Ethical Governance and Intelligent Monitoring Model of the Digital Temple Big Data Center

With the rapid progress of the digitalization of the religious field, temples increasingly depend on large data centers to regulate the flow of worshipers, religious rites, records of merit, and public affairs, and this also leads to the tension and confrontation between moral governance and intelligent surveillance. To solve this problem, we propose a hybrid framework, which combines ethical governance and intelligent monitoring, denoted as EGIM-Net (Ethical Governance and Intelligent Monitoring Network), that is extended and innovated based on the state-of-the-art multi-agent reinforcement learning (MARL) and interpretable graph neural ethical reasoning models. EGIM-Net is composed of three core modules: (1) Multi-Context Behavior Encoder (MCBE) that accurately models the spatiotemporal dynamic features of crowd behavior at various places of the temple based on hierarchical temporal attention mechanism and scene embedding vectors (2) Ethical Ontology Inference Module (EOIM) that extends religious ethic knowledge graph and employs a neural symbolic reasoning network to dynamically judge whether the actions meet Buddhist precepts and normative context; (3) Dual-Cycle Intelligent Monitoring Optimizer (DLSO) that integrates a Privacy-Preserving RL mechanism and federated Transformer-based anomaly detection network to ensure that the monitoring activities are real-time, sensitive, and culturally adaptive. Experiments on a simulated digital temple dataset prove that EGIM-Net can improve the precision of recognizing ethical offense by 19.4% accuracy over all existing methodologies.