## 4 Result and Discussion
### Simulation Model Construction  
Following the game hypothesis and analysis mentioned above, this study employs Vensim PLE 7.3.5 and MATLAB 9.12.0 to construct a two-agent evolutionary game SD model comprising two subsystems: the coal mining enterprise and the local government. As illustrated in Figure 4, the model contains two horizontal variables, two rate variables, eight intermediate variables, and twenty external variables. Specifically, the rate of coal mining enterprises choosing the execution of intelligent construction and the rate of local government executing supervision strictly are expressed as two horizontal variables; change rate of intelligent construction and change rate of strict supervision are expressed as two rate variables; and the external variables are in alignment with the variable values presented in the income matrix presented in Table 2. Furthermore, the functional relationships between variables and the flow rate formulas in the SD model are primarily derived from the game analysis above and replication dynamic equations, precisely Type 11 and Type 14. In light of the aforementioned considerations, the spillover penalty mechanism of the coal mining enterprise, as outlined in Hypothesis 3, is subjected to analysis. That is, in the event of a fine being imposed on a particular enterprise, the liability system, which also holds local enterprises in the same area to account, is put into effect. This paper presents an enhanced version of the evolutionary system, as illustrated in Figure 5.  
Taking the coal mining intelligence investigation as a working opportunity, we examined the exploitation and construction status of internal intelligent equipment in 15 coal mines located in Datong City, Lvliang City, and Taiyuan City of Shanxi Province. These regions were selected due to their diverse geological conditions and varying degrees of intelligent transformation. Specifically, Datong coal mines face high gas content and ventilation challenges, while Lvliang mines are characterized by fragmented strata and complex fault structures. Taiyuan mines, in contrast, are located in areas with relatively stable geological formations but face environmental and policy constraints. In addition, based on the internal supervision reward and punishment systems and the actual income and expenditure records of each coal mine, combined with relevant data from the China  
Figure 4: Evolutionary game SD model of coal mining intelligent construction. Figure 5: Evolutionary game SD model of coal mining intelligent construction under the spillover effect. According to the randomized trial results of [PERSON], [PERSON] and the research by [PERSON] into financial markets, the risk preference coefficient \(\beta\), \(\theta\) is 0.88, the risk preference coefficient \(\varphi\), \(\sigma\) is 0.98 [48]. The sensitivity of valence and cost loss avoidance (\(\lambda\) and \(\delta\)) is 2. The model is set as follows: INITIAL TIME = 0, FINAL TIME = 100, TIME STEP = 0.03125, Units for Time: Year, Integration Type: Euler. The numerical simulation of the model indicates that the actual situation is consistent with the situation depicted in Figure 3p of the above analysis. In other words, with the gradual and rigorous oversight of the government, enterprises will ultimately achieve comprehensive integration of intelligent construction. In practice, the government will undoubtedly anticipate that enterprises will proactively adopt this behavior, which is conducive to humanity's future survival and development due to self-restraint or regulation. Consequently, in the idealized state, the government would like to see that in the absence of government regulation, enterprises can still fully implement intelligent construction, i.e., reach the state in Figure 3n. To this end, we explore the key influences on evolutionary