def build_causal_graph(kg, target_concepts):
    ig = prune_knowledge_graph(kg, causal_rules)
    third_party_sets = traverse_influence_paths(ig, target_concepts)
    causal_structure = identify_causal_mechanisms(third_party_sets)
    return validate_temporal_constraints(causal_structure)

class AdaptiveWeighting(nn.Module):
    def __init__(self, num_modalities=2):
        super(AdaptiveWeighting, self).__init__()
        self.weights = nn.Parameter(torch.ones(num_modalities))

    def forward(self, modalities):
        normalized_weights = F.softmax(self.weights, dim=-1)
        weighted_sum = sum([w * m for w, m in zip(normalized_weights, modalities)])
        return weighted_sum

class CausalDecisionEngine:
    def __init__(self, causal_graph, reward_model):
        self.graph = causal_graph
        self.reward = reward_model

    def update_intervention(self, new_data):
        updated_graph = self.graph.update(new_data)
        optimal_policy = self._compute_optimal_policy(updated_graph)
        return self._generate_treatment_plan(optimal_policy)

    def _compute_optimal_policy(self, graph):
        # 使用深度Q网络计算最优策略
        return dq_network.predict(graph)

def adaptive_weight_update(current_weights, new_data, learning_rate=0.01):
    gradient = compute_gradient(new_data)
    updated_weights = current_weights - learning_rate * gradient
    return F.softmax(updated_weights, dim=-1)

import numpy as np
from sklearn.decomposition import PCA

def multimodal_fusion(electrical_signal, vibration_data, thermal_image):
    elec_features = extract_electrical_features(electrical_signal)
    vib_features = extract_vibration_features(vibration_data)
    thermal_features = extract_thermal_features(thermal_image)

    fused_features = np.hstack((elec_features, vib_features, thermal_features))
    pca = PCA(n_components=0.95)
    reduced_features = pca.fit_transform(fused_features)
    return reduced_features