Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Analysis on the Applicability of Meter Data Acquisition Methods Based on the Analytic Hierarchy Process

The choice of meter data acquisition methods has important significance for the electric energy management. Based on the comprehensive analysis of several meter data acquisition methods, this paper assess the performance of each one by analytic hierarchy process. We can draw a conclusion by calculating＂ The local automatic meter reading, the prepaid electric energy metering and the remote automatic meter reading have almost the same performance. They are better than the manual meter reading and the vehicle mounted mobile automatic meter reading. So we can choose any one of the three. Among them, the prepaid electric energy metering performs best. This can be a reference for grid company＇ s decision.

An Application of a Multi-Tier Data Warehouse in Oil and Gas Drilling Information Management

Expenditure on wells constitute a significant part of the operational costs for a petroleum enterprise, where most of the cost results from drilling. This has prompted drilling departments to continuously look for ways to reduce their drilling costs and be as efficient as possible. A system called the Drilling Comprehensive Information Management and Application System (DCIMAS) is developed and presented here, with an aim at collecting, storing and making full use of the valuable well data and information relating to all drilling activities and operations. The DCIMAS comprises three main parts, including a data collection and transmission system, a data warehouse (DW) management system, and an integrated platform of core applications. With the support of the application platform, the DW management system is introduced, whereby the operation data are captured at well sites and transmitted electronically to a data warehouse via transmission equipment and ETL (extract, transformation and load) tools. With the high quality of the data guaranteed, our central task is to make the best use of the operation data and information for drilling analysis and to provide further information to guide later production stages. Applications have been developed and integrated on a uniform platform to interface directly with different layers of the multi-tier DW. Now, engineers in every department spend less time on data handling and more time on applying technology in their real work with the system.

Application of FPGA in Process Tomography Systems

This paper will provide some insights on the application of Field Programmable Gate Array (FPGA) in process tomography. The focus of this paper will be to investigate the performance of the technology with respect to various tomography systems and comparison to other similar technologies including the Application Specific Integrated Circuit (ASIC), Graphics Processing Unit (GPU) and the microcontroller. Fundamentally, the FPGA is primarily used in the Data Acquisition System (DAQ) due to its better performance and better trade-off as compared to competitor technologies. However, the drawback of using FPGA is that it is relatively more expensive.

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。

基于中子活化瞬发伽马能谱确定地层元素含量的方法

中子技术能够利用中子与物质的相互作用确定其组成等性质,广泛应用于医学、环境科学、核反应堆建设、金属矿产勘查及石油工业中。在石油天然气领域,中子探测技术主要被用于确定井孔中的孔隙度、密度和流体饱和度等参数。随着测井技术的不断发展,基于瞬发伽马射线的中子活化分析技术也已应用于井下确定地层元素含量,并成为复杂岩性和非常规油气储层获得矿物含量的唯一手段。通过归纳地层元素伽马能谱测井的方法,利用蒙特卡罗方法模拟不同地层条件下的伽马能谱,构建地层元素的标准谱库,研究奇异值分解和极大似然估计能谱数据处理方法,并利用得到的元素产额及氧闭合模型实现地层元素含量的确定。利用蒙特卡罗方法构建已知地层的伽马能谱,采用上述数据处理方法得到元素含量与地层真实元素含量误差均在4%之内,Si误差最大为2.8%,Ca最大误差为3.3%,研究结果为井下确定地层元素成分及含量提供了有效的技术支持。

Extending JASAG with data processing techniques for speeding up agricultural simulation applications:A case study with Simugan

Resource-intensive agricultural simulation applications have increased the need for gridification tools–i.e.,software to transform and scale up the applications using Grid infrastructures–.Previous research has proposed JASAG,a generic gridification tool for agricultural applications,through which the performance of a whole-farm simulation application called Simugan improved considerably.However,JASAG still lacks proper support for efficiently exploiting Grid storage resources,causing significant delays for assembling and summarizing the generated data.In this application note,two different data processing techniques in the context of JASAG are presented to tackle this problem.Simugan was again employed to validate the benefits of these techniques.Experiments using data processing techniques show that the execution time of Simugan was accelerated by a factor of up to 34.34.

面向直升机装配生产线虚拟运行的多源数据感知与融合应用技术

针对直升机装配生产线中存在的数据来源繁多、描述形式不统一、利用率低等问题,对直升机装配生产线运行过程中的多源数据感知与融合应用技术进行研究。首先,定义生产线运行中的多源数据感知与融合技术内涵及其整体框架,包括生产线多源数据采集系统构建、多源数据融合技术构建、装配过程多源数据分析预测模型构建,以及生产线多源数据分析应用模块;其次,采用标准化的生产物联、基于本体的语义描述与基于多源数据的质量映射等方法,为实际装配生产线多源数据的感知与融合提供可行的解决方案;最后,阐述上述技术在生产线虚拟运行状态分析、设备使用状态监测和装配质量提升等方面的应用措施,为直升机装配生产线的数字孪生构建与虚拟高效运行提供优质数据基础。

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

大数据分析在IT计算机应用中的创新实践

文章回顾了大数据技术的发展历程,包括其起源、演变及在IT领域的应用趋势,并详细分析了当前大数据技术面临的主要挑战。深入探讨了大数据分析在IT应用中的核心角色和作用,特别是在数据处理和系统优化方面的应用。文章着重于大数据技术的创新实践措施与策略分析,如技术创新、策略调整及与其他IT技术的融合,旨在通过对大数据技术在IT应用中的综合分析,为未来的技术发展和应用提供参考。

应用型高校在线学习过程评价系统研究

针对应用型高校的在线学习评价体系存在的评价内容单一、评价方法传统、评价效果不理想等诸多不足,文章将过程评价的理念和方法与大数据技术的特点和功能相结合,设计了一种在线学习过程评价系统。该系统旨在通过对学习者在线学习过程数据的采集、分析和评价,实现对学习者的个性化诊断和引导,提高在线学习评价的效率和效果,为应用型高校的在线教学和管理提供新的视角和参考。

高光谱遥感在自然资源调查监测中的应用

综述了高光谱遥感在自然资源调查监测中的服务与应用。首先,介绍了高光谱遥感的基本原理和技术特点,包括高光谱数据获取与处理、高光谱类型与特征识别等。随后,重点关注了高光谱遥感在不同自然资源调查方面的应用,包括植被覆盖、土地利用与土地覆盖、水资源、矿产资源等。此外,还讨论了高光谱遥感在自然资源调查中的优势与局限。

基于大数据分析的网络安全威胁检测与防范研究

本文探讨了基于大数据分析的网络安全威胁检测与防范方法,通过分析基于大数据分析的信息安全技术的原理和优缺点,阐述网络安全威胁检测与防范的关键技术,提出利用大数据分析技术提升网络安全威胁检测和防范能力的策略,从数据采集与处理、特征提取与选取、模型构建与培训、威胁探测与预防4个方面,对大数据环境下的网络安全威胁进行了研究,总结了基于大数据分析的网络安全威胁检测与防范的有效手段。本研究旨在为网络安全管理提供更加高效、智能的解决方案,应对不断演变的网络威胁。

施工过程管控多维模型与数字孪生架构研究

在建设工程施工过程引入数字孪生技术,运用动态数字模型模拟施工过程状态与行为,通过虚实映射实现对工程管理的分析预测,可解决工程管理数据采集失真、信息共享困难、数据利用无效等问题。文章从施工项目管控特点出发建立施工过程管控多维度模型,细化信息管控过程建立施工过程数字孪生架构,并依托实际项目需求构建施工现场管控数字孪生系统探索应用场景。

大数据时代下计算机软件技术的应用研究

随着互联网技术的应用和普及,人们的生产和生活方式发生很大了改变,相关业务场景也越来越数据化,数据化的场景会形成海量的数据信息,对这些庞杂的信息进行挖掘可以分析到有价值的信息,为人们的生产和学习提供指导和决策。在当前的大数据时代,数据的收集、存储以及价值挖掘离不开计算机软件技术。文章在概述大数据技术与计算机软件技术的基础上,对大数据时代计算机软件技术的应用进行总结和研究,以供参考。

计算机科学技术对物联网发展的推动作用分析

计算机科学技术在物联网技术发展过程中发挥着关键的推动作用。然而,随着物联网规模的不断扩大,其面临着性能提升、应用拓展和安全保障等方面的挑战。本文从物联网核心技术出发,分析嵌入式技术、传感器技术和RFID技术在物联网中的应用。论述计算机科学技术对物联网发展的重要性,并提出提升系统性能、拓展应用领域和加强安全保障的策略,旨在通过实施这些策略实现物联网系统的更高性能、更广泛应用和更可靠安全保障,推动物联网技术持续健康发展。

遥感技术在矿山地质测绘数据处理中的应用

遥感技术的广泛应用,使得整体矿山地质测绘数据处理逐步向智能化的方向转变,利用这部分科技含量较高的技术手段,可以进一步消除以往测绘数据处理阶段中存在的局限性,为矿山开采行业的稳定发展奠定坚实基础。因此,文章首先对矿山地质测绘中遥感技术的基本概述加以明确;其次,对遥感技术与矿山地质测绘数据处理中的要点与优势展开深入分析;在此基础上,提出遥感技术在矿山地质测绘数据处理中的具体应用措施。

基于过程管理的输变电工程造价管控系统设计

以实现输变电工程造价全过程管控为目的,设计基于过程管理的输变电工程造价管控系统。将输变电工程相关数据导入到数据存储器中,数据由Web应用服务传输到过程管控模块中,利用工程造价模块对输变电工程实施分阶段结算计划制定、工程量分析与对比,采用工程造价估算优化模型实现全过程输变电工程造价管控。结果表明:该系统可靠性高,可移植性好,可有效呈现输变电工程项目结构和造价,具备较好输变电工程造价管控效果。

计量科学管理及其在软件应用中的专业性

计量科学是一门重要的工程学科,涵盖了测量、标准化、校准以及质量控制等方面的知识和技术。在今天的工业和科研领域,计量科学管理变得至关重要,因为它有助于确保产品和数据的准确性、可追溯性和可重复性。本论文旨在深入探讨计量科学管理的基本原则,并介绍了在这一领域中广泛使用的软件工具,以帮助计量工程师更好地管理测量和质量控制过程。