Research On Application Of Context Computing In Energy Saving In Smart Campus

With the continuous improvement of China's education level,the scale of school enrollment is also expanding.To this end,schools need to add more equipment to serve teachers and students.However,at present,most schools still manage equipment manually,which will cause a series of problems such as complex management processes,heavy workload and difficult maintenance,which will easily lead to waste of campus resources.The thesis combines with the special scientific research project of Shaanxi Provincial Department of Education: "Research on Key Technologies for Complex Event Processing and Their Application in the Smart Campus Internet of Things"(project number: 17JK0376).Relevant technologies such as networking,big data,and machine learning have been explicitly introduced into smart campus energy management.A set of multi-source data that can pre-process,reason,analyze,and predict campus nature,humanities,space-time,and personalization has been proposed to meet high-level decision-making application needs Intelligent processing method of multi-dimensional situational information,and tested and applied the research theoretical results.The thesis first elaborates the research background,problems and significance of the application of contextual computing in smart campus energy saving,and then gives the research content,research method and overall structure arrangement of the paper.Secondly,the key technical concepts involved in this article are introduced in detail.It consists of three major aspects: smart campus,context energy saving and personalized information service,and then provides a basic reference for the construction of multi-dimensional context information processing methods.Then,by adopting techniques such as event object model,queue model,Kalman filter method,semantic web and logical reasoning,a multi-dimensional situational information processing mechanism capable of pre-processing and fusion analysis of campus multi-source data such as nature,humanities,space-time and so on is proposed.In order to quickly identify,reason and integrate the current situation,and produce inference results,to achieve the purpose of smart campus energy-saving control.Fourth,based on the multi-dimensional context information processing mechanism and inter-tree similarity calculation method,a processing mode capable of real-time collection,storage,matching,and inference of personalized context information is designed to effectively identify and process campus personalized context data,To generate personalized context services,to achieve personalized energy-saving control and management on campus.Fifth,a combined pre-processing technology and KNN improved prediction model were used to construct a campus situation trend prediction method for accurately and quickly predicting thedevelopment trend of campus situations.Finally,the smart campus energy-saving control and management system is deployed in a realistic experimental scenario to complete the construction of related software and hardware equipment.During the operation,the functional modules of the system were tested in time,and the expected results were achieved.Comprehensive experimental results show that the proposed multi-dimensional context information processing method proposed in this paper can fuse real-time,intelligent,multi-dimensional context information such as nature,humanities,space-time and personalization,and formulate equipment energy-saving control schemes,ultimately achieving the purpose of campus energy conservation.