As intelligent sensing expands into high-privacy environments such as restrooms and changing rooms, the field faces a critical privacy-security paradox. Traditional RGB surveillance raises significant concerns regarding visual recording and storage, while existing privacy-preserving methods-ranging from physical desensitization to traditional cryptographic or obfuscation techniques-often compromise semantic understanding capabilities or fail to guarantee mathematical irreversibility against reconstruction attacks. To address these challenges, this study presents a novel privacy-preserving perception technology based on the AI Flow theoretical framework and an edge-cloud collaborative architecture. The proposed methodology integrates source desensitization with irreversible feature mapping. Leveraging Information Bottleneck theory, the edge device performs millisecond-level processing to transform raw imagery into abstract feature vectors via non-linear mapping and stochastic noise injection. This process constructs a unidirectional information flow that strips identity-sensitive attributes, rendering the reconstruction of original images impossible. Subsequently, the cloud platform utilizes multimodal family models to perform joint inference solely on these abstract vectors to detect abnormal behaviors. This approach fundamentally severs the path to privacy leakage at the architectural level, achieving a breakthrough from video surveillance to de-identified behavior perception and offering a robust solution for risk management in high-sensitivity public spaces.

翻译：随着智能感知技术向卫生间、更衣室等高隐私环境扩展，该领域面临严峻的隐私-安全悖论。传统RGB监控在视觉记录与存储方面存在显著隐患，而现有隐私保护方法——从物理脱敏到传统密码学或混淆技术——往往损害语义理解能力，或无法保证对抗重建攻击的数学不可逆性。为应对这些挑战，本研究提出一种基于AI Flow理论框架与边缘-云协同架构的新型隐私保护感知技术。该方法融合了源端脱敏与不可逆特征映射：边缘设备依托信息瓶颈理论，通过非线性映射与随机噪声注入，在毫秒级时间内将原始图像转换为抽象特征向量。该过程构建了单向信息流，剥离身份敏感属性，使原始图像重建成为不可能。云端平台随后利用多模态家族模型，仅基于这些抽象向量进行联合推理以检测异常行为。该方案从架构层面根本性切断了隐私泄露路径，实现了从视频监控到去身份化行为感知的突破，为高敏感性公共空间的风险管控提供了可靠解决方案。