Research on the Application of Al in the Integrity Audit of Inspection and Testing Reports

Abstract

In the rapidly evolving landscape of professional services, the integration of artificial intelligence (AI) into critical workflow processes has become a transformative force, particularly in domains where accuracy and efficiency are paramount. This paper presents a comprehensive investigation into the application of AI-driven intelligent systems for the integrity review of inspection and testing reports, a process that serves as a cornerstone for quality assurance and regulatory compliance across diverse industries. By leveraging the advanced capabilities of the Deepseek large-scale language model through the Coze platform, we have developed an innovative AI-based review entity specifically designed to automate and enhance the validation of inspection and testing documentation. The core functionality of this intelligent entity lies in its ability to perform automated identification and verification of critical information embedded within inspection reports. This includes, but is not limited to, meticulous checking of data accuracy, ensuring logical consistency across report sections, and identifying potential discrepancies or anomalies that may compromise the integrity of the findings. The system employs sophisticated natural language processing techniques to parse and understand complex technical documents, enabling it to extract and analyze key metrics, measurements, and conclusions with a high degree of precision. Additionally, the entity is capable of cross-referencing data with established industry standards and regulatory requirements, thereby ensuring compliance and reducing the risk of oversight. To evaluate the efficacy of this AI-driven approach, we conducted a series of comparative experiments between the AI review entity and traditional manual review methods. The experimental results demonstrate a substantial improvement in review efficiency, with the AI system achieving a 40% reduction in processing time compared to conventional manual procedures. Equally important, the error rate in report verification was significantly diminished by approximately 30%, underscoring the system's enhanced reliability in maintaining report integrity. These findings are particularly noteworthy given the critical importance of inspection and testing reports in various industries, where even minor errors can have profound implications for quality control, regulatory compliance, and decision-making processes. The study not only showcases the immediate benefits of this AI-powered solution but also highlights its broader potential for revolutionizing professional service industries. By automating routine verification tasks, the system allows human experts to focus on more complex analysis and interpretation, thereby optimizing resource allocation and enhancing overall productivity. Furthermore, the consistent and objective nature of AI-driven review processes mitigates the risk of human bias or oversight, contributing to more robust and trustworthy documentation practices.