Abstract: To address the difficulty of bounding end-to-end latency in high-load mixed-traffic scenarios, the degradation of transmission determinism caused by priority inversion and head-of-line blocking, and the large model size and low solving efficiency of joint scheduling over dual-homed redundant topologies, a deterministic transmission architecture and scheduling method based on Time-Sensitive Networking (TSN) is proposed for train-control services. In terms of architecture, an eight-car dual-homed ring network is constructed according to the hierarchical structure of the train backbone and consist networks. A/B-plane isolation and end-to-end cross-layer mapping are employed to support differentiated transmission and redundant-path switching for services with different priorities. In terms of scheduling, a two-stage optimization model combining spatial routing and temporal gating is established based on the time-aware shaping mechanism. The first stage determines transmission paths over the two planes, whereas the second generates conflict-free transmission phases within each scheduling period. To address the rapid growth of disjunctive constraints with the number of flows in the full-scale model, binary routing reduction, a forward-list heuristic, and accelerated binary search are applied sequentially. These strategies discretize routing decisions into plane selections, fix the transmission order of frames competing for the same port, and perform a bounded search for the end-to-end latency upper bound. Under the monotonic-feasibility condition, the binary search terminates within a finite number of steps. For cases in which the demand of critical flows exceeds the available network capacity, a two-stage overload admission mechanism is designed. A priority-based bandwidth-feasibility precheck is first performed, after which the reduced model is solved for the flow set that passes the precheck. If the flow set is infeasible, the lowest-priority flow is removed and the model is solved again until a feasible solution is obtained or a rejection result is produced. Simulations are conducted on an eight-car dual-homed ring topology with background loads ranging from 20% to 95% and with scenarios containing 10 and 20 highest-priority flows. The proposed method is compared with Strict Priority (SP) scheduling, routing-only optimization, and the Online Scalable Scheduling and Routing (OSSR) algorithm. The results show that the proposed method limits the maximum end-to-end latency of critical control flows to less than 0.6ms. For the evaluated static flow sets, it reduces the maximum end-to-end latency by 33.6%–51.9%, 38.6%–48.2%, and 37.5%–43.6% compared with SP scheduling, routing-only optimization, and OSSR, respectively. With 20 highest-priority flows, the three-stage reduction decreases the number of disjunctive constraints from 738 to 0, limits peak memory consumption to approximately 4% of that required by the full-scale model, and achieves a cumulative solving time of approximately 0.5s and a speedup of approximately 1.56 fold. The binary search converges after six feasibility checks. Under overload conditions, the method admits critical flows according to capacity constraints and service priorities while rejecting requests that exceed the available capacity, thereby providing deterministic transmission guarantees for safety-critical train-control services.

摘要： 针对列车控制业务在高负载混合流量场景下端到端时延难以界定、优先级反转和线头阻塞削弱传输确定性，以及双归属冗余拓扑下联合调度模型规模大、求解效率低等问题，提出一种面向列车控制业务的时间敏感网络（Time-Sensitive Networking, TSN）确定性传输架构与调度方法。架构方面，依据列车骨干网与编组网的分层结构，构建八节编组列车双归属环形网络，采用A/B双平面隔离和全链路跨层映射，实现不同优先级业务的分类传输及冗余路径切换。调度方面，基于时间感知整形机制建立空间路由与时域门控相结合的两阶段优化模型：第一阶段确定双平面传输路径，第二阶段生成周期内无冲突的发送相位。针对全量模型中析取约束随流数量快速增长的问题，依次采用二元路由降维、前向列表启发式和二分搜索加速策略，将路由决策离散为平面选择，固定竞争端口中的帧发送顺序，并对端到端时延上界进行有界搜索。在可行性单调条件下，二分搜索能够有限步终止。针对关键流需求超过网络容量的情况，设计两阶段过载准入机制，先执行基于优先级的带宽可行性预检，再对通过预检的流集进行降维求解；若不可满足，则逐次剔除最低优先级流并重新求解，直至获得可行方案或输出拒绝结果。基于八节编组双归属环形拓扑开展仿真实验，背景负载率覆盖20%-95%，并设置10条和20条最高优先级流场景，与严格优先级（Strict Priority, SP）、纯路由优化及在线可扩展调度与路由（Online Scalable Scheduling and Routing, OSSR）算法进行对比。结果表明，所提方法可将关键控制流的最大端到端时延控制在0.6ms以内；在所测静态流集下，相较于SP、纯路由优化和OSSR，最大端到端时延分别降低33.6%-51.9%、38.6%-48.2%和37.5%-43.6%。在20条最高优先级流场景下，三阶段降维将析取约束数量由738降至0，峰值内存约为全量求解的4%，累计求解时间约为0.5s，加速比约为1.56；二分搜索经过6次可行性判定完成收敛。过载场景下，该方法能够依据容量约束和业务优先级完成关键流准入并拒绝超容量请求，为列车安全关键业务提供确定性传输保障。