CN114610809A - Power grid data structured processing method and device - Google Patents

Power grid data structured processing method and device Download PDF

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Publication number
CN114610809A
CN114610809A CN202210269169.1A CN202210269169A CN114610809A CN 114610809 A CN114610809 A CN 114610809A CN 202210269169 A CN202210269169 A CN 202210269169A CN 114610809 A CN114610809 A CN 114610809A
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Prior art keywords
data
power grid
grid data
structured
processing
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Chinese (zh)
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窦如婷
石嘉豪
陶秀杰
周育忠
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CSG Electric Power Research Institute
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CSG Electric Power Research Institute
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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
    • G06F16/25—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
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • 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
    • G06F16/258—Data format conversion from or to a database

Abstract

The invention relates to a power grid data structured processing method and a device, wherein a data extraction model is adjusted by updating data of a structure type, and power grid data of different data sources are extracted and processed according to the adjusted data extraction model to obtain power grid data of multiple structure types. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.

Description

Power grid data structured processing method and device
Technical Field
The invention relates to the technical field of data processing, in particular to a power grid data structured processing method and device.
Background
The power grid refers to a whole formed by a substation with various voltages and a power transmission and distribution line in a power system, comprises units of power transformation, power transmission, power distribution and the like, and is a basic component of the power system. In a broad sense, a power grid refers to an overall system for securing a narrow-sense power grid, and includes multiple system departments such as power generation, power distribution, and power management. The generalized power grid system can generate diversified power grid data such as various internal data such as power grid production case data, major accident event data, power grid system data, power grid report result data, power grid equipment management information data and power grid new technology product knowledge data and external data such as internet data and equipment acquisition data when guaranteeing normal operation of a narrow-sense power grid, and provides knowledge support and technical support for normal operation of the power grid.
In normal operation of the generalized power grid system, relevant personnel need to acquire relevant power grid data generated during operation. However, due to the diversification and complication of the types of generalized power grid data, the universality, the sharing performance, the portability and the data analysis reliability of the power grid data are poor, and great interference is caused to the information acquisition or further data processing of related personnel.
Disclosure of Invention
Therefore, it is necessary to provide a method and a device for processing power grid data in a structured manner, which are used in normal operation of a generalized power grid system, and have the defects of poor generality, sharing, portability and data analysis reliability of the power grid data, and large interference on information acquisition of related personnel or further data processing.
A power grid data structuring processing method comprises the following steps:
acquiring data update of each structure type, and adjusting a data extraction model;
performing data extraction processing on the power grid data of different data sources according to the data extraction model to obtain power grid data of multiple structure types;
converting the multi-structure type power grid data into structured power grid data;
and converting the structured grid data into a structured processing result with a corresponding format according to a preset output mode or a user request mode.
According to the power grid data structuring processing method, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is carried out on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the readable and visual requirements or the processing requirements of third parties for data, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
In one embodiment, the process of obtaining data updates for each structure type includes the steps of:
obtaining model category updates, entity tag updates, relationship tag updates, and/or attribute tag updates.
In one embodiment, the data extraction process includes full extraction, incremental extraction and real-time extraction.
In one embodiment, the process of adapting a data extraction model comprises the steps of:
and processing a data updating result through a machine learning algorithm, and training a data extraction model.
In one embodiment, a process for converting multiple structure types of grid data into structured grid data comprises the steps of:
and respectively carrying out data filtering processing, data conversion processing, data loading processing, data cleaning processing, dirty data processing and data standardization processing on the multi-structure type power grid data to obtain structured power grid data.
In one embodiment, the data cleansing process includes non-null checking, primary key duplication, illegal code cleansing, illegal value cleansing, data format checking, and record count checking.
In one embodiment, the different data sources include databases such as Oracle, sql server, MySql, Hbase, Hive, greenply, GBase, PostgreSQL, SOLR, Redis, ODPS, OTS, and GDS, and the file types of the grid data of the different data sources include files such as FTP, XML, CSV, JSON, and EXCEL.
In one embodiment, the corresponding format includes a chart, a query or a report, etc.
A grid data structured processing apparatus, comprising:
the model adjusting module is used for acquiring data update of each structure type and adjusting the data extraction model;
the data extraction module is used for performing data extraction processing on the power grid data of different data sources according to the data extraction model to obtain power grid data of multiple structure types;
the data conversion module is used for converting the multi-structure type power grid data into structured power grid data;
and the result generation module is used for converting the structured grid data into a structured processing result in a corresponding format according to a preset output mode or a user request mode.
According to the power grid data structuring processing device, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is carried out on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the grid data structuring processing method of any of the above embodiments when executing the computer program.
According to the computer equipment, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is carried out on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, realizes the steps of the grid data structuring method of any of the above embodiments.
According to the computer-readable storage medium, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is performed on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
Drawings
FIG. 1 is a flow diagram of a grid data structuring process according to an embodiment;
FIG. 2 is a flow chart of a grid data structuring process according to another embodiment;
FIG. 3 is a flow chart of a grid data structuring process according to yet another embodiment;
fig. 4 is a block diagram of a grid data structuring processing apparatus according to an embodiment.
Detailed Description
For better understanding of the objects, technical solutions and effects of the present invention, the present invention will be further explained with reference to the accompanying drawings and examples. Meanwhile, the following described examples are only for explaining the present invention, and are not intended to limit the present invention.
The embodiment of the invention provides a power grid data structured processing method.
Fig. 1 is a flowchart of a grid data structuring processing method according to an embodiment, and as shown in fig. 1, the grid data structuring processing method according to an embodiment includes steps S100 to S103:
s100, acquiring data update of each structure type, and adjusting a data extraction model;
the data extraction model is a built model facing related personnel or users and can be adjusted according to multi-channel and multi-dimensional data. And updating and acquiring data of various structure types in real time to adjust the content of the data extraction model.
In one embodiment, fig. 2 is a power grid data structuring processing method according to another embodiment, and as shown in fig. 2, the process of acquiring data updates of each structure type in step S100 includes step S200:
s200, model category updating, entity label updating, relation label updating and/or attribute label updating are obtained.
The data extraction model comprises model categories, entity labels, relationship labels and attribute labels related to the power industry. The data update of each structure type acquired in step S200 is used to support the structured construction of the data extraction model knowledge graph and the construction of new words. Correspondingly, in one embodiment, the data extraction model comprises an entity extraction model, a relation extraction model and an attribute extraction model.
In one embodiment, as shown in fig. 2, the process of adjusting the data extraction model in step S100 includes step S300:
and S300, processing a data updating result through a machine learning algorithm, and training a data extraction model.
The data of each structure type is updated, the brought data volume is borne by a machine learning algorithm, and the workload of an artificial training data extraction model is reduced.
The data extraction model formulates an associated data element criterion for each data item and defines certain processing rules for each criterion data element. These processing logic include: data conversion, data verification, data splicing assignment and the like. Based on technologies such as machine learning, the data fields are recognized and identified, and the problem of data irregularity encountered in the data processing process is solved through the automatic benchmarking technology of data.
The data items are mapped to standard library data items: and simplifying manual operation by means of machine learning recommendation, recommending data item associated data table fields with highest similarity according to semantic similarity and sampling value domain test, and selecting a proper conversion rule according to data characteristics to perform automatic standardized test. And automatically generating the auditing task of the fields according to the rule template of the data item.
The rule system contains many data processing logics: converting data items in various time formats in different data sources into a uniform timestamp format; encrypting or performing hash conversion on the data item; for example, the identification number is checked, whether the identification number is a legal 18-bit identification number is checked, and if the identification number is 15-bit, the identification number is uniformly converted into 18 bits; connecting a plurality of data items into one data item through a designated splicing symbol; assigning a certain constant or variable value to a certain data item, etc.
S101, performing data extraction processing on the power grid data of different data sources according to a data extraction model to obtain power grid data of multiple structure types;
the power grid data comprises internal resource data and external resource data. The internal resource data comprises power grid production case data, major accident event data, power grid system data, power grid report result data, power grid equipment management information data and power grid new technology product knowledge data; the external resource data consists of known network data, internet data and equipment acquisition data.
In one embodiment, the data sources of the grid data include databases such as Oracle, SqlServer, MySql, Hbase, Hive, greenply, GBase, PostgreSQL, SOLR, Redis, ODPS, OTS, and GDS, based on the difference of the types of the target libraries. According to the distinction of the file types, the file types of the power grid data comprise files such as FTP, XML, CSV, JSON and EXCEL.
In one embodiment, the acquisition tool based on cloud computing and distributed storage is used as a tool means for data extraction processing to realize the unified extraction of structured data, semi-structured data and unstructured data.
In one embodiment, the data extraction processing mode of the power grid data comprises full extraction, incremental extraction and real-time extraction. The full extraction is data mirroring or data copying, and the power grid data of the table or view in the data source is extracted from the data source as it is. The incremental extraction of the power grid data is the extraction of newly added or modified power grid data in a data source after the last time of data extraction of the power grid data is completed. The real-time extraction is to extract the power grid data from the data source in real time.
S102, converting the multi-structure type power grid data into structured power grid data;
wherein the grid data includes structured grid data, semi-structured grid data, and unstructured grid data. The multi-structure type grid data that performs the data conversion includes semi-structured grid data and unstructured grid data. After the power grid data with dimensions, multiple sources and multiple structures are gathered, the power grid data are processed, and some error data are corrected and repaired, and the power grid data are merged and sorted.
In one embodiment, the unstructured grid data is converted into the structured data, and the unstructured grid data can be converted into semi-structured grid data and then into the structured grid data by methods such as feature value extraction.
In one embodiment, fig. 3 is a power grid data structured processing method according to yet another embodiment, and as shown in fig. 3, a process of converting power grid data of multiple structure types into structured power grid data in step S102 includes step S400:
and S400, respectively carrying out data filtering processing, data conversion processing, data loading processing, data cleaning processing, dirty data processing and data normalization processing on the multi-structure type power grid data to obtain structured power grid data.
In one embodiment, the data filtering is realized by filtering data which does not conform to the application rule or are invalid in the power grid data, so that the data standard is unified.
In one embodiment, the data conversion processing realizes the conversion of the conflict of the format, the information code and the value of the power grid data.
In one embodiment, the data loading process comprises an insert operation and a modify operation, and clean data and dirty data are respectively inserted into different data tables. The data loading process is mainly realized by building a database environment. And when the loading data volume is large, the data loading processing is carried out by combining the big data storage technology with the script program processing.
In one embodiment, the data cleaning process supports data cleaning and rule customization of data specification, and mainly comprises NULL value replacement, character string operation, data type conversion, function dependence, regular processing, field combination, data comparison, customized SQL script execution, JSON output and other data conversion rules, problem data cleaning rules of similar repeated records, abnormal attribute values and the like, and MD5 encryption rules. Wherein, the data cleaning rule comprises: non-null checking, main key repetition, illegal code cleaning, illegal value cleaning, data format checking and record number checking.
And when the non-null checking indicates that the required field is non-null, checking the field data.
The main key is repeatedly used for checking the same type of data in a plurality of service systems in order to ensure the uniqueness of the main key when the same type of data is cleaned and stored uniformly.
The illegal code and the illegal value are cleaned into illegal code problems including the inconsistency of the illegal code and the code with the data standard and the like, the illegal value problems include value errors, format errors, redundant characters, messy codes and the like, and the checking and the correction are carried out according to specific conditions.
The data format check is to check whether the format of the attribute value in the table is correct to measure the accuracy, such as time format, currency format, redundant characters and messy codes.
The record number checking is checking of the total data number among data related to each system or checking of fluctuation of daily data quantity in a data table.
In one embodiment, dirty data is processed as the condition of vacancy values, outliers and inconsistent data which are ubiquitous in data quality, and such dirty data can be cleaned by adopting methods such as manual detection, statistical methods, clustering, classification, distance-based methods, association rules and the like. According to the defect type classification, dirty data can be divided into three core problem data of missing value data, error data and error associated data for data cleaning.
In one embodiment, the data normalization process is different for different systems, such as different data code standards, different data formats, different data identifications, different data errors, and the like. Therefore, the data needs to be normalized, so that the analysis is performed under the same index, and the reliability of the data analysis result is ensured. For example, for attribute values of a database, a uniqueness rule, a continuity rule, a null value rule, and the like can be established to check and constrain data. Uniqueness rules refer to filling unique constraints for primary keys or other attributes such that each value of a given attribute is different from the other values of the attribute; continuity rules refer to the absence of missing values between the maximum and minimum values of an attribute, and each value is also unique, typically used for check numbers; null rules refer to the use of other special symbols in place of null values and how such values should be handled.
The standardization of the data can improve the universality, the sharing property and the portability of the data and the reliability of data analysis. The data normalization process is to be generic, following either industry or national standards.
S103, converting the structured grid data into a structured processing result with a corresponding format according to a preset output mode or a user request mode.
After the structured power grid data are obtained, the structured power grid data are converted into a structured processing result in a specific format according to a preset output mode, or the structured power grid data are converted into a structured processing result in a corresponding format according to a user request mode, so that the data requirements of a third party are met.
In one embodiment, the corresponding format includes a chart, a query or a report, etc. As a better implementation mode, the corresponding format is a report, and the functions of drawing and generating a chart, searching a power grid knowledge base, searching known network literature data, analyzing natural language, mining and analyzing data, intelligently generating a report and the like are supported.
In the power grid data structuring processing method in any embodiment, the data extraction model is adjusted by updating the data of the structure type, and the data extraction processing is performed on the power grid data of different data sources according to the adjusted data extraction model, so as to obtain the power grid data of multiple structure types. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
The embodiment of the invention also provides a power grid data structured processing device.
Fig. 4 is a block diagram of an embodiment of a grid data structuring device, and as shown in fig. 4, the grid data structuring device of an embodiment includes a module 100, a module 101, a module 102, and a module 103:
the model adjusting module 100 is used for acquiring data updates of various structure types and adjusting a data extraction model;
the data extraction module 101 is used for performing data extraction processing on the power grid data of different data sources according to the data extraction model to obtain power grid data of multiple structure types;
the data conversion module 102 is used for converting the multi-structure type power grid data into structured power grid data;
the result generating module 103 is configured to convert the structured grid data into a structured processing result in a corresponding format according to a preset output mode or a user request mode.
According to the power grid data structuring processing device, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is carried out on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
The embodiment of the present invention further provides a computer storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the method for processing the power grid data structure according to any of the above embodiments is implemented.
Those skilled in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Random Access Memory (RAM), a Read-Only Memory (ROM), a magnetic disk, and an optical disk.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a RAM, a ROM, a magnetic or optical disk, or various other media that can store program code.
Corresponding to the computer storage medium, in one embodiment, a computer device is further provided, where the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the method for processing the grid data structure according to any one of the embodiments described above is implemented.
According to the computer equipment, the data extraction model is adjusted through the data updating of the structure type, and the data extraction processing is carried out on the power grid data of different data sources according to the adjusted data extraction model, so that the power grid data of multiple structure types are obtained. Further, the multi-structure type power grid data are converted into structured power grid data, and the structured power grid data are converted into structured processing results in corresponding formats according to a preset output mode or a user request mode. Based on the method, the power grid data are standardized and normalized, and structured processing results in different formats are provided for the structured power grid data, so that the method adapts to the data readable, visual or processable requirements of a third party, and improves the universality, the sharing property, the portability and the data analysis reliability of the power grid data.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A power grid data structuring processing method is characterized by comprising the following steps:
acquiring data update of each structure type, and adjusting a data extraction model;
performing data extraction processing on the power grid data of different data sources according to the data extraction model to obtain power grid data of multiple structure types;
converting the multi-structure type power grid data into structured power grid data;
and converting the structured grid data into a structured processing result with a corresponding format according to a preset output mode or a user request mode.
2. The grid data structuring processing method according to claim 1, wherein the process of obtaining data updates for each structure type includes the steps of:
obtaining model category updates, entity tag updates, relationship tag updates, and/or attribute tag updates.
3. The grid data structuring processing method according to claim 1, wherein the data extraction processing includes full extraction, incremental extraction and real-time extraction.
4. The grid data structuring processing method according to claim 1, wherein the process of adjusting the data extraction model comprises the steps of:
and processing a data updating result through a machine learning algorithm, and training the data extraction model.
5. The grid data structuring process according to claim 1, wherein said process of converting said multiple structure type grid data into structured grid data comprises the steps of:
and respectively carrying out data filtering processing, data conversion processing, data loading processing, data cleaning processing, dirty data processing and data normalization processing on the multi-structure type power grid data to obtain the structured power grid data.
6. The grid data structuring processing method according to claim 5, wherein the data washing processing includes non-null checking, primary key repetition, illegal code washing, illegal value washing, data format checking, and record number checking.
7. The grid data structuring processing method according to any one of claims 1 to 6, wherein the different data sources include databases such as Oracle, sqlServer, MySql, Hbase, Hive, GreenPlum, GBase, PostgreSQL, SOLR, Redis, ODPS, OTS, and GDS, and the file types of the grid data of the different data sources include files such as FTP, XML, CSV, JSON, and EXCEL.
8. A grid data structured processing apparatus, comprising:
the model adjusting module is used for acquiring data update of each structure type and adjusting the data extraction model;
the data extraction module is used for performing data extraction processing on the power grid data of different data sources according to the data extraction model to obtain power grid data of multiple structure types;
the data conversion module is used for converting the multi-structure type power grid data into structured power grid data;
and the result generation module is used for converting the structured grid data into a structured processing result in a corresponding format according to a preset output mode or a user request mode.
9. A computer arrangement comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the grid data structuring method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the grid data structuring method according to any one of claims 1 to 7.
CN202210269169.1A 2022-03-18 2022-03-18 Power grid data structured processing method and device Pending CN114610809A (en)

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