Research On The Grain Storage Basic Physical Parameters Expert System

The current majority monitoring and controlling system of grain storage expert system is a combination of software and hardware。It has some limitations in updating and sharing the expert knowledge. And many research institutions and experts obtain a large number of expert knowledge in the research process, but there are some flaws in the management of expert knowledge. Thus, these knowledges can not be informed timely by the grain storage management staff, resulting in a waste and loss. This paper designs and implements a new grain storage basic physical parameters expert system to solve this problem. The main content are as follows:1. Based on the existing grain storage expert system research, this paper presents the design scheme, using the SSH framework and Drools reasoning engine for the system. And this paper discusses the traditional matching algorithm and the Rete algorithm which is used in the Drools reasoning engine.2. The basic physical parameters in this expert system mainly are numerical data. For this feature, this paper uses the idea of least squares method to improve the conflict resolution strategy in this expert system in order to find the optimal.3. Analyze and design the logical structure, user roles and permissions, functional requirements, and database structure for the system. Using Java language to develop and deploy the system, and to achieve the various functional modules. This system uses the MySQL database to manage the data.4. Test the system, in order to find and solve the problem, and to improve the system further. The gain storage basic physical parameters expert system uses SSH framework to build the B/S architecture system platform. It is not restrict by region and hardware. And it effectively increases the sharing rate and popularizing rate of the expert knowledge in grain storage physical parameters. The conflict resolution strategy is improved according to its own characteristics, so that the efficiency is not affected when the data reaches a certain scale.