CN115599923A - Power grid standard knowledge extraction method - Google Patents

Power grid standard knowledge extraction method Download PDF

Info

Publication number
CN115599923A
CN115599923A CN202211299499.1A CN202211299499A CN115599923A CN 115599923 A CN115599923 A CN 115599923A CN 202211299499 A CN202211299499 A CN 202211299499A CN 115599923 A CN115599923 A CN 115599923A
Authority
CN
China
Prior art keywords
extraction
data
knowledge
model
entities
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202211299499.1A
Other languages
Chinese (zh)
Inventor
段勇
涂亮
林正平
周育忠
王宏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China South Power Grid International Co ltd
Yunnan Power Grid Co Ltd
Original Assignee
China South Power Grid International Co ltd
Yunnan Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China South Power Grid International Co ltd, Yunnan Power Grid Co Ltd filed Critical China South Power Grid International Co ltd
Priority to CN202211299499.1A priority Critical patent/CN115599923A/en
Publication of CN115599923A publication Critical patent/CN115599923A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • Water Supply & Treatment (AREA)
  • Human Resources & Organizations (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Public Health (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention provides a power grid standard knowledge extraction method, which belongs to the technical field of power grid data, and is characterized in that the power grid standard knowledge extraction method acquires original data of various forms, which are introduced in the process of updating data, defines a knowledge production target by a body construction system and outputs a data extraction model; performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data; and converting the power grid data of each type into knowledge map data, and establishing entities and relationships among the entities. And the automatic construction from the source database data to the knowledge graph is completed through structured and unstructured data access, and the structured data graph entering capability is provided. By integrating knowledge organization, storage, extraction and reasoning methods and tools, a large amount of time of professional technicians is saved, and a foundation is provided for rapidly changing knowledge such as power scheduling, equipment management, data interaction, service inquiry and the like.

Description

Power grid standard knowledge extraction method
Technical Field
The invention relates to the technical field of power grid data, in particular to a power grid standard knowledge extraction method.
Background
Traditional knowledge organization and management approaches have failed to meet the needs of current power systems. Currently, knowledge bases based on knowledge representation and knowledge reasoning are used in power systems, such as: and an intelligent decision system, a fault positioning system, a power transmission network planning decision and the like are combined with the traditional expert system.
However, most of these knowledge bases rely on the traditional knowledge management method of extracting, sorting and storing data in the form of icons in the database by experts, the knowledge structure that can be stored is single, and each update requires a great deal of time for professional technicians. Particularly for the fields with rapid knowledge change, such as power dispatching, equipment management, data interaction, service inquiry and the like, the existing knowledge management mode seriously lags behind the development requirement of the system.
The electric power system needs a novel, automatic and intelligent knowledge organization, storage, extraction and reasoning method and tool to aggregate massive discrete information points into a semantic network, and the mature and stable map construction functional system in the industry is introduced to enable the link to be done with half the effort. Therefore, we propose a power grid standard knowledge extraction method to solve the above problems in the technical background.
Disclosure of Invention
In order to make up for the defects, the invention provides a power grid standard knowledge extraction method, and aims to solve the problem of power grid knowledge extraction in the prior art.
The invention is realized by the following steps: a power grid standard knowledge extraction method comprises
Acquiring data updating, introducing various forms of original data, defining knowledge production targets by a body construction system, and outputting a data extraction model;
performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data;
and converting the various types of power grid data into knowledge map data, and establishing entities and relationships among the entities.
In a preferred technical scheme of the invention, the ontology construction system complies with the technical specifications of standardized digital processing and standardized bidding on the technical architecture, and carries out the support services of standardized digital processing, the construction of a standard knowledge base, the construction of a standard knowledge graph and the construction of a tool model and a construction process.
In a preferred embodiment of the present invention, the process of the data model includes the steps of: and processing a data updating result through a machine learning algorithm, and training a data extraction model.
In a preferred technical solution of the present invention, the data extraction model includes a chapter extraction, a term extraction, an index extraction, a formula extraction, a picture extraction, and a table extraction.
Chapter bar: extracting and processing the seal strip of each level, supporting the seal strip association nesting, namely hierarchical processing, and the seal strip of the parent level can contain the seal strip of the child level.
The terms: and the extraction of the terms is supported, and the extraction result comprises term names, term definitions and the like.
Index and index value: and extracting indexes (including character type clauses and numerical types) in the standard and outputting the indexes in a key-value form.
The formula: the formula in the standard is extracted, and the formula name and the specific formula are required to be included.
Picture: and extracting the non-pure character pictures in the standard, and outputting picture resources which need to include picture names and picture resource files.
Table (b): and extracting the table in the standard, wherein the extraction result supports the extraction of two types, namely a picture and an excel table, including table name (if any), table header (if any), row data, column data and the like.
In a preferred technical solution of the present invention, the knowledge extraction of each type of power grid data includes structured extraction, semi-structured extraction, and unstructured extraction, and the knowledge graph is constructed by using conversion of the structured extraction, the semi-structured extraction, and the unstructured extraction.
In a preferred technical solution of the present invention, the specific functions of the knowledge-graph include: the method supports the identification of entities, relations and attributes from the free text, and can optimize the accuracy of free text extraction in a mode of manual intervention model; the method supports direct data conversion from a structured data source, aligns with the knowledge map schema mapping, and automatically produces knowledge map data; supporting a user-defined knowledge graph extraction model, including a tuning model, an optimized word list, a definition template and the like; the method supports a machine learning model, machine rules and manual modes to carry out mapping, cleaning, fusion, normalization, edge building and completion of entities, attributes and relationships; the whole process of map knowledge production supports visualization, white box and audit intervention.
In a preferred technical solution of the present invention, the system further includes an entity extraction module and an association extraction module.
In a preferred technical solution of the present invention, the entity extraction module is an entity that extracts data from text data, and the entity extraction module is composed of three small modules, where the three small modules are respectively three models, namely, a transform model, a BiGRU model, and a CRF model, and finally completes the task of entity extraction through the functions of each model of the three parts.
In the design idea of the system, named entities are identified as sequence labeling tasks to be processed. The general idea is to give a sequence, and mark or label each element in the sequence, where the label is a label in the biees. And for the element which is the entity, the element is treated as the entity according to the label, and for the label which is not the entity, the O label is used as the attribute of the corresponding entity, and then corresponding deduplication operation is carried out in the process of storing.
In a preferred technical solution of the present invention, the association extraction module is to determine a relationship between entities, extract the entities first, then determine the relationship between the entities, borrow a BiGRU model extracted from the entities, and then extract the relationship between the entities by combining another authorization, where the BiGRU portion obtains a corresponding label sequence of a sentence, and then extract the relationship by using an authorization for multiple categories of related relationships.
In a preferred embodiment of the present invention, the task of multi-classification in the Attention predicts classification tags of sentences through a softmax classifier.
The invention has the beneficial effects that: the invention relates to a power grid standard knowledge extraction method, which comprises the steps of acquiring original data of various forms, which are introduced during data updating, defining a knowledge production target by a body construction system, and outputting a data extraction model; performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data; and converting the power grid data of each type into knowledge map data, and establishing entities and relationships among the entities. Knowledge extraction provides knowledge extraction services aiming at different data sources, and all the knowledge extraction services run periodically in the background in a task mode, so that continuous access of various external data is guaranteed. And the automatic construction of the source database data to the knowledge graph is completed through structured and unstructured data access, and the structured data graph entering capability is provided. By integrating knowledge organizing, storing, extracting and reasoning methods and tools, massive discrete information points are aggregated into a semantic network, so that a great deal of time of professional technicians is saved, and a foundation is provided for rapid knowledge change of power dispatching, equipment management, data interaction, service query and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a power grid standard knowledge extraction method provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a knowledge extraction system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an entity extraction module model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Examples
Referring to fig. 1-3, the present invention provides a technical solution: a power grid standard knowledge extraction method comprises
Acquiring data updating, introducing various forms of original data, defining a knowledge production target by a body construction system, and outputting a data extraction model;
performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data;
and converting the power grid data of each type into knowledge map data, and establishing entities and relationships among the entities.
In the embodiment of the invention, the ontology construction system complies with the standardized digital processing and standard bidding technical specification on the technical architecture, and carries out the standardized digital processing, the standard knowledge base construction, the standard knowledge map construction tool model and the construction process support service.
In an embodiment of the present invention, a process of data modeling includes the steps of: and processing a data updating result through a machine learning algorithm, and training a data extraction model.
In the embodiment of the invention, the extraction modes of the data extraction model comprise chapter bar extraction, term extraction, index extraction, formula extraction, picture extraction and table extraction.
Chapter bar: extracting and processing the seal strip of each level, supporting the seal strip association nesting, namely hierarchical processing, and the seal strip of the parent level can contain the seal strip of the child level.
The terms: and the extraction of the terms is supported, and the extraction result comprises term names, term definitions and the like.
Index and index value: and extracting indexes (including character type clauses and numerical types) in the standard and outputting the indexes in a key-value form.
The formula: the formula in the standard is extracted, and the formula name and the specific formula are required to be included.
Picture: and extracting the non-pure character pictures in the standard, and outputting picture resources which need to include picture names and picture resource files.
Table (b): and extracting the table in the standard, wherein the extraction result supports the extraction of two types, namely a picture and an excel table, including table name (if any), table header (if any), row data, column data and the like.
In the embodiment of the invention, the knowledge extraction of each type of power grid data comprises structured extraction, semi-structured extraction and unstructured extraction, and the knowledge graph is constructed by using the conversion of the structured extraction, the semi-structured extraction and the unstructured extraction.
And (3) establishing a knowledge graph production tool model, providing support service in a graph establishing process, and assisting in completing the establishment of the standard knowledge graph of the transformer and the breaker equipment. The method comprises the following steps: the scheme and implementation tool of the knowledge modeling, extracting function, knowledge disambiguation, map building function and map storing and inquiring function supports extraction of knowledge, schema building, map modification, real-time display and the like.
In the embodiment of the invention, the specific functions of the knowledge graph comprise: the method supports the identification of entities, relations and attributes from the free text, and can optimize the accuracy of free text extraction in a mode of manual intervention model; the method supports direct data conversion from a structured data source, aligns with the knowledge map schema mapping, and automatically produces knowledge map data; supporting a user-defined knowledge graph extraction model, including a tuning model, an optimized word list, a definition template and the like; the method supports a machine learning model, machine rules and manual modes to carry out mapping, cleaning, fusion, normalization, edge building and completion of entities, attributes and relationships; the whole process of map knowledge production supports visualization, white box and audit intervention.
In the embodiment of the invention, the power grid standard knowledge extraction method further comprises an entity extraction module and an association extraction module.
Referring to fig. 3, in the embodiment of the present invention, the entity extraction module is to extract an entity in data from text data, and the entity extraction module is composed of three small modules, where the three small modules are respectively three models, i.e., a transform model, a BiGRU model, and a CRF model, and finally completes an entity extraction task through functions of each of the three models.
In the design idea of the system, named entities are identified as sequence labeling tasks to be processed. The general idea is to label each element in a given sequence or to label each element in the sequence, where the label is a label in biees. And for the element which is the entity, the element is treated as the entity according to the label, and for the label which is not the entity, the O label is used as the attribute of the corresponding entity, and then corresponding deduplication operation is carried out in the process of storing.
Transformer model
Positional embedding was obtained using a Transformer model. As shown in fig. 3, a sentence in a text is input into a transform model, spatial embedding of a related sentence is obtained after transform model operation, and then the spatial embedding is input into a next layer model as an input. In the construction of the Transformer model, construction was performed using a tensrflow.
BiGRU model
According to the principle of a BiGRU model, a double-layer GRU model is built, the number of GRU unit cells is determined according to actual requirements, the purpose of the model is to take the result of the previous part of the transform model as the input of the model, and the result obtained from the BiGRU model is that the result of the probability embedding in the transform model passes through a label sequence of the model, and the label sequence is subjected to the probability calculation of the final CRF to determine which words belong to the entity and which words do not belong to the entity. In the input aspect of the model, because it is impossible to dynamically change the size of the BiGRU input unit according to the length of the positional embedding during the model training process, it is necessary to reserve a sufficient input length in advance, and for the case that the positional embedding length is insufficient, 0 is used for supplement. The implementation mode of the BiGRU model writes corresponding codes through TensorFlow.
CRF model
The function of the CRF model is to calculate the probability of each sequence in the label sequences, and the most probable sequence determines whether or not it belongs to an entity. Wherein, the output label sequence of the BiGRU model is a matrix, each column has 5 numbers, and each row number in the column represents the probability in the BIOES mode; the input of CRF is a sequence, so that the resulting label sequence of BiGRU is used as the input of CRF model with each row as a sequence, then the probability of the column is calculated, then the maximum value in the label sequence is compared, and then which of the meta-sentences belongs to the entity is distinguished according to the probability in the label sequence.
In the part, besides the problem of entity extraction, the attribute extraction work in knowledge extraction is also partially realized, corresponding words are selected according to the labeling sequence, repeated parts are removed, and the rest words are used as the attributes of the entities extracted from sentences.
In the embodiment of the invention, the association extraction module judges a relationship between entities, extracts the entities firstly, then judges the relationship between the entities, borrows a BiGRU model extracted from the entities, and then extracts the relationship between the entities by combining with other Attention, wherein the BiGRU part obtains a corresponding label sequence of a sentence, and then extracts the relationship by using the idea of multi-classification of related relationships by utilizing the Attention.
In the embodiment of the invention, the task of multi-classification in the Attention carries out classification label prediction on the sentence through a softmax classifier.
In conclusion: the invention relates to a power grid standard knowledge extraction method, which comprises the steps of acquiring data updating, introducing various forms of original data, defining a knowledge production target by a body construction system, and outputting a data extraction model; performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data; and converting the power grid data of each type into knowledge map data, and establishing entities and relationships among the entities. Knowledge extraction services aiming at different data sources are provided, all the knowledge extraction services periodically run in a background in a task mode, and continuous access of various external data is guaranteed. And the automatic construction from the source database data to the knowledge graph is completed through structured and unstructured data access, and the structured data graph entering capability is provided. By integrating knowledge organization, storage, extraction and inference methods and tools, massive discrete information points are aggregated into a semantic network, a great deal of time of professional technicians is saved, and a foundation is provided for rapid knowledge change of power scheduling, equipment management, data interaction, service query and the like.
And after the knowledge is extracted, a knowledge storage system is adopted for storage, and the knowledge storage subsystem comprises a map storage engine and a text storage engine. And the map storage engine constructs a super-large-scale high-performance distributed map indexing and storage engine. The method supports common Graph models, property graphs and Graph-based Graph query languages similar to Gremlin, provides a Graph native storage engine, supports various storage media/systems, memories or direct SSDs on a storage framework, has distributed storage capacity, meets the storage requirement of mass Graph data, has multiple active instances, is rapid in fault switching, and achieves high availability of services. The text knowledge storage engine integrates the elastic search which is subjected to depth effect and performance optimization, provides a storage and retrieval system for large-scale text data, has expandable system capacity, and provides a series of optimizable configurations.
The map storage engine:
the graph database BGgraph is a high-performance commercial graph database and is suitable for application scenes of high data correlation and deep analysis. The core of BGraph is a high-performance graph database engine, which is applied and practiced in a knowledge graph system for years, can support hundreds of millions of entities and millisecond response delay, provides distributed and high availability capability, and meets the requirements of enterprise-level application.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A power grid standard knowledge extraction method is characterized by comprising
Acquiring data updating, introducing various forms of original data, defining a knowledge production target by a body construction system, and outputting a data extraction model;
performing data extraction processing on different original data according to the data extraction model to obtain various types of power grid data;
and converting the power grid data of each type into knowledge map data, and establishing entities and relationships among the entities.
2. The power grid standard knowledge extraction method according to claim 1, wherein the ontology construction system performs standard digital processing and standard knowledge base construction and standard knowledge graph construction tool model and construction process support services in compliance with standard digital processing and standard bidding technical specifications on a technical architecture.
3. The grid standard knowledge extraction method according to claim 1, wherein the data model process comprises the following steps: and processing a data updating result through a machine learning algorithm, and training a data extraction model.
4. The grid standard knowledge extraction method according to claim 1, wherein the data extraction model is extracted in a manner of chapter extraction, term extraction, index extraction, formula extraction, picture extraction and table extraction.
5. The grid standard knowledge extraction method according to claim 1, wherein the knowledge extraction of each type of grid data comprises structured extraction, semi-structured extraction and unstructured extraction, and the knowledge graph is constructed by using conversion of the structured extraction, the semi-structured extraction and the unstructured extraction.
6. The grid standard knowledge extraction method according to claim 5, wherein the specific functions of the knowledge graph comprise: the method supports the identification of entities, relations and attributes from the free text, and can optimize the accuracy of free text extraction in a mode of manual intervention model; the method supports direct data conversion from a structured data source, aligns with the knowledge map schema mapping, and automatically produces knowledge map data; supporting a user-defined knowledge graph extraction model, including a tuning model, an optimized word list, a definition template and the like; the method supports a machine learning model, machine rules and a manual mode to carry out mapping, cleaning, fusion, normalization, edge building and completion of entities, attributes and relationships; the whole process of map knowledge production supports visualization, white box and audit intervention.
7. The grid standard knowledge extraction method according to claim 1, further comprising an entity extraction module and an association extraction module.
8. The grid standard knowledge extraction method according to claim 7, wherein the entity extraction module is an entity extracting data from text data, and the entity extraction module is composed of three small modules, wherein the three small modules are respectively three models, namely a transform model, a BiGRU model and a CRF model, and the task of entity extraction is finally completed through the functions of the three models.
9. The method for extracting power grid standard knowledge according to claim 7, wherein the association extraction module is used for determining a relationship between entities, extracting the entities first, then determining the relationship between the entities, borrowing a BiGRU model extracted from the entities, and then extracting the relationship between the entities by combining with another Attention, wherein the BiGRU part obtains a corresponding label sequence of a sentence, and then extracting the relationship by using an idea of multi-classification of related relationships.
10. The grid standard knowledge extraction method according to claim 9, wherein the task of multi-classification in the Attention predicts classification tags of sentences through a softmax classifier.
CN202211299499.1A 2022-10-22 2022-10-22 Power grid standard knowledge extraction method Pending CN115599923A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211299499.1A CN115599923A (en) 2022-10-22 2022-10-22 Power grid standard knowledge extraction method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211299499.1A CN115599923A (en) 2022-10-22 2022-10-22 Power grid standard knowledge extraction method

Publications (1)

Publication Number Publication Date
CN115599923A true CN115599923A (en) 2023-01-13

Family

ID=84848365

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211299499.1A Pending CN115599923A (en) 2022-10-22 2022-10-22 Power grid standard knowledge extraction method

Country Status (1)

Country Link
CN (1) CN115599923A (en)

Similar Documents

Publication Publication Date Title
Yan et al. KnowIME: a system to construct a knowledge graph for intelligent manufacturing equipment
CN112612902A (en) Knowledge graph construction method and device for power grid main device
CN113553420B (en) Power grid fault processing rule recommendation method and system based on knowledge graph
CN111552813A (en) Power knowledge graph construction method based on power grid full-service data
CN106447346A (en) Method and system for construction of intelligent electric power customer service system
CN114077674A (en) Power grid dispatching knowledge graph data optimization method and system
CN113434634A (en) Knowledge graph construction method and device
CN110704577A (en) Method and system for searching power grid scheduling data
CN112685570B (en) Multi-label graph-based power grid network frame knowledge graph construction method
CN115757810A (en) Method for constructing standard ontology of knowledge graph
CN112100506B (en) Information pushing method, system, equipment and storage medium
CN115438199A (en) Knowledge platform system based on smart city scene data middling platform technology
CN113946686A (en) Electric power marketing knowledge map construction method and system
CN113220901A (en) Writing concept auxiliary system and network system based on enhanced intelligence
CN115858829A (en) Multi-source heterogeneous environment data asset construction method based on computational power network
CN114153821A (en) Electric quantity graph database construction and search method based on graph theory
CN111625596B (en) Multi-source data synchronous sharing method and system for real-time new energy consumption scheduling
CN115937881A (en) Method for automatically identifying content of knowledge graph construction standard form
CN117010373A (en) Recommendation method for category and group to which asset management data of power equipment belong
CN115238099A (en) Industrial Internet data middle platform construction method for energy industry equipment
CN116401338A (en) Design feature extraction and attention mechanism based on data asset intelligent retrieval input and output requirements and method thereof
CN115017251B (en) Standard mapping map establishing method and system for smart city
CN116011564A (en) Entity relationship completion method, system and application for power equipment
CN115827885A (en) Operation and maintenance knowledge graph construction method and device and electronic equipment
CN115757735A (en) Intelligent retrieval method and system for power grid digital construction result resources

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination