Multi-modal data acquisition and comprehensive analysis platform based on convolution decomposition depth model

Abstract

The invention discloses a multi-modal data acquisition and comprehensive analysis platform based on a convolution decomposition depth model. The multi-modal data acquisition and comprehensive analysisplatform comprises the following steps: S1, establishing a data interaction module; S2, establishing a data analysis module; and S3, establishing a user service module. According to the platform, multivariate data forms such as texts, voices and pictures are simultaneously supported. In the aspect of data collection, a default user mainly provides a source for data, so that a good interaction mode and a high-concurrency and high-availability database management mode are provided. In the aspect of data analysis, the pictures are trained and classified based on a deep learning CNN and an RNN, and text extraction and merging are performed by applying a TF-IDF word frequency network in NLP. A BP neural network constructed on the basis of a standard keras module and a tf.keras module under tensorflow is used, so that collection and accurate classification of the audio are realized.

Description

technical field [0001] The invention relates to a multimodal data collection and comprehensive analysis platform based on a convolutional decomposition depth model, which belongs to the technical field of computers. Background technique [0002] The artificial intelligence industry is in a period of vigorous development, core algorithms are constantly being broken through, computing power has been significantly improved, and the demand for various data and learning samples is showing explosive growth; second, there are many subdivisions of artificial intelligence, and smart phones , smart cars, smart homes, smart robots, Internet entertainment and social networking, and many other applications will inevitably lead to increasingly refined and directional data set requirements. Users hope to get the data sets they expect, but often find that the massive data on the Internet is too rough or discrete to meet their needs. [0003] Judging from the current macro environment, firs...

AI Technical Summary

Problems solved by technology

However, the existing data collection platforms often have the following problems: first, users want to obtain the data sets they expect, but often find that the massive data on the collection platform is too rough or discrete, and it is difficult to meet their refined use standards; , search matching is often not optimized and intelligent enough, which makes users often deviate in the operation of data

Third, the interaction between the platform and users is not high, and it is difficult to meet the design requirements of high concurrency and high availability

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