CN110717018A - Industrial equipment fault maintenance question-answering system based on knowledge graph - Google Patents
Industrial equipment fault maintenance question-answering system based on knowledge graph Download PDFInfo
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Abstract
The invention provides a Knowledge Graph (Knowledge Graph) -based industrial equipment fault maintenance question-answering system, which comprises the following steps of: acquiring expert experience knowledge and preprocessing disambiguation, and constructing an industrial fault maintenance field knowledge map by using a relationship extraction algorithm and an event extraction algorithm; the deep neural network and the hidden Markov model are combined to construct a voice recognition model, so that the accuracy rate of the Mandarin speech recognition is improved, and meanwhile, the voice recognition models in multiple regions are trained, and the accurate analysis of various dialect voices is realized. Semantic analysis is carried out on the voice recognition result based on fusion natural language processing technologies such as a Fastext intention recognition algorithm and the like, the voice query intention of a client is accurately recognized, and related fault maintenance opinions are queried through a knowledge graph.
Description
Technical Field
The invention relates to the field of equipment fault maintenance, the field of voice recognition and the field of natural language processing, in particular to a knowledge graph-based question-answering system for fault maintenance of industrial equipment.
Background
An industrial equipment fault maintenance question-answering system based on a knowledge graph. Establishing a knowledge graph based on expert experience knowledge in the professional field of related industrial equipment maintenance, analyzing the query intention of a user by using a voice recognition technology and a natural language processing technology, and providing related fault maintenance suggestions by querying the knowledge graph.
The general-purpose question-answering system generally needs to realize short dialogue question-answering by using a large amount of corpora as a basis. Such systems are often used in the entertainment field, and are less used in the industrial fault repair field, and the systems often have difficulty in extracting precise meanings in the user's question, and cannot accurately analyze the user's intention. Secondly, the industrial fault maintenance association field is wide, and the question-answering system can only inquire key words of question sentences in a traditional knowledge base according to questions provided by users, so that the positions of answers are positioned.
The question-answering system provides an industrial fault maintenance question-answering system based on the knowledge graph, and can adapt to continuously improved user requirements. The question-answering system is novel, can provide better user experience, provides a professional industrial fault maintenance scheme for users, and ensures stable operation of equipment.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides a knowledge graph-based industrial equipment fault question-answering system. And storing expert experience knowledge based on the knowledge graph. And performing voice recognition by using a neural network combined hidden Markov model method, recognizing the user intention based on a FastText model, finally completing the query of a knowledge graph, and providing a reasonable industrial fault maintenance scheme for the user.
The technical scheme of the invention is as follows:
the system for question-answering and trouble-shooting of industrial equipment based on the knowledge graph is characterized by comprising a knowledge acquisition module, a knowledge graph construction module, a voice recognition module and a semantic analysis module, wherein the knowledge graph query module comprises the following steps:
and (1) acquiring expert experience knowledge related to fault diagnosis and maintenance in a knowledge acquisition module in a mode of manual entry and internet collection. Forming effective fault maintenance related information through cleaning, screening and feature extraction;
step (2), in a knowledge graph construction module, extracting entity relations on the basis of dependency relations by carrying out entity relation extraction on the preprocessed industrial equipment maintenance related knowledge, namely identifying entities from text contents, further extracting semantic relations among the entities, carrying out event extraction on the basis of combined use of a pattern matching algorithm and an SVM algorithm, extracting specific fault maintenance patterns from unstructured information such as texts, and carrying out structured presentation;
step (3) in the speech recognition module, combining the deep neural network and the hidden Markov model to train a speech recognition model, training speech recognition models of a plurality of regions while improving the accuracy rate of the Mandarin speech recognition, converting speech data into text data, and realizing accurate analysis of various dialect speeches;
and (4) in a semantic analysis module, performing word segmentation processing on the converted text data, and then performing information such as fault types and the like of user query based on a FastText model.
And (5) in a knowledge graph query module, retrieving related fault maintenance information from a knowledge graph based on the recognition result of the user intention, obtaining candidate question sentences similar to the user question, and returning to a corresponding candidate result list.
The invention has the beneficial effects that:
(1) the method has the advantages that the industrial fault analysis knowledge graph is constructed through the relation extraction based on the dependency syntax analysis and the event extraction algorithm based on the combination of the pattern matching algorithm and the SVM algorithm, and the representing capability of expert fault maintenance experience knowledge is enhanced;
(2) the speech recognition model is trained by combining the deep neural network and the hidden Markov model, so that the speech recognition precision of various dialects is enhanced, and the algorithm processing accuracy is improved;
(3) the user query intention recognition algorithm based on FastText accurately recognizes the user voice query intention and provides an accurate and reasonable fault maintenance scheme for the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the operation of the knowledge-graph based trouble shooting system for industrial equipment;
FIG. 2 is a flowchart of the FastText-based user query intent recognition model training process of the present invention.
Detailed Description
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.
As shown in fig. 1, the system for troubleshooting and questioning and answering industrial equipment based on the knowledge graph comprises a knowledge acquisition module, a knowledge graph construction module, a voice recognition module, a semantic analysis module and a knowledge graph query module.
The specific flow of the knowledge-graph-based industrial equipment fault maintenance question-answering system is described in detail below with reference to fig. 1 and 2:
and (1) acquiring expert experience knowledge related to fault diagnosis and maintenance in a knowledge acquisition module in a mode of manual entry and internet collection. Forming effective fault maintenance related information through cleaning, screening and feature extraction;
step (2), in a knowledge graph construction module, extracting entity relations on the basis of dependency relations by carrying out entity relation extraction on the preprocessed industrial equipment maintenance related knowledge, namely identifying entities from text contents, further extracting semantic relations among the entities, carrying out event extraction on the basis of combined use of a pattern matching algorithm and an SVM algorithm, extracting specific fault maintenance patterns from unstructured information such as texts, and carrying out structured presentation;
step (3) in the speech recognition module, combining the deep neural network and the hidden Markov model to train a speech recognition model, training speech recognition models of a plurality of regions while improving the accuracy rate of the Mandarin speech recognition, converting speech data into text data, and realizing accurate analysis of various dialect speeches;
and (4) performing word segmentation processing on the converted text data in a semantic analysis module, then identifying the query intention of the user based on a FastText model, and analyzing information such as the fault type, the maintenance method and the like queried by the user.
And (5) in a knowledge graph query module, retrieving related fault maintenance information from a knowledge graph based on the recognition result of the query intention of the user to obtain candidate question sentences similar to the user question, and returning to a corresponding candidate result list.
The invention provides an industrial fault analysis expert system based on a knowledge graph and an industrial equipment fault question-answering system based on the knowledge graph. And storing expert experience knowledge based on the knowledge graph. And performing voice recognition by using a neural network combined hidden Markov model method, recognizing the user intention based on a FastText model, finally completing the query of a knowledge graph, and providing a reasonable industrial fault maintenance scheme for the user.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (1)
1. The system for question-answering and trouble-shooting of industrial equipment based on the knowledge graph is characterized by comprising a knowledge acquisition module, a knowledge graph construction module, a voice recognition module and a semantic analysis module, wherein the knowledge graph query module comprises the following steps:
and (1) acquiring expert experience knowledge related to fault diagnosis and maintenance in a knowledge acquisition module in a mode of manual entry and internet collection. Forming effective fault maintenance related information through cleaning, screening and feature extraction;
step (2), in a knowledge graph construction module, extracting entity relations on the basis of dependency relations by carrying out entity relation extraction on the preprocessed industrial equipment maintenance related knowledge, namely identifying entities from text contents, further extracting semantic relations among the entities, carrying out event extraction on the basis of combined use of a pattern matching algorithm and an SVM algorithm, extracting specific fault maintenance patterns from unstructured information such as texts, and carrying out structured presentation;
step (3) in the speech recognition module, combining the deep neural network and the hidden Markov model to train a speech recognition model, training speech recognition models of a plurality of regions while improving the accuracy rate of the Mandarin speech recognition, converting speech data into text data, and realizing accurate analysis of various dialect speeches;
and (4) performing word segmentation processing on the converted text data in a semantic analysis module, then performing intention recognition on user query based on a FastText model, and analyzing information such as query fault types and maintenance methods.
And (5) in a knowledge graph query module, retrieving related fault maintenance information from a knowledge graph based on the recognition result of the user intention, obtaining candidate question sentences similar to the user question, and returning to a corresponding candidate result list.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111309828A (en) * | 2020-03-27 | 2020-06-19 | 广东省智能制造研究所 | Knowledge graph construction method and device for large-scale equipment |
| CN111782800A (en) * | 2020-06-30 | 2020-10-16 | 上海仪电（集团）有限公司中央研究院 | Intelligent conference analysis method for event tracing |
| CN112015921A (en) * | 2020-09-15 | 2020-12-01 | 重庆广播电视大学重庆工商职业学院 | Natural language processing method based on learning-assisted knowledge graph |
| CN112270490A (en) * | 2020-11-11 | 2021-01-26 | 北京优锘科技有限公司 | Park intelligent facility management system based on knowledge graph of Internet of things |
| CN112435512A (en) * | 2020-11-12 | 2021-03-02 | 郑州大学 | Voice behavior assessment and evaluation method for rail transit simulation training |
| CN112527982A (en) * | 2020-11-26 | 2021-03-19 | 上海发电设备成套设计研究院有限责任公司 | Equipment management system, method, equipment and storage medium |
| CN112836972A (en) * | 2021-02-05 | 2021-05-25 | 南方电网调峰调频发电有限公司信息通信分公司 | IT equipment fault defect processing system and fault defect processing method |
| CN113051382A (en) * | 2021-04-08 | 2021-06-29 | 云南电网有限责任公司电力科学研究院 | Intelligent power failure question-answering method and device based on knowledge graph |
| CN113094512A (en) * | 2021-04-08 | 2021-07-09 | 达而观信息科技（上海）有限公司 | Fault analysis system and method in industrial production and manufacturing |
| CN113268604A (en) * | 2021-05-19 | 2021-08-17 | 国网辽宁省电力有限公司 | Self-adaptive expansion method and system for knowledge base |
| CN113449512A (en) * | 2020-03-25 | 2021-09-28 | 中国电信股份有限公司 | Information processing method, apparatus and computer readable storage medium |
| CN113486151A (en) * | 2021-07-13 | 2021-10-08 | 盛景智能科技(嘉兴)有限公司 | Fault repair knowledge point query method and device, electronic equipment and storage medium |
| CN114186022A (en) * | 2021-12-02 | 2022-03-15 | 国网山东省电力公司信息通信公司 | Scheduling instruction quality inspection method and system based on voice transcription and knowledge graph |
| CN114297350A (en) * | 2021-06-07 | 2022-04-08 | 北京大学 | Natural language-oriented urban domain knowledge model query method and device |
| CN114780743A (en) * | 2022-04-19 | 2022-07-22 | 红云红河烟草(集团)有限责任公司 | Knowledge graph-based parcel maintenance experience recommendation method |
| CN115033679A (en) * | 2022-08-10 | 2022-09-09 | 深圳联友科技有限公司 | Method for searching automobile maintenance data based on knowledge graph |
| CN115366157A (en) * | 2022-10-24 | 2022-11-22 | 北京奔驰汽车有限公司 | Industrial robot maintenance method and device |
| CN116821310A (en) * | 2023-08-28 | 2023-09-29 | 北京智谱华章科技有限公司 | Enterprise knowledge base question-answering system based on large model and knowledge graph |
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| CN113449512A (en) * | 2020-03-25 | 2021-09-28 | 中国电信股份有限公司 | Information processing method, apparatus and computer readable storage medium |
| CN111309828A (en) * | 2020-03-27 | 2020-06-19 | 广东省智能制造研究所 | Knowledge graph construction method and device for large-scale equipment |
| CN111309828B (en) * | 2020-03-27 | 2024-02-20 | 广东省智能制造研究所 | Knowledge graph construction method and device for large-scale equipment |
| CN111782800A (en) * | 2020-06-30 | 2020-10-16 | 上海仪电（集团）有限公司中央研究院 | Intelligent conference analysis method for event tracing |
| CN111782800B (en) * | 2020-06-30 | 2023-11-21 | 上海仪电（集团）有限公司中央研究院 | Intelligent conference analysis method for event tracing |
| CN112015921A (en) * | 2020-09-15 | 2020-12-01 | 重庆广播电视大学重庆工商职业学院 | Natural language processing method based on learning-assisted knowledge graph |
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| CN112270490A (en) * | 2020-11-11 | 2021-01-26 | 北京优锘科技有限公司 | Park intelligent facility management system based on knowledge graph of Internet of things |
| CN112435512B (en) * | 2020-11-12 | 2023-01-24 | 郑州大学 | Voice behavior assessment and evaluation method for rail transit simulation training |
| CN112435512A (en) * | 2020-11-12 | 2021-03-02 | 郑州大学 | Voice behavior assessment and evaluation method for rail transit simulation training |
| CN112527982A (en) * | 2020-11-26 | 2021-03-19 | 上海发电设备成套设计研究院有限责任公司 | Equipment management system, method, equipment and storage medium |
| CN112836972A (en) * | 2021-02-05 | 2021-05-25 | 南方电网调峰调频发电有限公司信息通信分公司 | IT equipment fault defect processing system and fault defect processing method |
| CN113094512A (en) * | 2021-04-08 | 2021-07-09 | 达而观信息科技（上海）有限公司 | Fault analysis system and method in industrial production and manufacturing |
| CN113094512B (en) * | 2021-04-08 | 2024-05-24 | 达观数据有限公司 | Fault analysis system and method in industrial production and manufacturing |
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| CN113268604B (en) * | 2021-05-19 | 2024-06-07 | 国网辽宁省电力有限公司 | Knowledge base self-adaptive expansion method and system |
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| CN114297350A (en) * | 2021-06-07 | 2022-04-08 | 北京大学 | Natural language-oriented urban domain knowledge model query method and device |
| CN113486151A (en) * | 2021-07-13 | 2021-10-08 | 盛景智能科技(嘉兴)有限公司 | Fault repair knowledge point query method and device, electronic equipment and storage medium |
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| CN115033679A (en) * | 2022-08-10 | 2022-09-09 | 深圳联友科技有限公司 | Method for searching automobile maintenance data based on knowledge graph |
| CN115033679B (en) * | 2022-08-10 | 2023-01-13 | 深圳联友科技有限公司 | Method for searching automobile maintenance data based on knowledge graph |
| CN115366157A (en) * | 2022-10-24 | 2022-11-22 | 北京奔驰汽车有限公司 | Industrial robot maintenance method and device |
| CN116821310B (en) * | 2023-08-28 | 2023-12-08 | 北京智谱华章科技有限公司 | Enterprise knowledge base question-answering system based on large model and knowledge graph |
| CN116821310A (en) * | 2023-08-28 | 2023-09-29 | 北京智谱华章科技有限公司 | Enterprise knowledge base question-answering system based on large model and knowledge graph |
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| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200121 |