Abstract: The present disclosure provides an energy-efficient (low energy consumption), carbon enriched cement production system and a method for producing cement clinker. This system involves raw material preheating and precalcining system and sequentially connected kiln inlet chamber, rotary kiln and cooler, wherein the raw material preheating and precalcining system involves precalciner and preheater, and the cooler involves first cooling zone and second cooling zone. The first cooling zone includes first cooling partition and second cooling partition, wherein a mixture of pure oxygen and high-concentration CO2 flue gas entering the inlet of the first cooling partition, high-concentration CO2 flue gas entering the inlet of the second cooling partition, and air entering the inlet of the second cooling zone have solved the problems in existing cement kiln CO2 enrichment technology, i.e., extensive air leakage and high energy consumption between the first cooling zone and the second cooling zone.

Abstract: Disclosed is a human-computer dialogue method including determining a set number of jump topics about a target topic, and generating a topic jump map converging to the target topic based on the correlation intensions among the set number of jump topics; after an initial response to a user's dialogue request, selecting from the topic jump map a jump topic to which the user's dialogue request relates as an initial topic for a first round of recommendation; after completing a human-machine dialogue of the initial topic, determining a jump topic to jump according to the jump probability of jumping out of the initial topic to the k jump topics at the downstream level for a next round of recommendation; and gradually guiding from the initial topic to the target topic by step-by-step recommendation. A more fluent and efficient human-machine dialogue based on a clear communication goal can be realized.

Abstract: The present application discloses a voice dialogue processing method and apparatus. The voice dialogue processing method includes: determining a voice semantics corresponding to a user voice to be processed; determining a reply sentence for the voice semantics based on a dialogue management engine, a training sample set of which is constructed from a dialogue business customization file including at least one dialogue flow, and the dialogue flow includes a plurality of dialogue nodes in a set order; and generating a customer service voice for replying to the user voice according to the determined reply sentence.

Abstract: The present application discloses a method of knowledge sharing between dialogue systems, including: receiving, by a first dialogue system, a knowledge sharing request sent by an external dialogue system, wherein the knowledge sharing request comprises at least feature information of a knowledge point to be shared by the external dialogue system; parsing the knowledge sharing request to determine the feature information of the knowledge point to be shared; and adding the feature information of the knowledge point to be shared to a knowledge base of the first dialogue system to form a shared knowledge base. The embodiment of the present application realizes the sharing of knowledge among different dialogue systems by sharing knowledge points among dialogue systems, and meets the cross-domain dialogue needs of users for dialogue robots to the greatest extent while minimizing costs.

Abstract: Disclosed is a human-computer dialogue method including determining a set number of jump topics about a target topic, and generating a topic jump map converging to the target topic based on the correlation intensions among the set number of jump topics; after an initial response to a user's dialogue request, selecting from the topic jump map a jump topic to which the user's dialogue request relates as an initial topic for a first round of recommendation; after completing a human-machine dialogue of the initial topic, determining a jump topic to jump according to the jump probability of jumping out of the initial topic to the k jump topics at the downstream level for a next round of recommendation; and gradually guiding from the initial topic to the target topic by step-by-step recommendation. A more fluent and efficient human-machine dialogue based on a clear communication goal can be realized.