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Research on microservice gateway technology based on data processing characteristics of Service-Oriented Architecture design models 2

Research on microservice gateway technology based on data pr...

引用

2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： He, Hongjie Hu, Yashan Wang, Fei Fan, Zikai Dai, Jiao Li, Wenpu Wang, Xingtao Xue, Meng State Grid Jiangsu Electric Power Co. Ltd. Economic and Technical Research Institute Nanjing210000 China State Grid Information and Communication Industry Group Co. Ltd. Beijing100052 China

ISBN: (纸本)9781510689053

With the rapid development of cloud computing, big data and IoT (Internet of Things) technologies, microservice architecture has gradually become the mainstream design model for enterprise IT (Information Technology) systems. Based on the SOA (Service-Oriented Architecture) design model, this study systematically analyzes the data processing capabilities of microservice gateways and proposes a comprehensive set of technical frameworks. The research centers around the key functions of microservice gateways, including message format conversion, data validation and filtering, as well as service routing and load balancing, to explore and innovate the technology. By implementing a unified message format standard and JSON Schema validation, this research ensures data accuracy for cross-service communication. In addition, by integrating JSON (JavaScript Object Notation) Schema and regular expressions, efficient data validation and security filtering are realized. For service management, this study employs Consul and Netflix Ribbon techniques to achieve dynamic discovery of services and load balancing. The experimental evaluation reveals the potential of the proposed technologies in enhancing data processing efficiency and system stability, providing professional guidance and practical basis for further research and application development of microservice gateways. © 2025 SPIE.

关键词： Service oriented architecture (SOA)

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Efficient grid and Density based EGDBSCAN Clustering Method to Identify Global Noisy Points 4

Efficient Grid and Density based EGDBSCAN Clustering Method ...

引用

4th international conference on Power, Control and computing Technologies, ICPC2T 2025

作者： Babu, S. Mallesh Sharma, V.K. Giri, M. Department of CSE Bhagwant University Rajasthan Ajmer India Department of ECE Bhagwant University Rajasthan Ajmer India Department CSE Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur India

ISBN: (纸本)9798331530402

Clustering methods are used in many applications like psychology, computers, biology, and so on. DBSCAN is a one of the clustering algorithm which comes under density based clustering technique. DBSCAN algorithm clustering data samples based on density values, but all clustering techniques are proposed with various types of challenges. DBSCAN clustering method is also fail to detect all noisy points. In this research paper we a proposed a new novel efficient DBSCAN (EGDBSCAN) algorithm to cluster data set. First data samples are placed into grid cells, for each cell calculate LNPS, MLNPS, and NNPG values, all new data samples are added one by one to grid space and update the above mentioned parameters, and finally identify list of top 'k' global noisy points. Performance of proposed clustering is compared with KNN noisy detection method and we come to know that proposed algorithm shows better results. © 2025 IEEE.

关键词： K-means clustering

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Adaptive Allocation Method of Terminal Container Parameters Based on Evolutionary Game 5th

Adaptive Allocation Method of Terminal Container Parameters ...

引用

5th international conference on Neural computing for Advanced applications (NCAA)

作者： Wu, Yu Wu, Linfeng Yang, Xiaowei Chen, Shunli Xie, Han State Grid Chongqing Elect Power Co Mkt Serv Ctr Chongqing 400023 Peoples R China Chongqing Guanghui Power Supply Serv Co Ltd Customer Serv Branch Chongqing 400023 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

In order to solve the problems of poor operational stability and high failure rate caused by the increasing number of terminal equipment devices and unreasonable resource allocation, an evolutionary game algorithm is used to construct an adaptive allocation method of terminal container parameters. First, deploy the smart metering terminal architecture and monitor the operating status of the terminal equipment. Subsequently, based on the idea of dominant resource balancing, the container placement order is determined by calculating the dominant resource request ratio of each terminal container, and a coarse-grained resource deployment strategy is obtained. Finally, an evolutionary game algorithm is used for fine-grained resource allocation and deployment to obtain the optimal strategy selection for terminal containers. Experimental results show that the proposed method can effectively improve resource utilization, improve the stable operation control capabilities and refined management level of remote terminal equipment, and ensure the reliable development of various upper-level business applications in marketing.

关键词： terminal container coarse-grained deployment evolutionary game adaptive parameter adjustment resource allocation and deployment

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Edge computing-Based Adaptive Data Compression Method for D-PMU in Digital Distribution Networks 2nd

Edge Computing-Based Adaptive Data Compression Method for D-...

引用

2nd international conference on Smart Electrical grid and Renewable Energy, SEGRE 2024

作者： Liu, Yinliang Duan, Shuyin Yuanlv, Zerui Yu, Lei Yang, Hao Yu, Binrong Bu, Yuntao Guangzhou510663 China China Southern Power Grid Co. Ltd. Guangzhou510663 China Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin300072 China

ISBN: (纸本)9789819619641

The widespread use of distribution-level phasor measurement units (D-PMUs) in digital distribution networks has introduced significant pressure on communication and storage systems due to the high precision and data transmission rates of these devices. This paper presents an adaptive compression algorithm for D-PMU data from edge computing devices in digital distribution networks. Utilizing a filter-based swing door trending compression algorithm combined with state discrimination methods, the proposed method applies distinct compression strategies for stable and fault operational states, achieving adaptive data compression. Validation of the algorithm’s effectiveness was performed through testing on system data during both stable and fault conditions. The results demonstrate that the proposed method effectively reduces the load on communication and storage systems while maintaining data accuracy and real-time performance, thereby proving its feasibility and efficacy in practical applications. This approach ensures that critical data is preserved during fault conditions while efficiently compressing data during stable operation, thus enhancing the overall efficiency of digital distribution networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Phasor measurement units

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A Training Method for Power grid Customer Data Model Based on Bayesian Algorithm 14th

A Training Method for Power Grid Customer Data Model Based o...

引用

14th international conference on Frontier computing, FC 2024

作者： Wu, Fangquan Liu, Yichi Ji, Yuan Zhou, Ling Guizhou Power Grid Co. Ltd. Guizhou550003 China

ISBN: (纸本)9789819627974

Currently, customer demand value evaluation work is still carried out through two methods: customer active reporting and customer manager on-site mining, with a single source of demand and a lack of intelligent methods for mining customer demand. In this paper, the prediction models in Power grid customer data information search process have been analyzed. Through comparing with the practicability of regression forecasting model, time series forecasting model, trend extrapolation forecasting model, gray prediction model and Bayesian forecasting model, the comprehensive evaluation method and the variation coefficient method are employed to establish the mathematical model. It is finally pointed out that the practicability of Bayesian forecasting model is maximum in the process of data information search. The results show that in the data mixed sampling, the accuracy and comprehensiveness of the Power grid customer data model based on the Bayesian forecasting are concentrated between 94% and 97%, while the accuracy and comprehensiveness of other models are unsatisfactory. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Energy Consumption Optimization using Big Data Analytics for Smart grid Management 5

Energy Consumption Optimization using Big Data Analytics for...

引用

5th international conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Devi, K. Sumithra Contractor, Dipenkumar Deepa, V. Lakshmi, T.K. Santhikala, M. Mounika, Kamati Kcg College of Technology Department of Computer Science and Engineering Chennai India Government Engineering College Department of Computer Engineering Dahod India Sri Ramakrishna College of Arts and Science Department of Information Technology Tamil Nadu Coimbatore India Malla Reddy University Department of Computer Science and Engineering Telangana Hyderabad India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Andhra Pradesh Tirupathi India

ISBN: (纸本)9798331522100

The application of big data analytics to improve energy usage efficiency in smart grids has been prompted by the rising need for power management. Adding to the above, it poses a big data solution for demand prediction, load consolidation, and fault diagnosis. Establishing the new and state-of-the-art innovative LSTM network that was employed in the forecast yielded a higher demand forecast accuracy of 94.2% compared to the previous systems. The load balancing index achieved 92% with an accuracy of 93.7% in fault detection, thus minimizing maintenance cost by 14.7% while improving grid reliability. The proposed model was found to give better results than previous conventional machine learning techniques and even modern ones which are more complex, especially for an ever-changing grid environment. Overall, these findings show the promise of big data solutions for handling the challenges of the contemporary smart grid while improving its efficiency through energy optimization. Consequently, this work lays a framework for subsequent studies on the remaining applications of advanced analytics in energy management systems. © 2025 IEEE.

关键词： Smart power grids

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Enhanced Power Quality in grid-Tied PV Systems Using an Optimized ANFIS Controller for a 5-Level MLI 5

Enhanced Power Quality in Grid-Tied PV Systems Using an Opti...

引用

5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Rekha, B. Ramesh, H.R. University Visvesvaraya College of Engineering Department of Electrical Engineering K R Circle Bangalore560001 India

ISBN: (纸本)9798350356236

Multilevel inverters (MLIs) provide a major advancement for renewable energy and industrial applications. They offer superior performance compared to conventional inverters by reducing switch rating size and increasing efficiency. However, it has some drawbacks, including a complex pulse width modulation control, voltage balancing challenges, and an increased number of switches. To overcome these issues, the proposed MLI topology is modelled to minimize number of DC sources and switches while also enhancing the modularity and expanding the number of output levels. Switching pulses for five-level MLI is optimized using an Adaptive Neuro-fuzzy interference system (ANFIS) controller to produce the most effective switching signals for the proposed inverter. Dynamic voltage restorer (DVR) is utilized in the system to address power quality (PQ) problems, including harmonics, voltage swells, and voltage sags. The findings show that the ANFIS controller produces a Total Harmonic Distortion (THD) of 1.66%, while the fuzzy controller results in a THD of 6.54%. © 2025 IEEE.

关键词： Solar fuels

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A Method for Identifying Fraudulent Behavior in Electricity Bill Collection Based on Nearest Neighbor Metric Matching 5th

A Method for Identifying Fraudulent Behavior in Electricity ...

引用

5th international conference on Neural computing for Advanced applications (NCAA)

作者： Han, Juan Yu, Limei Li, Aiqing Jiang, Yuanmeng Jiang, Shuai Tian, Mengyu State Grid Shandong Elect Power Co Mkt Serv Ctr Measurement Ctr Jinan 250001 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

In view of the current problems of very few samples of electricity bill collection fraud and difficulty in identifying abnormal power payment users, this paper proposes a method to identify electricity bill collection fraud based on nearest neighbor metric matching. First, by analyzing the characteristics of multi-source data on users' electricity consumption, the user payment behavior portrait categories are depicted. Subsequently, Gaussian process regression was used to build a user payment behavior prediction model based on normal user payment sample data. Finally, combined with dynamic multi-objective gravity search to obtain their nearest neighbor groups with different metrics, through abnormal probability distributionmatching, the abnormal payment thresholds and abnormal payment cycle thresholds of categories without abnormal payment user records are obtained, thereby generating electricity bill collection fraud screening rules and storing them on the blockchain as smart contracts, the accounts of abnormal transactions are checked through smart contracts. The experimental results show that the proposed method can promptly and accurately identify abnormal paying users, thereby effectively reducing fraud in electricity bill channels.

关键词： payment behavior portrait Gaussian process regression dynamic multi-objective gravity search electricity bill collection identification nearest neighbor metric matching

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A Comprehensive Study for Application of Blockchain Technologies for the Decentralized grid Utilization Possibilities

A Comprehensive Study for Application of Blockchain Technolo...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Upreti, Kamal Divakaran, Prakash Chaturvedi, Vandana Mishra Upreti, Shitiz Vats, Prashant Goyal, Kamal Dept. of Computer Science Delhi NCR India Himalayan University Arunachal Pradesh Itanagar India D Y Patil Deemed to be University Navi Mumbai India Mullana Haryana Ambala India Manipal University Jaipur Dept. of Cse Rajasthan Jaipur India Pimpri Chinchwad University Pune India

ISBN: (纸本)9798331527495

This thorough research explores the world of blockchain technology and its significant effects on the use of decentralized grids. Decentralized networks have shown promise in addressing the rising need for renewable energy and effective resource management. Blockchain, a technology based on distributed ledgers, presents creative approaches to improve grid oversight, enable through peer-to- energy trade, and guarantee openness and protection in the management of the grid. This paper investigates the advantages and disadvantages of using blockchain in decentralized grids. From trading in renewable energy to grid optimization and demand response, we examine a variety of use cases and applications. We offer insight into the practical viability and scalability of blockchain-based solutions through a thorough analysis of real-world deployments and case reports. We also address the legislative and technological challenges to be solved before blockchain technology can realize its full potential in decentralized grid setups. Our research strongly emphasizes that regulatory architectures, seamless integration, and standardization all contribute to supporting the harmonious adaptation of blockchain technology worldwide. This study provides lawmakers, industry players, and investigators in the efforts to build a sustainable and effective energy future with an informative tool: it will shed light on the potential and constraints presented by blockchain technology in a decentralized environment of grid usage. © 2025 IEEE.

关键词： Resource allocation

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Optimizing Cloud computing Task Schedules Through Advanced Intelligent Optimization Methods

Optimizing Cloud Computing Task Schedules Through Advanced I...

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Ammari, Ahmed Chiheb Al Hmouz, Rami Khriji, Lazhar Zhou, MengChu Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9789819747832

The number of Distributed Green Cloud Datacenters (DGCDs) is globally increasing. Such DGCDs deploy different types of renewable sources to generate clean energy and save money. They are located in different regions depending on the availability of renewable energy sources, costs of bandwidth and grid electricity prices. This paper focuses on applications within them, which are sensitive to delay and looks into a way to schedule different applications with respect to time constraint. The paper uses the Firefly algorithm, bat algorithm and simulated annealing-bat algorithms as optimization techniques to minimize total operational cost of such DGCDs. These algorithms have been compared through data-driven experiments carried out in this study. Of particular note is the Firefly algorithm's superior performance when compared with others. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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