An Analysis of the Cultivation Methods of Visual Communication Design in New Media Environment

In order to better adapt to the teaching development needs of visual communication design in the new media environment, a method of visual communication design training based on the ecological environment of the new media environment is proposed. This method constructs a web-based professional course network teaching platform by cultivating practical design talents under the new platform, new environment, and new technology, starting from the perspective of practical application, starting from the teaching process of visual communication design specialty, combined with the needs of social development for students' creative needs, so as to provide rich visual communication design cases, design practice, and exchange and discussion platform for professional teaching. The results show that the local display time of the system page is too long. It takes 3.3 seconds to enter the query page manually, and the script test time is 0.3 seconds, so the page display time takes about 3 seconds. The system basically meets the functional requirements, but from the perspective of resource occupation, the program requirements are further optimized to reduce the utilization of CRU. From the analysis of memory usage, it is suggested to optimize the memory recycling mechanism.

High-Intensity Injury Recognition Pattern of Sports Athletes Based on the Deep Neural Network

In order to solve the problem of low efficiency and accuracy of injury image recognition for sports athletes in high-intensity injury treatment, this paper proposes an injury recognition mode based on the deep neural network. In this paper, the image of sports injury is converted to gray level, and the contour of the injury part in the image is extracted according to the combination of adaptive thresholding and mathematical morphology. In this model, the seed points are selected, the active contour is used to approximate the initial contour, and the curve fitting method is used to fit the obtained discrete points to obtain the final damaged contour. The digital matrix is constructed by using the extracted number of pixels at the damaged position and relevant information. The images are arranged into feature vectors with a length of 64 according to the mode of column concatenation. The overall mean vector of the image is calculated. The calculation results, training samples, and image samples to be recognized are substituted into the Euclidean distance to obtain the preliminary recognition results of the damaged position of the image of sports injury. Then, the image segmentation is realized by clustering. The clustering segmentation results are used to color describe the pixel categories of the original image, calculate the relative damage proportion area in the sports injury image, and identify the damage parts of the high-intensity sports injury image. The experimental results show that the recognition rate of the neural network is 80%-100%, and the recognition time of this method is 0-0.6/s. The above method can improve the accuracy of the recognition of the damaged part of the sports injury image and shorten the recognition time and has certain feasibility in determining the sports injury part.