Research and Application of Maize Precision Intelligence Spatial Decision Support System

In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.

Study on Material ManagementSystem of Coal Enterprise Basedon Data Warehouse

On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).

The Design of the Assistant Decision Support System of Cross-Regional Rural Labor Flow

The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

A Decision Support System for Construction Projects Based on Standardized Exchanged Documents

Large amounts of documents are exchanged during the construction phase of projects, which covers the important management information. To utilize the exchanged documents to support decisionmaking of the management staffs, the requirement analysis was carried out based on the interviews to the practitioners. A decision support system called Explyzer+ was developed based on the previous prototype system Explyzer. The latter was enhanced by adding the functions to automate the whole process and the techniques of data mining including decision tree analysis and clustering analysis. A case study for in-depth information analysis was conducted based on the data obtained from a large construction project to demonstrate its feasibility and effectiveness. The system could effectively assist the management staffs to carry out in-depth information analysis for decision-making in construction projects.

Big Data Analysis in Smart Manufacturing: A Review

The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

基于SCADA和支持向量回归的风电机组状态在线评估方法

为提高风电机组并网运行的实时可靠性、优化机组维修策略、降低风力发电成本,有必要充分考虑风电机组各部件或子系统之间的相互作用和耦合关系。利用数据挖掘技术,建立了一个针对风电机组整体运行状态的在线评估模型。首先,分析了数据采集与监控(SCADA)报警系统的不足,提出了基于回归预测模型和SCADA报警系统相配合的鲁棒性更强的在线评估方案;其次,对评估方案中的回归预测模型进行了详细说明,建立了以SCADA系统的部分监测项目为输入量、以风电机组有功功率为输出量的基于支持向量回归(SVR)算法的回归预测模型。最后,利用某风电场的实测数据对所提出的在线评估模型进行了验证,结果证明了此方法的可行性。

基于云计算平台的风电场SCADA系统的设计与应用

针对目前风电场积累的海量多源异构的大数据,借助云计算平台架构灵活多变、数据并行处理的优势,设计了基于云计算平台的风电场数据采集与监控(SCADA)系统。在该系统的云计算平台上采用智能化数据挖掘算法,完成了风机数据的远程采集、智能分析、故障预判。最后选取反映风电场性能的功率和转子转速数据,以自适应权重(AFW)的K-means算法和最小二乘支持向量机两种典型的数据挖掘算法为例,对数据进行了聚类分析和回归分析,验证了系统的可行性。

高速铁路牵引供电SCADA系统调度端数据仓库设计

针对我国高速铁路(HSR)牵引供电(TPS)SCADA调度端保存的海量数据,分析了传统数据库的特点及不足,提出利用数据仓库技术将操作型数据从日常事务处理(OLTP)系统应用环境中分离出来,构造分析决策型应用环境,提高牵引供电SCADA系统决策支持能力。设计了牵引供电SCADA系统数据仓库体系结构,以实现对牵引供电SCADA设备故障进行深入分析,为对保护动作进行评判及事故预测提供依据。根据挖掘主题,对牵引供电SCADA调度端数据仓库给出了详细的设计(包括事实表、公共维表和私有维表设计),并对数据仓库的实施作了阐述。

基于长输管道SCADA运行数据的调控操作分析与研究

长输管道SCADA系统建设日趋成熟,在各级油气管道调控中心也搭建了数据中心,实现了对多条管道SCADA运行数据的汇聚,积累了大量的管道生产运行数据,这些数据详实地记录了管道生产运行和调控操作的全过程。而调控中心面临着如何对管道调控操作规律、管道复杂度和调度员操作水平进行科学评估与分析的难题。本文介绍了采用数据挖掘中描述性分析技术,基于海量的长输管道SCADA运行数据,对调控操作基础数据进行工况判断规律的提取、分析和完善,最终识别判断出调度人员在各种工况下操作全貌,并基于工况自动判断结果进行各种纬度的统计分析,为调控运行管理提供科学的决策支持数据和有效分析方法。

数据挖掘技术在管理信息系统中的应用

阐述数据挖掘技术的原理和算法,包括分类、聚类、关联规则挖掘,探讨数据挖掘技术在管理信息系统中的应用。通过数据挖掘技术,企业可以识别风险和异常情况,从而提供预测和决策支持。分析数据挖掘技术在管理信息系统中的挑战和发展方向。

中文医学术语集在真实世界临床研究中的应用

目的:通过分析国内外医学术语集的应用进展,拓展中文临床术语集在中文电子病历环境下的科研应用场景及范围。方法:检索四川大学数字图书馆出版物,通过文献回顾进行分析归纳。结果:中文医学术语集对电子病历概念识别和数据挖掘具有重要作用,有助于语义互操作。基于语义网及语义知识库,可以更深入地进行多种风险预测模型、语义相似度计算和推理性决策支持系统的研究。结论:中文医学术语集是基础性的信息化标准,可支撑多种临床智慧应用构建,建议纳入医学教育。

基于医疗大数据挖掘的护理决策支持系统模型设计研究

探讨基于医疗大数据挖掘的护理决策支持系统模型在临床实践中的应用价值,采用多源医疗数据集、数据预处理及集成技术,利用机器学习算法构建决策支持模型。结果显示,该模型在预测护理需求、患者病情变化及应对随机事件方面表现出较高的准确性,能够有效减少护理人员工作负担,提高护理精确度和响应速度。通过深度挖掘患者数据,系统为护理提供个性化建议,促进患者护理结果优化。

基于关联规则挖掘算法的集团型企业业务数据管理系统

为了在决策支持系统中快速挖掘出关联规则,以帮助管理者做出更好的决策,提出了一种非冗余高效用关联规则算法。建立了一个高效用项目集合,在其中识别出高效用项。在此基础上,开发了一种从构建的集合中生成规则的高效算法。将提出的新算法分别在稀疏型数据集和密集型数据集上进行了验证,实验结果表明,提出的非冗余高效用关联规则算法运行时间更短,且所占用内存更小。所提出的算法可以与各种应用集成,并且可以很好地与外部系统结合,例如物联网(IoT)和分布式计算机系统,其在决策支持系统中具有较高的使用价值。

基于大数据技术的新型配电网指挥系统设计

阐述基于大数据技术的新型配电网供电服务指挥系统的设计,包括系统架构、数据采集与传输技术、数据分析和挖掘算法,以及服务指挥与决策支持功能。通过实际应用案例,展示系统的有效性。

基于大数据技术的地铁电客车车门维保数据分析与决策支持研究

利用大数据技术分析地铁电客车车门维保数据,以提供更有效的决策支持。收集了大量的维保数据,并运用数据挖掘技术和机器学习方法,对车门维保过程中的关键指标进行深入分析。研究了车门故障的概率分布,制定了维保策略,并提出了决策支持系统。通过数学建模和实际数据计算,为地铁运营商提供了更准确、高效的维保决策,以提高车门的可靠性和安全性。

电力系统通信中的大数据分析与智能决策支持系统设计

文章旨在探讨电力系统通信中的大数据分析与智能决策支持系统设计。通过分析电力系统通信的架构与组成,以及大数据技术在其中的应用,提出了一套智能决策支持系统的设计方案。系统综合运用数据采集、处理、分析以及挖掘等技术手段,旨在实现对电力系统通信的实时监测和智能决策支持。通过对案例的研究和验证,证明了该系统在提高电力系统通信运行效率和智能化水平方面的有效性与可行性。通过文章的研究,能够提高电力系统通信的运行效率和智能化水平,为电力系统的稳定运行提供支持。

电子信息科学与大数据分析的结合与应用研究

文章围绕电子信息科学与大数据分析的结合与应用进行研究。首先介绍电子信息科学与大数据分析的相关基础理论,及其在各领域的关键应用。随后,通过利用大数据工具和技术,深入进行电子信息的采集,预处理和分析处理。研究表明,电子信息科学与大数据分析的结合,可有效挖掘电子信息的内在价值,并为决策支持提供更为强大的科学依据。最后,对可能存在的问题和挑战进行探讨,提出相应解决方案。

基于数据仓库和数据挖掘技术的温室决策支持系统

农业领域的专家知识多是描述性和经验性的知识,难以进行精确的数学描述,这给农业决策支持系统的构建带来了一定的困难。为解决这种传统的决策支持系统的不足,介绍了基于数据仓库技术和数据挖掘技术构建的温室决策支持系统。该系统通过建立数据仓库来存储各种来自异质的信息源的数据,利用联机分析处理实现多维数据分析,然后通过数据挖掘技术从数据仓库中获得新的农业知识,以丰富决策支持系统的知识库。在系统的实现过程中,采用了SQL Server分析服务,实现数据挖掘与数据仓库以及应用程序的紧密耦合,从而大大提高了数据挖掘效率。将数据仓库、联机分析处理、数据挖掘三者结合起来形成了一种新的温室决策支持系统。