数据标注管理工具：AI燃料工厂的精益引擎

# CVAT Webhook自动质检模块
from cvat_sdk import WebhookReceiver
from quality_engine import PolygonValidator

webhook = WebhookReceiver(secret="YOUR_SECRET")

@webhook.handler(event='annotation:created')
def realtime_quality_check(event):
    annotation = event.annotation
    
    # 动态加载质检规则（YAML配置驱动）
    rules = load_rules_for_task(event.task_id)
    
    # 执行多边形闭合检测
    if not PolygonValidator.check_closed(annotation):
        trigger_rejection(annotation, "未闭合多边形")
    
    # 医学影像专项检测
    if event.project == "CT_Liver":
        if not check_dicom_consistency(annotation):
            freeze_annotator(event.user_id)  # 自动冻结账号

# 集成到标注界面
cvat.add_custom_button("即时质检", run_quality_check)

graph TD
  A[新任务] --> B{任务解析器}
  B -->|医疗影像| C[匹配医学背景标注员]
  B -->|3D点云| D[匹配LiDAR经验者]
  C --> E[技能画像库]
  D --> E
  E --> F[实时工作队列]
  F --> G[自动负载均衡]
  
  classDef critical fill:#ffebee,stroke:#e53935;
  class A critical;

def dynamic_scheduler(task, annotators):
    # 计算任务紧急度（截止时间/延期惩罚）
    urgency = calculate_urgency(task)
    
    # 构建技能匹配矩阵
    skill_matrix = build_skill_matrix(annotators, task)
    
    # 动态优先级调整（紧急任务插队）
    if urgency > URGENCY_THRESHOLD:
        task.priority = MAX_PRIORITY
        
    # 求解最优分配（带约束优化）
    assignment = solve_assignment(skill_matrix, task.priority)
    return assignment

# 集成Scale AI API实现联邦调度
scale_api.apply_schedule(assignment)

// 标注进度Exporter（Go实现）
package main

import (
    "prometheus/client_golang/prometheus"
    "db_connector"
)

var (
    taskProgress = prometheus.NewGaugeVec(
        prometheus.GaugeOpts{
            Name: "annotation_progress_per_task",
            Help: "Real-time task progress",
        },
        []string{"task_id", "project"},
    )
)

func recordProgress(taskID string) {
    // 从数据库获取毫秒级进度
    progress := db.GetProgress(taskID)
    
    // 推送Prometheus指标
    taskProgress.WithLabelValues(taskID, "CT_Liver").Set(progress)
    
    // 风险预警（进度<阈值）
    if progress < RISK_THRESHOLD {
        alertManager.Send("任务延迟: "+taskID)
    }
}

func init() {
    prometheus.MustRegister(taskProgress)
}

// 连接质检系统与进度监控
board.connectModule('quality', {
  source: 'cvat',
  rules: '/configs/medical_rules.yaml',
  onReject: (data) => {
    board.triggerAlert(`质检失败: ${data.task_id}`)
    db.updateTaskStatus(data.task_id, 'rejected')
  }
})

// 自动生成调度指令
board.on('task_created', (task) => {
  const command = `优先处理${task.project}标注`
  const schedule = nlpParser.parse(command)  // NLP解析指令
  scale_api.adjustSchedule(schedule)
})

class AITrainer:
    def __init__(self, annotator_id):
        self.camera = ARGlassStream(annotator_id)
        self.llm = FineTunedGPT("标注专家模型")
    
    def realtime_feedback(self):
        while True:
            frame = self.camera.get_frame()
            # 检测标注动作（如多边形绘制）
            action = detect_annotation_action(frame)
            
            # 生成纠正建议
            if action.error > ACCEPTABLE_THRESHOLD:
                suggestion = self.llm.generate(
                    f"纠正建议: {action.type} 位置偏移{action.offset}px"
                )
                ar_display.show(suggestion)  # AR眼镜实时提示