辽宁金融职业学院（Liaoning Finance Vocational College）坐落于辽宁省沈阳市，是辽宁省人民政府举办的全日制普通专科院校，是辽宁省职业教育改革发展示范学校建设单位。2002年1月，学校由辽宁银行学校与辽宁省金融职工大学合并而成。辽宁银行学校前身可追溯至成立于1954年的中国人民银行辽宁省分行干部学校。辽宁省金融职工大学创建于1985年。截至2018年12月，学校占地面积约10万平方米，有7个教学单位，开设30个高职专科专业；有教职工297人，全日制在校生5840人。
The constantly-increasing intricacy of household energy consumption in smart cities increases the demand for accurate forecasting models that will enable efficient energy management and sustainability. Most traditional statistical methods fail to model nonlinear temporal and electrical dependencies in real-world IoT data. An integrated IoT-enabled smart energy management framework is developed that couples advanced machine learning and deep learning models with a metaheuristic optimization algorithm. Real-world IoT data from the UCI household power consumption dataset were utilized, where ensemble-based models (XGBoost, LightGBM, HGB) and neural network architectures (MLP and LSTM) were optimized using the Golden Jackal Optimizer (GJO). Among all models, the optimized GJO-MLP model obtained the highest predictive performance with an RMSE of 0.222 and R2 of 0.946, while the GJO-LightGBM model came in second. SHAP-based feature interpretation identified short-term lags of global active power and voltage as the dominant predictors. This framework enhances transparency, interpretability, and predictive accuracy in real-time demand forecasting. This work has shown that AI-driven IoT energy management systems can be used to make significant improvement in demand-side management, energy efficiency, and operational stability in future smart grids.
Based on the SERVQUAL evaluation model, this paper examines the characteristics and service model of university archive services and establishes a knowledge service model framework for university smart archives. The service framework utilizes information fusion technology to quantitatively analyze the archive situation through the combination of 6 dimensions and 23 secondary indicators. One of the key points is to combine the whitening weight function of upper and lower limit measurements and the central whitening weight function to form a grey clustering algorithm in order to solve the “cliff” effect of the whitening weight function of the traditional upper and lower limit measurements. After the development of the system was completed, the index weights were first calculated by means of a questionnaire and then practiced in the school. The results show that responsiveness has the highest weight, 0.8612; security is second, 0.6321. And tangibility is the last, only 0.1792. The first college scored 0.461 in C1, and the second college scored only 1.87 in E1, which pulled the average score of the first-level index to 4.52. After the practice, the survey proved that the system meets the needs of the college and can point out areas for archive improvement. Archives improvement direction.
Campus media undertakes the important mission of network education, which has a profound impact on students’ behavior concept and consciousness development, information mastery and internet thinking. Based on the analysis of the significance of the development of campus media integration in colleges and universities, this paper puts forward the strategies to improve the educational function of campus media in the perspective of media integration, that is, paying attention to the audience group of campus media and clarifying the educational orientation,combining theory with practice to improve the effectiveness of education, integrating of resources to achieve media integration and collaborative education.
