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Research on the Practical Application of RPA Robot for Automatic Intelligent Investment Statistics in PIS System

Published: 01 June 2024 Publication History

Abstract

Nowadays, due to the wide range of professions involved in investment statistics, the long statistical process, large number of grassroots units, and richness of the types of project statistics, statisticians at all levels are faced with the difficulties such as the cross-professional coordination, the diverse data verification, and the low consistency in data formats. At the same time, the verification of a large number of data is often done manually, and business personnel need to repeat the system's operating procedures, which consumes a lot of labor costs. RPA is an application technology that simulates human-machine interaction, using software robots to automatically execute workflows, replacing or assisting humans in completing repetitive labor with clear rules. This paper explores the application of RPA technology in the field of investment statistics, innovates the management mode of power grid investment plan, improves the quality and efficiency of investment statistics, and is committed to meeting the actual needs of improving the quality and efficiency of investment statistics with high efficiency and low cost, and responding to the company's requirements for improving quality and efficiency.

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Kim, C and Hong, Y. 2022. Research on intelligent statistical operation robot for power grid system based on RPA technology. Modern Industrial Economics and Information Technology (08), 309-310+313.
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Wang, L. 2023.Research on the application of RPA financial robot in enterprises. Finance and accounting study(26),7-9.
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    AIBDF '23: Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum
    September 2023
    577 pages
    ISBN:9798400716362
    DOI:10.1145/3660395
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 01 June 2024

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