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CN104092578A - Pipeline-based data flow monitoring method - Google Patents

Pipeline-based data flow monitoring method Download PDF

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CN104092578A
CN104092578A CN201310741890.7A CN201310741890A CN104092578A CN 104092578 A CN104092578 A CN 104092578A CN 201310741890 A CN201310741890 A CN 201310741890A CN 104092578 A CN104092578 A CN 104092578A
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pipeline
monitoring
data flow
monitored item
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CN104092578B (en
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尼加提
陈建新
马斌
王晓磊
马天福
朱银涛
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Information and Telecommunication Branch of State Grid Xinjiang Electric Power Co Ltd
State Grid Corp of China SGCC
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Information and Telecommunication Branch of State Grid Xinjiang Electric Power Co Ltd
State Grid Corp of China SGCC
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Abstract

The invention discloses a data stream monitoring method based on a pipe method. The data stream monitoring method includes the following steps: through application system interfaces, enabling data of different kinds of application systems to be integrated; receiving changed data of the plurality of application systems in a real-time manner; constructing data streams, constructing a pipe model and establishing linkage among data stream correlation, data stream observation items and the data streams; and through a monitoring engine, carrying out monitoring analysis on the basis of a curve cross section on a plurality of data streams in one pipe. The data stream monitoring method based on the pipe method is capable of establishing an incidence relation between the data streams and in the computer application systems, a data correlation change condition is monitored continuously and a data monitoring result in direct expression means such as curved surfaces, waveforms and tables and the like is output so that a function of monitoring linkage data streams based on time periods can be realized.

Description

基于管道方式的数据流监控方法Pipeline-based data flow monitoring method

技术领域 technical field

本发明涉及信息监测技术,特别是基于管道方式的数据流监控方法。 The invention relates to information monitoring technology, in particular to a pipeline-based data flow monitoring method.

背景技术 Background technique

随着信息技术在企业生产经营过程中被进一步深化应用,在各类信息系统中产生了越来越多的生产、项目、资金和物资等数据,对这些数据长期、持续性变化形成的数据流的监控和分析能够有效帮助企业做出经营决策,所以,逐渐受到企业的重视。由于这些数据产生于不同的系统,对数据的监控方法都分布于各自系统中,仅能借助在若干时间点上的检索查询、统计手段等方可实现。也有部分企业采用了基于数据中心的数据监控方式,其各业务部门针对的被监控对象集中于各领域的数据流,例如,生产部门关注于生产、产品数据流的变化,财务部门专注于资金数据流的变化和趋势分析。 With the further application of information technology in the production and operation of enterprises, more and more production, project, capital and material data have been generated in various information systems. The data flow formed by long-term and continuous changes of these data Advanced monitoring and analysis can effectively help enterprises make business decisions, so they are gradually valued by enterprises. Since these data are generated in different systems, the monitoring methods for the data are distributed in their respective systems, which can only be realized with the help of retrieval queries and statistical means at several points in time. There are also some companies that have adopted a data center-based data monitoring method. The monitored objects of each business department focus on the data flow in various fields. For example, the production department focuses on changes in production and product data flow, and the financial department focuses on capital data. Flow change and trend analysis.

当前的数据流监控方法,能针对特定数据流进行监控,然而,忽视了业务数据隐含的信息关联性、数据流变化的联动性及数据流变化具有的延迟性,没有综合建立数据流之间的关联关系,从而无法对多条数据流的联动性进行综合监控和分析。 The current data flow monitoring method can monitor specific data flows. However, it ignores the hidden information relevance of business data, the linkage of data flow changes, and the delay of data flow changes, and does not comprehensively establish the relationship between data flows. Therefore, it is impossible to comprehensively monitor and analyze the linkage of multiple data streams.

发明内容 Contents of the invention

本发明的目的在于提供一种基于管道方式的数据流监控方法,能建立数据流之间关联关系,在计算机应用系统中持续性监测数据关联变化情况,并输出以曲面、波形、表格等为直观表达方式的数据监控结果,能够实现基于时间段监控联动性数据流的功能。 The purpose of the present invention is to provide a pipeline-based data flow monitoring method, which can establish the association relationship between data flows, continuously monitor the data association changes in the computer application system, and output curved surfaces, waveforms, tables, etc. as intuitive The data monitoring result of the expression mode can realize the function of monitoring the linkage data flow based on the time period.

本发明的目的是这样实现的:一种基于管道方式的数据流监控方法,①构建数据流模型:通过数据流构造器构建一个存储业务数据流的数据结构,定义数据流的静态属性—名称标识、编码标识、描述和监控项集合,在该结构中继承动态属性—数据访问、监控项递增,在构建数据流模型后,保存到管道数据库中;②构建管道模型:通过管道构造器构造一个管道,定义管道标识,将第一步构造的数据流结构添加到管道中,每条管道可以添加多条有业务关联关系的数据流;③建立输入通道:应用系统调用本方法中的接口,当业务数据变化时触发数据的传递,通过接口将业务数据传递到本方法中,建立本方法与应用系统之间的数据交互通道,传递的参数包括管道标识、数据流标识、监控项标识、监控项类型、监控项的数据;④启动监控引擎:通过构造器构建的数据流和管道,在监控流程中加载和实例化,从管道数据库装载一个管道,接收应用系统经接口传递的业务数据,启动管道并将业务数据加载到各数据流的监控项中,将实时接收的数据保存在监控数据库中,并实时监控数据流中监控项的变化,记录变化的联动性、时间点;⑤启动联动自适应模块:根据监控数据库中监控数据的实际变化顺序、变化的时间间隔,来自动修正管道中定义的监控项的优先级和关联顺序;⑥输出曲面监控结果:通过关联性连线在时间轴上截取变化时的监控项,从而形成一个基于时间段、由监控项数据构成的曲面;⑦管道的启动和关闭:当监控引擎加载并实例化管道后,启动管道,在注入实际数据后进行实时监控;当管道被手动停止后,监控引擎停止管道实例化,将监控数据库中的管道置于挂起状态,不再接收该管道的实时数据;当手动关闭或接收到数据流结束状态后,监控引擎停止管道实例化,卸载管道模型,关闭该管道接收通道,从监控数据库中移除管道数据。  The purpose of the present invention is achieved in this way: a data flow monitoring method based on the pipeline mode, ① building a data flow model: constructing a data structure for storing business data flow through the data flow constructor, and defining the static attribute of the data flow—name identification , Encoding identification, description and monitoring item collection, inherit dynamic attributes in this structure - data access, monitoring item increment, after building the data flow model, save it in the pipeline database; ②Building pipeline model: Construct a pipeline through the pipeline constructor , define the pipeline identifier, add the data flow structure constructed in the first step to the pipeline, and each pipeline can add multiple data flows with business relationships; ③ Establish an input channel: the application system calls the interface in this method, when the business Data transfer is triggered when the data changes, business data is transferred to this method through the interface, and a data interaction channel between this method and the application system is established. The parameters passed include pipeline ID, data flow ID, monitoring item ID, and monitoring item type , the data of monitoring items; ④Start the monitoring engine: load and instantiate the data flow and pipeline constructed by the constructor in the monitoring process, load a pipeline from the pipeline database, receive the business data transmitted by the application system through the interface, start the pipeline and Load the business data into the monitoring items of each data flow, save the real-time received data in the monitoring database, and monitor the changes of the monitoring items in the data flow in real time, and record the linkage and time point of the change; ⑤Start the linkage adaptive module : According to the actual change sequence and time interval of the monitoring data in the monitoring database, automatically correct the priority and correlation sequence of the monitoring items defined in the pipeline; ⑥Output surface monitoring results: Intercept changes on the time axis through associative connections Time-based monitoring items, thus forming a time-based surface composed of monitoring item data; ⑦Starting and closing of the pipeline: when the monitoring engine loads and instantiates the pipeline, start the pipeline, and perform real-time monitoring after injecting actual data; After the pipeline is manually stopped, the monitoring engine stops the pipeline instantiation, puts the pipeline in the monitoring database in a suspended state, and no longer receives the real-time data of the pipeline; when it is manually closed or receives the end of the data stream, the monitoring engine stops the pipeline Instantiate, unload the pipeline model, close the pipeline receiving channel, and remove the pipeline data from the monitoring database. the

本发明技术方案生成的原因:业务数据随时间轴的变化形成了业务数据流,不同业务之间存在的关联性隐含在业务数据流的变化中,但由于业务被不同应用系统承载,因此,应用系统接口能形成数据集成通道,管道方式能约束具有隐含关系的业务数据流,通过关联数据流之间监控项执行实时监控数据流的任务能够有效揭示在一段时间内不同业务数据流之间的联动性。 The reason for the generation of the technical solution of the present invention: the change of business data with the time axis forms a business data flow, and the correlation between different businesses is implied in the change of business data flow, but because the business is carried by different application systems, therefore, The application system interface can form a data integration channel, and the pipeline method can constrain the business data flow with implicit relationships. The task of real-time monitoring of data flow through the monitoring items between associated data flows can effectively reveal the relationship between different business data flows within a period of time. linkage.

本发明的优点:一、管道约束效应能绑定存在于不同应用系统中的业务数据流,通过数据流之间关联关系、数据流监控项之间的关联关系对数据流实时监控,就能有效揭示业务数据变化的联动性;二、监控曲面的手段能够有效消除在时间点监控方式中业务数据变化关联的不明显性,并展示在一段时间内数据变化的相互影响关系。 Advantages of the present invention: 1. The pipeline constraint effect can bind business data streams existing in different application systems, and real-time monitoring of data streams through the association relationship between data streams and the association relationship between data stream monitoring items can effectively Reveal the linkage of business data changes; 2. The method of monitoring surface can effectively eliminate the inconspicuousness of business data change correlation in the point-in-time monitoring method, and show the mutual influence relationship of data changes over a period of time.

本发明通过应用系统接口使各类应用系统的数据集成起来,实时接收多个应用系统的变化数据,构造数据流,构建管道模型,建立数据流关联性、数据流观察项及数据流之间的联动性,并通过监控引擎,对在一个管道内多条数据流进行基于曲线截面的监控分析。 The invention integrates the data of various application systems through the application system interface, receives the changing data of multiple application systems in real time, constructs the data flow, builds the pipeline model, and establishes the relationship between data flow, data flow observation items and data flow. Linkage, and through the monitoring engine, monitor and analyze multiple data streams in a pipeline based on curve sections.

本发明能建立数据流之间关联关系,在计算机应用系统中持续性监测数据关联变化情况,并输出以曲面、波形、表格等为直观表达方式的数据监控结果,能够实现基于时间段监控联动性数据流的功能。 The invention can establish the correlation between data streams, continuously monitor the data correlation changes in the computer application system, and output the data monitoring results in the form of curved surfaces, waveforms, tables, etc. as intuitive expressions, and can realize monitoring linkage based on time periods The function of the data flow.

附图说明 Description of drawings

下面将结合附图对本发明作进一步说明。 The present invention will be further described below in conjunction with accompanying drawing.

图1为本发明的工作流程图; Fig. 1 is a work flow chart of the present invention;

图2为本发明管道模型构建示意图; Fig. 2 is the construction schematic diagram of pipeline model of the present invention;

图3为本发明基于曲面的监控原理示意图。 Fig. 3 is a schematic diagram of the monitoring principle based on the curved surface of the present invention.

具体实施方式 Detailed ways

一种基于管道方式的数据流监控方法,如图1所示,①构建数据流模型:通过数据流构造器构建一个存储业务数据流的数据结构,定义数据流的名称标识、编码标识、描述、监控项集合等静态属性,在该结构中继承数据访问、监控项递增等动态属性,在构建数据流模型后,保存到管道数据库中;②构建管道模型:通过管道构造器构造一个管道,定义管道标识,将第一步构造的数据流结构添加到管道中,每条管道可以添加多条有业务关联关系的数据流,如附图2所示;为管道中的两个数据流之间,添加基于监控项的关联关系对,通过关联关系约束相关业务数据流的联动变化,将构建成功的管道模型保存在管道数据库中;③建立输入通道:应用系统调用本方法中的接口,当业务数据变化时触发数据的传递,通过接口将业务数据传递到本方法中,建立本方法与应用系统之间的数据交互通道,传递的参数包括管道标识、数据流标识、监控项标识、监控项类型、监控项的数据;④启动监控引擎:通过构造器构建的数据流和管道,是一个没有实际数据的模型,并不能直接应用于实时监控,需要在监控流程中加载和实例化才能应用,监控引擎从管道数据库装载一个管道,接收应用系统接口传递的业务数据,启动管道并加载业务数据到各数据流的监控项中,将实时接收的数据保存在监控数据库中,并实时监控数据流中监控项的变化,记录变化的联动性、时间点;⑤启动联动自适应模块:根据监控数据库中监控数据的实际变化顺序、变化的时间间隔,来自动修正管道中定义的监控项的优先级、关联顺序等关联关系;⑥输出曲面监控结果:由于数据变化引起的联动性具有延迟性,所以发起变化的监控项的时间点较早,引起其他监控项变化的时间点较晚,通过关联性连线在时间轴上截取变化时的监控项,从而形成一个基于时间段、由监控项数据构成的曲面,如图3所示,输出结果包括一个数据流中各监控项的持续性波动曲线、变化值,基于时间轴的一个管道中所有关联数据流中监控项的联动性曲面图形和变化值;⑦管道的启动和关闭:管道构建后,就处于就绪状态,此时管道没有实际数据,不能应用于实时监控;当监控引擎加载并实例化管道后,就启动了管道,此时管道处于激活状态,在注入实际数据后即可实时监控;当管道被手动停止后,监控引擎停止管道实例化,将监控数据库中的管道置于挂起状态,不再接收该管道的实时数据;当手动关闭或接收到数据流结束状态后,监控引擎停止管道实例化、卸载管道模型,关闭该管道接收通道,从监控数据库中移除管道数据。 A pipeline-based data flow monitoring method, as shown in Figure 1, ① Constructing a data flow model: constructing a data structure for storing business data flows through the data flow constructor, defining the name identification, coding identification, description, Static attributes such as monitoring item collection, in which dynamic attributes such as data access and monitoring item increment are inherited, and saved in the pipeline database after building the data flow model; To identify, add the data flow structure constructed in the first step to the pipeline, and each pipeline can add multiple data flows with business relationships, as shown in Figure 2; between the two data flows in the pipeline, add Based on the association relationship pairs of monitoring items, the linkage changes of related business data flows are constrained by the association relationship, and the successfully constructed pipeline model is saved in the pipeline database; ③ Establish an input channel: the application system calls the interface in this method, when the business data changes When triggering data transfer, the business data is transferred to this method through the interface, and the data interaction channel between this method and the application system is established. The parameters passed include pipeline ID, data flow ID, monitoring item ID, monitoring item type, monitoring item data; ④Start the monitoring engine: the data flow and pipeline built by the constructor is a model without actual data, and cannot be directly applied to real-time monitoring. It needs to be loaded and instantiated in the monitoring process before it can be applied. The monitoring engine starts from The pipeline database loads a pipeline, receives the business data transmitted by the application system interface, starts the pipeline and loads the business data into the monitoring items of each data stream, saves the real-time received data in the monitoring database, and monitors the monitoring items in the data stream in real time Change, record the linkage and time point of the change; ⑤Start the linkage adaptive module: according to the actual change order and time interval of the monitoring data in the monitoring database, automatically correct the priority and association order of the monitoring items defined in the pipeline, etc. Correlation relationship; ⑥ output surface monitoring results: due to the delay in the linkage caused by data changes, the time point of the monitoring item that initiates the change is earlier, and the time point that causes other monitoring items to change is later. The monitoring items when changing are intercepted on the axis to form a surface based on the time period and composed of monitoring item data, as shown in Figure 3. The output results include the continuous fluctuation curve and change value of each monitoring item in a data stream, based on Linkage surface graphics and change values of monitoring items in all associated data streams in a pipeline on the time axis; ⑦Starting and closing of the pipeline: After the pipeline is built, it is in a ready state. At this time, the pipeline has no actual data and cannot be applied to real-time monitoring ;When the monitoring engine loads and instantiates the pipeline, it starts the pipeline. At this time, the pipeline is in an active state, and real-time monitoring can be performed after injecting actual data; when the pipeline is manually stopped, the monitoring engine stops the pipeline instantiation, and the monitoring database The pipeline in the pipeline is placed in a suspended state and no longer receives real-time data from the pipeline; when the pipeline is manually closed or the end of the data flow is received, the monitoring engine stops the pipeline instantiation, unloads the pipeline model, closes the pipeline receiving channel, and retrieves data from the monitoring database Remove pipeline data in .

本发明通过应用系统接口构建各类应用系统的业务数据集成通道,通过数据流构造器和管道构造器建立基于数据流监控的数据实体,监控引擎将业务数据的变化装载到数据流和管道中,实时监控各业务数据流变化的联动性,联动性自适应模块则根据数据流中监控项的变化频率、对其它数据流中监控项的影响顺序,动态地调整数据流中关联关系的优先级和监控项的优先级,最后,将一段时间内形成的具有联动关系的数据流节点的变化情况通过输出模块展示出来。本发明的流程说明如下:①数据流构造器:用于构造一个存储业务数据流的数据结构,通过数据流标识、确认一个业务数据流,包括数据流标识、数据流名称、监控项链表,每个业务数据流包含多个监控项。②监控项:一个业务数据流中的数据项用于实时记录数据的变化和时间点,也是用户关注和监控的业务数据,通过应用系统接口接收数据,在监控引擎中启动管道后,再加载到数据流的监控项中。③管道构造器:用于构造一个管道,一个管道就是约束若干个数据流的数据结构,管道包括一个数据流链表、数据流关联关系表,数据流关联关系表包括数据流关系对和数据流监控项的关系对;数据流关系对表述了两个数据流之间的相互影响关系,而这种相互影响关系通过监控项关系对被具体体现出来;构造一个管道模型,就是生成针对每个管道的唯一标识,在管道中加入已经定义好的数据流并添加数据流之间的约束(即定义数据流关系),管道模型构建示意图如图2所示。④监控引擎:用于装载一个管道、接收经应用系统接口传递的业务数据,启动管道并将业务数据加载到各数据流的监控项中,以实时监控数据流中监控项的变化,记录变化的联动性和时间点。⑤联动性自适应模块:由于管道中数据流的关联关系是在模型中设置的,在监控引擎启动管道、加载数据后,自适应模块会根据实际监控项的变化状态和联动变化情况修正关联关系中的优先级和关联顺序。⑥曲面监控输出模块:用于输出一个管道中所有数据流联动变化的状态,该状态由各数据流在不同时间点的一个监控项关联对构成;由于数据变化的延时性,由数据变化所连接的监控项关系对组成的联动性闭合连线,在管道的时间轴上构成了一个曲线截面,该曲线截面表达了在一段时间内各业务数据流之间的联动性,其基于曲面的监控原理示意图如图3所示。 The present invention constructs business data integration channels of various application systems through application system interfaces, establishes data entities based on data flow monitoring through data flow constructors and pipeline constructors, and the monitoring engine loads changes in business data into data flows and pipelines, Real-time monitoring of the linkage of changes in the data streams of each business, and the linkage adaptive module dynamically adjusts the priority and The priority of monitoring items, and finally, the changes of data flow nodes with linkage relationship formed within a period of time are displayed through the output module. The process flow of the present invention is described as follows: 1. data stream constructor: used to construct a data structure for storing business data streams, identify and confirm a business data stream through data streams, including data stream identifiers, data stream names, and monitoring necklace lists, each A business data flow contains multiple monitoring items. ②Monitoring items: Data items in a business data stream are used to record data changes and time points in real time. They are also business data that users pay attention to and monitor. The data is received through the application system interface, and after the pipeline is started in the monitoring engine, it is loaded into the In the monitoring item of the data flow. ③Pipeline constructor: used to construct a pipeline. A pipeline is a data structure that constrains several data streams. The pipeline includes a data stream linked list and data stream association relationship table. The data stream association relationship table includes data flow relationship pairs and data flow monitoring Item relationship pairs; data flow relationship pairs express the mutual influence relationship between two data flows, and this mutual influence relationship is embodied through monitoring item relationship pairs; to construct a pipeline model is to generate a pipeline model for each pipeline Uniquely identify, add the defined data flow to the pipeline and add constraints between the data flows (that is, define the data flow relationship). The schematic diagram of the pipeline model construction is shown in Figure 2. ④Monitoring engine: used to load a pipeline, receive business data transmitted through the application system interface, start the pipeline and load the business data into the monitoring items of each data stream, so as to monitor the changes of the monitoring items in the data stream in real time and record the changes. linkage and timing. ⑤Linkage self-adaptation module: Since the relationship between the data streams in the pipeline is set in the model, after the monitoring engine starts the pipeline and loads data, the self-adaptive module will correct the relationship according to the change status of the actual monitoring items and the linkage change Priority and association order in . ⑥Surface monitoring output module: used to output the status of all data stream linkage changes in a pipeline, which is composed of a monitoring item association pair of each data stream at different time points; The linkage closed line formed by the relationship between the connected monitoring items forms a curved section on the time axis of the pipeline, which expresses the linkage between various business data flows within a period of time, and its surface-based monitoring The schematic diagram of the principle is shown in Figure 3.

Claims (1)

1. the data stream monitoring method based on pipe method, it is characterized in that: 1. build data flow model: the data structure that builds a storage service data flow by data flow constructor, static attribute-name identification, code identification, description and the monitored item set of definition data flow, in this structure, inherit dynamic attribute-data access, monitored item increases progressively, building after data flow model, be saved in pipe database; 2. build pipeline model: by a pipeline of pipe configuration device structure, definition pipeline identification, adds the data flow architecture of first step structure in pipeline to, and every pipeline can add many data flow that have business association relation; 3. set up input channel: application system is called the interface in this method, the transmission of trigger data in the time that business datum changes, by interface, business datum is delivered in this method, set up the data exchange channels between this method and application system, the parameter of transmission comprises the data of pipeline identification, stream identification, monitored item mark, monitored item type, monitored item; 4. start supervisor engine: the data flow building by constructor and pipeline, in monitoring flow process, load and instantiation, load a pipeline from pipe database, receive the business datum that application system is transmitted through interface, start pipeline and business datum be loaded in the monitored item of each data flow, the data that receive are in real time kept in monitor database, and the in real time variation of monitored item in monitor data stream, linkage, time point that record changes; 5. start interlock adaptation module: according to the actual change order of monitor data in monitor database, the time interval changing, automatically revise priority and the shut sequence of the monitored item defining in pipeline; 6. export curved surface monitored results: on time shaft, intercept the monitored item while variation by relevance line, thereby form a curved surface forming based on the time period, by monitored item data; 7. the startup of pipeline and closing: when supervisor engine load and instantiation pipeline after, start pipeline, monitor in real time injecting after real data; After pipeline is manually stopped, supervisor engine stops pipeline instantiation, and the pipeline in monitor database is placed in to suspended state, no longer receives the real time data of this pipeline; When manual-lock or receive after data flow done state, supervisor engine stops pipeline instantiation, and unloading pipeline model, closes this pipeline receive path, removes pipeline data from monitor database.
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