## 4 Discussion  
The results of our study were conducted in public building spaces with random movement of people. Based on the group universal thermal comfort, the _PMV-PPD_ model was modified in real-time by using personalized discomfort body language expression to obtain predicted values that are closer to the real thermal comfort, and the means of combining commonality and personalized thermal comfort characterization provides new research ideas and possibilities for the prediction of thermal dissatisfaction rate, which further provides a basis for the reasonable setting of indoor temperature and humidity. In addition, this paper can reduce the disruption to the normal office of people in public building spaces. In this study, the camera captures human thermal discomfort body language to demonstrate its feasibility in expressing thermal discomfort. For example, when users feel hotter, they produce fanning, wiping sweat, etc., and when users feel colder, they produce breathing into their hands and stamping their feet. There were significant \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reinforcement Learning Algorithms** & **Indoor Temperature Setting/\({}^{\circ}\)C** & **Relative Humidity Setting/\%** & **PMV** &
\begin{tabular}{c} **Thermal** \\ **Dissatisfaction Rate** \\ \end{tabular} \\ \hline DDPG & 25.8 & 49.5\% & 0.23 & 6.09\% \\ DQN & 25.9 & 48.7\% & 0.25 & 6.29\% \\ Q-Learning & 26.1 & 49.1\% & 0.33 & 7.26\% \\ SARSA & 26.3 & 48.5\% & 0.4 & 8.33\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The final setting value is based on _PMV-PPD_ Model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reinforcement Learning Algorithms** & **Indoor Temperature Setting/\({}^{\circ}\)C** & **Relative Humidity Setting/\%** & **PMV** &
\begin{tabular}{c} **Thermal** \\ **Dissatisfaction Rate** \\ \end{tabular} \\ \hline DDPG & 25.5 & 45.6\% & 0.09 & 16.58\% \\ DQN & 25.8 & 45.3\% & 0.2 & 16.9\% \\ Q-Learning & 25.9 & 45.2\% & 0.23 & 17.8\% \\ SARSA & 26.0 & 44.8\% & 0.26 & 18.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The final setting value is based on _PMV-BPD_ Model. differences in the number of people expressing thermal discomfort body language when the room temperature changed, and the type of body language expression was not fixed. Consistent with the conclusions reached in the study [31], the proposed method for predicting the thermal dissatisfaction rate for a given condition considering the randomness caused by inter- or intra-individual thermal sensory variability to obtain a comprehensive understanding of the comfortable thermal environment and the human thermal comfort response has shown good performance for predicting the occupant thermal dissatisfaction rate. Compared with the _PMV-PPD_ model, which considers indoor environmental parameters and individual parameters to predict thermal dissatisfaction rate, this study adds human thermal discomfort body language expressions based on this model and integrates environmental parameters, human body parameters and thermal discomfort body language expressions to predict thermal dissatisfaction rate, realizing real-time correction of the _PMV-PPD_ model, and the prediction effect is greatly improved. The improved _PMV-PPD_ model can evaluate the influence of individual differences on human thermal comfort more conveniently and more closely to the real situation, and the evaluation effect is better.