Abstract：Digital twin is an essential technology in smart manufacturing and has practical value in factories. At a semiconductor testing services company, a digital twin system was used to collect over 200 000 alarm data points from probe stations. The analysis showed that more than 90% of the alarms were resolved remotely using the digital twin system. To improve the digital twin model and alarm management, alarms were replicated within the model by simulating different scenarios. Operating the digital twin system manually reduced equipment downtime by over 64% compared to traditional methods. Additionally, using an AI backend to automatically operate the digital twin system further reduced equipment waiting time by 78%. This highlights the importance of personnel response time to equipment alarms in affecting equipment downtime. Therefore, enhancing the use of digital twin models for automated alarm handling can significantly reduce equipment downtime and improve overall equipment utilization.

Abstract：Currently, the business operations of power systems primarily rely on microservices, resulting in significant changes in business architecture. Data security capabilities need to be deeply integrated with business operations. However, existing data security measures are still based on traditional software and hardware architectures, making them inadequate for the dynamic and elastic protection required in cross-domain scenarios, and unable to adapt to the evolving business architecture. There is an urgent need to develop data sharing and interaction security protection technologies based on microservices architecture. Given the massive amount of data generated by power systems and the varying security requirements of different data types, ordinary microservices architectures struggle to address load imbalances under high concurrency scenarios in power systems. To tackle these issues, this paper proposes a microservice scheduling algorithm for data security capabilities based on the Kepler Optimization Algorithm (KOA), aiming to achieve load balancing and enhance the system's high concurrency handling capacity. By thoroughly modeling the resources of cloud cluster nodes and the performance of microservices, an optimization model is constructed with the goal of balancing cluster load and minimizing microservice runtime. Experimental results show that the KOA-based data security capability microservice scheduling algorithm significantly improves server load balancing, enhances cluster system processing efficiency, and reduces task response time, effectively boosting the system's concurrency performance.

Abstract：For the data center of modern power systems, identifying data inference risks during user access to data is particularly crucial. Especially when multiple users collude to steal data, it may lead to the inference of sensitive data from non sensitive data, resulting in sensitive data leakage and posing a serious threat to power dispatch and national security. Traditional access control mechanisms cannot identify this risk. Therefore, this article proposes a multi-party association data security risk identification model MPA-BN based on Bayesian networks, which comprehensively considers user access behavior, time patterns, interface types, and data interaction methods. Bayesian networks are used to analyze the access relationship between users and service interfaces, deeply explore the dependency relationship and probability characteristics between data, identify the correlation between external service interfaces in data, and potential risks of user combinations. The dataset used in this study is from the desensitization logs of the power company's data center, which includes 10 000 visiting users and generates approximately 1 million log entries. The experimental results show that the model can effectively identify the risk of multiple users colluding to steal sensitive data, providing stronger protection for the security of power system data.

Abstract：In response to the problems of insufficient technical support, inconsistent standards and specifications, and incomplete supporting tools faced by high-voltage power customer electricity safety management, this paper elaborates on customer side electricity safety policies and regulations, key technologies, standard specifications, and software and hardware equipment. The current research status at home and abroad is analyzed and summarized, and an online inspection technology system for electricity customer electricity safety is established. Key technical routes and solutions are provided from four aspects: safety characteristic modeling, safety information perception, intelligent fault diagnosis, and safety risk assessment. An "online+mobile" electrical safety online inspection equipment system has been established, and the main functions of the equipment have been designed.