With the in-depth application of AI technologies such as Deep Seek in medical scenarios, Fwone has taken the lead in completing the intelligent restructuring of its operation and maintenance (O&M) system. Our independently developed localized intelligent application, "Fwone · Niuyun", as a multi-intelligent AI application, can optimize O&M management scenarios, improve the efficiency of various hospital processes, and inject core technological momentum into the construction of smart healthcare.
This application adopts a "large model + vertical knowledge base" architecture to achieve in-depth integration of the in-hospital knowledge base and AI models. Meanwhile, it seamlessly connects with Fwone’s integrated O&M monitoring platform, building a full-process platform featuring unified platform, intelligent analysis, and closed-loop management. It covers all scenarios including diagnosis and treatment assistance, early warning monitoring, and logistics management.
Fwone · Niuyun: Empowering integrated O&M management with an AI management core and restructuring management processes.
The independently developed Fwone · Niuyun multi-agent application adopts a domain-specific agent architecture to achieve accurate responses in scenarios such as consultation, O&M, and medical care. The system integrates large model algorithms and medical knowledge graphs, supporting multi-modal interaction and vertical scenario adaptation. It not only solves the problem of insufficient generalization of single models but also significantly improves hospital management efficiency and service accuracy through mechanisms such as automated task distribution.
O&M needs are responded to in seconds, fault handling efficiency is increased by 70%, and intelligent recommendation of O&M strategies is supported.
The system automatically generates standardized work orders and intelligently assigns priorities, forming a hierarchical mechanism of "ordinary issues solved independently by business departments, complex issues handled collaboratively by professional teams".
This mechanism shortens equipment maintenance response time, improves logistics management efficiency, and ultimately achieves smart O&M through a "perception-decision-execution" closed loop.
The O&M agent can directly call real-time data from the monitoring system and monitor the operation status of multiple agents in real time. It predicts potential faults through algorithms, transforming traditional "post-event troubleshooting" into "pre-event intervention". This greatly shortens equipment maintenance response time and effectively improves the management efficiency of various departments.
Engineers and intelligent customer service collaborate to build an all-time intelligent service system. Through a human-machine collaboration model, a 24/7 intelligent response closed loop is formed, realizing the continuity and intelligent upgrading of O&M services. Problems are handled intelligently and proactively – analysis and handling are completed before issues occur, effectively ensuring business continuity.
It enhances work efficiency through multi-dimensional scenario-based empowerment, such as:
Assisting the information department in data organization and analysis report generation;
Helping clinical research teams integrate global medical guidelines and in-hospital knowledge bases for literature output;
Providing knowledge indexing for clinical diagnosis and treatment, assisting doctors in diagnosis and treatment, and offering real-time decision-making suggestions.
Beyond being an intelligent tool, Fwone provides one-stop O&M solutions covering the entire lifecycle of informatization projects (consultation, development, implementation, and operation).
Relying on rich hospital O&M experience, the Fwone team customizes smart medical systems with dynamic adaptability and self-healing capabilities for hospitals.
(Figure: Large Model Integration Process Configuration Management)
Based on the "large model + vertical knowledge base" architecture, Fwone integrates in-hospital O&M data and large model algorithms to build a solution library that accurately adapts to various in-hospital scenarios, providing precise and personalized solutions for different hospital scenarios.
(Figure: Daily Average Large Model Call Statistical Analysis)
Equipped with rich components and customizable modules, it automatically generates multi-dimensional O&M reports (fault proportion / work order trend / maintenance efficiency).
Supports custom data dashboards to provide visual support for management decision-making.
(Figure: Large Model System Plug-in and Tool Call Management)
(Figure: Large Model Data Source Processing System)
O&M personnel go deep into on-site scenarios to handle daily hospital issues accurately and in a timely manner. They provide 5×8 hours of immediate response, solve business problems accurately and promptly, and ensure the smooth progress of daily work. Serving core hospitals in Central China, Fwone’s mature O&M experience directly solves over 70% of daily issues!
The intelligent O&M integrated box integrates core services such as O&M, monitoring, and AI analysis, supporting plug-and-play deployment. Users only need to connect a network cable to realize device access and service activation – no additional hardware configuration is required, significantly reducing O&M complexity.
The system supports on-premises server deployment, enabling local storage and analysis of O&M data to ensure the security and compliance of medical data.
It adopts a private cloud independent deployment architecture, supporting containerized deployment and microservice architecture. It can run independently in the hospital’s exclusive private cloud environment, ensuring system flexibility and medical data security through intelligent resource scheduling and data encryption mechanisms.
Through knowledge transfer, complex large models are compressed into lightweight small models, enabling efficient inference and low-cost deployment.
In the future, Fwone will continue to expand AI application scenarios to various business fields, pursuing more accurate diagnosis, more efficient management, and more humanized services to build a new ecosystem for smart healthcare!
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