控制爆破法处理卡钻事故

介绍了在包头市某工程实施管道穿越黄河施工中,采用爆破法处理卡钻的经验。针对深水环境条件及钻杆内径小不宜采用集团装药的条件,确定采用"小直径爆破筒,钻杆内部装药"的爆破方案,阐述了爆破设计及施工注意事项。可供类似工程参考。     

智能数据分区与布局研究

大数据时代,数据规模庞大,由数据进行驱动的应用分析场景日益增多.如何快速、高效地从这些海量数据中提取出用以分析决策的信息,给数据库系统带来重大挑战.同时,现代商业分析决策对分析数据的实时性要求数据库系统能够同时快速处理ACID事务和复杂的分析查询.然而,传统的数据分区粒度太粗,且不能适应动态变化的复杂分析负载;传统的数据布局单一,不能应对现代大量增加的混合事务分析应用场景.为了解决以上问题,“智能数据分区与布局”成为当前的研究热点之一,它通过数据挖掘、机器学习等技术抽取工作负载的有效特征,设计最佳的分区策略来避免扫描大量不相关的数据,指导布局结构设计以适应不同类型的工作负载.首先介绍了智能数据分区与布局的相关背景知识,然后对智能数据分区与布局技术的研究动机、发展趋势、关键技术进行详细的阐述.最后,对智能数据分区与布局技术的研究前景做出总结与展望.     

基于大数据的智慧林业系统设计

With the development of 5G, big data, cloud computing, IOT, mobile Internet and other information technologies of new generation, the design of forestry information system establishes a new mode of forestry development integrating three-dimensional perception, collaborative management and internal and external services by means of perception, IOT and intelligence. Intelligent forestry system is a comprehensive information management system integrating forest management, intelligent forestry business management, green maintenance management and emergency command and dispatching management. Based on the specific project practice, this paper puts forward the overall framework design scheme and management platform design scheme of intelligent forest- ry.     

“电网一张图”时空信息管理系统

文章首先总结了电力企业信息管理系统的现状,提出了多源信息管理系统融合集成的邦联、子集和完全融合3种方式,分析了3种方式的优缺点,以建设企业"互联网搜索"型数字引擎为目标,提出了融合基于本体的图数据库建模,多维关联与路径因果分析,和人机共生互动可视化决策于一体的企业信息管理系统技术发展趋势;在系统分析目前正在快速发展的图数据库技术与传统关系数据库之间的本质区别基础上,总结原创的电力图计算平台技术的特点;在此基础上讨论"电网一张图"在建设能源互联网时空信息管理平台数据关联索引图中的核心作用,提出了支持能源互联网建设的"电网一张图"时空信息管理系统的技术架构与特点,最后总结"电网一张图"时空信息管理系统在提高电网安全运行水平、提升客户服务水平、增强清洁能源消纳水平、改善经营绩效水平、以及为综合能源服务和建设能源生态等提供数据服务方面的广阔应用场景。     

Study and comparison of the unique selling propositions (USPs) of free-to-use multinational patent search systems

The article analyses and compares some free-to-use patent search services which are not managed by patent authorities but which may offer unique selling propositions (USPs) beyond the expected, standard, search functions. Those services selected for this study are Google Patents, Lens, Patent Inspiration, Free Patents Online, SureChEMBL and Octimine. For this comparative study a total set of 66 criteria were analysed for each of the six search services. Furthermore the USPs and perceived advantages and disadvantages were identified. The study concluded that some of these search services are better suited for general prior art searches or patent analysis while others are applicable for patent searches in specific fields like biotechnology or pharmaceuticals.     

电力系统自动化发展方向分析

计算机技术、控制技术及信息技术的发展,电力系统自动化面临着空前的变革,多媒体技术、智能控制技术将迅速进入电力系统自动化领域。     

基于DSP的智能电源控制单元设计与实现

本文利用TI公司TMS320F28335芯片高效的浮点运算能力,结合片上丰富的外设,设计并实现了一种具有高可靠性的智能电源控制单元。该控制单元周期性地对各片上外设进行自检维护,完成多路负载通道控制、电压、电流的实时监控,并对故障进行指示、处理和上报,同时提供人机交互界面更新状态信息。经过验证,该控制单元工作稳定,具备良好的工程应用价值。     

基于Fusion Compute虚拟化平台的HIRFL数据库迁移及性能优化

为解决兰州重离子加速器（HIRFL）数据中心资源利用率低下、硬件和维护成本较高等问题，HIRFL数据中心正在向虚拟化集群模式进行迁移。本文以华为Fusion Compute虚拟化平台为基础，构建了HIRFL数据库的迁移方案。通过物理主机、虚拟机计算资源、内存、存储和网络资源性能优化，提出了1套完整的HIRFL数据库虚拟机性能优化方案。采用业界主流的数据库性能测试工具Benchmark Factory对迁移前后的数据库进行了测试，对比实验表明，本文提出的方案具有有效性和可行性，降低了数据中心的运行和维护成本，提升了HIRFL数据库系统的安全性和稳定性。     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.