Construction of Student Safety Management System based on Big Data and Machine Learning

With the development of science and technology, more and more industries begin to involve big data. This paper aims to explore and build a student safety management model based on big data and machine learning. On the basis of summarizing the basic theories, research processes and basic paradigms involved in the previous studies of "data mining and knowledge discovery" and "information extraction and information analysis", a data-driven PMDA-Diki public security intelligence analysis and extraction system model is independently proposed. This model describes the basic evolution path of data processing flow and data existence form in the process of transforming multi-source data into security intelligence, and also defines the operation steps of extracting public security intelligence based on data, namely: By means of "processing → data mining → knowledge discovery → algorithm activation", the data can be gradually extracted into the required public security information according to the order of "data → information → knowledge → intelligence". Big data technology, in short, is the ability to extract valuable information quickly from a wide variety of types of data. Combining with rich contour feature visible light image, using the scale invariant feature transform (SIFT) algorithm for image registration fusion processing, image fusion is both advantages, through the deconvolution of fusion image feature extraction of feature extraction algorithm is exclusive feature mapping matrix, different from traditional random initialization convolution neural network learning method of convolution kernels, An improved neural network identification model, through the characteristics of the migration study will get mapping matrix as the initial convolution convolution neural network model of nuclear matrix, using the improved network model testing of the visible light and infrared images of repeated iterative learning, training a good model can improve the students' safety equipment management system identification accuracy, So as to realize the intelligent identification of equipment. Through the above research, it can realize the intelligent identification and fault warning of safety management equipment, and transform the traditional manual monitoring mode into intelligent monitoring. The rationality and effectiveness of the proposed method are verified by an example analysis of the measured data.