The Design And Implementation Of Physical Education Teaching Net Work Aided System

In the bidding activities, building and managing a database of experts is an important link of regulating the quality of bidding activities. Since the “invite public bidding of People’s Republic of China bids law” enacted on January 1, 2000, some enterprises and public institutions of one petroleum company had formulated the bidding evaluation expert management rules one after another. But different company has different management style. Differences among different styles bring some trouble in practice.After surveying current construction situation of databases of experts, a management system of bidding experts is designed and implemented. This system contains six modules: management of basic information, expert information maintenance, management of expert extraction, management of expert check, management of expert evaluation and information querying. These modules are organically integrated,establishing a complete expert management mechanism. Based on the management of expert basic information, experts can be extracted to take part in bidding evaluation. When the bidding finished, it needs to check bidding experts considered their performance. The system is based on B/S structure, and designed with Struts framework which based on MVC and layered architecture. Data view layer using AJAX technique and JQuery framework. Using database Oracle to store and manage data, the designed database meets the Third Normal Form.In addition, because the utilization rate of expert repeats highly when they are extracted by the way of extracting randomly, a kind of recommendation algorithm is designed and realized after the numerous studies of expert system extraction module and current recommendation algorithms. This algorithm is serviced for the bidding expert system technically. And it has experimented on the dataset that contains real data and experimental data generated randomly. Compare and analysis the two results that getting from random extraction and t he recommendation algorithm respectively, it is easy to know that the degree of mental fatigue of experts that recommended by recommendation algorithm is less than 48% of that extracted by random extraction, and the score of experts is more than 26%. Besides, aiming at multiple projects group recommendation algorithm is proposed, compared with single project recommendation algorithm, project contribution is improved. Hope that this algorithm makes some contribution to the expert extraction.