The Feasibility Evaluation Model of Industrial Robot Entrepreneurship Based on Data Collection

J. Interconnect. Networks Pub Date : 2022-02-04 DOI:10.1142/s0219265921470174

Jingjing Zhou, Jianwei Han, Cui Fu, Jingjing Liu

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Abstract

Artificial intelligence and robots are changing the economic and entrepreneurship environment in the industrial revolution. Artificial intelligence and robotics have become prevalent in modern economic, professional, social, and daily lives. As a function of its ability to update and develop business processes and innovative ideas, services, and products and resolve difficult tasks to achieve new, entrepreneurship has experienced massive development. Significant changes are occurring in entrepreneurship and economic growth due to artificial intelligence. Therefore, this paper aims to understand the effect and components of data entrepreneurship overall with the help of an artificial intelligence-based feasibility evaluation model (AI-FEM). Robot, edge and physical resource layers are described in depth in this document. We first deploy an edge node near the data sources to combine multiple devices’ interfaces and function as a raw data filter. Then it provides opportunity recognition, opportunity development, and opportunity implementation processes are part of the framework’s processes described in this paper. This paper aims to develop a basic framework for evaluating AI’s potential implications for the interaction between entrepreneurship and economic growth. The economic growth of industrial robots reduces basic labor costs. However, it increases hourly compensation, suggesting that the productivity-enhancing advantage of industrial robots equals the wage-increasing influence. The results show that the system is feasible and performs better in real-time and network transmission than in an AI-based industry scenario. The experimental results of AI-FEM show the high-performance ratio of 95.5%, productivity ratio of 96.3%, reliability ratio of 93.4%, the employment rate of 92.6%, an efficiency ratio of 93.6%, industrial management ratio of 90.3%, and cost-effectiveness ratio of 20.3% compared to other methods.

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基于数据收集的工业机器人创业可行性评价模型

人工智能和机器人正在改变工业革命中的经济和创业环境。人工智能和机器人技术在现代经济、职业、社会和日常生活中已经变得非常普遍。作为其更新和发展业务流程、创新理念、服务和产品以及解决困难任务以实现新目标的能力的一种功能，创业经历了巨大的发展。由于人工智能，创业和经济增长正在发生重大变化。因此，本文旨在借助基于人工智能的可行性评估模型(AI-FEM)全面了解数据创业的影响和组成部分。机器人、边缘和物理资源层在本文档中有深入的描述。我们首先在数据源附近部署一个边缘节点，以组合多个设备的接口并作为原始数据过滤器。然后提出了机会识别、机会开发和机会实现过程是本文描述的框架过程的一部分。本文旨在建立一个基本框架，以评估人工智能对创业和经济增长之间相互作用的潜在影响。工业机器人的经济增长降低了基本的劳动力成本。然而，它增加了时薪，这表明工业机器人提高生产率的优势等于提高工资的影响。结果表明，该系统是可行的，并且在实时传输和网络传输方面优于基于人工智能的工业场景。实验结果表明，与其他方法相比，AI-FEM方法的效率比为95.5%，生产率比为96.3%，可靠性比为93.4%，就业率为92.6%，效率比为93.6%，工业管理率为90.3%，成本效益比为20.3%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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J. Interconnect. Networks

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