With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.
During the production process of solar panels, it is inevitable to have some defects, such as cracks on the surface of solar panels due to extrusion or damage due to quality issues. This article improves the Serre standard model, which can simulate the ventral visual pathway with object recognition ability, based on the latest research progress and results of simulating biological visual mechanism models in computer vision, to improve the recognition effect of surface defects on solar panels. At the same time, a pre-processing scheme combining Gaussian Laplace operator operator and adaptive Wiener filter to remove noise spots is studied, and the local Gabor Binary Pattern Histogram Sequence (LGBPHS) features are obtained through pre-processing. The Percolation-Based image processing method for detecting obvious cracks was used to determine the location of the algorithm and the calculation results based on the improved standard model method. It mainly refers to the MAX value output by the C2 layer and the classification and identification results of whether there are cracks, and the crack location function is completed. The experimental results show that the proposed method has an accuracy rate of 98.86% in training and 98.64% in testing, and both the false detection rate and the missed detection rate do not exceed 1%. Therefore, the method proposed in the study has a high accuracy and can effectively identify the surface defects of solar panels.INDEX TERMS Image processing, solar panels, defect recognition, local binary mode.
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