Data processing method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a data processing method and device, equipment, a storage medium and a program product. The method comprises the steps that in response to determination of target data, the target data is stored to a target area, the data stored to the target area has a corresponding identifier, and the target data is determined based on received user input; based on the target identifier corresponding to the target data, executing data analysis for the target data; and presenting a reply to the user input based on a result of the data analysis. Therefore, after the data are queried, the data are not directly exposed to a subsequent data analysis link, but are provided to the subsequent data analysis link in an identification form. On one hand, the amount of data communication between different links can be reduced, and on the other hand, the data is isolated between the different links, so that the security can be improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and in particular, to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for data processing. Background Art

[0002] With the development of information technology, various terminal devices can provide various services to people in work and life. Applications that provide services can be deployed in terminal devices. Terminal devices present corresponding content and interact with users through the user interface of the application to meet various needs of users. In some cases, various data need to be processed in order to meet the needs of users. Therefore, how to improve the security of data processing is a concern. Summary of the invention

[0003] In a first aspect of the present disclosure, a data processing method is provided. The method includes: in response to determining target data, storing the target data in a target area, the data stored in the target area having a corresponding identifier, the target data being determined based on received user input; performing data analysis on the target data based on the target identifier corresponding to the target data; and presenting a reply to the user input based on a result of the data analysis.

[0004] In a second aspect of the present disclosure, there is provided a device for data processing, comprising: a data storage module, configured to store the target data in a target area in response to determining target data, the data stored in the target area having a corresponding identifier, the target data being determined based on received user input; an analysis execution module, configured to perform data analysis on the target data based on a target identifier corresponding to the target data; and a reply presentation module, configured to present a reply to the user input based on a result of the data analysis.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the electronic device executes the method of the first aspect.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the medium, and when the computer program is executed by a processor, the method of the first aspect is implemented.

[0007] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0008] It should be understood that the content described in this section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented;

[0011] Figure 2 A flowchart showing a process of information processing according to some embodiments of the present disclosure;

[0012] Figure 3 A schematic diagram showing an example of information processing according to some embodiments of the present disclosure;

[0013] Figure 4 A schematic structural block diagram of a device for data processing according to some embodiments of the present disclosure is shown;

[0014] Figure 5 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0017] Herein, unless explicitly stated, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.

[0018] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0019] It is understandable that before using the technical solutions disclosed in the various embodiments of the present disclosure, the types, scopes of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and their authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

[0020] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to software or hardware such as an electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.

[0021] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message is sent to the relevant user, for example, in the form of a pop-up window, in which the prompt message may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.

[0022] It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not limit the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure. The activation of the digital assistant related functions, the acquired data, the processing and storage of the data, etc. in the embodiment of the present disclosure shall obtain the prior authorization of the user and other rights holders associated with the user, and shall comply with the relevant laws and regulations and the agreement rules between the rights holders.

[0023] As used herein, the term "model" can learn the association between the corresponding input and output from the training data, so that after the training is completed, the corresponding output can be generated for a given input. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multi-layer processing units. A neural network model is an example of a model based on deep learning. In this article, "model" may also be referred to as "machine learning model", "learning model", "machine learning network" or "learning network", and these terms are used interchangeably in this article.

[0024] Figure 1A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. Environment 100 involves an application management platform 110, which can support the creation of applications and / or the running of applications. In some embodiments, the portion of the application management platform 110 that supports the creation of applications can also be referred to as an application creation portion. In some embodiments, the portion of the application management platform 110 that supports the running of applications can also be referred to as an application running portion.

[0025] As shown in the figure, the application creation part can provide a user 105 with an environment for creating and publishing applications. User 105 can be referred to as an application creation user or creator. In some embodiments, the application creation part can be a low-code platform that provides a collection of tools for application creation. The application creation part can support visual development of various types of applications, so that developers can skip the process of manual coding and speed up the development cycle and cost of applications. The application creation part can support any appropriate platform for users to develop one or more types of applications, for example, it can include a platform based on application platform as a service (aPaaS). Such a platform can support users to efficiently develop applications and realize operations such as application creation and application function adjustment.

[0026] The application creation part can be deployed locally on the terminal device of user 105, and / or can be supported by a server-side device. For example, the terminal device of user 105 can run a client of the application creation part, which can support the interaction between the user and the application creation part provided by the server. In the case where the application creation part runs locally on the user's terminal device, the user 105 can directly interact with the local application creation part using the terminal device. In the case where the application creation part runs on the server-side device, the server-side device can, based on the communication connection with the terminal device, implement service provision for the client running in the terminal device. The application creation part can present a corresponding page 130 to the user 105 based on the operation of the user 105, so as to output to the user 105 and / or receive from the user 105 information related to application creation.

[0027] In some embodiments, the application creation part can be associated with a corresponding database, which stores data or information required for the application creation process supported by the application creation part. For example, the database can store the code and description information corresponding to each functional module that constitutes the application. The application creation part can also perform operations such as calling, adding, deleting, and updating the functional modules in the database. The database can also store operations that can be performed on different functional blocks. Exemplarily, in a scenario where an application is to be created, the application creation part can call the corresponding functional blocks from the database to build the application.

[0028] In an embodiment of the present disclosure, a user 105 can create a target application 120 as needed on the application creation part and publish the target application 120. The target application 120 can be published to any appropriate application running part, as long as the application running part can support the operation of the target application 120. After publishing, the target application 120 can be used to be operated by one or more terminal users 145. The terminal user 145 can operate the target application 120 through an associated terminal device 146, and then interact with the application management platform 110. The terminal user 145 can be referred to as the terminal user of the target application 120. In some embodiments, the target application 120 may include or be implemented as a digital assistant 122.

[0029] The digital assistant 122 can be configured to have the ability of intelligent dialogue. In the example shown in the figure, the digital assistant 122 can be integrated into the target application 120, as part of the target application 120 to assist in the task processing within the target application 120. In other examples, the digital assistant 122 can be configured as an independently running application, such as a web application or other types of applications. In such an example, the digital assistant 122 and the target application 120 can be regarded as the same application. The digital assistant 122 is provided to assist users in various task processing requirements in different applications and scenarios. In the interaction process with the digital assistant 122, the user inputs an interactive message, and the digital assistant 122 provides a reply message in response to the user input. Typically, the digital assistant 122 can support users to input questions in a natural language, and perform tasks and provide replies based on the understanding of natural language input and logical reasoning ability.

[0030] In some embodiments, digital assistant 122 can interact with end user 145 as a contact of end user 145. For example, digital assistant 122 can be implemented in an instant messaging (IM) application. Digital assistant 122 can interact with end user 145 in a single chat session with end user 145. In some embodiments, digital assistant 122 can interact with multiple users in a group chat session including multiple users.

[0031] For each terminal user 145, the client of the application running part can present an interactive window 142 of the target application 120 or the digital assistant 122 in the client interface, such as a conversation window with the digital assistant 122. The terminal user 145 can enter a conversation message in the conversation window, and the target application 120 can determine the reply message of the digital assistant 122 based on the created configuration information and present it to the user in the interactive window 142. In some embodiments, depending on the configuration of the target application 120, the interactive message with the target application 120 may include a message in a multimodal form, such as a text message (e.g., a natural language text), a voice message, an image message, a video message, and the like.

[0032] Similar to the application creation part, the application running part can be deployed locally on the terminal device of each terminal user 145, and / or can be supported by the server device. For example, the terminal device of the terminal user 145 can run a client with the application running part, and the client can support the interaction between the user and the application running part provided by the server. In the case where the application running part runs locally on the user's terminal device, the terminal user 145 can directly use the terminal device to interact with the local application running part. In the case where the application running part runs on the server device, the server device can realize the service supply to the client running in the terminal device based on the communication connection with the terminal device. The application running part can present the corresponding application page to the terminal user 145 based on the operation of the terminal user 145, so as to output and / or receive information related to the use of the application to the terminal user 145.

[0033] In some embodiments, the implementation of at least part of the functions of the target application 120 and / or the implementation of at least part of the functions of the digital assistant 122 in the target application 120 can be implemented based on the model. During the creation or operation of the target application 120, one or more models 155, such as the capabilities of the model 155, can be called. In the target application 120, the digital assistant 122 can use the model 155 to understand the user input and provide a reply to the user based on the output of the model 155.

[0034] During the creation process, the application management platform 110 needs to use the model 155 to determine whether the operation result of the target application 120 meets expectations when testing the target application 120. During the operation process, in response to different operation requests of the user of the target application 120, the application operation part may need to use the model 155 to determine the response result to the user.

[0035] Although shown as being independent of the application management platform 110, one or more models 155 may run on the application management platform 110, or other remote servers. In some embodiments, the model 155 may be a machine learning model, a deep learning model, a learning model, a neural network, etc. In some embodiments, the model may be based on a language model (LM). The language model can have question-answering capabilities by learning from a large amount of corpus. The model 155 may also be based on other appropriate models.

[0036] The application management platform 110 can run on appropriate electronic devices. The electronic devices here can be any type of device with computing capabilities, including terminal devices or server devices. The terminal device can be any type of mobile terminal, fixed terminal or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication systems (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio broadcast receivers, e-book devices, gaming devices, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. The server device can include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like. In some embodiments, the management platform 110 can be implemented based on cloud services.

[0037] It should be understood that the structure and functionality of the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. For example, although a single user interacting with the application creation portion and a single user interacting with the application execution portion are shown, in reality multiple users can access the application management platform 110 to each create a digital assistant, and each digital assistant can be used to interact with multiple users.

[0038] As mentioned above, users can initiate data processing requests within the application to process data within the application. The processing of data may involve multiple links. Traditionally, applications can transfer the data to be processed between multiple links. Doing so, on the one hand, increases the amount of data communication between different links, and on the other hand, it also brings data security risks. For example, in some links, data needs to be provided to a machine learning model to achieve data processing with the help of a machine learning model, such as performing data analysis on the data with the help of a machine learning model. However, providing data to a machine learning model may affect the security of the data and increase the risk of data leakage.

[0039] In view of this, in an embodiment of the present disclosure, an improved data processing scheme is provided. In this scheme, in response to determining target data, the target data is stored in a target area, the data stored in the target area has a corresponding identifier, and the target data is determined based on the received user input. Based on the target identifier corresponding to the target data, data analysis is performed on the target data. Based on the result of the data analysis, a reply to the user input is presented.

[0040] In this way, after the data to be processed is queried, the data is not directly exposed to the subsequent data analysis link, but provided to the subsequent data analysis link in the form of an identifier. On the one hand, the amount of data communication between different links can be reduced, and on the other hand, the data is isolated between different links, which can improve security.

[0041] Some example embodiments of the present disclosure will be described in detail below with reference to examples of the accompanying drawings.

[0042] The task management process described in the embodiments of the present disclosure may be implemented on an application management platform, a terminal device on which the application management platform is installed, and / or a server corresponding to the application management platform. In the following examples, for the sake of discussion, the description is from the perspective of the application management platform, for example Figure 1 The application management platform 110 shown. The user interface presented by the application management platform 110 can be presented via the terminal device of the user 145, and the application management platform 110 can receive user input via the terminal device of the user 145. In some embodiments of the present disclosure, the user 145 is the terminal user of the target application 120. It should be understood that the user interface presented by the application management platform 110 can also be presented via the terminal device of the user 105, and the application management platform 110 can also receive user input via the terminal device of the user 105. In some embodiments of the present disclosure, the user 105 is the creator, manager or maintainer of the target application 120.

[0043] Figure 2 Schematic diagram of an architecture 200 for data processing according to some embodiments of the present disclosure is shown. The architecture 200 may be implemented at the application management platform 110. In some embodiments, the operations performed by the application management platform 110 may be specifically performed by the application running part of the application management platform 110. Figure 1 The architecture 200 is described with reference to the environment 100 of FIG. The architecture 200 involves a first functional block 210 , a target area 220 , and a second functional block 230 .

[0044] In some embodiments, the application management platform 110 may receive user input 201, and the user input 201 may include any appropriate type of input such as text, image, video, audio, data table, etc. The application management platform 110 may receive the user input 201 in any appropriate manner. For example, the application management platform 110 may receive the user input 201 from other electronic devices based on the communication connection between other electronic devices. For example, the application management platform 110 may collect the user input 201 via one or more of its own display screen, physical controls, microphone, camera, etc. The user input 201 may be presented in an interactive window (e.g., interactive window 142), for example. In some embodiments, when the user input 201 is a non-text type of user input, the application management platform 110 may process the user input to determine the text corresponding to the user input.

[0045] The application management platform 110 may determine the target data 214 based on the received user input 201. Regarding the specific manner of determining the target data 214, in some embodiments, the first function block 210 in the application management platform 110 may be configured to generate a data query instruction 212. The application management platform 110 may generate the data query instruction 212 by calling a predetermined first function block 210. Exemplarily, the first function block 210 may be a structured query language (SQL) function block, and the data query instruction 212 generated by the first function block 210 may be SQL.

[0046] The data query instruction 212 can tell how to query the target data 214, which can help better understand the data. In some embodiments, the first functional block 210 can generate the data query instruction 212 with the help of a machine learning model. The machine learning model can be a model deployed locally on the application management platform 110, or a model deployed at other electronic devices.

[0047] The application management platform 110 can retrieve target data 214 from one or more data sources by executing data query instructions 212. The data source can be any appropriate data source, including but not limited to structured objects and unstructured objects (e.g., web pages, documents), etc. A structured object can be any appropriate type of object that can store or represent information in a structured manner, which can include but not limited to data tables, databases, etc. The target data 214 can, for example, be the original data retrieved from the data source. Taking the data source including a personnel information data table as an example, the application management platform 110 can, for example, retrieve the target data 214 from the data table by executing the data query instruction 212. The target data 214 can, for example, be several rows of data in the data table (i.e., the personnel information of several people).

[0048] The application management platform 110 may store the target data 214 in the target area 220 in response to determining the target data 214. The target area 220 may include, for example, a key-value data storage system. The data stored in the target area 220 may have a corresponding identifier. This identifier may be used to indicate the storage location of the corresponding data in the target area. In some embodiments, different data in the target area 220 correspond to different identifiers (that is, each data corresponds to a unique identifier). The target identifier 222 corresponding to the target data 214 may include, for example, a key in a key-value data storage system.

[0049] The second function block 230 in the application management platform 110 may generate a data analysis instruction 232 for data analysis for the target data based at least on the user input 201. The data analysis instruction 232 may be an instruction in any appropriate programming language, for example, it may be a Python instruction.

[0050] In some embodiments, the second function block 230 may also obtain auxiliary information 224 related to the use of the target data 214, and generate a data analysis instruction 232 based on the user input 201 and the auxiliary information 224. The auxiliary information 224 may be determined by the application management platform 110 based on the target data 214 in response to determining the target data 214. In some embodiments, the auxiliary information 224 may be stored in the target area 220. The second function block 230 may read the auxiliary information 224 corresponding to the target identifier 222 from the target identifier 222 based on the target identifier 222. For example, the data in the target area 220 may exist in the form of "identity-data-auxiliary information", and the second function block 230 may retrieve the target area 220 based on the target identifier 222 to obtain the auxiliary information 224 corresponding to the target identifier 222.

[0051] In some embodiments, the auxiliary information 224 may not be stored in the target area 220, and the auxiliary information 224 may be associated with the target data 214 in any appropriate manner. Alternatively or additionally, in some embodiments, the auxiliary information 224 may also be directly provided to the second functional block 230. The second functional block 230 may subsequently generate a data analysis instruction 232 directly using the pre-acquired auxiliary information 224 in response to the need to perform data analysis. In some embodiments, the auxiliary information may be stored in a separate information library, and the data in the information library may exist in the form of "identification-auxiliary information". The second functional block 230 may directly retrieve the auxiliary information 224 from the information library based on the target identification 222. For another example, the second functional block 230 may also directly search for auxiliary information 224 associated with the target data 214 from one or more data sources based on the target identification 222. The present disclosure does not limit the specific method of obtaining the auxiliary information 224.

[0052] The auxiliary information 224 may include meta information of the target data 214, which may also be referred to as metadata. The meta information of the target data 214 may, for example, include fields included in the target data 214. This may help the second functional block 230 generate more accurate data analysis instructions 232. Alternatively or additionally, the auxiliary information 224 may include source information of the target data 214. The source information of the target data 214 may, for example, indicate the source of the target data 214 and may include a description of the target data and / or the source of the target data. Alternatively or additionally, the auxiliary information 224 may include corresponding description information of one or more fields included in the target data 214. For example, if the field of the target data includes a field specific to a certain field, the auxiliary information 224 may include a description of the field, which may help better understand the field. It is understood that the auxiliary information 224 may include one or more items of a plurality of information, and the auxiliary information 224 may also include any other appropriate content.

[0053] In some embodiments, the second functional block 230 can generate data analysis instructions 232 using a trained machine learning model. This machine learning model can be a model deployed locally on the application management platform 110, or a model deployed at other electronic devices, and this machine learning model can be based on any appropriate model structure. The second functional block 230 can, for example, provide auxiliary information 224 and user input 201 to the machine learning model to generate data analysis instructions 232 using the machine learning model. Thus, specific data does not need to be provided to the model, and the model can understand the information necessary for data analysis based on the auxiliary information, without affecting the accuracy of the data analysis.

[0054] The data analysis instruction 232 may, for example, instruct to summarize, cluster, calculate, and the like the target data 214. After the application management platform 110 obtains the data analysis instruction 232, it may execute the data analysis instruction 232 on the target data 214 based on the target identifier 222. Specifically, the second function block 230 in the application management platform 110 may read the target data 214 from the target area 220 based on the target identifier 222. The second function block 230 may load the read target data 214 into the execution environment of the data analysis instruction 232, and execute the data analysis instruction 232 on the target data 214 loaded into the execution environment. The execution environment may be a secure isolation environment, such as a trusted execution environment. For example, if the data analysis instruction 232 is a Python instruction, the corresponding execution environment is the Python execution environment. The second function block 230 may load the target data 214 into Python, and execute the Python instruction in Python to process the target data 214.

[0055] In response to the data analysis instruction 232 being executed, the second function block 230 can obtain the result of the data analysis on the target data 214 (also referred to as the analysis result) 234. For example, the second function block 230 can obtain the execution result of the data analysis instruction 232 from the execution environment, which is also the analysis result of the target data 214. The analysis result 234 can include, for example, at least one chart type of content and / or at least one form type of content. For example, the analysis result of the target data 214 can be a bar chart, which can more clearly present the differences between different data in the target data 214.

[0056] The application management platform 110 may present a reply to the user input 201 based on the analysis result 234. Exemplarily, the application management platform 110 may provide the analysis result 234 to the terminal device 146. The terminal device 146 may present an interactive interface (e.g., an interactive window 142 between the user 145 and the digital assistant 122), and present the analysis result 234 in the interactive interface.

[0057] In summary, according to the embodiments of the present disclosure, after the data is queried, the data is not directly exposed to the subsequent data analysis link, but is provided to the subsequent data analysis link in the form of an identifier. On the one hand, the amount of data communication between different links can be reduced, and on the other hand, the data is isolated between different links, which can improve security.

[0058] Figure 3 1 is a flowchart of a method 300 for data processing according to some embodiments of the present disclosure. The method 300 may be implemented in the application management platform 110, for example, by the application running part of the application management platform 110. Figure 1 The method 300 is described with reference to the environment 100 of FIG.

[0059] In block 310 , in response to determining the target data, the application management platform 110 stores the target data in a target area, the data stored in the target area having a corresponding identifier, the target data being determined based on the received user input.

[0060] In block 320 , the application management platform 110 performs data analysis on the target data based on the target identifier corresponding to the target data.

[0061] At block 330 , the application management platform 110 presents a response to the user input based on the results of the data analysis.

[0062] In some embodiments, performing data analysis on target data includes: obtaining auxiliary information related to the use of the target data; generating data analysis instructions for the target data using a machine learning model based on user input and the auxiliary information; and executing the data analysis instructions on the target data based on a target identifier.

[0063] In some embodiments, the method 300 further includes: in response to determining the target data, storing auxiliary information in the target area, and acquiring the auxiliary information includes: reading the auxiliary information from the target area based on the target identifier.

[0064] In some embodiments, the auxiliary information includes at least one of the following: meta information of the target data, source information of the target data, or corresponding description information of one or more fields included in the target data.

[0065] In some embodiments, performing data analysis on target data includes: obtaining data analysis instructions for the target data; based on a target identifier, loading the target data into an execution environment of the data analysis instructions; and executing the data analysis instructions on the target data loaded into the execution environment.

[0066] In some embodiments, the target data is determined by: calling a predetermined first function block to generate a data query instruction; retrieving the target data from one or more data sources by executing the data query instruction, and wherein the data analysis instruction for data analysis is implemented by calling a predetermined second function block.

[0067] In some embodiments, the result of the data analysis includes at least one of the following types of content: at least one chart type, at least one form type.

[0068] In some embodiments, the target area includes a key-value data storage system, and the target identification includes a key in the key-value data storage system.

[0069] The embodiments of the present disclosure also provide corresponding devices for implementing the above methods or processes. Figure 4 A schematic structural block diagram of an apparatus 400 for data processing according to some embodiments of the present disclosure is shown. The apparatus 400 may be implemented in or included in the application management platform 110, for example. Each module / component in the apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0070] As shown in the figure, the device 400 includes a data storage module 410, which is configured to store the target data in a target area in response to determining the target data, the data stored in the target area has a corresponding identifier, and the target data is determined based on the received user input. The device 400 also includes an analysis execution module 420, which is configured to perform data analysis on the target data based on the target identifier corresponding to the target data. The device 400 also includes a reply presentation module 430, which is configured to present a reply to the user input based on the result of the data analysis.

[0071] In some embodiments, the analysis execution module 420 is further configured to: obtain auxiliary information related to the use of the target data; generate data analysis instructions for the target data based on user input and auxiliary information using a machine learning model; and execute data analysis instructions on the target data based on a target identifier.

[0072] In some embodiments, the apparatus 400 further includes an auxiliary information storage module configured to store auxiliary information in the target area in response to determining the target data, and the analysis execution module 420 is further configured to read the auxiliary information from the target area based on the target identifier.

[0073] In some embodiments, the auxiliary information includes at least one of the following: meta information of the target data, source information of the target data, or corresponding description information of one or more fields included in the target data.

[0074] In some embodiments, the analysis execution module 420 is further configured to: obtain data analysis instructions for target data; load the target data into an execution environment of the data analysis instructions based on a target identifier; and execute the data analysis instructions on the target data loaded into the execution environment.

[0075] In some embodiments, the target data is determined by: calling a predetermined first function block to generate a data query instruction; retrieving the target data from one or more data sources by executing the data query instruction, and wherein the data analysis instruction for data analysis is implemented by calling a predetermined second function block.

[0076] In some embodiments, the result of the data analysis includes at least one of the following types of content: at least one chart type, at least one form type.

[0077] In some embodiments, the target area includes a key-value data storage system, and the target identification includes a key in the key-value data storage system.

[0078] The units and / or modules included in the device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 400 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0079] Figure 5 1 shows a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 5 The electronic device 500 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown may include or be implemented as Figure 1 application management platform 110, or Figure 4 Device 400.

[0080] like Figure 5 As shown, the electronic device 500 is in the form of a general electronic device. The components of the electronic device 500 may include, but are not limited to, one or more processors or processing units 510, a memory 520, a storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing unit 510 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 520. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0081] The electronic device 500 typically includes a plurality of computer storage media. Such media may be any accessible media that is accessible to the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 may be a volatile memory (e.g., registers, caches, random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 may be a removable or non-removable medium, and may include a machine-readable medium, such as a flash drive, a disk, or any other medium, which may be capable of being used to store information and / or data and may be accessed within the electronic device 500.

[0082] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5 As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. The memory 520 may include a computer program product 525 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0083] The communication unit 540 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0084] The input device 550 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 560 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 may also communicate with one or more external devices (not shown) through the communication unit 540 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 500, or communicate with any device that allows the electronic device 500 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0085] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0086] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0087] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0088] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0089] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0090] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A data processing method, comprising: In response to determining target data, storing the target data in a target area, the data stored in the target area having a corresponding identifier, the target data being determined based on the received user input; Based on the target identifier corresponding to the target data, performing data analysis on the target data; as well as Based on the results of the data analysis, a response to the user input is presented.

2. The method according to claim 1, wherein performing data analysis on the target data comprises: Acquiring auxiliary information related to the use of the target data; Based on the user input and the auxiliary information, using a machine learning model, generating a data analysis instruction for the target data; as well as Based on the target identifier, the data analysis instruction is executed on the target data.

3. The method according to claim 2, further comprising: In response to determining the target data, storing the auxiliary information in the target area, and acquiring the auxiliary information comprises: Based on the target identifier, the auxiliary information is read from the target area.

4. The method according to claim 2, wherein the auxiliary information comprises at least one of the following: Meta information of the target data, The source information of the target data, or Corresponding description information of one or more fields included in the target data.

5. The method according to claim 1, wherein performing data analysis on the target data comprises: Obtaining a data analysis instruction for the target data; Based on the target identifier, loading the target data into an execution environment of the data analysis instruction; as well as The data analysis instruction is executed on the target data loaded into the execution environment.

6. The method according to claim 1, wherein the target data is determined by: Calling a predetermined first function block to generate a data query instruction; By executing the data query instruction, the target data is retrieved from one or more data sources, and The data analysis instruction for the data analysis is implemented by calling a predetermined second function block.

7. The method according to claim 1, wherein the result of the data analysis includes at least one of the following types of content: At least one chart type, At least one form type. 8 . The method of claim 1 , wherein the target area comprises a key-value data storage system, and the target identifier comprises a key in the key-value data storage system.

9. A device for data processing, comprising: A data storage module configured to store the target data in a target area in response to determining the target data, the data stored in the target area having a corresponding identifier, the target data being determined based on the received user input; an analysis execution module, configured to execute data analysis on the target data based on a target identifier corresponding to the target data; as well as The answer presentation module is configured to present an answer to the user input based on the result of the data analysis.

10. An electronic device comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 8 when executed by the at least one processing unit.

11. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, wherein the computer program implements the method according to any one of claims 1 to 8 when executed by a processor.

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