CN112507006A - Power grid enterprise operation data integration system based on cloud - Google Patents
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Abstract
The embodiment of the application provides a power grid enterprise operation data integration system based on high in clouds, including the data access layer: the data source layer is used for extracting, converting and synchronizing various heterogeneous data from the data source layer to the upper layer; a data management layer: the enterprise data management and control system is used for realizing enterprise data management and control in a mode including metadata management, data dictionary and main data management and data quality management; data model layer: the basic table is used for classifying the universe data according to the theme to form a full-service basic model; general analysis model layer: the system is used for providing data service for a value-added service scene of a high-quality client and a loan credit granting scene of a financial institution; a data service layer: for providing data servitization tools and high available service response capabilities. The data processing process is limited in multiple aspects such as data extraction, main body classification and the like by redefining the specific content and application function of the multilayer data model, so that the data processing efficiency is improved.
Description
Technical Field
The application belongs to the field of data processing, and particularly relates to a power grid enterprise operation data integration system based on a cloud.
Background
The existing cloud-end data processing method is single, only can realize operation, control, storage and analysis of data, but cannot realize mining processing of the data, cannot realize intelligent mining and analysis of associated data by using a large database according to the type of the data required by a user, cannot achieve the purpose of improving the intelligent degree of data processing, cannot achieve the purpose of establishing a tree induction classifier and mining and integrating data through user editing constraint conditions, cannot achieve the purpose of performing association mining according to integrated data information, and therefore brings great inconvenience to people who utilize a cloud computing processing system. Data classification is too complicated among the current power grid enterprise, and the work load when carrying out data classification among the prior art is very huge, has seriously reduced work efficiency.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the cloud-based power grid enterprise operation data integration system is provided, and the data processing process is limited from multiple aspects such as data extraction, main body classification and the like by redefining the specific content and application function of a multilayer data model, so that the data processing efficiency is improved.
Specifically, the power grid enterprise operation data integration system based on high in clouds that this application embodiment provided includes:
a data access layer: the data source layer is used for extracting, converting and synchronizing various heterogeneous data from the data source layer to the upper layer;
a data management layer: the enterprise data management and control system is used for realizing enterprise data management and control in a mode including metadata management, data dictionary and main data management and data quality management;
data model layer: the system comprises a data asset directory management tool, a global data base table, a wide table base table and a global data base table, wherein the data asset directory management tool is used for classifying the global data base table according to subjects to form a full-service base model, aggregating the wide table base table into a wide table model according to the subjects and the incidence relation between main data and multi-dimensional data, and managing and controlling the wide table base table by using the;
general analysis model layer: the system comprises a general analysis model, a fault diagnosis model and a fraud early warning model, wherein the general analysis model is used for providing data service for value-added service scenes of high-quality customers and loan and credit scenes of financial institutions;
a data service layer: the method is used for providing a data servitization tool and high available service response capability, monitoring service performance, recording the calling frequency of service and data through a data access log, and further measuring the data heat.
Optionally, in the data access layer:
the collection tools including ETL including button, real-time collection mode including socketRocket, collection mode including Flume log, FTP tool including git-FTP were used.
Optionally, a data governance system of Apache Atlas is used in the data governance layer.
Optionally, in the data model layer and the general analysis model layer:
in the aspect of computing and storing, Spark and Flink are used for real-time streaming computing, MapReduce is used for offline computing, HDFS, a distributed columnar database Hbase, a self-developed transactional database GreatDB and a time sequence database Timescale DB are used for storing, and GreenPlum and Gbase8a are used for an MPP database;
in the aspect of data modeling, a self-developed 'M +' platform is used or a data mining platform such as RapidMiner, KNIME and the like is adopted.
Optionally, in the data service layer:
data service directory management needs to be self-developed, a data service is published by using SpringCloud, a data service router uses Nginx, and service performance monitoring uses APM.
Optionally, in the integrated system:
the graphic report uses Tableau/Echarts, Bosun is used for early warning and monitoring, and an SLCD visualization tool is used for large-screen display;
the auxiliary components include message queue using Kafka, resource management using YARN, platform scheduling using ZooKeeper, and task scheduling using Oozie or Azkaban.
Optionally, the integration system further comprises automaton complex event pattern monitoring;
the method specifically comprises the following steps that Cayuga adopts a processing model of a production line, each input result can be output to the next processing module in real time, and meanwhile, a priority queue is adopted to process a plurality of events with the same timestamp and equivalent states together.
Optionally, the integration system further comprises matching tree complex event pattern monitoring;
the method specifically comprises the steps that a tree structure is used for expressing the aggregation relation of composite events, child nodes express the most basic events, middle nodes express the composite events, and root nodes express the most complex events;
when a tree child node receives a simple event, a father node matches the received simple event transmitted by the child node according to a rule defined in advance, if the simple event is successful, the father node and the father node are analogized, the father node reaches the last root node, the whole process is a recursive process, otherwise, the complex event does not occur.
Optionally, the integrated system further includes directed graph complex event mode monitoring;
the method specifically comprises the steps that a composite rule of an event is represented by an edge of a directed graph, when the event is triggered, the carried rule can be started, the disadvantage of a directed graph complex event mode is that the triggering and starting of a basic event are not considered in the sequence of the event, and a directed graph complex event monitoring mode system comprises SNOOP and TelegraphCQ.
Optionally, the integration system further comprises a Petri net complex event pattern monitoring;
the system specifically comprises a library station, a transition, a directed arc W and a dragging key, wherein an input library is used for modeling simple events, an output library is used for modeling complex events, and an auxiliary library is used for describing the dependency relationship among the events;
the dragging is dependent on the relative position of the main dragging, and the Petri net analyzes the logical relation and the conditional constraint between the events according to the hierarchical idea.
The beneficial effect that technical scheme that this application provided brought is:
drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a cloud-based power grid enterprise operation data integration system provided in an embodiment of the present application.
Detailed Description
To make the structure and advantages of the present application clearer, the structure of the present application will be further described with reference to the accompanying drawings.
Example one
Specifically, the power grid enterprise operation data integration system based on cloud that this application embodiment provided, as shown in fig. 1, includes:
a data access layer: the data source layer is used for extracting, converting and synchronizing various heterogeneous data from the data source layer to the upper layer;
a data management layer: the enterprise data management and control system is used for realizing enterprise data management and control in a mode including metadata management, data dictionary and main data management and data quality management;
data model layer: the system comprises a data asset directory management tool, a global data base table, a wide table base table and a global data base table, wherein the data asset directory management tool is used for classifying the global data base table according to subjects to form a full-service base model, aggregating the wide table base table into a wide table model according to the subjects and the incidence relation between main data and multi-dimensional data, and managing and controlling the wide table base table by using the;
general analysis model layer: the system comprises a general analysis model, a fault diagnosis model and a fraud early warning model, wherein the general analysis model is used for providing data service for value-added service scenes of high-quality customers and loan and credit scenes of financial institutions;
a data service layer: the method is used for providing a data servitization tool and high available service response capability, monitoring service performance, recording the calling frequency of service and data through a data access log, and further measuring the data heat.
In practice, the data for the project design is roughly divided into the following categories according to the needs of the project study:
(1) distribution network automation system data
The primary data required by project research are distribution network automation system data, and the data content is grid model data (CIME file) and measurement data (DAT file) of a typical region.
Claim description: the required models, measurement and utilization need to be directed at the same feeder line, and the number of the feeder lines is more than 10.
(2) Marketing system
The required marketing system data is: user basic data (including client basic information data, client power supply information, metering data, load information and the like), marketing account data (transformer substation information, power supply area information, power supply line information, public transformer information, metering terminal information and the like)
(3) User acquisition of system data
The project-required acquisition system data includes: medium voltage side, low pressure side data, every data message includes: voltage, current, power data;
data requirements are as follows: distribution network measurement and medium-low voltage application need be directed at same time quantum, and the data scale is at least 1 year and the interval is 60 minutes data mostly.
(4) PMS system data
Project research requires that the data content in the PMS system is: and line and equipment parameter data in the power grid.
(5) GIS platform and other data
The project research needs power grid GIS platform data information, and in addition, needs data capable of expressing station-line-change and relation data thereof, including: power line information, transformer data, device association data, metering device information, and the like.
In order to integrate the data, the cloud-based power grid enterprise operation data integration architecture is divided into 5 levels. As shown in fig. 1, the following steps are performed from bottom to top in sequence:
1) a data access layer: the support extracts, converts and synchronizes various types of heterogeneous data from the data source layer to the upper layer. The main mode is as follows: ETL, real-time collection, data replication, streaming data access, FTP, log collection, etc.
2) A data management layer: the enterprise data management and control are realized through a series of modes such as metadata management, data dictionary and main data management, data quality management and the like, the data distribution condition is clearly known, the data reliability is improved to the maximum extent, and a solid data base is provided for the upper layer.
3) Data model layer: firstly, the basic table of the universe data is classified according to the theme to form a full-service basic model, such as personnel, finance, customers, power grid and the like. In order to facilitate rapid analysis and application, a series of wide-list models such as a personnel wide list, a financial wide list, a customer wide list, a power grid wide list and the like are aggregated according to the association relationship between the theme and the main data and the multi-dimensional data. The model forms data assets and is controlled by a data asset directory management tool.
4) General analysis model layer: the model is a high-level model with universality abstracted close to a service scene, such as a credit rating model, and can simultaneously provide data services for value-added service scenes (internal) of high-quality customers, loan credit granting scenes (external) of financial institutions and the like. Other general analysis models include prediction models, fault diagnosis models, fraud early warning models, and the like.
5) A data service layer: the data service tool and the high available service response capability are provided, the service performance is monitored, and in addition, the data access log records the calling frequency of the service and the data, thereby measuring the data heat. The upper application can access the data of the data model layer and the high-level model of the general analysis model layer through the data service layer, and the purposes of quickly constructing the data application and exploring and mining are achieved.
In order to support the data integration architecture, an open and open-source technical framework is adopted, the most appropriate mature component is selected at each level, and in the data access layer: the collection tools including ETL including button, real-time collection mode including socketRocket, collection mode including Flume log, FTP tool including git-FTP were used.
The data governance system of Apache Atlas is used in the data governance layer.
In the data model layer and the general analysis model layer:
in the aspect of computing and storing, Spark and Flink are used for real-time streaming computing, MapReduce is used for offline computing, HDFS, a distributed columnar database Hbase, a self-developed transactional database GreatDB and a time sequence database Timescale DB are used for storing, and GreenPlum and Gbase8a are used for an MPP data warehouse. In the aspect of data modeling, a self-developed 'M +' platform can be used, and data mining platforms such as RapidMiner and KNIME can also be adopted. In addition, the picture video analysis components opencv, VideoEye, and speech semantic recognition simon, etc. may be needed in some real-time monitoring scenes.
In the data service layer:
data service directory management needs to be self-developed, a data service is published by using SpringCloud, a data service router uses Nginx, and service performance monitoring uses APM.
In the integrated system: the graphic report uses Tableau/Echarts, Bosun is used for early warning and monitoring, and an SLCD visualization tool is used for large-screen display;
the auxiliary components include message queue using Kafka, resource management using YARN, platform scheduling using ZooKeeper, and task scheduling using Oozie or Azkaban.
Automaton complex event pattern monitoring
Automaton complex event pattern monitoring is mature on stream event handling problems, and automatons change state only if atomic events occur. When the schema of the automaton is accepted, it is determined that a composite event has occurred. The regular table is constructed by using an automaton pattern. The extended automaton may W define constraints between events (time limits or content constraints) that need additional saving. Systems using automaton complex event monitoring mode include ODE, SASE, Cayuga, etc. The Cayuga adopts a subscription-publishing technology, has good expansibility, supports concurrent subscription events (Subscribe events), and provides good scalability. And by adopting a processing model of a production line, each input result can be output to the next processing module in real time. A priority queue is employed to process multiple events together that have equivalent states for the same timestamp. The kernel is single-threaded, and the performance of the Cayuga system is not obviously improved.
Matching tree complex event pattern monitoring
The complex event pattern monitoring of the matching tree is definite in relation of hierarchical expression, the tree structure is used for expressing the aggregation relation of the composite events, child nodes express the most basic events, middle nodes express the composite events, and root nodes express the most complex events. When a tree child node receives a simple event, a father node matches the received simple event transmitted by the child node according to a rule defined in advance, if the simple event is successful, the father node and the father node are analogized, the father node reaches the last root node, the whole process is a recursive process, otherwise, the complex event does not occur. The Esper system adopts a matching tree complex event monitoring mode, and adopts two modes of bottom-up and top-down based on the matching complex event monitoring of the tree. The top-down detection is that the father node inquires the son node, and is suitable for detecting a negative event.
Directed graph complex event pattern monitoring
In the monitoring of the complex event mode of the directed graph, the nodes also have rules to represent the most basic events, the synthesis rules of the events are represented by the edges of the directed graph, and the rules are also started when the events are triggered. The directed graph complex event model has the disadvantage that the triggering of the basic events is initiated without regard to the order of the events. The system adopting the directed graph complex event monitoring mode comprises SNOOP, TelegraphCQ and the like.
Petri net complex event pattern monitoring
The Petri network complex event mode monitoring is a complex event mode for inputting a basic event and outputting a composite event, when the composite event occurs, the last node in a sequence is marked, and only simple events arriving in a matching sequence are detected. Mainly comprises a custody, a transition, a directed arc W and a dragging. The input library models simple events, the output library models complex events, and the auxiliary library describes the dependency relationship among the events. The drag is dependent on the relative position of the primary drag. The Petri net analyzes logical relations and conditional constraints among events according to a layered idea.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. Power grid enterprise operation data integration system based on high in clouds, its characterized in that, integration system includes:
a data access layer: the data source layer is used for extracting, converting and synchronizing various heterogeneous data from the data source layer to the upper layer;
a data management layer: the enterprise data management and control system is used for realizing enterprise data management and control in a mode including metadata management, data dictionary and main data management and data quality management;
data model layer: the system comprises a data asset directory management tool, a global data base table, a wide table base table and a global data base table, wherein the data asset directory management tool is used for classifying the global data base table according to subjects to form a full-service base model, aggregating the wide table base table into a wide table model according to the subjects and the incidence relation between main data and multi-dimensional data, and managing and controlling the wide table base table by using the;
general analysis model layer: the system comprises a general analysis model, a fault diagnosis model and a fraud early warning model, wherein the general analysis model is used for providing data service for value-added service scenes of high-quality customers and loan and credit scenes of financial institutions;
a data service layer: the method is used for providing a data servitization tool and high available service response capability, monitoring service performance, recording the calling frequency of service and data through a data access log, and further measuring the data heat.
2. The cloud-based power grid enterprise operation data integration system of claim 1, wherein in the data access layer:
the collection tools including ETL including button, real-time collection mode including socketRocket, collection mode including Flume log, FTP tool including git-FTP were used.
3. The cloud-based grid enterprise operation data integration system of claim 1, wherein an Apache Atlas data governance system is used in the data governance layer.
4. The cloud-based grid enterprise operation data integration system of claim 1, wherein in the data model layer and the generic analysis model layer:
in the aspect of computing and storing, Spark and Flink are used for real-time streaming computing, MapReduce is used for offline computing, HDFS, a distributed columnar database Hbase, a self-developed transactional database GreatDB and a time sequence database Timescale DB are used for storing, and GreenPlum and Gbase8a are used for an MPP database;
in the aspect of data modeling, a self-developed 'M +' platform is used or a data mining platform such as RapidMiner, KNIME and the like is adopted.
5. The cloud-based grid enterprise operation data integration system of claim 1, wherein in the data services layer:
data service directory management needs to be self-developed, a data service is published by using SpringCloud, a data service router uses Nginx, and service performance monitoring uses APM.
6. The cloud-based grid enterprise operation data integration system of claim 1, wherein in the integration system:
the graphic report uses Tableau/Echarts, Bosun is used for early warning and monitoring, and an SLCD visualization tool is used for large-screen display;
the auxiliary components include message queue using Kafka, resource management using YARN, platform scheduling using ZooKeeper, and task scheduling using Oozie or Azkaban.
7. The cloud-based grid enterprise operation data integration system of claim 1, further comprising automaton complex event pattern monitoring;
the method specifically comprises the following steps that Cayuga adopts a processing model of a production line, each input result can be output to the next processing module in real time, and meanwhile, a priority queue is adopted to process a plurality of events with the same timestamp and equivalent states together.
8. The cloud-based grid enterprise operation data integration system of claim 1, further comprising matching tree complex event pattern monitoring;
the method specifically comprises the steps that a tree structure is used for expressing the aggregation relation of composite events, child nodes express the most basic events, middle nodes express the composite events, and root nodes express the most complex events;
when a tree child node receives a simple event, a father node matches the received simple event transmitted by the child node according to a rule defined in advance, if the simple event is successful, the father node and the father node are analogized, the father node reaches the last root node, the whole process is a recursive process, otherwise, the complex event does not occur.
9. The cloud-based grid enterprise operation data integration system of claim 1, further comprising directed graph complex event pattern monitoring;
the method specifically comprises the steps that a composite rule of an event is represented by an edge of a directed graph, when the event is triggered, the carried rule can be started, the disadvantage of a directed graph complex event mode is that the triggering and starting of a basic event are not considered in the sequence of the event, and a directed graph complex event monitoring mode system comprises SNOOP and TelegraphCQ.
10. The cloud-based power grid enterprise operation data integration system of claim 1, further comprising Petri Net complex event pattern monitoring;
the system specifically comprises a library station, a transition, a directed arc W and a dragging key, wherein an input library is used for modeling simple events, an output library is used for modeling complex events, and an auxiliary library is used for describing the dependency relationship among the events;
the dragging is dependent on the relative position of the main dragging, and the Petri net analyzes the logical relation and the conditional constraint between the events according to the hierarchical idea.
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