Because a single model cannot determine a complicated mapping relationship, monitoring index and risk rank is important; a reservoir water level monitoring index in the tailing dam is posed by a multiple set value map. First, the information from a reservoir water level and risk rank of the tailings dam is collected, characterizing and constructing corresponding domains with multiple possible values. Second, by extending a point mapping to a fuzzy set mapping, various set-valued mapping relationships between the reservoir water level and the risk rank are established, which obtains joint-falling shadows. Third, by evaluating the risk rank of the tailings dam, a risk assessment model between the reservoir water level and the risk rank is proposed, which fuses the multiple set-valued mapping and configuration and combines the results. Finally, the proposed method is verified by an example. The results show that not only is the proposed method a new method for establishing the complex mapping relationship between the reservoir water level and the risk rank of the tailings dam, but also for evaluating results that are consistent with the actual situation of the dam.

In complex dynamic environments, infrared and visible video sequences exhibit highly variable and unpredictable feature distributions. Existing fusion algorithms with fixed architectures cannot adaptively respond to these dynamic feature changes, resulting in blurred fusion outcomes and the loss of critical detail information. To address this limitation, we propose a salient feature-driven mimic fusion algorithm that continuously monitors feature variations and dynamically reconfigures the fusion architecture to maintain optimized fusion performance. First, we extract amplitude and frequency attributes from infrared and visible video features and perform weighted fusion to calculate single-modality temporal features and cross-modal intra-frame difference features. Second, based on clustering statistical properties of feature distributions, we construct possibility distribution functions to quantify the degree of feature variation, design synthesis rules to derive comprehensive possibility values of feature change, and utilize significantly changing features as driving factors for subsequent mimic variant adjustments. Building upon this foundation, we establish a fusion validity evaluation function by analyzing correlation coefficients between feature changes and fusion quality metrics, and accordingly construct mapping relationships between features and mimic variants. Finally, we determine the optimal mimic variant combination by synthesizing various feature change characteristics to implement mimic fusion. Experimental evaluation demonstrates that our proposed method significantly outperforms existing approaches in adaptive fusion performance in dynamic scenes, with superior preservation of edge and texture details in the fusion results.