Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

Research on Intelligent Technology Management and Service Platform

In recent years,there are some problems in science and technology management,such as untimely task supervision and independent information system,which makes it difficult to achieve accurate,quantitative and standardized management.The storage of scientific research test data is scattered,and there are many deficiencies in the management,promotion and use of existing intellectual property.In this paper,on the basis of the knowledge economy,intelligence economy under the conditions of knowledge management concept and cutting-edge technology,technology management and service management related data,information,knowledge,method of blend together.Based on science and technology management,knowledge management as the core of management and service platform of science and technology technical route,main function,construction principles and standards.In order to realize the management systematization of science and technology,intelligent,better educated,multi-level and multi-angle application services.

Machine learning-based zero-touch network and service management:a survey

The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.

Effective Approach to Elevate the Intelligence of Management Decision System

Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to learn by means of adopting three types of heterogeneous knowledge representation and knowledge management measures.At length,this paper outlines the basic framework of an intelligence system for the sake of management decision problem.

Improving the Patient Discharge Planning Process through Knowledge Management by Using the Internet of Things

The UK National Health Service (NHS) is faced with problems of managing patient discharge and preventing the problems that result from it such as frequent readmissions, delayed discharge, long waiting lists, bed blocking and other such consequences. The problem is exacerbated by the growth in size, complexity and the number of chronic diseases in the NHS. In addition, there is an increase in demand for high quality care, processes and planning. Effective Discharge Planning (DP) requires practitioners to have appropriate, patient personalised and updated knowledge in order to be able to make informed and holistic decisions about a patients’ discharge. This paper examines the role of Knowledge Management (KM) in both sharing knowledge and using tacit knowledge to create appropriate patient discharge pathways. The paper details the factors resulting in inadequate DP, and demonstrates the use of Internet of Things (IoT) and Machine2Machine (M2M) as candidate technologies and possible solutions which can help reduce the problem. The use of devices that a patient can take home and devices which are perused in the hospital generate information, which can serve useful when presented to the right person at the right time, thus harvesting knowledge. The knowledge when fed back can support practitioners in making holistic decisions with regards to a patients’ discharge.

An Intelligent System for a Resource Allocation Problem Based on Fuzzy Reasoning

The development of the assistive abilities regarding the decision-making process o fan Intelligent Control System （ICS） like a fuzzy expert system implies the development of its functionality and its ability of specification. Fuzzy expert systems can model fuzzy controllers, i.e., the knowledge representation and the abilities of making decisions corresponding to fuzzy expert systems are much more complicated that in the case of standard fuzzy controllers. The expert system acts also as a supervisor, creating meta-level reasoning on a set of fuzzy controllers, in order to choose the best one for the management of the process. Knowledge Management Systems （KMSs） is a new development paradigm of Intelligent Systems which has resulted from a synergy between fuzzy sets, artificial neural networks, evolutionary computation, machine learning, etc., broadening computer science, physics, economics, engineering, mathematics. This paper presents, after a synergic new paradigm of intelligent systems, as a practical case study the fuzzy and temporal properties of knowledge formalism embedded in an ICS. We are not dealing high with level reasoning methods, because we think that real-time problems can only be solved by rather low-level reasoning. Solving the match-time predictability problem would allow us to build much more powerful reasoning techniques.

具身智能知识服务:元宇宙与AIGC等新技术赋能下的知识服务新图景

[目的/意义]智能技术的发展为知识服务领域带来前所未有的变革。研究具身智能知识服务的理论逻辑、技术基础、体系框架和应用前景,可以为知识服务领域提供新的理论参照和实践指导。[方法/过程]基于具身认知理论,深刻理解具身智能知识服务的在场性、主动性和连接性特征。进一步分析以元宇宙、AIGC、数智人和人形机器人等作为技术基座所构建的具身智能知识服务体系框架,涵盖具身知识的生成、加工、传播、反馈等要素,并探讨具身智能知识服务在教育培训、公共文化、文化旅游和医疗健康等不同服务场景中的应用。[结果/结论]分析具身智能知识服务体系框架的构建和应用图景,有助于建立更全面和智能的知识服务网络,满足用户的知识需求,提高具身智能知识服务的效能并推动具身智能知识服务的高质量发展。

知识服务与新质生产力:双向赋能机制与实践路径

[目的/意义]新质生产力是数智背景下生产力发展的最新形态,探讨知识服务与其双向赋能的机制与实践路径,对于发挥数据要素新价值、数智技术新动能、智能服务新范式的效用,促进培育新质生产力和提升知识服务,具有重要的理论意义和现实意义。[方法/过程]本研究以总结知识服务发展历程和在新质生产力视角下的新阐释为基础,通过剖析知识服务与新质生产力双向赋能的内在关联、外在表现,构建出知识服务与新质生产力的双向赋能机制,并提出双向赋能路径。[结果/结论]“要素重组是重要抓手、科技创新是关键举措、优质服务是终极目标”成为知识服务与新质生产力双向赋能的内在关联;知识服务通过助力科学研究加速科技创新,优化跨学科跨领域知识关联与挖掘开辟新赛道,助力产业智能化转型升级孕育新业态,培育新质生产力;新质生产力通过化解资源潜在价值挖掘受限困境、技术模型算法依赖困境、人才数量与质量欠佳困境和增加需求、增长点与竞争优势驱动知识服务发展,提升知识服务。最后,通过以市场需求为导向,以数据要素为核心,以技术创新为关键,以应用场景为抓手,把握新质增长引擎,压实智能驱动底座,夯实融合应用基础,落实双向赋能机制。

“人工智能+大数据”在医院服务流程管理中的应用与实践

智慧医疗和数字化转型是医疗领域发展的方向。以吉林大学中日联谊医院实践为例,探讨了“人工智能+大数据”在医院精细化服务流程管理中的应用,着重分析其在提升医疗服务水平和满足患者需求方面的潜在优势。通过智慧医疗路径设计和构建精细化智慧管理服务流程的框架,为医院数字化转型提供思路和方法。

新一代人工智能驱动档案信息化建设的现状和趋势探讨——基于医疗机构的考察

探讨新一代人工智能技术在档案信息化建设中的应用现状和趋势,并以医疗机构为调查对象进行实证分析。通过分析新一代人工智能技术在档案信息化建设中的应用现状和趋势,结合在医疗机构的调查分析,提出了调查设计和方法,并进行了调查结果统计和分析。探讨了医疗档案信息化建设的特点和需求,分析了新一代人工智能技术在医疗档案信息化建设中的现状、趋势、挑战和未来的发展方向,提出了新一代人工智能技术驱动档案信息化建设建议和展望。

生成式人工智能背景下企业知识管理系统的构建及发展

通过文献调查法和类比分析法,以企业知识创新产出为核心目标,将知识管理的发展归纳为3个不同的时代,并鉴于仿生学、工程学、管理学三者的交叉对比,构建智能生成时代知识管理系统的基本框架,并将人工智能(AI)技术、智能中心、知识资产作为智能知识管理的三大要素,最后提出元宇宙虚拟世界和现实世界相结合的开放型知识管理构想理念模型。研究(1)明晰了生成式AI未来将渗透到各个组织层面的发展趋势;(2)结合主客观因素提出了生成式AI企业知识管理系统的基本框架及运作机理,为有效解决组织知识创新产出的内涵及运转提供了新的思路;(3)理论上建立了以智能中心为核心驱动力的知识创新管理理论系统,为生成式AI产业的智能发展提供了一定的理论支撑。

人工智能视域下的医护科研人员知识服务

随着ChatGPT的兴起,如何将人工智能更好地运用于医护科研人员知识服务体系中,成为推进“健康中国”建设的重要举措。文章以医护科研人员为研究对象,以Wilson信息行为模型和信息搜寻行为的情境为理论基础,采用问卷调查法和因子分析法设计用户画像标签体系,构建医护科研人员用户画像。研究发现:医护科研人员可细分为科研成熟型、科研进阶型、科研起步型和科研潜在型四类群体;图书馆员可通过积极采纳AI服务理念进行用户特征的个性化与动态化识别,使用自然语言处理与机器学习实现医学资源自动化管理,以AI教学平台提升医护科研人员对新工具的掌握效率等途径,实现人工智能视域下医护科研人员知识服务效能的提升。

面向数智化服务设计的创新模型构建及其实践

聚焦服务设计创新模糊前端创新演化博弈机制,为传统中小企业嵌入数智化供应链生态平台提供先进设计方法与启示,帮助中小企业从“多维平台嵌套”数智化平台的共创成果中获利,并扩展原有平台的功能边界。对智米科技等数智化平台进行单案例分析,而后针对所形成的服务设计实践启示,整合与优化了工程领域的ARIZ等跨学科技术,最终构建了一套面向数智化服务设计创新的ASD模型。验证阶段以数智化与双碳背景下的民宿酒店设计为实践对象,验证了ASD模型在服务设计创新模糊前端具有促进利他偏好演化的作用,有助于中小企业设计创新管理的模式构建。研究获得的启示,构建的模型能够为缺乏经验的传统服务型制造企业,实施服务设计创新战略,具有较高的管理启示价值。

国内外应急情报研究热点、演化趋势与未来展望(1990-2023)

应急情报在重塑现代化应急管理体系中承担了数据化、智能化的重要角色,识别分析应急情报研究热点与趋势,能为完备应急情报体系、促进应急管理方式蜕变提供决策支持。文章从文献分布特征、科研合作、术语和引文关系等方面对国内外应急情报研究进行计量分析,通过关键词共现、聚类分析提取应急情报研究热点,并根据可视化研究脉络分析总结演化趋势。研究结果显示:当前应急情报研究呈现出面向国家重大需求的融合趋势、应急情报核心研究力量亟待形成、围绕数智转型的多元化发展态势和聚焦公众参与的群众路线倾向的特征,未来研究方向主要有建设应急情报学科、深化应急情报体系、创新应急情报数智技术和探索应急管理情报实践。

赋能视角下中国高校知识服务能力提升路径研究

为提升中国高校知识服务效能,激发高校教师创新潜能、促进知识创新,在知识服务领域引入赋能理论,对中国9所高校26位知识服务主体进行深入访谈,结合C9高校发布的9份网络报道,运用扎根理论的编码技术进行数据处理,挖掘出技术赋能、市场赋能、知识赋能、心理赋能、制度赋能和组织赋能6个核心范畴,基于分析结果构建双因素视角下高校知识服务赋能理论模型(IMK-TPO模型),并采用问卷调查法对模型进行验证。结果发现,在高校知识服务赋能路径中,既要以制度赋能、市场赋能和知识赋能等保健赋能模式为保障,维持原有服务效能,还需以技术赋能、心理赋能和组织赋能等激励赋能模式为驱动,提升整体服务效能。其中,制度赋能和市场赋能需要政府等组织的协同支持;而知识赋能则需要通过持续学习来提升。基于此,提出由政府高校个人合力建立信息共享平台、搭建合作网络,强化学科交融互动的发展策略,以期促进中国高校知识服务发展。

智能建造背景下工程管理本科生知识需求调查

智能建造快速发展,工程管理的内容也在迅速变革,亟需调查分析由此导致的工程管理本科层次人才知识需求的变化。从行业需求的角度面向智能建造相关企业开展问卷调查,共获得了102份有效问卷,涵盖50余家企业。结果表明:工程基础、传统工程管理、BIM、人工智能及大数据、工厂生产管理知识的“需要及非常需要”累计占比分别为90.19%、87.25%、80.39%、75.49%、75.49%。据此,建议高校工程管理专业应着重考虑上述类型知识的传授。当前,为满足工厂生产管理方面的知识需求,还迫切需要高校及行业单位联合编写相关教材。在当前智能建造快速发展背景下,研究结果可为高校工程管理本科生培养方案的修订提供关键依据。

新质生产力战略下AIGC赋能的知识和情报

[目的/意义]以人工智能生成内容为代表的AI技术赋能新质生产力涌现多维价值意蕴,可以更好地助力我国数字经济实现高质量发展,更好地助推我国知识和情报服务创新。分析AIGC赋能知识和情报创新服务的新机制、新风险和新路径,对于推动信息资源管理学科在新质生产力重大国家战略下的蓬勃发展具有重要的理论和现实意义。[方法/过程]本文依据新质生产力的理论内涵,从劳动对象、劳动者和劳动资料3个角度剖析AIGC赋能知识和情报服务创新的内在机制,依据信息传递三要素从信息源、信道和信息受体3个维度识别AIGC赋能知识和情报服务创新的风险并提出治理策略,从新质生产力构成阐释AIGC赋能知识和情报创新服务的实践路径。[结果/结论]研究表明,新质生产力战略下,AIGC以数据要素为基础,以基础模型为支撑,以人智协作为核心,形成知识和情报服务创新的内在机制;数据隐私安全风险、网络空间安全风险和科技伦理安全风险需要更好地发挥产学研协同优势进行治理;提出通过构建以数据要素驱动的新模式,组建人智协作的新团队和开发可靠可信的智能化模型促进AIGC赋能知识和情报服务创新的实践路径。

近20年高校图书馆知识服务研究热点与趋势

[目的/意义]分析2001―2022年高校图书馆知识服务领域的研究现状、研究热点及研究前沿,以期为我国图书馆知识服务研究与实践提供借鉴与参考。[方法/过程]检索CNKI平台中2001―2022年高校图书馆知识服务领域的相关文献,利用CiteSpace软件对其从时间、机构、作者、关键词四个方面进行文献计量,生成可视化知识图谱,呈现出国内高校图书馆知识服务领域研究热点、发展脉络及前沿趋势。[局限]2023年文献尚未纳入,后续将持续追踪最新研究成果,完善本研究的发展趋势分析;限于文献篇幅,部分研究主题的脉络分析略为粗糙,有待深入细化。[结果/结论]高校图书馆知识服务领域整体发文量以2019年为分界点,呈长期上升、近期下降趋势;各作者、机构进行独立研究的现象较为普遍;研究热点为信息素养、学科服务、知识服务、知识产权信息服务、数据管理服务、智库服务、数字学术服务、智慧服务,研究前沿为智慧服务、数字人文、数据素养、数字学术、知识产权、数据管理等方向。