Two-Dimensional Cloud Model for Comfort Evaluation

In the field of comfort evaluation, the two-dimensional cloud model provides an effective visualization tool for handling the fuzziness and randomness in subjective assessments. The model operates through the following core methodology:

Conceptual Foundation Extends the traditional one-dimensional cloud model to a two-dimensional space, simultaneously considering two key comfort indicators such as temperature and humidity. Each dimension is characterized by three numerical features (expected value, entropy, and hyper-entropy) to depict data distribution patterns through mathematical formulations like: Ex = mean(data_x), En = std(data_x)/sqrt(2*pi), He = std(En_samples)

Dynamic Generation Logic The model utilizes a forward cloud generator algorithm to convert quantitative inputs into qualitative concepts. The algorithm implementation follows these steps: 1. Generate core cloud droplets (high-certainty central regions) based on preset parameters using normal distribution sampling: x_i = Norm(Ex, En) and y_i = Norm(Ey, En) 2. Create probability-diffused cloud clusters through secondary randomization: x_final = Norm(x_i, He) and y_final = Norm(y_i, He) 3. Forms gradient comfort boundaries through iterative coordinate generation, visualized as a scatter plot with density-based coloring

Annotation Application Value The generated cloud map serves as a basis for comfort level classification, for example: - Core cloud area corresponds to "optimal comfort zone" with concentrated high-membership points - Diffused cloud layers reflect "acceptable but fluctuating" transition zones with moderate membership values - Sparse areas indicate "discomfort" critical states characterized by low point density and membership degrees

Technical Extension Directions The model can be further enhanced by: - Incorporating time dimensions to form dynamic clouds through sliding window parameter updates - Integrating machine learning algorithms (e.g., gradient descent) for adaptive parameter adjustment - Applying to real-time comfort control scenarios like smart homes and vehicle environments using Python/Matlab implementation with matplotlib visualization libraries