The Research And Development Of Integrative Decision-Making And Supporting System Of Insect Management In Agriculture Based C/S/S Structure

The basements of successful integrated pest management (IPM) are correct pest identification, accurate forecasting and rational controlling protocol and so on. Basing those knowledge and high-techs in the fields of computer and internet, Systems of Artificial Intelligence were developed rapidly.We simply reviewed the agricultural expert system, and analyzed some problems in expert system of insect identification, such as faint function, low efficacy and demand of more knowledge of taxonomy, etc. Aiming to those points, we brought solutions forward including depressing requirement of taxonomical knowledge while emphasizing employment on knowledge in biology and ecology. In order to achieve distributed calculation and facilitate updating of components in the expert system, we designed some others which are relatively independent using COM technique. We organized knowledge database in frame structure to decrease the cost of maintenance of database.Utilizing some powerful tools (such as Microsoft Visual Basic, Visual InterDev and Macromedia Dreamweaver, Firework), we established the expert system project which assist farmers and technician making decision in IPM. There are generally six organizing and expressing systems of knowledge in expert system which including Production System, Meaning Net System, Frames System, Object-Oriented System, Case Based Reasoning (CBR) and Model Based Reasoning (MBR). From the point of view of IPM, we compared those systems, chose two most adaptive systems to establishment of our expert system of controlling pests and elucidated the reasons and principles of combined use of Object-Oriented System and Frame System. In this paper, we designed Frame Knowledge Database of Pest Identification and Knowledge Database of Making-Decision and orient them to controlling of most important economic and agricultural pest species. At the same time, combining expert system database with IPM knowledge, we created an Expert System Reasoning Engine and selected a strategy of knot killed which is based on Professional Importance Quotient (PIQ) and Property Fuzzy Power (PFP). As we had expected, heuristic-wide-prior search strategies had been come true after PIQ and PFP were applied into the engine.Using COM technology, we exploited the COM components of expert system, including Expert System shell, Expert System reasoning machine and Expert System plug-in tools. Expert System's three-layer C/S/S structure which was composed of those components can realize distributed computation and enhance the reasoning efficacy. Moreover, our Expert Systems (including Pest Identification System and Making-decision System) realized by distributed computation have two interfaces in the shell and engine, respectively, which improve the expandable property of the system.In addition, we also designed some accessorial tools of Expert System such as Exposition Building Engine (EBE) and Expert System Knowledge Database Modifying Tool (MT). EBE can provide dynamics HTML pages' explanations to text information with explaining and demonstrating database. MT can assure correct data link in System Logic Table and stable logical structure, which help programmer make secondary development easily.