An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.

New development of longwall mining equipment based on automation and intelligent technology for thin seam coal

The paper introduced complete sets of automatic equipment and technology used in thin seam coal face, and proposed the comprehensive mechanization and automation of safe and high efficiency mining models based on the thin seam drum shearer. The key technology of short length and high power thin seam drum shearer, and new type roof support with big extension ratio and plate canopy were introduced. The new research achievement on automatic control system of complete sets of equipment for the thin seam coal, which composed of electronic-hydraulic system, compact thin seam roof supports, high effective shearer with intelligent control system, and characterized by automatical follow-up and remote control technology, was described in this paper..

A review of intelligent ore sorting technology and equipment development

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.

Key Technologies and Development Trends in Advanced Intelligent Sawing Equipments

As a starting point in equipment manufacturing,sawing plays an important role in industrial production.Intelligent manufacturing equipment is an important carrier of intelligent manufacturing technologies.Due to the backwardness of intelligent technology,the comprehensive performance of sawing equipments in China is obviously different from that in foreign countries.State of the art of advanced sawing equipments is investigated along with the technical bottleneck of sawing machine tool manufacturing,and a new industrial scheme of replacing turning-milling by sawing is described.The key technologies of processing-measuring integrated control,multi-body dynamic optimization,the collaborative sawing network framework,the distributed cloud sawing platform,and the self-adapting service method are analyzed;with consideration of the problems of poor processing control stableness,low single machine intelligence level,no on-line processing data service and active flutter suppression of sawing with wide-width and heavy-load working conditions.Suggested directions for further research,industry implementation,and industry-research collaboration are provided.

Intelligent Fault-tolerant Management of Electromechanical Equipment

In this paper, a method of intelligent fault tolerant management on electromechanical equipment is presented. It is based on condition monitoring of equipment and realized by condition prediction and condition control. An example is introduced and analyzed in this paper.

Application of Intelligent Equipment on Novel Coronavirus Pneumonia Designated Hospital

Objective: To explore the application of intelligent equipment in non-negative pressure isolation ward for COVID-2019 patients. Method: From February 1 to March 17, 2020, intelligent equipment, such as communication interaction system, intelligent disinfection robot, delivering robot, were used in non- negative pressure isolation ward of COVID-2019. With the help of communication interaction system to supervise the implementation of infection prevention and control, and observe the incorrect situation of pee use and personal behavior before and after the implementation. The disinfection robot and meal delivery robot were used in ward disinfection and life nursing combined with nursing practice. Result: Through the supervision of communication interaction system, the frequency of pee use and personal behavior was reduced. The frequency of bad articles before and after improvement was wearing protective clothing (2.80%/0.84%), taking off protective clothing (5.87%/0.84%), personal behavior observation (8.38%/1.90%), P < 0.01. The robot disinfected and delivered medicine for 912.5 h, saving 225 shifts of nursing staff. Conclusion: Intelligent equipment is a good option for infection control in isolation ward of COVID-2019. It can not only reduce the workload of health workers, but also the cross-infection.

China National Research Center of Intelligent Equipment for Agriculture

China National Research Center of Intelligent Equipment for Agriculture （NRCIEA） was established in 2009 on the basis of Beijing Research Center of Intelligent Equipment for Agriculture. According to the development trend of world Intelligent Equipment for Agriculture （lEA） and China＇s needs of modern agriculture, NRCIEA is engaged in solving the key, fundamental and common technical problems in lEA.

The personal protective equipment(PPE)based on individual combat:A systematic review and trend analysis

With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.

Evolution and Development Trend Prospect of Metal Milling Equipment

The world is currently undergoing profound changes which have never happened within the past century.Global competition in the technology and industry fields is becoming increasingly fierce.The strategic competition of the major powers further focuses on the manufacturing industry.Developed countries such as the United States,Germany,and Japan have successively put forward strategic plans such as“re-industrialization”and“return of manufacturing industry”,aiming to seize the commanding heights of a new round of global high-end technology competition and expand international market share.Standing at the historic intersection of a new round of scientific and technological revolution and China's accelerated high-quality development,the“14th Five-Year Plan”clearly pointed out that intelligent manufacturing is the main development trend to promote China's manufacturing to the medium-high end of the global value chain.This reflects the importance of advanced manufacturing for national strategic layout.To better grasp the development direction of advanced manufacturing equipment,the development process and current application status of manufacturing equipment are summarized,and thereafter the characteristics of manufacturing equipment in different development stages of the manufacturing industry are analyzed.Finally,the development trend of advanced milling equipment is prospected.

Intelligent Metal Detection and Disposal Automation Equipment Based on Geometric Optimization Driving Algorithm

In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.

ANINTELLIGENT DECISION SUPPORTSYSTEM（IDSS） ONTHEPROCESSOFNICKELMATTESMELTER

This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules ale specially suggested. The IDSS possesses the self-learning and adaptive properties, andhas been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.

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.

地下金属矿开采装备短间隔智能调度体系研究

作为矿山生产管理的重要内容,生产调度优化与管控可以充分利用产能、降低开采成本,因此成为现代矿山降本增效的有效手段。通过精细化调度管理和智能决策方法,实现复杂条件下的开采装备高效协同作业,充分提高调度管控效率和矿山生产能力,是现阶段矿山开采装备智能调度需要解决的关键问题。以地下金属矿山开采装备调度过程为研究对象,以提高开采装备的作业效率和生产能力为目的,研究了开采装备智能调度管控模式、决策支持算法和集成化应用。针对现代地下金属矿生产实时化、精准化与智能化管控的新需求,提出了开采装备短间隔智能调度新模式,形成了以“任务分解—任务配置—运输配置—作业指令—响应修正—分析反馈”为特征的短间隔闭环调度方案。设计了包括开采装备任务配置与调度优化算法、面向多级动态配矿的矿石流优化算法、无轨装备集群调度控制优化算法的智能化决策支持算法库,实现由人工经验到智能决策的转变。将开采装备短间隔智能调度体系进行综合示范应用,满足了现代矿山精细化管控和智能化决策需求。研究成果对于地下矿山开采装备作业过程的精细化管控与开采效率提升具有指导意义,同时也为现代地下金属矿山的生产调度管理提供了理论和方法支撑。

多尺度熵方法在机械故障诊断中的应用研究进展

机械设备状态监测与故障诊断的关键是故障特征的表征与提取,采用基于熵及相关方法建立的非线性动力学指标能够提取蕴藏在振动信号中的非线性故障特征信息。自熵方法引入以来,通过不断修改和改进来提高熵估计的准确性,多尺度熵进一步拓展了时间序列其他尺度上包含的复杂度信息,其在设备状态监测与故障诊断中得到广泛应用。本文对单一尺度熵及多尺度样本熵、多尺度模糊熵、多尺度排列熵和多尺度散布熵等多尺度熵方法在机械智能故障诊断中的应用进行综述,总结不同方法的特点优势与不足;针对多变量数据处理问题,综述由单变量推广到多变量的多元多尺度熵的应用发展过程。最后结合多尺度熵相关方法在机械智能故障诊断中面临的问题与挑战,对未来发展方向进行展望,即在工业大数据应用、故障机理、可解释性角度构建基于熵的深度学习模型。

选矿厂智能化建设目标与实施路径

选矿厂的智能化升级转型是建设现代矿山企业的必然选择,其建设目标及实施路径与广义智能工厂建设有着显著差异。本文结合选矿厂智能化建设背景、面临的问题,探讨了选矿厂智能化的建设目标,提出了智能选矿厂的建设路径:智能装备与智能感知、过程自动化稳定运行、工艺优化控制、信息化生产管理以及与之匹配的生产组织管理体系,最后给出了选矿厂智能化建设的实施步骤建议。

上海轨道交通提升盾构隧道结构本质安全的创新实践

[目的]盾构隧道是柔性结构,具有多缝特征。受施工初始质量缺陷、周围载荷变异、外部环境恶化以及管养不到位等因素影响,易出现结构变形超限、渗漏超标以及耐久性劣化等问题,仅依靠运营期加固和整治无法彻底解决。且随着线网规模的扩大,维修成本和夜间天窗时间资源的代价也会急剧增加,最终影响城市轨道交通全网的运维效率和质量。因此需研究提升盾构隧道结构本质安全的技术措施。[方法]分析了上海轨道交通盾构隧道的主要病害及影响盾构隧道结构安全的主要因素;介绍了上海轨道交通提升盾构隧道结构本质安全的技术措施以及确保盾构隧道结构本质安全的创新实践。[结果及结论]上海轨道交通采取的提升盾构隧道结构本质安全的技术措施包括优化扩大盾构隧道内径、采用错缝拼装方式、提升接缝密封防水设计,确保盾构隧道结构本质安全的创新实践包括采用预埋承插式管片新型盾构隧道结构、采用推拼同步智能盾构装备及隧道智能建造技术。从源头把握盾构隧道结构本质安全,通过优化隧道结构设计、创新拼装技术以及采用智能化装备,在建设阶段提升盾构隧道的成型质量、减少结构初始缺陷,能够降低运营期盾构隧道结构发生病害的可能性,可为隧道的长期运营安全奠定坚实基础。

丘陵山区机械化灌溉现状与发展思考

针对目前丘陵山区机械化灌溉装备与技术单一,适应性差,缺少绿色化、智能化的灌溉模式等一系列问题,系统阐述了目前相关研究的现状和未来的发展趋势;全面了解目前丘陵山区机械化灌溉水平是后续开展相关研究的关键.通过分析目前丘陵山区机械化灌溉的现状,适应丘陵山区机械化发展的要求,在机械化灌溉方面从国家政策入手到丘陵分区,结合丘陵山区的灌溉特点、目前的灌溉现状和相关经济因素进行了系统的总结.根据中国丘陵山区的国情以及相关政策,对丘陵山区机械化灌溉提出了相应的建议.可为未来丘陵山区机械化灌溉改造提供参考,补齐丘陵山区机械化灌溉装备与技术的短板,还可提高对丘陵山区机械化灌溉方面的重视,提升丘陵山区农业的灌溉水平,促进农业跨领域深度联合,推动农业可持续和现代化发展.

智慧时代园林植物水肥资源利用效率的提升路径研究

随着智慧时代的到来,云计算、大数据技术为园林植物生长信息的分析和利用提供了新方法,为园林植物种植和管理提供了强大助力。水肥管理是园林植物种植过程中的关键环节,如何利用前沿技术提升园林植物水肥资源效率,是园林产业升级面临的现实问题之一。首先分析智慧时代的技术特征,其次分析园林植物种类和水肥需求特性,最后根据传统园林植物水肥利用效率提升模式存在的不足,提出水肥利用效率的提升路径,以期为先进技术更好地赋能园林植物水肥管理提供参考。