在低空经济背景下,建筑行业的快速发展带来了大量的建筑垃圾,对环境和城市管理提出了严峻挑战。本文研究低空经济背景下建筑垃圾治理的智能决策支持系统。首先,利用物联网传感器从建筑工地和废物处理站实时收集建筑垃圾的相关数据(建筑...在低空经济背景下,建筑行业的快速发展带来了大量的建筑垃圾,对环境和城市管理提出了严峻挑战。本文研究低空经济背景下建筑垃圾治理的智能决策支持系统。首先,利用物联网传感器从建筑工地和废物处理站实时收集建筑垃圾的相关数据(建筑垃圾的重量、体积和成分)。然后应用K-means聚类算法对收集的数据进行清洗和预处理,提取有用的特征。基于随机森林算法,构建预测模型,用于预测建筑垃圾的产生量和组成成分。最后,利用遗传算法设计出最佳的垃圾处理方案,包括分类、回收和运输路径优化等。实验结果表明,该智能化决策支持系统在提升建筑垃圾处理效率和优化资源利用方面具有较为明显的效果。In the context of the low-altitude economy, rapid development of the construction industry has generated a large amount of construction waste, posing severe challenges to the environment and urban management. This paper studies an intelligent decision support system for construction waste management under the low-altitude economy. Firstly, IoT sensors are used to collect real-time data related to construction waste (such as weight, volume, and composition) from construction sites and waste treatment stations. Then, K-means clustering algorithm is applied to clean and preprocess the collected data to extract useful features. Based on Random Forest algorithm, a prediction model is constructed to predict the amount and composition of construction waste. Finally, Genetic Algorithm is used to design optimal waste treatment plan, including classification, recycling, and transportation route optimization. Experimental results show that this intelligent decision support system significantly improves the efficiency of construction waste treatment and optimizes resource utilization.展开更多
Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new genera...Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.展开更多
文摘在低空经济背景下,建筑行业的快速发展带来了大量的建筑垃圾,对环境和城市管理提出了严峻挑战。本文研究低空经济背景下建筑垃圾治理的智能决策支持系统。首先,利用物联网传感器从建筑工地和废物处理站实时收集建筑垃圾的相关数据(建筑垃圾的重量、体积和成分)。然后应用K-means聚类算法对收集的数据进行清洗和预处理,提取有用的特征。基于随机森林算法,构建预测模型,用于预测建筑垃圾的产生量和组成成分。最后,利用遗传算法设计出最佳的垃圾处理方案,包括分类、回收和运输路径优化等。实验结果表明,该智能化决策支持系统在提升建筑垃圾处理效率和优化资源利用方面具有较为明显的效果。In the context of the low-altitude economy, rapid development of the construction industry has generated a large amount of construction waste, posing severe challenges to the environment and urban management. This paper studies an intelligent decision support system for construction waste management under the low-altitude economy. Firstly, IoT sensors are used to collect real-time data related to construction waste (such as weight, volume, and composition) from construction sites and waste treatment stations. Then, K-means clustering algorithm is applied to clean and preprocess the collected data to extract useful features. Based on Random Forest algorithm, a prediction model is constructed to predict the amount and composition of construction waste. Finally, Genetic Algorithm is used to design optimal waste treatment plan, including classification, recycling, and transportation route optimization. Experimental results show that this intelligent decision support system significantly improves the efficiency of construction waste treatment and optimizes resource utilization.
文摘Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.
