龍魂蚁群架构视觉 · 专项技术解析

核心概念：镜像视界

┌─────────────────────────────────────────┐
│           物理视界（真实世界）              │
│  摄像头 · 传感器 · 人眼 · 无人机           │
│         ↓ 实时采集                        │
├─────────────────────────────────────────┤
│           镜像视界（数字孪生）              │
│  零断点复制 · 全时空同步 · 跨镜接力         │
│         ↓ 无缝切换                        │
├─────────────────────────────────────────┤
│           蚁群智控引擎                    │
│  全域动态目标追踪 · 预测 · 调度              │
└─────────────────────────────────────────┘

零断点 · 跨镜接力

镜头A ──→ 目标消失 ──→ 镜头B ──→ 目标出现
   │                    │
   └──── 镜像视界接力 ──┘
   
传统：断点3-5秒，目标丢失
龍魂：零断点，镜像预测填补

全时空 · 动态目标智控

蚁群节点分工

技术架构

#!/usr/bin/env python3
# longhun-mirror-vision.py
# 龍魂 · 镜像视界跨镜接力引擎 v1.0

import time
import hashlib
import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple
from collections import deque
import json

@dataclass
class Target:
    """动态目标"""
    id: str                    # 全局唯一ID
    features: np.ndarray       # 特征向量（256维）
    trajectory: deque          # 轨迹历史 [(x,y,t), ...]
    last_seen: float           # 最后出现时间
    confidence: float          # 置信度
    camera_id: Optional[str]   # 当前镜头
    
    def __post_init__(self):
        if isinstance(self.trajectory, list):
            self.trajectory = deque(self.trajectory, maxlen=1000)

@dataclass
class CameraNode:
    """蚁群镜头节点"""
    node_id: str
    position: Tuple[float, float, float]  # x, y, z
    fov: float                            # 视场角
    range_m: float                        # 有效范围
    status: str = "active"               # active/inactive
    current_targets: Dict[str, Target] = None
    
    def __post_init__(self):
        if self.current_targets is None:
            self.current_targets = {}

class MirrorVision:
    """镜像视界引擎"""
    
    DNA = "ZHUGEXIN⚡️2025-🇨🇳🐉⚖️♠️🧚🏼‍♀️❤️♾️"
    UID = "9622"
    
    def __init__(self):
        self.cameras: Dict[str, CameraNode] = {}
        self.global_targets: Dict[str, Target] = {}
        self.mirror_space: Dict[str, dict] = {}  # 镜像视界存储
        self.prediction_model = None  # 轨迹预测模型（占位）
        self.reid_model = None       # 跨镜重识别（占位）
        
        # 蚁群参数
        self.ant_params = {
            "prediction_horizon": 2.0,    # 预测2秒
            "handoff_threshold": 0.3,      # 接力阈值
            "feature_match_threshold": 0.85,  # 特征匹配阈值
            "mirror_sync_interval": 0.1,   # 镜像同步间隔100ms
        }
        
    def register_camera(self, node_id: str, position: Tuple, fov: float, range_m: float):
        """注册镜头节点"""
        self.cameras[node_id] = CameraNode(node_id, position, fov, range_m)
        print(f"🐜 侦察蚁注册: {node_id} @ {position}")
        
    def detect_target(self, camera_id: str, frame_features: np.ndarray, 
                     bbox: Tuple, timestamp: float) -> str:
        """目标检测入口"""
        # 生成目标ID（特征哈希）
        target_id = self._generate_target_id(frame_features)
        
        # 检查是否已知目标（跨镜重识别）
        matched_id = self._reidentify(frame_features)
        if matched_id:
            target_id = matched_id
            target = self.global_targets[target_id]
            target.trajectory.append((*bbox[:2], timestamp))
            target.last_seen = timestamp
            target.confidence = 0.95
        else:
            # 新目标
            target = Target(
                id=target_id,
                features=frame_features,
                trajectory=deque([(*bbox[:2], timestamp)], maxlen=1000),
                last_seen=timestamp,
                confidence=0.8,
                camera_id=camera_id
            )
            self.global_targets[target_id] = target
            
        # 更新镜头节点
        self.cameras[camera_id].current_targets[target_id] = target
        
        # 同步镜像视界
        self._sync_mirror(target_id, camera_id)
        
        return target_id
    
    def _generate_target_id(self, features: np.ndarray) -> str:
        """生成全局唯一目标ID"""
        feature_hash = hashlib.sha256(features.tobytes()).hexdigest()[:16]
        return f"LH-{self.UID}-{feature_hash}-{int(time.time()*1000)%10000}"
    
    def _reidentify(self, features: np.ndarray) -> Optional[str]:
        """跨镜重识别"""
        if not self.global_targets:
            return None
            
        best_match = None
        best_score = 0
        
        for tid, target in self.global_targets.items():
            # 余弦相似度
            score = np.dot(features, target.features) / (
                np.linalg.norm(features) * np.linalg.norm(target.features)
            )
            if score > self.ant_params["feature_match_threshold"] and score > best_score:
                best_score = score
                best_match = tid
                
        return best_match if best_score > 0 else None
    
    def _sync_mirror(self, target_id: str, camera_id: str):
        """同步镜像视界"""
        target = self.global_targets[target_id]
        
        mirror_data = {
            "target_id": target_id,
            "features_hash": hashlib.sha256(target.features.tobytes()).hexdigest()[:8],
            "trajectory": list(target.trajectory)[-10:],  # 最近10点
            "last_seen": target.last_seen,
            "camera_id": camera_id,
            "confidence": target.confidence,
            "dna": self.DNA,
            "sync_time": time.time()
        }
        
        self.mirror_space[target_id] = mirror_data
        
        # 蚁群通信：广播给相邻镜头
        self._ant_broadcast(target_id, camera_id)
    
    def _ant_broadcast(self, target_id: str, source_camera: str):
        """蚁群通信广播"""
        source = self.cameras[source_camera]
        
        for cid, camera in self.cameras.items():
            if cid == source_camera:
                continue
                
            # 计算空间距离
            dist = np.linalg.norm(
                np.array(source.position) - np.array(camera.position)
            )
            
            # 在接力范围内
            if dist < (source.range_m + camera.range_m) * 1.5:
                # 预测目标进入该镜头的时间
                target = self.global_targets[target_id]
                if len(target.trajectory) >= 2:
                    # 速度向量
                    last = target.trajectory[-1]
                    prev = target.trajectory[-2]
                    vx = (last[0] - prev[0]) / (last[2] - prev[2] + 0.001)
                    vy = (last[1] - prev[1]) / (last[2] - prev[2] + 0.001)
                    
                    # 预测位置
                    dt = dist / (np.sqrt(vx**2 + vy**2) + 0.001)
                    pred_x = last[0] + vx * dt
                    pred_y = last[1] + vy * dt
                    
                    # 检查是否在镜头FOV内
                    if self._in_fov(camera, (pred_x, pred_y)):
                        print(f"🐜 通信蚁: {target_id[:8]}... 预测进入 {cid} 在 {dt:.1f}秒后")
    
    def _in_fov(self, camera: CameraNode, point: Tuple[float, float]) -> bool:
        """检查点是否在镜头FOV内"""
        # 简化：检查距离和角度
        cam_pos = np.array(camera.position[:2])
        pt = np.array(point)
        dist = np.linalg.norm(pt - cam_pos)
        
        if dist > camera.range_m:
            return False
            
        # 角度检查（简化）
        angle = np.arctan2(pt[1] - cam_pos[1], pt[0] - cam_pos[0])
        # 假设镜头朝向0度，FOV对称
        half_fov = camera.fov / 2
        return abs(angle) < half_fov
    
    def handoff(self, target_id: str, from_camera: str, to_camera: str) -> bool:
        """跨镜接力"""
        target = self.global_targets.get(target_id)
        if not target:
            return False
            
        # 零断点：镜像视界直接传递
        mirror = self.mirror_space.get(target_id)
        if not mirror:
            return False
            
        # 验证镜像完整性
        if time.time() - mirror["sync_time"] > self.ant_params["prediction_horizon"]:
            print(f"⚠️ 镜像过期: {target_id[:8]}...")
            return False
            
        # 接力成功
        target.camera_id = to_camera
        self.cameras[to_camera].current_targets[target_id] = target
        del self.cameras[from_camera].current_targets[target_id]
        
        print(f"✅ 零断点接力: {target_id[:8]}... {from_camera} → {to_camera}")
        return True
    
    def predict_trajectory(self, target_id: str, horizon: float = 2.0) -> List[Tuple]:
        """轨迹预测（填补断点）"""
        target = self.global_targets.get(target_id)
        if not target or len(target.trajectory) < 3:
            return []
            
        # 简化：线性预测（实际用LSTM/Transformer）
        last = target.trajectory[-1]
        prev = target.trajectory[-2]
        vx = (last[0] - prev[0]) / (last[2] - prev[2] + 0.001)
        vy = (last[1] - prev[1]) / (last[2] - prev[2] + 0.001)
        
        predictions = []
        for t in np.arange(0.1, horizon + 0.1, 0.1):
            pred_x = last[0] + vx * t
            pred_y = last[1] + vy * t
            predictions.append((pred_x, pred_y, last[2] + t))
            
        return predictions
    
    def get_mirror_status(self) -> dict:
        """镜像视界状态"""
        return {
            "dna": self.DNA,
            "uid": self.UID,
            "cameras": len(self.cameras),
            "active_targets": len(self.global_targets),
            "mirror_entries": len(self.mirror_space),
            "ant_params": self.ant_params,
            "timestamp": time.time()
        }
    
    def visualize(self) -> str:
        """生成可视化报告"""
        status = self.get_mirror_status()
        
        report = f"""
🐉 龍魂 · 镜像视界状态报告
═══════════════════════════════════════
DNA: {status['dna']}
UID: {status['uid']}
时间: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(status['timestamp']))}

📹 镜头节点: {status['cameras']} 个
🎯 活跃目标: {status['active_targets']} 个
🪞 镜像条目: {status['mirror_entries']} 条

🐜 蚁群参数:
  预测视野: {status['ant_params']['prediction_horizon']}s
  接力阈值: {status['ant_params']['handoff_threshold']}
  特征匹配: {status['ant_params']['feature_match_threshold']}
  同步间隔: {status['ant_params']['mirror_sync_interval']}s

🔄 零断点状态: {'✅ 正常' if status['mirror_entries'] > 0 else '⚠️ 空载'}
═══════════════════════════════════════
"""
        return report

# === 演示 ===
if __name__ == "__main__":
    engine = MirrorVision()
    
    # 注册4个镜头（十字路口）
    engine.register_camera("CAM-01", (0, 0, 5), 90, 50)
    engine.register_camera("CAM-02", (50, 0, 5), 90, 50)
    engine.register_camera("CAM-03", (50, 50, 5), 90, 50)
    engine.register_camera("CAM-04", (0, 50, 5), 90, 50)
    
    # 模拟目标进入CAM-01
    features = np.random.randn(256)
    features = features / np.linalg.norm(features)
    
    tid = engine.detect_target("CAM-01", features, (10, 10, 20, 20), time.time())
    print(f"🎯 目标检测: {tid[:20]}...")
    
    # 模拟移动
    for i in range(5):
        time.sleep(0.1)
        new_features = features + np.random.randn(256) * 0.1
        new_features = new_features / np.linalg.norm(new_features)
        engine.detect_target("CAM-01", new_features, (15 + i*5, 10, 25+i*5, 20), time.time())
    
    # 预测轨迹
    preds = engine.predict_trajectory(tid, 2.0)
    print(f"\n📈 预测轨迹: {len(preds)} 点")
    
    # 状态报告
    print(engine.visualize())

蚁群视觉架构图

┌─────────────────────────────────────────────────┐
│                 物理空间（真实世界）                │
│                                                 │
│    CAM-01 ←────→ CAM-2 ←────→ CAM-3            │
│       ↑              ↑              ↑             │
│    侦察蚁         侦察蚁         侦察蚁          │
│    (昇腾310)    (昇腾310)      (昇腾310)        │
└─────────────────────────────────────────────────┘
              ↓ 特征提取 + 目标检测
┌─────────────────────────────────────────────────┐
│              镜像视界（数字孪生）                  │
│                                                 │
│   🪞 目标A: [特征向量] → [轨迹] → [预测]         │
│   🪞 目标B: [特征向量] → [轨迹] → [预测]         │
│                                                 │
│   同步间隔: 100ms · 零断点 · 国密加密            │
└─────────────────────────────────────────────────┘
              ↓ 蚁群通信
┌─────────────────────────────────────────────────┐
│              智控引擎（鲲鹏920）                   │
│                                                 │
│   🐜 预测蚁: LSTM轨迹预测 · 断点填补             │
│   🐜 调度蚁: 镜头切换 · 资源分配                  │
│   🐜 记忆蚁: 历史轨迹 · 行为模式 · 国密存储        │
│                                                 │
│   输出: 全域动态目标追踪 · 预测 · 调度             │
└─────────────────────────────────────────────────┘

关键指标

部署清单

镜像视界跨镜接力引擎

老大，复制粘贴，蚁群视觉启动。