Information processing method and apparatus, device, and storage medium

Abstract

Provided are an information processing method and apparatus, a device, and a storage medium. The method comprises: in response to receiving a user input, acquiring target data indicated by the user input (210); on the basis of source information related to a source of the target data, using a target model to generate at least one processing instruction for the target data (220); executing the at least one processing instruction on the target data to acquire at least one processing result (230); and on the basis of the at least one processing result, using the target model to generate an analysis result for the target data to serve as a response to the user input (240).

Description

Information processing method, apparatus, device, and storage medium

[0001] The present application claims priority to the Chinese Patent Application No. 202410851923.1, filed on June 27, 2024, entitled “Information processing method, apparatus, device, and storage medium”, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] Example embodiments of the present disclosure generally relate to the field of computers, and in particular, to an information processing method, apparatus, device, and computer-readable storage medium. BACKGROUND

[0003] With the development of information technology, various terminal devices can provide people with various services in work and life, etc. Applications providing services can be deployed in the terminal devices. The terminal devices present corresponding content through the user interface of the application and implement interaction with the user to meet various needs of the user. In some cases, the user can initiate an information processing request within the application. Therefore, how to improve the efficiency of information processing is a problem of concern. SUMMARY

[0004] In a first aspect of the present disclosure, an information processing method is provided. The method comprises: in response to receiving a user input, obtaining target data indicated by the user input; generating at least one processing instruction for the target data by using a target model based on source information related to a source of the target data; obtaining at least one processing result by executing the at least one processing instruction on the target data, respectively; and generating an analysis result for the target data by using the target model based on the at least one processing result as a response to the user input.

[0005] In a second aspect of the present disclosure, an apparatus for information processing is provided, comprising: a target data obtaining module configured to obtain target data indicated by a user input in response to receiving the user input; a processing instruction generating module configured to generate at least one processing instruction for the target data by using a target model based on source information related to a source of the target data; a processing result obtaining module configured to obtain at least one processing result by executing the at least one processing instruction on the target data, respectively; and an analysis result generating module configured to generate an analysis result for the target data by using the target model based on the at least one processing result as a response to the user input.

[0006] In a third aspect of the disclosure, an electronic device is provided. The device includes at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. The instructions, when executed by the at least one processor, cause the electronic device to perform the method of the first aspect.

[0007] In a fourth aspect of the disclosure, a computer-readable storage medium is provided. The medium has stored thereon computer-executable instructions that, when executed by a processor, implement the method of the first aspect.

[0008] According to a fifth aspect of the disclosure, a computer program product is provided, which is tangibly stored in a computer storage medium and includes computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to the first aspect of the disclosure.

[0009] It should be understood that the details described in this section are not intended to limit key or important features of embodiments of the disclosure or limit the scope of the disclosure. Other features of the disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0010] The above and other features, advantages, and aspects of embodiments of the disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings in which:

[0011] FIG. 1 shows a schematic diagram of an example environment in which embodiments of the disclosure can be implemented;

[0012] FIG. 2 shows a flowchart of a process of information processing according to some embodiments of the disclosure;

[0013] FIG. 3 shows a schematic diagram of an example of information processing according to some embodiments of the disclosure;

[0014] FIG. 4 shows a schematic structural block diagram of an apparatus for information processing according to some embodiments of the disclosure;

[0015] FIG. 5 shows a block diagram of an electronic device that can implement one or more embodiments of the disclosure. DETAILED DESCRIPTION

[0016] 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 drawings, it is understood that the present disclosure can be implemented in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided so as to more completely and thoroughly understand the present disclosure. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of protection of the present disclosure.

[0017] In the description of embodiments of the present disclosure, the term "comprising" and its conjugations should be understood to encompass the meaning of "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 can also be included below.

[0018] In this document, unless explicitly stated, performing a step "in response to A" does not mean performing the step immediately after A, but can include one or more intermediate steps.

[0019] It can be understood that the data involved in the technical solutions of the present disclosure (including but not limited to the data itself, the obtaining, use, storage or deletion of the data) should comply with the requirements of relevant laws and regulations and relevant provisions.

[0020] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the type of information involved in the present disclosure, the scope of use, the use scenario, etc. should be informed to the relevant user and the authorization of the relevant user should be obtained by appropriate means, wherein the relevant user can include any type of right subject, such as an individual, an enterprise or a group.

[0021] For example, in response to receiving the active request of the user, a prompt information is sent to the relevant user to explicitly prompt the relevant user that the operation requested to be performed will require the information of the relevant user to be obtained and used, so that the relevant user can voluntarily choose whether to provide the information to the software or hardware such as electronic device, application program, server or storage medium, etc. performing the operation of the technical solutions of the present disclosure according to the prompt information.

[0022] As an optional but non-limiting implementation manner, in response to receiving the active request of the relevant user, the manner of sending the prompt information to the relevant user can be, for example, the manner of pop-up window, and the prompt information can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry selection controls for the user to select "agree" or "disagree" to provide information to the electronic device.

[0023] It can be understood that the above notification and user authorization obtaining process is only illustrative, and does not limit the implementation of the present disclosure, and other ways that meet relevant laws and regulations can also be applied to the implementation of the present disclosure. The enabling of the digital assistant related functions of the embodiments of the present disclosure, the obtained data, the processing and storage manner of the data, and the like should obtain the prior authorization of the user and other right subjects associated with the user, and should comply with relevant laws and regulations, the agreement rules between right subjects.

[0024] As used herein, the term “model” can learn an association between respective inputs and outputs from training data, such that after training is complete, a corresponding output can be generated for a given input. The generation of a model can be based on machine learning techniques. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. A neural network model is one example of a model based on deep learning. In this document, a “model” can also be referred to as a “machine learning model,” a “learning model,” a “machine learning network,” or a “learning network,” which terms are used interchangeably herein.

[0025] FIG. 1 illustrates a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. The 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 part of the application management platform 110 for supporting the creation of applications can also be referred to as an application creation part. In some embodiments, the part of the application management platform 110 for supporting the running of applications can also be referred to as an application running part.

[0026] As shown, the application creation part can provide a creation and publishing environment of applications for a user 105. The user 105 can be referred to as an application creation user, creator. In some embodiments, the application creation part can be a low-code platform, which provides a toolset for application creation. The application creation part can support the visual development of various types of applications, such that a developer can skip the process of manual coding, and accelerate the development cycle and cost of the application. The application creation part can support any appropriate platform for the user to develop one or more types of applications, which can include, for example, a platform based on application platform as a service (aPaaS). Such a platform can enable the user to efficiently develop an application, implement operations such as application creation, adjustment of application functions, and the like.

[0027] The application creation portion can be deployed locally at the terminal device of the user 105 and / or can be supported by a server device. For example, the terminal device of the user 105 can run a client of the application creation portion, which can support the user's interaction with the application creation portion provided by the server. In the case that the application creation portion is run locally at the terminal device of the user, the user 105 can directly interact with the local application creation portion using the terminal device. In the case that the application creation portion is run at the server device, the server device can implement the service provision to the client run at the terminal device based on the communication connection between the server device and the terminal device. The application creation portion can present a corresponding page 130 to the user 105 based on the operation of the user 105 to output and / or receive information related to the application creation to / from the user 105.

[0028] In some embodiments, the application creation portion can be associated to a corresponding database, in which data or information required by the application creation process supported by the application creation portion is stored. For example, the database can store the code and description information corresponding to each functional module for composing the application, etc. The application creation portion can also perform operations such as calling, adding, deleting, updating, etc. on the functional modules in the database. The database can also store operations executable on different functional blocks. For example, in the scenario of creating an application, the application creation portion can call the corresponding functional blocks from the database to build the application.

[0029] In embodiments of the present disclosure, the user 105 can create a target application 120 on the application creation portion as needed and publish the target application 120. The target application 120 can be published to any appropriate application running portion as long as the application running portion can support the running of the target application 120. After publication, the target application 120 can be used for operation by one or more terminal users 145. The terminal user 145 can operate the target application 120 through the associated terminal device 146 and further interact with the application management platform 110. The terminal user 145 can be referred to as a terminal user of the target application 120. In some embodiments, the target application 120 can include or be implemented as a digital assistant 122.

[0030] The digital assistant 122 can be configured to have the capability of intelligent conversation. In the example shown in the figure, the digital assistant 122 can be integrated within the target application 120 as a part of the target application 120 to assist in performing task processing within the target application 120. In other examples, the digital assistant 122 can be configured as a standalone application, such as a web application or other type of application. In such examples, the digital assistant 122 and the target application 120 can be considered as the same application. The digital assistant 122 is provided to assist users in various task processing needs in different applications, scenarios. During the interaction with the digital assistant 122, the user inputs an interaction message, and the digital assistant 122 provides a reply message in response to the user input. Typically, the digital assistant 122 is capable of supporting the user to input a question in a natural language manner, and perform a task and provide a reply based on the understanding of the natural language input and logical reasoning capability.

[0031] In some embodiments, the digital assistant 122 can interact with the end user 145 as a contact of the end user 145. For example, the digital assistant 122 can be implemented in an instant messaging (IM) application. The digital assistant 122 can interact with the end user 145 in a one-on-one chat session with the end user 145. In some embodiments, the digital assistant 122 can interact with multiple users in a group chat session including multiple users.

[0032] For each end user 145, the application running portion of the client can present an interaction window 142 of the target application 120 or the digital assistant 122, such as a conversation window with the digital assistant 122, in a client interface. The end user 145 can input a conversation message in the conversation window, and the target application 120 can determine a reply message of the digital assistant 122 based on the created configuration information and present to the user in the interaction window 142. In some embodiments, depending on the configuration of the target application 120, the interaction message with the target application 120 can include messages in multiple modalities, such as text messages (e.g., natural language text), voice messages, image messages, video messages, etc.

[0033] Similar to the application creation part, the application running part can be deployed locally at the terminal device of each end user 145, and / or can be supported by the service end device. For example, the terminal device of the end user 145 can run a client of the application running part, which can support the interaction of the user with the application running part provided by the service end. In the case that the application running part runs locally at the terminal device of the user, the end user 145 can directly utilize the terminal device to interact with the local application running part. In the case that the application running part runs at the service end device, the service end device can implement the service provision to the client running in the terminal device based on the communication connection between the terminal device. The application running part can present a corresponding application page to the end user 145 based on the operation of the end user 145, to output and / or receive information related to the application use to / from the end user 145.

[0034] 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 a model. During the creation or running of the target application 120, one or more models 155, such as the capabilities of the model 155, can be invoked. In the target application 120, the digital assistant 122 can utilize the model 155 to understand the user input, and provide a reply to the user based on the output of the model 155.

[0035] During the creation process, the test of the target application 120 by the application management platform 110 needs to utilize the model 155 to determine that the running result of the target application 120 meets the expectation. During the running process, in response to different operation requests of the user of the target application 120, the application running part can need to utilize the model 155 to determine the response result to the user.

[0036] Although shown as being independent of the application management platform 110, one or more models 155 can run on the application management platform 110, or other remote servers. In some embodiments, the model 155 can be a machine learning model, a deep learning model, a learning model, a neural network, etc. In some embodiments, the model can be based on a language model (LM). The language model can have the ability of question and answer by learning from a large amount of corpus. The model 155 can also be based on other appropriate models.

[0037] The application management platform 110 can be running on a suitable electronic device. The electronic device herein can be any type of device with computing capability, including an end device or a server device. The end device can be any type of mobile terminal, fixed terminal, or portable terminal including a mobile handset, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a game device, or any combination thereof, including accessories and peripherals of such 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 a cloud service.

[0038] It should be appreciated that the structure and functionality of the environment 100 are described for illustrative purposes only and are not intended to imply any limitation on the scope of the present disclosure. For example, while the figures illustrate a single user interacting with the application creation portion and a single user interacting with the application running portion, in practice 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.

[0039] As mentioned previously, a user can initiate a task processing request within an application to process information within the application. Traditionally, an application can implement data analysis, e.g., provide data insights, with the aid of an analysis framework. However, when the amount of data is large, the application can have difficulty determining a relatively accurate data analysis result in a convenient and efficient manner. To improve the accuracy of data analysis, human labor can also be used to perform data analysis, which can increase the labor cost of data analysis. Performing data analysis by human labor can further reduce the efficiency of data analysis.

[0040] In view of the foregoing, in embodiments of the present disclosure, an improved solution for information processing is provided. In the solution, in response to receiving a user input, target data indicated by the user input is obtained. Based on source information about a source of the target data, at least one processing instruction for the target data is generated by utilizing a target model. At least one processing result is obtained by performing the at least one processing instruction on the target data, respectively. Based on the at least one processing result, an analysis result for the target data is generated by utilizing the target model as a response to the user input.

[0041] In this way, the processing instruction for the data can be generated by means of the model, and the analysis result of the data can be determined by means of the model based on the processing result of the data instruction. The specific data processing can not be performed by the model, but can be completed by the instruction execution engine. Thus, the data processing which the model can not be good at can be stripped from the model. This helps to improve the efficiency and accuracy of information processing.

[0042] Some example embodiments of the present disclosure will be described in detail below with reference to examples of the accompanying drawings.

[0043] The task management process described in the embodiments of the present disclosure can 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 examples below, for the sake of discussion, the perspective of the application management platform is described, such as the application management platform 110 shown in FIG. 1. 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 a 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 a creator, manager or maintainer of the target application 120.

[0044] FIG. 2 shows a flowchart of a process 200 of information processing according to some embodiments of the present disclosure. The process 200 can be implemented in the application management platform 110, for example, can be implemented by the application running part of the application management platform 110. The task processing process shown in FIG. 2 is described below in conjunction with FIG. 1.

[0045] At block 210, the application management platform 110 acquires the target data indicated by the user input in response to receiving the user input.

[0046] The user input can be input from any suitable user, e.g., it can be user input from the user 145. The user input can be of any suitable type, e.g., it can be of text type, voice type, gesture type, etc. The application management platform 110 can receive the user input via any suitable manner, e.g., it can receive user input of text type via an input box, receive user input of audio type via a microphone, etc. The user input can be presented in an interaction window, e.g., the interaction window 142. In some embodiments, where the user input is of non-text type, the application management platform 110 can process the user input to determine the text corresponding to the user input. For example, where the user input is of audio type, the application management platform 110 can convert the audio corresponding to the user input to text.

[0047] The target data can include raw data in a data object. The data object can be a structured object, e.g. The structured object can be any suitable type of object that can store or represent information in a structured manner, which can include but is not limited to a data table, a database, etc. For example, the target data can include raw data in a data table. The target data can also include data determined based on the raw data. For example, the target data can include data computed from the raw data.

[0048] In some embodiments, the application management platform 110 can determine the intent of the user input, and obtain the target data based on the intent. For example, where the user input is "In which month did the highest sales of product A in the past year occur?", the application management platform 110 can determine that the intent of the user input is to analyze the sales data of product A in the past year, and can then obtain the sales data of product A in the past year from a data table. Based on the sales data in the past year, the sales in each month in the past year can be computed. In this example, the sales in each month and the sales data in the past year can be the target data.

[0049] In some embodiments, obtaining the target data from the user input can be implemented using a model. For example, the model can generate a data query instruction based on the user input to query the sales in each month in the past year.

[0050] At block 220, the application management platform 110 generates at least one processing instruction for the target data using a target model based on the source information related to the source of the target data. The processing instruction can be used to perform any type of data processing on the target data, such as but not limited to data conversion, data cleaning, sorting, various numerical operations, etc.

[0051] The provenance information may, for example, include one or more data query instructions used to obtain the target data. The data query instructions may, for example, be a structured query language (SQL) that can inform the target model how the target data was queried, which can help the target model better understand the data. The provenance information may, for example, also include meta-information of one or more data objects from which the target data originated, such as structure information of a data table. The meta-information of the data objects may, for example, include fields included in the data objects. The provenance information may, for example, also include data record examples in the one or more data objects. Taking a data table as an example of a data object, the data record examples may, for example, include the first few rows of the data table.

[0052] The target model can be a model deployed locally at the application management platform 110 or a model deployed at another electronic device. The target model can be based on any suitable model structure, including but not limited to a Transformer model, a convolutional neural network (CNN), a recurrent neural network (RNN), a deep neural network (DNN), etc. In some embodiments, the target model can be a language model (LM).

[0053] In some embodiments, the application management platform 110 can obtain address information for the target data. The address information can indicate a storage location of the target data. In some embodiments, the application management platform 110 can store the target data to an instruction execution environment before generating the at least one processing instruction. In this case, the address information obtained by the application management platform 110 can indicate a storage location of the target data in the instruction execution environment. As an example, the instruction execution environment can be a restricted runtime environment that allows a user to execute data processing instructions, such as code in a predetermined language, in an isolated secure space. Such an instruction execution environment provides a secure, isolated, and controllable environment. A sandbox is an example of an instruction execution environment.

[0054] The application management platform 110 may, for example, generate a prompt input for the target model based on the address information, the provenance information, and the user input, which can also be referred to as prompt information. The application management platform 110 may, for example, obtain a prompt template for the target model and determine the prompt information for the target model by filling the address information, the provenance information, and the user input into the prompt template. In some embodiments, the application management platform 110 can generate only one prompt information and obtain the at least one processing instruction by providing the prompt information to the target model.

[0055] For example, the application management platform 110 can generate a first prompt information for the target model based on the address information, the source information and the user input. The application management platform 110 can provide the first prompt information to the target model. Upon receiving the first prompt information, the target model can generate a corresponding model output based on the first prompt information. The model output may, for example, indicate a first processing instruction of the at least one processing instruction. The application management platform 110 can obtain the model output from the target model and determine the first processing instruction of the at least one processing instruction based on the model output.

[0056] In this way, the prompt information can be generated based on the address information and the source information of the data, and such prompt information can be provided to the target model, which can reduce the amount of data processed by the target model and improve the accuracy of information processing. In addition, by providing the address of the target data to the model instead of providing all the target data to the model, the input to the model can be avoided as much as possible to exceed the input capacity.

[0057] In some embodiments, the application management platform 110 can further generate at least one prompt information and obtain at least one processing instruction by providing the at least one prompt information to the target model, each processing instruction corresponding to one prompt information. Specifically, the application management platform 110 may, for example, determine at least one task indicated by the user input by using the target model. If the at least one task includes a plurality of tasks, the application management platform 110 can determine a corresponding prompt information for each task. The application management platform 110 can further determine a processing instruction corresponding to each task based on the prompt information corresponding to each task.

[0058] At block 230, the application management platform 110 obtains at least one processing result by executing the at least one processing instruction on the target data. In some embodiments, if the target data is stored in the instruction execution environment, the at least one processing instruction generated by the application management platform 110 is executed in the preset instruction execution environment. The application management platform 110 may, for example, send the at least one processing instruction to the preset instruction execution environment to execute the at least one processing instruction in the instruction execution environment. The application management platform 110 can obtain at least one processing result of the at least one processing instruction from the instruction execution environment.

[0059] If the at least one processing instruction includes multiple processing instructions, the multiple processing instructions can be independent of each other or dependent on each other. For example, the application management platform 110 can determine another processing instruction based on a processing result of one processing instruction. For example, the application management platform 110 can determine that the user input indicates a first task and a second task using a target model. For example, if the user input includes the text "What is the month in which the highest sales of Product A occurred in the past year? What is the monthly average sales of Product A in the past year?", the application management platform 110 can determine that the user input indicates a task A "determine the month in which the highest sales of Product A occurred in the past year" and a task B "determine the monthly average sales of Product A in the past year" using a target model.

[0060] The application management platform 110 can generate first prompt information associated with the first task based on the address information, the source information, and the user input. The application management platform 110 can provide the first prompt information to the target model to obtain a first processing instruction of the at least one processing instruction. The first processing instruction is for the first task. The application management platform 110 can obtain a first processing result of the first processing instruction. For example, the first processing instruction can be an instruction to find the maximum value of the 12 sales, and the first processing result is the maximum value and the corresponding month.

[0061] The application management platform 110 can generate second prompt information associated with the second task based on the address information, the source information, and the first processing result. The application management platform 110 can provide the second prompt information to the target model to obtain a second processing instruction of the at least one processing instruction. The second processing instruction is for the second task. For example, the second processing instruction can be an instruction to calculate the average value of the 12 sales, and the second processing result is the average value.

[0062] In some embodiments, the application management platform 110 can also update the target data based on a processing result of a given processing instruction of the at least one processing instruction in response to completion of execution of the given processing instruction, and generation of a processing instruction after the given processing instruction is based on the updated target data. For example, if a processing result of a processing instruction A indicates that new data is generated, the new data is to be added to the target data. A processing instruction after the processing instruction A is generated based on the target data with the new data added. If a processing result of a processing instruction B indicates that the target data is cleaned, the application management platform 110 removes the cleaned data from the target data. A processing instruction after the processing instruction B is generated based on the target data with the cleaned data removed.

[0063] At block 240, the application management platform 110 generates, based on the at least one processing result, an analysis result for the target data as a response to the user input using the target model. The analysis result may, for example, include various insight analysis related to the user input. Continuing the example above, the analysis result may, for example, include a summary of the past year sales change pattern of item A.

[0064] In some embodiments, the analysis result may, for example, include reference information indicating a source of one or more pieces of data in the analysis result. In such embodiments, the model can be instructed in the prompt provided to the model to give a source of data on which the analysis result relies, thereby facilitating to strengthen the credibility of the insight and to provide the user with a credible analysis result.

[0065] The application management platform 110 may, for example, receive the user input via an interaction window (e.g., the interaction window 142) and provide the analysis result via the interaction window. The analysis result may, for example, be provided to the user in the form of a conversational message from the digital assistant.

[0066] The application management platform 110 may, for example, provide the at least one processing result to the target model to generate the analysis result for the target data using the target model. In some embodiments, the application management platform 110 can generate prompt word inputs for the target model based on the at least one processing result and provide the generated prompt word inputs to the target model. The application management platform 110 may, for example, generate one prompt word input based on the at least one processing result, the prompt word input can instruct the target model to analyze the at least one processing result. The application management platform 110 may, for example, also generate at least one prompt word input based on the at least one processing result, each prompt word input instructs to analyze a corresponding processing result.

[0067] In some embodiments, the application management platform 110 can further determine an input consumption amount of the source information and the at least one processing result and an input capacity of the target model. The input consumption amount can represent an amount of data of the source information and the at least one processing result as input of the target model. An example of the input consumption amount can be a number of tokens consumed by the input of the model, and accordingly, the input capacity can be a total number of tokens that can be provided to the model in one input. The application management platform 110 can compare the input consumption amount and the input capacity of the target model to determine whether the input consumption amount exceeds the input capacity of the target model. If the input consumption amount does not exceed the input capacity, the application management platform 110 can directly utilize the target model to process the at least one processing result to determine an analysis result for the target data. If the input consumption amount exceeds the input capacity, the application management platform 110 can generate third prompt information based on the at least one processing result to instruct the target model to summarize the at least one processing result. The application management platform 110 can provide the third prompt information to the target model to obtain the analysis result.

[0068] Exemplarily, taking an input capacity of 50 (e.g., in the unit of token number) as an example, if the input consumption amount is 40, the application management platform 110 can determine that the input consumption amount does not exceed the input capacity, and thus directly utilize the target model to process the at least one processing result to determine an analysis result for the target data. If the input consumption amount is 60, the application management platform 110 can determine that the input consumption amount exceeds the input capacity, and thus can generate third prompt information based on the at least one processing result to instruct the target model to summarize the at least one processing result. The application management platform 110 can provide the third prompt information to the target model to obtain the analysis result.

[0069] In this way, when the input consumption amount reaches the upper limit (i.e., reaches the input capacity), the application management platform 110 can prompt the target model to summarize according to the current existing result, can provide the user with at least part of the analysis result, and can improve the user experience of the user.

[0070] Referring to FIG. 3, FIG. 3 illustrates a schematic diagram of an example 300 of information processing according to some embodiments of the present disclosure. As shown in FIG. 3, the application management platform 110 can obtain the target data 304 indicated by the user input 340 in response to obtaining the user input 340. Continuing the example above, if the user input 340 is “summarize the sales of item A in the past year”, the target data 304 can be the sales data of item A in the past year. The application management platform 110 can generate at least one processing instruction for the target data 304 based on the source information 306 related to the source of the target data 301, using a target model (e.g., the model 330). The processing instruction can be used to perform any type of data processing on the target data, such as but not limited to data conversion, data cleaning, sorting, various numerical operations, etc. Continuing the example above, if the user input 340 is “summarize the sales of item A in the past year”, the processing instruction can be used to calculate the sales and / or the sales volume of item A in each of the past 12 months, etc.

[0071] As previously described at block 220, the application management platform 110 can generate the hint information for the model 330 based at least on the source information 306, and provide the hint information to the model 330. The model 330 can output at least one processing instruction for the target data 304. The model 330 can be a model local to the application management platform 110, or a model deployed at a remote device. If the model 330 is deployed locally to the application management platform 110, the application management platform 110 can directly utilize the model 330 to determine the at least one processing instruction. If the model 330 is deployed at a remote device, the application management platform 110 can invoke the model 330 deployed at the remote device via a communication connection between the application management platform 110 and the remote device to determine the at least one processing instruction.

[0072] The source information 306 can include one or more data query instructions used to obtain the target data, meta information 302 of one or more data objects from which the target data is derived, and / or data record examples 303 in the one or more data objects. For example, the source information 306 can include one or more query instructions used to obtain the sales data of item A in the past year. The application management platform 110 can upload the target data 304 to the instruction execution environment 320 in advance to store the target data 304 to the instruction execution environment 320, e.g., before generating the at least one processing instruction. The application management platform 110 can obtain address information of the target data 304, the address information indicating a storage location of the target data 304 in the instruction execution environment 320. The application management platform 110 can generate the hint information 310 (e.g., the first hint information) for the target model based on the address information, the source information 306, and the user input 340.

[0073] As to the specific way of generating the at least one processing instruction, the application management platform 110 may, for example, perform a plurality of steps included in the block 350 to generate the at least one processing instruction. Specifically, at the block 351, the application management platform 110 may, for example, determine a plurality of tasks indicated by the user input 340. For example, the application management platform 110 may, for example, provide the prompt information generated based on the user input 340 to the target model, and parse the tasks by the target model. At the block 352, the application management platform 110 may, for example, generate a plurality of prompt information based on the plurality of tasks. At the block 353, the application management platform 110 may, for example, provide the plurality of prompt information to the target model, and determine a plurality of processing instructions corresponding to the plurality of tasks by the target model. It is to be noted that at least one of the plurality of steps included in the block 350 may, for example, be determined by the application management platform 110 by means of the model 330. The application management platform 110 may, for example, directly utilize the model 330 or invoke the model 330 from a remote device to perform the at least one step. As an example, at the block 351, the application management platform 110 may, for example, provide the prompt information generated based on the user input 340 to the target model, and parse the tasks by the target model. At the block 352, the application management platform 110 may, for example, generate a corresponding prompt information for each of the tasks. At the block 353, the generated prompt information may, for example, be provided to the target model, and the processing instructions corresponding to the tasks may, for example, be generated by the target model.

[0074] Further, at the block 360, the application management platform 110 may, for example, obtain at least one processing result by performing the at least one processing instruction on the target data 304. For example, the processing result may, for example, be the calculated sales volume and / or sales amount of the product A in each month of the past year. In some embodiments, if a plurality of processing instructions are included, the application management platform 110 may, for example, perform the processing instructions on the target data 304 multiple times to obtain a plurality of processing results. The application management platform 110 may, for example, provide the at least one processing instruction to the instruction execution environment 320, and obtain the at least one processing result corresponding to the at least one processing instruction from the instruction execution environment 320.

[0075] At block 370, the application management platform 110 can provide the at least one processing result to the target model (again, for example, can be the model 330) to generate an analysis result for the target data 304 as a response to the user input 340 by utilizing the target model. Continuing the example above, the monthly sales volume and / or sales amount of the product A can be provided to the target model. In this way, the target model can output an analysis result for the sales volume and / or sales amount, such as a change trend of the sales volume and / or sales amount, a month when the highest sales volume and / or sales amount occurs, a month when the lowest sales volume and / or sales amount occurs, etc. In some embodiments, the application management platform 110 can determine whether the input consumption 301 of the source information 306 and the at least one processing result exceeds the input capacity 305 of the target model. The application management platform 110 can generate, in response to determining that the input consumption 301 exceeds the input capacity 305, prompt information indicating that the target model summarizes the at least one processing result based on the at least one processing result. The application management platform 110 can provide the prompt information to the target model to obtain the analysis result. The application management platform 110 can directly provide the at least one processing result to the target model to obtain the analysis result in response to determining that the input consumption 301 does not exceed the input capacity 305.

[0076] In summary, according to embodiments of the present disclosure, processing instructions for data can be generated by means of a model, and analysis results for data can be determined based on processing results of the data instructions by means of a model. This helps to improve the efficiency and accuracy of information processing. In addition, the processing capability of a model for complex information and large amount of information is also enhanced.

[0077] Embodiments of the present disclosure also provide a corresponding apparatus for implementing the above method or process. FIG. 4 shows a schematic structural block diagram of an apparatus 400 for information processing according to some embodiments of the present disclosure. The apparatus 400 may, for example, be implemented in or included in the application management platform 110. Various modules / components in the apparatus 400 can be implemented by hardware, software, firmware, or any combination thereof.

[0078] As shown, the apparatus 400 includes a target data acquisition module 410 configured to acquire target data indicated by a user input in response to receiving the user input. The apparatus 400 also includes a processing instruction generation module 420 configured to generate at least one processing instruction for the target data by utilizing a target model based on source information related to a source of the target data. The apparatus 400 further includes a processing result acquisition module 430 configured to acquire at least one processing result by executing the at least one processing instruction on the target data, respectively. The apparatus 400 also includes an analysis result generation module 440 configured to generate an analysis result for the target data as a response to the user input by utilizing the target model based on the at least one processing result.

[0079] In some embodiments, the processing instruction generation module 420 comprises: an address information obtaining module configured to obtain address information of the target data, the address information indicating a storage location of the target data; a first prompt information generation module configured to generate first prompt information for the target model based on the address information, the source information and the user input; and a first processing instruction obtaining module configured to provide the first prompt information to the target model to obtain a first processing instruction of the at least one processing instruction.

[0080] In some embodiments, the at least one processing instruction is executed in a preset instruction execution environment, and the apparatus 400 further comprises: a target data storage module configured to store the target data to the instruction execution environment before the at least one processing instruction is generated, the address information indicating a storage location of the target data in the instruction execution environment.

[0081] In some embodiments, the apparatus 400 further comprises: a task determination module configured to determine, by using the target model, that the user input indicates at least a first task and a second task, and wherein the first processing instruction is associated with the first task, and the processing instruction generation module 420 further comprises: a second prompt information generation module configured to generate second prompt information for the target model based on the address information, the source information and a first processing result of the first processing instruction; and a second processing instruction obtaining module configured to provide the second prompt information to the target model to obtain a second processing instruction of the at least one processing instruction for the second task.

[0082] In some embodiments, the source information comprises at least one of: one or more data query instructions used to obtain the target data, meta information of one or more data objects from which the target data is derived, or a data record example in the one or more data objects.

[0083] In some embodiments, the analysis result generation module 440 comprises: a capacity determination module configured to determine whether an input consumption amount of the source information and the at least one processing result exceeds an input capacity of the target model, the input consumption amount representing an amount of data of the source information and the at least one processing result as input of the target model; a third prompt information generation module configured to generate third prompt information based on the at least one processing result to indicate that the target model summarizes the at least one processing result, in response to determining that the input consumption amount exceeds the input capacity; and an analysis result obtaining module configured to provide the third prompt information to the target model to obtain the analysis result.

[0084] In some embodiments, the apparatus 400 further includes a target data updating module configured to, in response to a given processing instruction in the at least one processing instruction completing execution, update the target data based on a processing result of the given processing instruction, and generation of a processing instruction subsequent to the given processing instruction is based on the updated target data.

[0085] In some embodiments, the analysis result includes reference information indicating a source of one or more pieces of data in the analysis result.

[0086] The units and / or modules included in the apparatus 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, e.g., machine executable instructions stored on a storage medium. In addition to or alternatively, some or all of the units and / or modules in the apparatus 400 can be implemented at least partially by one or more hardware logic components. As an example and not by way of limitation, example 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), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0087] FIG. 5 illustrates a block diagram of an electronic device 500 in which one or more embodiments of the disclosure can be implemented. It should be understood that the electronic device 500 illustrated in FIG. 5 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. The electronic device 500 illustrated in FIG. 5 can include or be implemented as the application management platform 110 of FIG. 1, or the apparatus 400 of FIG. 4.

[0088] As shown in FIG. 5, the electronic device 500 is in the form of a general electronic device. Components of the electronic device 500 can include, but are not limited to, one or more processors 510 or processing units, 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 processor 510 can be a real or virtual processor and is capable of performing various processing according to programs stored in the memory 520. In a multi-processor system, multiple processors perform computer-executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0089] The electronic device 500 typically includes a plurality of computer storage media. Such media can be any available media that is accessible by the electronic device 500 and includes both volatile and nonvolatile media, removable and non-removable media. The memory 520 can be volatile (such as register, cache, RAM), non-volatile (such as ROM, EEPROM, flash memory), or some combination of the two. The storage device 530 can be a removable or non-removable media, and can include machine-readable media, such as flash drives, magnetic disks, or any other media that can be used to store information and / or data and that can be accessed by the electronic device 500.

[0090] The electronic device 500 can further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 5, a disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk (e.g., a CD-ROM) can be provided. In such instances, each drive can be connected to the bus (not shown) by one or more data media interfaces. The memory 520 can include a computer program product 525 having one or more program modules configured to carry out the various methods or actions of the various embodiments of the present disclosure.

[0091] The communication unit 540 enables communication with other electronic devices over communication media. Additionally, the functionality of the components of the electronic device 500 can be implemented in a single computing cluster or a plurality of computer machines capable of communication over a communication connection. Thus, the electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network nodes.

[0092] The input device 550 can be one or more input devices, such as a mouse, a keyboard, a trackball, etc. The output device 560 can be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 can also communicate with one or more external devices (not shown) such as a storage device, a display device, etc. through the communication unit 540, as needed, one or more devices that enable a user to interact with the electronic device 500, or any devices (e.g., a network card, a modem, etc.) that enable the electronic device 500 to communicate with one or more other electronic devices. Such communication can be carried out via an input / output (I / O) interface (not shown).

[0093] According to an example implementation of the present disclosure, a computer readable storage medium is provided having computer executable instructions stored thereon, where the computer executable instructions are executed by a processor to implement the method described above. According to an example implementation of the present disclosure, a computer program product is also provided that is tangibly stored on a non-transitory computer readable medium and includes computer executable instructions, where the computer executable instructions are executed by a processor to implement the method described above.

[0094] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0095] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium. The instructions stored on the computer readable storage medium can be used to program a computer, a programmable data processing apparatus, and / or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0096] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0097] The computer program product of the present disclosure can have a signal including said computer program. This signal can be electronic, electromagnetic, optical, or any other suitable type of signal. Such a signal can be provided through a communication connection, such as electrical wiring, optical fiber, wireless interface, etc. Examples of computer program products include computer program implemented on a personal computer, server, or other networked device. A non-transitory computer readable medium, such as a floppy disk, CD-ROM, DVD-ROM, Blu-ray Disc, hard disk drive, or any other suitable non-transitory computer readable medium can store the computer program product.

[0098] Various implementations of the disclosure have been described in detail above. The foregoing description is exemplary and explanatory only, and is not intended to be exhaustive or to limit various implementations of the disclosure to the precise forms disclosed. Many modifications and variations are possible in light of the above teachings without departing from the scope and spirit of the disclosure. It is intended that the scope of the disclosure be limited only by the claims and the equivalents thereof. The use of the terms "including," "containing," "comprising," "having," "in involving," "portions," "elements," "components," "steps," "phases," "processes," "operations," "steps," "stages," "procedures," "methods," "mechanisms," "devices," "systems," "apparatuses," "units," "means," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "apparatuses," "units," "devices," "systems," "

Claims

1. An information processing method, comprising: In response to receiving user input, the target data indicated by the user input is obtained; Based on source information related to the source of the target data, at least one processing instruction for the target data is generated using the target model; By executing the at least one processing instruction on the target data, at least one processing result is obtained respectively; as well as Based on the at least one processing result, the target model is used to generate an analysis result for the target data as a response to the user input.

2. The method of claim 1, wherein generating at least one processing instruction for the target data comprises: Obtain address information for the target data, wherein the address information indicates the storage location of the target data; Based on the address information, the source information, and the user input, a first prompt message is generated for the target model; as well as The first prompt information is provided to the target model to obtain the first processing instruction among the at least one processing instruction.

3. The method according to claim 2, wherein the at least one processing instruction is executed in a preset instruction execution environment, and the method further comprises: Before generating the at least one processing instruction, the target data is stored in the instruction execution environment, and the address information indicates the storage location of the target data in the instruction execution environment.

4. The method according to claim 2, further comprising: Using the target model, it is determined that the user input indicates at least a first task and a second task, and The first processing instruction is associated with the first task, and generating at least one processing instruction for the target data further includes: Based on the address information, the source information, and the first processing result of the first processing instruction, a second prompt message is generated for the target model; and The second prompt information is provided to the target model to obtain a second processing instruction from the at least one processing instruction, for use in the second task.

5. The method of claim 1, wherein the source information includes at least one of the following: One or more data query instructions for retrieving the target data. The target data originates from the metadata of one or more data objects, or Examples of data records in the one or more data objects.

6. The method of claim 1, wherein generating an analysis result for the target data as a response to the user input comprises: Determine whether the input consumption of the source information and the at least one processing result exceeds the input capacity of the target model, wherein the input consumption represents the amount of data that uses the source information and the at least one processing result as input to the target model; In response to determining that the input consumption exceeds the input capacity, a third prompt message is generated based on the at least one processing result to instruct the target model to summarize the at least one processing result; as well as The third prompt information is provided to the target model to obtain the analysis results.

7. The method according to claim 1, further comprising: In response to the completion of execution of a given processing instruction among the at least one processing instruction, the target data is updated based on the processing result of the given processing instruction, and The generation of subsequent processing instructions is based on the updated target data.

8. The method of claim 1, wherein the analysis results include citation information indicating the source of one or more data in the analysis results.

9. An apparatus for information processing, comprising: The target data acquisition module is configured to acquire the target data indicated by the user input in response to receiving user input; The processing instruction generation module is configured to generate at least one processing instruction for the target data based on source information related to the source of the target data and using a target model; The processing result acquisition module is configured to acquire at least one processing result by executing at least one processing instruction on the target data; as well as The analysis result generation module is configured to generate analysis results for the target data as a response to the user input, based on the at least one processing result and using the target model.

10. An electronic device, comprising: At least one processor; as well as At least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, 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 processor.

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

12. A computer program product tangibly stored in a computer storage medium and comprising computer-executable instructions that, when executed by a device, cause the device to perform the method according to any one of claims 1-8.

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