Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based on OPC UA is designed. Firstly, combining the process flow and data source characteristics of array antenna assembly, a data collection and transmission scheme based on OPC UA was designed. Secondly, a process information model of array antenna assembly was established to realize data perception and transmission and solve the difficulties of complex data structure, high real-time requirements, and heterogeneous data in digital twin workshop. Finally, the proposed method and model were applied to the performance prediction platform for an array antenna assembly process based on digital twins, achieving perception of process data during the assembly process of array antennas, and achieving performance prediction and visualization for various stages of array antennas based on assembly process data.

Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control.

This paper focuses on enhancing the resilience of microgrids—localized power systems that integrate multiple energy sources—against challenges such as natural disasters, technological obstacles, and human errors. It begins by defining the specific connotation of microgrid resilience and then proposes an innovative solution centered on the use of advanced sensor technology to continuously monitor the microgrid and its operational environment, ensuring accurate and timely data collection under dynamic conditions. Subsequently, a decision risk assessment framework is constructed, integrating data quality evaluation and operational risk considerations, to drive strategy optimization through in-depth data analysis. At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. Both decision processes skillfully utilize Monte Carlo simulation and multi-objective genetic algorithms to effectively manage the uncertainty risks in the decision-making process, thereby significantly enhancing the overall resilience of the microgrid.