基于IFC的轨道交通BIM构件标准库研究

针对轨道交通工程的建筑信息模型(building information modeling,BIM)建模方法不统一、信息不规范,导致BIM模型数据难以在后期有效统一应用的弊端,研究基于工业基础类(industry foundation classes,IFC)标准的轨道交通BIM构件标准库。首先,根据轨道交通工程各专业构件的表达需求,研究构件类型、构件信息的IFC表达,以及适配IFC标准的扩展机制;其次,提出轨道交通BIM构件标准库技术框架,主要包括基础数据层、技术支撑层、应用场景层、用户层;然后,采用国家标准的分类编码方法定义轨道交通各专业BIM构件的分类编码,设计了构件信息模板,规范各专业构件的属性信息及其资源链接方式;最后,研究BIM构件模型的加解密方法,以保障轨道交通工程BIM模型数据的安全性。研究结果表明,通过搭建统一的BIM构件标准库,能够促进参建方采用标准化的BIM构件模型创建轨道交通BIM项目模型,保障BIM模型的规范性。

融合IFC语义信息与几何相似性的BIM构件实例信息提取方法

建筑信息模型(BIM)能够对建筑结构、部件组成及其业务语义属性进行准确表达,在智慧城市时空底板建设及建筑物“规、建、养、运”全生命周期数字化管理中发挥着重要的作用。BIM模型的数量级大、引用关系与层级结构复杂,增加了BIM构件实例信息的提取难度,从而导致目前的CIM平台难以直接利用BIM构件实例信息实现轻量化的数据传输、流畅的可视化及BIM分析计算。针对此问题,本文提出了一种融合IFC语义信息与几何相似性的BIM构件实例信息提取方法,在充分考虑IFC几何引用语义信息的基础上,使用ICP算法和豪斯多夫距离计算几何相似性,实现对BIM构件实例信息的精确提取;此外,还针对BIM场景中常见的拉伸体构件,提出了一种基于特殊拉伸体的实例信息快速提取方法;最后选取了5个不同BIM专业的试验示例数据进行详尽分析。试验结果显示,本文提出的BIM构件实例信息提取方法在各种示例数据中的文件平均压缩率为29.79%,能够显著地减小文件体积;在BIM构件实例信息提取能力方面,实现了79.41%的平均构件实例化率及22.47%的平均实体压缩率,且每个实例化构件平均包含的子构件数量高达49.24。本文方案可以充分提取IFC文件中的BIM构件实例信息,能够为海量BIM模型的轻量化提供强有力的技术支撑。

基于IFC的结构BIM模型创建和数据管理技术研究和实现

结构计算模型与BIM模型的融合是实现BIM在结构工程中全面应用的关键环节,本文研究钢筋混凝土结构计算模型的数据格式,根据计算模型中的构件数据和配筋数据分别生成包含混凝土构件和钢筋的IFC模型,并基于分布式数据库存储完成IFC模型合并以及数据管理的相关技术。基于本文研究成果和开发的相关软件,可以由结构计算模型生成准确的结构BIM模型,该模型可应用于碰撞检测、工程量统计、施工图生成等场景。通过项目应用验证了本文方法的有效性。

基于IFC的建筑结构设计信息交互框架研究与实现

为了解决基于BIM的建筑与结构设计工作模式中信息交互度低的问题,提出了基于IFC的建筑结构信息交互框架。该框架由4个算法模块、5个步骤组成。借助于IfcOpenShell工具包,用Python语言开发了前两个算法模块即IFC文件优化算法模块和结构信息模型提取算法模块。选取案例模型对所开发算法的有效性进行了验证。验证结果表明:优化算法模块可以有效地过滤IFC文件中的无用信息,查找并删除重复的实体,使IFC文件轻量化,且可间接提高提取算法模块的运行效率;提取算法模块可以准确地从IFC文件中提取结构信息模型,未出现IFC语法错误、构件丢失、构件重叠等情况。IFC文件优化算法模块和结构信息模型提取算法模块的成功开发为信息交互框架的完全实现奠定了基础。

Potential industrial applications of photo/electrocatalysis: Recent progress and future challenges

Nowadays,the rapid development of the social economy inevitably leads to global energy and environmental crisis.For this reason,more and more scholars focus on the development of photocatalysis and/or electrocatalysis technology for the advantage in the sustainable production of high-value-added products,and the high efficiency in pollutants remediation.Although there is plenty of outstanding research has been put forward continuously,most of them focuses on catalysis performance and reaction mechanisms in laboratory conditions.Realizing industrial application of photo/electrocatalytic processes is still a challenge that needs to be overcome by social demand.In this regard,this review comprehensively summarized several explorations in thefield of photo/electrocatalytic reduction towards potential industrial applications in recent years.Special attention is paid to the successful attempts and the current status of photo/electrocatalytic water splitting,carbon dioxide conversion,resource utilization from waste,etc.,by using advanced reactors.The key problems and challenges of photo/electrocatalysis in future industrial practice are also discussed,and the possible development directions are also pointed out from the industry view.

基于IFC标准的机场道面结构信息模型存储与传递方法

为解决机场跑道系统在全生命周期建筑信息模型(BIM)应用中的信息传递和数据交互问题,将工业基础类(IFC)标准在机场跑道系统进行扩展,建立机场道面结构的IFC实体架构,在此基础上创建面向运维应用的刚性道面板实体,并进行几何信息与属性信息的扩展;提出基于IFC的属性信息添加与传递方法,对信息传递过程中的属性信息丢失率进行分析。研究表明:通过建立IFC标准模型,可以实现机场跑道系统的信息传递和数据交互;采用基础模型族扩展法进行机场道面模型传递的属性信息丢失率高达84.21%;采用IFC解析扩展法首次传递可保证信息不丢失,但二次传递信息存在丢失问题;族模型IFC解析扩展法可实现信息首次传递不丢失,二次传递属性信息丢失率低至16.7%,并且采用不同IFC标准版本对模型传递过程中的属性信息丢失率产生较大影响。应用IFC标准进行机场领域扩展具有较高的可行性,所提的信息传递方法可实现机场道面全生命周期信息传递。研究成果可为机场飞行区新IFC实体的扩展提供支持,为机场工程数字化提供技术参考。

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Can Industrial Structure Upgrading Restrain Industrial Land Expansion?Evidence from China

China has made great achievements in industrial development and is transforming into a powerful manufacturing country.Meanwhile,the industrial land scale is also expanding.However,whether industrial structure upgrading achieves the purpose of restraining industrial land expansion remains unanswered.By calculating the industrial land structure index(ILSI)and industrial land expansion scale(ILES),this study analyzed their temporal and spatial distribution characteristics at both regional and city levels from 2007to 2020 in China.Results show that industrial land expansion presents a different trend in the four regions,the ILES in the eastern region is the largest,and the speed of industrial land expansion has declined since 2013,but it has gradually increased since 2016.The ILSI of the eastern and central regions is higher than that of the western and northeastern regions.Furthermore,a spatial Durbin model(SDM)has been established to estimate the spatial effect of industrial structure upgrading on industrial land expansion from 2007 to2020.Notably,industrial structure upgrading has not slowed industrial land expansion.The eastern and western regions require a greater amount of industrial land while upgrading the industrial structure.The improvement of the infrastructure level and international trade level has promoted industrial land expansion.

基于IFC规范的模型扩展物联数据方法研究

针对建筑行业中海量物联数据传递的问题,提出一种基于IFC规范的模型扩展物联数据方法。将物联数据的数据请求作为IFC模型的附加属性集,与模型中的测点进行绑定,可实现在BIM模型传递的同时将物联数据同步传递。研究内容重点描述了物联监测数据随模型传递的优势,并通过模型扩展物联数据方法定义、原理及实现等3个方面展开,以及进行了属性集定义描述和相关配置软件的开发;采用该方法,可应用于施工期智慧工地平台、服役期智慧运维平台以及专项数据分析等场景;通过某桥梁模型的具体应用,结合桌面端和Web端的IFC模型解析器,验证了扩展物联模型在信息传递过程中的平台无关性。

Spatial Patterns and Drivers of Intelligent Manufacturing from a‘Glob-allocal'Perspective:A Study of China's Industrial Robotics

The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

基于IFC拓展模型的邻近施工对既有地铁隧道变形影响研究

随着土木行业的信息化发展,BIM技术在土木工程中的应用不断拓展。为解决以往邻近施工过程中难以准确反映施工加卸载对既有建(构)筑物影响的问题,文章依托某邻近既有地铁隧道施工的建筑工程,首先在概述IFC模型多种拓展机制并对比其优缺点的基础上,对BIM模型进行IFC属性集工况信息拓展;然后,通过数值模拟方法,进行邻近施工加卸载荷载模拟,并据此建立IFC实体数据与加卸载施工对地铁隧道变形影响值的数学定量关系;最后,基于IFC模型中的几何数据信息及拓展属性信息,提出能够直观展现施工加卸载对既有地铁隧道变形影响的信息化方法,并对其计算出的影响值与实际施工过程中地铁隧道竖向变形值进行相关性分析。研究结果表明,IFC模型属性集拓展方法能够对工况信息进行有效补充;邻近施工加卸载荷载对既有地铁隧道变形的影响规律呈近似指数函数关系;所提方法算出的施工加卸载对既有地铁隧道变形影响值与地铁隧道变形实际监测值具有较高的相关性,能够准确反映施工加卸载对邻近地铁隧道变形的影响情况。

Technological anxiety:Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood.Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an“icing on the cake”or“adding fuel to the fire”needs further study.This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy.First,from the theoretical logic and the economic development law,this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs.Then,based on the panel data of 269 cities in China from 2006 to 2021,we use the two-way fixed effect model,dynamic threshold model,and two-stage intermediary effect model.The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism.Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the“creation effect”.In other words,advanced digital technology has created additional jobs,and the overall supply of employment in the labor market has increased.The conclusion is still valid after the endogeneity identification and robustness test.In addition,the positive effect has a nonlinear effect on the network scale.When the installation density of industrial robots exceeds a particular threshold value,the division of labor continues to deepen under the combined action of the production efficiency and compensation effects,which will cause enterprises to increase labor demand further.Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions.This study concludes that in the digital China era,the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market.This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

China’s Industrial Modernization:Development Rationale,Current Status,and Policy Directions

Economies that have effectively escaped the“middle-income trap”demonstrate common traits in their industrial restructuring as they progressed to high-income status.These include a relatively stable share of an economy’s manufacturing sector,a reasonable economic structure,enhanced industrial capabilities,and growth driven by innovation.However,late-moving countries face a number of hurdles as they strive to cross this threshold.China’s development advantages include,among other things,a complete industrial system,a more balanced industrial structure,growing indigenous innovation capabilities,continual expansion and upgrading of domestic demand,and a greater degree of openness.These capabilities have provided continuous momentum for industrial growth,allowing China to capitalize on the next wave of technological and industrial revolutions while also promoting long-term,steady industrial development.During its modernization efforts,China has seen substantial changes in the external environment surrounding its industrial development.We must not only recognize the increasing complexity,intensity,and uncertainty of these changes,but also take proactive steps to solve diverse issues and capitalize on opportunities arising from global digital and green transitions.Equal focus should be placed on strengthening reforms and promoting high-level openness,improving policy coordination and consistency,and pursuing an innovation-driven strategy.This will speed the development of a modern industrial system and encourage the formation of new,high-quality productive forces.

DTAIS:Distributed trusted active identity resolution systems for the Industrial Internet

In recent years,the Industrial Internet and Industry 4.0 came into being.With the development of modern industrial intelligent manufacturing technology,digital twins,Web3 and many other digital entity applications are also proposed.These applications apply architectures such as distributed learning,resource sharing,and arithmetic trading,which make high demands on identity authentication,asset authentication,resource addressing,and service location.Therefore,an efficient,secure,and trustworthy Industrial Internet identity resolution system is needed.However,most of the traditional identity resolution systems follow DNS architecture or tree structure,which has the risk of a single point of failure and DDoS attack.And they cannot guarantee the security and privacy of digital identity,personal assets,and device information.So we consider a decentralized approach for identity management,identity authentication,and asset verification.In this paper,we propose a distributed trusted active identity resolution system based on the inter-planetary file system(IPFS)and non-fungible token(NFT),which can provide distributed identity resolution services.And we have designed the system architecture,identity service process,load balancing strategy and smart contract service.In addition,we use Jmeter to verify the performance of the system,and the results show that the system has good high concurrent performance and robustness.

Development and Validation of Integrated Nutrient Management Practices of Industrial Processing Varieties: Asterix and Courage in Bangladesh

An experiment was meticulously conducted at the research field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh, during the 2011-2012 potato growing season to develop integrated crop management practices for the potato seed production of industrial processing varieties Asterix and Courage. Significantly, higher growth and yield parameters were found in the BADC-recommended practice. Later, another experiment was conducted to validate the BADC practice during the 2013-2014 potato growing season in two locations in Bangladesh. Results showed that the production of tuber per hill, tuber weight per hill as well as gross tuber yield per plot, higher proportion of storable seed tubers, and more quality seed potatoes (A-grade and B-grade) seed tubers were found significantly higher in the “BADC developed practice” compared to other treatments. Viral diseases (PLRV and PVY) prevalence was lower in “BADC developed practice”. Moreover, “BADC developed practice” contributed more economic yield by minimizing input cost compared to “Munshiganj advanced farmers’ practice”. Therefore, the “BADC developed practice” was found “superior” regarding yield, quality, and profitability in seed potato production of industrial varieties—Asterix and Courage in Bangladesh.

Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

Attributes of Specialized Households'Resilience and Its Impact on Rural Industrial Advancement:A Case Study of National Musical Instrument Production Specialized Households in Lankao County,Henan,China

Specialized households serve as the primary units within specialized villages in China,and their capacity to withstand risks and external influences significantly shapes the future trajectory of specialized villages and the overall vitality of the rural economy.In this study,we established a measurement indicator system based on the definition of specialized households’resilience,elucidating the logical connection between specialized households’resilience and rural industrial development in China.The musical instrument industry in Lankao County,Henan Province of China,was employed as a case;survey data,the entropy method,and an obstacle diagnosis model were used to examine how instrument production specialized households responded to the challenges posed by Corona Virus Disease 2019(COVID-19)and the tightening of national environmental protection policies,yielding the following key findings:1)there exists substantial variation in the comprehensive resilience levels among different specialized households;2)the ability to learn and adapt is the most significant contributor to the overall resilience level of specialized households;3)technological proficiency and access to skilled talent emerge as pivotal factors influencing specialized households’resilience;4)the positioning of specialized households within the industrial supply chain and the stability of their income have a direct bearing on their resilience level.The influence of specialized households’resilience on industrial development primarily manifests in the following ways:stronger resilience correlates with increased stability in production and sales,fostering a more proactive approach to future actions.However,heightened exposure to the external macroeconomic environment can lead to a higher rate of export reduction.To enhance the development resilience of entities like specialized households and family farms,and to invigorate rural economic development,escalating investments in rural science and technology and prioritizing the training of technical talent become imperative.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.