With the spread of compulsory education emerged continuous school management problems, and the quality of school management in compulsory education has attracted a great deal of attention in China. However, the application of information technology in the field is not yet detailed and wide, resulting in problems concerning heavy workloads and high difficulty in the whole evaluation process. As such, we have utilized big data technologies, including Apache Spark, Apache Hive, and SPSS, to effectively carry out data cleaning, correlation analysis, dynamic factor analysis, principal component analysis, and visual display on a sample of 1760 data points from 40 primary and secondary schools located in the Q Province of China. This has enabled us to construct a model for evaluating school management quality in the compulsory education stage, reducing the previous 22 management tasks required for evaluation down to just 5. Such streamlining has greatly reduced the workload and difficulty previously associated with evaluation, providing a more efficient and effective solution for assessing quality management in schools. It has improved the efficiency and accuracy of evaluation and further promoted the simultaneous development of education and education equity in the compulsory education stage.

The world population is aging at an accelerating pace, and the rate of disability caused by chronic diseases and trauma is on the rise. The traditional rehabilitation model has some structural defects, such as subjective evaluation, homogeneity in treatment schemes, imbalance of resource allocation, and insufficient intervention accuracy. The deep integration of big data analysis, artificial intelligence (AI), edge computing, digital twins, federated learning and multimodal large models is promoting a shift in the rehabilitation system from an experience-driven paradigm to a data-driven paradigm. This review systematically summarizes the development and evolution of big data analysis and artificial intelligence pertaining to intelligent rehabilitation, proposes a four-tier progressive technical architecture, summarizes the standardized governance paradigm of heterogeneous rehabilitation big data, and analyzes the mechanism and application boundaries of artificial intelligence algorithms in rehabilitation scenarios, such as neurology, orthopedics, elderly balance, and speech cognition. By comparing typical intelligent rehabilitation platforms horizontally, the core bottlenecks, including data islands, lack of clinical interpretability, weak robustness in complex environments and lack of evidence-based support, were identified. A future development path for integrating federated learning, lightweight multimodal models, human digital twins, flexible wearable sensing and other technologies is proposed to provide theoretical support for the next generation of rehabilitation systems.

With the spread of compulsory education emerged school management problems continued, and the quality of school management in compulsory education has attracted a great deal of attention in China. However, the application of information technology in the field is not yet detailed and wide, resulting in problems of heavy workload and high difficulty in the whole evaluation process. Accordingly, we use big data technologies such as Apache Spark, Apache Hive, and SPSS to carry out data cleaning, correlation analysis, dynamic factor analysis, principal component analysis, and visual display on 1760 sample data from 40 primary and secondary schools in Q Province in China, and constructs a model school management of quality evaluation in the compulsory education stage, which reduces the 22 management tasks required for previous evaluation to 5, greatly reducing the workload and difficulty of evaluation. It has improved the efficiency and accuracy of evaluation, and further promoted the simultaneous development of education of five domains and education equity in the compulsory education stage.