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Apriori算法的氢安全事故统计分析

为探究氢安全事故不同特征因素间的相关性,以827起涉及氢气系统的安全事故为数据集,统计分析事故时间、应用阶段、点火源、事故致因及事故后果等特征因素。首先,构建事故致因的关联规则网络,讨论事故致因相关性;然后,交互分析事故致因和事故后果,明确事故因果关系;最后,基于Apriori算法挖掘氢安全事故的6类特征因素的关联规则。结果表明:氢安全事故中设备失效与设计错误、操作错误与意外反应等6个事故致因组合相关性最高;未点燃的氢气释放与压力释放装置、通风系统等8个因素强关联,氢火灾与螺栓、自燃等9个因素强关联,氢爆炸与制氢、电火花、压缩机等5个因素强关联。

基于Apriori的视觉传感网络关联数据高效识别方法

在视觉传感网络关联数据识别中,由于数据变化的规律难以捕捉,造成识别结果精确度降低,为此,提出了基于Apriori的视觉传感网络关联数据高效识别方法。通过设计视觉传感网络联合监控系统监控视觉传感网络数据变化情况,获取视觉传感网络关联数据。该系统由处理器单元、数据传输单元等四个模块构成。提取图像中的感兴趣区域完成ROI提取,并通过腐蚀处理等方式对视觉传感网络关联数据实施形态学处理。基于Apriori算法实施视觉传感网络关联数据之间关联规则的挖掘,实现视觉传感网络关联数据的识别。布设实验环境后,使用三个数据集测试该方法的识别性能。测试结果表明,所提方法的微平均查全率均高于94.86%,微平均查准率高于93.02%,最高召回率为98.52%,三个项目的测试值均较高,证明了该方法对视觉传感网络关联数据识别比较准确。

基于Apriori算法的生鲜蔬菜购买的关联分析

为满足当下生鲜超市日常补货和定价需求,基于某商超经销的6个蔬菜品类从2020年7月1日至2023年6月30日的销售流水明细与批发价格的相关数据,采用Apriori算法和K-means++算法研究各生鲜蔬菜的销售时间分布规律及消费者购买各生鲜蔬菜的相关关系,进而从蔬菜种类销售购买关联度及相关性角度为生鲜超市的策略制定提供新的视角。

Apriori算法在重庆垫江南部土壤养分元素组合研究中的应用

研究基于重庆垫江南部地区3250份土壤表层样品的54类地球化学数据,应用Python编程语言进行Apriori关联规则算法研究,尝试通过大数据分析的方法提取出隐藏在数据集中的土壤养分元素与其他各元素之间的组合规律,为垫江地区的土地利用规划、生态环境保护、现代农业发展以及土地污染防治等工作的开展提供科学依据。通过Apriori方法进行的重庆垫江南部土壤元素数据分析最终反映出各元素之间共存在八种关联关系。Fe、Zn、Co、V、Mg、Cu六类土壤养分元素的关联性极强,会出现共同富集的现象;土壤中Mo、Ca元素的缺乏与Li、I、U、P元素的匮乏存在一定关联;其他土壤养分元素则相对独立,无法建立与其他元素之间的关联规则。相较于传统地球化学分析方法,关联规则算法具有快捷、准确、完全基于数据深入挖掘等特点,对于未来土壤养分元素多寡的指示以及富集等级的划分具有重要的学术与应用价值。

基于Apriori的离心式压缩机故障影响因素分析

离心式压缩机随着在役数量和服役年数的增加,故障率也逐渐上升,利用数据挖掘等方法可以建立压缩机故障模型,有效降低压缩机的故障率。文章利用5M1E分析法对某管线离心式压缩机的故障记录进行分析处理,再通过Apriori算法筛选压缩机故障的影响因素,定量分析出各影响因素间的关联规则,发现了压缩机元件老化程度和压缩机元件性能下降程度这两条强关联规则,为后续压缩机管理提供科学参考。

基于Apriori算法的接转站集输系统能耗分析与优化

某油田接转站集输系统的能量供应形式为加热炉+集输泵,加热炉热效率和泵机组效率相对较低,站场能耗较高。针对这一问题,在对数据修复、离散化的基础上,采用K-means和Apriori算法完成了推导加热炉热效率和泵机组效率的多条关联规则。结果表明,加热炉的能耗水平与过剩空气系数、排烟温度、炉膛内压等参数相关;集输泵的能耗水平与功率因数、节流损失率、工作电流、进口压力等参数相关,依据强关联规则的结果优化站内集输系统,全年累积可节约燃气36×10^(4) m^(3),节约电量8×10^(4) kWh,节约运行费用59.6万元,节气、节电效果显著。研究结果可为减少集输系统能耗提供实际参考。

基于改进遗传算法和Apriori算法的气旋强度预测方法

为了提高气旋强度预测的准确度,在遗传算法和多支持度阈值的Apriori算法基础上提出了气旋预测算法GA-MSApriori。利用滑动窗口对历史气旋监测数据进行处理,经过处理后的数据能够描述各监测要素值所处范围以及要素值在过去时间内的变化情况,并以此作为输入数据,最终输出预测准确度较高的气旋强度预测规则。相较于传统的预测方法,气旋强度的预测准确度提高了6%~10%。

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。