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一、结构优化与内容整合编程方案

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1. 强化MCP协议的技术映射

技术实现：上下文关联与动态资源适配

代码方案：基于Spring Cloud + OpenTelemetry的MCP协议集成

// MCP协议全局Trace ID生成与传递（Java示例）
@Configuration
public class MCPTraceConfig {@Beanpublic SpanProcessor addMCPContext() {return SpanProcessor.composite(// 注入多模态特征标识（影像ID+病历哈希）(span, context) -> span.setAttribute("mcp.context", MDC.get("image_id") + "|" + MDC.get("emr_hash")),BatchSpanProcessor.builder(OtlpGrpcSpanExporter.builder().build()).build());}
}// GPU资源动态调度（Kubernetes适配）
apiVersion: apps/v1
kind: Deployment
metadata:name: image-service
spec:template:spec:nodeSelector:gpu-type: a100  # 指定GPU节点containers:- name: image-analyzerresources:limits:nvidia.com/gpu: 1  # GPU资源声明
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apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:name: image-service-hpa
spec:metrics:- type: Resourceresource:name: nvidia.com/gputarget:type: UtilizationaverageUtilization: 80  # GPU利用率触发扩容

OpenTelemetry兼容性设计

# MCP协议与OpenTelemetry元数据映射（Python示例）
from opentelemetry import trace
from opentelemetry.sdk.resources import Resourcetracer = trace.get_tracer_provider().get_tracer("mcp-tracer",resource=Resource.create({"service.name": "diagnosis-ai","mcp.version": "1.2"})
)with tracer.start_as_current_span("ct_analysis") as span:span.set_attribute("mcp.modality", "IMAGE")  # 标记模态类型span.set_attribute("mcp.priority", "HIGH")   # 设置任务优先级

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2. 案例与数据深度整合

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