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(1) 制定网络传输与牵引调控系统的数字镜像整体构建计划与框架布局，将网络传输、调控模块和调控目标分为三个模拟单元，分别采用专用模拟平台进行模型化，并整合至统一环境中，实现数字镜像系统的组装。在此基础上，展示网络传输与牵引调控数字镜像的应用案例，并评估多种运行条件下的模拟输出。面向模型构建，本文介绍了两种模拟模型的组建策略，即模拟置换和虚实结合两种途径，并完成了相应模型的开发。这种框架的设计强调了模块间的无缝衔接，能够模拟真实高铁运行中的数据流动和控制响应。例如，在处理网络延迟时，该模型能实时调整参数，验证不同拓扑下的性能差异，从而为实际部署提供参考。通过这种方法，不仅加速了设计验证过程，还降低了物理测试的成本，确保高铁系统的可靠性和效率。
(2) 聚焦以太网和时间敏感传输技术，设计基于优先级排序的调度机制与基于线性规划的传输门控表优化算法的联合方案。建立多网络融合的数据传输模型，对单环和双环拓扑进行仿真测试，并通过多种运行情境模拟，对比性能指标，确认时间敏感功能的实用性，针对各种应用场合，给出利用该技术减少延迟的指导意见。通过车辆实际验证，证实了模拟模型和测试结论的可靠性。这种方案的核心在于算法的协同，能够在高负载下维持低时延传输。例如，在模拟高峰期数据洪峰时，该方法能优先处理关键控制信号，避免拥堵，确保牵引系统的稳定响应。通过优化门控表，该技术显著降低了端到端延迟，为高铁通信升级提供了技术路径，进一步提升了车辆整体调控的实时性。
(3) 基于模块化理念和模拟置换测试需求，以高压模块、牵引模块、门控与气候模块、故障识别模块等高铁系统中的调控逻辑和时序为基础，建立中央调控单元的过程变量与功能逻辑的映射关系。同样，对于牵引模块，实现基于电流预测的双环调控、四象限转换器的控制、异步电机转矩直接调控，以及恒定速度策略。通过系统功能评估和互操作性测试，验证了模拟策略和模型的有效性。这种模块化方法允许独立开发各子系统，然后集成测试，提高了开发效率。例如，在门控模块模拟中，该框架能处理开门序列和故障注入，模拟真实响应，帮助识别潜在问题。通过这种方式，不仅优化了调控逻辑的调试，还为多系统协作提供了可靠平台，确保高铁在复杂环境下的平稳运行。
import networkx as nx import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim from scipy.optimize import linprog import random from collections import deque def build_network_topology(nodes, edges): G = nx.Graph() G.add_nodes_from(nodes) G.add_edges_from(edges) return G def simulate_single_ring(G, traffic): delays = [] for packet in traffic: path = nx.shortest_path(G, packet['source'], packet['dest']) delay = len(path) * 0.01 + random.uniform(0.001, 0.005) delays.append(delay) return np.mean(delays) def simulate_double_ring(G1, G2, traffic): delays = [] for packet in traffic: if random.random() > 0.5: path = nx.shortest_path(G1, packet['source'], packet['dest']) else: path = nx.shortest_path(G2, packet['source'], packet['dest']) delay = len(path) * 0.01 + random.uniform(0.001, 0.005) delays.append(delay) return np.mean(delays) def priority_scheduling(queues): scheduled = [] priorities = sorted(queues.keys(), reverse=True) for prio in priorities: while queues[prio]: scheduled.append(queues[prio].popleft()) return scheduled def tsn_gate_list_optimization(flows, constraints): c = np.ones(len(flows)) A_ub = np.random.rand(len(constraints), len(flows)) b_ub = np.ones(len(constraints)) res = linprog(c, A_ub=A_ub, b_ub=b_ub) return res.x def build_control_model(input_size, hidden_size, output_size): class ControlNet(nn.Module): def __init__(self): super(ControlNet, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, output_size) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x return ControlNet() def train_control_model(model, data, epochs=50): optimizer = optim.SGD(model.parameters(), lr=0.01) criterion = nn.MSELoss() for epoch in range(epochs): inputs = torch.tensor(data[:-1], dtype=torch.float32) targets = torch.tensor(data[1:], dtype=torch.float32) outputs = model(inputs) loss = criterion(outputs, targets) optimizer.zero_grad() loss.backward() optimizer.step() return model def high_fidelity_simulation(params): results = [] for p in params: sim = p['speed'] * 1.1 + p['load'] * 0.5 + random.gauss(0, 0.1) results.append(sim) return results def reduced_order_simulation(params): results = [] for p in params: sim = p['speed'] + p['load'] * 0.3 results.append(sim) return results def multi_condition_test(scenarios): outputs = {} for scenario in scenarios: if scenario['type'] == 'traction': out = random.uniform(100, 200) elif scenario['type'] == 'braking': out = random.uniform(50, 100) outputs[scenario['id']] = out return outputs def obdpa_algorithm(data_fusion): predictions = [] for d in data_fusion: dist = d['speed']**2 / (2 * d['decel']) + random.uniform(5, 10) predictions.append(dist) return predictions def nnap_method(inputs): accel_preds = [] for inp in inputs: accel = inp['torque'] / inp['mass'] + random.gauss(0, 0.05) accel_preds.append(accel) return accel_preds def validate_with_real_data(sim_data, real_data): errors = np.abs(np.array(sim_data) - np.array(real_data)) return np.mean(errors) nodes = ['node1', 'node2', 'node3', 'node4'] edges_single = [('node1', 'node2'), ('node2', 'node3'), ('node3', 'node4'), ('node4', 'node1')] G_single = build_network_topology(nodes, edges_single) edges_double1 = [('node1', 'node2'), ('node2', 'node3')] edges_double2 = [('node3', 'node4'), ('node4', 'node1')] G_double1 = build_network_topology(nodes, edges_double1) G_double2 = build_network_topology(nodes, edges_double2) traffic = [{'source': 'node1', 'dest': 'node3'} for _ in range(100)] delay_single = simulate_single_ring(G_single, traffic) delay_double = simulate_double_ring(G_double1, G_double2, traffic) queues = {1: deque([1,2,3]), 2: deque([4,5]), 3: deque([6])} sched = priority_scheduling(queues) flows = [1,2,3,4] consts = [1,2] gate_list = tsn_gate_list_optimization(flows, consts) control_model = build_control_model(5, 10, 1) data = np.random.rand(100) trained_model = train_control_model(control_model, data) params = [{'speed': 100, 'load': 50} for _ in range(10)] hi_fi = high_fidelity_simulation(params) red_ord = reduced_order_simulation(params) scens = [{'type': 'traction', 'id': 1}, {'type': 'braking', 'id': 2}] tests = multi_condition_test(scens) fusion_data = [{'speed': 120, 'decel': 5} for _ in range(5)] obdpa_preds = obdpa_algorithm(fusion_data) nn_inputs = [{'torque': 2000, 'mass': 10000} for _ in range(5)] nn_preds = nnap_method(nn_inputs) real = [150, 80] error = validate_with_real_data([hi_fi[0], red_ord[0]], real) print(error)如有问题，可以直接沟通
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