构建面向销售服务的运营商管理支撑系统

在电信运营行业竞争日益加剧的大环境下,市场对传统运营商运营模式的转变提出了更加具体的要求。面向销售服务的运营商管理支撑系统为市场经营"围绕客户、细分需求、有效沟通"提供了信息化解决方案。文章结合某电信运营商的具体案例,分析了现有的支撑系统,提出了销售服务管理支撑系统进一步建设的必要性、目的性、设计思路、规范原则、总体架构和技术体系,描述了系统的相关功能,归纳了其主要特点,并思考了系统实施过程中应注意的一些问题。     

地震断裂带高铁大跨拱桥深基坑钢板桩围堰支护试验与监测

针对大跨度高速铁路拱桥建设对深基坑开挖施工的安全性要求,为了研究钢板桩围堰支护在大跨度高速铁路拱桥深基坑施工中的可行性,以新建徐盐高铁徐洪河特大桥工程项目为依托,通过数值模拟及现场试验监测,对深基坑钢板桩围堰支护在施工过程中的形变及受力情况进行分析。结果表明:通过有限元模拟分析,钢板桩围堰最危险受力点处于钢板桩中部、中上部及四角连接处,围堰第4道内支撑内力较其他3道内支撑大。整个施工过程中工况5为施工最不利工况。由监测结果可知,在施工过程中钢板桩单日最大位移值为2.8mm,最大累计位移值为24.0mm,均未达到预警值,施工过程中钢板桩形变稳定。钢支撑轴力受基坑周边荷载的影响明显,围堰钢支撑最大压力值为147.31kN,最大拉力值为24.95kN,未达到预警值,结构安全。研究结果可供同类型工程施工参考。     

洪水预报智能模型在中国半干旱半湿润区的应用对比

为探究不同洪水预报智能模型在我国半干旱半湿润区的应用效果,选用决策树、多层感知器、随机森林和支持向量机4种模型在陕西省3个典型流域进行逐时洪水预报;选择相关系数、纳什效率系数、均方根误差、平均绝对误差和相对误差等评价指标,比较不同预见期下4种模型在半干旱半湿润典型流域洪水预报的适用性。结果表明:在短预见期预报中,4种模型在半湿润区典型流域均可获得较高的预报结果,在半干旱典型流域模拟精度相对偏低,仅支持向量机模型满足预报要求;随着预见期延长,不同模型性能变化差异大,支持向量机模型整体稳定,在小流域实时洪水预报中具有明显优势;随机森林模型与决策树模型精度随预见期延长而缓慢下降,前者适应性更好;多层感知器模型精度随预见期延长而骤减,模型稳定性较差。     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

液压支架工作状态模糊识别系统研究

针对目前综采工作面液压支架压力监测系统只能监测综采工作面压力,不能对液压支架工作状态进行识别的问题,设计了一种液压支架工作状态模糊识别系统。该系统可对现有的综采工作面液压支架压力监测系统监测到的压力数据进行模糊识别,根据模糊识别输出值即可判断液压支架的5种工作状态,即降架、移架、升架、增阻、卸压保持;当液压支架处于增阻工作状态且增阻时间过长时,该系统可及时通知液压支架操作工增大液压支架压力,辅助实现顶煤破碎。测试结果验证了该系统的可行性。     

煤炭企业决策支持信息系统

以龙口矿业集团有限公司的决策支持信息系统为例,详细介绍了煤炭企业决策支持信息系统的总体架构、系统数据来源及处理、关键指标信息、系统功能。该系统通过DTS、中间件等技术获取数据;采用面向主题的数据仓库多维表结构和与业务系统对应的ODS层表结构存储数据;通过分析各种数据指标的类型及属性,建立起一套共包含45个关键指标的企业关键指标体系结构;并采用同比分析、环比分析、趋势分析等数学模型对数据进行分析和展现,从而实现辅助决策。     

液压支架电液控制系统软件在线升级方案设计

针对现有液压支架间架控制器与端头控制器的软件升级需要技术人员在井下对控制器逐一拆装操作,且耗时长、效率低的问题,提出了一种液压支架电液控制系统软件在线升级方案,详细介绍了液压支架电液控制系统组成、代码存储空间变化及在线升级的相关程序流程,并给出了实验数据及分析。该方案实现了对间架控制器和端头控制器的远程在线升级,升级过程简单方便,且升级速度快。     

基于超声波的围岩松动圈实时监测系统设计

实时地掌握围岩松动圈的范围对矿藏的安全生产具有重要的意义。利用超声波探测的灵活性和准确性,本文提出了一种基于超声波的围岩松动圈监测系统设计方法。此方案利用超声波在岩体中的传播速度与岩体的受力状态和裂隙程度的关系来实时地监测围岩松动圈的情况,具有方便、灵活、易于操作等特点。     

SVM-based ontology matching approach

There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.