CN115129722A - Energy optimization and promotion method for multi-source heterogeneous data - Google Patents

Energy optimization and promotion method for multi-source heterogeneous data Download PDF

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Publication number
CN115129722A
CN115129722A CN202210864344.1A CN202210864344A CN115129722A CN 115129722 A CN115129722 A CN 115129722A CN 202210864344 A CN202210864344 A CN 202210864344A CN 115129722 A CN115129722 A CN 115129722A
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data
database
source heterogeneous
mapping
energy optimization
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张玉迁
王庆国
于修和
邹春龙
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Zhongchuang Energy Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2471—Distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Fuzzy Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention relates to the technical field of data collection and management, and discloses an energy optimization and promotion method for multi-source heterogeneous data, which comprises the following steps: receiving and constructing a database lake library, receiving multi-source heterogeneous data and constructing a body, and the second step is as follows: extracting the semantics of the content of the imported data file, and the third step: and according to the RDF description and referring to the related ontology, a fourth step of: the invention optimizes the problems of data entry and query, complexity and convenience of semantic retrieval, realizes automatic establishment and convenient retrieval, can receive data of different sources and different structures, isomorphizes the data, and greatly improves the semantization, the user experience and the efficiency.

Description

Energy optimization and promotion method for multi-source heterogeneous data
Technical Field
The invention relates to the technical field of data collection and management, in particular to an energy optimization and promotion method for multi-source heterogeneous data.
Background
In the internet era, enterprises pay great attention to aspects such as storage, use, safety of data, and many business development are based on data drive, and data is exactly the core of enterprise, and under the big data environment, the enterprise faces diversified data structure: relational database, file data, No-SQL type, stream data, inverted index; the data source is also rich: an enterprise will typically have multiple relational databases; different types of files of different departments can be stored in different places, for example, 5 million cast body slice images exist in an analysis test center and cannot be stored in the warehouse in time due to unstable information systems and complex warehousing rules; inconvenience is brought to researchers to use the slice data to research oil and gas reservoirs. The superior department requires to put these photo data in storage, through extracting well name, depth, pairing with sample batch number in the database, standardizing photo name, rejecting duplicate photo, rejecting existing photo in the existing database, inputting photo index information, uploading photo file and so on multiple steps; the data arrangement work is complicated and the workload is huge.
Therefore, an energy optimization and promotion method for multi-source heterogeneous data is provided.
Disclosure of Invention
The invention mainly solves the technical problems in the prior art and provides an energy optimization and promotion method for multi-source heterogeneous data.
In order to achieve the purpose, the invention adopts the following technical scheme that the energy optimization and promotion method of the multi-source heterogeneous data comprises the following steps: the first step is as follows: receiving and constructing a database, a lake and reservoir library, receiving multi-source heterogeneous data, mapping the multi-source heterogeneous data into isomorphic data through a mapper, establishing an index, constructing an ontology, confirming the attribute and the parameter of the ontology, further adding attribute parameter description of the ontology, and storing the attribute parameter description into a graph, database, lake and reservoir library of a database, and a library server;
the second step is that: extracting the semantics of the content of the imported data file, establishing RDF description, and storing the established RDF description into a document type database of a database-in-lake repository server;
the third step: according to the RDF description and by referring to the related ontology, the association of the semantic hierarchy between the file corresponding to the RDF description and the ontology is realized, and the association is written into a database;
the fourth step: inquiring according to the index through an inquirer;
as a further limitation of the above solution, the mapping step includes:
a configuration step: configuring a mapper table for each type of source data of the multi-source heterogeneous data;
and a mapping finishing step: mapping the source data into a table structure according to the mapper table through a mapper;
index establishment: and establishing the index for the data of the table structure.
As a further limitation of the above solution, the original field is mapped to a corresponding table field during the mapping, and the original field and the table field are in one-to-one correspondence.
As a further limitation of the above solution, the mapping completion step further includes: maintaining the mapping relation by using a json data format, and enabling the source data field to be: id. origin _ name and origin _ desc are mapped to id, table _ name and table _ desc, respectively
As a further limitation of the above solution, in the index creating step, a table primary key field or any other unique value field is selected to create the index.
As a further limitation of the above solution, the RDF description includes nodes and edges, where a node represents an entity/resource/attribute, and an edge represents a relationship between an entity and a relationship between an entity and an attribute.
As a further limitation of the above scheme, the data-storage-and-library server is a data storage and management service platform including four databases, namely a relational database, a document database, a distributed file system and a graph database, the platform adopts a distributed operation and storage architecture, integrates various computers and single computers with data storage and operation functions, servers and computer clusters/server clusters, and provides various functional components including data management and algorithm development
As a further limitation of the above solution, the data storage and management service platform organizes and manages data files and their storage and exchange through log files and metadata files, where log record data contained in the log files exists in a key-value pair form and contains fields corresponding to the following contents: the name of the operator of the current operation, the type of the operation, the time of the operation, the state of the operation, the description of the current data and the storage place of the current data.
As a further limitation of the above solution, the graph database is Neo4j or any one of Cayley or grappg db; the document type database is any one of MongoDB or CouchDB.
Advantageous effects
The invention provides an energy optimization and promotion method for multi-source heterogeneous data. The method has the following beneficial effects:
(1) the invention optimizes the problems of data entry, query and semantic retrieval complexity and convenience, realizes automatic establishment and convenient retrieval, can receive data of different sources and different structures, can completely isomorphize a data system with larger difference such as a relational database and a non-relational database, can receive data of different sources and different structures, can also completely isomorphize the data system with larger difference such as the relational database and the non-relational database, and can map source data into table data through mapper relational mapping so as to hide the source data.
(2) The combined query method enables a user to query the multi-source heterogeneous data more simply, and meets the requirements of complex business on the multi-source data.
(3) According to the energy optimization and promotion method for the multi-source heterogeneous data, the problem of accuracy of body construction can be solved, and perfect body construction is realized; (3) the invention can solve the problem of multi-element isomerism of the data to be stored so as to realize that various data can be stored in the data lake and reservoir; the invention has the advantages of convenient use, traceable retrieval process, convenient management and convenient further detailed acquisition of retrieval result documents.
(4) According to the energy optimization and promotion method for the multi-source heterogeneous data, the convenience of semantic data lakes and reservoirs and the convenience of retrieval can be built, and manual intervention operation and convenience of semantic data lakes and reservoirs retrieval are achieved; the data storage security and stability can be guaranteed, and the data storage security and stability guarantee can be guaranteed.
Detailed Description
The embodiment I is a method for optimizing and improving energy of multi-source heterogeneous data, and the method comprises the following steps: the first step is as follows: receiving and constructing a database, a lake and reservoir library, receiving multi-source heterogeneous data, mapping the multi-source heterogeneous data into isomorphic data through a mapper, establishing an index, constructing an ontology, confirming the attribute and the parameter of the ontology, further adding attribute parameter description of the ontology, and storing the attribute parameter description into a graph, database, lake and reservoir library of a database, and a library server;
the second step is that: extracting the semantics of the content of the imported data file, establishing RDF description, and storing the established RDF description into a document type database of a database-in-lake repository server;
the third step: according to the RDF description and referring to the related ontology, the file corresponding to the RDF description is associated with the ontology by a semantic level, and the association is written into a graph database;
the fourth step: inquiring according to the index through an inquirer;
the mapping step includes:
a configuration step: configuring a mapper table for each type of source data of the multi-source heterogeneous data;
and a mapping finishing step: mapping the source data into a table structure according to the mapper table through a mapper;
index establishment: and establishing the index for the data of the table structure.
During the mapping, the original field is mapped into a corresponding table field, and the original field and the table field are in one-to-one correspondence.
The mapping completion step further comprises: maintaining the mapping relation by using a json data format, and enabling the source data field to be: id. origin _ name and origin _ desc are mapped to id, table _ name and table _ desc, respectively
In the index establishing step, a table primary key field or any other unique value field is selected to establish the index.
The RDF description contains nodes and edges, wherein the nodes represent entities/resources/attributes, and the edges represent relationships between entities and relationships between entities and attributes.
The data storage and management service platform integrates various single computers, servers and computer clusters/server clusters with data storage and operation functions and provides various functional components including data management and algorithm development, and the data storage and management service platform organizes and manages data files and storage and exchange thereof through log files and metadata files, wherein log record data contained in the log files exist in a key value pair mode and contain fields corresponding to the following contents: the method comprises the following steps of (1) obtaining an operator name, an operation type, an operation time, an operation state, a description of current data and a storage place of the current data of a current operation, wherein the graph database is Neo4j or any one of Cayley or GrapgDB; the document database is any one of MongoDB or CouchDB, and an ontology is inquired in the semantic data lake-library through a database to obtain attributes related to the ontology;
the semantic data lake-reservoir library provides a graphical retrieval interface and a layer-by-layer query interface, supports data relation map display of query results, and supports related operations of maps;
obtaining a source file corresponding to a query result, a matching list of files or data and contents of the queryable files;
the user can further confirm the node in the network map and refine the query result, and a group of engineering drawing files from a certain FTP is provided, wherein the IP address is 192.168.12.101, the port is 8080, the user name is admin, and the password is password.
And the data lake and reservoir server starts a connection service on a software interface of the data lake and reservoir server, inputs access interface information of the file data source and is successfully connected.
Further, 20 PDF files to be imported are seen on the interface, the 20 PDF engineering drawing files are imported according to an interface menu, and the data lake library server background executes the following operations on each PDF file while importing the files:
1. converting the PDF file into a plain text;
2. extracting semantic information and key words in the text by using a natural language processing method;
3. examining the semantics and keywords extracted in the previous step, comparing the semantics and keywords with the existing graph database body, attributes and tags, establishing RDF association between the body and the PDF file, and writing the RDF association into a graph database Neo4 j; the identity of the ontology is recorded in Neo4j, and other information (e.g., ID information for the source file corresponding to the query result) such as the pointer of the PDF file associated with the ontology is stored in the MongoDB.
The second embodiment provides an energy optimization and promotion method for multi-source heterogeneous data, which comprises the following steps: the first step is as follows: receiving and constructing a database, a database warehouse and a warehouse, receiving multi-source heterogeneous data, mapping the multi-source heterogeneous data into isomorphic data through a mapper, establishing an index, constructing an ontology, confirming the attribute and the parameter of the ontology, further adding the attribute parameter description of the ontology, and storing the attribute parameter description into a graph database, a warehouse and a warehouse of a database warehouse server;
the second step is that: extracting the semantics of the content of the imported data file, establishing RDF description, and storing the established RDF description into a document type database of a database-in-lake-reservoir library server;
the third step: according to the RDF description and referring to the related ontology, the file corresponding to the RDF description is associated with the ontology by a semantic level, and the association is written into a graph database;
the fourth step: inquiring according to the index through an inquirer;
the mapping step includes:
a configuration step: configuring a mapper table for each type of source data of the multi-source heterogeneous data;
and a mapping finishing step: mapping the source data into a table structure according to the mapper table through a mapper;
index establishment: and establishing the index for the data of the table structure.
During the mapping, the original field is mapped into a corresponding table field, and the original field and the table field are in one-to-one correspondence.
The mapping completion step further includes: maintaining the mapping relation by using a json data format, and enabling the source data field to be: id. origin _ name and origin _ desc are mapped to id, table _ name and table _ desc, respectively
In the index establishing step, a table primary key field or any other unique value field is selected to establish the index.
The RDF description contains nodes and edges, wherein the nodes represent entities/resources/attributes, and the edges represent relationships between the entities and the attributes.
The data storage and management service platform integrates various single computers, servers and computer clusters/server clusters with data storage and operation functions and provides various functional components including data management and algorithm development, and the data storage and management service platform organizes and manages data files and storage and exchange thereof through log files and metadata files, wherein log record data contained in the log files exist in a key value pair mode and contain fields corresponding to the following contents: the method comprises the following steps of (1) obtaining an operator name, an operation type, an operation time, an operation state, a description of current data and a storage place of the current data of a current operation, wherein the graph database is Neo4j or any one of Cayley or GrapgDB; the document type database is any one of MongoDB or CouchDB, and the local data lake and library server is composed of a relational database MariaDB, a document type database MongoDB, a distributed file system HDFS and a database Neo4 j. There is a set of data record files from a certain FTP and the files are in XLS format with IP address 192.168.12.101, port 8080, username admin and password passswd.
And the data lake and reservoir server starts a connection service on a software interface of the data lake and reservoir server, inputs access interface information of the file data source and is successfully connected.
Further, seeing the XLS file to be imported on the interface, importing the XLS data file according to the interface menu, and simultaneously importing the files, executing the following operations on each line in the file by the background of the database library server:
1. reading the row of data records;
2. extracting semantic information and keywords in the row of data records;
3. examining the semantics and keywords extracted in the previous step, comparing the semantics and keywords with the existing database body, attributes and tags, establishing RDF association between the body and the XLS file record, and writing the RDF association into a database Neo4 j; the identity of the ontology is recorded in Neo4j, and other information (e.g., ID information, source files for the query result) such as a pointer to the PDF file associated with the ontology is stored in the MongoDB.
The embodiment III provides an energy optimization and promotion method for multi-source heterogeneous data, which comprises the following steps: the first step is as follows: receiving and constructing a database, a lake and reservoir library, receiving multi-source heterogeneous data, mapping the multi-source heterogeneous data into isomorphic data through a mapper, establishing an index, constructing an ontology, confirming the attribute and the parameter of the ontology, further adding attribute parameter description of the ontology, and storing the attribute parameter description into a graph, database, lake and reservoir library of a database, and a library server;
the second step is that: extracting the semantics of the content of the imported data file, establishing RDF description, and storing the established RDF description into a document type database of a database-in-lake-reservoir library server;
the third step: according to the RDF description and referring to the related ontology, the file corresponding to the RDF description is associated with the ontology by a semantic level, and the association is written into a graph database;
the fourth step: inquiring according to the index through an inquirer;
the mapping step includes:
a configuration step: configuring a mapper table for each type of source data of the multi-source heterogeneous data;
and a mapping finishing step: mapping the source data into a table structure according to the mapper table through a mapper;
index establishment: and establishing the index for the data of the table structure.
During the mapping, the original field is mapped into a corresponding table field, and the original field and the table field are in one-to-one correspondence.
The mapping completion step further includes: maintaining the mapping relation by using a json data format, and enabling the source data field to be: id. origin _ name and origin _ desc are mapped to id, table _ name and table _ desc, respectively
In the index establishing step, a table main key field or any other unique value field is selected to establish the index.
The RDF description contains nodes and edges, wherein the nodes represent entities/resources/attributes, and the edges represent relationships between the entities and the attributes.
The data storage and management service platform organizes and manages data files and storage and exchange thereof through log files and metadata files, wherein the log record data contained in the log files exist in a key value pair mode and comprise fields corresponding to the following contents: the method comprises the following steps of (1) obtaining an operator name, an operation type, an operation time, an operation state, a description of current data and a storage place of the current data of a current operation, wherein the graph database is Neo4j or any one of Cayley or GrapgDB; the document database is any one of MongoDB or CouchDB, and the read: querier, a single data processing engine, mapper: mapper and relation table, table: isomorphic data source after mapping, field: a data source field.
Through optimizing data entry and data query, can receive the data of different sources and different structures, and with its isomorphism, and also can be completely isomorphism to the great data system of difference such as relational database and non-relational database, mainly through mapper relational mapping, map the source data into table data, make the source data hide, so, the user uses is to face oneself data structure, semantization and user experience have been improved greatly, it is simpler when making the user inquire the heterogeneous multisource data, needn't go to pay close attention to the storage mode of bottom layer data during the inquiry, only need accurately handle the mapping relation and the index of upper data, satisfy the higher use scene of requirement to multisource data.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. The energy optimization and promotion method for multi-source heterogeneous data is characterized by comprising the following steps of: the method comprises the following steps:
the first step is as follows: receiving and constructing a database, a lake and reservoir library, receiving multi-source heterogeneous data, mapping the multi-source heterogeneous data into isomorphic data through a mapper, establishing an index, constructing an ontology, confirming the attribute and the parameter of the ontology, further adding attribute parameter description of the ontology, and storing the attribute parameter description into a graph, database, lake and reservoir library of a database, and a library server;
the second step is that: extracting the semantics of the content of the imported data file, establishing RDF description, and storing the established RDF description into a document type database of a database-in-lake repository server;
the third step: according to the RDF description and referring to the related ontology, the file corresponding to the RDF description is associated with the ontology by a semantic level, and the association is written into a graph database;
the fourth step: and querying according to the index through a querier.
2. The energy optimization and promotion method for multi-source heterogeneous data according to claim 1, characterized in that: the mapping step includes:
a configuration step: configuring a mapper table for each type of source data of the multi-source heterogeneous data;
and a mapping finishing step: mapping the source data into a table structure according to the mapper table through a mapper;
index establishment: and establishing the index for the data of the table structure.
3. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 2, characterized in that: during the mapping, the original field is mapped to a corresponding table field, and the original field and the table field are in one-to-one correspondence.
4. The energy optimization and promotion method for multi-source heterogeneous data according to claim 3, characterized in that: the mapping completion step further comprises: maintaining the mapping relation by using a json data format, and enabling the source data field to be: id. origin _ name and origin _ desc are mapped to id, table _ name and table _ desc, respectively.
5. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 4, characterized in that: in the index establishing step, a table main key field or any other unique value field is selected to establish the index.
6. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 5, characterized in that: the RDF description contains nodes and edges, wherein the nodes represent entities/resources/attributes, and the edges represent relationships between entities and relationships between entities and attributes.
7. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 5, characterized in that: the data storage and management service platform comprises four databases, namely a relational database, a document database, a distributed file system and a graph database, adopts a distributed operation and storage architecture, integrates various computers, servers and computer clusters/server clusters with data storage and operation functions, and provides various functional components including data management and algorithm development.
8. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 5, characterized in that: the data storage and management service platform organizes and manages data files and storage and exchange thereof through log files and metadata files, wherein log record data contained in the log files exist in a key value pair mode and contain fields corresponding to the following contents: the name of the operator of the current operation, the type of the operation, the time of the operation, the state of the operation, the description of the current data and the storage place of the current data.
9. The energy optimization and promotion method for the multi-source heterogeneous data according to claim 5, characterized in that: the graph database is any one of Neo4j, Cayley or GrapgDB; the document type database is any one of MongoDB or CouchDB.
CN202210864344.1A 2022-07-21 2022-07-21 Energy optimization and promotion method for multi-source heterogeneous data Withdrawn CN115129722A (en)

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Application publication date: 20220930