The Chain Effect of B&B Cluster Driving Tourism Income Increment in Heritage Sites: Economic Multiplier Simulation of LoRa Sensor Network and System Dynamics

World Heritage Sites generally face the core contradiction between tourism economic development and heritage resource protection. Due to the lagging data collection and limitations of static analysis, traditional management models struggle to accurately quantify the dynamic balance between economic value-added and ecological protection. Addressing the identification challenge of the chain effect triggered by the cluster development of home stays, this study proposes a three-in-one technical framework of “sensory network-dynamic simulation-decision support.†Based on the LoRa low-power wide-area network, a multinode monitoring system is constructed to break through the bottleneck of real-time data collection in karst landforms, achieving high-precision positioning of tourist behavior with 1.37–2.68 m accuracy and second-level dynamic tracking of consumption trajectories. Coupling system dynamics models with nonlinear threshold analysis, this study identifies the critical inflection point of home stay cluster density for economic value-added in karst landform heritage sites. This threshold identification complements existing studies on heritage carrying capacity and cluster optimization, and at this threshold, the growth rate of tourism revenue reaches a theoretical peak, the decay rate of heritage integrity is minimized, and the efficiency of protection input–output is significantly improved. Empirical verification in ancient villages of southern Anhui shows that the dynamic regulation mechanism achieves second-level delay in emergency response, technology-enabled scenarios effectively stimulate the synergistic effect of the tertiary industry, the return visit rate of tourists increases by over 50%, and the added value of the local industrial chain doubles. The research results have established a “monitoring-simulation-decision†closed-loop management paradigm, which effectively integrates contemporary theories of heritage tourism economics, destination competitiveness, smart tourism ecosystems, and data-driven analysis, The empirical results from Xidi and Hongcun are consistent with cross-case findings on heritage tourism governance in ancient village, supporting the broader applicability of this framework to karst heritage sites with similar cluster characteristics.