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聯(lián)合收獲機(jī)知識庫數(shù)據(jù)多表聯(lián)合查詢方法研究
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國家重點(diǎn)研發(fā)計劃項(xiàng)目(2017YFD0700100)


Multi-table Joint Query Method for Combine Harvester Knowledge Base Data
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    摘要:

    聯(lián)合收獲機(jī)知識庫系統(tǒng)使用SQL Server數(shù)據(jù)庫,,數(shù)據(jù)庫中的眾多數(shù)據(jù)表具有獨(dú)立性,便于構(gòu)建和管理,。但當(dāng)知識庫數(shù)據(jù)量達(dá)到一定規(guī)模后,,對數(shù)據(jù)表逐一查詢不具有可操作性,將全部數(shù)據(jù)表融合又會導(dǎo)致數(shù)據(jù)結(jié)構(gòu)混亂,、內(nèi)容表述不清,、技術(shù)無法實(shí)現(xiàn)。針對這一問題,,提出聯(lián)合收獲機(jī)知識庫數(shù)據(jù)多表聯(lián)合查詢方法,。從多角度劃分?jǐn)?shù)據(jù)表類型,分析聯(lián)合收獲機(jī)知識庫數(shù)據(jù)存儲結(jié)構(gòu),,設(shè)置多表聯(lián)合數(shù)據(jù)管理范圍,;應(yīng)用SQL(Structured query language)語言將多表信息融合為數(shù)據(jù)集并存入臨時表中,實(shí)現(xiàn)多表聯(lián)合運(yùn)行,;利用人機(jī)交互界面將用戶查詢需求轉(zhuǎn)換成多表聯(lián)合查詢語句,,生成查詢結(jié)果,實(shí)現(xiàn)多表近似范圍查詢和多表精確定位查詢,。多表聯(lián)合查詢與傳統(tǒng)單表知識查詢之間的測試結(jié)果表明,,多表近似范圍查詢比系統(tǒng)原有的單表近似范圍查詢節(jié)約用戶操作時間50%及以上,最高達(dá)到90.4%,;多表精準(zhǔn)定位查詢比系統(tǒng)原有的單表精準(zhǔn)定位查詢節(jié)約用戶操作時間48.1%及以上,,最高達(dá)到89.6%。多表聯(lián)合查詢的實(shí)現(xiàn)使聯(lián)合收獲機(jī)知識庫系統(tǒng)具有實(shí)用性與可行性,可為同類知識庫系統(tǒng)數(shù)據(jù)管理提供可借鑒的思路和方法,。

    Abstract:

    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, 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. In response to this problem, a multi-table joint query method of combine harvester knowledge base data was proposed. The data table types was divided from multiple perspectives, the data storage structure of the combine harvester knowledge base was analyzed and the management scope for multi-table joint data was set. The application structured query language (SQL) fused multi-table information into a dataset and stored it into a temporary table to achieve multi-table joint operation. The human-computer interactive interface was 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 were realized. The test results between multi-table joint query and traditional single-table knowledge query showed that on the one hand, multi-table approximate range query saved user operation time by 50% or more than the original single-table approximate range query of the system, and the highest reached 90.4%;on the other hand, the multi-table precise positioning query saved 48.1% or more user operation time compared with the original single-table precise positioning query of the system, and the highest reached 89.6%. The implementation of multi-table joint query made the combine harvester knowledge base system practical and feasible and provided a reference idea and method for data management of similar knowledge base system.

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劉宏新,張一鳴,解勇濤,趙一健,郭麗峰.聯(lián)合收獲機(jī)知識庫數(shù)據(jù)多表聯(lián)合查詢方法研究[J].農(nóng)業(yè)機(jī)械學(xué)報,2023,54(5):150-162. LIU Hongxin, ZHANG Yiming, XIE Yongtao, ZHAO Yijian, GUO Lifeng. Multi-table Joint Query Method for Combine Harvester Knowledge Base Data[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(5):150-162.

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  • 收稿日期:2022-07-15
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  • 在線發(fā)布日期: 2023-05-10
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