Research on the Application of Intelligent Construction Technology in Hangzhouxi Railway Station

Relying on the construction management of Hangzhouxi Railway Station,this paper analyses the comprehensive application of intelligent construction technology and the establishment of the common data environment(CDE)by using BIM technology.This paper gives the idea that such issues are deeply explored as large-span curved surface structure improvement,steel structure construction monitoring,special-shaped ticket check canopy construction,prefabricated machine room construction,grid construction management,etc.so as to form an intelligent construction management system based on BIM technology.The system has achieved good application results in economic benefits,social benefits and environmental benefits,which can promote the gradual transformation to a more digitalized,networked and intelligent Hangzhouxi Railway Station,and lay a solid foundation for achieving the construction goals of controllable construction period,excellent quality,green and low carbon,etc.

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive Real-Time Control of Flexible Manufacturing Systems

This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

Construction of Intelligent Garden

The new generation of information technology is rapidly integrating into various professional fields and becoming the link of crossing and converging disciplines.Intelligent rest station of garden is an innovative research achievement in the development of intelligent garden.It aims to serve intelligent city development of intelligent decision-making and technological innovation of green industry,benefit the people,and improve the living environment of sustainable development.

面向抽水蓄能电站的巡检机器人关键技术研究

针对抽水蓄能电站中混凝土墙体现场检测难度大且传统巡检方式效率低的问题,提出了一种基于深度学习的裂缝检测系统。通过FCN网络和CNN网络的相似性匹配,可以有效地判断混凝土裂缝的位置、尺寸及深度等信息,实现对裂缝的自动检测。同时,设计了巡检机器人硬件构架与软件系统,实现巡检路线自主规划、自主导航和检测区域的自主识别等功能。基于CrackForest数据集对所提检测模型的性能进行了验证测试,结果表明所提模型的识别精度可达87.74%,平均误差仅为0.45,综合性能良好。

Prototyping low-cost automatic weather stations for natural disaster monitoring

Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.

Accurate time synchronization of power reference station based on BD3 system

A Beidou 3(BD3)system-based power reference station can provide high-precision time synchronization for power distribution systems by sending synchronization data packets to devices in a multi-hop routing fashion.However,optimizing route selection to reduce both time synchronization error and delay is a challenging problem.In this paper,we establish a software-defined network-enabled power reference station time synchronization framework based on BD3.Then,we formulate the joint problem to minimize cumulative synchronization error and delay through multi-hop route selection optimization.A back propagation(BP)neural network-improved intelligent time synchronization route selection algorithm named BP-RS is proposed to learn the optimal route selection,which uses a BP neural network to dynamically adjust the exploration factor to achieve rapid convergence.Simulation results show the superior performance of BP-RS in synchronization delay,synchronization error,and adaptability with changing routing topologies.

Application and Innovation of Equipment Reliability Management at Nuclear Power Stations

To achieve excellent production capability and maintain high security for nuclear power stations, equipment reliability is studied as a key issue. As the main branch, equipment reliability management is considered as an effective approach to make plant management strategy and organize routine work. Some achievements on research and application of equipment reliability management at Daya Bay Nuclear Power Station are introduced in this paper. By studying and implementing 1NPO （Institute of Nuclear Power Operations） AP-913 equipment reliability process, reliability-centered maintenance （RCM）, critical component management （CCM）, and many other similar methods, equipment reliability management system has been established at Daya Bay. And by continuous effort on improving application effect of these technologies, some innovation has also been made. Some innovative products, such as iSOM Intelligent Expert System, reliability and technique centered maintenance （RtCM）, predictive maintenance system （PdM）, and so on, have been developed. Based on these achievements, Daya Bay Nuclear Power Station has achieved excellent operation performance and won several prizes in WANO （World Association of Nuclear Operators） competition every year.

基于LoRa通信的无线发射台信号智能监测技术研究

为实现大面积无线发射台信号覆盖监测,有效地抵御监测过程中多径干扰和频率选择性衰减等干扰因素的影响,研究基于LoRa通信的无线发射台信号智能监测技术。在无线发射台发出信号后,转换成数字信号进行对比,识别出有效发射台信号后,通过级联型LoRa组网方式以及LoRa通信技术,将数据发送到服务器中存储,实现无线发射台信号的传输。利用循环位移因子对基带产生的Chirp信号完成调频扩频调制,扩展信号频谱,增强信号抗干扰能力以及传输可靠性,增强信号监测的效果。实验结果表明:该方法能够在距离LoRa终端设备2.5 km处依旧接收到发射台信号,丢包率为12%,能够准确地监测到无线发射台是否存在发射信号。

铁路智能客站数字孪生模型研究

铁路智能客站数字孪生模型是智能客站的重要组成部分,对于客运组织方案、设备配置及布局优化、安全应急等仿真具有重要意义。研究提出智能客站数字孪生五维模型,给出客站物理实体、客站数字孪生体、客站虚实连接、客站孪生数据、客站孪生体服务的内涵;设计客站孪生体服务的具体内容,包括可视化调度指挥、可视化监控运维等车站可视化服务,以及客运组织方案仿真、设备配置及布局优化模拟、安全应急仿真、环境舒适度调控仿真等模拟仿真服务;研究客站数字孪生体虚拟静模型与动行为模型建模关键技术。构建清河站数字孪生体并开展试验,验证可视化调度指挥、设备虚拟巡检、车站安全应急仿真等功能,实现清河站通过数字孪生体全面掌控车站状况。

发射台站智能化任务分配算法研究

基于发射台站智能化管理系统的数据处理模型,研究任务分配算法。以分支定界算法代表精确算法、贪心算法代表近似算法、遗传算法代表智能算法,进行算法性能测试,并对计算数据进行分析,总结不同算法的优缺点和应用场景。

基于数字孪生的客站安全态势推演技术研究

为实现铁路客站实时掌握安全变化趋势,将数字孪生技术引入客站安全管理领域。通过设计客站安全态势推演框架,构建客站安全觉察、理解、预测的安全态势感知模型和安全结果的推演4层体系;设计基于数字孪生的客站安全态势推演框架;根据旅客乘车流程为主线,抽取日常与突发事件时,客站人员-设备-列车-作业联动行为变化,形成客站数字孪生协作动行为模型,镜像反映客站安全态势推演过程;并以客站水灾事件为典型案例,细化态势推演协作动行为具体内容。研究结果表明:从外观保真性、数据采集准确性、过程模拟忠诚度3个孪生指标,验证清河站数字孪生技术在客站安全态势推演中的有效性。研究结果可为客站安全管理提供强有力的技术支撑。

发射台站发射任务智能化分配数据处理模型的研究与构建

本文基于发射台站智能化系统建设中任务分配问题的研究,提出了发射任务分配调度算法对数据处理的具体需求,设计了任务分配数据处理模型,包括发射任务模型、任务评估矩阵、发射分配模型和效果目标函数,并对数据处理模型的参数和计算关系进行了公式化描述。通过具体的任务分配操作示例,展示了数据处理模型在实际应用中的操作流程和效果,验证了模型的实用性和有效性。

多源融合降水和智能网格预报在渭河一次洪水预报中的应用

选取2018年6月28日至7月15日渭河干流魏家堡水文站洪水过程为研究对象,开展多源融合降水和智能网格预报在洪水模拟中的应用试验。结果表明,以多源融合和站点实况降水为输入均能较好地模拟洪水过程,因此在无降水观测资料的流域,采用多源融合降水产品代替站点实况降水进行洪水模拟分析和计算是可行的,但多源融合降水模拟的径流深和洪峰流量较小。在魏家堡水文站两次洪水过程洪峰出现前48 h的面雨量相差不大的情况下,产流量相差显著,说明前期土壤湿度对渭河流域的产流影响很大,防汛时特别要关注持续性降水的影响。以落地雨进行洪水预报的预见期为12 h,以智能网格预报产品作为降水输入,能够提前84 h预报出超保证流量的洪峰流量,将洪水预见期由12 h延长至84 h。但以智能网格降水预报计算的面雨量与实况面雨量相比有一定的高估,造成了模拟的洪峰流量和径流深相对误差较大。建议针对不同的天气背景开展面雨量订正方法研究,以提高面雨量预报准确率,在延长洪水预见期的同时,进一步提高洪水预报精度。

智能机器人在大港油田西二联站库巡检中的应用与实践

长期以来,油田站库主要以传统人工运维方式进行管理,该种管理方式暴露出巡检效率低下、巡检工作不到位及生产管理落后等问题,造成站库安全隐患不能及时发现并处置。为了进一步优化站库巡检制度、简化工作流程,在大港油田西二联站库搭建了站库智能机器人巡检系统,该系统代替了传统系统工作效率低、成本高、效果差的人工巡检模式。智能机器人巡检系统的应用降低了员工劳动强度、提升了站库工作效率、保障了站库安全生产,有效地提高了油田安全生产的管理水平,为油田智能化生产管理开辟了新的技术路线。

基于系统论事故分析模型的油气智慧管道系统信息物理风险辨识

为了辨识油气智慧管道系统中存在的信息安全风险,通过基于系统论事故分析模型(systems-theoretic accident modeling and process,STAMP)的方法,对油气智慧管道系统的信息物理安全进行全面评估与分析。首先,系统综合分析了油气智慧管道涉及的设备、设施、工艺、元件,评估其安全性。其次,通过建立STAMP模型,深入分析了各层级、元件之间的反馈信息与控制动作,形成了明确的控制反馈回路,突显了元件之间的关联与控制关系。在此基础上,系统辨识出了潜在的信息风险因素,推导并构建了可能发生的系统失效场景。以天然气输气首站油气智慧管道系统为例,研究验证了基于STAMP模型的可行性和有效性。结果显示,该方法不仅直观地描述了元件之间的关联与控制关系,而且从物理层功能安全的角度全面考虑了信息风险,特别凸显了过程控制系统(process control systems,PCS)及易受攻击的操作员站。与传统方法相比,本研究所提出的方法将信息物理安全风险因素的识别率提升至80%以上,提高了40%以上,有助于避免不必要的安全措施冗余设计,提高了安全风险管控的准确性。

基于HMM-MLP的泵站监测健康诊断系统研究

为实现泵站工程在生产运行过程中有效预测设备潜在故障风险,提升泵站设备运行效率,在数字孪生水利工程数据底板基础上,基于现有硬件设备,以结构故障机理为导向,提出了一种HMM-MLP的泵站设备故障预测方法。先由连续小波包变换处理设备的运行信号,然后通过HMM模型生成设备运行状态序列作为MLP网络的输入预测设备故障,最后通过仿真实验表明,HMM-MLP模型可有效提高泵站设备故障的预测准确率。同时,依托在线监测数据和离线检查与试验数据,建立了设备健康评价指标体系,并开发了泵站监测健康诊断系统,协助运行管理人员充分了解和掌握机组设备的“健康”状态,提升设备管理的信息化水平。结果表明:该研究可为泵站健康系统建设提供实际案例指导与经验启示。

高铁客站建设的创新实践

随着国家战略发展布局需要,铁路枢纽高铁站房建设规模和数量剧增,为客站品质、乘客体验提升带来了新的挑战。以近年来中铁建工集团有限公司承建的铁路站房为例,按照“畅通融合、绿色温馨、经济艺术、智能便捷”的建设方针和“精心、精细、精致、精品”的站房建设新要求,从客站建设的新思路、新技术、新工艺和新标准4个维度,结合客站建设实践经验,梳理新时代高铁客站建设的创新方法,为我国高铁站房建设进一步向智能化、工业化、人文化、绿色化方向发展提供有益参考。

跨省域智慧能源管理大数据平台架构及关键技术研究与实践

随着广东省能源集团公司下属电站的不断建设,集团公司对各个电站的运营、运维、人员的管理问题愈发凸显。为有效提升各能源场站的综合管理水平,通过搭建基于物联网的智慧能源管理大数据平台对能源场进行集约管理。系统以工业互联网为硬件基础,以大数据平台为软件核心,实现了系统对风电场、光伏电站、煤层气电站等区域的实时远程监控及各项业务管理。实践证明,本系统能减少运行维护成本,提升集团公司的整体核心竞争力。

Library-Inside:AI赋能图书馆新质生产力的一种基础模型

AI正引发知识服务的生产力×生产关系的革命性变革,迫切需要推动AI与图书馆服务深度融合,打造符合新的发展理念的先进生产力质态。本文提出,从第一性原则思维出发,面向复杂适应性系统,以问题驱动和场景驱动的创新和设计驱动的创新,不断演进发展AI赋能的图书馆服务的新质生产力。在回归原点分析“人们到图书馆干什么”的基础上,基于大语言模型+AI智能体提供的能力,提出Library-Inside作为AI赋能图书馆新质生产力的一种基础模型,并描述了三种可能的实现模式:Library-Inside 1.0作为用户知识应用的智能助手,Library-Inside 2.0作为用户知识生产的智慧座舱和Library-Inside 3.0(Library×)打造用户AI赋能知识生产力的生态养育站。本文指出,要以战略创新推动图书馆新质生产力发展,要注意克服战略创新中的内在的结构性缺失和系统性偏差。