Design and Implementation of Cloud Platform for Intelligent Logistics in the Trend of Intellectualization

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer(SaaS), platform layer(PaaS) and infrastructure(IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-winlogistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

Intelligent Support System for Healthcare Logistics 4.0 Optimization in the Covid Pandemic Context

The covid pandemic points out inconsistencies and points to improve in the organization of healthcare logistics. Indeed, the dangerousness and the propagation process of the virus imply to increase health security (patient and personal health). In this context, healthcare logistics flows require a new and safety organization improving the hospital performance. The purpose of this paper consists in optimizing healthcare logistics flows by solving problems associated to the internal logistics such as reduction of the personal health wasting time and the protection of both patients and personal health. Then, the methodology corresponds to the use of the hospital sustainable digital transformation as a response to healthcare flows and safety problems. Indeed, social, societal and environmental aspects have to be considered in addition to new technologies such as artificial intelligence (AI), Internet of Things (IoTs), Big data and analytics. These parameters could be used in the healthcare for increasing doctor, nurse, caregiver performance during their daily operations, and patient satisfaction. Indeed, this hospital digital transformation requires the use of large data associated to patients and personal health, algorithms, a performance measurement tool (actual and future state) and a general approach for transforming digitally the hospital flows. The paper findings show that the healthcare logistics performance could be improved with a sustainable digital transformation methodology and an intelligent software tool. This paper aims to develop this healthcare logistics 4.0 methodology and to elaborate the intelligent support system. After an introduction presenting the common hospital flows and their main problems, a literature review will be detailed for showing how existing concepts could contribute to the elaboration of a structured methodology. The structure of the intelligent software tool for the healthcare digital transformation and the tool development processes will be presented. An example will be given for illustrating the development of the tool.

Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

The Study on the Framework and Implementation Scheme of the Railway Intelligent Cold Chain Logistics System

With the development of intelligent and communication technology, traditional logistics has gradually transformed into intelligent logistics. The construction of the railway intelligent cold chain logistics system is an effective measure to implement the structural adjustment of supply, which conforms to the times. Firstly, basic features and design principles of the railway intelligent cold chain logistics system were described. Secondly, target functions and the system framework of the railway intelligent cold chain logistics were put forward, which takes information service as a foundation, management service as a guarantee, business capability as kernel and expansion capability as support. Subsequently, the technical approaches were studied at the hardware, processing and software levels. Finally, development strategies of the railway intelligent cold chain logistics were discussed, including optimizing the layout of infrastructure, improving the performance of equipment, extending the reach of services, deepening the cooperation of technologies and promoting the formulation of standards which provides feasible references for the railway cold chain logistics to explore modern business models.

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

基于Logistic回归分析构建恶性肺结节CT人工智能技术和肿瘤标志物的预测模型

目的探讨CT人工智能技术与肿瘤标志物联合鉴别诊断恶性肺结节有效性。方法选取2018年1月至2021年1月我院收治的453例肺结节患者,根据病理学结果分为良性组(n=317)和恶性组(n=136),比较2组临床资料、CT人工智能参数、血清胃泌素释放肽前体(Gastrin-Releasing Peptide,Pro-GRP)、神经元特异性烯醇化酶(Neuron-Specific Enolase,NSE)、癌胚抗原(Carcinoembryonic Antigen,CEA)、细胞角蛋白片段19(Cytokeratin Fragment 19,CYFRA21-1)、鳞状细胞癌抗原(Squamous Cell Carcinoma Antigen,SCC),采用多因素Logistic回归方程分析恶性肺结节相关影响因素,采用R语言绘制预测恶性结节的列线图。结果恶性组吸烟史、既往肺部外恶性肿瘤史患者多于良性组(P<0.05);恶性组结节直径、恶性概率高于良性组,毛刺征、位于上叶、病灶形态不规则、空泡征表现多于良性组(P<0.05);各单独指标评估恶性肺结节价值:恶性概率的AUC大于结节直径、毛刺征、位于上叶、病灶形态不规则、空泡征(P<0.05);恶性组Pro-GRP、NSE、CEA、CYFRA21-1、SCC高于良性组(P<0.05)。多因素分析显示,吸烟史、既往肺部外恶性肿瘤史、恶性概率、Pro-GRP、NSE、CEA、CYFRA21-1、SCC均是恶性结节的相关危险因素(P<0.05)。基于以上多因素分析筛选出的各危险因素,绘制恶性肺结节的列线图预测模型,Bootstrap内部验证显示校正曲线与理想曲线拟合良好,C-index指数为0.984,AUC为0.925,敏感度为85.29%,特异性为83.91%,说明本研究列线图模型具有较好的预测能力。结论基于CT人工智能技术和Pro-GRP、NSE、CEA、CYFRA21-1、SCC构建的恶性肺结节预测模型,能准确、便捷鉴别肺结节的性质,可为临床诊疗提供参考依据。

基于人工智能的皮革企业物流系统设计研究

皮革产业对物流体系具有极高的依赖性,物流水平可以直接影响企业的经营成本、产业效益等。以人工智能为基础提出了一种皮革企业物流系统的设计思路,从设备层、边缘层、云端应用层3个视角构建了具体的皮革企业物流系统框架,实现了皮革企业物流系统的数据采集与传送、物流业务协调与系统互联、业务和数据建模等功能,旨在为人工智能与皮革产业物流体系的融合提供借鉴。

基于“一中心四平台”的公立医院后勤运营管理实践研究

目的探讨基于“一中心四平台”的医院后勤运营管理模式成效。方法以医院智慧管理分级评估标准为依据,依托大数据、物联网、互联网+、人工智能等技术运用,打造后勤智慧运维管理中心以及设备管控信息化平台、安全防范信息化平台、能耗监测信息化平台、运营管理信息化平台的“一中心四平台”运营管理模式。结果经过样本医院的管理实践证明:基于“智慧联动”的设备故障处置能力得到显著提升,2022年故障报警次数同比2021年下降28.63%,其中高级、普通报警次数同比2021年分别下降69.53%、33.15%;基于“一站式”的后勤服务保障能力得到显著提升,2022年同比2021年维修次数下降2.92%,维修质量有所提高,反复维修频次降低;基于“数据挖掘”的能耗管控能力得到显著提升,2022年万元收入能耗支出同比2021年下降8.71%。结论“一中心四平台”可以显著提高后勤运维管理水平。

基于Unity3D的智慧物流实验室孪生研究

为了共享实验资源,文章采用数字孪生技术,通过Unity3D虚拟现实开发平台对智慧物流实验室进行研究并设计一套云实验系统。根据已有的智慧物流实验设备,包括立体仓库、传送设备、堆垛机、分拣装置和AGV等为研究对象,运用SolidWorks与3DMax完成对实验设备三维模型的建立,运用S7-PLCSIM Advancde完成对物流设备的控制程序,通过PLC与Unity3D的通信和数据交互,最终在Unity3D的虚拟环境中完成物流运输的虚拟仿真实验系统。通过该系统可以在虚拟空间可以实现物料的传送、入库、出库等物流运输过程,实现物流仓储及运输工作过程的虚拟可视化,也可以通过编写的PLC程序完成物流设备的工作动作,实现异地云实验。该系统可以有效解决高端实验设备不足等问题,让资源匮乏的高校学生得到应有的实验训练、提高操作技能。

基于机器视觉的智能物流搬运机器人的设计与研究

第七届全国大学生工程训练综合能力竞赛智能+赛道基于机器视觉的智能物流搬运机器人,对OpenMV4视觉模块进行研究,应用该模块进行二维码、不同颜色物料等多种目标的识别,信息经过模块上STM32F427微控制器的处理,与Arduino Mega 2560板通信,机器人识别场地上的三个区域,用PID算法驱动麦克纳姆轮机器人的四个直流电机旋转并且定位机器人,通过PCA9685模块的I2C通信协议,发送PWM脉冲信号给机械臂上的四个关节舵机,使智能物流搬运机器人对场地上的物料进行自动搬运。实验证明其可以较为精确地完成各项搬运任务。

轨道物流实时跟踪系统在某院中心药房的实践应用

中心药房是负责医院患者药物配发的中心枢纽,承担着保障全院患者及时治疗的重任。智能轨道物流系统的使用极大地节省了人力,但由于频繁使用,易因设备出现差错和故障而影响工作效率。医院通过引入轨道物流跟踪系统,提高了医院物流管理的水平及医院中心药房的工作质量和服务水平,降低了医院内耗,为进一步实现医院现代化物流管理提供了保障。

互联网背景下河北智慧物流管理模式的发展策略研究

互联网时代,各类信息技术在我国各行各业都得到了广泛应用,也深刻改变了传统行业的发展格局,其中对物流行业领域的影响表现得尤为显著。基于互联网信息技术的发展,我国传统物流体系逐步转变为智慧物流模式,有效提高了物流体系的服务质量。本文首先阐述了互联网时代背景下智慧物流管理模式的发展现状,其次明确了智慧物流管理模式的具体建设内容,最后从完善智慧物流的政策体系、细化智慧物流的管理制度、优化控制智慧物流的成本、加快信息技术的融入进程、完善智慧物流人才培养机制五个方面,探讨了河北省智慧物流管理模式的发展策略。

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。

集团化医院多院区后勤机电设备设施集成化管理探索与实践

分析了集团化医院多院区后勤机电系统管理信息化的现状,以及医院后勤管理流程存在的问题和挑战。以华中科技大学同济医学院附属协和医院为例,介绍医院后勤一体化综合管理平台对于后勤机电设备的集成管理实践,包括平台的功能框架、部署方式以及数据标准化和流程优化等。通过平台的部署和集成,实现了多院区后勤机电设备和系统的数据共享和协同管理。实践表明,基于综合管理平台的集成管理是解决集团化医院多院区后勤机电系统管理问题的有效途径。

ChatGPT对物流行业发展的影响分析

传统物流业在订单处理、仓储管理、货物运输配送和客户服务等环节存在污染排放量大、成本高昂及效率低下等诸多问题。生成式人工智能ChatGPT可有效帮助物流行业各环节解决现存问题,如释放生产力潜能、重塑生产关系框架、推动行业转型升级和产业模式创新,但也会带来失业、人员结构和经营模式变革、法律纠纷、行业竞争壁垒增加等多项挑战。对此,文章提出,未来物流行业发展应在企业层面坚持自主技术研发与组织结构优化并行,在政府层面坚持政策支持与法规约束并行,在个体层面坚持融入浪潮与明确风险并行,并充分发挥各条路径间的协同效应来加以应对。

三级公立医院智慧后勤管理平台建设与应用

目的建设智慧后勤管理平台,实现对医院人、财、物、技术的协同管理,达到高安全、优品质、低成本的运营管理目标。方法结合医院实际运营需求,引入“平台+现场管家专业运维”模式,打通医院后勤各系统专业服务数据,助力医院数字化转型。结果建成的智慧后勤管理平台,能够实现对后勤各系统专业服务数据的统一协调、统一调度、闭环跟踪,真正实现了后勤一站式管理,提升了医院的社会效益和经济效益。结论三级公立医院智慧后勤管理平台能推动医院后勤部门数字化转型,提高医院运行效率和服务质量。