Abstract: The reconfigurable intelligent surface (RIS) -assisted integrated sensing and communication (ISAC) system is a key technology for future 6G networks. A cooperative dual-active RIS-assisted ISAC system is studied, utilizing dual-reflection links to achieve both communication between a base station and multiple users in non-line-of-sight areas and multi-target sensing tasks. To maximize the minimum sensing beampattern gain, the communication quality of service, sensing mean square cross-correlation, and power constraints for both the base station and active RIS were modeled, forming a joint optimization problem for key parameters including the communication beamforming and sensing covariance matrix at the base station, and the reflection coefficient matrix and amplification power allocation of the active RIS. Due to the strong coupling between the beamforming vectors, sensing covariance matrix at the base station, and the reflection coefficient matrices of the two active RISs, this joint optimization problem is non-convex and highly complex to solve. Therefore, a distributed iterative suboptimal algorithm was designed to decouple the joint optimization into three subproblems: optimizing the communication beamforming, sensing covariance matrix, and active RIS amplification power allocation at the base station, and optimizing the reflection coefficient matrices at both active RISs. The simulation results demonstrate that the proposed distributed optimization algorithm exhibits good convergence performance. Compared with other schemes, optimizing the amplification power allocation between the two active RIS units can improve the beampattern gain by approximately 2 dB under the same system power budget and number of RIS elements. This leads to higher power utilization efficiency.