In the realm of consumer electronics, the integration of knowledge graphs with causal inference significantly advances recommendation systems within the Artificial Intelligence of Things (AIoT). This paper introduces a novel method that addresses the limitations of traditional AIoT-based systems, which tend to prioritize correlation over causality and demonstrate limitations in navigating inference paths across knowledge graphs. A reinforcement learning based knowledge graph model is designed to enhance interpretability and trustworthiness in recommendation processes. A soft reward strategy is employed within a Markov decision process, utilizing a multi-hop scoring function to ensure rational outcome assessments. Additionally, a graph search algorithm with user-conditional action pruning is incorporated to facilitate efficient and accurate sampling of inference paths. Experimental results indicate significant improvements in key performance metrics such as normalized discounted cumulative gain, recall, hit ratio, and precision ratio over existing methods. Furthermore, interpretable reasoning paths are provided, establishing a new benchmark in the AIoT-driven recommendation landscape for consumer electronics.

In recent years, with the widespread use of location-aware handheld devices and the development of wireless networks, trajectory data have shown a trend of rapid growth in data volume and coverage, which has led to the prosperous development of location-based services (LBS). Spatio-temporal range query, as the basis of many services, remains a challenge in supporting efficient analysis and calculation of data, especially when large volumes of trajectory data have been accumulated. We propose a Hilbert-GeoSOT spatio-temporal meshing and coding method called HGST to improve the efficiency of spatio-temporal range queries on massive trajectory data. First, the method uses Hilbert to encode the grids obtained based on the GeoSOT space division model, and then constructs a unified time division standard to generate the space–time location identification of trajectory data. Second, this paper builds a novel spatio-temporal index to organize trajectory data, and designs an adaptive spatio-temporal scaling and coding method based on HGST to improve the query performance on indexed records. Finally, we implement a prototype system based on HBase and Spark, and develop a Spark-based algorithm to accelerate the spatio-temporal range query for huge trajectory data. Extensive experiments on a real taxi trajectory dataset demonstrate that HGST improves query efficiency levels by approximately 14.77% and 34.93% compared with GeoSOT-ST and GeoMesa at various spatial scales, respectively, and has better scalability under different data volumes.