The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security vulnerabilities in protecting patient and doctor information and storing health data effectively. As an important research branch in the field of the Internet of Things, the Internet of Medical Things is important for the overall improvement of public health in terms of how to safely conduct technology development and application research and to effectively implement healthcare needs. Blockchain technology is decentralized and untrusted as well as prevents tampering with data and reduces the cost of trust. Its good performance has a strong developmental nature in the healthcare field. This paper analyses how to solve security problems through access control under the Internet of Medical Things, and optimizes three access control methods. The Internet of Medical Things accesses control approach that introduces blockchain technology enhances computational and storage capabilities and is a good solution to the problem of third-party trustworthiness. Even in the face of the rapid growth of end devices, blockchain technology can solve some of the problems arising from access control of massive devices through three directions: hierarchical management, compressed storage and performance optimization. Finally, it provides directions for future research on the security aspects of blockchain technology under the Internet of Medical Things.

In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO2, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO2 particles in high temperature, a method based on the improved DeepLab v 3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLab v 3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.