CN113918537A - XML-based power grid multidimensional data modeling method - Google Patents
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Abstract
The invention discloses a power grid multidimensional data modeling method based on XML, which divides power grid dimension modeling into two stages, namely storage layer modeling and application layer modeling, so that a power grid dimension model is quickly, accurately and flexibly constructed; establishing a relational data model with multidimensional characteristics in a storage layer by using a relational database according to a star chart method; and designing an XML Schema at an application layer, and describing the multidimensional data cube and physical mapping. The OLAP based on the XML provides an effective modeling method for multi-dimensional data modeling of a storage layer and an application layer respectively aiming at the problems of data in power grid services, and has the characteristics of high efficiency, accuracy and flexibility; various multidimensional data models are established according to different data requirements, and the problems of non-uniform data standards, data isolated islands and the like are solved; the data are quickly integrated and shared, the management level of a power grid company is further improved and perfected, and greater competitiveness is brought to the power grid company.
Description
Technical Field
The invention relates to power grid dimension modeling, in particular to a power grid multidimensional data modeling method based on XML.
Background
With the rapid development of the informatization of the power grid industry, various data resources are continuously accumulated, and the requirement on data sharing among different service systems is urgent. And the problems of data isolated island, standard predicament, sharing difficulty and the like occur due to the lack of unified workflow management and data standards among the systems, so that data barriers are caused, and data enabling is hindered. The data warehouse technology can well solve the problems, and the most important work in the construction of the data warehouse is to establish a data model suitable for decision analysis. The dimension model is suitable for Processing data of large-scale data volume, better supports operation based On data warehouse mining and On-Line Analytical Processing (OLAP), can analyze a large amount of business data in multiple angles and multiple layers, and helps analysts to understand the data more deeply.
The difficulty of constructing a data model also rises continuously along with more and more heterogeneous data sources, larger data volume and more complex demand analysis of a service system in the power grid industry.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a power grid multidimensional data modeling method based on XML (extensive Makeup language) so as to meet the requirement of quickly, accurately and flexibly constructing a power grid dimension model.
The technical scheme is as follows: the invention relates to a power grid multidimensional data modeling method based on XML, which comprises the following steps:
(1) abstracting a multidimensional data model of OLAP into a cube containing a multidimensional space;
in the multidimensional space inside the cube, there are multiple dimensions of axes, each perpendicular to the other (the vertical concept of multidimensional space has surpassed the general three-dimensional vertical). Data of interest to the user is distributed in a multi-dimensional coordinate system composed of all dimensions.
Subject matter: the name of the data of an aspect to be analyzed, which may have a plurality of cubes; cube: is a multi-dimensional data set, which is composed of a fact table and a plurality of dimensions, and one dimension can be a dimension table; indexes are as follows: or called fact, corresponding to a specific fact table, corresponding to the decision support center, which is the center of the star model and contains all the metric values concerned by the user; maintaining: the dimension defines the central axis of the fact research, and the dimension needs to correspond to a dimension table which contains the values of various aggregation modes which can be taken by the dimension; dimension level: the values of the dimensions tend to have different hierarchical granularities, a dimension hierarchy being one that is partitioned from other, more detailed dimensions, in which each descending level corresponds to a more detailed level in detail. The number of levels in the dimension hierarchy corresponds to the significance level of the granularity in the query; and (3) measurement: a metric is a variable or measure corresponding to the center point of the study. A measure is a numerical value assigned to a column in the fact table; the attributes are as follows: attributes are objects that are used to define a dimension, and attributes are typically assigned to columns in a dimension table.
A topic may have multiple cubes, each cube consisting of a fact, multiple dimensions and multiple measurements, and there may be multiple dimension levels below the dimensions, and there may be multiple attributes for each topic, cube fact, dimension level.
(2) Modeling a storage layer, establishing a data storage structure conforming to multidimensional characteristics, and providing data support for an upper layer;
(2.1) analyzing historical business data of the enterprise, and finding and extracting multi-dimensional data objects in the historical business data;
and (2.2) adjusting and modifying the data storage structure in a multidimensional way to finally obtain a storage layer data model according with multidimensional characteristics.
And establishing a multidimensional data model based on the relational database by using a star chart method. According to the star diagram method, the process of designing the multidimensional data model is to establish two most important relational tables of an index table and a dimension table, define the association between the index table and the dimension table and finally obtain a relational model in star distribution.
(3) Modeling an application layer, and establishing a logical multidimensional data cube; the multidimensional data cube is oriented to multidimensional operation and multidimensional processing, and provides object support for online analysis and decision making.
(3.1) defining various data objects of the multidimensional data cube and specifying a physical mapping of each data object;
and (3.2) binding a well-structured multidimensional data cube with the data in the storage layer to form a complete OLAP multidimensional data model.
The multidimensional data model of the XML-based application layer, i.e., the multidimensional data cube, is defined by model metadata. The model metadata includes definitions of the various multidimensional data objects within the cube, and also specifies how the cube maps onto a relational model, or physical mapping, of the storage layer. Using XML, the metadata of the multidimensional data cube can be designed as an XML Schema. The application layer defines and stores a complete multidimensional data cube through XML Schema, and the system realizes the organization and storage of model metadata by means of the XMLSchema. Establishing a multidimensional data cube Schema, wherein a series of XML tags are required to be designed firstly, and various multidimensional data objects and physical mapping thereof are described by using self-defined tags; and then, forming a complete multidimensional data cube by various multidimensional data objects in a mode of nesting XML labels step by step.
A computer storage medium having stored thereon a computer program which, when executed by a processor, implements an XML-based grid multidimensional data modeling method as described above.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the reprocessor, the processor implementing a method for XML-based multidimensional data modeling of an electrical grid as described above when executing the computer program.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the problems of multiple heterogeneous data sources, large data volume, complex demand analysis and difficulty in constructing a data model in the power grid industry are solved;
2. the OLAP modeling scheme based on the XML provides an effective modeling method for multi-dimensional data modeling of a storage layer and an application layer respectively aiming at the problems of data in power grid services, and has the characteristics of high efficiency, accuracy and flexibility;
3. various multidimensional data models are established according to different data requirements, and the problems of non-uniform data standards, data isolated islands and the like are solved;
4. the method provides meaningful reference for theoretical research of OLAP modeling and application in the power grid, can establish various multidimensional data models for the data requirements of different service application scenes of the power grid company for different application scenes, and provides data support for service personnel and analysis decision personnel of the power grid company from different dimensions;
5. the method and the system quickly realize the integration and sharing of data, provide data support for the service application of the power grid company, further improve and perfect the management level of the power grid company and bring greater competitiveness to the power grid company.
Drawings
FIG. 1 is a multi-dimensional data model cube construction;
FIG. 2 is storage layer modeling;
FIG. 3 is application layer modeling.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
Example 1:
a power grid multidimensional data modeling method based on XML comprises the following steps:
(1) abstracting a multidimensional data model of OLAP into a cube containing a multidimensional space;
in the multidimensional space inside the cube, there are multiple dimensions of axes, each perpendicular to the other (the vertical concept of multidimensional space has surpassed the general three-dimensional vertical). Data of interest to the user is distributed in a multi-dimensional coordinate system composed of all dimensions.
Subject matter: the name of the data of an aspect to be analyzed, which may have a plurality of cubes; cube: is a multi-dimensional data set, which is composed of a fact table and a plurality of dimensions, and one dimension can be a dimension table; indexes are as follows: or called fact, corresponding to a specific fact table, corresponding to the decision support center, which is the center of the star model and contains all the metric values concerned by the user; maintaining: the dimension defines the central axis of the fact research, and the dimension needs to correspond to a dimension table which contains the values of various aggregation modes which can be taken by the dimension; dimension level: the values of the dimensions tend to have different hierarchical granularities, a dimension hierarchy being one that is partitioned from other, more detailed dimensions, in which each descending level corresponds to a more detailed level in detail. The number of levels in the dimension hierarchy corresponds to the significance level of the granularity in the query; and (3) measurement: a metric is a variable or measure corresponding to the center point of the study. A measure is a numerical value assigned to a column in the fact table; the attributes are as follows: attributes are objects that are used to define a dimension, and attributes are typically assigned to columns in a dimension table.
As shown in FIG. 1, a topic may have a plurality of cubes, each cube consisting of a fact, a plurality of dimensions, and a plurality of measures, and a plurality of dimension hierarchies may be below a dimension, and each topic, cube fact, dimension, and dimension hierarchy may have a plurality of attributes.
(2) Modeling a storage layer, establishing a data storage structure conforming to multidimensional characteristics, and providing data support for an upper layer;
(2.1) analyzing historical business data of the enterprise, and finding and extracting multi-dimensional data objects in the historical business data;
and (2.2) adjusting and modifying the data storage structure in a multidimensional way to finally obtain a storage layer data model according with multidimensional characteristics.
As shown in FIG. 2, a relational database-based multidimensional data model is built using a star graph approach. According to the star diagram method, the process of designing the multidimensional data model is to establish two most important relational tables of an index table and a dimension table, define the association between the index table and the dimension table and finally obtain a relational model in star distribution.
The index table is composed of an index column and a dimension foreign key. The index column is used for storing index data, and the other columns except the index column are used for storing dimension foreign keys. The dimension outer key points to the main key of each dimension table and is a key for the interconnection of the index table and the dimension table. The index epitope is in the middle of the star plot and is indicated by a rectangle. Each dimension table represents a certain dimension of the data object and is used for storing dimension data. The main keys of the dimension table correspond to the outer keys of the index table one by one. Each dimension table contains all dimension hierarchies for that dimension. Each dimension hierarchy occupies a column in the dimension table. Each dimension table is distributed on one corner of the star model and is represented by a diamond. After the index table and the dimension tables are established, model combination is carried out by using the association of the index table and the main keys and the foreign keys of the dimension tables, and therefore a complete star model can be obtained.
The method is analyzed by a star model which is commonly used in the power grid and relates to the analysis subject of electric quantity and electric charge. As shown in fig. 2, the fact table of electricity quantity and electricity charge is located at the center, and six dimension tables are distributed on the edges of the star chart, which are respectively a time dimension table, a region dimension table, a user attribute dimension table, a user category dimension table, an industry attribute dimension table and a line station area dimension table. The primary key of each dimension table is associated with the corresponding foreign key of the index table.
(3) Modeling an application layer, and establishing a logical multidimensional data cube; the multidimensional data cube is oriented to multidimensional operation and multidimensional processing, and provides object support for online analysis and decision making.
(3.1) defining various data objects of the multidimensional data cube and specifying a physical mapping of each data object;
and (3.2) binding a well-structured multidimensional data cube with the data in the storage layer to form a complete OLAP multidimensional data model.
As shown in FIG. 3, the multidimensional data model of the XML-based application layer, i.e., the multidimensional data cube, is defined by model metadata. The model metadata includes definitions of the various multidimensional data objects within the cube, and also specifies how the cube maps onto a relational model, or physical mapping, of the storage layer.
Using XML, the metadata of the multidimensional data cube can be designed as an XML Schema. The application layer defines and stores a complete multidimensional data cube through XML Schema, and the system realizes the organization and storage of model metadata by means of the XMLSchema. Establishing a multidimensional data cube Schema, wherein a series of XML tags are required to be designed firstly, and various multidimensional data objects and physical mapping thereof are described by using self-defined tags; and then, forming a complete multidimensional data cube by various multidimensional data objects in a mode of nesting XML labels step by step.
Aiming at different multidimensional data objects, taking the electric quantity and electric charge analysis subject as an example, the following XML tags are designed:
< Cube >: < Cube > defines the entire multidimensional data Cube, which is a collection of dimensions and indices. Cube is generally the top-most label of the multidimensional data model Schema. In terms of physical mapping, since the index Table is in the core position of the Cube, the mapping of the index Table is directly defined inside the < Cube > tag, which is called Table attribute.
< Measure >: < Measure > is index data, which is metric data most concerned by users in the cube. The physical mapping of the metric data is defined by the column attribute of the Measure tag.
< Dimension >: < Dimension > represents a certain Dimension. Each dimension divides the index data into smaller categories. For the physical layer mapping aspect, one dimension corresponds to one dimension table and also corresponds to one external key pointing to the dimension table. The inner layer tag < Table > attribute of the Dimension tag is used for defining the Dimension Table of the Dimension mapping; the foreignKey attribute of the Dimension tag defines the pointer table foreign key associated with the Dimension table.
< Level >: < Level > is located inside < Dimension > and represents a Dimension Level in a Dimension. A dimension may be conceptually divided into multiple levels. Each dimension hierarchy maps to a column in the dimension table on physical storage, and the physical mapping is defined by column attributes. And combining the XML tags together in a progressive nesting mode to obtain a complete XML Schema. The multidimensional data modeling scheme based on XML has wide application in the power grid, and an XML Schema for describing the service condition of the power grid is given below.
Example 2:
a computer storage medium having stored thereon a computer program which, when executed by a processor, implements an XML-based grid multidimensional data modeling method as described above.
Example 3:
a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the reprocessor, the processor implementing a method for XML-based multidimensional data modeling of an electrical grid as described above when executing the computer program.
Claims (6)
1. A power grid multidimensional data modeling method based on XML is characterized by comprising the following steps:
(1) abstracting a multidimensional data model of OLAP into a cube containing a multidimensional space;
(2) modeling a storage layer, establishing a data storage structure conforming to multidimensional characteristics, and providing data support for an upper layer;
(3) and modeling an application layer, and establishing a logically multidimensional data cube.
2. The XML-based power grid multidimensional data modeling method according to claim 1, wherein the step (2) is specifically as follows:
(2.1) analyzing historical business data of the enterprise, and finding and extracting multi-dimensional data objects in the historical business data;
and (2.2) adjusting and modifying the data storage structure in a multidimensional way to finally obtain a storage layer data model according with multidimensional characteristics.
3. The XML-based power grid multidimensional data modeling method according to claim 1, wherein the step (3) is specifically as follows:
(3.1) defining various data objects of the multidimensional data cube and specifying a physical mapping of each data object;
and (3.2) binding a well-structured multidimensional data cube with the data in the storage layer to form a complete OLAP multidimensional data model.
4. The XML-based power grid multidimensional data modeling method as claimed in claim 1, wherein the multidimensional data cube in step (3) is oriented to multidimensional operation and multidimensional processing, and provides object support for online analysis and decision making.
5. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements an XML-based grid multidimensional data modeling method as claimed in any one of claims 1 to 4.
6. A computer device comprising a storage, a processor and a computer program stored on the storage and executable on the reprocessor, wherein the processor when executing the computer program implements an XML-based grid multidimensional data modeling method as claimed in any one of claims 1 to 4.
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