The present disclosure provides a human-computer intelligence chatting method and device. The method includes: receiving a multimodal input signal, the multimodal input signal comprising at least one of a speech signal, an image signal, a sensor signal and an event driving signal; processing the multimodal input signal to obtain text data, and obtaining an intention of a user according to the text data; obtaining an answer corresponding to the intention of the user, and converting the answer to a multimodal output signal; and outputting the multimodal output signal.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A human-computer intelligence chatting method based on artificial intelligence, comprising: receiving a multimodal input signal, the multimodal input signal comprising at least one of a speech signal, an image signal, a sensor signal and an event driving signal; processing the multimodal input signal to obtain text data, and obtaining an intention of a user according to the text data; obtaining an answer corresponding to the intention of the user, and converting the answer to a multimodal output signal; and outputting the multimodal output signal. 2. The method according to claim 1, wherein obtaining an intention of a user according to the text data comprises: analyzing the text data and generating the intention of the user according to a result of analyzing the text data. 3. The method according to claim 2, wherein analyzing the text data comprises: performing a syntactic structure analysis on the text data, and performing a semantic analysis based on words, a domain multi-classification recognition based on a topic model, a semantic disambiguation, and an auto-completion based on grammatical structures and context information. 4. The method according to claim 1, further comprising: storing the intention of the user into historical intentions of the user. 5. The method according to claim 1, wherein obtaining an answer corresponding to the intention of the user comprises: searching a memory system according to the intention of the user, so as to obtain constraint conditions on the intention of the user; searching a topic model and a domain entity database according to the intention of the user, so as to obtain variables and attributes associated with the intention of the user; obtaining a similarity between a current chat context and a pre-stored chat mode via an active learning module; accessing an open service interface, and obtaining a result returned via the open service interface; and obtaining the answer corresponding to the intention of the user according to the intention of the user in combination with the constraint conditions on the intention of the user, the variables and attributes associated with the intention of the user, the result returned via the open service interface and the similarity between the current chat context and the pre-stored chat mode. 6. The method according to claim 5, further comprising: storing the intention of the user, the constraint conditions on the intention of the user, and the variables and attributes associated with the intention of the user into a dialogue model; establishing a transition probability map according to a statistical result stored in the dialogue model, and generating a new topic according to the transition probability map at an appropriate time, wherein the statistical result is obtained according to the intention of the user, the constraint conditions on the intention of the user, and the variables and attributes associated with the intention of the user. 7. The method according to claim 5, after processing the multimodal input signal to obtain text data, further comprising: storing content favorable for memorizing into the memory system after obtaining the text data. 8. The method according to claim 7, wherein the memory system comprises a short-term memory system and a long-term memory system; storing content favorable for memorizing into the memory system comprises: storing content favorable for short-term memorizing into the short-term memory system, in which the content favorable for short-term memorizing comprises historical dialogue records of the user, a topic status sequence established based on the historical dialogue records and entity-related attributes extracted from the historical dialogue records; and storing content favorable for long-term memorizing into the long-term memory system, in which the content favorable for long-term memorizing comprises personal information and population attributes of the user, preferences of the user, historical geographic records of the user, historical purchase records of the user, personal information and population attributes in the system, and preferences of the system. 9. The method according to claim 5, after processing the multimodal input signal to obtain text data, further comprising: recording topics extracted from the text data in the topic model and recording entity attributes extracted from the text data in the domain entity database. 10. The method according to claim 5, wherein obtaining a similarity between a current chat context and a pre-stored chat mode via an active learning module comprises: performing a numeralization on the chat mode of human beings according to the dialogue model, the topic model and the domain entity database, so as to obtain a numerical chat mode; storing the numerical chat mode in the active learning module; and detecting by the active learning module, the similarity between the current chat context and the numerical chat mode. 11. A human-computer intelligence chatting device based on artificial intelligence, comprising: a processor; and a memory, configured to store instructions executable by the processor, wherein, the processor is configured to: receive a multimodal input signal, the multimodal input signal comprising at least one of a speech signal, an image signal, a sensor signal and an event driving signal; process the multimodal input signal to obtain text data, and obtain an intention of a user according to the text data; obtain an answer corresponding to the intention of the user, and convert the answer to a multimodal output signal; and output the multimodal output signal. 12. The device according to claim 11, wherein the processor is configured to analyze the text data and generate the intention of the user according to a result of analyzing the text data, in which the processor is specifically configured to perform a syntactic structure analysis on the text data, and perform a semantic analysis based on words, a domain multi-classification recognition based on a topic model, a semantic disambiguation, and an auto-completion based on grammatical structures and context information. 13. The device according to claim 11, wherein the processor is further configured to store the intention of the user into historical intentions of the user. 14. The device according to claim 11, wherein the processor is configured to: search a memory system according to the intention of the user, so as to obtain constraint conditions on the intention of the user; search a topic model and a domain entity database according to the intention of the user, so as to obtain variables and attributes associated with the intention of the user; obtain a similarity between a current chat context and a pre-stored chat mode via the active learning module; access an open service interface and obtain a result returned via the open service interface; and obtain the answer corresponding to the intention of the user according to the intention of the user in combination with the constraint conditions on the intention of the user, the variables and attributes associated with the intention of the user, the result returned via the open service interface and the similarity between the current chat context and the pre-stored chat mode. 15. The device according to claim 14, wherein the processor is further configured to: store the intention of the user, the constraint conditions on the intention of the user, and the variables and attributes associated with the intention of the user into a dialogue model; establish a transition probability map according to a statistical result stored in the dialogue model, and generate a new topic according to the transition probability map at an appropriate time, wherein the statistical result is obtained according to the intention of the user, the constraint conditions on the intention of the user, and the variables and attributes associated with the intention of the user. 16. The device according to claim 14, wherein the processor is further configured to: store content favorable for memorizing into the memory system after obtaining the text data. 17. The device according to claim 16, wherein the memory system comprises a short-term memory system and a long-term memory system, and the processor is further configured to store content favorable for short-term memorizing into the short-term memory system, and to store content favorable for long-term memorizing into the long-term memory system, in which the content favorable for short-term memorizing comprises historical dialogue records of the user, a topic status sequence established based on the historical dialogue records and entity-related attributes extracted from the historical dialogue records; and the content favorable for long-term memorizing comprises personal information and population attributes of the user, preferences of the user, historical geographic records of the user, historical purchase records of the user, personal information and population attributes in the system and preferences of the system. 18. The device according to claim 14, wherein the processor is further configured to: record topics extracted from the text data in the topic model, and record entity attributes extracted from the text data in the domain entity database after obtaining the text data. 19. The device according to claim 14, wherein the processor is configured to: perform a numeralization on the chat mode of human beings according to the dialogue model, the topic model and the domain entity database, so as to obtain a numerical chat mode; store the numerical chat mode in the active learning module; and obtain the similarity between the current chat context and the numerical chat mode from the active learning module. 20. A non-transitory computer-readable storage medium having stored therein instructions that, when executed by a processor of a device, causes the device to perform a human-computer intelligence chatting method based on artificial intelligence, the method comprising: receiving a multimodal input signal, the multimodal input signal comprising at least one of a speech signal, an image signal, a sensor signal and an event driving signal; processing the multimodal input signal to obtain text data, and obtaining an intention of a user according to the text data; obtaining an answer corresponding to the intention of the user, and converting the answer to a multimodal output signal; and outputting the multimodal output signal.