Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems

MA Xiaoliang; LIU Ying; ZHAO Ruqiang; YANG Bangxing; GAO Jie; DENG Congjian

doi:10.11959/j.issn.1000-0801.2024184

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Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems

Research and Development | 更新时间：2024-09-23

- Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems
- Telecommunications Science Vol. 40, Issue 8, Pages: 130-137(2024)
- 作者机构：
  
  1.西安电子科技大学，陕西西安 710071
  2.中国电信股份有限公司广州分公司，广东广州 510620
  3.马晓亮劳模与工匠人才创新工作室，广东广州 510620
  4.广州云趣信息科技有限公司，广东广州 510665
  5.中数通信息有限公司，广东广州 510630
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024184
  CLC： TP393
- Received：29 April 2024，
  
  Revised：2024-06-12，
  
  Published：20 August 2024
- 稿件说明：
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马晓亮,刘英,赵汝强等.绿色AI效率评价模型的构建与应用[J].电信科学,2024,40(08):130-137.

MA Xiaoliang,LIU Ying,ZHAO Ruqiang,et al.Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems[J].Telecommunications Science,2024,40(08):130-137.
马晓亮,刘英,赵汝强等.绿色AI效率评价模型的构建与应用[J].电信科学,2024,40(08):130-137. DOI： 10.11959/j.issn.1000-0801.2024184.

MA Xiaoliang,LIU Ying,ZHAO Ruqiang,et al.Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems[J].Telecommunications Science,2024,40(08):130-137. DOI： 10.11959/j.issn.1000-0801.2024184.

摘要

随着人工智能（artificial intelligence，AI）的兴起，大模型（large language model，LLM）日益成为知识推介和多轮对话的核心技术。伴随而来，AI大模型在数据处理、模型训练和部署过程中的高能耗问题亟须有效评估，以便在模型优化后进行前后量化对比。提出一种AI大模型能耗的评估方法，旨在量化评估AI模型的服务效率（efficiency，E）。该模型使用训练收敛时间（time，T）、模型参数规模（parameter，P）和浮点运算量（floating-point operations，F）等多维度因素，通过构建能源消耗函数

（

，

）实现量化分析；同时，运用非线性最小二乘法，得出模型参数。该分析方法不仅适用于电信运营商客服AI模型的运行效率分析，也可泛化于其他行业的AI模型能耗评估。

Abstract

The rise of AI and the increasing prominence of LLM are positioned as core technologies for knowledge dissemination and multi-turn dialogues. Alongside their growth

the high energy consumption associated with data processing

model training

and deployment in AI large models is necessitating effective evaluation to facilitate quantitative comparisons before and after model optimization. An assessment method for the energy consumption of AI large models was introduced

aimed at quantitatively evaluating the service efficiency (E) of AI models. This model was incorporated with multiple dimensions such as training convergence time (T)

model parameter size (P)

and floating-point operations (F)

and quantitative analysis was achieved through the construction of an energy consumption function C(T

F). Furthermore

by employing the nonlinear least squares method

model parameters were derived. This analysis method was not only applicable to the operational efficiency analysis of AI models used by telecommunications operators but can also be generalized for energy consumption assessment of AI models across various industries.

关键词

Keywords

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