Research And Implementation Of Key Technologies On Water Treatment Mis Based On B/S Structure

Currently, the water treatment industry receives more and more attention, especially in the management of water treatment equipment, on which the quality of management directly determines the development and competitiveness of enterprises. However, we still lack platform which can manage the water treatment equipment better at home. To solve this problem, the paper studied the key technologies on water treatment MIS deeply and completed a detailed design and implementation of the system based on B/S structure. The main achievements of this paper can be summarized as follows.(1) Completion of the overall analysis and design of the system. It was completed of the demand analysis in accordance with the actual situation. Completed the systematic extended analysis and modeling using the principle of the hybrid system. Completed the system design using the hierarchical thinking and the technology frame design using the SSH.(2) Completed in-depth study on key technologies of the system. First, we researched the wireless network communication technology, mainly analyzed GPRS and WIFI technologies which were on the most commonly used and conducted experiments by corresponding industrial-grade module to compare them. The experiments showed that the industrial WIFI module fully meet the needs of the system. Secondly, we made comparative analysis on network applications technology, mainly on Apache MINA and Netty. Then we measured the response speed and stability by actual experiments, which showed that Netty was more suitable for the system. Finally, we studied the massive data processing technology and proposed solutions for the system by the use of partitioning and iing technique. And at last, we completed the experimental test and the results showed that this scheme can meet the operating performance of the system.(3) We studied the Q&A module in the system and proposed an improved similarity calculation algorithm. The algorithm was based on word2vec and used the concept of the word vector to compute sen similarity. The algorithm made the similarity calculation more reasonable in a combination of the words weights and semantic information. Then the experiments were made to compare the algorithm with the traditional TFIDF algorithm, and the results showed that the accuracy of the algorithm was significantly higher than TFIDF algorithm.(4) Based on the above analysis, the design of the system’s database and the design of the system security and the design of the functional sub-module were completed. Finally, we completed the development of the whole system using technologies related to B/S structure.