With the gradual application of edge computing technology in practical scenarios, the issues of system security and credibility have become the core difficulties affecting its performance and promotion. Blockchain technology, with its characteristics of decentralization and data immutability, provides a solid guarantee for task offloading and identity authentication in edge computing. However, there is an obvious contradiction in the allocation of computing resources between the blockchain consensus process and the execution of edge computing tasks. If the resource allocation is unreasonable, it will lead to the decline of system efficiency and the waste of resources. In response to this challenge, this paper constructs a collaborative computing model including multiple terminals, multiple edge nodes and cloud servers. Systematically combining the network environment, task processing mechanism and blockchain consensus mechanism, a joint optimization strategy based on the double-delay deep deterministic policy gradient algorithm is proposed. This strategy realizes the coordinated allocation of computing resources between blockchain consensus and edge task offloading. The optimization method designed in this paper comprehensively considers task offloading decisions, transmission power control and computing resource allocation, significantly shortening the overall task processing delay of the terminal and reducing energy consumption, while ensuring the timeliness and security of the consensus mechanism. For the high-dimensional and complex resource scheduling problem, this paper adopts the double-delay deep deterministic policy gradient algorithm combined with fast numerical computing technology, which effectively improves the training efficiency and stability of the model. Through multi-scenario simulation experiments, the method proposed in this paper has improved the system performance by an average of more than 20% compared with the traditional scheme, fully verifying its superiority in improving the credibility and resource utilization rate of the system. The research results of this paper not only enrich the theoretical basis of the integration of blockchain and edge computing, but also provide practical and feasible solutions for the rational allocation of computing resources in actual engineering.

Wenjie Jiang, Yan Ren. Research on Joint Optimization of Blockchain Consensus and Edge Computing Task Offloading Computing Power Matching Based on Double-Delay Deep Deterministic Policy Gradient Algorithm. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 13-26. https://doi.org/10.38007/ML.2025.050102.

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