To solve the timeliness of flood control data transmission in large reservoirs, a flood control data push method based on artificial intelligence technology is proposed. By constructing a multi-objective flood control data push model and combining with convolutional neural network algorithm, the data transmission from the database to the data stream and then to the model is realized. Compared with the method in literature [2–4], this method has the highest storage capacity adjustment during 8 typical days, with 10 adjustment peaks and 11 minimum values, and has significant advantages in timeliness of information transmission, which can fully utilize flood control system information communication to adjust reservoir capacity in time. In terms of solving efficiency, this research method improves with the increase of iterations, approaching 100% in 60 iterations, which is far higher than the solution efficiency of 70%, 65% and 30% in references [2–4] under the same iterations. The results show that this method can effectively reduce flood peaks, alleviate flood processes, and improve reservoir water levels through effective flood regulation while ensuring flood safety.
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