融合深度学习与动态偏差预警机制的电影公共服务观影人数智能核准模型研究OA

Audience statistics in digital cinema public services serve as a critical metric for measuring service effectiveness.To address challenges such as complex rural screening environment,subjective biases in manual reporting,and the ineffi-ciency of traditional oversight,this paper proposes an intelligent audit model integrating deep learning with a dynamic de-viation warning mechanism.The proposed model employs the CSRNet crowd counting network to automatically estimate instantaneous audience numbers from on-site photos.A deviation coefficient is introduced to quantitatively model the dis-crepancy between actual reported numbers and algorithmic results.Statistical analysis of experimental samples verifies that K approximates a normal distribution,leading to the determination of Kthreshold=1.3 as the dynamic tolerance threshold.This forms an automated judgment formula for audit and warning.Results indicate that the model achieves a 91.5%auto-matic approval rate for compliant reports,effectively balancing automated processing with anomaly detection,which pro-vides a scalable technical paradigm for audience statistics and regulation in complex scenarios such as digital cinema pub-lic services.