Improvement of Wired Drill Pipe Data Quality via Data Validation and Reconciliation

Wired drill pipe（WDP）technology is one of the most promising data acquisition technologies in today s oil and gas industry.For the first time it allows sensors to be positioned along the drill string which enables collecting and transmitting valuable data not only from the bottom hole assembly（BHA）,but also along the entire length of the wellbore to the drill floor.The technology has received industry acceptance as a viable alternative to the typical logging while drilling（LWD）method.Recently more and more WDP applications can be found in the challenging drilling environments around the world,leading to many innovations to the industry.Nevertheless most of the data acquired from WDP can be noisy and in some circumstances of very poor quality.Diverse factors contribute to the poor data quality.Most common sources include mis-calibrated sensors,sensor drifting,errors during data transmission,or some abnormal conditions in the well,etc.The challenge of improving the data quality has attracted more and more focus from many researchers during the past decade.This paper has proposed a promising solution to address such challenge by making corrections of the raw WDP data and estimating unmeasurable parameters to reveal downhole behaviors.An advanced data processing method,data validation and reconciliation（DVR）has been employed,which makes use of the redundant data from multiple WDP sensors to filter/remove the noise from the measurements and ensures the coherence of all sensors and models.Moreover it has the ability to distinguish the accurate measurements from the inaccurate ones.In addition,the data with improved quality can be used for estimating some crucial parameters in the drilling process which are unmeasurable in the first place,hence provide better model calibrations for integrated well planning and realtime operations.

Quality Assessment of FY-4A Lightning Data in Inland China

The Lightning Mapping Imager(LMI)equipped on the FY-4 A(Feng Yun-4 A)geostationary satellite achieves lightning positioning through optical imaging and has the advantages of high temporal resolution,high stability,and continuous observation.In this study,FY-4 A LMI lightning event,group and flash data from April to August 2018 are selected,and their quality are assessed through qualitative and quantitative comparison with the ground-based Advanced Time of Arrival and Direction system(ADTD)lightning observation network data and the American International Space Station(ISS)lightning imaging sensor(LIS)data.The results show that the spatial distributions of FY-4 A lightning are consistent with those of the ground-based ADTD and ISS LIS.The temporal variation in FY-4 A lightning group frequency is consistent with that of ADTD stroke,which reflects that FY-4 A LMI can capture the lightning occurrence in inland China.Quantitative statistics show that the consistency rate of FY-4 A LMI and ISS LIS events is relatively high but their consistency rate is lower in terms of lightning group and flash data.Compared with the lightning observations by the ISS LIS and the ground-based ADTD,FY-4 A LMI reports fewer lightning events in the Tibetan Plateau.The application of Tibetan Plateau lightning data requires further processing and consideration.

Compliance verification and probabilistic analysis of state-wide power quality monitoring data

This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.

Modeling data quality for risk assessment of GIS

This paper presents a methodology to determine three data quality (DQ) risk characteristics: accuracy, comprehensiveness and nonmembership. The methodology provides a set of quantitative models to confirm the information quality risks for the database of the geographical information system (GIS). Four quantitative measures are introduced to examine how the quality risks of source information affect the quality of information outputs produced using the relational algebra operations Selection, Projection, and Cubic Product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The GIS is the prime source of information on the location of cables, and detection time strongly depends on whether maps indicate the presence of cables in the construction business. Poor data quality in the GIS can contribute to increased risk or higher risk avoidance costs. A case study provides a numerical example of the calculation of the trade-offs between risk and detection costs and provides an example of the calculation of the costs of data quality. We conclude that the model contributes valuable new insight.

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.

Provenance Method of Electronic Archives Based on Knowledge Graph in Big Data Environment

With the advent of the era of big data,the Provenance Method of electronic archives based on knowledge graph under the environment of big data has produced a large number of electronic archives due to the development of science and technology.How to guarantee the credential characteristics of electronic archives in the big data environment has attracted wide attention of the academic community.Provenance is an important technical means to guarantee the certification of electronic archives.In this paper,knowledge graph technology is used to provide the concept provenance of electronic archives in large data environment.It not only enriches the provenance method,but also guarantees the certification of electronic archives in the large data environment.

基于ArcGIS Data Reviewer的天地图融合数据质量检查方法

从天地图融合数据质量检查出发,依据数据标准,通过结合具体的质检规则,研究了一种基于ArcGIS Data Reviewer模块的自动化、批量化并且可使数据在处理阶段就可进行检查的天地图融合数据检验方法,这种灵活的质检机制大大减少了数据融合过程中的人工反复处理,提高了生产单位的作业效率及成果质量,也可为其他项目的质检系统开发提供借鉴。

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.

智慧供热系统数据运行核查方法研究

高质量数据是智慧供热系统数据分析的前提和分析结论可靠性的保障,计量器具的良好工作状态是获得高质量数据的基础。本文讨论了计量器具的检定、校准及运行核查的区别,研究了智慧供热系统计量器具在线运行核查方法。研究表明:流量计根据其实际安装工作条件,采用安装条件修正系数可解决直管段长度不够带来的测量误差问题;采用基准表法对使用的流量计进行技术性能核查,可正确判别单台流量计的工作状态;示踪法是解决大口径贸易结算表数据质量问题的有效方法。本研究可为及时发现工作异常的计量器具、确保测量数据的准确性及可靠性提供理论指导及技术支撑。

桥梁健康监测数据的质量评估方法研究

桥梁健康监测数据的挖掘和分析工作只有在整体数据质量符合基本要求的有效数据基础上进行,才能保障如模态参数识别、损伤识别和状态评估等后续工作的准确性。因此,基于量化改进的探索性分析方法(Exploratory Data Analysis,EDA)和相关性分析从数据完整性、准确性和一致性的角度建立了桥梁健康监测静、动态数据的质量评估方法。对某大跨度斜拉桥健康监测系统的静、动态数据进行质量评估,通过对比分析了不同评估质量的温度数据、静挠度数据和不同评估质量的主梁竖向加速度动力信号的模态参数识别的稳定图,验证了所提方法的正确性。结果表明,所提评估方法能够快速有效地判断数据质量的好坏,进而确保桥梁结构的服役性能评估和预测的准确性,有利于提高健康监测数据的可用性和效能。

基于间接核查法的流量计云在线运行核查方法

在供热物理设备网中,除不同规模的流量计群外,还设有大量与流量有关的设备及部件。本文研究了利用供热系统的机理、被核查流量计的相关参数、供热物理设备网中设置的具有流量特性的设备或部件来判断被核查流量计工作状态的方法,并在此基础上提出了流量计的云在线运行核查技术流程,以快速判断在线应用的流量计的技术性能是否满足使用要求,解决供热企业核查人员及核查仪器设备不足的缺陷,有效提升智慧供热系统数据质量和提高控制系统的安全。

人工智能囊胚形态评估数据集构建与质控专家共识

囊胚形态人工智能(AI)评估是AI医疗器械发展的新兴方向,也是AI在辅助生殖领域的重要应用。AI在新领域应用的起步阶段,数据集的构建与质控对产品质量有重要影响。目前,囊胚形态学AI评估在数据采集、标注、质控等方面尚未形成统一的规范。在参考AI医疗器械、辅助生殖医疗器械现有国家行业标准的基础上,本文以囊胚形态AI评估数据集为主题,对数据集构建与质控要求进行了探讨,对数据集质量特性进行了解析,旨在指导数据集制造责任方加强数据集全生命周期管理,更好地为产品研发、测试、临床试验等环节提供质量保障,助力产业发展。

气象敏感性疾病综合数据平台设计与实现

目的/意义设计综合数据平台,以满足气象敏感性疾病调查数据收集和共享的需要,提升我国对气象敏感性疾病的监测能力。方法/过程通过数据抽取、数据交换、数据导入、定制采集等多种方式,将疾病数据、气象数据、环境数据、诊疗数据等加工处理,纳入平台统一管理。结果/结论该平台实现了数据采集、汇交、可视化展现、数据共享功能,可为全国各基地的科研人员更好地管理和利用气象敏感性疾病调查数据提供支撑。

基于EDA统计图量化的桥梁动态监测数据质量评估

探索性数据分析统计图在桥梁健康监测动态数据质量评估中已有广泛应用。为了减少人工观察统计图的主观性,通过近似度量方法实现统计图的量化分析,得到多个指标对监测数据进行快速质量评估。在运营环境激励作用下,桥梁结构动力响应具有短时线性平稳性,近似服从正态分布。以某大跨斜拉桥振动数据为研究对象,首先,绘制样本数据直方图和Q-Q图,通过观察数据分布特征预先判断数据质量,确定优、良和差3个等级。然后,分别通过KL散度和余弦相似度2种近似度量方法对样本数据直方图和Q-Q图进行量化,得到数据服从正态分布程度的指标;通过箱线图检测样本数据全局异常点,得到正常数据占比;统计分析得到量化值和先验质量等级的对应关系,确定以直方图KL散度和余弦相似度为主、以箱线图正常数据占比为辅的数据质量评估标准。最后,取部分数据为验证集,进一步验证所提方法各个指标的合理性,并给出该方法在实际工程上的应用结果。

实施国家义务教育英语学习质量监测,促进英语教育高质量发展

2021年,教育部印发《国家义务教育质量监测方案(2021年修订版)》,首次将英语学科纳入监测范围。2022年5月,教育部基础教育质量监测中心第一次在全国范围内实施了国家义务教育英语学习质量监测。通过本轮监测,监测中心客观掌握国家义务教育英语学习质量总体水平,全面了解影响英语教育质量的主要因素,进而推动英语教学改革,提高英语学习效率。未来,英语监测将在诊断教育问题、引领教育改革方面发挥重要作用,助力国家义务教育高质量发展。本文将从英语监测启动的意义、高质量英语监测的特征以及英语监测如何推动教育质量发展等方面,系统介绍国家义务教育英语学习质量监测。

Survey of nutrients and quality assessment of crab paste by 1~H NMR spectroscopy and multivariate data analysis

Crab paste is a traditional and popular food for people in the coastal area of China.Visual inspection is currently the only method of quality assessment.We developed a new analytical method of a comprehensive survey of nutrients and quality assessment for crab paste based on a combination of 1 H NMR spectroscopy with multivariate data analysis.The aqueous extract of crab paste was dominated by amino acids,sugars,carboxylic acids,nucleotides and amines(including 19 first-time-reported compounds such as choline,uracil and guanosine).Two grades of crab paste had significant compositional differences in terms of amino acids,lactate,N-acetylglutamate,choline,dimethylamine,uridine,1-methylnicotinamide and 2-pyridienmethanol.These results provided important information on the grade-dependence of crab-paste composition,and demonstrated that NMR-MDA was effective not only for the comprehensive survey of nutrients,but also for quality assessment of crab paste.

基于组合赋权法和模糊综合评价的电力设备状态数据质量评估

随着电力网络的扩大以及工业信息化的迅速发展,在电力领域中采集和待处理的数据量呈现爆发式增长,数据的丢失、冗余、异常、冲突等问题也日益突出,影响数据的质量。数据质量评估作为保证数据质量的关键一环,发挥着重要作用。针对电力设备的状态数据提出一种数据质量评估方法,选择完整性、准确性、唯一性、一致性、及时性5个评估指标构建数据质量评估体系,设置了定量计算的评估规则;组合运用模糊层次分析法和熵权法来确定各评估维度的权重,以提高数据质量评估的科学性;再运用模糊综合评价法,依据隶属函数来确定数据质量的所属等级。最后运用上述方法,对某地的油色谱数据质量进行评估,该方法计算得到数据质量的评分为77.15,处于“中等”等级。评估结果与实际应用场景相符,表明该文提出的方法适用于电力设备状态数据质量评估。

基于点云数据的预制叠合板尺寸质量智能检测方法

为保证预制叠合板在施工现场能顺利安装,在出厂前通常需要对其进行尺寸质量检测,现有预制叠合板的尺寸质量检测方法难以全面准确地测量叠合板的实际三维尺寸。提出一种基于预制叠合板点云数据的多尺寸质量智能检测方法。对采集的点云数据进行预处理后,利用机器学习算法完成预制叠合板点云的自动定位与分割;依据不同的检测任务,将目标点云沿不同方向降维,映射为二维灰度图像;利用图像特征检测算法,分别实现叠合板底板的长宽、预留胡子筋的出筋长度与间距及桁架钢筋高度的自动检测;在验证试验中,对3块预制叠合板的点云数据进行尺寸质量检测。结果表明,提出的智能检测方法能全面准确地完成预制叠合板出厂尺寸质量检测,能进一步提高预制叠合板非接触质量检测结果的科学性与精准性。

风云三号微波温度计长序列再定标历史数据集质量评估

以METOP-A、Suomi-NPP历史资料作为参照,系统分析比较了2008—2020年4颗风云三号卫星的微波温度计(Microwave Temperature Sounder, MWTS)再定标历史资料质量。结果表明,4颗卫星的MWTS探测性能稳步上升,再定标数据集有效消除了遥感仪器在轨期间数据异常跳变、寿命期内遥感仪器辐射响应衰变、不同卫星间的辐射定标差异等因素影响,大幅提升了MWTS历史数据集的准确性和均匀性,使得再定标后的对流层和平流层通道数据与国外同类型仪器数据偏差在±0.1 K范围内。本文还重点分析比较了对流层中高层和平流层低层两个探测通道,结果表明FY-3D MWTS再定标数据和美国NOAA卫星应用研究中心STAR长序列数据集针对中高层大气的表现类似,平均亮温在时间变化和空间分布具有相似的特征,月均全球高空亮温年变化趋势差异最大为0.002 4。因此,2020年之后的FY-3D再定标数据,可以接续STAR长序列数据集,用于中高层大气的温度变化检测与分析。

工业多元时序数据质量评估方法

现有的数据质量评估(DQA)方法通常只从特定数据质量维度(DQD)的基本概念分析,忽略了能够反映数据质量(DQ)关键信息的细粒度的子维度对评估结果的影响。针对上述问题,提出一种工业多元时序数据质量评估(IMTSDQA)方法。首先,对于待评估的DQD,如完整性、规范性、一致性、唯一性和准确性等进行细粒度划分,考虑同一DQD内或不同DQD间各子维度的相关性以确定这些子维度的度量;其次,对完整性的属性完整性、记录完整性、数值完整性,规范性的类型规范性、精度规范性,一致性的顺序一致性、逻辑一致性,唯一性的属性唯一性、记录唯一性,准确性的范围准确性、数值准确性等子维度进行权重分配,进而充分挖掘DQD的深层次信息,从而获得反映DQ详情的评估结果。实验结果表明,与现有的基于框架定性分析、依据DQD基本定义构建模型的方法相比,IMTSDQA能更详细、更全面地评估DQ,且不同DQD的评估结果更能客观准确地反映DQ问题。