在实时政策约束下的野火疏散物流网络的自监督时序模式挖掘

Source: Dev.to

引言：点燃此研究的学习之旅

正值 2023 年野火季节，我在分析北加州的疏散路线失效时，获得了突破性的领悟。我一直在尝试使用传统的监督学习模型来预测疏散瓶颈，但当政策约束在疏散过程中发生变化时，这些模型屡屡失效。模型是基于历史数据训练的，而实时的政策变动——例如突发的道路封闭或收容所容量调整——使它们几乎毫无用处。

在浏览计算机视觉领域的自监督学习论文时，我发现了一件令人着迷的事：让模型从无标签图像中学习表征的技术，同样可以迁移到疏散物流的时间序列上。对对比学习方法的研究表明，如果把疏散数据的不同时间窗口视为同一底层过程的不同“视图”，就可以构建在没有显式标签的情况下学习稳健时间模式的模型。这便是我在自监督时间模式挖掘用于野火疏散网络的研究起点。

野火疏散物流是应急管理中最具挑战性的时间优化问题之一。该系统涉及多个动态要素：

• 火势蔓延的时间模式（小时/日循环、天气依赖）
• 人类行为模式（疏散决策时机、路线偏好）
• 基础设施动态（道路容量衰减、通信网络故障）
• 政策约束（疏散指令、资源分配规则、司法边界）

在调查现有疏散模型时，我发现大多数方法将这些要素视为相互独立，或对它们的交互做出简化假设。突破出现在我开始将整个疏散生态系统视为时间图时：节点代表决策点，边代表时间依赖关系。

通过学习自监督学习的最新进展，我认识到处理时间数据的关键在于设计有意义的前置任务（pre‑text tasks），迫使模型学习有用的表征。针对疏散网络，我设计了三项核心前置任务：

1. 时间对比预测——学习区分正常与异常的时间模式。
2. 遮蔽时间建模——预测时间序列中缺失的片段。
3. 时间对齐——学习在不同时间尺度上对齐模式。

在对这些任务进行实验时，一个有趣的发现是时间对比学习在政策约束场景下产生了最稳健的表征。模型能够在没有显式监督的情况下识别出违反政策约束的时间模式。

我对时间数据的 Transformer 架构的探索促使我开发出一种混合模型，将时间卷积网络与注意力机制相结合。关键创新在于通过约束感知遮蔽（constraint‑aware masking）将政策约束直接嵌入注意力机制中。

策略约束时序注意力 (PyTorch)

import torch
import torch.nn as nn
import torch.nn.functional as F

class PolicyConstrainedTemporalAttention(nn.Module):
    def __init__(self, d_model, n_heads, max_seq_len=96):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads

        # Linear projections
        self.query = nn.Linear(d_model, d_model)
        self.key   = nn.Linear(d_model, d_model)
        self.value = nn.Linear(d_model, d_model)

        # Policy‑constraint embeddings (10 constraint types)
        self.policy_embedding = nn.Embedding(10, d_model)
        # Positional embeddings for temporal information
        self.temporal_position = nn.Embedding(max_seq_len, d_model)

    def forward(self, x, policy_mask, temporal_positions):
        """
        x                : [batch, seq_len, d_model]
        policy_mask      : [batch, seq_len] (indices of constraint types)
        temporal_positions: [batch, seq_len] (position indices)
        """
        batch_size, seq_len, _ = x.shape

        # Add temporal and policy information
        x = x + self.temporal_position(temporal_positions)
        x = x + self.policy_embedding(policy_mask)

        # Multi‑head projections
        Q = self.query(x).view(batch_size, seq_len, self.n_heads, self.head_dim)
        K = self.key(x).view(batch_size, seq_len, self.n_heads, self.head_dim)
        V = self.value(x).view(batch_size, seq_len, self.n_heads, self.head_dim)

        # Scaled dot‑product attention
        attn_scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) / (self.head_dim ** 0.5)

        # Apply policy‑constraint mask (0 = forbidden)
        policy_mask_matrix = self._create_policy_mask(policy_mask)  # [batch, heads, seq_len, seq_len]
        attn_scores = attn_scores.masked_fill(policy_mask_matrix == 0, float('-inf'))

        attn_weights = F.softmax(attn_scores, dim=-1)
        out = torch.einsum('bhqk,bkhd->bqhd', attn_weights, V)
        out = out.reshape(batch_size, seq_len, self.d_model)

        return out

    def _create_policy_mask(self, policy_mask):
        """
        Dummy implementation – replace with actual logic that creates a
        [batch, heads, seq_len, seq_len] mask based on policy constraints.
        """
        batch, seq_len = policy_mask.shape
        mask = torch.ones(batch, self.n_heads, seq_len, seq_len, device=policy_mask.device)
        # Example: zero‑out attention where constraint type == 0
        mask[:, :, :, :] = (policy_mask.unsqueeze(1).unsqueeze(-1) != 0).float()
        return mask

时序对比损失（PyTorch）

class TemporalContrastiveLoss(nn.Module):
    def __init__(self, temperature=0.1, temporal_window=6):
        super().__init__()
        self.temperature = temperature
        self.temporal_window = temporal_window

    def forward(self, embeddings, temporal_labels):
        """
        embeddings      : [batch, seq_len, embed_dim]
        temporal_labels : [batch, seq_len] (segment identifiers)
        """
        batch_size, seq_len, embed_dim = embeddings.shape
        loss = 0.0

        # Iterate over sliding windows to form anchor‑positive pairs
        for i in range(seq_len - self.temporal_window):
            # Anchor: mean embedding of the current window
            anchor = embeddings[:, i:i + self.temporal_window].mean(dim=1)  # [batch, embed_dim]

            # Positive: mean embedding of the next window (temporal proximity)
            positive = embeddings[:, i + 1:i + 1 + self.temporal_window].mean(dim=1)

            # Compute cosine similarity
            sim_pos = F.cosine_similarity(anchor, positive) / self.temperature

            # Negatives: all other windows in the batch
            neg_mask = torch.arange(seq_len - self.temporal_window) != i
            negatives = embeddings[:, neg_mask][:, :, :].view(batch_size, -1, embed_dim)
            neg_sim = F.cosine_similarity(
                anchor.unsqueeze(1), negatives, dim=-1
            ) / self.temperature  # [batch, num_neg]

            # InfoNCE loss
            logits = torch.cat([sim_pos.unsqueeze(1), neg_sim], dim=1)  # [batch, 1+num_neg]
            labels = torch.zeros(batch_size, dtype=torch.long, device=logits.device)  # anchor is class 0
            loss += F.cross_entropy(logits, labels)

        return loss / (seq_len - self.temporal_window)

要点

将 时序对比学习 与 策略感知注意力机制 相结合，可得到在实时疏散政策变化时仍保持鲁棒性的表征。该框架可扩展到其他时间敏感、受约束的领域，如洪水响应、疫情物流以及大规模电网恢复等。

对比损失计算

# Positive: nearby temporal window
pos_start = i + self.temporal_window
pos_end = pos_start + self.temporal_window
positive = embeddings[:, pos_start:pos_end].mean(dim=1)

# Negatives: distant temporal windows
negative_indices = torch.randint(0, seq_len, (batch_size, 10))
negatives = embeddings[torch.arange(batch_size).unsqueeze(1),
                        negative_indices].mean(dim=1)

# Compute contrastive loss
pos_sim = F.cosine_similarity(anchor, positive, dim=-1)
neg_sim = F.cosine_similarity(anchor.unsqueeze(1), negatives, dim=-1)

logits = torch.cat([pos_sim.unsqueeze(1), neg_sim], dim=1) / self.temperature
labels = torch.zeros(batch_size, dtype=torch.long, device=embeddings.device)

loss += F.cross_entropy(logits, labels)

return loss / (seq_len - self.temporal_window)

可微分策略层（PyTorch）

class DifferentiablePolicyLayer(nn.Module):
    def __init__(self, constraint_types, max_constraints=5):
        super().__init__()
        self.constraint_types = constraint_types
        self.constraint_encoder = nn.Linear(constraint_types, 128)
        self.temporal_projection = nn.Linear(128, 256)

    def forward(self, temporal_patterns, policy_constraints, current_time):
        """
        temporal_patterns:   [batch_size, seq_len, features]
        policy_constraints:  [batch_size, num_constraints, constraint_dim]
        current_time:        scalar representing current time step
        """
        batch_size, seq_len, _ = temporal_patterns.shape

        # Encode policy constraints
        constraint_emb = self.constraint_encoder(policy_constraints)
        constraint_emb = torch.mean(constraint_emb, dim=1)  # Aggregate constraints

        # Project to temporal dimension
        temporal_constraints = self.temporal_projection(constraint_emb)
        temporal_constraints = temporal_constraints.unsqueeze(1).expand(-1, seq_len, -1)

        # Apply constraints as attention modulation
        constrained_patterns = temporal_patterns * torch.sigmoid(temporal_constraints)

        # Time‑aware constraint enforcement
        time_weights = self._compute_time_weights(current_time, seq_len)
        constrained_patterns = constrained_patterns * time_weights.unsqueeze(-1)

        return constrained_patterns

    def _compute_time_weights(self, current_time, seq_len):
        """Compute weights based on temporal proximity to policy changes."""
        time_steps = torch.arange(seq_len, device=self.constraint_encoder.weight.device)
        time_diff = torch.abs(time_steps - current_time)
        weights = torch.exp(-time_diff / 10.0)  # Exponential decay
        return weights

时序路径优化器 (PyTorch)

class TemporalRouteOptimizer:
    def __init__(self, pattern_miner, constraint_manager):
        self.pattern_miner = pattern_miner
        self.constraint_manager = constraint_manager

    def optimize_evacuation_routes(self, current_state, time_horizon, policy_updates):
        """
        current_state:   当前疏散网络状态
        time_horizon:   需要优化的未来时间步数
        policy_updates: 实时政策变更
        """
        # 从当前状态提取时序模式
        temporal_features = self._extract_temporal_features(current_state)

        # 应用政策约束
        constrained_features = self.constraint_manager.apply_constraints(
            temporal_features, policy_updates
        )

        # 挖掘时序模式
        patterns = self.pattern_miner.mine_patterns(constrained_features)

        # 生成疏散计划
        plans = []
        for t in range(time_horizon):
            # 使用学习到的模式预测未来状态
            future_state = self._predict_state(patterns, t)

            # 为此时间步优化路径
            routes = self._optimize_routes(future_state, policy_updates)
            plans.append(routes)

            # 根据新信息更新模式
            patterns = self._update_patterns(patterns, routes)

        return plans

    def _extract_temporal_features(self, state):
        """从网络状态中提取时序特征。"""
        features = []
        # 道路网络时序特征
        features.append(state['road_congestion_trend'])
        features.append(state['evacuation_rate'])
        features.append(state['resource_availability'])

        # 环境时序特征
        features.append(state['fire_spread_rate'])
        features.append(state['weather_conditions'])

        return torch.stack(features, dim=-1)

策略自适应注意力 (PyTorch)

class PolicyAdaptiveAttention(nn.Module):
    def __init__(self, base_model, adaptation_layers=3):
        super().__init__()
        self.base_model = base_mod  # Note: original variable name truncated; ensure correct reference
        # Additional adaptation layers would be defined here
        # ...

    # Placeholder for further implementation

带策略条件层的自适应模型

self.adaptation_layers = nn.ModuleList([
    nn.Linear(base_model.hidden_size, base_model.hidden_size)
    for _ in range(adaptation_layers)
])

def forward(self, x, new_policy_constraints):
    # 获取基础表示
    base_repr = self.base_model(x)

    # 对新策略进行快速适应
    adapted_repr = base_repr
    for layer in self.adaptation_layers:
        # 拼接策略信息
        policy_expanded = new_policy_constraints.unsqueeze(1).expand(
            -1, adapted_repr.size(1), -1
        )
        combined = torch.cat([adapted_repr, policy_expanded], dim=-1)

        # 应用适应（残差连接）
        adapted_repr = layer(combined) + adapted_repr

    return adapted_repr

合成疏散数据生成 (PyTorch)

class SyntheticEvacuationGenerator:
    def __init__(self, pattern_miner, physics_simulator):
        self.pattern_miner = pattern_miner
        self.physics_simulator = physics_simulator

    def generate_scenarios(self, base_patterns, num_scenarios, variability=0.3):
        """Generate synthetic evacuation scenarios."""
        scenarios = []

        for _ in range(num_scenarios):
            # Sample from learned patterns
            pattern_idx = torch.randint(0, len(base_patterns), (1,))
            base_pattern = base_patterns[pattern_idx]

            # Apply realistic variations
            varied_pattern = self._apply_variations(base_pattern, variability)

            # Simulate physics‑based constraints
            physics_constraints = self.physics_simulator.simulate(varied_pattern)

            # Combine patterns with physics
            full_scenario = self._combine_patterns(varied_pattern, physics_constraints)
            scenarios.append(full_scenario)

        return torch.stack(scenarios)

知识蒸馏用于边缘部署（PyTorch）

class TemporalKnowledgeDistillation:
    def __init__(self, teacher_model, student_model, temperature=2.0):
        self.teacher = teacher_model
        self.student = student_model
        self.temperature = temperature
        self.criterion = nn.KLDivLoss(reduction='batchmean')

    def distill(self, data_loader, optimizer, epochs=5):
        self.teacher.eval()
        self.student.train()

        for epoch in range(epochs):
            for batch in data_loader:
                inputs = batch['inputs']
                with torch.no_grad():
                    teacher_logits = self.teacher(inputs) / self.temperature

                student_logits = self.student(inputs) / self.temperature
                loss = self.criterion(
                    F.log_softmax(student_logits, dim=-1),
                    F.softmax(teacher_logits, dim=-1)
                ) * (self.temperature ** 2)

                optimizer.zero_grad()
                loss.backward()
                optimizer.step()