Advancing Production Operation Safety with Virtual Reality Solutions and AI-Driven Computer Vision

Abstract

Artificial intelligence computer vision has always attracted much attention in the global academic community, and their method of analyzing security issues is mainly through simulation experiments. This article will conduct research based on human-computer interaction technology image processing and other related theories. First, this article introduces the principles of production operation cockpit. Then, this article elaborates on the computer vision algorithm and its mathematical derivation method and illustrates the feasibility and practicability of the research through examples. Finally, this article proposes an instrument panel safety design strategy based on the results of artificial intelligence computer vision research and conducts a safe driving simulation experiment. The experimental results show: (1) Security model testing: In terms of stability function testing, the wall display module is 0.857, the management prediction core module is 0.895, the operation management service database is 0.883, and the enterprise resource planning module is 0.837; in terms of throughput, the wall display module is 7478849, the management prediction core module is 7889587, the operation management service database is 895890, and the enterprise resource planning module is 742689; in terms of the number of concurrent users, these four modules are all 5,000; in terms of recovery failure time, the wall display module is 4 seconds, the management prediction core module is 3 seconds, the running management service database is 8 seconds, and the enterprise resource planning module is 4 seconds; the security performance of its model wall display module is 94%, the security performance of the management prediction core module is 97%, the security performance of the operation management service database is 99%, and the enterprise resource planning module has a security performance of 96%; in terms of page loading speed test, its wall display module is 4m/s, management prediction core module is 5m/s, operation management service database is 8m/s, and enterprise resource planning module is 6m/s. (2) In terms of computer vision algorithm performance: the accuracy of the computer vision algorithm is between 0.907-0.996, the recall rate is between 0.768-0.897, and the number of frames is 122HZ and 144HZ; the regression error range of the computer vision algorithm is between 0.047-0.79; the processor utilization of the algorithm is between 2% and 5%.