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

Abstract

The embodiment of the invention provides an information processing method and device, equipment, a storage medium and a program product. The method comprises the following steps: receiving user input for indicating a processing task, wherein the processing task is related to personnel information; based on the user input, at least one execution stage of the processing task and at least one function block corresponding to the at least one execution stage are determined, and the function block in the at least one function block is configured to execute a preset type of operation; and respectively calling at least one function block in at least one execution stage to obtain a processing result of the processing task. Therefore, diversified task processing requirements related to personnel information can be met, the diversity, flexibility or processing quality of task processing can be improved, and the development difficulty of applications can be reduced.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to information processing methods, apparatuses, devices, computer-readable storage media, and computer program products. 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 applications to meet various needs of users. In some cases, users may want applications to handle a variety of tasks to meet their diverse task processing needs. Summary of the invention

[0003] In a first aspect of the present disclosure, an information processing method is provided. The method includes: receiving a user input indicating a processing task; based on the user input, determining at least one execution phase of the processing task and at least one function block corresponding to the at least one execution phase, wherein the function block in the at least one function block is configured to perform a predetermined type of operation; and obtaining a processing result of the processing task by respectively calling at least one function block in at least one execution phase.

[0004] In a second aspect of the present disclosure, an information processing device is provided. The device includes: a receiving module configured to receive user input indicating a processing task; a determining module configured to determine, based on the user input, at least one execution phase of the processing task and at least one function block corresponding to the at least one execution phase, wherein the function block in the at least one function block is configured to perform a predetermined type of operation; and an acquiring module configured to acquire a processing result of the processing task by respectively calling at least one function block in at least one execution phase.

[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] In 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 structural block diagram of an apparatus for information processing according to some embodiments of the present disclosure is shown; and

[0013] Figure 4 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. DETAILED DESCRIPTION

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] As described above, 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. The 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, users may want applications to handle a variety of tasks to meet the user's diverse task processing needs.

[0038] Traditionally, in order to meet the diverse task processing needs of users, developers usually need to develop multiple task processing processes. This not only increases the development difficulty for developers, but also increases system resource consumption, slows down response speed, and is not conducive to improving task processing quality. Especially in data query scenarios, there are diverse data query needs, which greatly increases the development difficulty for developers.

[0039] In view of this, an embodiment of the present disclosure provides an improved solution for information processing. In the solution, a user input indicating a processing task is received. Based on the user input, at least one execution phase of the processing task and at least one function block corresponding to the at least one execution phase are determined. The function blocks in the at least one function block are configured to perform a predetermined type of operation. Afterwards, the processing result of the processing task is obtained by calling the at least one function block in the at least one execution phase.

[0040] Thus, the function blocks are pre-packaged for complex processing. In this way, during task execution, the task is broken down into different stages and the corresponding function blocks are called for processing without considering the specific complex processing. In this way, the diverse task processing requirements can be met, the diversity, flexibility or processing quality of task processing can be improved, and the difficulty of application development can be reduced.

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

[0042] The information processing process described in the embodiments of the present disclosure may be implemented in an application management platform, a terminal device installed with the application management platform, and / or a server corresponding to the application management platform. In the following examples, for the purpose 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 1 is a flowchart of a process 200 of information processing according to some embodiments of the present disclosure. The process 200 may be implemented in the application management platform 110, for example, by the application running part of the application management platform 110. Figure 1 To describe Figure 2 Process 200 is shown.

[0044] In block 210 of process 200, the application management platform 110 receives user input indicating a processing task. The processing task may be, for example, a query task, such as querying various types of information within the organization where the user is located, or information in a data source to which the user has permission to access, etc. Exemplarily, the user 145 may input the user input indicating the processing task through the terminal device 146 or an attached device of the terminal device 146. The application management platform 110 may receive the user input indicating the processing task related to the target type information from the terminal device 146 based on the communication connection between the terminal device 146 and the application management platform 110.

[0045] The user input may include input content in various modes such as text, voice, image, video, operation instruction, etc. input by the user. For example, the user 145 may input text indicating a data query task through a conversation window with the digital assistant 122. For another example, the user 145 may input a voice indicating a data processing task through an audio collection device such as a microphone.

[0046] In block 220 of process 200, application management platform 110 determines at least one execution phase of processing task and at least one function block corresponding to at least one execution phase respectively based on user input. Function blocks in the at least one function block are configured to perform operations of a predetermined type. Examples of function blocks may include, but are not limited to, function blocks for performing specific data processing, function blocks for generating data query instructions, function blocks for generating data processing instructions, function blocks for analyzing data, function blocks for calling functions, function blocks for calling interfaces, function blocks for calling models, function blocks for reading and writing data, function blocks for decision making or logical judgment, and the like.

[0047] Alternatively or additionally, for each execution stage, the stage task of the execution stage can be completed by one functional block, or by multiple functional blocks. It is understandable that the specific number of execution stages and the number of functional blocks required for each execution stage mainly depend on the task processing requirements of the processing task.

[0048] Alternatively or additionally, the application management platform 110 may pre-build a plurality of predetermined function blocks, each of which is configured to perform a predetermined type of operation. For example, a plurality of predetermined function blocks may be pre-created by the application creation portion of the application management platform 110. The predetermined type of operation that a predetermined function block can perform may also be referred to as the capability or skill of the predetermined function block. In the application operation phase, the application management platform 110 may determine at least one execution phase of the processing task based on the capabilities or skills of the plurality of predetermined function blocks. The application management platform 110 may also determine at least one function block from the plurality of predetermined function blocks that corresponds to the at least one execution phase.

[0049] In some embodiments, the application management platform 110 may obtain corresponding configuration information of multiple predetermined function blocks. Based on the corresponding configuration information of multiple predetermined function blocks and the prompt information generated by the user input, the prompt information is provided to the machine learning model to obtain the model output of the machine learning model. Afterwards, the application management platform 110 may determine at least one execution stage and at least one function block based on the model output. The configuration information of the predetermined function block includes at least the identification of the predetermined function block and the description information of the predetermined function block, and the description information may be the characteristics of one or more dimensions of the predetermined function block. For example, the description information may include a description of the predetermined type of operation performed by the predetermined function block, a description of the capabilities or skills possessed by the predetermined function block, a description of the input parameters and output parameters of the predetermined function block, and the like. Determining at least one execution stage of the processing task and at least one function block corresponding to the at least one execution stage through the machine learning model is conducive to simplifying the determination process of the execution stage and the function block. This is also conducive to expanding the scope of application of the processing task, so that its execution stage and the required function block are determined for a variety of processing tasks.

[0050] It is understandable that the use of machine learning models to determine the execution stage and functional blocks is only exemplary. In actual applications, the application management platform 110 can also determine the execution stage and functional blocks through other means, and it should not be understood as being limited to the use of machine learning models to determine the execution stage and functional blocks.

[0051] In block 230 of process 200, the application management platform 110 obtains the processing result of the processing task by respectively calling at least one function block in at least one execution stage. Alternatively or additionally, in each execution stage of the at least one execution stage, the application management platform 110 may respectively call at least one function block to perform a predetermined type of operation using at least one call instruction for the at least one function block to obtain at least one execution result of the at least one execution stage. Thereafter, the application management platform 110 may determine the processing result of the processing task based on the at least one execution result.

[0052] In some embodiments, when a processing task has multiple execution stages, the application management platform 110 may execute the multiple execution stages sequentially, or may execute at least some of the multiple execution stages synchronously. Accordingly, the application management platform 110 may call multiple function blocks sequentially to respectively execute respective predetermined types of operations. The application management platform 110 may also call multiple function blocks synchronously to synchronously execute respective predetermined types of operations.

[0053] In some embodiments, the application management platform 110 may generate prompt information (sometimes also referred to herein as first prompt information) based on user input, and provide the generated prompt information to the machine learning model to obtain the model output of the machine learning model. Subsequently, the application management platform 110 may determine the first execution stage of the processing task, the first function block corresponding to the first execution stage, and the first call instruction for calling the first function block based on the model output. In the first execution stage, the application management platform 110 may call the first function block based on the first call instruction. The first function block performs a predetermined type of operation of the first function block to obtain a first execution result of the first execution stage. In the case where the processing task includes only one execution stage, the first execution result may be the processing result of the processing task. In the case where the processing task includes multiple execution stages, the first execution result may not be the processing result of the processing task, but may only be an intermediate result of the processing task.

[0054] In some embodiments, the application management platform 110 may also generate prompt information again (sometimes also referred to as second prompt information in this article) based on the first execution result of the first execution stage and the user input. The generated prompt information is provided to the machine learning model to obtain the model output of the machine learning model. The application management platform 110 may determine the second execution stage of the processing task, the second function block corresponding to the second execution stage, and the second call instruction for calling the second function block based on the model output. In the second execution stage, the application management platform 110 may call the second function block based on the second call instruction to obtain the second execution result of the second execution stage. The second execution result may be the processing result of the processing task, or the second execution result may be a part of the processing result of the processing task. The second execution result may also be another intermediate result of the processing task. The application management platform 110 may also execute one or more execution stages such as the third execution stage, the fourth execution stage, the fifth execution stage, etc. based on the second execution result to obtain the processing result of the processing task.

[0055] In some embodiments, the processing task may be a data query task related to target type information. Here, the target type information may include any appropriate type of information. Exemplarily, the target type information may include information related to a certain type of object, such as information related to networked devices purchased within an organization. However, this is only exemplary and is not intended to be limiting, and the embodiments of the present disclosure are not limited in this regard. On this basis, the at least one execution stage includes at least one of the following stages: a first execution stage, a second execution stage, a third execution stage, a fourth execution stage, or a fifth execution stage. The first execution stage is used to obtain reference information required to generate a data query instruction. The second execution stage is used to list the name of the structured data related to the target type information. The third execution stage is used to list the data structure of the structured data related to the target type information. The fourth execution stage is used to execute the obtained data query instruction related to the target type information to query the data related to the target type information from the structured data related to the target type information. The fifth execution stage is used to display the data related to the target type information. Here, the structured data may include various data with a predetermined data structure, such as a table, a database structure, etc. The data query instruction corresponds to the structured data and is used to query data from the structured data. For example, the structured data may include table data in a structured database, and the data query instruction may include an SQL query instruction written based on the Structured Query Language (SQL).

[0056] Exemplarily, the digital assistant 122 may be configured to perform processing tasks related to the target type information. For example, the digital assistant 122 is configured to perform data query tasks and data analysis tasks related to the target type information using Structured Query Language (SQL). User input may be used to instruct the digital assistant 122 to perform, for example, a query for XXXXX in XX month of XXXX year.

[0057] In response to receiving user input, the application management platform 110 may obtain an assistant introduction, a task description, and configuration information of a plurality of predetermined functional blocks of the digital assistant 122. The assistant introduction may be used to describe the functions of the digital assistant 122. An exemplary assistant introduction is shown below.

[0058] Table 1

[0059]

[0060] The task description may be used to describe data query tasks and data analysis tasks related to target type information. Alternatively or additionally, the task description may describe multiple candidate execution stages that the digital assistant 122 may select during the execution of the data query tasks and data analysis tasks related to target type information. An exemplary task description is shown below.

[0061] Table 2

[0062]

[0063]

[0064] The application management platform 110 can generate prompt words for the machine learning model based on information such as user input, assistant description, task description, and configuration information of multiple predetermined function blocks. The prompt words are provided to the machine learning model to obtain the model output of the machine learning model. The application management platform 110 can determine the first execution phase, the first function block of the first execution phase, and the first call instruction for calling the first function block based on the model output. The application management platform 110 can use the first call instruction to call the first function block (such as a reference information query function block) to obtain the reference information required to write SQL statements. The reference information may include but is not limited to the basic structure of SQL statements, SQL syntax, SQL writing instructions, etc. The reference information can be used as the first execution result of the first execution phase.

[0065] The application management platform 110 can generate prompt information of the machine learning model again based on the reference information to obtain the model output of the machine learning model. Based on the model output, the second execution phase (e.g., table name query phase) of the data query task for the target type information, the second function block of the second execution phase, and the second call instruction for calling the second function block are determined. The application management platform 110 can use the second call instruction to call the second function block (e.g., table name query function block), and the second function block can query the table name of the table related to the target type information in the database as the second execution result.

[0066] The application management platform 110 can also generate prompt information of the machine learning model again based on the table name to obtain the model output of the machine learning model. Based on the model output, the third execution phase (e.g., table structure query phase) of the query task of the target type information, the third function block of the third execution phase, and the third call instruction for calling the third function block are determined. The application management platform 110 can use the third call instruction to call the third function block (e.g., table structure query function block), and the third function block can query the table structure of one or more tables related to the query task of the target type information as the third execution result.

[0067] The application management platform 110 can generate prompt information again based on the table structure of the one or more tables to obtain the model output of the machine learning model. Based on the model output, the fourth execution stage (e.g., data query stage) of the query task for the target type information is determined, the fourth function block of the fourth execution stage, and the fourth call instruction for calling the fourth function block. The fourth function block may include, for example, a data query function block. The fourth call instruction may include information indicating the data query function block, and may also include a data query instruction (e.g., a query instruction written using an SQL statement) for the data query function to execute a data query. The data query function block can use the data query instruction to query data related to the target type information.

[0068] The application management platform 110 can generate prompt information again based on the data related to the target type information to obtain the model output of the machine learning model. Based on the model output, the fifth execution stage (e.g., data display stage) of the query task of the target type information, the fifth function block (e.g., data display function block) of the fifth execution stage and the fifth call instruction for calling the fifth function block are determined. The application management platform 110 can use the fifth call instruction to call the data display function block, and use the data display function block to display the queried data related to the target type information.

[0069] It should be noted that the execution process of the above-mentioned processing tasks is only exemplary and should not be understood as being limited to the execution of the above-mentioned processing tasks, nor should it be understood as being limited to the use of the above-mentioned execution process to process tasks. In practical applications, any appropriate processing task can be processed according to actual needs, and any appropriate execution process can be used to process tasks according to the actual needs of the processing tasks.

[0070] In some embodiments, the application management platform 110 may provide prompt information generated based on corresponding configuration information of at least one functional block to the machine learning model to obtain at least one call instruction corresponding to at least one execution stage. In at least one execution stage, at least one functional block is called based on at least one call instruction to obtain the corresponding execution result of at least one execution stage. Thereafter, the processing result of the processing task is determined based on the corresponding execution result of at least one execution stage.

[0071] Exemplarily, when it is determined that the processing task has a first execution stage, a second execution stage, and a third execution stage, and a first function block corresponding to the first execution stage, a second function block corresponding to the second execution stage, and a third function block corresponding to the third execution stage, the application management platform 110 can generate prompt information of the machine learning model based on the configuration information of the first function block, the configuration information of the second function block, and the configuration information of the third function block. The prompt information is provided to the machine learning model to obtain the model output of the machine learning model. The first call instruction, the second call instruction, and the third call instruction are determined based on the model data. The application management platform 110 can use the first call instruction to call the first function block to obtain the first execution result, use the second call instruction to call the second function block to obtain the second execution result, and use the third call instruction to call the third function block to obtain the third execution result. Here, the second function block can perform a predetermined type of operation based on the first execution result, and the third function block can also perform a predetermined type of operation based on the second execution result. The application management platform 110 can determine the processing result of the processing task based at least on the third execution result.

[0072] In some embodiments, when the application management platform 110 determines that the at least one execution stage includes a first execution stage and a second execution stage, it can generate prompt information for the machine learning model (sometimes also referred to as first prompt information in this article) based on the configuration information of the first function block corresponding to the first execution stage in at least one execution stage and the user input to obtain a first call instruction for the first execution stage. The first function block is called based on the first call instruction to obtain a first execution result of the first execution stage. The application management platform 110 can also generate prompt information for the machine learning model (sometimes also referred to as second prompt information in this article) again based on the configuration information and the first execution result of the second function block corresponding to the second execution stage in at least one execution stage to obtain a second call instruction for the second execution stage. The second function block is called based on the second call instruction to obtain a second execution result of the second execution stage. Thereafter, the processing result of the processing task is determined based on the second execution result.

[0073] It should be understood that the above process of determining the execution phase, function block and call instruction of the processing task is only exemplary. In practical application, any appropriate method can be selected according to actual needs to determine the execution phase, function block and call instruction of the processing task.

[0074] In summary, according to the embodiments of the present disclosure, diverse task processing requirements can be met, the diversity, flexibility or processing quality of task processing can be improved, and the difficulty of application development can be reduced.

[0075] Figure 3A schematic structural block diagram of an information processing device according to some embodiments of the present disclosure is shown. The device 300 may be implemented in or included in the application management platform 110, for example. Each module / component in the device 300 may be implemented by hardware, software, firmware, or any combination thereof.

[0076] As shown in the figure, the apparatus 300 includes a receiving module 310, a determining module 320 and an acquiring module 330. The receiving module 310 is configured to receive a user input indicating a processing task. The determining module 320 is configured to determine at least one execution phase of the processing task and at least one function block corresponding to the at least one execution phase based on the user input, wherein the function block in the at least one function block is configured to perform a predetermined type of operation. The acquiring module 330 is configured to acquire the processing result of the processing task by respectively calling at least one function block in at least one execution phase.

[0077] In some embodiments, the determination module 320 is further configured to: obtain corresponding configuration information of multiple predetermined functional blocks, the configuration information of a predetermined functional block among the multiple predetermined functional blocks at least includes an identifier of the predetermined functional block and description information of the predetermined functional block; and provide prompt information generated based on the corresponding configuration information of the multiple predetermined functional blocks and user input to the machine learning model to determine at least one execution stage and at least one functional block.

[0078] In some embodiments, the acquisition module 330 is further configured to: provide prompt information generated based on the corresponding configuration information of at least one functional block to the machine learning model to obtain at least one calling instruction corresponding to at least one execution stage; in at least one execution stage, call at least one functional block based on at least one calling instruction to obtain the corresponding execution result of at least one execution stage; and determine the processing result of the processing task based on the corresponding execution result of at least one execution stage.

[0079] In some embodiments, the acquisition module 330 is further configured to: generate first prompt information for the machine learning model based on configuration information of a first function block corresponding to a first execution stage in at least one execution stage and user input to obtain a first call instruction for the first execution stage; call the first function block based on the first call instruction to obtain a first execution result of the first execution stage; and determine a processing result of the processing task based on the first execution result.

[0080] In some embodiments, the acquisition module 330 is further configured to: generate second prompt information for the machine learning model based on the configuration information of the second function block corresponding to the second execution stage in at least one execution stage and the first execution result to obtain a second call instruction for the second execution stage; call the second function block based on the second call instruction to obtain a second execution result of the second execution stage; and determine the processing result of the processing task based on the second execution result.

[0081] In some embodiments, the determination module 320 is further configured to provide first prompt information generated based on user input to the machine learning model to determine the first execution stage of the processing task, the first function block corresponding to the first execution stage, and the first call instruction for calling the first function block.

[0082] In some embodiments, the determination module 320 is further configured to: provide second prompt information generated based on the first execution result of the first execution stage and user input to the machine learning model to determine the second execution stage of the processing task, the second function block corresponding to the second execution stage, and the second calling instruction for calling the second function block, the first execution result is obtained by calling the first function block based on the first calling instruction; and the acquisition module 330 is further configured to: call the second function block based on the second calling instruction to obtain the second execution result of the second execution stage; and determine the processing result of the processing task based on the second execution result.

[0083] In some embodiments, the processing task includes a data query task for querying target type information, and at least one execution stage includes at least one of the following stages: a first execution stage for acquiring reference information required to generate a data query instruction, a second execution stage for listing the names of structured data related to the target type information, a third execution stage for listing the data structure of the structured data related to the target type information, a fourth execution stage for executing the obtained data query instruction related to the target type information to query data related to the target type information from the structured data related to the target type information, or a fifth execution stage for displaying data related to the target type information.

[0084] The units and / or modules included in the device 300 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 300 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.

[0085] Figure 4 4 shows a block diagram of an electronic device 400 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 4 The electronic device 400 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Figure 4 The electronic device 400 shown may include or be implemented as Figure 1 application management platform 110, or Figure 3 device 300.

[0086] like Figure 4 As shown, the electronic device 400 is in the form of a general electronic device. The components of the electronic device 400 may include, but are not limited to, one or more processors or processing units 410, a memory 420, a storage device 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. The processing unit 410 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 420. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 400.

[0087] The electronic device 400 typically includes a plurality of computer storage media. Such media may be any accessible media that is accessible to the electronic device 400, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 420 may be a volatile memory (e.g., a register, a cache, a 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 430 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 400.

[0088] The electronic device 400 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 4 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 420 may include a computer program product 425 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0089] The communication unit 440 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 400 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 400 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.

[0090] The input device 450 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 460 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 400 may also communicate with one or more external devices (not shown) through the communication unit 440 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 400, or communicate with any device that allows the electronic device 400 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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. An information processing method, comprising: receiving user input instructing a processing task; Based on the user input, determining at least one execution phase of the processing task and at least one function block respectively corresponding to the at least one execution phase, wherein a function block in the at least one function block is configured to perform a predetermined type of operation; as well as The processing result of the processing task is obtained by respectively calling the at least one function block in the at least one execution stage.

2. The method of claim 1 , wherein determining the at least one execution phase and the at least one functional block comprises: Obtaining corresponding configuration information of a plurality of predetermined function blocks, wherein the configuration information of a predetermined function block among the plurality of predetermined function blocks at least includes an identifier of the predetermined function block and description information of the predetermined function block; as well as Prompt information generated based on the corresponding configuration information of the multiple predetermined functional blocks and the user input is provided to the machine learning model to determine the at least one execution stage and the at least one functional block.

3. The method according to claim 1, wherein obtaining the processing result of the processing task comprises: Providing prompt information generated based on the corresponding configuration information of the at least one functional block to the machine learning model to obtain at least one call instruction corresponding to the at least one execution stage respectively; In the at least one execution phase, calling the at least one function block based on the at least one calling instruction respectively to obtain the corresponding execution result of the at least one execution phase; as well as Based on the corresponding execution result of the at least one execution stage, a processing result of the processing task is determined.

4. The method according to claim 1, wherein obtaining the processing result of the processing task comprises: Based on configuration information of a first function block corresponding to a first execution stage in the at least one execution stage and the user input, generate first prompt information for a machine learning model to obtain a first call instruction for the first execution stage; Calling the first function block based on the first calling instruction to obtain a first execution result of the first execution stage; as well as A processing result of the processing task is determined based on the first execution result.

5. The method according to claim 4, wherein determining the processing result of the processing task based on the first execution result comprises: Based on configuration information of a second function block corresponding to a second execution stage in the at least one execution stage and the first execution result, generating second prompt information for the machine learning model to obtain a second call instruction for the second execution stage; Calling the second function block based on the second calling instruction to obtain a second execution result of the second execution stage; as well as A processing result of the processing task is determined based on the second execution result.

6. The method of claim 1 , wherein determining the at least one execution phase and the at least one functional block comprises: The first prompt information generated based on the user input is provided to the machine learning model to determine the first execution stage of the processing task, the first function block corresponding to the first execution stage, and the first calling instruction for calling the first function block.

7. The method of claim 6, wherein determining the at least one execution phase and the at least one functional block further comprises: providing second prompt information generated based on a first execution result of the first execution stage and the user input to the machine learning model to determine a second execution stage of the processing task, a second function block corresponding to the second execution stage, and a second calling instruction for calling the second function block, wherein the first execution result is obtained by calling the first function block based on the first calling instruction; and Wherein obtaining the processing result of the processing task includes: Calling the second function block based on the second calling instruction to obtain a second execution result of the second execution stage; as well as A processing result of the processing task is determined based on the second execution result.

8. The method according to claim 1, wherein the processing task comprises a data query task for querying target type information, and the at least one execution stage comprises at least one of the following stages: The first execution phase is used to obtain the reference information required to generate data query instructions. The second execution phase is used to list the names of structured data related to the target type information. The third execution phase is used to list the data structure of structured data related to the target type information. The fourth execution stage is used to execute the obtained data query instruction related to the target type information to query the data related to the target type information from the structured data related to the target type information, or The fifth execution stage is used to display the data related to the personnel information.

9. An information processing device, comprising: a receiving module configured to receive a user input indicating a processing task; a determination module configured to determine, based on the user input, at least one execution phase of the processing task and at least one function block respectively corresponding to the at least one execution phase, wherein a function block in the at least one function block is configured to perform a predetermined type of operation; as well as The acquisition module is configured to acquire the processing result of the processing task by respectively calling the at least one function block in the at least one execution stage.

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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