【作者】 高洪伟；

【导师】 何瑞银；

【作者基本信息】 南京农业大学 ， 农业机械化， 2004， 硕士

【摘要】 近年来，我国联合收获机的产销量迅速增加，目前我国联合收割机的保有量达到了30余万台，提高了稻麦的机收水平，改善了劳动条件，创造了显著的社会效益和经济效益，但是由于技术上的难点，标准化、通用化程度低以及经济问题等都制约着稻麦机收市场的发展，总体机收水平不高。再就是一直以来，对于稻麦收获机的选购，都是凭一些专家的主观评价和经验，缺乏可靠性。 目前，我国普遍存在的稻麦联合收获机械问题是收获作业成本较高、资金合理利用率差，在机务计划制定和机具优化配备方面，与发达国家相比有很大差距。针对这些问题，本文借鉴国内外的主要研究成果，结合江苏省农垦集团的实际情况，以农业智能系统为平台研究开发了稻麦收获机械选型的决策支持系统。该决策支持系统由五大部分组成，包括用户界面(人机会话系统)、智能决策模块、数据库及其管理系统、知识库及其管理系统和数据查询模块。 用户通过友好的用户界面，直接输入预计收获作物面积、收获作物类型、是否要秸秆还田、可靠性以及配件供应要求等决策参数，智能决策模块根据用户所提供的输入调用数据库中的数据(数据库存储收获机的型号、功率、实际生产率、平均油耗、购买价格、可靠性、配件供应、喂入形式、机手工资等最初数据)，并运用知识库中存放的专家经验知识综合考虑以上决策参数进行推理和解释，产生符合要求的稻麦联合收获机型号，然后计算出这些机型的收获作业成本，选取其中作业成本最低的收获机即为最佳收获机型号。该系统以农业智能系统开发平台PAID4.0(单机版)开发的，具有友好的用户界面，使一般没有计算机知识的农民用户也可方便地使用。一般用户的权限是进行数据的输入、决策和查询。 研究结果表明，该系统可有效地指导一般用户进行收获机选型，避免了传统的凭经验进行选型的片面性和盲目性，按作业成本最低的原则进行选型能够有效地提高资金的利用率，从而显著提高农业生产的经济效益。更多还原

【Abstract】 In recent years, in our country the production and sale of combines have increased rapidly. At present, the combine’s retained quantity of our country is about 300 thousand suits, which have improved the machinery harvesting level of rice and wheat, and improved the laboring conditions and created distinct social benefit and economic benefit. However, because of the technical difficulties, low standardization, low all-purpose capacity and economic problems etc., all of these factors restrict the development of the rice and wheat machinery harvesting market, and then the machinery harvesting level is still low. Another reason is that we always follow experts’ subjective appraisements and experiences to select combines, but these experiences are lack of reliability sometimes.At present, in our country the general existing problems on rice and wheat combines are high harvesting operation costs and low efficiency of fund using, and we are still lack of practicable way to make field planning and select combines compared with developed country. In order to solve these problems, this thesis, referring to domestic and foreign main research achievements and integrating the actual situation of Co., Ltd. of agriculture reclamation Group of Jiangsu province, developed Decision Support System (DSS) of rice and wheat combine selection by agricultural intelligent system as platform. This DSS is composed of five components, including user interface (person and computer dialoguing system), intelligent decision module, database and its management system, knowledge base and its management system and data-querying module (including fact-querying and result-querying).By convenient user interface, user can directly input decision-making parameters such as prospective harvesting crop areas, harvesting crop type, conduction of straw return to fields or not, reliability and fittings provisions requirement and so on. Then the intelligent decision module uses data in the database (in the database storing initial data about combines’ size, power, actual productivity, average fuel consumption, purchase price, reliability, fittings provision, feeding mode and operators’ wage etc.) and exerts experts’ experiences and knowledge stored in knowledge base to deduce and explain through considering all above decision parameters. In this process this system can suggest user some rice and wheat combine models (probably more than one) that satisfies therequirement, and then this system calculates these combines’ harvesting unit costs and selects the combine with minimum unit cost among these as the best combine. This system is developed by agricultural intelligent system developing platform PAID4.0 (stand-alone version), which has convenient user interface that makes farmers can easily use it without any computer knowledge. User only has the right to input data, make decision and query.The research result shows that this system can be used to guide user to select combines effectively, whdch can avoid unilateralism and blindness of traditional selection through experience. Based on minimum unit cost to select combine, we can effectively improve the fund return rate, and then increase the economic benefit of agricultural production.更多还原