WO2013067724A1 - 一种云端用户映射系统和方法 - Google Patents
一种云端用户映射系统和方法 Download PDFInfo
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- WO2013067724A1 WO2013067724A1 PCT/CN2011/083199 CN2011083199W WO2013067724A1 WO 2013067724 A1 WO2013067724 A1 WO 2013067724A1 CN 2011083199 W CN2011083199 W CN 2011083199W WO 2013067724 A1 WO2013067724 A1 WO 2013067724A1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
Definitions
- the present application relates to the field of cloud technologies, and in particular, to a cloud user mapping system and method. Background technique
- the traditional software self-learning function is performed locally, and needs to occupy the storage and computing resources of the user terminal.
- the user replaces the device, and the original data resources and self-learning results cannot be reused in the new device.
- the self-learning function is isolated in each software, the user history data in the software is only used for self-learning of the software, and the self-learning effect is general.
- the self-learning of each software application is independent of each other. These self-learning data cannot be shared between applications, and cannot be synchronized with the network. The previous self-learning data cannot be used after replacing the device or software.
- the technical problem to be solved by the present application is to provide a cloud user mapping system and method, which realizes that a user accesses a cloud service system through an application of a different terminal, and a unique user mapping object corresponding to the user exists in the cloud (Reflect ion Object, abbreviated R0)
- the system manages user data in a unified manner and improves the quality of cloud services through self-learning.
- a cloud user mapping system including: a user mapping object module, including an authentication module, a personalized information storage module, and an information capture module; the authentication module is configured to use the user identity information Authenticate user permissions and target users
- the request information of the application is associated with the job module;
- the storage module is configured to store the identity information and the personalized information of the user;
- the information capture module is configured to obtain the user personalized information processed by the user behavior analysis module;
- At least one job module configured to perform personalized processing on the request information based on the configuration information of the user, and send the processing result to the target application;
- a self-learning module configured to perform self-learning according to the request information of the user and the processing result information of the corresponding working module to optimize configuration information of the user in the working module;
- the user behavior analysis module is configured to analyze the user's personalized information according to the user's various request information for different applications and the processing result information of the corresponding requested job module.
- the self-learning module is further configured to: optimize the configuration information of the user in each working module according to the personalized information stored in the user mapping object module.
- the method further includes:
- the user history data storage module is configured to store various request information of the user and processing result information of the corresponding request.
- the authentication module includes:
- a first authentication module configured to perform authentication according to identity information registered by the user in the cloud
- a second authentication module configured to perform identity authentication according to the third-party passport of the user, where the third-party passport is Identification of an authorized third-party platform.
- the process of authenticating according to the user's third party passport includes:
- the user's authority is confirmed by the second authentication module according to the registration information of the user on the third-party platform and the passport of the third-party platform.
- the application also discloses a cloud user mapping method, including:
- the user transmits the request information to the cloud through an application
- the user rights are authenticated based on the registration information in the user mapping object in the cloud.
- the job module corresponding to the current request processes the request based on the configuration information of the current user, and sends the processed result information to the target.
- the configuration information of the job module is self-learned and optimized based on the user personalized information stored in the user mapping object;
- the target application uses the processing result of the personalized job module and the request information of the user to return the feedback data to the user after completing the corresponding operation;
- the personalized information of the user is obtained according to the various request information of the user and the processing result information of the corresponding request, and stored in the user mapping object.
- the configuration information can also be optimized by the following steps:
- the process of authenticating the user's rights based on the registration information in the user mapping object in the cloud includes:
- the first authentication step is performed according to the identity information registered by the user in the cloud; and/or the second authentication step is performed according to the third party passport of the user, wherein the third party passport is an authorized third party. Proof of identity of the platform.
- the method further includes:
- the user history data storing step stores the request information of the user and the processing result information of the corresponding request in the user history data storage module. Compared with the prior art, the present application has the following advantages:
- the self-learning result and the user behavior analysis result of the user using various application softwares are saved to the R0 of the user corresponding to the user and unified into each of the cloud services.
- the operation module completely breaks the independent self-learning situation of each module, and realizes unified user data management and self-learning function in the cloud. Even if the user replaces the terminal device, as long as the user accesses the cloud service system through the registered account, the corresponding information can be found in the cloud. User R0, thus continuing the high quality cloud service experience.
- FIG. 1 is a schematic structural diagram of a cloud user mapping system according to the present application.
- FIG. 2 is a schematic flowchart of a cloud user mapping method according to the present application. detailed description
- FIG. 1 a schematic structural diagram of a cloud user mapping system of the present application is shown, including: a user mapping object module, at least one job module, a self-learning module, and a user behavior analysis module.
- the user mapping object module includes an authentication module, a personalized information storage module, and an information capture module.
- the authentication module is configured to authenticate user rights according to user identity information, and request information and operations of the user for the target application.
- the module performs the correspondence;
- the storage module is configured to store user personalized information; and
- the information capture module is configured to obtain user personalized information processed by the user behavior analysis module.
- the terminal When a user uses a target application through a terminal, the terminal sends the identity information of the user to the cloud, and sends the request information for the target application to the cloud, and the user mapping object (RO) module of the cloud.
- the authentication module in the user mapping object (RO) module authenticates the user rights according to the user identity information, and corresponds the request information of the user to the target application to the job module capable of processing the request. .
- the authentication module includes:
- the first authentication module is configured to perform authentication according to the identity information registered by the user in the cloud.
- the cloud can directly pass the user in the cloud.
- the registration information confirms the user's RO, and the next step is performed for the user through the user's RO.
- a second authentication module configured to perform identity verification according to the third party passport of the user, where the third party passport is an identity certificate of the authorized third party platform; wherein, according to the user by the second authentication module
- the registration information of the third-party platform and the passport of the third-party platform confirm the user's authority. For example, if a third-party platform cooperates with the cloud system of the present application, and the various applications provided by the third-party platform adopt the function of a certain operation module in the cloud of the application, the cloud allocates one for the third-party platform. Passport, that is, the third-party platform can use the permission of the cloud function of this application.
- the cloud When a user registers with a third-party platform and uses the application provided by the third-party platform, the cloud will be based on the user's registration information on the third-party platform and the third-party platform. Passport confirmation user The RO, then through the RO for the user to proceed to the next step.
- the information capture module of the user mapping object (RO) module acquires the user behavior analysis module.
- the user personalized information is processed, and the storage module of the user mapping object (RO) module stores the personalized information of the user, and also stores the identity information of the user.
- the personalized information includes the user's points of interest and habits, and the identity information includes the user's registration information and the like.
- the at least one job module is configured to personalize the request information based on the configuration information of the user, and send the processing result to the target application.
- the cloud there may be multiple job modules, such as a voice recognition job module, a handwriting recognition job module, and the like.
- the corresponding job module personalizes the request based on the configuration information of the corresponding user in the job module, for example, according to the user's interest. Points are prioritized for parts related to points of interest.
- the configuration information is information optimized by the self-learning module according to various data of the user.
- the voice recognition module is based on the voice recognition module.
- the user's configuration information personalizes the voice recorded by the user. For example, if the user speaks a Chongqing dialect and the point of interest is in the economic field, the voice recognition module preferentially identifies in the vocabulary corresponding to the economic field in the Chongqing dialect library. This can greatly improve the accuracy of speech recognition for the user.
- the handwriting recognition function of the software is a handwriting recognition module from the cloud.
- the handwriting recognition module is based on The user's configuration information personalizes the strokes input by the user. For example, if the user is accustomed to the pen and the point of interest is in the sports field, the handwriting improves the accuracy of the handwriting recognition of the user.
- the self-learning module is configured to perform self-learning according to the request information of the user and the processing result information of the corresponding working module to optimize configuration information of the user in the working module.
- the self-learning optimization is performed on the handwriting recognition function of the handwriting recognition module (i.e., the configuration file corresponding to the user).
- Self-learning can be performed by methods such as cluster analysis, feature extraction, and SVM classifier.
- the trajectory of the handwriting recognition input will be transmitted and stored in a two-dimensional array.
- the character corresponding to the handwritten trajectory will be found, and the candidate result of the character will be returned to the user, and the user selects the final
- the handwriting recognition process is completed, and the stored user request data (trajectory of handwriting input) and the result data (result of handwriting recognition) are used to build a model, and the handwriting recognition engine is trained to be more adapted to the user's personalized handwriting input. Need (for example, the user writes more sloppy, and there is a partial inverted pen behavior.
- the selection of the recognition result may not be the target word input by the user, and the user needs to manually select the candidate characters, after a certain amount of The user data is accumulated, the training model is established according to the handwritten trajectory data input by the user and the target character result data selected by the user, and the handwriting recognition engine is personalizedly trained and optimized.
- the user uses the handwriting input again, even if the handwriting is originally scribbled, the user Input The probability that characters appear in one selection will be greatly improved.
- the user does not have to select candidate words again, and can directly input them continuously.
- the self-learning training model is continuously improved, and the handwriting recognition engine will be handwritten for the user.
- Input habits are continuously optimized to improve the efficiency of handwriting input and improve the quality of cloud services.
- the results of self-learning are also stored in the self-learning module. Users improve the user information in the RO model to optimize cloud services in other jobs. quality.
- the user When the user next uses a handwriting recognition application, it can be identified based on the optimized handwriting recognition module corresponding to the user, so that it can accurately recognize the user's strokes.
- the self-learning module can continuously optimize the job module according to the request information of the user and the processing result information of the corresponding job module, so that the processing result of the user in the job module is further improved. Precise and fast.
- the self-learning module is further configured to: according to the personalized information stored in the user mapping object module, the user in each working module Configuration information is optimized,
- each operation module can continue to update the personalized information in the user RO, and then perform self-learning to optimize the configuration information of the user. For example, when the user RO obtains the points of interest and habits obtained by analyzing the various data of the user by the user behavior analysis module, the configuration information of the user in each operation module is optimized, so that each module is within a more precise range. Work on it.
- the user behavior analysis module is configured to analyze personalized information of the user according to various request information of the user for different applications and processing result information of the corresponding requested job module.
- a user may use different applications, and the application modules used by each application in the cloud may also be different, but the information reflects the user's relevant personalized characteristics, through various request information for different applications for users and
- the analysis of the processing result information corresponding to the requested job module can obtain personalized information of the user, such as the user's interest points and behavior habits. For example, users often write football-related vocabulary such as football, UEFA graduates League, and Premier League in various handwriting recognition applications, and after the recognition, the user also selects these sports-related vocabulary, and then processes the data and the handwriting recognition module input by the user.
- the analysis of the data can be used to get users interested in sports. If further users are interested in the game, they will be credited with personalized information.
- the information in the user RO captures the personalized information and stores it in the personalized information storage module.
- the user behavior analysis module can add the user's new request data and the result data processed by the cloud corresponding job module to the data source for analysis, and continuously optimize and optimize the user's personalized information.
- the application further includes a user history data storage module, configured to store various request information of the user and processing result information of the corresponding request.
- the information can be stored in the user history data storage module, and supplied to the self-learning module and the user behavior analysis module for processing.
- FIG. 2 a schematic flowchart of a cloud user mapping method according to the present application is shown.
- Step 210 The user transmits the request information to the cloud through an application.
- Step 220 Authenticate the user's authority based on the registration information in the user mapping object in the cloud. After the authentication is passed, the job module corresponding to the current request processes the request based on the configuration information of the current user, and sends the processed result information to the target application; the configuration information of the job module is based on the user mapping object.
- the stored user personalized information is self-learned and optimized;
- the process of authenticating the user based on the registration information in the user mapping object in the cloud includes:
- the first authentication step is performed according to the identity information registered by the user in the cloud; and/or the second authentication step is performed according to the third party passport of the user, wherein the third party passport is an authorized third party. Proof of identity of the platform.
- the configuration information may also be optimized by the following steps:
- Step 230 The target application uses the processing result of the personalized job module and the request information of the user to return the feedback data to the user after completing the corresponding operation;
- Step 240 analyzes the personalized information of the user according to the various request information of the user and the processing result information of the corresponding request, and stores the personalized information to the user mapping object.
- the user history data storing step stores the request information of the user and the processing result information of the corresponding request in the user history data storage module.
- the user uses a handwriting input software through a smart phone, and the handwriting function of the software comes from the cloud.
- the handwriting input software of the smart phone transmits the character track input by the user handwriting to the cloud in a two-dimensional array.
- the user's identity information such as the id registered in the cloud or a third-party passport, will be transmitted to the cloud;
- the authentication module in the user mapping object (RO) module of the cloud performs authentication and authentication according to the identity information of the user, such as the registration id or a third-party passport.
- the user requests the handwritten software.
- the information is corresponding to the handwriting recognition job module of the cloud, and then the handwriting recognition job module inputs the user based on the configuration information of the current user (that is, the optimized configuration information according to the personalized information of the user and the previous handwritten application related data).
- the character track is identified, and the recognition result is returned to the user. This result can be directly returned to the cloud through the cloud.
- the client can also be returned to the client through a third-party platform.
- the user behavior analysis module of the cloud analyzes the various request information of the user and the processing result information of the corresponding request to obtain the personalized information of the user, and stores the information to the user mapping object (RO). .
- the various request information of the user and the processing result information of the corresponding request may be stored in the user history data storage module.
- the self-learning module optimizes the configuration information corresponding to the user of the handwriting recognition module according to the processing result data of the handwriting recognition module and the corresponding request information corresponding to the user, and the self-learning module further according to the user mapping object (RO)
- the user personalized information stored in the self-learning optimizes the configuration information of the handwriting recognition module corresponding to the user, and the self-learning module automatically optimizes the other according to the user personalized information stored in the user mapping object (RO).
- the job module corresponds to the user's configuration information, so that when the user uses an application in the future, the processing of the corresponding application according to the corresponding module can be more accurate and rapid.
- the role of user R0 is to provide users with a unique mapping in the cloud. Users can replace the device or software, and still use the optimized handwriting recognition engine to experience the same cloud service.
- the user's habits and Points of interest (such as the user's habit of writing a pen, like to write sports-related words) continue to improve to the user R0, the next time the user uses the software with voice recognition, the cloud will find R0 that has been perfected, with information such as user interest points.
- the accuracy of speech recognition will be greatly improved; R0 will continue to be improved during use, and the end result is that everyone has a unique R0 in the cloud, and all cloud services are personalized for the user. Customized, cloud service experience will be greatly improved.
- a user uses a voice recognition software through a mobile terminal, and the voice recognition function of the software comes from the cloud, and the application is provided by a third-party platform.
- the voice recognition software of the mobile terminal transmits the voice read by the user to the cloud in the form of data, and also transmits the identity information of the user, such as the id registered on the third-party platform and the passport of the third-party platform to the cloud. ;
- the authentication module in the cloud user mapping object (RO) module performs authentication and authentication according to the identity information of the user, such as the id registered on the third-party platform and the passport of the third-party platform. Passing the right, the user's request information for the voice recognition software is corresponding to the cloud voice recognition module, and then the voice recognition module is based on the current user's configuration information (ie, according to the user's personalized information and the previous voice application related The optimized information of the data is used to identify the voice read by the user, and then return the recognition result to the user. The result can be directly returned to the client through the cloud or returned to the client through the third-party platform.
- the identity information of the user such as the id registered on the third-party platform and the passport of the third-party platform.
- the user's request information for the voice recognition software is corresponding to the cloud voice recognition module, and then the voice recognition module is based on the current user's configuration information (ie, according to the user's personalized information and the previous voice application related
- the user behavior analysis module of the cloud analyzes the various request information of the user and the processing result information of the corresponding request to obtain the personalized information of the user, and stores the information to the user mapping object (RO). ).
- the various request information of the user and the processing result information of the corresponding request may be stored in the user history data storage module.
- the self-learning module optimizes the configuration information corresponding to the user of the voice recognition module according to the processing result data of the voice recognition module corresponding to the user and the corresponding request information, and the self-learning module further according to the user mapping object (RO)
- the user personalized information stored in the self-learning optimizes the configuration information of the voice recognition module corresponding to the user, and the self-learning module also self-learns and optimizes according to the user personalized information stored in the user mapping object (RO).
- the job module corresponds to the user's configuration information, so that when the user uses an application in the future, the processing of the corresponding application according to the corresponding module can be more accurate and rapid.
- the application provides a computer medium comprising: computer executable instructions having the method steps of performing any of the foregoing.
- the computer medium can also include both a storage medium and a transmission medium, including any mechanism for storing or transmitting information in a form readable by a computer (e.g., a computer).
- computer media includes read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash storage media, electrical, optical, acoustic, or other forms of propagated signals (eg, carrier, infrared Signals, digital signals, etc.).
- the cloud user mapping method of the present application can be performed on the computer medium, including: the user transmitting the request information to the cloud through an application;
- the user is authenticated based on the registration information in the user mapping object (RO) in the cloud.
- the job module corresponding to the current request processes the request based on the configuration information of the current user, and the processed result information is processed.
- Sending to the target application; the configuration information of the job module is self-learned and optimized based on the user personalized information stored in the user mapping object (RO);
- the target application uses the processing result of the personalized job module and the request information of the user to return the feedback data to the user after completing the corresponding operation;
- the personalized information of the user is obtained according to the various request information of the user and the processing result information of the corresponding request, and stored in the user mapping object (RO).
- configuration information may also be optimized by the following steps:
- the process of authenticating the user's rights based on the registration information in the user mapping object (RO) in the cloud includes:
- the first authentication step is performed according to the identity information registered by the user in the cloud; and/or the second authentication step is performed according to the third party passport of the user, wherein the third party passport is an authorized third party. Proof of identity of the platform.
- the user history data storing step stores the request information of the user and the processing result information of the corresponding request in the user history data storage module.
- This application can be used in a variety of general purpose or special purpose computing system environments or configurations.
- personal computer server computer, handheld or portable device, tablet device, multiprocessor system, mainframe computer, distributed computing environment including any of the above systems or devices, etc.
- the application can be described in the general context of computer-executable instructions executed by a computer, such as a program module.
- program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular types of abstract data.
- the present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices that are connected through a communication network.
- program modules can be located in both local and remote computer storage media including storage devices.
Abstract
本申请提供了一种云端用户映射系统和方法，涉及云技术领域。所述的系统包括：用户映射对象模块，包括鉴权模块、个性化信息存储模块和信息抓取模块；至少一个作业模块；自学习模块；用户行为分析模块。本申请通过在云端设置一个对应于云端的某一个用户映射对象即RO将用户使用各种应用软件后自学习结果和用户行为分析结果保存至云端的对应于该用户的RO中并统一至云服务中的各个作业模块，彻底打破了各个模块独立自学习的局面，在云端实现统一的用户数据管理和自学习功能，即使用户更换终端设备，只要用户通过注册账号访问云服务系统，就能在云端找到对应的用户RO，从而继续高质量的云服务体验。
Description
一种云端用户映射系统和方法
技术领域
本申请涉及云技术领域， 特别是涉及一种云端用户映射系统和方法。 背景技术
随着云技术的发展， 具备规模经济性、 虚拟化能力强、 高可靠性和价格 低廉等优势的云技术越来越受到重视。 同时， 现在许多的软件都具有自学习 的功能， 利用使用过程中的数据进行自学习以优化本软件的功能， 比如语音 识别软件， 通过语音识别引擎将语音直接转换为文字， 同时利用这些信息优 化语音识别引擎的效率和准确率。
现有技术中， 传统软件自学习功能是在本地进行的， 需要占用用户终 端的存储和运算资源， 用户更换了设备， 原有的数据资源和自学习成果在 新设备无法复用。 由于自学习功能是孤立存在于各个软件， 软件中的用户 历史数据只用来对该软件的自学习使用， 自学习效果一般。 当用户使用多 个软件应用后， 各个软件应用的自学习相互独立， 各个应用之间无法共享 这些自学习的数据， 并且无法与网络同步， 在更换设备或者软件后无法使 用之前的自学习数据。
发明内容
本申请所要解决的技术问题是提供一种云端用户映射系统和方法， 实现 用户通过不同终端的应用访问云服务系统，在云端都存在该用户相对应的唯 一用户映射对象（Ref lect ion Object , 简称 R0 ) 系统， 对用户数据进行统 一的管理， 并通过自学习， 提升云服务的质量。 为了解决上述问题， 本申请公开了一种云端用户映射系统， 包括： 用户映射对象模块 , 包括鉴权模块、 个性化信息存储模块和信息抓取模 块； 所述鉴权模块用于根据用户身份信息鉴定用户权限， 并将用户针对目标
应用的请求信息与作业模块进行对应； 所述存储模块用于存储用户的身份信 息和个性化信息； 所述信息抓取模块用于获取用户行为分析模块处理得到的 用户个性化信息；
至少一个作业模块，用于基于该用户的配置信息对所述请求信息进行个 性化处理， 并将处理结果发送给目标应用；
自学习模块，用于依据所述用户的请求信息和相应作业模块的处理结果 信息进行自学习优化该作业模块中该用户的配置信息；
用户行为分析模块，用于依据所述用户的针对不同应用的各种请求信息 和对应请求的作业模块的处理结果信息进行分析得到用户的个性化信息。
优选的， 当具有两个或者两个以上的作业模块时， 自学习模块还用于： 依据用户映射对象模块中存储的个性化信息对各作业模块中该用户的 配置信息进行优化。
优选的，还包括：
用户历史数据存储模块，用于存储所述用户的各种请求信息和对应请求 的处理结果信息。
优选的， 所述的鉴权模块包括：
第一鉴权模块， 用于依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权模块， 用于依据用户的第三方护照进行身份鉴定， 其中所述的 第三方护照为被授权的第三方平台的身份证明。
优选的， 依据用户的第三方护照进行身份鉴定的过程包括：
通过第二鉴权模块根据用户在第三方平台的注册信息与第三方平台的 护照确认用户的权限。
相应的， 本申请还公开了一种云端用户映射方法， 包括：
用户通过一应用将请求信息传送至云端；
在云端基于用户映射对象中的注册信息鉴定该用户的权限， 鉴权通过 后， 与当前请求对应的作业模块基于当前用户的配置信息对该请求进行处 理， 并将处理后的结果信息发送至目标应用； 所述作业模块的配置信息基于 该用户映射对象中所存储的用户个性化信息自学习优化得到；
所述目标应用利用所述个性化作业模块的处理结果和用户的请求信息， 完成相应操作后返回给用户反馈数据；
依据所述用户的各种请求信息和对应请求的处理结果信息进行分析得 到用户的个性化信息， 并存储至用户映射对象。
优选的， 所述配置信息还可以通过以下步骤进行优化：
依据所述用户的请求信息和相应作业模块的处理结果信息进行自学习 优化该作业模块中该用户的配置信息；
优选的，所述在云端基于用户映射对象中的注册信息鉴定该用户的权限 过程包括：
第一鉴权步骤， 依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权步骤， 依据用户的第三方护照进行身份鉴定， 其中所述的第三 方护照为被授权的第三方平台的身份证明。
优选的， 还包括：
用户历史数据存储步骤，将所述用户的各请求信息和对应请求的处理结 果信息存入用户历史数据存储模块中。 与现有技术相比， 本申请具有以下优点：
本申请通过在云端设置一个对应于某一个用户的 R0 , 将用户使用各种 应用软件后自学习结果和用户行为分析结果保存至云端的对应于该用户的 R0中并统一至云服务中的各个作业模块， 彻底打破了各个模块独立自学习 的局面， 在云端实现统一的用户数据管理和自学习功能， 即使用户更换终 端设备， 只要用户通过注册账号访问云服务系统， 就能在云端找到对应的 用户 R0 , 从而继续高质量的云服务体验。
附图说明
图 1是本申请一种云端用户映射系统的结构示意图；
图 2是本申请一种云端用户映射方法的流程示意图。
具体实施方式
为使本申请的上述目的、 特征和优点能够更加明显易懂， 下面结合附图 和具体实施方式对本申请作进一步详细的说明。
参照图 1 , 示出了本申请一种云端用户映射系统的结构示意图， 包括： 用户映射对象模块、 至少一个作业模块、 自学习模块和用户行为分析模块。
所述用户映射对象模块包括鉴权模块、个性化信息存储模块和信息抓取 模块； 其中， 所述鉴权模块用于根据用户身份信息鉴定用户权限， 并将用户 针对目标应用的请求信息与作业模块进行对应； 所述存储模块用于存储用户 个性化信息； 所述信息抓取模块用于获取用户行为分析模块处理得到的用户 个性化信息。
当某个用户通过一终端使用某个目标应用时，该终端会将该用户得身份 信息发送到云端， 也会将针对该目标应用的请求信息发送到云端， 云端的用 户映射对象 ( RO )模块在收到这些请求后， 通过所述的用户映射对象 ( RO ) 模块中的鉴权模块根据用户身份信息鉴定用户权限， 并将用户针对目标应用 的请求信息与能够处理该请求的作业模块进行对应。
其中， 所述的鉴权模块包括：
第一鉴权模块， 用于依据用户在云端注册的身份信息进行鉴定； 当用户 直接在云端进行注册后， 用户使用采用云端某个作业模块功能的目标应用 时， 云端可以直接通过该用户在云端的注册信息确认该用户的 RO, 并通过 该用户的 RO为用户进行下一步的工作。
和 /或， 第二鉴权模块， 用于依据用户的第三方护照进行身份鉴定， 其 中所述的第三方护照为被授权的第三方平台的身份证明； 其中， 通过第二鉴 权模块根据用户在第三方平台的注册信息与第三方平台的护照确认用户的 权限。 比如， 如果一个第三方平台与本申请的云端系统进行了合作， 该第三 方平台提供的各种应用采用了本申请云端中的某种作业模块的功能， 那么云 端会为该第三方平台分配一个护照， 即该第三方平台能够使用本申请云端功 能的权限证明， 当某个用户通过第三方平台注册并使用第三方平台提供的应 用， 云端会根据用户在第三方平台的注册信息和第三方平台的护照确认用户
的 RO, 然后通过该 RO为用户进行下一步工作。
其中， 当云端的用户行为分析模块对于每次用户使用一个目标应用后的 数据和其他数据进行分析得到用户个性化信息后， 用户映射对象（RO )模 块的信息抓取模块会获取用户行为分析模块处理得到的用户个性化信息， 用 户映射对象 ( RO )模块的存储模块会存储用户这些个性化信息， 同时还会 存储用户的身份信息。 所述的个性化信息包括用户的兴趣点和习惯， 所述的 身份信息包括用户的注册信息等。
所述至少一个作业模块，用于基于该用户的配置信息对所述请求信息进 行个性化处理， 并将处理结果发送给目标应用。
在云端中， 可以存在多个作业模块， 比如语音识别作业模块， 手写识别 作业模块等。 当用户 RO将用户针对目标应用的请求信息与作业模块进行对 应后，相应的作业模块会基于该作业模块中相应该用户的配置信息对所述请 求进行个性化处理， 比如才艮据用户的兴趣点优先处理与兴趣点相关的部分。 所述的配置信息是通过自学习模块根据该用户的各种数据进行优化过的信 息。
比如当用户使用某一种语音识别游戏，其中游戏的语言识别功能是来自 云端的语音识别模块， 当该用户的 RO确认其权限通过并与云端的语音识别 模块进行对应后，语音识别模块基于该用户的配置信息对用户录入的语音进 行个性化处理， 比如如果用户说的是重庆方言， 并且兴趣点在经济领域， 则 语音识别模块优先在重庆方言库中， 对应经济领域的词汇中进行识别， 这样 可大大提高对该用户语音识别的准确率。
又比如当用户使用某一种手写识别软件，其中软件的手写识别功能是来 自云端的手写识别模块， 当该用户的 RO确认其权限通过并与云端的手写识 别模块进行对应后， 手写识别模块基于该用户的配置信息对用户输入的笔画 进行个性化处理， 比如如果用户习惯连笔， 并且兴趣点在体育领域， 则手写 提高对该用户手写识别的准确率。
当用户的作用模块处理完毕后将处理结果发送给目标用户。
所述自学习模块，用于依据所述用户的请求信息和相应作业模块的处理 结果信息进行自学习优化该作业模块中该用户的配置信息。
比如对于用手写音识别软件的请求信息和对应手写识别模块的处理结 果信息， 进行自学习优化对手写识别模块应于该用户的手写识别功能（即对 应于该用户的配置文件）。 其中自学习可以通过聚类分析、 特征提取、 SVM 分类器等方法进行。
例如， 手写识别输入的轨迹， 会以二维数组的方式进行传送和存储， 在 相应的识别字典中， 会找到与手写轨迹相对应的字符， 将字符的候选结果返 回用户， 由用户选择最终的结果， 手写识别过程即完成， 利用存储的用户请 求数据（手写输入的轨迹）和结果数据（手写识别的结果）建立模型， 对手 写识别引擎进行训练， 使其更加适应于用户个性化手写输入的需要 (例如， 用户写字较为潦草， 且有部分倒插笔的行为， 用户首次输入时， 识别结果的 一选可能不是用户所输入的目标字， 用户需要手动选择候选中的字符， 经过 一定数量的用户数据积累，根据用户输入的手写轨迹数据和用户选择的目标 字符结果数据建立训练模型， 对手写识别引擎进行个性化训练与优化， 用户 再次使用手写输入时， 即便还是原先潦草的笔迹， 但是用户输入目标字符出 现在一选的概率会大大提升， 此时用户不必再次选择候选字， 直接连续输入 即可， 随着用户历史数据的不断增加， 自学习训练模型的不断完善， 手写识 别引擎会针对用户手写输入习惯不断优化， 从而提高手写输入的效率， 提升 云服务的质量）， 自学习的结果也会存储在自学习模块中， 用户完善 RO模 型中的用户信息， 以便优化在其他作业中的云服务质量。
当用户下一次使用某一个手写识别应用时，可以基于此对应于该用户的 优化后的手写识别模块进行识别， 使其能够准确识别用户的笔画。
当用户每次使用与该作业模块相关的应用， 自学习模块都可根据所述用 户的请求信息和相应作业模块的处理结果信息不断优化该作业模块，使用户 在该作业模块的处理结果更为精确和快速。
其中， 当具有两个或者两个以上的作业模块时， 自学习模块还用于： 依据用户映射对象模块中存储的个性化信息对各作业模块中该用户的
配置信息进行优化，
比如， 云端中有手写识别模块， 语音识别模块和其他作业模块， 各作业 模块可以继续更新用户 RO中的个性化信息， 再次进行自学习对该用户的配 置信息进行优化。 比如当用户 RO中获取了用户行为分析模块通过对用户的 各种数据进行分析后得到的兴趣点和习惯，对各作业模块中该用户的配置信 息进行优化， 使各模块在更精确的范围内进行处理工作。
所述用户行为分析模块， 用于依据所述用户针对不同应用的各种请求信 息和对应请求的作业模块的处理结果信息进行分析得到用户的个性化信息。
一个用户可能会使用不同的应用，各个应用在云端采用的作业模块也可 能各不相同， 但这些信息都反映了用户的相关个性化的特点， 通过对用户的 针对不同应用的各种请求信息和对应请求的作业模块的处理结果信息的分 析可以得到用户的个性化信息， 比如用户的兴趣点和行为习惯。 比如说用户 在各种手写识别应用中经常写入足球、 欧冠联赛、 英超等足球相关的词汇， 并且识别后用户也选择这些与体育相关的词汇， 则通过对用户输入的数据和 手写识别模块处理的数据进行分析可以得到用户对体育很感兴趣， 进一步的 用户对足^ 艮感兴趣， 则将这些兴趣点记入个性化信息。 用户 RO中的信息 抓取模块则会将这些个性化信息获取然后存入个性化信息存储模块。
用户每次使用某个应用， 用户行为分析模块都可将用户新的请求数据和 通过云端相应作业模块处理后的结果数据加入进行分析的数据源， 不断的对 用户的个性化信息进行优化完善。
其中， 本申请还包括用户历史数据存储模块， 用于存储所述用户的各种 请求信息和对应请求的处理结果信息。
用户每次使用某个应用的请求信息和对应请求的处理结果信息都可存 储进入用户历史数据存储模块，供给自学习模块和用户行为分析模块进行处 理。
参照图 2, 其示出了本申请一种云端用户映射方法的流程示意图。
步骤 210, 用户通过一应用将请求信息传送至云端；
步骤 220, 在云端基于用户映射对象中的注册信息鉴定该用户的权限，
鉴权通过后， 与当前请求对应的作业模块基于当前用户的配置信息对该请求 进行处理， 并将处理后的结果信息发送至目标应用； 所述作业模块的配置信 息基于该用户映射对象中所存储的用户个性化信息自学习优化得到；
其中， 所述在云端基于用户映射对象中的注册信息鉴定该用户的权限过 程包括：
第一鉴权步骤， 依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权步骤， 依据用户的第三方护照进行身份鉴定， 其中所述的第三 方护照为被授权的第三方平台的身份证明。
其中， 所述配置信息还可以通过以下步骤进行优化：
依据所述用户的请求信息和相应作业模块的处理结果信息进行自学习 优化该作业模块中该用户的配置信息。
步骤 230, 所述目标应用利用所述个性化作业模块的处理结果和用户的 请求信息， 完成相应操作后返回给用户反馈数据；
步骤 240依据所述用户的各种请求信息和对应请求的处理结果信息进行 分析得到用户的个性化信息， 并存储至用户映射对象。
其中， 还包括：
用户历史数据存储步骤，将所述用户的各请求信息和对应请求的处理结 果信息存入用户历史数据存储模块中。
比如用户通过某款智能手机使用一款手写输入软件， 而该软件的手写功 能来自云端， 该智能手机的该款手写输入软件会将用户手写输入的字符轨迹 以二维数组的方式传至云端， 同时还会将该用户的身份信息比如在云端注册 的 id或第三方护照传至云端；
然后云端的用户映射对象（RO )模块中的鉴权模块会根据该用户的身 份信息比如所述注册 id或第三方护照进行验证鉴权， 当鉴权通过，则将用户 针对该手写软件的请求信息与云端的手写识别作业模块进行对应， 然后由该 手写识别作业模块基于当前用户的配置信息 (即根据该用户的个性化信息和 以前的手写应用相关数据进行优化后的配置信息 )对用户输入的字符轨迹进 行识别， 然后将识别结果返回给用户， 这个结果可以通过云端直接返回给用
户端也可以通过第三方平台返回给用户端。
在完成该次手写应用的处理过程后， 云端的用户行为分析模块会将用户 的各种请求信息和对应请求的处理结果信息进行分析得到用户的个性化信 息， 并存储至用户映射对象（RO )。 其中， 所述的用户的各种请求信息和对 应请求的处理结果信息可以存储在用户历史数据存储模块中。 自学习模块会 根据用户对应进行该次处理的手写识别模块的处理结果数据和相应的请求 信息优化该手写识别模块对应于该用户的配置信息， 自学习模块还会根据该 用户映射对象（RO ) 中所存储的用户个性化信息自学习优化该手写识别模 块的对应于该用户的配置信息， 同时自学习模块还会根据该用户映射对象 ( RO ) 中所存储的用户个性化信息自学习优化其他作业模块对应该用户的 配置信息， 使用户以后使用某个应用时， 根据相应模块对相应应用的请求进 行处理的时候能更精确和迅速。
例如用户习惯倒插笔输入字符和连笔输入字符，通过对请求数据和结果 数据的自学习， 不断优化手写识别引擎， 提升手写识别的准确率和效率。 用 户 R0起到的作用是为用户在云端提供唯一映射， 用户更换设备或软件， 仍 然可以使用优化后的手写识别引擎， 体验同样的云服务； 通过对用户行为的 分析，会将用户的习惯和兴趣点（比如用户习惯连笔，喜欢写体育相关的字） 不断完善至用户 R0 ,用户下次使用具有语音识别功能的软件时，在云端会找 到已经完善过， 具有用户兴趣点等信息的 R0 , 此时， 语音识别的准确性会大 大提高； R0也会在使用过程中不断得到完善，最终的结果是每个人在云端都 有一个且唯一的 R0 ,所有的云服务的是为用户个性化定制的，云服务的体验 将得到大幅度提升。
又比如用户通过某个移动终端使用一款语音识别软件， 而该软件的语音 识别功能来自云端， 并且该应用是由第三方平台提供。 该移动终端的该款语 音识别软件会将用户读入的语音以数据的方式传至云端， 同时还会将该用户 的身份信息比如在第三方平台注册的 id和第三方平台的护照传至云端；
然后云端的用户映射对象（RO )模块中的鉴权模块会根据该用户的身 份信息比如在第三方平台注册的 id和第三方平台的护照进行验证鉴权，当鉴
权通过， 则将用户针对该语音识别软件的请求信息与云端的语音识别模块进 行对应， 然后由该语音识别模块基于当前用户的配置信息 (即根据该用户的 个性化信息和以前的语音应用相关数据进行优化后的配置信息 )对用户读入 的语音进行识别， 然后将识别结果返回给用户， 这个结果可以通过云端直接 返回给用户端也可以通过第三方平台返回给用户端。
在完成该次语音识别应用的处理过程后， 云端的用户行为分析模块会将 用户的各种请求信息和对应请求的处理结果信息进行分析得到用户的个性 化信息， 并存储至用户映射对象（RO )。 其中， 所述的用户的各种请求信息 和对应请求的处理结果信息可以存储在用户历史数据存储模块中。 自学习模 块会根据用户对应进行该次处理的语音识别模块的处理结果数据和相应的 请求信息优化该语音识别模块对应于该用户的配置信息， 自学习模块还会根 据该用户映射对象（RO ) 中所存储的用户个性化信息自学习优化该语音识 别模块的对应于该用户的配置信息， 同时自学习模块还会根据该用户映射对 象（ RO ) 中所存储的用户个性化信息自学习优化其他作业模块对应该用户 的配置信息， 使用户以后使用某个应用时， 根据相应模块对相应应用的请求 进行处理的时候能更精确和迅速。
相应的， 本申请提供了一种计算机介质， 包括： 具有执行前述任一的方 法步骤的计算机可执行指令。 所述计算机介质也可以包括存储型介质和传输 型介质， 包括用于以计算机（例如计算机）可读的形式存储或传送信息的任 何机制。 例如， 计算机介质包括只读存储器（ROM )、 随机存取存储器（ RAM )、 磁盘存储介质、 光存储介质、 闪速存储介质、 电、 光、 声或其他形式的传播 信号 （例如， 载波、 红外信号、 数字信号等）等。
在该计算机介质上可执行本申请一种云端用户映射方法， 包括： 用户通过一应用将请求信息传送至云端；
在云端基于用户映射对象（RO ) 中的注册信息鉴定该用户的权限， 鉴 权通过后， 与当前请求对应的作业模块基于当前用户的配置信息对该请求进 行处理， 并将处理后的结果信息发送至目标应用； 所述作业模块的配置信息 基于该用户映射对象（RO ) 中所存储的用户个性化信息自学习优化得到；
所述目标应用利用所述个性化作业模块的处理结果和用户的请求信息， 完成相应操作后返回给用户反馈数据；
依据所述用户的各种请求信息和对应请求的处理结果信息进行分析得 到用户的个性化信息， 并存储至用户映射对象（RO )。
进一步的， 所述配置信息还可以通过以下步骤进行优化：
依据所述用户的请求信息和相应作业模块的处理结果信息进行自学习 优化该作业模块中该用户的配置信息；
进一步的， 所述在云端基于用户映射对象（RO ) 中的注册信息鉴定该 用户的权限过程包括：
第一鉴权步骤， 依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权步骤， 依据用户的第三方护照进行身份鉴定， 其中所述的第三 方护照为被授权的第三方平台的身份证明。
进一步的， 还包括：
用户历史数据存储步骤，将所述用户的各请求信息和对应请求的处理结 果信息存入用户历史数据存储模块中。
本申请可用于众多通用或专用的计算系统环境或配置中。 例如： 个 人计算机、 服务器计算机、 手持设备或便携式设备、 平板型设备、 多处 理器系统、 大型计算机、 包括以上任何系统或设备的分布式计算环境等 等。
本申请可以在由计算机执行的计算机可执行指令的一般上下文中描 述， 例如程序模块。 一般地， 程序模块包括执行特定任务或实现特定抽 象数据类型的例程、 程序、 对象、 组件、 数据结构等等。 也可以在分布 式计算环境中实践本申请， 在这些分布式计算环境中， 由通过通信网络 而被连接的远程处理设备来执行任务。 在分布式计算环境中， 程序模块 可以位于包括存储设备在内的本地和远程计算机存储介质中。
以上对本申请所提供的一种云端用户映射系统和方法， 进行了详细介 绍， 本文中应用了具体个例对本申请的原理及实施方式进行了阐述， 以上实 施例的说明只是用于帮助理解本申请的方法及其核心思想； 同时， 对于本领
域的一般技术人员， 依据本申请的思想， 在具体实施方式及应用范围上均会 有改变之处， 综上所述， 本说明书内容不应理解为对本申请的限制。
+
Claims
1、 一种云端用户映射系统， 其特征在于， 包括：
用户映射对象模块 , 包括鉴权模块、 个性化信息存储模块和信息抓取模 块； 所述鉴权模块用于根据用户身份信息鉴定用户权限， 并将用户针对目标 应用的请求信息与作业模块进行对应； 所述存储模块用于存储用户的身份信 息和个性化信息； 所述信息抓取模块用于获取用户行为分析模块处理得到的 用户个性化信息；
至少一个作业模块，用于基于该用户的配置信息对所述请求信息进行个 性化处理， 并将处理结果发送给目标应用；
自学习模块，用于依据所述用户的请求信息和相应作业模块的处理结果 信息进行自学习优化该作业模块中该用户的配置信息；
用户行为分析模块，用于依据所述用户的针对不同应用的各种请求信息 和对应请求的作业模块的处理结果信息进行分析得到用户的个性化信息。
2、 如权利要求 1所述的系统， 其特征在于：
当具有两个或者两个以上的作业模块时， 自学习模块还用于：
依据用户映射对象模块中存储的个性化信息对各作业模块中该用户的 配置信息进行优化。
3、 如权利要求 1所述的系统， 其特征在于,还包括：
用户历史数据存储模块，用于存储所述用户的各种请求信息和对应请求 的处理结果信息。
4、 如权利要求 1所述的系统， 其特征在于， 所述的鉴权模块包括： 第一鉴权模块， 用于依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权模块， 用于依据用户的第三方护照进行身份鉴定， 其中所述的 第三方护照为被授权的第三方平台的身份证明。
5、 如权利要求 4所述的系统， 其特征在于， 依据用户的第三方护照进 行身份鉴定的过程包括：
通过第二鉴权模块根据用户在第三方平台的注册信息与第三方平台的 护照确认用户的权限。
6、 一种云端用户映射方法， 其特征在于， 包括： 用户通过一应用将请求信息传送至云端；
在云端基于用户映射对象中的注册信息鉴定该用户的权限， 鉴权通过 后， 与当前请求对应的作业模块基于当前用户的配置信息对该请求进行处 理， 并将处理后的结果信息发送至目标应用； 所述作业模块的配置信息基于 该用户映射对象中所存储的用户个性化信息自学习优化得到；
所述目标应用利用所述个性化作业模块的处理结果和用户的请求信息， 完成相应操作后返回给用户反馈数据；
依据所述用户的各种请求信息和对应请求的处理结果信息进行分析得 到用户的个性化信息， 并存储至用户映射对象。
7、 如权利要求 6所述的方法， 其特征在于， 所述配置信息还可以通过 以下步骤进行优化：
依据所述用户的请求信息和相应作业模块的处理结果信息进行自学习 优化该作业模块中该用户的配置信息；
8、 如权利要求 6所述的方法， 其特征在于， 所述在云端基于用户映射 对象中的注册信息鉴定该用户的权限过程包括：
第一鉴权步骤， 依据用户在云端注册的身份信息进行鉴定； 和 /或， 第二鉴权步骤， 依据用户的第三方护照进行身份鉴定， 其中所述的第三 方护照为被授权的第三方平台的身份证明。
9、 如权利要求 6所述的方法， 其特征在于， 还包括：
用户历史数据存储步骤，将所述用户的各请求信息和对应请求的处理结 果信息存入用户历史数据存储模块中。
10、 一种计算机介质， 包括： 具有执行权利要求 6至 9中任一的方法步
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| US20110138050A1 (en) * | 2009-12-03 | 2011-06-09 | International Business Machines Corporation | Optimizing cloud service delivery within a cloud computing environment |
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