NL generation SQL method for big data operation and maintenance of new power supply urban rail trains
A technology for urban rail trains and big data, which is applied in the field of NL generating SQL for big data operation and maintenance of new power supply urban rail trains, and can solve problems such as inability to apply operation and maintenance data of new power supply urban rail trains
Active Publication Date: 2021-11-23
ZHEJIANG UNIV
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Problems solved by technology
[0007] Aiming at the deficiencies in the prior art, the object of the present invention is to provide an NL-generated SQL method for large data operation and maintenance of new-type power supply urban rail trains, which solves the multi-table query operation in the database and solves the problem of existing natural language generation databases. The application scenario of query statement technology is limited to the data query in a single table in the database and cannot be applied to the technical problem of operation and maintenance data of complex new power supply urban rail trains
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[0030] The data solution of the present invention will be further described in conjunction with specific implementation and examples.
[0031] Such as figure 1 , specific embodiments of the present invention and its implementation process are as follows:
[0032] Step 1: The new power supply urban rail train will generate a large amount of data during operation. These historical data are of great reference to the operation and maintenance personnel when operating and maintaining the train. In this example, the data is collected and structured and stored in multiple data tables of the MySQL relational database for subsequent query and management. The input of this step is the collected data, and the output is multiple data tables containing the structured data, and the size of the data tables is 20.
[0033] Step 2: Encoding is required before inputting natural language and table and column name information in the database into the deep learning classification model. In this...
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The invention discloses an NL generating SQL method for large data operation and maintenance of new-type power supply urban rail trains. Collect a variety of data generated during operation from the new power supply urban rail train, organize them into their own structured data, and store them in multiple data tables of the relational database; name to obtain two types of codes; build a syntax analysis tree template that conforms to the grammatical structure, and train and predict the parts that need to be filled in the syntax analysis tree template; input the natural language to be queried into the deep learning model to obtain the information that needs to be queried in the database Table name information, generate a complete database query statement, and execute the database query statement to obtain the required query data. The invention automatically generates database query sentences according to natural language questions, improves the work efficiency of operation and maintenance data, and can accurately return the required query data during the multi-table query operation of the database involved in different scenarios.
Description
technical field [0001] The invention relates to a database natural language query processing method in the field of artificial intelligence, in particular to a method for generating SQL (database query statement) from NL (natural language) oriented to big data operation and maintenance of new power supply urban rail trains. Background technique [0002] In recent years, with the continuous growth of my country's economy, the infrastructure construction industry has also grown rapidly, and the rail transit industry has also ushered in the dividends of development. For the massive data operation and maintenance of the new power supply urban rail trains, the operation and maintenance personnel are not experts in the database field, but expect to query the database through natural language to improve the efficiency of operation and maintenance. Therefore, by studying how to convert human natural language questions into executable database query sentences to improve the efficienc...
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IPC IPC(8): G06F16/2452G06F16/22G06F16/2455G06F16/2458G06F16/28
CPCG06F16/2282G06F16/24522G06F16/2456G06F16/2458G06F16/284
Inventor 陈刚刘晋潘硕李辉曹金超吴晓凡江大伟陈珂
Owner ZHEJIANG UNIV