Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare systems.With the advent of the“Smart Healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic diseases.Among them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state.However,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be addressed.Artificial intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases.In addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data management.Integrating AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered one.In this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease management.Finally,the shortcomings of this new paradigm and future research directions are also discussed.

Reliability Management for Information System

An integrated intelligent management is presented to help organizations manage many heterogeneous resources in their information system. A general architecture of management for information system reliability is proposed, and the architecture from two aspects, process model and hierarchical model, described. Data mining techniques are used in data analysis. A data analysis system applicable to real-time data analysis is developed by improved data mining on the critical processes. The framework of the integrated management for information system reliability based on real-time data mining is illustrated, and the development of integrated and intelligent management of information system discussed.

Intelligent Information Management in Aquaponics to Increase Mutual Benefits

Aquaponics are feedback and two player systems, in which fish and crops mutually benefit from one another and, therefore require close monitoring, management and control. Vast amount of data and information flow from the aquaponics plant itself with its huge amount of smart sensors for water quality, fish and plant growth, system state etc. and from the stakeholder, e.g., farmers, retailers and end consumers. The intelligent management of aquaponics is only possible if this data and information are managed and used in an intelligent way. Therefore, the main focus of this paper is to introduce an intelligent information management (IIM) for aquaponics. It will be shown how the information can be used to create services such as predictive analytics, system optimization and anomaly detection to improve the aquaponics system. The results show that the system enabled full traceability and transparency in the aquaponics processes (customers can follow what is going on at the farm), reduced water and energy use and increased revenue through early fault detection. In this, paper the information management approach will be introduced and the key benefits of the digitized aquaponics system will be given.

Future technology-enabled care for diabetes and hyperglycemia in the hospital setting

Patients with diabetes are increasingly common in hospital settings where optimal glycemic control remains challenging.Inpatient technology-enabled support systems are being designed,adapted and evaluated to meet this challenge.Insulin pump use,increasingly common in outpatients,has been shown to be safe among select inpatients.Dedicated pump protocols and provider training are needed to optimize pump use in the hospital.Continuous glucose monitoring(CGM)has been shown to be comparable to usual care for blood glucose surveillance in intensive care unit(ICU)settings but data on cost effectiveness is lacking.CGM use in non-ICU settings remains investigational and patient use of home CGM in inpatient settings is not recommended due to safety concerns.Compared to unstructured insulin prescription,a continuum of effective electronic medical record-based support for insulin prescription exists from passive order sets to clinical decision support to fully automated electronic Glycemic Management Systems.Relative efficacy and cost among these systems remains unanswered.An array of novel platforms are being evaluated to engage patients in technology-enabled diabetes education in the hospital.These hold tremendous promise in affording universal access to hospitalized patients with diabetes to effective self-management education and its attendant short/long term clinical benefits.

IoT-Enabled RFID in Supply Chain Management: A Comprehensive Survey and Future Directions

Radio frequency identification (RFID) has emerged as a pivotal technology in supply chain management (SCM), significantly enhancing its efficiency and effectiveness. When integrated with the internet of things (IoT) to form RFID-IoT, this technology brings transformative advancements to SCM, enabling automated sensing, pervasive computing, and ubiquitous data access across the entire supply chain, from manufacturers and distributors to retailers and consumers. This integration facilitates real-time identification and monitoring of products, enhances various processes, improves logistic tracking, and ensures better product quality management. Despite its promising benefits, the adoption of RFID-IoT in SCM faces several challenges, including technical complexities, data security concerns, and high implementation costs. However, the future potential of RFID-IoT technology remains substantial. It is anticipated that further integration with other emerging technologies, such as block chain and artificial intelligence, will lead to more comprehensive and robust SCM solutions, offering unprecedented levels of transparency, efficiency, and automation in supply chain operations.

浅议BIM技术、智能监测、绿色节能在建设工程项目管理中的应用

文章研究了BIM和智能监测的建设工程项目管理在绿色节能领域的创新应用。通过详细分析和探讨,揭示这一整合应用的核心原理和关键作用。通过综合BIM、智能监测和绿色节能理念,强调了这一整合应用对于实现可持续建筑和更高效能源利用的重要性,为未来建筑工程的发展提供了方向。

基于光电技术的智能输液管理系统的开发与应用

目的:基于光电技术构建智能输液管理系统,并探讨该系统在静脉输液过程中的应用效果。方法:建立由智能输液监控、信息自动集成、护士手持掌上电脑(PDA)、终端质量监测与分析组成的智能输液管理系统,基于输液闭环管理通过智能调节输液速度、卡停输液异常等功能进行全程智能输液管控。选取杭州某三级甲等综合性医院2个病区的输液病人,其中2022年6月—12月的136例病人作为对照组,2023年1月—6月的132例病人作为观察组,观察组使用基于光电技术的智能化系统进行静脉输液管理,对照组采用传统人工方法进行输液管理。比较两组病人输液相关护理耗时、输液相关并发症发生率和病人满意率。结果:观察组质控输液滴速护理耗时、处理输液异常耗时均短于对照组(P<0.001),静脉炎、液体渗出发生率低于对照组(P<0.05)。结论:应用基于光电技术的新智能输液管理系统可以优化输液流程,提升护理工作效率和病人满意度。

母猪精准养殖智能感知技术研究进展

母猪的生长状况、繁殖性能与健康状况是猪场管理的重要生产要素,直接关系到猪场的经济效益。我国母猪养殖还存在生产管理智能化水平低、健康管理效率低、生产力水平低等突出问题,制约了我国养殖业的高质量发展。本文从母猪生长信息感知技术、繁殖行为监测技术、健康状态感知技术等3个方面,总结了母猪精准养殖管理中生长、生理、健康监测的研究和发展现状,分析了母猪精准养殖技术的薄弱环节,对母猪养殖智慧管控系统的建设提出了建议,并对未来的发展趋势进行了展望,以期为我国养猪业绿色高效智能转型升级与智能养猪工厂的建设提供参考。

基于环境安全的实验室智能监控管理系统

高校实验室是开展科学研究和人才培养的重要场所,但近年实验室频发的安全事故对正常的教学科研工作带来了不利影响。为有效地进行实验安全管理,设计了一套实验室智能监控管理系统,能实现实验室环境全天候智能化的监测和安全管理。系统分为上位机和下位机两部分,上位机安装在办公室或其他场所,能远程监控实验室的各项环境参数并控制实验室的环境设备;下位机安装在实验室实时采集实验室的各项环境参数,并连接实验室的环境设备。系统采用基于ARM的芯片作为主芯片,选择了一系列适合的传感器采集环境参数,包括一氧化碳、二氧化碳、温湿度、漏水传感器等。该系统已在华中师范大学电工电子湖北省实验教学示范中心试用,试用期间运行正常,为实验室的管理工作带来很大便利。

某院PIVAS智能环境实时监控系统的建立与应用

目的保障静脉用药调配中心(PIVAS)输液调配质量及患者的用药安全。方法医院依托PIVAS洁净室安全生产监测及预警平台,与某公司联合开发了可实现洁净室净化空调系统与操作台智能控制,以及浮游菌及尘埃粒子、压差及温湿度、净化机组状态等的动态监测及预警功能的智能环境实时监控系统。比较系统运行前(2021年7月至12月)、运行后(2022年1月至6月)的相关指标监测结果。结果与系统运行前比较,系统运行后异常监测结果月均发现次数与压差月均正常天数均显著增多(P<0.05);异常监测次均处理用时与浮游菌检测及报告用时均显著缩短(P<0.05);各监测位置(除百级操作台外)浮游菌及尘埃粒子的实时监测数值均显著下降(P<0.05)。结论该系统实现了PIVAS洁净室环境关键指标的实时收集、动态监测及预警,有效地保证了PIVAS输液的安全生产及全流程信息可追溯,推动了智慧PIVAS体系的建设,进一步保障了患者的用药安全。

大连湾海底隧道结构健康监测系统研究与应用

结构健康监测是通过无损、实时的方式识别外部环境变化及结构自身变化,实现损伤识别、评估分析、安全预警等功能,从而快速辨别结构的健康状态的监测手段。结构健康监测系统可为项目的智慧管养、运维提供决策和支持,达到降低运营风险、延长结构使用寿命、节约运营成本的目的。文中从监测系统中监测内容的设计、监测平台的软硬件构建等方面对大连湾海底隧道结构健康监测系统进行介绍,应用反馈的监测数据验证了监测系统的准确有效性。

基于边缘算法的无人机消防应急管理平台设计

阐述基于边缘算法的无人机智慧消防应急管理平台的设计与实施。通过结合边缘计算和无人机技术,构建一套高效、智能的消防应急管理系统,实现火灾现场的实时监控、快速响应和智能决策。

大型复杂船舶建造过程中的智慧消防监测预警系统设计

针对大型复杂船舶建造过程中的安全管理,设计智慧消防监测预警系统。构建包含感知层、传输层和处理层等3层结构的系统模型,设置自动预警、预警信息推送、远程指令下发、设备自检和设备联动等系统功能,并采用组网与数据传输技术、协同预警技术、面向现场作业的预警信息推送技术和预警子系统构建技术等关键技术。该系统可提供安全风险监测预警,有效提升大型复杂船舶建造过程中的应急响应能力。

基于智能监测的山地城市边坡安全管理系统建设

重庆作为典型的山地城市,地质情况复杂,岩土边坡众多且分布广泛,边坡安全是城市安全和管理中的重要环节,当前城镇边坡尚未形成完整的安全运维体系。该文提出了基于智能监测的山地城市边坡安全管理系统建设方案,实现山地城市边坡安全的“信息管理”“状态感知”“评估预警”“应急管理”和“安全运维”五大核心功能,实现安全管理“一张图”,安全信息“可视化”、风险感知“实时化”、运维管理“智能化”,解决山地城市边坡安全管理痛点,为城市安全提供有力保障。

老年全子宫切除围术期舒适化监测与疼痛智能化管理的应用研究

目的:探讨围术期舒适化监测与疼痛智能化管理在老年全子宫切除中的应用效果。方法:选取2020年8月至2022年8月郑州大学第五附属医院收治的82例拟行腹腔镜全子宫切除术的老年患者作为研究对象,采用随机数字表法分为观察组和对照组,各41例。两组患者均采用常规镇痛管理,对照组患者接受围术期舒适化监测,观察组在对照组基础上联合应用疼痛智能化管理,均观察7d。观察比较两组患者术后排气时间、排便时间、住院时间、舒适度及满意度,应用视觉模拟评分法(Visual analogue scale,VAS)评估两组患者术后当天、术后3 d、术后7 d时疼痛情况。结果:两组患者术后排气时间、排便时间、住院时间、术后当天VAS评分无明显差异(P>0.05);观察组舒适状况量表(General comfort questionnaire,GCQ)评分、满意度均明显大于对照组(P<0.05);观察组术后3 d、术后7 d时VAS评分均明显小于对照组(P<0.05)。结论:围术期舒适化监测与疼痛智能化管理联合应用,可有效提高老年全子宫切除患者的舒适度及满意度,减轻疼痛情况。

医院集中带量采购品种可替代药品监测和精细化管理探索

目的提升医院集中带量采购(简称集采)品种可替代药品的监测和精细化管理水平。方法根据国家、省际联盟药品集采相关政策,结合目前医院药品集采工作执行情况,按照药品的解剖学、治疗学及化学分类系统,药品说明书,指南等,采用Power BI软件,在前期集采药品智能监控系统基础上扩展可替代药品监测和管理相关功能模块。结果扩展新增了可替代药品监测和管理模块及重点科室使用监测功能模块,进一步实现了可替代药品监控、集采中选药品缺药时的可替代药品管理、医疗机构基本用药目录优化、重点科室使用监测等功能。结论功能扩展后的集采药品智能监控系统,可促进集采药品管理工作的精细化、智能化,为医疗机构建立完善的集采药品全流程科学管理提供技术支持。

广州市大型公立医院医保智能监控体系的构建与提升策略研究

随着医疗保障体系的持续发展和完善,对医保管理提出了更高的标准化和效率要求。选取广州市一所著名的双一流大学附属医院作为案例,探讨了医保智能监控体系的构建和提升策略。首先,从国家、政策和机构层面进行了分析,强调了构建医疗保险智能监控体系的迫切性和必要性。接着,对当前国内外研究现状进行了全面回顾,突出了国内智能审计系统在智能水平、医院级智能监控的复杂性以及规则库标准化方面面临的挑战。之后,详细介绍了广州市公立医院在医保智能监控体系上的实践,包括体系构成、组织工作模式和规则库标准化建设。最后,总结了这些实践对医疗服务行为规范和医保质量管理效率提升的积极影响,并提出了未来研究方向的见解。

关于铁路机电设备管理系统设计规范解读

从铁路机电设备管理系统技术发展、应用需求、系统建设等方面阐述了新颁布标准TB10037—2022《铁路机电设备管理系统设计规范》编制的必要性和编制原则,从铁路机电设备管理系统及其子系统的组成、设置、架构、联动控制、监控和管理的功能及对象等多个维度对新颁布标准的重点技术内容进行解读。该标准以实现铁路机电设备节能增效为目的,注重智能绿色理念,为服务国家节能战略和铁路高质量发展奠定基础。

基于人工智能的太阳能光伏发电系统的应用

随着人工智能技术的不断发展,其在能源领域的应用逐渐成为热点。介绍了太阳能光伏发电系统的工作原理,探讨了人工智能在光伏发电系统中的关键作用:可实现最大化能源产出,有助于及时发现并解决问题,提高系统的可靠性和安全性。基于人工智能的太阳能光伏发电系统展现了其在推动清洁能源技术发展方面的巨大潜力,为未来可持续能源系统的建设提供了新思路。