CN114548408A - Knowledge reasoning method for hydrogen-oxygen engine - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000001301 oxygen Substances 0.000 title claims description 13
- 229910052760 oxygen Inorganic materials 0.000 title claims description 13
- 238000000605 extraction Methods 0.000 claims abstract description 20
- 238000012795 verification Methods 0.000 claims abstract description 15
- 238000005457 optimization Methods 0.000 claims abstract description 10
- 230000010365 information processing Effects 0.000 claims description 8
- 239000000463 material Substances 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 210000004556 brain Anatomy 0.000 claims description 3
- 230000002265 prevention Effects 0.000 claims description 3
- 239000003380 propellant Substances 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 239000007788 liquid Substances 0.000 abstract description 17
- 239000001257 hydrogen Substances 0.000 abstract description 14
- 229910052739 hydrogen Inorganic materials 0.000 abstract description 14
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 abstract description 11
- 238000013461 design Methods 0.000 abstract description 2
- 230000007547 defect Effects 0.000 abstract 1
- MYMOFIZGZYHOMD-UHFFFAOYSA-N Dioxygen Chemical compound O=O MYMOFIZGZYHOMD-UHFFFAOYSA-N 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 6
- 238000010276 construction Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 4
- 238000013135 deep learning Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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Abstract
The invention provides a knowledge reasoning method for an oxyhydrogen engine, which comprises the following steps: a knowledge extraction system and a verification optimization system; the knowledge extraction system comprises a standardized knowledge graph system and a non-standardized knowledge graph system; the standardized knowledge graph system is a structured data sum; the non-standardized knowledge graph system includes information-processed semi-structured data and unstructured data. The invention overcomes the defects of the prior art, has reasonable design and compact structure, and constructs the knowledge map of the professional field by surrounding the engineering field of the liquid oxygen-liquid hydrogen engine. And performing operations such as knowledge extraction and the like on unstructured data acquired from a business system platform, wherein the operations comprise entity extraction and relation extraction, converting the unstructured data into structured data, and constructing a knowledge graph of the liquid oxygen-liquid hydrogen engine engineering field by combining the structured data and corpora acquired from the system to form a knowledge graph storage database.
Description
Technical Field
The invention relates to the technical field of liquid oxygen and liquid hydrogen engines, in particular to a knowledge reasoning method for an oxygen-hydrogen engine.
Background
The knowledge graph graphically describes the complex relationship between concepts and entities in the real world, and the internet can transmit information, organize and manage information and enable people to better understand knowledge in a more acceptable world-aware mode. The knowledge graph can also make great contribution to the development of intelligent science and technology in China by combining big data, deep learning and the like.
Knowledge graph design is a technology which mainly comprises three aspects of knowledge representation, graph construction and graph application. The knowledge representation is a method research aiming at representing and processing objective event knowledge in a computer; the knowledge graph is built to solve the problem of how to construct an algorithm to acquire internet knowledge of objective events from objective worlds or various data resources, and the main task of knowledge graph application is to research how to use the knowledge graph to better solve the actual problem in real life.
The construction of the knowledge graph needs to extract valuable information from complex and various internet information by using the technologies of regional learning, information extraction and the like based on a specific knowledge representation model, provides a data source for the knowledge graph and lays a foundation for graph construction, wherein the core technology is information extraction and semantic integration. The construction method of the knowledge graph is influenced by a plurality of factors, including three factors: one is to learn knowledge from what data resources, and the original web page data includes three kinds of data, structured (e.g., database), semi-structured (e.g., table on web page) and unstructured (e.g., plain text data); secondly, learning what knowledge mainly comprises concept hierarchy, fact knowledge, event knowledge and the like; third, what learning approach is used to obtain knowledge.
The knowledge graph is a structured semantic knowledge base and is used for describing concepts and mutual relations in the physical world in a symbolic form, the basic composition units of the knowledge graph are 'entity-relation-entity' triples, and entities and related attribute values thereof, and the entities are mutually connected through relations to form a network knowledge structure.
The liquid oxygen-liquid hydrogen engine refers to an engine using liquid hydrogen and liquid oxygen as fuel. The oxyhydrogen engine is one of the trends of the technological development of the world rocket engine, and the mastery of the oxyhydrogen engine technology is one of the signs of the nation becoming the strong aerospace country. The development of a high-thrust hydrogen-oxygen engine is the development trend of liquid rocket engine technology at home and abroad, so that the construction of a map of the liquid-oxygen liquid-hydrogen engine is very necessary. At present, a knowledge graph is not constructed in the field of liquid oxygen and liquid hydrogen engines.
Therefore, a knowledge reasoning method of the hydrogen-oxygen engine is provided.
Disclosure of Invention
It is an object of the present invention to solve or at least alleviate problems in the prior art.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a knowledge reasoning method for a hydrogen-oxygen engine, comprising: a knowledge extraction system and a verification optimization system;
the knowledge extraction system comprises a standardized knowledge graph system and a non-standardized knowledge graph system;
the standardized knowledge graph system is a structured data sum;
the non-standardized knowledge graph system comprises semi-structured data and unstructured data which are subjected to information processing; the information processing mode comprises entity identification and relationship identification;
the verification optimization system includes verification of a standardized knowledge graph system and a non-standardized knowledge graph system.
Optionally, the structured data includes universal linguistic data in the field, technical dictionaries and linguistic data adopted by technical manuals which can be directly used.
Optionally, the entities include, but are not limited to, equipment model, engine class, performance parameters, operating conditions, structure, propellants, cycles, tests, materials, processes, faults, others, environment, management, and attribute values.
Optionally, the relationship includes, but is not limited to, description, composition, advantages, disadvantages, failure, cause of failure, and prevention of failure.
Optionally, the information processing manner further includes a semantic parsing system and an entity recognition model system.
Optionally, the semantic parsing system includes recognizing entities in the semi-structured data or the unstructured data, and parsing the relationship between the entities according to semantics to form a non-standardized knowledge graph system.
Optionally, the entity recognition model is implemented by decomposing input semi-structured data or unstructured data through a semantic decomposition system according to a computer network or a human brain recognition system and expanded corpus information in the field.
Optionally, the system further comprises a knowledge fusion system, wherein the knowledge fusion system comprises a knowledge disambiguation system; a knowledge alignment system; the normalization system integrates two or more entities or relationships with the same meaning into one.
Optionally, the verification optimization system includes, but is not limited to, computer verification and manual verification, and analyzes the accuracy of the corpus by analyzing whether the corpus disassembled by the knowledge graph system conforms to the actual product.
Optionally, when the disassembled corpus is the same as the actual product, the disassembled corpus is included in the knowledge graph in the field, and if the disassembled corpus is not consistent with the actual product, and/or the newly supplemented corpus in the field is included in the entity recognition model in the field together for model training again.
The embodiment of the invention provides a knowledge reasoning method for an oxyhydrogen engine. The method has the following beneficial effects: the invention constructs a knowledge map in the professional field by surrounding the engineering field of the liquid oxygen-liquid hydrogen engine. The method comprises the steps of carrying out operations such as knowledge extraction and the like on unstructured data collected from a business system platform, wherein the operations comprise entity extraction and relation extraction, converting the unstructured data into structured data, and then combining the structured data and corpora collected from the system to construct a knowledge map in the liquid oxygen hydrogen engine engineering field to form a knowledge map storage database, so that the intelligent application of a typical business system or tool based on the knowledge map is realized, and corresponding introduction can be obtained based on intelligent semantic retrieval of the knowledge map, intelligent search based on the knowledge map and knowledge recommendation based on the knowledge map.
Drawings
FIG. 1 is a schematic flow chart of the system of the present invention;
FIG. 2 is a schematic flow diagram of a knowledge extraction system of the present invention;
FIG. 3 is a schematic flow chart of a semantic parsing system according to the present invention;
FIG. 4 is a schematic flow chart of the entity recognition model system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Example 1
Referring to fig. 1-4, a knowledge inference method for an oxyhydrogen engine comprises: firstly, a knowledge extraction system is included; secondly, a knowledge fusion system; thirdly, verifying an optimization system;
firstly, a knowledge extraction system; the system comprises a standardized knowledge graph system and a non-standardized knowledge graph system;
a normalized knowledge graph system, i.e., a representation that is capable of directly absorbing a structured data sum;
the structured data comprises universal language materials in the field, databases, technical dictionaries and language materials adopted by technical manuals, such as named entity language materials in the field of liquid oxygen liquid hydrogen rocket engine engineering, which can be directly used, and the sources of the structured data comprise various file materials. These structured data are the products of a generalized summary, and the sum of these structured data can form a standardized, standardized knowledge-graph building block, such as: the standardized knowledge graph system is equivalent to a dictionary, the structured data is equivalent to entries and interpretations thereof in the dictionary, and the dictionary can directly reference new entries and interpretations thereof during arrangement, so that the content of the dictionary is expanded.
The non-standardized knowledge graph system comprises semi-structured data and unstructured data which are subjected to information processing; the information processing mode comprises entity identification and relationship identification;
the semi-structured data and the unstructured data are understood according to dictionary examples to be equivalent to pure text articles, short sentences, tables on web pages and the like which cannot be directly utilized, the contents comprise new terms or new relations, the short sentences can be disassembled through semantic analysis or deep learning, and the disassembly of the new terms is equivalent to entity recognition in the application, wherein the relation disassembly is equivalent to relation recognition in the application.
The processing modes of the semi-structured data and the unstructured data are divided into two types:
one is as follows: the semi-structured data and the unstructured data can be directly disassembled through a semantic disassembling system;
the method comprises the steps of identifying entities in semi-structured data or unstructured data, wherein the entities are equipment names defined by linguistic data in the liquid hydrogen liquid oxygen engine engineering field, and resolving the relations among the entities according to semantics to form a non-standardized knowledge map system.
Entities include, but are not limited to, equipment model (vehicle), engine class, performance parameters, operating conditions, structure, propellant, cycle, test, material, process, fault, other, environmental, regulatory, and attribute values.
Relationships are shown in FIG. 3, including but not limited to description, composition, advantages, disadvantages, faults, cause of fault, and prevention of fault;
the second step is as follows: the semi-structured data and the unstructured data can not be disassembled through the semantic disassembling system, and the semi-structured data and the unstructured data can only be disassembled after the entity recognition model system is processed.
The entity recognition model system is to disassemble the semi-structured data or the unstructured data to be input through semantic relations according to a computer network or a human brain recognition system and expanded corpus information in the field.
Through the two modes, the entities in the semi-structured data or the unstructured data can be identified, and meanwhile, the relation between the entities is extracted according to semantics, so that the entities and the relation are integrated into a non-standard knowledge graph system.
The knowledge fusion system comprises a knowledge disambiguation system; a knowledge alignment system; a normalization system;
the knowledge disambiguation system is able to disambiguate entities or relationships, ensuring that the meaning of each entity or relationship expression is accurate.
The knowledge alignment system generalizes and summarizes the entities or the relationship contents of the same type;
the normalization system integrates two or more entities or relations with the same meaning into one entity, so that the storage capacity is effectively reduced, and the attribute value combination and data are more standard.
Thirdly, the verification optimization system verifies a standardized knowledge graph system and a non-standardized knowledge graph system;
the verification optimization system is mainly manually verified, whether the linguistic data disassembled through the knowledge graph system is consistent with an actual product or not is manually analyzed, when the disassembled linguistic data is the same as the actual product, the linguistic data is brought into a knowledge graph in the field to form an accurate and complete database, and if the disassembled linguistic data is not consistent with the actual product, and/or newly supplemented linguistic data in the field are brought into an entity recognition model in the field together to perform model training again, so that the accuracy of the linguistic data is improved.
In conclusion, the knowledge map of the professional field is constructed by surrounding the engineering field of the liquid oxygen-liquid hydrogen engine. The method comprises the steps of carrying out operations such as knowledge extraction and the like on unstructured data collected from a business system platform, including entity extraction and relation extraction, converting the unstructured data into structured data, and reasoning and constructing a knowledge graph in the engineering field of the liquid oxygen liquid hydrogen engine by combining the structured data and corpora collected from the system to form a knowledge graph storage database so as to realize intelligent application of a typical business system or tool based on the knowledge graph.
Example 2
Intelligent entity retrieval based on knowledge graph
The invention also comprises the processes of intelligent entity retrieval based on the knowledge graph, intelligent search based on the knowledge graph and knowledge recommendation based on the knowledge graph, which can be obtained;
by inputting the corresponding entity into the knowledge graph, the structured data, the semi-structured data and the unstructured data corresponding to the entity are searched and displayed on the knowledge graph at the moment, the data are often related to the introduction of the entity, and the data are related to the plain text articles, short sentences and tables on a webpage of the entity, so that the detailed information of the entity can be obtained, the information can be conveniently searched by workers, and the efficiency of knowledge acquisition is improved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A knowledge reasoning method for a hydrogen-oxygen engine, comprising: a knowledge extraction system and a verification optimization system;
the method is characterized in that: the knowledge extraction system comprises a standardized knowledge graph system and a non-standardized knowledge graph system;
the standardized knowledge graph system is a structured data sum;
the non-standardized knowledge graph system comprises semi-structured data and unstructured data which are subjected to information processing; the information processing mode comprises entity identification and relationship identification;
the verification optimization system includes verification of a standardized knowledge graph system and a non-standardized knowledge graph system.
2. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 1, characterized in that: the structured data comprises universal linguistic data in the field, technical dictionaries and linguistic data adopted by technical manuals which can be directly used.
3. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 2, characterized in that: the entities include, but are not limited to, equipment model, engine class, performance parameters, operating conditions, structure, propellants, cycles, tests, materials, processes, faults, others, environment, management, and attribute values.
4. The knowledge inference method of an oxyhydrogen engine according to claim 2, characterized in that: the relationships include, but are not limited to, description, composition, advantages, disadvantages, faults, causes of faults, and fault prevention.
5. The knowledge inference method of an oxyhydrogen engine according to any one of claims 1 to 4, characterized in that: the information processing mode further comprises a semantic disassembling system and an entity recognition model system.
6. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 5, wherein: the semantic disassembling system comprises the steps of identifying entities in semi-structured data or unstructured data, and disassembling the relation among the entities according to semantics to form a non-standardized knowledge map system.
7. The hydrogen-oxygen engine knowledge inference method of any one of claims 5 or 6, characterized by: the entity recognition model is to enable the input semi-structured data or unstructured data to be disassembled through a semantic disassembling system according to a computer network or a human brain recognition system and expanded corpus information in the field.
8. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 1, characterized in that: the system also comprises a knowledge fusion system, wherein the knowledge fusion system comprises a knowledge disambiguation system; a knowledge alignment system; the normalization system integrates two or more entities or relationships of the same meaning into one.
9. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 1, characterized in that: the verification optimization system comprises but not limited to computer verification and manual verification, and the accuracy of the corpus is analyzed by analyzing whether the corpus disassembled by the knowledge graph system is consistent with an actual product or not.
10. The knowledge inference method of a hydrogen-oxygen engine as claimed in claim 9, characterized in that: and when the disassembled corpus is the same as the actual product, the disassembled corpus is incorporated into the knowledge map in the field, and if the disassembled corpus is not consistent with the actual product, and/or the newly supplemented corpus in the field is incorporated into the entity recognition model in the field together for model training again.
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| CN111444351A (en) * | 2020-03-24 | 2020-07-24 | 清华苏州环境创新研究院 | Method and device for constructing knowledge graph in industrial process field |
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| CN111444351A (en) * | 2020-03-24 | 2020-07-24 | 清华苏州环境创新研究院 | Method and device for constructing knowledge graph in industrial process field |
| CN111813974A (en) * | 2020-07-08 | 2020-10-23 | 广州市多米教育科技有限公司 | Self-adaptive practice system based on image semantic analysis |
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