The big data of mobile resources under the new computing network architecture is repetitive and redundant, which leads to poor classification in the process of data scheduling and detection. In order to reduce the error rate of big data deduplication and unloading of mobile resources under the new computing network architecture, a new method of big data deduplication and unloading of mobile resources under the new computing network architecture based on redundant data elimination is proposed. Autocorrelation matched filter detection model is used to filter redundant data and suppress symbol interval interference on the prior features of mobile resource big data under the new computing network architecture with random sampling, and the clustering convergence characteristic parameters of mobile resource big data under the new computing network architecture are extracted by using sample fuzzy regression analysis and least squares sample block fusion detection method. The constrained evolution method of multi-level iterative regression analysis is used to estimate the classification features of mobile resources big data under the new computing network framework. The classification target features are input into the BP neural network classifier, and the adaptive weight distribution control of BP neural network classification is carried out by combining the adaptive clustering center optimization control algorithm, which improves the adaptability of data classification and realizes the unloading of mobile resources big data under the new computing network framework. The simulation results show that the algorithm can effectively reduce the interference of redundant data, and the fidelity rate of data classification is high and the error rate is low, which improves the dynamic management ability of mobile resource data under the new computing network architecture.
In order to improve the accuracy of Internet of Things access control and the clustering of data transmission, a big data peak clustering method based on reinforcement learning is proposed. A big database management model of Internet of Things access control architecture is established by adopting global data pattern. Based on heterogeneous parameters among big data sources of Internet of Things access control architecture, combined with structural feature analysis of data sources, a big data interference filtering model of Internet of Things access control architecture is established by adopting the feature analysis method of blockchain fusion control and association rule mining, and feature extraction of big data peaks of Internet of Things access control architecture is carried out through reinforcement learning algorithm. According to the change of Internet of Things access mode, cluster analysis and pattern recognition of Internet of Things access control architecture big data peak are realized. By constructing the spatial-temporal distribution model of Internet of Things access control architecture big data and Internet of Things transmission channel, the spectral density cluster analysis method is adopted, according to the quantitative parameter analysis of real-time Internet of Things access control architecture data stream, the quantitative recursive analysis method is adopted, and the online Internet of Things access control architecture big data cleaning is used to realize the identification and cluster analysis of Internet of Things access control architecture big data peak features, so as to improve the Internet of Things access control ability. The simulation results show that this method is highly reliable for peak clustering of big data in the access control architecture of the Internet of Things, and has strong dynamic analysis and recognition ability for Internet of Things access and data scheduling, good convergence of data clustering, and low error rate.
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