Aiming at the dynamics and uncertainties of natural colors affected by the natural environment, a color P-law generation model based on the natural environment is proposed to develop algorithms and to provide a theoretical basis for plant dynamic color simulation and color sensor data transmission. Based on the HSL (Hue, Saturation, Lightness) color solid, the proposed method uses the function P-set to provide a color P-law generation model and an algorithm of the Dynamic Colors System (DCS), establishing the DCS modeling theory of the natural environment and the color P-reasoning simulation based on the HSL color solid. The experimental results show that based on the color P-law, for the DCS of the natural environment, when the external factors change, the color of the plant changes, accordingly, verifying the effectiveness of the color P-law generation model and the algorithm of the DCS. In the dynamic color intelligent simulation system, when external factors change, the dynamic change of plant color generally conforms to the basic laws of the natural environment. This enables the effective extraction of color data from the Internet of Things (IoT)based color sensors and provides an effective way to significantly reduce the data transmission bandwidth of the IoT network.

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.