Optimization of fitness data monitoring system based on Internet of Things and cloud computing

In the service dimension, the construction of fitness science data supervision service mode is discussed. Based on the stakeholder theory, through the statistical analysis of the stakeholders of fitness science data supervision, three core stakeholders of the government, users and data service personnel are identified. Based on these three dimensions, we find out the core concepts of government policy model, user demand model and service model. At the same time, each dimension is deeply analyzed. Through the relationship analysis between these three dimensions, the user-oriented collaborative supervision service model of fitness scientific data is expected to guide the specific service practice of fitness scientific data supervision through the establishment of this model. In addition, an unsupervised learning method in machine learning, the isolation forest algorithm, is introduced to detect abnormal data; at the same time, using real fitness data sets, through comparative experiments with local anomaly factor algorithms, it is verified that the isolation forest algorithm has a good effect of anomaly detection; this article also uses redis cache to optimize the performance of the fitness data monitoring system, which solves the access pressure of the main database in a multi-user high-concurrency environment; Finally, the usability and stability of the system are verified by functional tests and stress tests.