Establishment of Person and Enterprise Process Model in Product Data Management System

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person User Role Group” net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

PERFORMANCE EVALUATION METHOD FOR BUSINESS PROCESS OF MACHINERY MANUFACTURER BASED ON DEA/AHP HYBRID MODEL

A set of indices for performance evaluation for business processes with multiple inputs and multiple outputs is proposed, which are found in machinery manufacturers. Based on the traditional methods of data envelopment analysis （DEA） and analytical hierarchical process （AHP）, a hybrid model called DEA/AHP model is proposed to deal with the evaluation of business process performance. With the proposed method, the DEA is firstly used to develop a pairwise comparison matrix, and then the AHP is applied to evaluate the performance of business process using the pairwise comparison matrix. The significant advantage of this hybrid model is the use of objective data instead of subjective human judgment for performance evaluation. In the case study, a project of business process reengineering （BPR） with a hydraulic machinery manufacturer is used to demonstrate the effectiveness of the DEA/AHP model.

Fault monitoring based on mutual information feature engineering modeling in chemical process

A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

Application of Model-Based Data Transmission Techniques to Gravitational Model Data

The transmission of scientific data over long distances is required to enable interplanetary science expeditions. Current approaches include transmitting all collected data or transmitting low resolution data to enable ground controller review and selection of data for transmission. Model-based data transmission (MBDT) seeks to increase the amount of knowledge conveyed per unit of data transmitted by comparing high-resolution data collected in situ to a pre-existing (or potentially co-transmitted) model. This paper describes the application of MBDT to gravitational data and characterizes its utility and performance. This is performed by applying the MBDT technique to a selection of gravitational data previously collected for the Earth and comparing the transmission requirements to the level required for raw data transmis-sion and non-application-aware compression. Levels of transmission reduction up to 31.8% (without the use maximum-error-thresholding) and up to 97.17% (with the use of maximum-error-thresholding) resulted. These levels significantly exceed what is possible with non-application-aware compression.

Sedimentary Microfacies and Porosity Modeling of Deep-Water Sandy Debris Flows by Combining Sedimentary Patterns with Seismic Data： An Example from Unit I of Gas Field A, South China Sea

Sandy debris flow deposits are present in Unit I during Miocene of Gas Field A in the Baiyun Depression of the South China Sea. The paucity of well data and the great variability of the sedimentary microfacies make it difficult to identify and predict the distribution patterns of the main gas reservoir, and have seriously hindered further exploration and development of the gas field. Therefore, making full use of the available seismic data is extremely important for predicting the spatial distribution of sedimentary microfacies when constructing three-dimensional reservoir models. A suitable reservoir modeling strategy or workflow controlled by sedimentary microfacies and seismic data has been developed. Five types of seismic attributes were selected to correlate with the sand percentage, and the root mean square （RMS） amplitude performed the best. The relation between the RMS amplitude and the sand percentage was used to construct a reservoir sand distribution map. Three types of main sedimentary microfacies were identified： debris channels, fan lobes, and natural levees. Using constraints from the sedimentary microfacies boundaries, a sedimentary microfacies model was constructed using the sequential indicator and assigned value simulation methods. Finally, reservoir models of physical properties for sandy debris flow deposits controlled by sedimentary microfacies and seismic inversion data were established. Property cutoff values were adopted because the sedimentary microfacies and the reservoir properties from well-logging interpretation are intrinsically different. Selection of appropriate reservoir property cutoffs is a key step in reservoir modeling when using simulation methods based on sedimentary microfacies control. When the abnormal data are truncated and the reservoir properties probability distribution fits a normal distribution, microfacies-controlled reservoir property models are more reliable than those obtained from the sequence Gauss simulation method. The cutoffs for effective porosity of the debris channel, fan lobe, and natural levee facies were 0.2, 0.09, and 0.12, respectively; the corresponding average effective porosities were 0.24, 0.13, and 0.15. The proposed modeling method makes full use of seismic attributes and seismic inversion data, and also makes the property data of single-well depositional microfacies more conformable to a normal distribution with geological significance. Thus, the method allows use of more reliable input data when we construct a model of a sandy debris flow.

基于Transformer的司法文书命名实体识别方法

命名实体识别是自然语言处理领域的关键任务之一,是实现下游任务的基础。目前针对司法领域的相关研究相对较少,司法系统的信息化和智能化转型仍有许多问题亟需解决。相比其他领域的文本,司法文书存在专业性强、语料资源少等局限,导致现有的司法文书识别结果较低。因此,从以下3方面开展研究:首先,提出了一种多标签层级迭代的文本标注方式,可以对原始司法文书文本进行自动化标注,同时有效地提升司法文书命名实体识别任务的实体识别效果;其次,提出了一种交融式的Transformer神经网络模型,对汉字固有属性的深层特征进行了充分利用,用于对司法文书进行命名实体识别;最后,对所提出的标注方法和模型与其他神经网络模型进行了对比实验。所提出的文本标注方式可以较为准确地实现司法文书的标注任务;同时,所提出的模型在通用数据集中相对于对照模型有较大的提高,并在司法领域数据集中取得了良好的效果。

基于赛教融合的智能交通课程实践教学研究

结合全国大学生智能汽车竞赛百度智慧交通创意组竞赛项目,设计智能交通课程实践教学内容,从竞赛内容凝练与课程知识点结合的教学案例和实验项目,实现赛教资源融合和赛教一体化实践平台共享。设计的实验教学项目融合了图像处理、数据分析、人工智能、智能车硬件平台等内容,包括数据采集、数据分析、数据集构建,深度学习模型建立与智能车平台部署等实践环节。学生从编写简单程序开始,不断提高,到构建复杂的深度学习模型实现智能车自动巡航控制实验。将竞赛任务具有挑战性和对抗性的特点融入课程教学,吸引学生兴趣,增强学生求知欲,提升学生创新能力和工程实践能力。

基于特征匹配的逆向工程模型重建方法

逆向工程(RE)也称反求工程,以实物、样件、软件(包括图样、程序、技术文件等)或影像(图片、照片等)为对象,应用产品设计方法学、系统工程学、计算机辅助技术方法进行系统分析和研究,探索掌握其关键技术,进而开发出同类或更先进产品。从数据采集、数据处理和模型构建3个阶段给出了RE中的模型重建过程。首先基于特征的点云匹配方法进行数据采集,获取物体表面的点云数据,然后采取降噪、平滑等方式进行预处理,最后通过曲面拟合、曲面特征提取和数据拼接完成实物到模型的建模。同时给出了各阶段的通用方法和实际应用的注意事项。RE可快速准确地确定实物的结构特点、内部构造等信息,并为后续设计提供参考。

面向云边端协同的多模态数据建模技术及其应用

云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据.

基于OPC UA的纤维缠绕机信息模型开发和应用

为实现对复合材料纤维缠绕机生产过程的监测,以缠绕机及其辅助设备为研究对象,针对实际加工设备的多样性造成的加工数据采集的异构性,基于OPC UA建立缠绕机的信息模型架构。根据OPC基金会发布的OPC UA规范和相关的行业标准,建立纤维缠绕机系统的信息模型和实例化。基于开源项目open62541,导入编译后的缠绕机的信息模型代码,自定义开发出符合使用规范的OPC UA服务器,然后利用UA客户端实现对服务器的连接,客户端能够通过服务器查询系统的加工数据,解决加工数据的异构性问题以达到监测缠绕机生产加工过程的目的;最后验证了建立的纤维缠绕机信息模型能够正确导入到开源项目open62541中,实现了加工数据监测。

发射台站发射任务智能化分配数据处理模型的研究与构建

本文基于发射台站智能化系统建设中任务分配问题的研究,提出了发射任务分配调度算法对数据处理的具体需求,设计了任务分配数据处理模型,包括发射任务模型、任务评估矩阵、发射分配模型和效果目标函数,并对数据处理模型的参数和计算关系进行了公式化描述。通过具体的任务分配操作示例,展示了数据处理模型在实际应用中的操作流程和效果,验证了模型的实用性和有效性。

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

基于YOLO模型的车流量实时采集系统研究

对于一座现代化城市来说,合理的交通规划是一个城市高效运行的关键,作为交通规划的关键信息的城市车流量信息,原本需要人工进行识别、获取、验证的提取方式,随着计算机视觉技术的蓬勃发展弊端尽显,终将退出历史的舞台。为了提高城市车流量信息的准确性和及时性,利用现有的计算机技术设计一种基于YOLO模型的车流量实时采集系统。该系统基于YOLO视觉检测模型,采用DeepSORT算法对检测到的目标车辆进行跟踪识别、判断车辆的运行状态、实现当前路段的车流量统计、对已记录车流量信息进行可视化展示以及数据输出等。该系统可以有效地代替传统消耗人力的死板工作,实现自动化数据收集以及道路交通情况的快速监测。该系统操作简单,交互性强,为城市的交通管理和交通规划提供准确实时的信息数据。

OSS Effort Expense Optimization Based on Wiener Process Model and GA

Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.

基于特征标记的油田无人值守站运行异常辨识研究

油田无人值守站的运行异常受多种因素综合影响,产生的数据可能存在噪声或错误,导致油田无人值守站运行异常辨识难度上升,所以研究一种新的基于特征标记的油田无人值守站运行异常辨识方法。按照既定时长采集无人值守站运行数据,对采集到的数据进行分析和降噪处理。以数据处理结果为基础,结合均值、方差和均方根值提取异常数据特征,并对异常状态特征进行标记处理,结合油田无人值守站运行异常辨识模型得到相关的辨识结果。实验结果表明,该设计的基于特征标记的油田无人值守站运行异常辨识方法的辨识准确率为93.77%,辨识效果好,可以在相关领域实现广泛应用。

多源数据融合的高层建筑物三维建模研究

随着无人机的大规模普及,倾斜摄影测量技术应用越来越广泛,但是由于其技术特点,在数据采集过程中受角度、遮蔽等因素的影响,导致三维模型极易出现拉花、扭曲等问题。基于此,探讨了融合机载激光雷达(Light Laser Detection and Ranging,LiDAR)、地面数据采集和倾斜摄影技术的方法,以提高高层建筑物三维建模的精度和细节。机载LiDAR技术通过激光雷达扫描获取点云数据,提供建筑物的几何形状和细节信息,倾斜摄影技术可以提供建筑物纹理信息,与机载LiDAR技术结合可以提高建模的真实感和精度。点面三维激光扫描仪能够有效补充前两项技术的缺失,如补充细节数据。研究融合三种数据源的信息,综合利用它们的优势,为城市规划、建设和管理提供更准确、可靠的数据支持。

大数据视域下区块链技术在数据溯源中的应用探究

随着大数据时代的到来,数据的采集、存储和处理变得更加便捷和高效,区块链技术作为一种去中心化的分布式账本技术,具有不可篡改、可追溯和去中心化的特点,为数据溯源提供了新的解决方案。文章介绍了大数据和区块链技术的基本概念,阐述了大数据视域下区块链技术在数据溯源中的应用流程,包括数据采集、存储、查询、统计和溯源,构建了大数据视域下区块链技术在数据溯源中的应用模型,研究其在不同领域的具体应用,以供参考。

超大规模数据处理中并行计算技术的应用研究

随着人工智能和大数据时代的到来,超大规模数据处理成了一个重要的研究领域。该文主要探讨并行计算技术在超大规模数据处理中的应用,首先详细阐述并行计算和超大规模数据处理的基本理论与概念,特别是并行计算的编程模型与工具,最后通过分析并行计算在搜索引擎、气象预报和金融分析等中的实际案例,阐述并行计算技术在超大规模数据处理中的实际应用。

Multimode Process Monitoring Based on the Density-Based Support Vector Data Description

Complex industry processes often need multiple operation modes to meet the change of production conditions. In the same mode,there are discrete samples belonging to this mode. Therefore,it is important to consider the samples which are sparse in the mode.To solve this issue,a new approach called density-based support vector data description( DBSVDD) is proposed. In this article,an algorithm using Gaussian mixture model( GMM) with the DBSVDD technique is proposed for process monitoring. The GMM method is used to obtain the center of each mode and determine the number of the modes. Considering the complexity of the data distribution and discrete samples in monitoring process,the DBSVDD is utilized for process monitoring. Finally,the validity and effectiveness of the DBSVDD method are illustrated through the Tennessee Eastman( TE) process.

数字孪生在轨道交通电力牵引系统应用的探讨与展望

随着我国轨道交通电力牵引系统的快速发展与技术成熟,其可靠性评估与智能化监控技术逐渐引起关注,数字孪生是基于数据与机器学习对实际物理系统的虚拟刻画技术,可以实现对实际物理系统的行为特征模拟与参数监测,因此将数字孪生技术引入轨道交通电力牵引系统领域,可为其数字化、智能化监控与运维提供发展思路与技术手段。文章首先对数字孪生技术目前在电力牵引系统中的应用现状进行了综述,进而列举了电力牵引系统数字孪生构建过程中所需的关键技术及其发展情况——在电力牵引系统领域,数字孪生及其相关技术仍在理论研究阶段,只有针对部分子系统的探索性研究,尚未形成完善的系统建模与智能监控体系;最后,文章展望了数字孪生技术在电力牵引系统领域的工程化实现前景,探讨了其投入工程应用可能会面临的内部技术问题与外部客观挑战,旨在为后续技术研究与实践提供参考。