Web 3.0 is envisioned as a decentralized paradigm, where blockchain serves as a core technology for transparent and tamper-proof data management. Among various blockchain architectures, consortium blockchains have emerged as the preferred platform for enterprise-grade Web 3.0. For consortium blockchains, newly generated blocks are generally propagated to all consensus nodes for validation through the gossip protocol. However, gossip-based propagation may introduce substantial message redundancy and tail latency. Moreover, the consensus nodes exhibit heterogeneous availability patterns, and existing block propagation schemes often overlook such temporal constraints. Therefore, the joint optimization of propagation timeliness and delivery coverage remains an open problem. In this paper, we propose a deliverable block propagation optimization framework for consortium blockchain-enabled Web 3.0. We first propose a delivery-aware timeliness metric called Age of Validated Block (AoVB), which excludes block receptions occurring outside the availability window of each consensus node, thereby measuring only actionable synchronization latency. This metric is unified with the block arrival rate into a hybrid cost objective that balances timeliness against delivery. To solve this complex optimization problem, we propose a Graph-based Hierarchical Deep Reinforcement Learning (GHDRL) method, which comprises a graph isomorphism network-based assignment module and a graph attention network-based propagation module. The two modules are optimized jointly under a two-stage training strategy. Numerical results show that GHDRL consistently outperforms all compared schemes across network scales from 50 to 500 peers, achieving up to 19.2% lower hybrid cost than the best-performing neural baseline. Moreover, the model generalizes from 100-peer training instances to 500-peer deployments without retraining.

翻译：Web 3.0被设想为一种去中心化范式，其中区块链作为透明且不可篡改数据管理的核心技术。在各种区块链架构中，联盟链已成为企业级Web 3.0的首选平台。对于联盟链而言，新生成的区块通常通过Gossip协议传播给所有共识节点进行验证。然而，基于Gossip的传播可能引入大量消息冗余和尾部延迟。此外，共识节点表现出异构的可用性模式，而现有区块传播方案往往忽视了此类时间约束。因此，传播及时性与交付覆盖率的联合优化仍然是一个开放性问题。本文针对联盟链驱动的Web 3.0提出了一种可交付区块传播优化框架。我们首先提出一种称为验证区块年龄（AoVB）的交付感知及时性度量，该度量排除了每个共识节点可用窗口之外的区块接收情况，从而仅度量可操作的同步延迟。该度量与区块到达率统一为一个平衡及时性与交付的混合成本目标。为解决这一复杂优化问题，我们提出了一种基于图的分层深度强化学习方法（GHDRL），该方法包含一个基于图同构网络的分配模块和一个基于图注意力网络的传播模块。两个模块通过两阶段训练策略联合优化。数值结果表明，GHDRL在50至500个节点的网络规模下始终优于所有对比方案，相比最佳神经基线方法，混合成本降低高达19.2%。此外，该模型无需重新训练即可从100个节点的训练实例泛化至500个节点的部署场景。