A digital human generation method and system, where the method includes: defining a digital human model, where the digital human model includes multiple dimensions of user profile models; acquiring multiple dimensions of data of a specific user that is from multiple data sources; and processing, based on the multiple dimensions of user profile models included in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user, where the multiple dimensions of user profiles of the specific user form a digital human corresponding to the specific user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A digital human generation method, comprising: defining a digital human model, wherein the digital human model comprises multiple dimensions of user profile models; acquiring multiple dimensions of data of a specific user that is from multiple data sources; and processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user, wherein the multiple dimensions of user profiles of the specific user form a digital human corresponding to the specific user. 2. The method according to claim 1, wherein acquiring multiple dimensions of data of the specific user that is from multiple data sources comprises: acquiring multiple dimensions of data of multiple users that is from multiple data sources; and determining, among the multiple dimensions of data of the multiple users that is from the multiple data sources and according to a belonging relationship between data and a user, the multiple dimensions of data belonging to the specific user that is from the multiple data sources. 3. The method according to claim 2, wherein acquiring multiple dimensions of data of multiple users that is from multiple data sources comprises acquiring the multiple dimensions of data of the multiple users that is from the multiple data sources by using at least one device of a terminal, a communications network element, and a data collection agent. 4. The method according to claim 1, wherein the multiple dimensions of user profiles comprise at least two of the following: a user profile in an image dimension, a user profile in a health dimension, a user profile in a behavioral habit dimension, a user profile in a social pattern dimension, a user profile in a consumption habit dimension, and a user profile in an interest and hobby dimension. 5. The method according to claim 1, wherein the method further comprises: performing data cleaning on the multiple dimensions of data of the specific user that is from the multiple data sources; extracting time and a keyword that are corresponding to content of cleaned data; and annotating the cleaned data by using the time and the keyword as annotation information, wherein processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user comprises processing annotated data based on the multiple dimensions of user profile models in the digital human model, to generate the multiple dimensions of user profiles corresponding to the specific user. 6. The method according to claim 1, wherein the method further comprises: performing data cleaning on the multiple dimensions of data of the specific user that is from the multiple data sources; extracting time, a location, and a keyword that are corresponding to content of cleaned data; and annotating the cleaned data by using the time, the location, and the keyword as annotation information, wherein the processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user comprises processing annotated data based on the multiple dimensions of user profile models in the digital human model, to generate the multiple dimensions of user profiles corresponding to the specific user. 7. The method according to claim 5, wherein the method further comprises storing the annotated data. 8. The method according to claim 1, wherein after processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user, the method further comprises providing, according to a query condition input by a client, the client with a user profile of a digital human corresponding to the query condition. 9. The method according to claim 1, wherein before defining the digital human model, the method further comprises creating, according to a requirement of the client, user profile models that are used to generate user profiles and corresponding to the requirement. 10. The method according to claim 1, wherein the processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources, to generate multiple dimensions of user profiles corresponding to the specific user comprises processing, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources by using at least one of the following algorithms, to generate the multiple dimensions of user profiles corresponding to the specific user: a classification algorithm, a clustering algorithm, a regression algorithm, a reinforcement learning algorithm, a transfer learning algorithm, a deep learning algorithm, and an active learning algorithm. 11. A digital human generation system, comprising: a defining module configured to define a digital human model, wherein the digital human model comprises multiple dimensions of user profile models; an acquiring module configured to acquire multiple dimensions of data of a specific user that is from multiple data sources; and a generating module configured to process, based on the multiple dimensions of user profile models in the digital human model defined by the defining module, the multiple dimensions of data of the specific user that is from the multiple data sources and acquired by the acquiring module, to generate multiple dimensions of user profiles corresponding to the specific user, wherein the multiple dimensions of user profiles of the specific user form a digital human corresponding to the specific user. 12. The system according to claim 11, wherein the acquiring module comprises: an acquiring unit configured to acquire multiple dimensions of data of multiple users that is from multiple data sources; and a determining unit configured to determine, among the multiple dimensions of data of the multiple users that is from the multiple data sources and acquired by the acquiring unit and according to a belonging relationship between data and a user, the multiple dimensions of data belonging to the specific user that is from the multiple data sources. 13. The system according to claim 12, wherein the acquiring unit is further configured to acquire the multiple dimensions of data of the multiple users that is from the multiple data sources by using at least one device of a terminal, a communications network element, and a data collection agent. 14. The system according to claim 11, wherein the multiple dimensions of user profiles comprise at least two of the following: a user profile in an image dimension, a user profile in a health dimension, a user profile in a behavioral habit dimension, a user profile in a social pattern dimension, a user profile in a consumption habit dimension, and a user profile in an interest and hobby dimension. 15. The system according to claim 11, wherein the system further comprises: a cleaning module configured to perform data cleaning on the multiple dimensions of data of the specific user that is from the multiple data sources and acquired by the acquiring module; an extracting module configured to extract time and a keyword that are corresponding to content of data obtained by cleaning by the cleaning module; and an annotating module configured to annotate, by using the time and the keyword as annotation information, the data obtained by cleaning by the cleaning module, wherein the generating module is further configured to process annotated data based on the multiple dimensions of user profile models in the digital human model, to generate the multiple dimensions of user profiles corresponding to the specific user. 16. The system according to claim 11, wherein the system further comprises: a cleaning module configured to perform data cleaning on the multiple dimensions of data of the specific user that is from the multiple data sources and acquired by the acquiring module; an extracting module configured to extract time, a location, and a keyword that are corresponding to content of data obtained by cleaning by the cleaning module; and an annotating module configured to annotate, by using the time, the location and the keyword as annotation information, the data obtained by cleaning by the cleaning module, wherein the generating module is specifically configured to process annotated data based on the multiple dimensions of user profile models in the digital human model, to generate the multiple dimensions of user profiles corresponding to the specific user. 17. The system according to claim 15, wherein the system further comprises: a storing module configured to store the data annotated by the annotating module. 18. The system according to claim 11, wherein the system further comprises: a query module configured to provide, according to a query condition input by a client, the client with a user profile of a digital human corresponding to the query condition. 19. The system according to claim 11, wherein the system further comprises: a creating module configured to create, according to a requirement of the client, user profile models that are used to generate user profiles and corresponding to the requirement. 20. The system according to claim 11, wherein the generating module is further configured to process, based on the multiple dimensions of user profile models in the digital human model, the multiple dimensions of data of the specific user that is from the multiple data sources by using at least one of the following algorithms, to generate the multiple dimensions of user profiles corresponding to the specific user: a classification algorithm, a clustering algorithm, a regression algorithm, a reinforcement learning algorithm, a transfer learning algorithm, a deep learning algorithm, and an active learning algorithm. 21. A digital human generation system, comprising: a receiving module configured to receive multiple dimensions of data of multiple users that is from multiple data sources; a data preprocessing module configured to determine a user to which the data received by the receiving module belongs, and perform data cleaning and annotation on the data; a data storing module configured to store data preprocessed by the data preprocessing module; a user identity management module configured to manage accounts of the user in the multiple data sources, to determine a belonging relationship between data of multiple users that is stored in the storing module and a user; a user profile model configuration library configured to define user profile models for generating user profiles; an algorithm library configured to store and update multiple algorithms used to generate user profiles; a digital human generating and maintaining module configured to process, based on the user profile models in the user profile model configuration library and according to an algorithm in the algorithm library, the data stored in the storing module, to generate corresponding user profiles, wherein the user profiles form a digital human corresponding to the user; and a digital human application programming interface (API) configured to interact with a client, so that the client queries a user profile of a digital human that is generated by the digital human generating and maintaining module or accepts a requirement raised by the client to create a user profile model.