| 序号 | 专利名 | 申请号 | 申请日 | 公开（公告）号 | 公开（公告）日 | 发明人 |
|---|---|---|---|---|---|---|
| 121 | 锥形量热仪用的烟气预处理、收集及在线分析装置 | CN201720409087.7 | 2017-04-18 | CN206740687U | 2017-12-12 | 黄海龙; 范明豪; 谢辉; 张佳庆; 郭祥军; 刘单华; 钟世强 |
| 一种锥形量热仪用的烟气预处理、收集及在线分析装置，包括上位机、PLC及由环形取样器、除水精密过滤器、制冷管路、取样泵、两位三通手动阀、干燥剂和气体分析仪串联连接而成的烟气试验管路，还包括由烟气储存罐、温湿度氧气变送器、SO2/HCL变送器、NO/NOX变送器、CO/CO2变送器、两位三通手动阀和抽气泵串联连接而成的烟气收集与分析管路，以及用于对环形取样器、制冷管路、烟气试验管路和烟气收集与分析管路清理的气体吹扫管路，烟气储存罐进口与制冷管路出口和取样泵进口相通，气体分析仪和抽气泵的出口有烟气处理装置。本实用新型能够解决现有取样管路易堵塞或不通畅，清理困难，不能直观有效地反映相关特征气体，也不能将烟气收集供后续分析研究的技术问题。(ESM)同样的发明创造已同日申请发明专利 | ||||||
| 122 | 一种适用煤制气工艺样品在线分析仪表的预处理装置 | CN201921130864.X | 2019-07-18 | CN210269833U | 2020-04-07 | 曲卫方 |
| 本实用新型公开了一种适用煤制气工艺样品在线分析仪表的预处理装置，包括管道、电机和进料斗，所述管道的底部固定有电机，且电机的输出端连接有导杆，所述导杆位于管道内，且导杆的外侧固定有搅拌杆，所述管道内轴连接有活动杆，且活动杆的外侧安装有过滤板，所述进料斗固定于管道的顶部，且进料斗内轴连接有分料杆，所述导杆的顶部边缘处连接有定位杆，且定位杆的一端位于定位槽内，并且定位槽开设于进料斗底部的内侧，所述定位杆的底部边缘处预留有限位槽，且限位槽内安装有限位球。该适用煤制气工艺样品在线分析仪表的预处理装置，对管道中的水煤气进行脱硫和过滤，同时通过对水煤气和脱硫剂的搅拌，提高脱硫效率。 | ||||||
| 123 | 一种在线分析前处理和离线分析一体液相色谱仪 | CN201920582275.9 | 2019-04-25 | CN209858494U | 2019-12-27 | 章骅 |
| 本实用新型公开了一种在线分析前处理和离线分析一体液相色谱仪,包括自动进样器、两个色谱泵、两个六通阀、在线净化柱、色谱分离柱、紫外检测器、荧光检测器，可以根据使用需要在离线分析和在线前处理分析两种模式之间切换。用作在线前处理分析时，样品可不经前处理，简单提取后直接检测，大大缩短了检测流程，减少了人工操作，降低了分析成本。作离线分析时不需要对系统进行变动，直接切换即可，有效的提高了仪器的使用效率和分析能力。(ESM)同样的发明创造已同日申请发明专利 | ||||||
| 124 | 一种双通道在线分析前处理和离线分析一体液相色谱仪 | CN201920580040.6 | 2019-04-25 | CN209858493U | 2019-12-27 | 章骅 |
| 本实用新型公开了一种双通道在线分析前处理和离线分析一体液相色谱仪，包括自动进样器、两个色谱泵、三个六通阀、在线净化柱、两个色谱分离柱、光化学衍生器、紫外检测器和荧光检测器，可以根据使用需要在离线分析和在线前处理分析两种模式之间切换。根据待分析样品的性质在线切换分析通道，使不同性质的物质在不同的色谱柱上实现离线或在线分离分析，解决传统液相色谱仪只能做离线分析或者在线净化分析的缺点。用做在线前处理分析时，样品可不经前处理，简单提取后直接检测，大大缩短检测流程，减少了人工操作，降低了分析成本。作离线分析时不需要对系统进行变动，直接切换即可，有效提高仪器的使用效率和分析能力。(ESM)同样的发明创造已同日申请发明专利 | ||||||
| 125 | 一种化产回收过程气体在线分析预处理净化装置 | CN201920599663.8 | 2019-04-29 | CN209841508U | 2019-12-24 | 陈勇波; 李娇; 赵凯; 盛荣芬; 刘帮; 骆毅 |
| 一种化产回收过程气体在线分析预处理净化装置，包括洗涤器本体，属于气体预处理技术领域，所述洗涤器本体顶部敞口处密封设置有洗涤器盖（9）而底部连通设置有排液管，所述排液管上设置有排液阀（6），所述洗涤器盖（9）上贯穿设置有导气管（1）和排气管（2），所述洗涤器内从下至上依次填充有冷凝液层（5）和洗涤剂层（3），所述洗涤器内设置有多孔板（7），所述多孔板（7）位于所述洗涤剂层（3）内，所述多孔板（7）与所述洗涤剂层（3）顶部液面之间空间堆积有填料（4）。本实用新型提供的装置对气体净化脱水，确保样气经此装置干燥脱水后不影响待测组分含量。 | ||||||
| 126 | 一种化学法氨逃逸在线分析监测仪的取样预处理装置 | CN201721581605.X | 2017-11-23 | CN207540841U | 2018-06-26 | 陈云龙; 丁瑞峰; 边宝丽 |
| 本实用新型涉及一种化学法氨逃逸在线分析监测仪的取样预处理装置。现有的烟气取样过程中，逃逸氨极易在取样装置与伴热管连接处结晶，严重影响了后续烟气逃逸氨含量检测的真实有效性。本实用新型所述的取样模块通过高温伴热管路和单独伴热处理的采集阀将过滤后的烟气送至逃逸氨分析测量模块，以避免逃逸氨结晶，而反吹模块的压缩吹扫空气对取样管路及滤芯进行吹扫清洁，可有效保证装置内部管路清洁，以实现整个装置长期高效地运行。采用本实用新型所述的化学法氨逃逸在线分析监测仪的取样预处理装置能够持续稳定地为后续分析测量模块提供清洁不失真的样气。 | ||||||
| 127 | SYSTEMS AND METHODS FOR PERFORMING ONLINE ANALYTICAL PROCESSING | PCT/IL2012000012 | 2012-01-09 | WO2012095839A4 | 2013-01-24 | ALSHEICH EREZ; ROSENSTEIN DANNY; MEISELES MOSHE |
| Near real time online analytical processing system, the system includes an analysis and aggregation module, a database and a query engine, the analysis and aggregation module, the analysis and aggregation module receiving transactional data, the analysis and aggregation module producing raw data, measured metrics statistics data, aggregated transactional data, and aggregated measured metrics statistics data, the database being coupled with the analysis and aggregation module, the database storing the raw data, the measured metrics statistics data, the aggregated transactional data and the aggregated measured metrics statistics data, the query engine being coupled with the database, the query engine receiving a user query, and answering the user query by retrieving data from the database. | ||||||
| 128 | 데이터베이스 시스템에서 온라인 분석 처리를 위한 데이터를 유지하는 방법 및 장치 | KR1020167018873 | 2014-01-02 | KR101805561B1 | 2017-12-07 | 비라라가반비노쓰; 라마무르티프라사나벤카테시; 첸지비아오 |
| 본발명의실시예는데이터베이스시스템에서온라인분석처리를위한데이터를유지하는방법및 장치를개시한다. 본방법은: 메인프로세스에서변경된페이지를추적하는단계; 및온라인분석처리를위한자식프로세스에변경된페이지를동기화하는단계를포함한다. 온라인분석처리를위한데이터를유지하는방법및 장치에서는, 변경된페이지가추적되고자식프로세스가변경된페이지와동기화된다. 따라서주기적인포크가회피되고, 종래기술에서주기적포크에의해발생하는포크오버헤드가제거되며, 변경된페이지만이동기화되므로동기화가빨라지고, 온라인데이터처리의성능이향상된다. | ||||||
| 129 | 다차원 온라인 분석 처리 시스템에서의 큐브를 재구성하는 방법 및 장치 | KR1020167027904 | 2015-02-09 | KR1020160130489A | 2016-11-11 | 장융; 윈비안; 투단단 |
| 본발명의실시예들은다차원온라인분석처리시스템에서큐브를재구성하는방법및 장치를제공하는데, 큐브가수신된재구성요청과기존큐브내에저장되어있는데이터에기초하여재구성되어, 데이터베이스로부터, 큐브를갱신하는데필요한데이터를획득할필요가없어서, 데이터베이스의 MOLAP 시스템에서모델재구성과데이터재구성이수행될때에도데이터의완전성이보장된다. | ||||||
| 130 | 온라인 분석처리툴을 이용한 고객정보 분석 시스템 및 방법 | KR1020000045595 | 2000-08-07 | KR1020020012339A | 2002-02-16 | 이한수; 안승진; 권병준 |
| PURPOSE: A system and a method for analyzing client information using an on-line analytical processing(OLAP) is provided to perform a request and receipt of a query to an iOLAP server by an interactive action of an iOLAP component included in a web browser with an iOLAP server without through a web server when a query of a user is requested using an ActiveX technique. CONSTITUTION: In a system having a web browser(10) installed in a user PC and a web server(20) including an iOLAP server(30') being connected through an HTTP/TCP-IP, the iOLAP server(30') stores an iOLAP component. When the web browser(10) is connected to the web server(20), if the iOLAP component(30) is not installed in a user PC, the iOLAP server(30') downloads the iOLAP component(30) in the iOLAP server(30') and installs the iOLAP component(30) in the user PC. The user inquires an analyzed result through the iOLAP component(30). When a query of a user is requested, a request and receipt of a query to the iOLAP server(30') are performed by an interactive action of the iOLAP component(30) included in the web browser(10) with the iOLAP server(30') without through a web server. Thus, a response result is suggested to an analysis request of the user. | ||||||
| 131 | 온라인 분석 처리 시스템의 질의 처리 고속화를 위한 비트맵 인덱스 구조 | KR1020020077147 | 2002-12-06 | KR1020030022091A | 2003-03-15 | 김명; 임윤선 |
| PURPOSE: A bit map index structure for a high-speed query process of an on-line analytical processing system is provided to increase an OLAP(On-Line Analytical Processing) main calculating speed including a range query by creating a small-sized index capable of accessing chunks rapidly when an OLAP(On-Line Analytical Processing) cube is created based on a chunk. CONSTITUTION: Black chunks such as '0', '3', '8', etc. are dense chunks. Gray chunks such as '1', '4', '6', etc. are thin chunks. White chunks such as '2', '5', '7', etc. are null chunks. When chunks of a cube are stored in a disk, bit sequences are created. When a chunk 'k' is stored, an index with respect to the chunk is created. The index of the chunk 'k' has 3-bit. The sequences are divided by one block of a disk. An address of the first dense chunk and an address of the first thin chunk included in a corresponding block are stored at the front of all index blocks. | ||||||
| 132 | DISTRIBUTED, REAL-TIME ONLINE ANALYTICAL PROCESSING (OLAP) | PCT/US2013/048006 | 2013-06-26 | WO2014004741A1 | 2014-01-03 | LANPHEAR, Damon; BISWAS, Prabuddha |
Source data of an event stream is parsed and supplemented with additional data from reference data sources, producing an enriched event stream from the parsed event stream data. The data records of the enriched event stream are partitioned into data fields designated as a dimension partition and a metric partition, which are partitioned into sub-dimension projections mapped to a plurality of storage keys, such that each of the storage keys includes one or more placeholder wildcard values and each of the storage keys is stored into a database of the computer system by the computer processor. The stored storage keys are then aggregated onto a two-dimensional coordinate vector such that, if the computer processor identifies a permuted storage key having metric values for which a corresponding storage key already exists in the database, then the computer processor aggregates the metric values of the identified storage key with the metric values of the corresponding storage key, and if the computer processor does not identify the permuted storage key as having a corresponding storage key that already exists in the database, then the computer processor writes the metric values of the permuted storage key into the database, comprising initial values for the key combination of dimension values. |
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| 133 | SYSTEMS AND METHODS FOR PERFORMING ONLINE ANALYTICAL PROCESSING | PCT/IL2012000012 | 2012-01-09 | WO2012095839A2 | 2012-07-19 | ALSHEICH EREZ; ROSENSTEIN DANNY; MEISELES MOSHE |
| Near real time online analytical processing system, the system includes an analysis and aggregation module, a database and a query engine, the analysis and aggregation module, the analysis and aggregation module receiving transactional data, the analysis and aggregation module producing raw data, measured metrics statistics data, aggregated transactional data, and aggregated measured metrics statistics data, the database being coupled with the analysis and aggregation module, the database storing the raw data, the measured metrics statistics data, the aggregated transactional data and the aggregated measured metrics statistics data, the query engine being coupled with the database, the query engine receiving a user query, and answering the user query by retrieving data from the database. | ||||||
| 134 | 데이터베이스 시스템에서 온라인 분석 처리를 위한 데이터를 유지하는 방법 및 장치 | KR1020167018873 | 2014-01-02 | KR1020160097362A | 2016-08-17 | 비라라가반비노쓰; 라마무르티프라사나벤카테시; 첸지비아오 |
| 본발명의실시예는데이터베이스시스템에서온라인분석처리를위한데이터를유지하는방법및 장치를개시한다. 본방법은: 메인프로세스에서변경된페이지를추적하는단계; 및온라인분석처리를위한자식프로세스에변경된페이지를동기화하는단계를포함한다. 온라인분석처리를위한데이터를유지하는방법및 장치에서는, 변경된페이지가추적되고자식프로세스가변경된페이지와동기화된다. 따라서주기적인포크가회피되고, 종래기술에서주기적포크에의해발생하는포크오버헤드가제거되며, 변경된페이지만이동기화되므로동기화가빨라지고, 온라인데이터처리의성능이향상된다. | ||||||
| 135 | 데이터 웨어하우스 상의 온라인 분석 처리(OLAP) 연산의 최적화된 시퀀싱을 위한 하이퍼 격자 모델 | KR1020137002253 | 2010-11-30 | KR101526514B1 | 2015-06-05 | 차키,나벤두; 센,소우미야 |
| 데이터웨어하우스내에포함되는데이터의온라인분석처리를위한시스템및 방법이제공된다. 일예시에따르면, 차원을하이퍼격자구조에추가하기위한방법이제공되며, 방법은새로운기저큐보이드를데이터웨어하우스의기능을설명하는하이퍼격자에추가하는단계를포함한다. 다른예시에따르면, 기존의하이퍼격자를통한최적의경로를결정함으로써소스큐보이드로부터목적큐보이드를생성하기위한방법이또한제공된다. | ||||||
| 136 | 데이터 웨어하우스 상의 온라인 분석 처리(OLAP) 연산의 최적화된 시퀀싱을 위한 하이퍼 격자 모델 | KR1020137002253 | 2010-11-30 | KR1020130083899A | 2013-07-23 | 차키,나벤두; 센,소우미야 |
| 데이터 웨어하우스 내에 포함되는 데이터의 온라인 분석 처리를 위한 시스템 및 방법이 제공된다. 일 예시에 따르면, 차원을 하이퍼 격자 구조에 추가하기 위한 방법이 제공되며, 방법은 새로운 기저 큐보이드를 데이터 웨어하우스의 기능을 설명하는 하이퍼 격자에 추가하는 단계를 포함한다. 다른 예시에 따르면, 기존의 하이퍼 격자를 통한 최적의 경로를 결정함으로써 소스 큐보이드로부터 목적 큐보이드를 생성하기 위한 방법이 또한 제공된다. | ||||||
| 137 | 데이터 처리 향상 방법, 컴퓨터 판독가능 기록매체, 데이터베이스 액세스 시스템, 데이터베이스를 액세스하기 위한 컴퓨터 하드웨어 구현된 시스템, 데이터 검색 방법 | KR1020040046510 | 2004-06-22 | KR101085666B1 | 2011-11-22 | 페트쿨레스쿠,크리스티안; 네트즈,아미르; 파수만스키,모샤; 두미트루,마리우스; 버거,알렉산더; 샌더스,폴조나단 |
| OLAP 시스템에서 관계형 데이터베이스의 데이터를 액세스하기 위한 데이터 모델은 다계층구조 차원을 이용한다. 이 차원은 속성들의 셋트를 포함한다. 각 속성은 관계형 데이터베이스의 컬럼에 결합되어 있다. 속성들 간의 관계를 나타내는 논리적 구조가 정의된다. 계층구조들이 정의된다. 각 계층구조는 속성들의 시퀀스를 포함한다. 계층구조는 데이터베이스 사용자가 데이터베이스를 액세스하는 데 사용할 수 있는 공통의 드릴다운 경로를 제공한다. 계층구조는 단일의 속성을 포함할 수도 있고, 속성들의 조합을 포함할 수도 있다. 속성들 간의 관계 및 계층구조 내의 속성들의 시퀀스 둘다는 데이터베이스에 관한 어떠한 제한 조건에도 독립적으로 정의된다. 데이터베이스, 차원, 속성, 계층구조, 큐브, OLAP, 드릴다운 경로 | ||||||
| 138 | 크롬염 처리 용액중의 3가크롬 및 6가크롬의 온-라인 분석시스템 | KR1019900022691 | 1990-12-31 | KR1019940004663B1 | 1994-05-27 | 소재춘 |
| The chrome on-line analyzing system includes a chrome processing solution container and barium nitrate installed on the left upper portion of a reactor, metering pumps installed at the intermediate portion of a liquid supply pipe, an automatic valve installed between the metering pump and the reactor to automatically control the flow amount of a sample solution, a fluorescent X-ray analyzing liquid sample preparing device coupled to the automatic valve, a fluorescent X-ray analyzing liquid sample preparing device coupled to the liquid supply pipe, a pulse filter adjoining the reactor, a withdrawal container installed between the pulse filter and liquid sample preparing device, a washing water reservoir coupled to the liquid supply pipe, a sludge discharger operating by a motor, and an agitator diagonally installed under the reactor, thereby greatly enhancing working efficiency of chrome processing. | ||||||
| 139 | NoSql online analytical processing architecture | US14030677 | 2013-09-18 | US08903872B1 | 2014-12-02 | Craig Weissman |
| A breadth-first join module receives, at a runtime, a query for travel data, wherein the travel data is stored in a data store, the data store comprising parent and child data structures arranged in a hierarchy. The breadth-first join module identifies a first child data structure associated with the travel data, the first child data structure comprising a plurality of pointers, wherein each of the plurality of pointers is associated with one of a plurality of first level parent data structures in the data store and queries each of the plurality of first level parent data structures to resolve the plurality of pointers in the first child data structure, wherein the plurality of first level parent data structures comprise travel data objects associated with the plurality of pointers, and wherein at least one of the plurality of first level parent data structures comprises a pointer to a second level parent data structure. The breadth-first join module queries the second level parent data structure to resolve the pointer in the at least one first level parent data structure, wherein all of the plurality of pointers in the first child data structure are resolved prior to querying the second level parent data structure. | ||||||
| 140 | Online analytical processing (OLAP) | US11948033 | 2007-11-30 | US07774302B2 | 2010-08-10 | Patrick Arras; Alfons Steinhoff |
| A computer program product and computer system for implementing a method that generates an n-dimensional matrix presentation of at least part of an m-dimensional database including data records and at least two key dimensions. Each key dimension includes data value fields. Each data value field has real data therein. A sorting sequence of fact numbers is calculated. The fact numbers identify the data records. Entry points for each dimension of the n-dimensional matrix presentation are calculated. Each entry point is calculated to be linked with a corresponding fact number of the sorting sequence. A cardinality of the n-dimensional matrix presentation is calculated in dependence on the calculated sorting sequence and the calculated corresponding entry points. The n-dimensional matrix presentation is generated based on the calculated cardinality and the calculated entry points. | ||||||
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