CN113434658A - Thermal power generating unit operation question-answer generation method, system, equipment and readable storage medium - Google Patents
Thermal power generating unit operation question-answer generation method, system, equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a method, a system, equipment and a readable storage medium for generating questions and answers for the operation of a thermal power generating unit, which are used for collecting data with knowledge text information, processing the collected data to obtain queryable natural language, performing abstraction and business modeling processing in a knowledge database, and matching the results of abstraction and business modeling with the queried natural language through knowledge modeling to obtain an entity group; fusing different expression forms of the same entity from different sources in a knowledge database, and storing the fused entity to generate a knowledge graph; the operator can input the keywords of the questions to be inquired in the knowledge database, and the answers can be obtained through system reasoning; according to the method, the thermal power generating unit operation question-answering system is constructed through the knowledge map technology, and corresponding answers can be deduced quickly, accurately and objectively according to the input of a user, so that operators can be helped to quickly and effectively position problems.
Description
Technical Field
The invention relates to the field of operation of thermal power generating units, in particular to a method, a system and equipment for generating an operation question and answer of a thermal power generating unit and a readable storage medium.
Background
On the upper computer part of a Distributed Control System (DCS), a knowledge graph technology is utilized to model text data generated in the field of power plant operation, and a thermal power generating unit question-answering system is constructed, so that operators are helped to quickly locate answers related to questions. A knowledge graph is a network of connected knowledge. The node is an entity, the edge is the relationship between two entities, and both the node and the edge can have attributes. Besides inquiring the attributes of the entities, the knowledge graph can conveniently find related entities and attribute information from one entity in a traversal relation mode. The power plant operation field comprises centralized control operation, electric operation, boiler operation and the like. The centralized control operation mainly comprises the steps of inspecting a system, filling in inspection records, operation records, power generation operation equipment and the like; the electric operation is mainly to finish the operation maintenance work of the electric profession and to finish the record of the report form related to the electric profession; the boiler operation mainly comprises the work of maintaining, inspecting and the like on the operation of the boiler. Because in the actual operation process of the power plant, a plurality of problems which need to be solved urgently are generated, and most of the problems are solved according to the experience of operators, a standard unified solution is not available.
Disclosure of Invention
The invention provides a question-answer generating method, a question-answer generating system, a question-answer generating device and a readable storage medium for thermal power generating unit operation, which can effectively position the question and improve the problem solving timeliness and the problem answering accuracy.
The invention is realized by the following technical scheme:
a thermal power generating unit operation question-answer generating method comprises the following steps:
acquiring data, wherein the data is text information with knowledge;
carrying out data processing on the text information with knowledge to obtain a queryable natural language;
carrying out abstraction and business modeling to obtain an abstraction and business modeling result;
matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group;
fusing and storing different expression forms of different sources and the same entity in a knowledge database;
generating a knowledge graph in a knowledge database;
and sending the knowledge graph.
Preferably, the method for acquiring the text information with knowledge comprises the following steps:
and automatically acquiring related data sets from the network by writing scripts or manually constructing the related data sets to obtain text information with knowledge.
Preferably, the data processing is data knowledge extraction, and the data knowledge extraction comprises word segmentation processing, entity identification and relationship identification;
the word segmentation processing is to segment a sentence;
the entity recognition is to recognize entity information related in the sentence according to the result after word segmentation;
relationship recognition is the definition of relationships between different entities in a sentence.
Preferably, the abstraction and business modeling process includes entity definition, relationship definition and event definition; the results of abstraction and service modeling are expressed in a triple form through knowledge modeling, the knowledge modeling is realized by a serialization method of an RDF resource description framework, the RDF resource description framework comprises nodes and edges, and the nodes are entities/resources and attributes; edges are relationships between entities and relationships between entities and attributes.
Preferably, the fusion operation includes entity alignment and entity disambiguation;
the method comprises the following steps of aligning entities, judging whether two or more entities with different information sources point to the same object in the real world or not, constructing an alignment relation among the entities when the entities represent the same object, and fusing and aggregating information contained in the entities;
and (3) entity disambiguation, namely mapping the ambiguous naming nominal item to the entity concept actually pointed by the ambiguous naming nominal item, and solving the problem that the nominal item of a named entity corresponds to a plurality of entity concepts.
Preferably, the storage is performed by using a neo4j technology storage method based on a graph database.
According to the thermal power unit operation question-answer generation method, after the knowledge graph is sent, the method further comprises the following steps:
receiving a knowledge graph;
and outputting an answer entity through input sentence entity extraction and intention classification results.
A thermal power generating unit operation question-answering system comprises:
the data acquisition module is used for acquiring data, and the data is text information with knowledge;
the data processing module is used for carrying out data processing on the text information with knowledge to obtain a natural language capable of being inquired;
the first data control module is used for carrying out abstraction and business modeling to obtain an abstraction and business modeling result;
the second data control module is used for matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group;
the data storage module fuses and stores different expression forms of different sources and the same entity in the knowledge database;
the knowledge graph generator is used for generating a knowledge graph in a knowledge database;
and the data transmitting module is used for transmitting the knowledge graph.
A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the thermal power generating unit operation question-answer generating method.
A computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for generating questions and answers for operating a thermal power generating unit.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a question-answer generating method for the operation of a thermal power generating unit, which comprises the steps of firstly collecting data with knowledge text information, processing the collected data to obtain queryable natural language, carrying out abstraction and business modeling processing in a knowledge database, and matching the results of the abstraction and the business modeling with the queried natural language through knowledge modeling to obtain an entity group; fusing different expression forms of the same entity from different sources in the knowledge database, and storing the fused entity to generate a knowledge graph; compared with the prior method for solving the problems generated in the unit operation process by depending on individual experience, the method for establishing the thermal power unit operation question-answering system by the knowledge map technology can rapidly, accurately and objectively deduce the corresponding answers according to the input of the user, thereby helping the operators to rapidly and effectively position the problems.
Drawings
FIG. 1 is a flow structure diagram of a thermal power generating unit operation question-answer generating method of the present invention;
FIG. 2 is a structural diagram of a thermal power generating unit operation question-answering system in the invention;
FIG. 3 is a structural diagram of an SPO triple in the RDF resource description framework serialization method in knowledge modeling according to the present invention;
FIG. 4 is a schematic diagram of the neo4j data storage structure in the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, in an embodiment of the present invention, a method for generating a question and an answer for operation of a thermal power generating unit is provided, so that an operator can obtain an answer inferred by the system by inputting a keyword for a question to be queried, thereby assisting the operator in effectively positioning the question, specifically including the following steps:
acquiring data, wherein the data is text information with knowledge;
carrying out data processing on the text information with knowledge to obtain a queryable natural language;
carrying out abstraction and business modeling to obtain an abstraction and business modeling result;
matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group;
fusing and storing different expression forms of different sources and the same entity in a knowledge database;
generating a knowledge graph in a knowledge database;
and sending the knowledge graph.
Specifically, the method for acquiring the text information with knowledge comprises the following steps:
and automatically acquiring related data sets from the network by writing scripts or manually constructing the related data sets to obtain text information with knowledge. The acquisition of related data sets in the network still needs manual review, and the data sets constructed in the invention belong to the category of unstructured data, and after knowledge is acquired, the data are processed in sequence, namely the knowledge is extracted in a manner of operation in natural language fields such as word segmentation processing, entity recognition, relationship recognition and the like; the word segmentation process is to segment a sentence, for example, "i love Beijing Tiananmen" can be divided into "I", "love", "Beijing", "Tiananmen" after word segmentation process; the entity identification is to identify entity information related in a sentence according to a result after word segmentation; relationship identification is to clarify the relationship between different entities in a sentence, such as causal relationship.
Specifically, the abstraction and business modeling process includes entity definition, relationship definition and event definition; the results of abstraction and service modeling are expressed in a triple form through knowledge modeling, the knowledge modeling is realized by a serialization method of an RDF resource description framework, the RDF resource description framework comprises nodes and edges, and the nodes are entities/resources and attributes; edges are relationships between entities and relationships between entities and attributes.
The RDF resource description framework serialization can realize the ontology-based reasoning capability, can extract the class, object and attribute information of a sentence, and can classify the relation among the class, the object and the attribute. The RDF resource description framework is essentially a data model, and is formally represented as an SPO triple, which is called a statement and also called a piece of knowledge in a knowledge graph, as shown in FIG. 3; the simple structure can clearly express the attribute of the entity in one statement; when there are multiple entities in a sentence, the relationship between the entities can also be represented by the connection of edges.
The data set constructed in the application is from multiple sources (crawler technology, manual construction), so that the fusion operation of knowledge information from different sources and repeated knowledge information needs to be carried out through the knowledge fusion operation. The invention adopts a data layer fusion method to fuse different expression forms of the same entity of data from different sources, and relates to the combination of entities, the combination operation of entity attributes and entity relations and the like. The fusion operation comprises entity alignment and entity disambiguation; entity alignment is intended to determine whether two or more entities from different sources are pointing to the same object in the real world. If a plurality of entities represent the same object, an alignment relation is constructed among the entities, and meanwhile information contained in the entities is fused and aggregated. Entity disambiguation mainly maps ambiguous naming terms to the actual entity concepts, and mainly solves the phenomenon that the naming terms of a named entity correspond to multiple entity concepts, namely 'word polysemy'.
According to the operational characteristics and the data scale of the power plant, the invention selects and adopts a neo4j technical storage method based on a graph database. neo4j data storage organizes data primarily by nodes and edges. The nodes represent the entities of sentences extracted in the knowledge modeling process, the edges represent the relationship among the entities and are directed edges, and the two ends of each edge respectively correspond to the starting node and the ending node. The categories of entities are classified by adding one or more labels on the nodes. The relational attributes and additional attributes of the entity are represented by a set of key-value pairs. The neo4j data storage structure is shown in fig. 4, and includes 2 points labeled Person: node 1 and Node 2, Node 1 has 2 attributes, respectively name: bob and age: 25; node 2 has 1 attribute name, Alice. bob LIKES Alice, represented by the edge like. Since 2 points only have one edge, the next edge pointer for the start and end points of the LIKES edge is null. It can be seen that using noe4j to store knowledge, the categories to which entities belong, and the attributes that entities have and the relationships between entities can be specified.
After the knowledge graph is sent, the invention also comprises the following steps:
receiving a knowledge graph;
and outputting an answer entity through input sentence entity extraction and intention classification results.
The entity extraction mainly comprises the steps of establishing an AC tree by using a feature library formed by entity names in a knowledge graph, and matching whether feature words exist in a question or not by using an AC algorithm, namely, inquiring whether knowledge graph entity nouns exist in the question or not to realize the entity extraction. The construction of the AC tree is mainly divided into two steps; firstly, constructing a plurality of keywords into a finite state pattern matching machine; and secondly, inputting the text character string as an input into a pattern matching machine for matching. The intention classification is to judge whether a specific entity category and a specific intention word exist in an input question based on a constructed knowledge graph so as to judge the intention, and belongs to template matching. And the Cypher statement generation module generates corresponding Cypher statements according to the results returned by entity extraction and intention classification. The query result output comprises two steps: firstly, according to a produced cypher statement, a neo4j graph database is linked for query, and a corresponding result is obtained. And then formatting and outputting the result to feed back to the user.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details not disclosed in the device embodiments, reference is made to the method embodiments of the invention.
In an embodiment of the present invention, an operation question-answering system for a thermal power generating unit is provided, as shown in fig. 2, which can be used to implement the operation question-answering generation method for the thermal power generating unit in the above embodiment, and specifically, the operation question-answering system for the thermal power generating unit includes:
the data acquisition module is used for acquiring data, and the data is text information with knowledge; the data processing module is used for carrying out data processing on the text information with knowledge to obtain a natural language capable of being inquired; the first data control module is used for carrying out abstraction and business modeling to obtain an abstraction and business modeling result; the second data control module is used for matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group; the data storage module fuses and stores different expression forms of different sources and the same entity in the knowledge database; the knowledge graph generator is used for generating a knowledge graph in a knowledge database; the data transmitting module is used for transmitting the knowledge graph; the data receiving module is used for receiving data; the data output module is used for outputting answer entities through input statement entity extraction and entity intention classification results; and the human-computer interaction module is used for displaying information.
In yet another embodiment of the present invention, a computer device is provided, comprising a memory for storing a computer program, a processor, and a computer program stored in the memory and executable on the processor, the computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for operating the question and answer generating method of the thermal power generating unit.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be an unstable memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the question-answer generating method for the operation of the thermal power generating unit in the above embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A thermal power generating unit operation question-answer generating method is characterized by comprising the following steps:
acquiring data, wherein the data is text information with knowledge;
carrying out data processing on the text information with knowledge to obtain a queryable natural language;
carrying out abstraction and business modeling to obtain an abstraction and business modeling result;
matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group;
fusing and storing different expression forms of different sources and the same entity in a knowledge database;
generating a knowledge graph in a knowledge database;
and sending the knowledge graph.
2. The thermal power generating unit operation question-answer generating method according to claim 1, characterized in that the acquisition method of the text information with knowledge is as follows:
and automatically acquiring related data sets from the network by writing scripts or manually constructing the related data sets to obtain text information with knowledge.
3. The thermal power generating unit operation question-answer generating method according to claim 1, characterized in that data processing is data knowledge extraction, and the data knowledge extraction comprises word segmentation processing, entity recognition and relationship recognition;
the word segmentation processing is to segment a sentence;
the entity recognition is to recognize entity information related in the sentence according to the result after word segmentation;
relationship recognition is the definition of relationships between different entities in a sentence.
4. The thermal power generating unit operation question-answer generating method according to claim 1, characterized in that abstraction and business modeling processing includes entity definition, relationship definition and event definition; the results of abstraction and service modeling are expressed in a triple form through knowledge modeling, the knowledge modeling is realized by a serialization method of an RDF resource description framework, the RDF resource description framework comprises nodes and edges, and the nodes are entities/resources and attributes; edges are relationships between entities and relationships between entities and attributes.
5. The thermal power generating unit operation question-answer generating method according to claim 1, characterized in that the fusion operation includes entity alignment and entity disambiguation;
the method comprises the following steps of aligning entities, judging whether two or more entities with different information sources point to the same object in the real world or not, constructing an alignment relation among the entities when the entities represent the same object, and fusing and aggregating information contained in the entities;
and (3) entity disambiguation, namely mapping the ambiguous naming nominal item to the entity concept actually pointed by the ambiguous naming nominal item, and solving the problem that the nominal item of a named entity corresponds to a plurality of entity concepts.
6. The thermal power generating unit operation question-answer generating method as claimed in claim 1, characterized in that the storage is performed by a neo4j technology storage method based on a graph database.
7. The thermal power generating unit operation question-answer generating method according to any one of claims 1 to 6, characterized by further comprising the following steps after the knowledge graph is sent:
receiving a knowledge graph;
and outputting an answer entity through input sentence entity extraction and intention classification results.
8. A thermal power generating unit operation question-answering system is characterized by comprising:
the data acquisition module is used for acquiring data, and the data is text information with knowledge;
the data processing module is used for carrying out data processing on the text information with knowledge to obtain a natural language capable of being inquired;
the first data control module is used for carrying out abstraction and business modeling to obtain an abstraction and business modeling result;
the second data control module is used for matching the abstract and business modeling results with queryable natural language through knowledge modeling to obtain an entity group;
the data storage module fuses and stores different expression forms of different sources and the same entity in the knowledge database;
the knowledge graph generator is used for generating a knowledge graph in a knowledge database;
and the data transmitting module is used for transmitting the knowledge graph.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the thermal power generating unit operation question and answer generating method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the thermal power generating unit operation question and answer generating method according to any one of claims 1 to 7.
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