Guoyao Wu, Fan Pan, Minyu Luo, Zhiqiang Lan · 5 authors
Conventional service evaluation systems are increasingly plagued by data opacity, susceptibility to tampering, and delayed feedback loops, which erode stakeholder trust and hinder effective quality governance. To address these critical challenges, this study proposes and empirically validates a blockchain-enabled framework for trusted closed-loop management of the entire service evaluation process. The proposed architecture synergizes distributed ledger technology, autonomous smart contracts, and a dynamic Bayesian trust scoring model to achieve real-time data verification, automated corrective feedback, and adaptive trust computation. We analyzed a comprehensive dataset of 1,200 service interactions across the hospitality, healthcare, and e-commerce sectors, characterized by customer satisfaction scores ranging from 5.1 to 9.8, reliability indices between 0.72 and 0.96, and normalized positive interaction frequencies from 0.42 to 0.89. Empirical results demonstrate that the integration of the blockchain framework significantly elevated mean trust scores from 0.71 (± 0.12) to 0.88 (± 0.09), representing a statistically significant 23.7% improvement. Furthermore, the system reduced the variance in satisfaction ratings by 0.48 and lowered overall service discrepancy rates by up to 15.4%. Sector-specific dynamic weight adjustments yielded optimized outcomes, including a 7.4% increase in reliability for healthcare and a 6.3% improvement in consistency for hospitality. Comparative analysis reveals that while conventional digital evaluation systems typically achieve only 5–12% performance gains, our blockchain-based approach substantially enhances trust, accuracy, and process transparency. Crucially, the closed-loop mechanism facilitated timely interventions, reducing critical service deviations by 17.5% in healthcare and 15.4% in e-commerce. These findings offer robust theoretical validation and practical guidelines for deploying transparent, accountable, and adaptive service evaluation ecosystems in diverse industrial contexts.
Electronic evidence of forensic medical images plays a key role in forensic identification. The existing deposit technology is difficult to cope with the dual challenges of image format change and AI forgery, and the fusion mechanism of digital watermarking and blockchain has the problems of robustness and traceability accuracy imbalance. This article proposes a dynamic trusted certificate storage system that integrates deep learning perceptual hash and alliance chain. A semantic hash generation network based on multi-scale frequency domain features is designed, and a lightweight intelligent contract architecture optimized by SM2/SM3 algorithm of state secrets is established. The full link traceability is realized by combining adaptive frequency domain and time domain nested watermarking algorithms. Experiments show that under the attacks of Gaussian noise, JPEG compression and geometric deformation, the Hamming distance of the hash is stable within 3 bits, which is better than the mutation of more than 30 bits in the traditional cryptographic hash. When the rotation is 10, the false recognition rate is less than 1%, and the sample collision probability is maintained at a very low order of magnitude; When the watermark embedding strength increases, the PSNR remains above 45 dB, and the normalized cross-correlation coefficient is higher than 0.92 under the condition of JPEG compression quality of 70. In the alliance chain scenario, the consensus delay of 30 nodes is 180 ms, and the delay rises to 320ms after the expansion of 100 nodes, and the system throughput is not significantly attenuated. In this study, the synergy between robustness, transparency and traceability efficiency is optimized, which can provide a reference technical scheme for judicial acceptance of forensic electronic evidence chain.