Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning

In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliabili...

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Veröffentlicht in:Journal of sensors Jg. 2022; S. 1 - 11
Hauptverfasser: Jiang, Mengmeng, Wu, Qiong, Li, Xuetao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 10.01.2022
John Wiley & Sons, Inc
Schlagworte:
Alarms
Algorithms
Artificial neural networks
Computer audits
Computers
Cooperation
Data analysis
Data integration
Data models
Decision analysis
Decision making
Decision trees
Digitization
False alarms
Fuzzy sets
Information sources
Information technology
Innovations
Machine learning
Methods
Reliability aspects
Remote sensing
Semantics
Sensors
ISSN:1687-725X, 1687-7268
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Zusammenfassung:In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.
Bibliographie:ObjectType-Article-1
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ISSN:1687-725X
1687-7268
DOI:10.1155/2022/8205929