Analysis of constraints on data management of intelligent platform for smart building in the era of big data

Zhang, Miyan; Lv, Hui; Chen, Hua-Peng; Hu, Qingxia

doi:10.1088/1755-1315/768/1/012109

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can alter as the demand and object change. Finally, it is applied to innovation and entrepreneurship projects [16][17][18] combined with innovation and entrepreneurship decision-making. e platform studies individuals' career direction and characteristics through massive innovation projects combined with personal conditions.…”

Section: Platform Structure and Operating Mechanismmentioning

confidence: 99%

Construction of College Innovation and Entrepreneurship Information-Sharing Platform under Big Data Analysis

2022

Mobile Information Systems

View full text Add to dashboard Cite

Achieving outcomes in today’s colleges will need a creative attitude to be associated with higher necessary reforms to ensure a long-term future. The goal of this study is to enhance college students’ creativity and entrepreneurship by providing them with enough innovation and entrepreneurship information (IEI). This exploration is to build a college IEI sharing platform under big data analysis (BDA). First, the existing problems of IEI sharing in colleges are analyzed, and the construction principles of IEI sharing platform in colleges are put forward. Then, the platform’s structure is designed, and its operation mechanism is explained. Next, the IEI sharing platform of colleges is designed and constructed through BDA. At the source layer, the platform checks the big data collection goal, confirms the scope and substance of the collected big data via the open access mechanism, and communicates the extracted target big data to the data layer via the data collector. In the data layer, the data table is streamlined, and the data processing method is built. They gathered a large amount of classified and preserved data, and then communicated to the analysis layer via the classified storage mechanism via the open data sharing interface. The analysis layer analyzes and processes large data using a BDA algorithm, allowing for adaptive scheduling and information access. The performance content may eventually alter with the demands by integrating the big data received through the integration and sharing method. The results of the experiments indicate that the platform’s data mining range is pretty consistent. When the number of data operation requests exceeds 10,000, the platform’s processing speed reaches 45000. Furthermore, the time cost remains constant within 2 s as the data scale rises. As the number of repetitions grows, the recall rate rises to 98%. The findings indicate that the platform offers significant data mining, processing, and scheduling skills, as well as the ability to distribute IEI in real time among universities. This study helps the promotion of entrepreneurship among college students by improving the present college IEI sharing platform.

show abstract

Section: Platform Structure and Operating Mechanismmentioning

confidence: 99%

Construction of College Innovation and Entrepreneurship Information-Sharing Platform under Big Data Analysis

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

Application of big data technology in electromechanical operation and maintenance intelligent platform

Yang¹,

Chan²,

Wang³

et al. 2023

Paladyn, Journal of Behavioral Robotics

View full text Add to dashboard Cite

Aiming at the data preprocessing requirements and label data cost issues arising from the intelligent operation and maintenance of electromechanical equipment, this article mainly studies structured data cleaning methods and fault prediction algorithms for a small number of label samples. First, this article introduces the overall architecture of the intelligent operation and maintenance system for electromechanical equipment. Second, based on the electromechanical equipment operation and maintenance data access service, data cleaning, and fault prediction, this article constructs an electromechanical equipment intelligent operation and maintenance platform based on Kafka message queue, Spark cluster, and other components, and introduces the functional composition of the system in detail. Finally, the article describes the functions of each component of data access service, data cleaning, and fault prediction in detail. To address the cost issue associated with sufficient labeled sample data for data analysis, we propose a semi-supervised learning algorithm, IF-GBDT, based on improved independent forests and Gradient Boosting Decision Tree. The independent forest algorithm supplements labels for unlabeled data based on the learning results of a small number of labeled samples. We also use the gradient lifting tree algorithm to train the model based on the new tag data set for fault prediction, thereby reducing the impact of lack of tags on the accuracy of the prediction model. Experiments show that this method improves classification accuracy and has good adaptability and concurrency performance for a small number of tags.

show abstract

Analysis of constraints on data management of intelligent platform for smart building in the era of big data

Cited by 2 publications

References 0 publications

Construction of College Innovation and Entrepreneurship Information-Sharing Platform under Big Data Analysis

Construction of College Innovation and Entrepreneurship Information-Sharing Platform under Big Data Analysis

Application of big data technology in electromechanical operation and maintenance intelligent platform

Contact Info

Product

Resources

About