Abstract: Disclosed are a power demand side speech interaction method and system. The method includes: obtaining original demand information, the original demand information including user's basic information, user demand information, and a user demand time; converting the original demand information into first information in text format; performing text statistical analysis based on an industry term on the first information in text format, to obtain second information; searching for corresponding user's actual information from a database according to the second information; outputting the user's actual information; searching for a corresponding forecasting model from the database, according to the second information and the user's basic information; calculating, according to a policy limit value of latest policy information in the database, a time for which the model corresponding to the user's basic information reaches the policy limit value; and transmitting an early warning message.

Abstract: Disclosed are various embodiments for deep reinforcement learning-based irrigation control to maintain or increase crop yield and/or other desired crop status, and/or reduce water use. One or more computing devices can be configured to determine an amount of water to be applied to at least one crop in at least one of a plurality of irrigation management zones through execution of a deep reinforcement learning routine. Further, the computing devices can determine a start time and an end time to be applied to the at least one of the plurality of irrigation management zones based at least in part on the amount of water determined by the deep reinforcement learning module. Finally, the computing devices can instruct an irrigation system to apply irrigation to the at least one of the plurality of irrigation management zones in accordance with the start time and the end time.