Optimization of student information collection and analysis platform based on big data and robotics technology

This paper focuses on the optimization of student information collection and analysis platform based on big data and robotics, aiming at building a student-centered intelligent education system to cope with the shortcomings of existing student information management in data dimension, collection efficiency and intelligent analysis. A collaborative acquisition system for multi-modal robots is studied and designed. The improved D* Lite path planning algorithm and multi-modal interaction protocol are adopted to realize automatic information acquisition in classrooms, laboratories, dormitories and other scenes. In terms of data processing, a multi-source heterogeneous data processing architecture is constructed, which covers spatio-temporal data fusion model and streaming computing pipeline. Data alignment and semantic association are realized by high-order tensor decomposition technology, and a distributed streaming computing engine is built by Apache Flink to meet the real-time requirements of data processing. The core analysis models include the classroom behavior recognition model based on the improved 3D-ResNet34 network and the LSTM academic performance prediction model integrating the sequential attention mechanism, which significantly improves the accuracy of behavior recognition and academic prediction. The system optimization strategy covers the edge-cloud collaborative computing architecture based on dynamic task unloading and the privacy protection framework combining federated learning and differential privacy, which effectively reduces energy consumption and ensures the security of students' information. The experimental results show that the improved D* Lite algorithm has excellent performance in dynamic environment navigation, the classroom behavior recognition model has excellent performance in accuracy and reasoning speed, the academic performance prediction model has good prediction stability and accuracy, and the whole system has good real-time data processing ability in multiple scenarios, which provides strong support for the construction of intelligent education system.