A data monitoring system for enterprise management
By combining a blockchain trusted storage and privacy protection module with a federated learning data fusion module, along with zero-knowledge proof technology and an adaptive weight adjustment mechanism, the problems of low data fusion efficiency and insufficient privacy security in traditional data monitoring systems are solved, thus realizing an efficient and secure data monitoring system.
CN122133190APending Publication Date: 2026-06-02YANCHENG KESEN SOFTWARE INFORMATION TECHNOLOGY CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- YANCHENG KESEN SOFTWARE INFORMATION TECHNOLOGY CO LTD
- Filing Date
- 2026-03-12
- Publication Date
- 2026-06-02
AI Technical Summary
Technical Problem
Traditional data monitoring systems suffer from low data fusion efficiency, insufficient privacy and security, and poor scalability, making it difficult to meet the needs of enterprises' digital transformation.
Method used
By employing a blockchain trusted storage and privacy protection module and a federated learning data fusion module, combined with zero-knowledge proof technology and an adaptive weight adjustment mechanism, secure data fusion and privacy protection are achieved.
Benefits of technology
It improves the efficiency and accuracy of data fusion, reduces data loss rate and leakage risk, adapts to the growth of enterprise business scale, and reduces deployment costs.
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Smart Images
Figure CN122133190A_ABST
Abstract
This invention discloses a data monitoring system for enterprise management, relating to the field of data monitoring technology. The invention includes a multi-source data acquisition module, a blockchain trusted storage and privacy protection module, a federated learning data fusion module, a data monitoring and analysis module, and a system management module; the blockchain trusted storage and privacy protection module incorporates zero-knowledge proof technology. This invention employs localized training through federated learning and lightweight data acquisition preprocessing, improving fusion efficiency compared to traditional centralized methods. The blockchain architecture supports high throughput, meeting the real-time monitoring needs of enterprises. Through adaptive weight adjustment and blockchain data verification, it effectively improves fusion accuracy and reduces data loss rate. Sensitive data privacy is protected through zero-knowledge proofs and asymmetric encryption. The blockchain ensures data immutability and traceability, meeting data security compliance requirements and reducing the risk of leakage.
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