The invention relates to the technical field of
system optimization, and discloses a
federated learning-based performance collaborative optimization method and
system for multiple sets of separation systems, and the method comprises the steps: obtaining the radioactivity value of the current batch of
raw material liquid of each set of At-211
separation system, and carrying out the analysis of the radioactivity value based on a deep neural network, generating a working condition sensitive
feature vector representing the influence of different batches of
radioactive activity values on the performance of each
separation system; through the working condition sensitive
feature vector, the working condition attribute factor, the activity working condition vector and the collaborative optimization coefficient which are sequentially generated in each step, the influence of different batches of
radioactive activity values can be accurately mined, the working condition association and difference of each
system are determined, the differential aggregation weight fine adjustment is realized, the out-of-distribution generalization failure of cross-activity migration is avoided, and the
system safety is improved. It is ensured that technological parameters adapt to the requirements of
raw material solutions with different activities, the cooperative capacity of multiple At-211 separation systems is improved, and the separation efficiency and the product purity are improved.