Design And Implementation Of An Industry-Education Integrated IoT Teaching Platform

In recent years,the rapid development and integration of the Internet of things(IoT),cloud computing and big data technology are continuously driving product and service innovation.At present,the practical teaching function of the existing IoT teaching platform is not perfect,and the industry coverage of teaching content is low.As a result,theoretical teaching and practical teaching are difficult to connect well,and it is difficult for students to understand and master the complete industry application process.To solve this problem,an IoT teaching platform with rich resources,comprehensive coverage and strong expansibility is designed and implemented in the thesis,which optimizes the practical teaching function and realizes the combination of theoretical teaching and practical teaching.The platform takes smart agriculture,smart home and smart medical as the prototype,and integrates IoT technology and multi-disciplinary technology.It can not only meet the needs of theoretical course teaching of IoT,but also integrate related technologies into a variety of IoT application fields.The research content of the thesis includes two aspects.(1)Rule engine is an important part of IoT teaching platform,which is the basis of realizing the dynamic configuration of business rules of practical teaching function.Based on the data characteristics of IoT application scenarios,a rete-based rule engine optimization algorithm is proposed,which improves the rete algorithm from the aspects of node sharing and pre-sorting and translation lookaside buffer(TLB)caching.First,a pre-sorting algorithm based on rule frequency is designed to pre-order the order of nodes according to the frequency of use of the rule pattern,and preferentially match the pattern with high frequency of use to increase the sharing rate of nodes and reduce the memory footprint of the inference network.Then,a TLB caching algorithm based on the principle of locality is designed,which stores the history of previously successfully matched rules to improve the matching efficiency of IoT-aware data.(2)A complete and available IoT teaching platform with convenient equipment management and real-time data visualization is designed and implemented,which combines theory with practice.The platform adopts the distributed architecture design,realizes the multi course self-help learning scheme in theory teaching module,and realizes the full link response and display of scene data in practice teaching module.First,the platform stores the IoT knowledge through theoretical teaching.Second,the underlying equipment is connected to the system through identity verification and protocol transformation.Then,the efficient rule matching is completed by improving the rule engine of rete.Finally,all kinds of datas of the scene are viewed through the visualization function.Tests on both functions and performance have effectively verified the feasibility and effectiveness of the system.The research in this thesis effectively realizes the combination of the IoT theory teaching and IoT practice teaching,which has a certain reference value for the design and implementation of the integrated teaching platform of industry and education.