019_具身人工智能的跨模态感知与安全融合：从多源数据到协同防御的深度解析

# 具身AI自适应跨模态融合框架示例
import numpy as np
from scipy.stats import entropy

class AdaptiveMultimodalFusion:
    def __init__(self, modalities_config):
        """
        初始化自适应跨模态融合系统
        参数:
            modalities_config: 包含各模态配置的字典
        """
        self.modalities = list(modalities_config.keys())
        self.modalities_config = modalities_config
        self.weights_history = []
        self.confidence_scores = {}
        self.uncertainty_measures = {}
        self.context_state = None
        self.adaptation_history = []
    
    def process_single_modality(self, modality_name, raw_data):
        """
        处理单个模态的数据
        参数:
            modality_name: 模态名称
            raw_data: 原始传感器数据
        返回:
            处理后的特征和置信度
        """
        if modality_name not in self.modalities:
            raise ValueError(f"未知的模态名称: {modality_name}")
        
        config = self.modalities_config[modality_name]
        
        # 数据预处理
        processed_data = self._preprocess_data(raw_data, config["preprocessing"])
        
        # 特征提取
        features = self._extract_features(processed_data, config["feature_extraction"])
        
        # 置信度估计
        confidence = self._estimate_confidence(features, processed_data, modality_name)
        
        # 不确定性估计
        uncertainty = self._estimate_uncertainty(features, processed_data, modality_name)
        
        # 更新状态
        self.confidence_scores[modality_name] = confidence
        self.uncertainty_measures[modality_name] = uncertainty
        
        return features, confidence, uncertainty
    
    def _preprocess_data(self, raw_data, preprocessing_config):
        """数据预处理"""
        # 实际实现中包含降噪、归一化等操作
        return raw_data  # 简化实现
    
    def _extract_features(self, processed_data, feature_config):
        """特征提取"""
        # 实际实现中包含各种特征提取算法
        return processed_data  # 简化实现
    
    def _estimate_confidence(self, features, processed_data, modality_name):
        """估计置信度分数"""
        # 简化实现，实际应基于多种因素计算置信度
        return np.random.uniform(0.5, 1.0)
    
    def _estimate_uncertainty(self, features, processed_data, modality_name):
        """估计不确定性度量"""
        # 简化实现，实际应基于熵、方差等计算不确定性
        return np.random.uniform(0.0, 0.5)
    
    def update_context(self, context_info):
        """
        更新上下文信息
        参数:
            context_info: 包含当前环境状态的信息
        """
        self.context_state = context_info
    
    def calculate_adaptive_weights(self):
        """
        根据当前状态计算自适应权重
        返回:
            各模态的权重字典
        """
        weights = {}
        total_confidence = 0.0
        
        # 基础权重基于置信度
        for modality in self.modalities:
            conf = self.confidence_scores.get(modality, 0.5)
            weights[modality] = conf
            total_confidence += conf
        
        # 归一化权重
        if total_confidence > 0:
            for modality in weights:
                weights[modality] = weights[modality] / total_confidence
        
        # 如果有上下文信息，根据场景调整权重
        if self.context_state:
            weights = self._adjust_weights_for_context(weights)
        
        # 考虑历史表现调整权重
        weights = self._adjust_weights_for_history(weights)
        
        # 记录权重历史
        self.weights_history.append({
            "timestamp": np.datetime64('now'),
            "weights": weights.copy(),
            "context": self.context_state
        })
        
        return weights
    
    def _adjust_weights_for_context(self, base_weights):
        """根据上下文调整权重"""
        # 实际实现中根据不同场景类型动态调整权重
        adjusted_weights = base_weights.copy()
        
        if self.context_state.get("lighting_condition") == "low":
            # 低光照条件下降低视觉权重，增加其他模态权重
            if "visual" in adjusted_weights:
                visual_weight = adjusted_weights["visual"]
                adjusted_weights["visual"] *= 0.5
                
                # 重新分配权重
                weight_increase = visual_weight * 0.5
                other_modalities = [m for m in adjusted_weights.keys() if m != "visual"]
                if other_modalities:
                    increase_per_modality = weight_increase / len(other_modalities)
                    for modality in other_modalities:
                        adjusted_weights[modality] += increase_per_modality
        
        # 归一化调整后的权重
        total = sum(adjusted_weights.values())
        if total > 0:
            for m in adjusted_weights:
                adjusted_weights[m] = adjusted_weights[m] / total
        
        return adjusted_weights
    
    def _adjust_weights_for_history(self, base_weights):
        """根据历史表现调整权重"""
        # 简化实现，实际应基于历史准确性动态调整
        return base_weights
    
    def fuse_features(self, modality_features, weights):
        """
        融合多模态特征
        参数:
            modality_features: 字典，键为模态名称，值为特征向量
            weights: 各模态的权重
        返回:
            融合后的特征向量
        """
        # 确保所有模态特征维度相同
        feature_dim = None
        for modality, features in modality_features.items():
            if feature_dim is None:
                feature_dim = len(features)
            elif len(features) != feature_dim:
                raise ValueError(f"特征维度不匹配: {modality} 有 {len(features)} 维，但期望 {feature_dim} 维")
        
        # 加权融合特征
        fused_features = np.zeros(feature_dim)
        for modality, features in modality_features.items():
            weight = weights.get(modality, 1.0/len(modality_features))
            fused_features += weight * np.array(features)
        
        return fused_features
    
    def fuse_decisions(self, modality_decisions, weights):
        """
        融合多模态决策结果
        参数:
            modality_decisions: 字典，键为模态名称，值为决策结果
            weights: 各模态的权重
        返回:
            融合后的决策结果
        """
        # 假设决策结果是类别概率分布
        num_classes = None
        for modality, decision in modality_decisions.items():
            if num_classes is None:
                num_classes = len(decision)
            elif len(decision) != num_classes:
                raise ValueError(f"类别数量不匹配: {modality} 有 {len(decision)} 个类别，但期望 {num_classes} 个")
        
        # 加权融合决策
        fused_decision = np.zeros(num_classes)
        for modality, decision in modality_decisions.items():
            weight = weights.get(modality, 1.0/len(modality_decisions))
            fused_decision += weight * np.array(decision)
        
        # 归一化
        if np.sum(fused_decision) > 0:
            fused_decision = fused_decision / np.sum(fused_decision)
        
        return fused_decision
    
    def detect_modality_anomalies(self):
        """
        检测各模态数据中的异常
        返回:
            异常检测结果字典
        """
        anomalies = {}
        
        for modality in self.modalities:
            # 基于置信度和不确定性检测异常
            confidence = self.confidence_scores.get(modality, 0.0)
            uncertainty = self.uncertainty_measures.get(modality, 1.0)
            
            # 简单的异常检测逻辑
            if confidence < 0.3 or uncertainty > 0.7:
                anomalies[modality] = {
                    "is_anomalous": True,
                    "confidence": confidence,
                    "uncertainty": uncertainty,
                    "suggestion": "降低该模态权重或重新校准传感器"
                }
            else:
                anomalies[modality] = {
                    "is_anomalous": False,
                    "confidence": confidence,
                    "uncertainty": uncertainty
                }
        
        # 检测模态间不一致性
        inter_modality_anomalies = self._detect_inter_modality_inconsistencies()
        
        return {
            "intra_modality_anomalies": anomalies,
            "inter_modality_anomalies": inter_modality_anomalies
        }
    
    def _detect_inter_modality_inconsistencies(self):
        """检测模态间的不一致性"""
        # 简化实现，实际应比较不同模态的输出一致性
        return {"detected": False, "details": "各模态数据一致"}
    
    def adapt_to_anomalies(self, anomaly_results):
        """
        根据异常检测结果调整系统
        参数:
            anomaly_results: 异常检测结果
        返回:
            适应措施
        """
        adaptations = []
        
        # 处理单模态异常
        for modality, result in anomaly_results["intra_modality_anomalies"].items():
            if result["is_anomalous"]:
                adaptations.append({
                    "type": "weight_adjustment",
                    "modality": modality,
                    "action": "decrease_weight",
                    "reason": f"置信度过低({result['confidence']:.2f})或不确定性过高({result['uncertainty']:.2f})"
                })
        
        # 处理模态间不一致
        if anomaly_results["inter_modality_anomalies"]["detected"]:
            adaptations.append({
                "type": "fusion_strategy_change",
                "action": "switch_to_robust_fusion",
                "reason": "检测到模态间数据不一致"
            })
        
        # 记录适应历史
        self.adaptation_history.append({
            "timestamp": np.datetime64('now'),
            "adaptations": adaptations,
            "anomaly_results": anomaly_results
        })
        
        return adaptations
    
    def generate_fusion_report(self):
        """
        生成融合系统报告
        返回:
            系统状态和性能报告
        """
        report = {
            "timestamp": np.datetime64('now'),
            "active_modalities": self.modalities,
            "current_confidence_scores": self.confidence_scores,
            "current_uncertainty": self.uncertainty_measures,
            "latest_weights": self.weights_history[-1]["weights"] if self.weights_history else {},
            "context_state": self.context_state,
            "adaptation_count": len(self.adaptation_history),
            "anomaly_status": self.detect_modality_anomalies()
        }
        
        return report

# 使用示例
def example_adaptive_fusion():
    # 定义模态配置
    modalities_config = {
        "visual": {
            "preprocessing": {"denoise": True, "normalize": True},
            "feature_extraction": {"type": "cnn_features"}
        },
        "audio": {
            "preprocessing": {"denoise": True, "resample": 16000},
            "feature_extraction": {"type": "mfcc_features"}
        },
        "proprioception": {
            "preprocessing": {"calibrate": True, "filter": True},
            "feature_extraction": {"type": "kinematic_features"}
        }
    }
    
    # 创建融合系统
    fusion_system = AdaptiveMultimodalFusion(modalities_config)
    
    # 模拟处理各模态数据
    visual_data = "模拟视觉数据"
    audio_data = "模拟音频数据"
    proprio_data = "模拟本体感受数据"
    
    visual_features, _, _ = fusion_system.process_single_modality("visual", visual_data)
    audio_features, _, _ = fusion_system.process_single_modality("audio", audio_data)
    proprio_features, _, _ = fusion_system.process_single_modality("proprioception", proprio_data)
    
    # 更新上下文
    context = {
        "lighting_condition": "normal",
        "noise_level": "low",
        "task_type": "navigation"
    }
    fusion_system.update_context(context)
    
    # 计算自适应权重
    weights = fusion_system.calculate_adaptive_weights()
    print("自适应权重:", weights)
    
    # 准备特征字典
    features = {
        "visual": np.random.random(128),  # 模拟特征向量
        "audio": np.random.random(128),
        "proprioception": np.random.random(128)
    }
    
    # 融合特征
    fused_features = fusion_system.fuse_features(features, weights)
    print(f"融合特征维度: {len(fused_features)}")
    
    # 模拟决策结果
    decisions = {
        "visual": np.array([0.8, 0.1, 0.1]),  # 类别概率分布
        "audio": np.array([0.1, 0.7, 0.2]),
        "proprioception": np.array([0.2, 0.3, 0.5])
    }
    
    # 融合决策
    fused_decision = fusion_system.fuse_decisions(decisions, weights)
    print("融合决策结果:", fused_decision)
    
    # 检测异常
    anomalies = fusion_system.detect_modality_anomalies()
    print("异常检测结果:", anomalies)
    
    # 适应异常
    adaptations = fusion_system.adapt_to_anomalies(anomalies)
    print("适应措施:", adaptations)
    
    # 生成报告
    report = fusion_system.generate_fusion_report()
    print(f"报告生成时间: {report['timestamp']}")
    
    return report