Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

The analysis of application of data mining technology in the system of intrusion detection

With the economic development and the popularity of application of electronic computer, electronic commerce has rapid development. More and more commerce and key business has been carried on the lnternet because Internet has the features of interaction, openness, sharing and so on. However, during the daily commerce, people worry about the security of the network system. So a new technology which can detect the unusual behavior in time has been invented in order to protect the security of network system. The system of intrusion detection needs a lot of new technology to protect the data of the network system. The application of data mining technology in the system of intrusion detection can provide a better assistant to the users to analyze the data and improve the accuracy of the checking system.

Research on the Data Mining Technology in College Students'Attendance System Based on the Big Data Architecture

With the development and improvement of the information technologies,the increasing of the upper application systems and the rapid expansion of the data accumulated in the campus information environment,a typical campus big data environment has initially been formed.Because of the characteristics of the higher education,students'mobility is great and their learning environment is uncertain,so that the students'attendance mostly used the manual naming.The student attendance system based on the big data architecture is relying on the campus network,and adopting the appropriate sensors.Through the data mining technology,combined with the campus One Card solution,we can realize the management of the attendance without naming in class.It can not only strengthen the management of the students,but can also improve the management levels of the colleges and universities.

Research on the big data feature mining technology based on the cloud computing

The cloud computing platform has the functions of efficiently allocating the dynamic resources, generating the dynamic computing and storage according to the user requests, and providing the good platform for the big data feature analysis and mining. The big data feature mining in the cloud computing environment is an effective method for the elficient application of the massive data in the information age. In the process of the big data mining, the method o f the big data feature mining based on the gradient sampling has the poor logicality. It only mines the big data features from a single-level perspective, which reduces the precision of the big data feature mining.

Research on the Development of Ideological and Political Education Resources for College Students Based on the Data Mining Technology

With the rapid development of the computer technologies, the rapid spread and development of the network resources have a significant impact on people's thinking and lifestyle. The ideological and political education in colleges and universities should keep pace with the times, and examine the new ways and resources of the ideological and political education in colleges and universities from the perspective of the times. The high-tech has been applied to the development of the modem education, expanding the teaching space-time, improving the teaching methods of the ideological and political course, and enriching the teaching contents of the ideological and political theoretical courses. The development mechanism of the ideological and political education resources for college students based on the data mining technology is not only in line with the trend of the times, but can also play an ideal role in optimizing the ideological and political education mechanism for college students.

The application of data mining technology in data service of micro service architecture

This paper introduces the application of data mining technology in data service. Data mining is a new technology, but its application time is long, and the application effect is obvious. Data mining technology can be used in enterprise customer service system, which can help enterprises to find potential customers, while retaining the most valuable customers. We pointed out the necessity of establishing data mining technology and service intelligence analysis system based on the combination of intelligence analysis and service characteristics and processes, the method of data mining, knowledge management ideas applied to intelligence analysis and service system.

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Research on the Big Data Cloud Computing Based on the Network Data Mining

The big data cloud computing is a new computing mode,which integrates the distributed processing,the parallel processing,the network computing,the virtualization technology,the load balancing and other network technologies.Under the operation of the big data cloud computing system,the computing resources can be distributed in a resource pool composed of a large number of the computers,allowing users to connect with the remote computer systems according to their own data information needs.

基于数据挖掘的前沿技术识别方法与实证研究

依据前沿技术的前瞻性、先导性、探索性及颠覆性特征,构建基于数据挖掘的前沿技术识别方法,可为高效制定技术发展策略、合理调整技术规划布局提供支撑。首先,以工程索引(engineering index,EI)和德温特创新(Derwent innovation,DI)平台数据库为数据源,制定检索表达式采集数据,并对数据进行预处理。其次,建立表征前沿技术特征的指标体系,并对据此筛选出的技术文本数据进行挖掘,识别前沿技术主题。最后,以量子计算领域为例开展实证研究,识别出量子纠错技术、光量子芯片技术等前沿技术点,经技术专家等验证,表明识别结果科学有效。

基于BP神经网络模型的矿井生产预警技术研究

矿井生产具有高动态、快时变、混沌和随机特点,生产过程中的人、设备、环境、管理等海量数据存在多源异构特征使得安全风险评估不确定性增加。使用智能算法对空间生产场景进行模拟建模,对影响矿井安全的因素建立预警指标体系并对指标量化与预处理,通过BP神经网络模型对预处理后的指标数据模型训练与测试,测试结果表明,随着迭代次数的增加,模型的收敛性与准确率极大提高,可以最大限度地提供矿井安全风险评估决策所用信息、为提高煤矿安全监测的效率和监测深度提供技术支持。

数据挖掘技术在高校学生评教中的应用研究

文章从当前高校学生评教的现实情况入手,概括论述了与数据挖掘技术相关的一系列内容,诸如数据挖掘的定义、过程以及方式,进而分析了数据挖掘技术在高校学生评教中的优势与价值,并重点阐述了数据挖掘技术在高校学生评教系统中的具体应用。旨在进一步突出数据挖掘技术与高校教育教学工作的紧密联系度,同时证明数据挖掘技术在促进高校学生评教方式改革以及切实提高高校教育教学质量中所产生的深远影响。

基于数据挖掘技术的医学检验技术线上教学效果评估系统设计

传统教学评估系统依赖人工或简单工具处理数据,难以深入分析海量数据,且易受个人偏见影响,评估结果不够准确公正。为优化医学检验技术线上教学评估效果,文章设计了基于数据挖掘技术的评估系统。在硬件方面,系统采用MSP430单片机与DS1302时钟芯片,确保实时性和准确性;在软件方面,构建评估结构,明确角色功能,建立数据仓库,细化数据结构表,满足查询需求。系统以赋分比和专业权重为依据生成评估报告,减少主观干扰,提升公正性和准确性。经测试,该系统性能卓越,具备实际应用价值。

基于改进SPRINT分类算法的数据挖掘模型

为解决目前数据挖掘模型分类时间长、挖掘准确率不高的问题,提出基于改进决策树分类算法(SPRINT)的数据挖掘模型。先采用最大-最小规范化公式完成原始数据线性变换,利用改进后的SPRINT分类算法按照输入数据特性进行分类,使用协同过滤技术生成与数据相近的属性集,计算数据属性相似度,生成语义规则集,为用户提供更优的数据服务。选取某公司营销数据集作为样本进行对比实验,结果表明,与对比模型相比,所提出的数据挖掘模型分类时间更短,挖掘准确率更高,能为用户提供更优质的数据服务。

基于数据库技术的计算机数据挖掘平台设计

数据库技术提供数据存储、查询等功能,数据挖掘技术则利用算法从数据中提取模式。基于数据库技术的计算机数据挖掘平台设计与实现,旨在融合两个领域的优点,开发出能够高效处理和分析大规模数据集的应用平台。文章介绍了基于数据库技术的计算机数据挖掘平台的设计与实现过程,探讨了计算机数据挖掘平台的具体设计,旨在提供高效可靠易用的数据挖掘平台。

数据挖掘技术在医院图书馆服务优化中的应用

为了提升医院图书馆的服务效率和信息化水平,文章设计并实现了一个基于数据挖掘技术的医院图书馆服务系统。该系统采用分层架构设计。其中,系统的资源采集层通过ETL技术有效采集医院相关文献资源;资源存储层运用决策树算法对采集的资源进行分类和存储;资源服务层通过访问接口为用户提供高效的服务支持;资源应用层利用数据挖掘技术优化检索和推荐机制,提高服务质量和效率。系统的测试结果表明,该系统能够显著提高医院图书馆文献资源的检索准确率,为医院图书馆的信息服务工作提供有力的支持。

融合“科学—技术—舆情”数据特征的技术筛选方法研究

[目的/意义]利用科技文献进行技术监测预警是科技情报工作的重要内容。目前,采用自然语言处理技术从科技文献中抽取的技术元素存在数量多、不易展示的问题,因此设计了一种融合“科学—技术—舆情”数据特征的技术筛选方法来实现科技文献技术挖掘结果的筛选。[方法/过程]以技术术语表示技术,基于词法结构分析和修饰符匹配方法构建技术术语层次结构体系,利用表征技术基础研究热度的论文数据、表征技术研发热度的专利数据、表征技术市场关注度的舆情数据,构建重要性、成长性、新颖性和持久性4类特征,采用机器学习方法训练和确定技术筛选模型。[结果/结论]通过与人工筛选结果对比发现,本方法能够更有效地筛选技术。在各种模型中,同时采用3类数据和4类特征构建的技术筛选模型效果最优,该方法可以为开展技术识别预测工作,研发技术挖掘工具提供依据。[局限]该方法仅在技术术语层次结构的第一层进行了效果验证,其领域适用性与数据类型方面还有待进一步研究。

基于数据挖掘的中医护理技术治疗术后疲劳综合征选穴规律分析

目的:基于数据挖掘技术,探讨中医护理技术干预术后疲劳综合征的选穴配伍规律。方法:检索中国知网、万方数据库、维普数据库、中国生物医学文献服务系统、PubMed、Web of Science建库至2023年12月31日收录的中医护理技术治疗术后疲劳综合征的文献,采用Excel 2016软件建立数据库并分析经络腧穴频次,基于SPSS Modeler18.0统计软件中的Apriori算法模型对腧穴配伍规律进行深层次分析。结果:纳入符合研究的文献87篇,涉及腧穴40个,使用频数居前3位的腧穴为足三里、神门、肾俞;选取的经脉以足太阳膀胱经、任脉和足阳明胃经为主;关联规则分析显示脾俞-肾俞、脾俞-肾俞-足三里为支持度最高的穴对和穴组。结论:中医护理技术干预术后疲劳综合征的选穴配伍规律分析,可为临床应用中医护理技术治疗术后疲劳综合征选穴提供一定的循证依据。

基于数据挖掘技术优化DRG临床用药目录

目的 优化疾病诊断相关分组(DRG)的临床用药目录,减少患者药品费用,提高DRG结付率。方法 选取某院神经内科BR23疾病组作为研究对象,应用数据挖掘技术,探索疾病组的用药规律,并利用药品综合评价方法对重点监测药品进行评分,进而优化疾病组的临床用药目录。选取2022年12月入组该疾病组患者的住院信息作为优化前数据,2023年9月入组该疾病组患者的住院信息作为优化后数据,通过比较两组患者的医疗质量及药品费用数据来评价优化目录的实施效果。结果 优化临床用药目录后,该疾病组的结付率由优化前的84.36%上升至104.70%,住院药品费用及住院总费用均显著降低(P<0.05),重点监测药品使用量明显下降。结论 数据挖掘技术有助于探索疾病组临床用药规律;药师可以此为依据,通过有效药学干预手段提高DRG结付率。

基于数据挖掘、网络药理学技术探讨藏药治疗慢性萎缩性胃炎的用药规律及作用机制

目的研究藏药治疗慢性萎缩性胃炎(chronic atrophic gastritis,CAG)的作用机制。方法在中国知网、万方数据库、维普期刊数据库检索藏药治疗CAG的藏药复方。对符合纳入排除条件的文献进行信息提取,构建数据库统计高频词藏药,进行关联度分析,获取藏药治疗CAG的核心药物。在TCMSP数据库检索藏药活性成分及作用靶点,利用GeneCads、OMIM在线数据库获取疾病靶点,对药物作用靶点及疾病相关靶点进行韦恩分析,确定藏药治疗CAG的潜在靶点,应用String数据库、Cytoscape 3.7.1软件构建“藏药-活性成分-潜在靶点”相互作用网络与蛋白相互作用(PPI)图。使用DAVID数据库对关键靶点进行GO功能和KEGG通路富集分析。结果共纳入藏药复方78个,包含136味藏药。根据置信度与关联性选取诃子、豆蔻、荜茇、石榴子四味核心药物,筛选出槲皮素、木犀草素、玫瑰树碱、鞣花酸、胡椒碱、去氢二异丁香酚等32个药物活性成分,核心靶点为AKT1、TNF、IL6、TP53、MMP9、HIF1A、CASP3、BCL2、IL1B、PTGS2等92个,获得747条GO条目,KEGG富集条目涉及癌症相关通路、脂质与动脉粥样硬化、AGE-RAGE等共139条通路。结论通过数据挖掘筛选出豆蔻、荜茇、石榴子和诃子这四味藏药作为藏药治疗CAG的核心药物,并运用网络药理学方法分析得出这四味藏药可能通过下调AKT1、TNF、IL6和TP53等基因的表达抑制炎症因子的释放;并通过调控癌症相关通路、脂质与动脉粥样硬化、AGE-RAGE、流体剪切应力与动脉粥样硬化、IL-17等信号通路直接或间接参与机体抗炎、抗氧化、促进血管再生等过程发挥治疗CAG的作用。

融合边缘计算技术与数据挖掘的大气污染物监测研究

随着城市化进程的加快,大气污染物的监测至关重要。研究引入了边缘计算技术进行施工现场的大气污染物监测。同时采用优化的数据挖掘技术,即长短期记忆-自回归整合移动平均模型(Long Short-Term Memory-Auto Regressive Integrated Moving Average Model,LSTM-ARIMA)进行大气污染监测。结果显示,采用边缘计算监测的PM 10浓度变化曲线与实际浓度变化曲线最吻合,监测的最大浓度偏差值仅为2.54μg/m 3。LSTM-ARIMA模型在均方误差最低,最大值仅为3.57,平均值仅为2.08。说明研究所提出的大气污染物监测方案具有显著的应用效果,为环境保护部门提供更有效的决策支持。