## 4 Content and Results of Research
### Content of Research
#### 4.1.2 Development of a Digital Twin Garden-Learning System
Additionally, the digital layer sets alert thresholds for various environmental monitoring data, enabling intelligent adjustments to environmental elements when parameter indicators exceed preset thresholds. Figure 8: Six-dimensional modeling of the digital twin garden-learning system. Twin data are the basis for the operation of the digital twin garden-learning system, and DD mainly include the data in the physical teaching space (Dp), the data in the virtual teaching space (Dv), the data in the cloud service (Ds), and the learning data (DI) (Equation (2)):  
\[DD=(Dp,Dv,Ds,Dk) \tag{2}\]  
Equation: Dp is mainly used to describe the physical elements that constitute the physical teaching space, such as the equipment, garden space, lighting, and other physical-element attribute data, as well as the dynamic process data that can reflect the operating state of the physical teaching space, the dynamic process data through the sensors, device interfaces, embedded acquisition cards, Internet of Things technology, etc. ; Dv can represent the data related to the model of the physical teaching space (including the geometric model, physical model, behavioral model, rule model, etc.). Dv can represent the data related to the physics-teaching-space model (including the geometric model, physical model, behavioral model, rule model, etc. ); Ds mainly includes models, algorithms, decision rules, database operations, etc., required for front-end display, as well as modal process data, human-computer interaction data, etc. ; DI includes expert knowledge, industry standards, commonly used algorithms, commonly used databases, commonly used API interfaces, and commonly used model-construction methods. Cloud services serve as the computational core for educational implementation, responsible for processing data collected from physical and digital spaces, as well as sending instructional commands. The smart brain enables functions such as learning analytics, natural language processing, artificial intelligence, learning alerts, audio-visual processing, and image processing. It can analyze and process relevant data based on learners' behavioral states and operational instructions, providing real-time intelligent feedback to assist learners in promptly refining their strategies. The smart brain offers services including learning monitoring, intelligent learning companions, smart tutors, and precise teaching, supporting autonomous learning and large-scale collaborative learning. With the support of the smart brain, data information throughout the entire learning cycle can be fully stored and utilized for the analysis of learning activities and prediction of learning behaviors, thereby providing robust support for improving learning performance and optimizing teaching strategies. Connection processing is the transmission service carrier of project-based teaching, which provides support through a two-by-two two-way interaction, protocol transmission, feedback regulation, and dynamic simulation for the physical learning space, virtual learning space, cloud service, and front-end display (Equation (3)):  
\[CN=(CNpv,CNpc,CNpd,CNvc,CN\_VF,CN\_CD,CN\_FD,CN\_FC) \tag{3}\]