The present disclosure provides a semantic analysis method and apparatus based on artificial intelligence. The method includes: matching input information to be processed with a preset semantic template, in which the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to a target semantic template successfully matched with the input information; normalizing the formative data and generating a semantic analysis result corresponding to the input information.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A semantic analysis method based on artificial intelligence, comprising: matching input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to the semantic template; normalizing the formative data and generating a semantic analysis result corresponding to the input information. 2. The method according to claim 1, wherein, the semantic slot information corresponding to the application scenario comprises: common semantic slot information and special semantic slot information. 3. The method according to claim 1, further comprising: when the input information is not matched with the semantic template, matching non target equipment information in the input information with the semantic slot information, and processing successfully matched semantic slot information to obtain candidate semantic slot information; when the input information contains target equipment information, matching the target equipment information with the equipment information; when the target equipment information is successfully matched with the equipment information, selecting target semantic slot information from the candidate semantic slot information according to preset semantic slot information corresponding to the target equipment information; and converting the input information to the formative data according to the target equipment information and the target semantic slot information. 4. The method according to claim 3, further comprising: when the target equipment information is not matched with the equipment information, feeding back semantic analysis failure information. 5. The method according to claim 3, further-comprising: when the input information does not include the target equipment information, detecting whether candidate equipment information corresponding to the candidate semantic slot information is unique; when the candidate equipment information is unique, converting the input information to the formative data according to the candidate equipment information and the candidate semantic slot information. 6. The method according to claim 5, further comprising: when the candidate equipment information is not unique, calculating score data corresponding to each candidate equipment information according to preset weights corresponding to the candidate semantic slot information; acquiring the target equipment information and the corresponding target semantic slot information from the candidate equipment information and the candidate semantic slot information according to the score data; converting the input information to the formative data according to the target equipment information and the target semantic slot information. 7. A semantic analysis apparatus based on artificial intelligence, comprising: a processor; and a memory, configured to store one or more software modules executable by the processor, wherein the one or more software modules comprise: a matching module, configured to match input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; a converting module, configured to convert the input information to formative data according to the preset semantic template, when the input information to be processed is successfully matched with the semantic template; a generating module, configured to normalize the formative data and generate a semantic analysis result corresponding to the input information. 8. The apparatus according to claim 7, wherein, the semantic slot information corresponding to the application scenario comprises: common semantic slot information and special semantic slot information. 9. The apparatus according to claim 7, wherein the one or more software modules further comprise an obtaining module and a selecting module, wherein the matching module is further configured to match non target equipment information in the input information with the semantic slot information, when the input information is not matched with the semantic template; the obtaining module is configured to process successfully matched semantic slot information to obtain candidate semantic slot information; the matching module is further configured to match target equipment information with the equipment information, when the input information contains the target equipment information; the selecting module is configured to select target semantic slot information from the candidate semantic slot information according to preset semantic slot information corresponding to the target equipment information, when the target equipment information is matched with the equipment information successfully; the converting module is configured to convert the input information to the formative data according to the target equipment information and the target semantic slot information. 10. The apparatus according to claim 9, wherein the one or more software modules further comprise: a feedback module, configured to feed back semantic analysis failure information when the target equipment information is not matched with the equipment information. 11. The apparatus according to claim 9, wherein the one or more software modules further comprise a detecting module, wherein the detecting module is configured to detect whether candidate equipment information corresponding to the candidate semantic slot information is unique when the input information does not include the target equipment information; the converting module is configured to convert the input information to the formative data according to the candidate equipment information and the candidate semantic slot information, when the candidate equipment information is unique. 12. The apparatus according to claim 11, wherein the one or more software modules further comprise a calculating module and a determining module, wherein the calculating module is configured to calculate score data corresponding to each candidate equipment information according to preset weights corresponding to the candidate semantic slot information, when the candidate equipment information is not unique; the determining module is configured to acquire the target equipment information and the corresponding target semantic slot information from the candidate equipment information and the candidate semantic slot information according to the score data; the converting module is configured to convert the input information to formative data according to the target equipment information and the target semantic slot information. 13. A non-transitory computer-readable storage medium, configured to store instructions that, when executed by a processor of a terminal, cause the terminal to perform a semantic analysis method based on artificial intelligence, the method comprising: matching input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to the semantic template; normalizing the formative data and generating a semantic analysis result corresponding to the input information.