# Robust Simulation of Cyber-Physical Systems for Environmental Monitoring on Construction Sites
## 4 E-CPS Modeling and Uncertainty-Factor Identification
### Development of the E-CPS Model Based on Ontology
When the \"environmental monitoring data\" exceed the limit, an \"alarm\" message is issued, and relevant instructions are then sent to the \"controller\" in order to take relevant measures. Finally, the \"evaluation of green construction\" is performed based on the \"environmental monitoring data\" collected by the monitoring \"equipment\". From the perspective of the environmental monitoring and evaluation, the object properties are hasarea, hasmonitorfacility, hassensor, hascontroller, conductalarm, and conductevaluation [50], thereby establishing the data relationship between ontology concepts and defining object properties, as shown in Figure 3.  
The data property establishes the relationship between classes and data and is simpler than the object properties. Data are associated with a class through a data property, and data properties depend on the class for their existence. This study entered the data properties in the data property hierarchy of the Protege software. Firstly, the data property was created, then all classes with the data property were added to \"domains\". Finally, the data form \"string\" in \"ranges\" was selected to complete the process of creating the data attribute. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**No.** & **Indicator** & **Description** & **Source** \\ \hline
1 & PM10 overrun times & Times of PM10 exceed the limit per month & PM10 sensor \\  & & Difference between the monthly average & \\
2 & TSP emissions & concentration of TSP and the urban background value (mg/m\({}^{3}\)) & TSP sensor \\
3 & Noise overrun times & Times of the monthly noise exceed the limit & Noise sensor \\
4 & Illegal construction times at night & Times of night construction violations without approval & Noise sensor \\
5 & Discharged sewage suspended solids content & The content of suspended solids in the sewage discharged from the construction site & Water quality monitoring sensor \\
6 & Muck truck cleaning situations & Whether departing vehicles are flushed as required & Vehicle flushing capture system \\
7 & Number of abnormal monitoring data & The number of abnormal monitoring data per month & Information platform \\
8 & Hardening of import and export roads & The main roads and sites on the construction site are hardened as required & Video Surveillance \\
9 & Enclosure around the construction site & Enclosure measures shall be taken around the construction site, and the front door and the vicinity of the enclosure shall be cleaned in time & Video Surveillance \\
10 & Bare ground coverage & The bare ground and mounds of the construction site shall be covered, solidified, or greened, as required & Video Surveillance \\
11 & Closed situation of key operation areas & The outer scaffolding is enclosed by dense mesh nets, metal safety nets & Video Surveillance \\
12 & Implementation of rectification and limit production and response measures according to the corresponding warning level & Information platform \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The environmental monitoring and evaluation indicator system. After creating the above ontology, object attributes, and data attributes, the E-CPS ontologies can be constructed as Figure 4.  
(2) Ontology reasoning  
The inference engine is the core of the ontology-based E-CPS model for construction sites. Considering that the inference engine should support the rule extension, the Jena inference engine is used for ontology reasoning in order to enhance the precision of ontology reasoning in the E-CPS [51]. Taking the PM10 over-limit early warning in the E-CPS as an example, the \"Nanjing Construction Site Intelligent Monitoring of Dust Monitoring Guide (Trial)\" stipulates that the value of early warning for PM10 is 100 \(\upmu\)g/m\({}^{3}\). When the monitoring data of PM10 reaches the limit, E-CPS issues an early warning signal. This rule aims to issue an orange warning when the monitoring value of PM10 is between 100 and 150. Similarly, other rules can be formulated.