## 4 Results
### Model Update Strategy
An operation data visualization monitoring interface has been developed on the Unity3D platform (as shown in Figure 21). The system adopts a multi-layered UI architecture, integrating basic components like RawImage and DropDown within a Canvas container, and combines them with C# scripts to achieve dynamic updates of transmission system parameters. It utilizes the Graph_Maker plugin to construct multi-dimensional data charts, including a pie chart for fault distribution, a bar chart for power generation changes, and a wind speed-power curve. The dynamic curves are updated using methods from the WMG_Series class. The Newtonsoft.Json parsing engine and the UnityWebRequest network module are integrated to obtain and display local weather data via an API. Figure 18: Model update strategy flowchart. Figure 19: Unity3D engine scene construction. Figure 20: Wind turbine status interface. As the core equipment of the wind energy conversion system, the wind turbine converts kinetic energy from the air into electrical energy. To ensure the stable and efficient operation of the unit, it is essential to monitor its operating parameters. By monitoring parameters such as generator speed, current, voltage, and power, which reflect the unit's operating conditions, its operation status can be accurately evaluated. As shown in Figure 22, the real-time monitoring data of these parameters serve as a critical foundation for evaluating operational status. Statistical analysis of wind turbine operational data is a vital tool for assessing its performance and stability. By collecting statistics on fault occurrences and daily power generation, the operational status of the turbine can be effectively understood. Figure 23 presents the number of faults and the trend of daily power generation for a wind turbine over a one-month cycle of operation. This data can be used to identify the fault frequency and fluctuations in power generation capacity over a certain period. Frequent faults or significant fluctuations in power generation may indicate potential technical problems or operational anomalies. The equipment's service life can be prolonged, and the power generation efficiency can be improved by timely fault detection and taking necessary maintenance and optimization measures. Figure 21: Main system interface. Figure 22: Operating parameters of wind turbine. As shown in Figure 24, in order to facilitate the observation and analysis of the changing trend of historical SCADA data, the Graph Maker visualization tool in the Unity3D engine was used to generate dynamic graphical displays of the historical monitoring data. Users can click to switch between viewing time-series curves of different parameters, including key operational indicators such as wind speed and power. To further monitor the internal status of the gearbox, the collected SCADA data is fed into the trained CNN-LSTM-Attention network model to calculate predicted values. The residuals between the predicted and actual values are then calculated. The processed results are sent back to the Unity3D platform. Subsequently, the Graph Marker component in Unity3D was used to plot the actual, predicted, and residual curves of the oil sump temperature, visually presenting the historical evolution of the gearbox's operational status. This enabled the implementation of a data-driven fault prediction function for the wind turbine gearbox. The trained CNN-LSTM-Attention network model learns the historical normal operation data to predict the oil sump temperature during gearbox operation and calculates the temperature residuals. When the system detects that a vibration signal or a residual exceeds a set threshold, it automatically triggers an alarm mechanism, thereby establishing a data-driven turbine gearbox fault warning system on the Unity3D visualization platform. The core mechanism of this system is to predict the residuals by monitoring the temperature of the gearbox oil sump.