applied and highest operation voltage.There are no guidelines or standards available for the design of 1 000-kV overhead transmission line in China.Study on key technologies and design schemes shall be carried out to ascertain the technical principles and construction standards for project construction,which are presented in this paper based on the Southeast Shanxi-Nanyang-Jingmen test and demonstration transmission line.A comparison and analysis of technical data and economic indices between UHV line and other lines are also described.

信息资源管理一级学科新发展理念下图情档资源的融合建设

在信息资源管理一级学科的推动指引下,在发展新质生产力的时代发展背景下,图书情报与档案资源融合建设有必要性且面临着一系列挑战和问题。这些问题主要集中在数据标准化不足、数据质量参差不齐、数据共享机制不完善以及专业人才短缺等方面。文章通过对这些问题进行深入分析,提出了相应的对策,以期能有效解决当前图书情报与档案资源融合建设中的问题,为信息服务的智能化和创新化提供有力支持,推动新质生产力的发展。

大数据时代建筑工程管理信息化建设研究

在大数据时代,数据的规模、复杂性和价值性不断提升,为各行各业的发展提供了全新的视角和可能。建筑工程管理作为建筑行业的重要组成部分,其信息化建设的进程直接影响到建筑行业的整体发展。文中对建筑工程管理信息化建设中大数据技术的体现进行了分析,探讨了建筑工程信息化管理系统架构,为建筑领域的发展提供了参考。

数据治理环境下研究生教育与管理数据资源化研究

在高校的数据治理环境下,围绕研究生教育与管理数据资源化重点,分析确立数据源,将其中的信息资源形成数据资产,探讨了相关数据在研究生教育工作、学位点建设、学科建设等方面如何发挥促进作用,使研究生教育与管理数据更好地服务于高校发展。

数据资产如何赋能企业高质量发展——对传统生产要素的优化机制

数据资产的资产属性和数据属性决定了其可以改善企业传统生产要素的配置状态,从而赋能企业高质量发展。根据企业年报,运用机器学习算法,通过“种子词筛选+AutoPhrase相似词扩充+SnowNLP情感极性分析”对企业的数据资产水平进行评估,以全要素生产率衡量企业高质量发展水平,进而采用沪深A股上市公司2007—2022年的数据进行分析,结果发现:数据资产增加显著促进了企业高质量发展,该作用主要由数据资产的处理和应用产生,数据资产获取对企业高质量发展的影响不显著;数据资产增加能够改善劳动力结构、提高技术水平、抑制非效率投资、降低管理成本,从而通过对传统生产要素的优化机制来赋能企业高质量发展;外部环境对数据资产赋能企业高质量发展具有调节作用,表现为环境不确定性、信息化水平、市场化水平和数字经济政策供给水平的提高会强化数据资产对企业高质量发展的促进作用。因此,企业应积极开发、积累和利用数据资产,政府应持续推进地区信息化、市场化和数字经济发展,以充分发挥数据资产的积极效应。

基于GIS+BIM的山区铁路施工进度管理系统研究

针对传统山区铁路施工进度采用电子表格管理所导致的标准化低、直观化差等问题,设计基于GIS+BIM的铁路施工进度管理系统。通过设计图形化的进度数据采集方式,为不同施工单位进度填报提供了统一的数据入口;对进度数据进行统计分析,融合工程进度计划数据,动态展示工程进展与计划情况,驱动工点BIM模型在三维场景中展示三维进度。应用表明,该系统能够提高进度数据的统一性和进度展示的直观性,为铁路施工管理数字化提供支撑。