Addressing heterogeneous data access and integrity assurance remains a critical challenge for modern smart electricity billing systems. Conventional solutions are often fragmented, with limited interoperability and inconsistent validation processes, which undermines scalability, data quality, and user trust. This paper proposes a Web engineering-driven framework that integrates service-oriented architecture, semantic modeling, and automated integrity verification into a unified, component-based Web application. Unlike prior energy-focused studies, the framework explicitly advances Web engineering principles by embedding metadata-driven routing, ontology-based interoperability, and real-time validation mechanisms into a distributed smart billing environment. The system architecture is designed for modular extensibility, with semantic reasoning enabling dynamic service orchestration and adaptive Web interfaces providing role-based usability across devices. Evaluation demonstrated low latency (120 ms), high availability (99.98%), and scalability to 5000 concurrent users, while the integrity module improved validation success rates from 96.2% to 98.9% over four weeks. Usability testing achieved a 97.5% task completion rate and a system usability score of 88.6, confirming accessibility and adaptability. By combining semantic-driven service orchestration, end-to-end integrity verification, and adaptive user interaction within a single Web framework, this work contributes a novel, holistic approach to Web engineering in critical infrastructure. Beyond smart billing, the proposed methods provide a transferable foundation for secure, interoperable, and user-centric Web applications in other distributed domains.

Ensuring data integrity in Web-based electricity fee channels is increasingly challenging due to dynamic energy data, complex topologies, and the rigidity of static monitoring mechanisms. This paper introduces a novel RL-driven monitoring framework, embedded in a modular, standards-compliant Web architecture, that autonomously detects and mitigates data integrity issues in real time. The proposed framework integrates a deep Q-learning agent with semantic metadata pipelines and RESTful microservices to dynamically adjust detection thresholds, refine anomaly classification policies, and incorporate human feedback into its learning loop. Unlike conventional rule-based systems, the RL agent continuously refines its decision policy through real-time interaction with dynamic data streams and operator feedback. Extensive experiments conducted on emulated smart grid datasets demonstrate the system’s practical benefits: a 20% absolute increase in anomaly detection accuracy (from 75% to 95%), a 53% reduction in false positive rate (from 15% to 7%), and a stable average detection latency of 240 ms, all without human-in-the-loop reconfiguration. The RL agent also demonstrates stable convergence and linear scalability, making it well-suited for growing smart grid infrastructures. The system also incorporates a Web-native dashboard that visualizes time-aligned energy consumption and anomaly events while enabling real-time operator feedback, which further optimizes the learning trajectory. These results highlight the feasibility and effectiveness of embedding adaptive, self-optimizing learning agents directly into Web-based infrastructure to ensure long-term data integrity, transparency, and operational resilience. The proposed framework contributes to advancing intelligent Web engineering practices and lays the groundwork for scalable, autonomous monitoring solutions across a wide range of data-intensive infrastructure domains.