In today’s rapidly accelerating process of global integration, emerging infectious diseases, antibiotic resistance, and zoonotic diseases have become common challenges that transcend borders, species, and professional fields. In the face of complex data from multiple domains and sources, traditional isolated response models are increasingly inadequate. To systematically enhance national biosecurity governance capabilities, One Health Cloud has established a core “intelligent hub”—the AI Agent system based on the DeepSeek dual-core large model. This system is dedicated to the deep integration and intelligent analysis of massive heterogeneous data, transforming data advantages into decision-making advantages, and comprehensively enhancing the capabilities for proactive perception, precise early warning, scientific decision-making, and efficient response to major public health threats.
1. Core Challenge: From Data Silos to Intelligent Integration
Currently, the processing of biosecurity data faces three major structural challenges:
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Data barriers are difficult to break down: Information such as human infectious disease reports, animal epidemic monitoring, environmental microbiological databases, and genomic sequences belong to different institutions and systems, with inconsistent standards, forming “data silos.”
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Analysis and decision-making are severely lagging: Relying on manual data comparison and epidemiological investigation is inefficient, often identifying issues only after the spread of an epidemic, missing the “golden window” for early intervention.
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Emergency response coordination is inefficient: In crisis management, the cross-departmental coordination of materials, personnel, and investigation information is complex, with a long chain of command transmission, leading to slow overall response.
Therefore, what we need is not just a data warehouse, but an “intelligent hub” with deep analytical capabilities and collaborative command functions. The design intention of the One Health Cloud AI Agent is to bridge the “last mile” from data to decision-making, consolidating fragmented information into unified situational awareness and action directives.
2. Technical Foundation: Precision Analysis Capabilities Driven by Dual-Core Engines
The intelligent core of this system is composed of the DeepSeek inference large model and the DeepSeek-Coder-V2 multimodal large model. The functions of both are deeply complementary, forming a complete closed loop from strategic assessment to tactical execution.
1. DeepSeek inference large model: Responsible for complex logical reasoning and strategic assessment As the system’s “strategic brain,” it focuses on processing unstructured information and conducting causal inference.
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Typical application: When the system receives reports of unusual deaths among wild birds in a certain area, migratory bird trajectories, and environmental genomic sequence information from nearby live poultry markets, DeepSeek can construct potential models of virus recombination and transmission pathways, assess the risk level of spillover to humans, and push the assessment conclusions and basis to the command center.
2. DeepSeek-Coder-V2 multimodal large model: Responsible for multimodal data parsing and automated execution As the system’s “tactical executor,” it translates complex directives into specific analytical tasks and operational processes.
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Typical application: Upon receiving an epidemic warning, Coder-V2 can automatically execute the following operations:
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Generate and execute SQL code to extract relevant case and population movement information from clinical databases and traffic data;
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Automatically write Python scripts to generate epidemic risk heat maps and transmission trend curves;
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Automatically calculate the demand and temporal gaps for key medical resources based on the projected scale of the epidemic.
The synergy of the two achieves the unity of “deep thinking” and “efficient execution.” For example, when DeepSeek determines that the input risk at a certain border checkpoint has increased, it can instruct Coder-V2 to automatically generate a strengthened monitoring plan and equipment allocation list for that checkpoint, directly translating decisions into actionable tasks.
3. Practical Empowerment: Intelligent Solutions for Four Major Business Scenarios
Scenario 1: Early Epidemic Perception—Achieving Automatic Identification of “Subtle Clues”
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Operational process: The AI Agent continuously scans global epidemic reports, academic platforms, and public information. When it identifies keywords such as “pneumonia of unknown cause” associated with specific geographical locations, it automatically initiates a multi-source data verification program.
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Core value: Transforming the traditional passive reporting model into a proactive, intelligent sniffing mode, significantly advancing the early warning window.
Scenario 2: Dynamic Transmission Early Warning—Providing a “Quantified, Visual” Risk Map
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Operational process: Based on real-time updated case, traffic, and population data, the AI Agent regularly runs transmission models to dynamically generate epidemic risk level maps by district for the near future.
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Core value: Providing scientific basis for precise implementation of targeted control measures, avoiding the significant social costs of a one-size-fits-all approach.
Scenarios 3 and 4: From Decision to Execution—Building a “Data-Driven” Emergency Closed Loop
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Operational process:
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Decision-making phase: Decision-makers input directives (e.g., “Assess the impact of a 7-day lockdown on Area A”), and DeepSeek simulates the triple impact of this measure on epidemic transmission, social economy, and medical burden, generating a comparative report.
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Execution phase: Once the plan is determined, Coder-V2 immediately activates, automatically generating an investigation task list, optimizing testing point layouts, and coordinating the logistics system to ensure precise delivery.
Core value: Elevating decision-making from “experience-based judgment” to “data simulation,” and upgrading execution from “manual scheduling” to “intelligent dispatching,” greatly compressing the cycle from decision to implementation.
4. Foundation and Vision: Autonomous and Controllable Evolving Infrastructure
Autonomy and control are the foundation of the One Health Cloud AI Agent. All core technologies utilize domestically developed large models, ensuring the independence, security, and continuous evolution of core analytical capabilities for national biosecurity under extreme circumstances.
Our goal is to build this system into a national-level infrastructure that continuously learns and autonomously evolves. As more data is integrated and application scenarios expand, its models will become increasingly precise, and reasoning will become deeper, ultimately becoming the core force that empowers various institutions and enhances the overall resilience of national biosecurity.
Conclusion: Calling for Collaborators to Build Future Defenses Together
The future of biosecurity is built on the foundation of data intelligence. The One Health Cloud AI Agent project is a key step in safeguarding national security with autonomous technology.
We understand that this endeavor requires the wisdom of all sectors. Here, we sincerely invite all public health institutions, research colleagues, and industry partners to join us in the iteration and improvement of this “intelligent hub,” transforming technological strength into a solid force for safeguarding life and health, and building a wise and reliable defense for national prosperity, stability, and the well-being of the people.
One Health Cloud AI Agent—Precise Insight, Scientific Decision-Making, Safeguarding Life.