Abstract: A filter debugging method, a device, an electronic apparatus and a readable storage medium are provided. The filter debugging method includes: step S1: inputting a current hole parameter and a current index value of a filter into a policy network which is pre-trained; step S2: determining, by the policy network, a target hole to be polished of the filter, according to the current hole parameter and the current index value of the filter; step S3: controlling a mechanical arm to polish the target hole of the filter; and step S4: determining whether the filter is qualified according to an index value of the polished filter; in a case that the filter is qualified, ending a process including the steps S1 to S4; in a case that the filter is unqualified, performing the steps S1 to S4 circularly until the filter is qualified.

Abstract: Embodiments of the present disclosure disclose a method, apparatus and electronic device for constructing a reinforcement learning model, and a computer readable storage medium, relate to the field of big data and deep learning technology. An implementation of the method can include: establishing a first simulation model between a calciner coal feed amount and a calciner temperature; establishing a second simulation model among a kiln head coal feed amount, a kiln current, a secondary air temperature, and a smoke chamber temperature; establishing a prediction model among: an under-grate pressure; the calciner temperature output by the first simulation model; the kiln current, the secondary air temperature, and the smoke chamber temperature content output by the second simulation model; and a free calcium; and constructing a reinforcement learning model according to a preset reinforcement learning model architecture, using the first simulation model, the second simulation model, and the prediction model.

Abstract: A filter debugging method, a device, an electronic apparatus and a readable storage medium are provided. The filter debugging method includes: step S1: inputting a current hole parameter and a current index value of a filter into a policy network which is pre-trained; step S2: determining, by the policy network, a target hole to be polished of the filter, according to the current hole parameter and the current index value of the filter; step S3: controlling a mechanical arm to polish the target hole of the filter; and step S4: determining whether the filter is qualified according to an index value of the polished filter; in a case that the filter is qualified, ending a process including the steps S1 to S4; in a case that the filter is unqualified, performing the steps S1 to S4 circularly until the filter is qualified.