Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

A Forensic Investigation of Terrorism in Nigeria: An Apriori Algorithm Approach

Investigations towards studying terrorist activities have recently attracted a great amount of research interest. In this paper, we investigate the use of the Apriori algorithm on the Global Terrorism Database (GTD) for forensic investigation purposes. Recently, the Apriori algorithm, which could be considered a forensic tool, has been used to study terrorist activities and patterns across the world. As such, our motivation is to utilise the Apriori algorithm approach on the GTD to study terrorist activities and the areas/states in Nigeria with high frequencies of terrorist activities. We observe that the most preferred method of terrorist attacks in Nigeria is through armed assault. Again, our experiment shows that attacks in Nigeria are mostly successful. Also, we observe from our investigations that most terrorists in Nigeria are not suicidal. The main application of this work can be used by forensic experts to assist law enforcement agencies in decision making when handling terrorist attacks in Nigeria.

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

An Improved Apriori Algorithm

In this paper,We study the Apriori and FP-growth algorithm in mining association rules and give a method for computing all the frequent item-sets in a database.Its basic idea is giving a concept based on the boolean vector business product,which be computed between all the businesses,then we can get all the two frequent item-sets(minsup=2).We basis their inclusive relation to construct a set-tree of item-sets in database transaction,and then traverse path in it and get all the frequent item-sets.Therefore,we can get minimal frequent item sets between transactions and items in the database without scanning the database and iteratively computing in Apriori algorithm.

Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

基于Apriori算法的煤矿安全事故分析

为分析煤矿事故报告中的危险致因因素,统计分析了2018—2022年全国煤矿事故报告数据,采用Apriori关联规则算法,并利用Gephi进行关联规则可视化,探究各个致因之间的复杂关系。首先对数据进行预处理,计算词频-逆向文件频率(Term Frequency-Inverse Document Frequency, TF-IDF),提取了78个煤矿事故致因因素,其中人因层包括31个因素,设备层包括9个因素,管理层包括31个因素,环境层包括7个因素;然后,经过关联规则挖掘算法,得到了585条关联规则,绘制了其支持度、置信度和提升度的散点图;最后,根据Gephi生成的事故致因复杂网络图,分别分析了高支持度、高置信度和高提升度关联规则致因因素。结果表明:基于Apriori算法的煤矿事故致因分析,得到了人因层、管理层、设备层和环境层4个方面的关键致因因素;对煤矿关键致因因素进行直观、多视图的展现,有助于提高煤矿安全管理水平。

谱聚类和Apriori算法在建筑坍塌事故致因组合分析中的应用

建筑坍塌事故是人员伤亡和经济损失较大的事故类型之一。为探究建筑坍塌事故不同致因之间的关联和相互依存关系,首先,选取国内2015—2020年231份建筑坍塌事故报告作为研究对象,借助R语言平台进行文本挖掘,得到43个致因。其次,运用Python进行谱聚类,根据致因之间的关联强度对其进行聚类。最后,利用关联规则挖掘Apriori算法确定建筑坍塌事故致因之间的关键关联组合。结果表明,43个事故致因可分为5类,在每一个簇类中确定了最关键的致因组合,并提出了针对性的预防措施,为坍塌事故的预防和控制提供一种新的思路。

基于Apriori多维传感参量沙棘质量监测系统设计

沙棘具有很高的营养价值,常被应用于植物原料提取行业。但由于沙棘品质监测技术的缺失,导致基于沙棘制造的副产品质量难以保证。目前沙棘质量检测主要采用单一的沙棘化学成份含量检测,难以满足对沙棘品质监测的技术需求。从沙棘生长特征出发,利用无线传感网技术和多传感技术对沙棘生长环境的多维度参数进行采集传输,基于改进Apriori(先验原理)对多维传感参量特征关联规则进行数据挖掘,分析标签值,选择出较高的沙棘副产品对应的沙棘生长环境特征数据,挖掘数据特征之间存在的映射关系,作为沙棘溯源质量监测评价的参考规范。基于Spark Streaming实时数据处理技术,构建沙棘质量监测系统。通过映射规则构造条形图直观展示,便于指导沙棘原料的筛选。从沙棘副产品质量监管出发,利用优质沙棘副产品溯源环境数据构建沙棘原料质量监测体系,提高以沙棘为原料的副产品品质监管。

基于SIF模型与Apriori算法的煤矿顶板事故致因关联分析

为更科学地预防煤矿顶板事故的发生,对煤矿顶板事故致因及其关联规则进行识别十分关键。首先,通过文本挖掘并结合SIF事故致因模型,确定56个影响顶板事故发生的致因;其次,通过构建顶板事故数据库并运用Apriori算法进行顶板事故致因关联规则挖掘;最后,绘制顶板事故致因关联规则复杂网络图,并综合分析顶板事故的核心致因及各致因间的关联规则。结果表明:安全培训教育和安全监督管理、作业人员安全意识淡薄和违反作业规程、当班管理人员在现场的管理不到位和其他事故致因之间有着很高的关联度以及提升度,这些因素是造成煤矿顶板事故发生的核心因素。

一种Apriori算法在监狱警察心理健康调查中的应用

为从复杂数据中挖掘监狱警察心理健康状况,提出一种改进的Apriori算法进行监狱警察心理健康监测和评估,并根据狱警心理表现症状进行疏导。针对传统Apriori算法计算量大、遍历数据长的问题,对数据库集心理健康调查结果进行矩阵化处理和和运算,采用自连接操作方式删除非频繁项集,降低数据计算量。将改进算法应用于监狱警察心理健康调查,应用结果表明:监狱警察普遍存在“内向、抑郁、焦虑、坚强”的心理特征,后续心理辅导工作中,需加强户外锻炼,多接触外界事物,培养活泼开朗、积极乐观的情绪。

基于关联规则数据挖掘的Apriori算法应用分析

基于关联规则的数据挖掘主要用于发现数据集中项目之间的联系。以Apriori算法为核心,利用店铺在线订单作为数据载体,深入挖掘顾客购买商品的历史记录,探究商品之间的关联性。通过分析这些关联规则,商家可以更精准地了解顾客的购物喜好和行为模式,从而制定出更具针对性和实效性的营销策略,有效提升商品的销售业绩。

基于改进Apriori算法的高校教育满意度关联规则挖掘

针对经典关联规则Apriori算法在大数据集情境下易产生冗余和误导性的关联规则,以及难以确认关键性关联规则等问题,提出支持度—置信度—权重检验系数框架与后项约束的改进Apriori算法。首先,定义相关性系数、提升系数、错误系数并进行证明分析,进而构建权重检验系数;其次,运用主成分分析法,提取指标中的高权重影响因素作为后项,通过后项约束过滤冗余关联信息,从而筛选出更为准确的关键性关联规则。将改进的Apriori算法应用于高校教育满意度调查数据的关联规则挖掘并进行分析对比,实验结果验证了该算法的合理性和有效性。

基于apriori算法对盆栽春小麦生理指标及产量的分析

【目的】研究土壤、残膜、灌溉制度等因素对新疆北疆春小麦的生长发育及产量的影响。【方法】试验采用盆栽试验,运用正交试验设计,选取土壤质地、灌溉方式、灌水定额、灌水次数、土壤地膜残留量5因素,各因素设置4水平,研究不同试验因素组合下春小麦株高、叶绿素含量、有效穗数、穗粒数、产量、水分利用效率和土壤含水率各指标的差异及变化;利用apriori算法分析小麦各生理指标间的关联性。【结果】全生育期内不同处理下小麦株高变化趋势基本一致,对小麦株高影响较大的因素为灌溉制度;小麦全生育期内叶绿素值整体上呈现单峰变化趋势,适当提高土壤肥力可以增产;土壤含水率与土壤质地和灌水量有明显的相关性。在灌浆期适度增加灌水能够提高小麦叶片光合速率,达到增产效果;灌水次数以及灌溉方式对小麦产量及产量构成因素有显著性影响,渗灌的增产效果表现突出。最高耗水量和最大穗粒数,最大有效穗数,最大株高具有强相关性;最高耗水量与最低干物质重、最低SPAD值有强关联性;最高株高与最低SPAD值呈强相关性。【结论】土壤质地为粘壤土,灌水方式为渗灌,灌水700 kg/hm^(2),地膜残留量为0时春小麦产量最高,能够达到71.56 g/盆;灌水定额700 kg/hm^(2)与最高耗水量和最大株高、最低SPAD值、最大有效穗数、最大穗粒数具有强相关性。过高的灌水量和耗水量会导致株高过高,SPAD值小。

基于Apriori算法的盘江矿区煤与瓦斯突出特征研究

为揭示盘江矿区煤与瓦斯突出灾害发生规律,提高瓦斯防治工作的针对性,根据Apriori算法规则的原理和方法,对盘江矿区37起煤与瓦斯突出事故点的瓦斯地质参数进行了统计分析,以最小支持度为40%、最小置信度为75%的二项关联规则基准开展了数据挖掘试验。结果表明:煤层瓦斯压力1~2 MPa与埋深200~400 m、煤厚2~4 m与煤的瓦斯放散初速度≥15mmHg、煤厚2~4 m与煤厚变异系数≤0.2、泥岩厚度≤5 m与围岩强度系数≥2,是具有强关联特征的参数组合类型,盘江矿区发生煤与瓦斯突出地点都呈现浅埋深、高瓦斯压力、厚煤层和围岩强度大的典型特征;在特殊的山地浅埋深条件下(200~400 m),煤层原始地应力往往不大,高瓦斯压力是煤与瓦斯突出的主要影响因素,煤层瓦斯压力普遍达到1~2 MPa;煤厚增大、瓦斯放散初速度高反映出煤体结构受到过强烈破坏,局部发育具有煤与瓦斯突出倾向的构造煤,煤厚变异系数小预示煤层遭受的破坏形式不以煤层局部变异为特征,可能存在以分层构造煤特征的煤体结构破坏的形式;在煤层的顶底板砂岩等强岩层普遍较厚的情况下,煤层高强度砂岩顶板在开采扰动下产生的动载荷条件,叠加形成了煤与瓦斯突出发生强应力场条件;高瓦斯压力、构造煤与顶板围岩特性是影响盘江矿区煤与瓦斯突出的主要因素,在煤矿开采过程中,应以瓦斯压力作为主导参数指标,并重视结合构造煤、顶板岩性的特征来开展煤层突出预测与治理工作。

基于改进Apriori算法的高速公路交通事故关联分析

针对现有交通数据中事故影响因素间的关联特性,为高速公路运营及管理部门提供精准化、细粒度的决策支持信息,该文考虑驾驶人、环境、道路和车辆4个维度,建立带约束的改进Apriori算法,挖掘影响高速公路交通事故的关联规则。在传统Apriori算法的基础上,规则前项和后项约束的增加,可以提高关联规则的准确性和挖掘效率。结合3 178条高速公路交通事故数据的分析结果表明:改进Apriori算法通过精准挖掘潜在因素和事故等级间的关联程度,降低无效关联规则数量,关联规则准确性、挖掘效率均大大提升。驾驶人性别、年龄、照明强度、车辆类型均与高速公路事故严重程度有强关联性;路面湿滑会使交通事故升级为一般事故;黑夜下的照明状况,是使轻微事故升级为一般和严重事故的主要因素。

基于改进Apriori算法的学生网上学习行为关联特征挖掘模型

学生在线学习行为与成绩之间存在密切关联,为了挖掘学生学习行为特征间的隐含关系,研究采用改进的关联规则挖掘算法对其进行相关性分析。实验结果表明,研究设计的算法在精确率及召回率指标上表现较好,相比传统的关联规则挖掘算法运行速度更快,用时降低幅度达26.49%。该方法从多维度实现了学习行为的分析与关联,为教学决策和研究提供了科学的支持和指导。

基于改进Apriori关联规则算法的信令分析

传统信令分析方法需要专业人员找出可能与失败码相关的聚集的信令字段值或值组合及其导致失败的概率,并定位网络问题,操作复杂且效率低。通过改进Apriori关联规则算法,将探寻聚集的字段值或其组合的过程转换成发现失败码和相关信令字段值的关联规则。在计算频繁项集时,通过设置最小支持度阈值找出包含失败码的频繁项,将待分析失败码作为后项,减少了算法的复杂度和算力要求,并通过置信度和提升度找出与后项强关联的属性,实现了对失败码集中属性的快速高效识别。

基于优化Apriori算法的印刷检测数据关联分析

为了解决印刷企业的印刷缺陷检测问题,采用优化Apriori关联分析挖掘方法,从而找出印刷缺陷检测数据之间的关联。首先,搭建印刷工厂管理与印刷数据采集系统;其次,优化Apriori算法的运行效率;最后,进行关联分析得出生产数据之间的关联规则。实验表明,该方法在印刷缺陷检测数据的分析中具有可行性,对降低印刷缺陷率有着重要的指导作用。