The Design and Application of Intelligent Power Supply System of Integrated Meteorological Business at the County Level

The system uses Japanese Mitsubishi PLC as the main control core,and intermediate relay is used for signal acquisition and conversion.The system is mainly composed of data processing module,municipal electricity monitoring module,wireless communication module,remote controller,speech recognition module,display module,sensor,switching output module and UPS power supply. The system can automatically start generator for power supply when municipal electricity is failure; when municipal electricity returns to normal,it automatically restores the power supply function of municipal electricity. In order to improve the stability of the system,the system has the functions of fault alarm,remote reset and remote query,etc.,which can realize seamless switching of municipal electricity and generator. It effectively avoids meteorological data loss,data delay,network interruption,etc. caused by unstable municipal electricity,solves the power supply problem of grass-roots meteorological station,especially at night,provides stable power supply for the integrated meteorological observation,forecasting,and early warning service.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

Study on Supply and Demand of Intelligent Home-based Aged Care in Shanghai

The 21st century is an era of aging population. With the accelerated aging of the population in our country, and the grim form of population aging, intelligent home-based care has become a concern for the elderly in the area of ？？care. This paper starts from the new concept of intelligent home-based care services and defines intelligent home care services. It also summarizes the current theoretical researches related to domestic research, which is of great significance for the investigation and research conducted in the later stage of research.

Application of Distributed Intelligent Power Supply Technology in Expressway Tunnels

With the coordinated development of today's social economy with science and technology,various advanced technologies are being used in highway engineering,especially the distributed intelligent power supply technology in expressway tunnels,which has a very significant advantage.In order to realize the effective application of this technology and promote the power supply effect in expressway tunnel,this study analyzes the advantages of this technology and its application in expressway tunnel,hoping to provide scientific reference for the application of distributed intelligent power supply technology and the engineering development of expressway tunnels.

Intelligent Manufacturing Systems in COVID‑19 Pandemic and Beyond:Framework and Impact Assessment

Pandemics like COVID-19 have created a spreading and ever-higher healthy threat to the humans in the manufacturing system which incurs severe disruptions and complex issues to industrial networks.The intelligent manufacturing(IM)systems are promising to create a safe working environment by using the automated manufacturing assets which are monitored by the networked sensors and controlled by the intelligent decision-making algorithms.The relief of the production disruption by IM technologies facilitates the reconnection of the good and service flows in the network,which mitigates the severity of industrial chain disruption.In this study,we create a novel intelligent manufacturing framework for the production recovery under the pandemic and build an assessment model to evaluate the impacts of the IM technologies on industrial networks.Considering the constraints of the IM resources,we formulate an optimization model to schedule the allocation of IM resources according to the mutual market demands and the severity of the pandemic.

Research on Supplying Mechanisms of Rural Economic Able-person in Construction of New Countryside——Based on the Phenomenon of Intelligence Returning in Under-developed Agricultural Plain Area of Henan

From a perspective of the discussions on researches of economic able-person by researchers at home,it can be concluded that economic able-person has become the important mode promoting rural development.The research makes a discussion on the rural intelligence returning theory and points out that as a marketing action,rural intelligence returning functions directly and effectively on the supplying to rural economic able-person.The research also makes an analysis of the supplying mechanisms of rural able-person intelligence returning of the under-developed agricultural plain in Henan province.And results show that transferring of rural surplus labors takes place among provinces;farmers accelerate in returning to hometowns to make money and returning phenomena would tend to be normal.With more and more popular the intelligence returning phenomenon in rural areas,the under-developed agricultural plain in Henan province would make enough profits.

Shaping the Next Generation Pharmaceutical Supply Chain Control Tower with Autonomous Intelligence

This paper summarizes the findings of an industry panel study evaluating how new Autonomous Intelligence technologies,such as artificial intelligence and machine learning,impact the system and operational architecture of supply chain control tower (CT) implementations that serve the pharmaceutical industry.Such technologies can shift CTs to a model in which real-time information gathering,analysis,and decision making are possible.This can be achieved by leveraging these technologies to better manage decision complexity and execute decisions at levels that cannot otherwise be managed easily by humans.Some of the key points identified are in the areas of the fundamental capabilities that need to be supported and the improved level of decision visibility that they provide.We also consider some the challenges in achieving this,which include data quality and integrity,collaboration and data sharing across supply chain tiers,cross-system interoperability,decision-validation and organizational impacts,among others.

Applications and Challenges of Smart Technologies in Supply Chain Management

The advent of intelligent technologies in supply chain management(SCM)through the use of artificial intelligence(AI),the Internet of Things(IoT),blockchain,big data analytics,and others is a game changer.These improvements increase clarity,effectiveness,and flexibility,allowing companies to quickly deal with market changes and interruptions.Nevertheless,adopting such technologies comes with difficulties,such as information security,compatibility,exorbitant prices,and employee opposition.The applications of intelligent technologies for SCM are examined in this article,as well as the importance of their implementation and solutions that provide a competitive advantage for businesses.

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.

基于Supply-Hub的智能制造企业库存管理

以产业集群为背景,在传统供应链体系中加入Supply-hub作为核心制造商的主要供货点,以信息共享平台为辅助,搭建了供应链结构模型,并对此结构下的主要运作模式进行了分析,总结了供应链中的横向、纵向、双向协同的模式。以此供应链体系为对象,建立了包括供应商库存成本、集配中心库存成本、制造商库存成本、制造商缺货成本、制造商协同补货成本和制造商协同收益等六个方面的成本模型,以测算供应链体系的整体成本情况。

Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems,which integrate the supply chain’s manufacturing operations,information technology,and telecommunication processes.Although supply chain 4.0 aims to improve supply chains’production systems and profitability,it is subject to different operational and disruptive risks.Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems.This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group(SCDG)algorithm.Exploration and exploitation mechanisms of the basic Sine Cosine Algorithm(CSA)are adjusted and controlled by two groups of agents that can be changed dynamically during the iterations.External and internal features were collected and analyzed from different data sources of service level agreements and transaction data from various KSA firms to validate the proposed algorithm’s efficiency.A balanced accuracy of 0.989 and a Mean Square Error(MSE)of 0.0476 were achieved compared with other optimization-based classifier techniques.A one-way analysis of variance(ANOVA)and Wilcoxon rank-sum tests were performed to show the superiority of the proposed SCDG algorithm.Thus,the experimental results indicate the effectiveness of the proposed SCDG algorithm-based voting classifier.

Understanding RFID Technology within a Business Intelligence Framework

Radio Frequency Identification (RFID) technology provides new and exciting opportunities for increasing organiza- tional, financial, and operational performance. With its focus on organizational efficiency and effectiveness, RFID technology is superior to barcodes in its ability to provide source automation features that increase the speed and volume of data collection for analysis. Today, applications that employ RFID are growing rapidly and this technology is in a continuous state of evolution and growth. As it continues to progress, RFID provides us with new opportunities to use business intelligence (BI) to monitor organizational operations and learn more about markets, as well as consumer attitudes, behaviors, and product preferences. This technology can even be used to prevent potentially faulty or spoiled products from ending up in the hands of consumers. However, RFID offers significant challenges to organizations that attempt to employ this technology. Most significantly, there exists the potential for RFID to overwhelm data collection and BI analytic efforts if organizations fail to effectively address RFID data integration issues. To this end, the purpose of this article is to explicate the dynamic technology of RFID and how it is being used today. Additionally, this article will provide insights into how RFID technology is evolving and how this technology relates to BI and issues related to data integration. This knowledge has never been more essential. While IT academic research into RFID development and issues has declined in recent years, RFID continues to be a vital area of exploration, especially as it relates to BI in the 21st century.

基于供需理论的生成式人工智能赋能情报工作范式模型构建与应用研究

[目的/意义]当前生成式人工智能的发展对社会产生了强烈冲击。为响应国家创新发展的迫切需求,如何利用生成式人工智能更好地完成情报工作,以满足用户的情报需求,已成为当前情报学领域关注的热点问题。[方法/过程]基于供需理论提出从数据供给侧(Supply)、智慧情报分析中台(Analysis)、情报需求侧(Demand)三方构建生成式人工智能赋能情报工作范式模型——SAD范式模型,深入分析生成式人工智能赋能情报工作机理,并结合国际前沿项目案例探讨生成式人工智能与情报工作的前瞻性融合发展。[结果/结论]生成式人工智能赋能情报工作范式模型SAD,可以更好地促进生成式人工智能赋能情报工作的各个环节,提高情报工作效率,为情报工作范式研究提供了新思路。

大数据下企业供应链风险管理与竞争情报融合模型构建--以华为公司为例

[研究目的]国际经贸对抗环境下,中美贸易摩擦将持续升级,国家层面的技术创新竞争使得企业面临的供应链风险大幅提升。同时,大数据改变了供应链风险管理模式,而竞争情报可以从理论、技术和方法等方面提升大数据下企业供应链风险管理能力,因此大数据下企业供应链风险管理与竞争情报的融合研究具有重要意义。[研究方法]采用文献分析、案例研究等方法,分析供应链风险管理与竞争情报的异同点,继而论证大数据下供应链风险管理与竞争情报融合的可行性和必要性,再辅之以标杆企业华为的供应链中断风险事件验证,最终构建大数据下华为供应链风险管理与竞争情报融合模型,并分析运行维护此模型的保障要素。[研究结论]研究表明,基于战略、组织、制度、技术和资源五要素的共同保障,大数据下供应链风险管理与竞争情报的融合能够实现供应链风险管理的全面性、及时性和有效性。

供应链金融对制造业数智化转型的影响研究

随着数字经济的快速发展,我国制造业企业迫切需要数智化转型,而供应链金融规模的逐年增长对企业数智化转型的影响正成为热点问题。文章以A股2017—2022年制造业上市公司相关数据为基础,实证研究了供应链金融对企业数智化转型的具体影响机制。结论表明:制造业发展供应链金融可以通过缓解融资约束促进企业数智化转型。文章揭示了“供应链金融—制造业数智化转型”的作用机理和理论边界,为促进制造业的高质量发展提供针对性政策建议。

智能物流机器人在医院后勤物资配送管理中的应用

目的开展智能物流机器人在医院后勤物流配送管理中的应用研究,以提高医院后勤及运行保障精细化管理的水平。方法基于智能订单调度系统和交通调度系统,打通智能物流机器人与医院信息系统的通道,整合即时定位与地图构建技术和5G技术,利用物联网技术将医院电梯、门禁与机器人紧密结合,实现智能物流机器人自动配送物资。结果提升了医院物资整体配送效率,降低了配送环节差错率,提高了医院整体运行效率,降低了医院的管理难度及运营成本。结论智能物流机器人具有高效、便捷、安全的特点,在医院物资配送管理中的应用效果良好,具有较强的可行性与推广性,可有效提高医院后勤精细化管理水平。

面向国家安全的情报需求管理:研究必要、机制构建与实践应用

[研究目的]当前,在国家安全形势日益复杂的背景下,由于情报需求增多、难度加大、情报人员能力不足、工作时间压缩等原因,情报机构能够提供的产品远远无法满足所有的情报需求,有必要开展面向国家安全的情报需求管理研究。[研究方法]从“人、事、物、流程、标准”等管理要素构成的视角解析国家安全情报需求管理的主体、内容范围、模式方法等,创设一套包括“情报管理者”“情报需求管理信息系统”“情报需求管理流程”在内的需求管理工作机制,提出在管理实践中需要注意保持决策者和情报工作机构之间的关系平衡、促进情报工作机构之间的多方协作、重视“机会分析”、控制需求管理信息系统中的战略情报需求占比等问题。[研究结论]研究表明,国家安全情报需求管理将有助于解决国家安全情报需求“过剩”与情报供应能力不足之间的矛盾,为中国国家情报工作规范化建设提供参考借鉴。

数智驱动的零售电商平台供应链发展研究

选取国内零售电商企业为研究对象,基于供应链视角,采用熵值法及描述性统计方法对当前数智驱动背景下零售电商平台供应链发展新格局对经济市场的辐射作用,以及国内零售电商上市公司供应链发展情况进行研究.研究结果发现,数智驱动下的零售电商企业供应链整体发展稳中求进,也证明了数智化对供应链发展具有重要的推动作用,数智驱动下的供应链仍然有发展进步空间与实际研究价值,提出电商平台供应链发展建议,对于零售电商企业具有实际参考价值.

回应性供给:农村老年人智能服务供给的实践逻辑

完善的智能服务供给是保障农村老年人共享数字红利的基础。然而,在智能服务供给实践中,一些基层政府却延续了传统的单向度供给模式,忽视了服务对象的差异性及农村老年人应享有的数字权利,导致智能服务供给的结构性困境。提升农村老年人智能服务供给实效,需遵循“需求锚定—结构调适—技术支持—精准回应”的回应性供给逻辑。构建数字乡村综合服务平台以锚定老年人智能服务需求,调适政府治理结构以形成矩阵式基层组织,打造数字化人才队伍和适老设计以构建技术支持体系,多元协同和主体赋能以实现精准回应,这将为农村老年人智能服务供给提供适配基础、组织保障、动力引擎和协同合力,保障其共享数字红利,解决数字应用“最后一公里”难题。