Objectives As maritime networks continue to expand, the compute-intensive tasks of offshore terminals are growing exponentially. Due to resource limitations and limited budgets, it is difficult to handle the increasingly diverse business needs, the traditional ocean communication models have the problem of not being able to simultaneously meet the demands of terminals in terms of energy saving and efficiency as well as effectively increasing the total computational task volumn of the system.

Methods This paper introduces a double-intelligent reflecting surface (IRS) collaborative architecture, considers deploying distributed IRS to assist users in offloading tasks to shore based MEC servers in uplink scenarios. Then, this paper considers the joint optimization of base station receiving beamforming, dual IRS joint phase shift matrix, user transmission power, and CPU computing frequency and designs a joint task offloading and resource allocation algorithm to maximize the total computational task volumn of the system under communication and computing resource constraints. This non-convex optimization problem is solved using the block coordinate descent (BCD) idea and an efficient alternating optimization algorithm based on maximal ratio combining (MRC), Lagrange multiplier method and bisection search.

Results The simulation results show that the proposed dual IRS-aided offshore cooperative offloading scheme can improve the system's total computational task volumn by about 7.03% compared to the baseline scheme when the total number of reflective elements is 800. This verifies that the offshore uplink energy-efficient offloading to introduce the dual IRS collaborative architecture can improve the total task volume requirement.

Conclusion The research results can provide reference for MEC communication system assisted offloading technology.