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Efficient Online Service Based on Go-Tensorflow in the Middle-Station Scenario of Grid Service

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Smart Computing and Communication (SmartCom 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13202))

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Abstract

The application of machine learning and deep learning is widely used in the business of the power grid. However, the business of the power grid is complicated, and the online service of deep learning faces greater performance challenges. In order to solve this problem, this paper proposes an online service EOSP based on go-tensorflow. EOSP service is divided into 3 modules, namely model configuration module, execution engine module and model management module. The model configuration module mainly includes functions such as online model configuration and model configuration information synchronization. The execution engine can execute graphical model calls, and has optimized performance based on the characteristics of golang language coroutines. The model management module is responsible for model registration, update, uninstallation and version management. Experiments show that the EOSP service is highly stable, which greatly reduces the time consumption of online services.

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Acknowledgment

This work financially supported by Science and Technology Program of State Grid Corporation of China under Grant No.: 5700-202055183A-0-0-00, which named Research on Technology of Big Data Monitoring Analysis in Power Grid by Coordination of Data Middle platform & Edge Calculation. Without their help, it would be much harder to finish the program and this paper.

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Authors and Affiliations

  1. State Grid Shanghai Energy Interconnection Research Institute, Beijing, 100000, China

    Peng Liu, Yiming Lu & Guoqing Wang

  2. China Southern Power Grid Digital Grid Research Institute Co. Ltd., Guangzhou, 510000, China

    Yiming Lu & Wang Zhou

Authors
  1. Peng Liu
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  2. Yiming Lu
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  3. Guoqing Wang
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  4. Wang Zhou
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Editor information

Editors and Affiliations

  1. Texas A&M University-Commerce, Commerce, TX, USA

    Meikang Qiu

  2. Beijing Institute of Technology, Beijing, Beijing, China

    Keke Gai

  3. Tsinghua University, Beijing, China

    Han Qiu

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Liu, P., Lu, Y., Wang, G., Zhou, W. (2022). Efficient Online Service Based on Go-Tensorflow in the Middle-Station Scenario of Grid Service. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-97774-0_1

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