期刊：
International Journal of Engineering Systems Modelling and Simulation,2025年16(1):52-62 ISSN：1755-9758
作者机构：
[Lianguang Mo] College of Management, Hunan City University, Yiyang, 413000, China;[Wenying Lu] School of Architectural Decoration, Jiangsu Vocational Institute of Architectural Technology, Xuzhou, 221116, China
关键词：
BIM model;high-rise buildings along the street;building surface;wind load;numerical simulation
摘要：
In order to overcome the problems of poor accuracy and low efficiency in the existing numerical simulation methods of building surface wind load, a new numerical simulation method of high-rise street building surface wind load based on BIM model is proposed. This method obtains the data of high-rise buildings along the street based on BIM model, and selects Realisable k-ε model as the turbulence model. The non-equilibrium wall function method is used to deal with the turbulence state on the building surface, the boundary conditions are set, and the turbulence model is calculated and solved by a separate solver to realise the numerical simulation of the surface wind load of high-rise street buildings. The experimental results show that the average error of the wind pressure coefficient of the proposed numerical simulation method is less than 0.4, which fully shows that the proposed numerical simulation method has good performance.
摘要：
In order to overcome the problems of poor scheduling accuracy, low recall rate and long scheduling time in the traditional scheduling method of ideological and political resources, the paper proposes a balanced scheduling method of resources based on clustering analysis algorithm. First, the load time series of the teaching resource server is determined and pre-processed. Secondly, cluster analysis is used to classify the data. Finally, according to the classification results, the balanced scheduling function of resources is constructed, and the particle swarm optimisation algorithm is used to solve the scheduling function to obtain the final scheduling strategy. The results show that the scheduling accuracy of the proposed method is 99.12%, the recall rate is up to 95%, the scheduling time is controlled within 7 s, and the resource balance scheduling effect is good.
期刊：
International Journal of Sustainable Development,2024年27(1-2):156-169 ISSN：0960-1406
作者机构：
[Zheng Xie] College of Management, Hunan City University, Yiyang, 413000, China
关键词：
Monte Carlo algorithm;logistics enterprises;supply chain operation risk;relationship network;factor set
摘要：
Aiming at the problems of low evaluation accuracy and long evaluation time in the supply chain operation risk evaluation of logistics enterprises, a method of supply chain operation risk evaluation of logistics enterprises based on the Monte Carlo algorithm is designed. First, set up the screening process of logistics enterprise supply chain operation risk evaluation indicators, and determine the key risk evaluation indicators. Then, the relationship network of risk evaluation indicators is constructed and the evaluation indicators are pretreated. Finally, the reliability degree of the index is calculated by the joint probability distribution function, and the construction of the supply chain operation risk evaluation model of logistics enterprises based on the Monte Carlo algorithm is completed. The test results show that the evaluation accuracy of the proposed method is always higher than 98%, and the time cost is always lower than 2 s, which can effectively improve the evaluation accuracy and shorten the evaluation time.
期刊：
International Journal of Continuing Engineering Education and Life-Long Learning,2024年34(4):354-367 ISSN：1560-4624
作者机构：
[Chun Liang] College of Management, Hunan City University, Yiyang, 413000, China;[Hai lin Peng] Faculty of Teacher Education, West Yunnan University, Lincang, 677000, China
关键词：
online teaching;deep belief network;DBN;teaching quality evaluation;restricted Boltzmann machine;RBM;evaluation index system
摘要：
In order to improve the evaluation effect and accuracy of online teaching quality, an online teaching quality evaluation method based on deep belief network is proposed. We establish the evaluation index system of online teaching quality, collect the data related to teaching quality, teaching attitude, teaching content, teaching methods and teaching influence by using crawler technology, and extract the data characteristics of online teaching quality evaluation index. Combined with the data characteristics, the online teaching quality evaluation model is constructed by using the deep belief network, and the evaluation index data is input into the evaluation model to obtain the online teaching quality score. The experimental results show that the error rate of the proposed method is only 4.9%, and the average accuracy rate of online teaching quality evaluation is as high as 97.2%, which has the characteristics that the accuracy of online teaching quality evaluation is higher than the effect.
