Research On Structure Optimization And Update Technology Of Combine Harvester Knowledge Base

The combine harvester knowledge base system uses the SQL server database,numerous data tables in the database are independent and easy to build and manage.But when the amount of knowledge base data reaches a certain size,the structure of the knowledge base system limits the full use of large-scale data,querying data tables one by one is not actionable and merging all the data tables will lead to confusion in the data structure,unclear content expression,and technical inability to achieve.Most of the knowledge update steps such as collection,sorting,and storage of knowledge in the knowledge base system are carried out in the form of manual,but when the knowledge base system is optimized and established into a large knowledge base system,the speed of manual update cannot meet the needs of knowledge base update.In response to the above two questions,a multi-table joint query method of combine harvester knowledge base data and update technology are proposed.The data table types is divided from multiple perspectives,the data storage structure of the combine harvester knowledge base is analyzed and the management scope for multi-table joint data is set.The application structured query language(SQL)fused multi-table information into a data sets and stores it into a temporary table to achieve multi-table joint operation.The human-computer interactive interface is used to convert the user query requirements into multi-table joint query statements to generate query results,and multi-table approximate range query and multi-table precise positioning query are realized.The framework and constituent elements of the target website are analyzed to locate the target information position on the web page;Web crawler is run to crawl target web pages,web page knowledge and data are obtained and processed;The knowledge and data types of the knowledge base system are analyzed to open the user file upload channel;Python and VB.net languages are mixed to enable the storage of knowledge and data and the updating of knowledge and data through human-computer interfaces.The main research contents include the following parts.(1)Adjustment of the existing architecture of the knowledge base systemThe existing structure of knowledge base system is analyzed,the role of knowledge base system in PDM system,which is a key component of combine harvester,is defined,the role of knowledge base system in PDM system is taken as the pull,the internal function of knowledge base system is adjusted,the same function is integrated,the similar function is connected,and the different function is modularized.Based on the combination of combine harvester pedigree hierarchy setting method and pedigree topology diagram,the knowledge base structure which is more in line with user needs is built and the existing architecture is adjusted.Associate knowledge base and model base.Knowledge base system and model base system are both subsystems in PDM system,which is a key component of combine harvester.The function between knowledge base system and model base system is defined.The model technical parameters obtained by browsing,inquiring,reasoning and matching of knowledge base system are transmitted by SQL Server database as the medium.In this way,the relation between knowledge base system and model base system is established.(2)Supplement of knowledge base system architectureFrom the perspective of the process of data query by users using data tables and the increase of data volume in knowledge base,this paper analyzes the existing data tables,divides the data tables into types according to the structure characteristics of data tables and the functional components they belong to,analyzes the data storage structure of knowledge base,sets the scope of multi-table joint data management,proposes the multi-table joint operation method,and applies the multi-table joint operation to user query.Multi-table approximate range query and multi-table precise location query are realized.(3)Acquisition and processing of knowledge and data in the knowledge base systemSelect an appropriate website as the target website for acquiring knowledge and data,analyze the source code of the website,summarize the distribution characteristics of knowledge and data on the website,locate the location of the source code of knowledge and data,use Python language to design a crawler to acquire knowledge and data,and process each part of knowledge and data into usable strings,tables,etc.Provides a channel for users to upload files,and limits the types of files to be uploaded according to the knowledge and data types required by the knowledge base system.(4)Storage and replacement of knowledge and data in knowledge base systemAnalyze the distribution of electronic warehouse storage structure,extract the acquired knowledge and data,store the two-dimensional table in the database,store other types of knowledge and data and the main data files and transaction log files in the form of files in the electronic warehouse.For files with the same name that already exist in the vault,they are stored in a substitution manner during file storage.(5)System technology integration and case analysisCombined with VB.net,SQL and Python language,knowledge transfer between design parameters in knowledge base system and parametric variations in model base system is realized.The multi-table joint operation and user query technology are integrated to realize multi-table approximate range query and multi-table precise location query.The website of National Agricultural Engineering Experimental Teaching Demonstration Center of Northeast Agricultural University was taken as the test object,and the test environment was simulated on the website to test the efficiency and quality of the knowledge and data updating of the webpage in the knowledge base system studied in this paper.The research and test results show that the multi-table approximate range query saves more than 50% of the user’s operation time,and the maximum is 90.4%,compared with the original single table approximate range query.Compared with the original single table precise positioning query,the multi-table precise positioning query saves 48.1% or more of the user’s operation time,and the maximum is 89.6%.Update determines whether the test web page is updated by detecting the change of the title in the web page.The update time of the test web page is 5.265 seconds.The realization of multi-table joint query and update makes the knowledge base system of the combine harvester have timeliness,practicability and feasibility,and provides a reference for the similar knowledge base system data management and data update ideas and methods.