Task processing method and apparatus, and device and computer-readable storage medium

Abstract

Provided in the embodiments of the present disclosure are a task processing method and apparatus, and a device and a computer-readable storage medium. The method comprises: parsing a received user input to obtain a parsing result, wherein the user input indicates a task execution request for a target application, and the parsing result indicates a plurality of sub-task requests corresponding to the task execution request; for each of the plurality of sub-task requests, determining a data requirement of the sub-task request; and in response to target data having been acquired on the basis of the data requirement, calling a specified function to process the sub-task request.

Description

Task processing method, apparatus, device, and computer-readable storage medium

[0001] The present application claims priority to the Chinese patent application No. 202410853011.8, filed on June 27, 2024, entitled “Task processing method, apparatus, device, and computer-readable 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 a task 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. The terminal device can be deployed with an application that provides services. The terminal device presents corresponding content through the user interface of the application and realizes interaction with the user to meet various needs of the user. In some cases, the user can initiate a task processing request in the application. Therefore, how to better achieve the goal of the task processing request is a problem of concern. SUMMARY

[0004] In a first aspect of the present disclosure, a task processing method is provided. The method comprises: parsing a received user input to obtain a parsing result, the user input indicating a task execution request for a target application, the parsing result indicating a plurality of sub-task requests corresponding to the task execution request. For a sub-task request in the plurality of sub-task requests, determining a data requirement of the sub-task request. In response to obtaining target data based on the data requirement, invoking a specified function to process the sub-task request.

[0005] In a second aspect of the present disclosure, a task processing apparatus is provided. The apparatus comprises: a parsing module configured to parse a received user input to obtain a parsing result, the user input indicating a task execution request for a target application, the parsing result indicating a plurality of sub-task requests corresponding to the task execution request. A query instruction generation module configured to determine, for a sub-task request in the plurality of sub-task requests, a data requirement of the sub-task request. An analysis instruction production module configured to, in response to obtaining target data based on the data requirement, invoke a specified function to process the sub-task request.

[0006] In a third aspect of the present disclosure, an electronic device is provided. The device comprises 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 present 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] In a fifth aspect of the present disclosure, a computer program product is provided. The computer program product is tangibly stored in a computer storage medium and includes computer-executable instructions that, when executed by a device, cause the device to perform the method of the first aspect.

[0009] It should be understood that all statements herein made regarding the exemplary embodiments are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (e.g., any elements developed that perform the same function, regardless of structure). Thus, the scope of the present disclosure should not be limited to the specific illustrative structures and methods described herein, but rather, should be given the broadest possible interpretation consistent with the principles of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0010] The above and other features, aspects, and advantages of various embodiments of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like reference numerals refer to like elements, wherein:

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

[0012] FIG. 2 shows a block diagram of a task processing process according to some embodiments of the present disclosure;

[0013] FIG. 3 shows a schematic diagram of a task processing process according to some embodiments of the present disclosure;

[0014] FIG. 4 shows a schematic diagram of a page showing results according to some embodiments of the present disclosure;

[0015] FIGS. 5A to 5C show schematic diagrams of charting example interfaces according to some embodiments of the present disclosure;

[0016] FIG. 6 shows a schematic block diagram of a task processing apparatus according to some embodiments of the present disclosure;

[0017] FIG. 7 shows a block diagram of an electronic device that can implement one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described herein below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein; rather, these embodiments are provided so that the present disclosure will be more thoroughly and completely understood. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and should not be construed as limiting the scope of the present disclosure.

[0019] In the description of embodiments of the disclosure, the term "comprising" and similar terms thereof are understood to be inclusive, i.e., "including but not limited to". The term "based on" is understood to mean "based at least in part on". The term "one embodiment" or "the embodiment" is understood to mean "at least one embodiment". The term "some embodiments" is understood to mean "at least some embodiments". Other explicit and implicit definitions can also be included below.

[0020] In this article, unless specifically 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.

[0021] It can be understood that the data involved in the technical solutions of the present application (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) should comply with the requirements of the relevant laws and regulations and the relevant provisions.

[0022] 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 individual, enterprise, group.

[0023] For example, in response to receiving the active request of the user, the 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.

[0024] As an optional but not limited implementation manner, in response to receiving the active request of the relevant user, the prompt information is sent to the relevant user, for example, in the form of a 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.

[0025] It can be understood that the above notification and user authorization process is only illustrative and does not limit the implementation of the present disclosure. Other ways that meet the 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 data obtained, the processing and storage mode of the data, etc. should be authorized in advance by the user and other right subjects associated with the user, and should comply with the agreement of the relevant laws and regulations and the agreement rules between the right subjects.

[0026] As used herein, the term “model” can learn the relationship 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 techniques. Deep learning is a machine learning algorithm that processes input and provides a corresponding output by using multiple layers of processors. The neural network model is an example of a model based on deep learning. In this article, “model” can also be referred to as “machine learning model”, “learning model”, “machine learning network” or “learning network”, which are used interchangeably herein.

[0027] FIG. 1 shows 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 an application and / or the running of an application. In some embodiments, the part of the application management platform 110 for supporting the creation of an application 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 an application can also be referred to as an application running part.

[0028] As shown, the application creation part can provide a creation and publishing environment of an application 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, so that the developer can skip the manual coding process, 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 an application platform as a service (aPaaS) based platform, for example. Such a platform can enable the user to efficiently develop the application, implement application creation, application function adjustment, etc.

[0029] The application creation part can be deployed locally on 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 part, which can support the user to interact with the application creation part provided by the server. In the case where the application creation part runs locally on the terminal device of the user, 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 device, the server device can implement service provision to the client running on the terminal device based on the communication connection between the terminal device and the server device. The application creation part 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.

[0030] In some embodiments, the application creation portion can be associated to a corresponding database in which data or information required by the application creation procedure supported by the application creation portion is stored. For example, the database can store code and description information corresponding to each functional module used to compose 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. Illustratively, in a scenario in which an application is to be created, the application creation portion can call corresponding functional blocks from the database to build the application.

[0031] 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 suitable application running portion as long as the application running portion is capable of supporting the running of the target application 120. After publication, the target application 120 can be used for operation by one or more end users 145. The end users 145 can operate the target application 120 through associated terminal devices 146 and in turn interact with the application management platform 110. The end users 145 can be referred to as end users of the target application 120. In some embodiments, the target application 120 can include or be implemented as a digital assistant 122.

[0032] 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 an independently running 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 and scenarios. During 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. Generally, 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 understanding of the natural language input and logical reasoning capability.

[0033] In some embodiments, the digital assistant 122 can interact with the end users 145 as contacts of the end users 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 users 145 in a one-on-one chat session with the end users 145. In some embodiments, the digital assistant 122 can interact with multiple users in a group chat session including multiple users.

[0034] For each end user 145, the client of the application running part can present an interaction window 142 of the target application 120 or the digital assistant 122 in the client interface, for example, a conversation window with the digital assistant 122. 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 the reply message 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, and the like.

[0035] Similar to the application creation part, the application running part can be deployed locally on the terminal device of each end user 145 and / or can be supported by the server device. For example, the terminal device of the end user 145 can run a client of the application running part, which can support the user's interaction with the application running part provided by the server. In the case where the application running part is run locally on the terminal device of the user, the end user 145 can directly interact with the local application running part using the terminal device. In the case where the application running part is run on the server device, the server device can implement service provision to the client running on 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 use of the application to / from the end user 145.

[0036] 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, for example, 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.

[0037] During the creation process, the testing 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.

[0038] 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 models 155 can be machine learning models, deep learning models, learning models, neural networks, etc. In some embodiments, the models can be based on language models (LMs). Language models can be capable of question answering by learning from a large corpus. The models 155 can also be based on other suitable models.

[0039] The application management platform 110 can run on a suitable electronic device. An electronic device here can be any type of device with computing capability, including an end device or a server device. An 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. A 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, etc. In some embodiments, the management platform 110 can be implemented based on cloud services.

[0040] It should be understood 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, although a single user is shown interacting with the application creation portion and a single user is shown 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.

[0041] Currently, a user input from a user, i.e., a task execution request for a target application, is usually received in a target application. In the case that the user input contains a clear instruction, it can be usually well completed. For example, the user input is “use line chart and column chart to reflect the sales of last month”, which contains clear line chart and column chart instructions, and thus has a better completion degree when executed. However, if the user input is relatively vague, it is difficult for related technologies to complete. For example, if the user input is “analyze the sales of last month”, the related technologies usually only get a sales data. That is, the related technologies lack sufficient recognition ability and multi-dimensional problem solving ability. For the target application 120, how to accurately understand the user’s expected target and give a solution from a non-angle is worth exploring.

[0042] In the embodiments of the present disclosure, an improved scheme of task processing is provided. In the scheme, a received user input is parsed to obtain a parsing result, and the user input indicates a target application. For a subtask request in a plurality of subtask requests, a data requirement of the subtask request is determined. In response to obtaining target data based on the data requirement, a specified function is called to process the subtask request. In this way, the user input is first parsed to determine a plurality of subtask requests, and each subtask request can correspond to a solution to the user input. Secondly, the data requirement of each subtask request is determined, that is, the data of each subtask can be the same as or different from other subtasks. For example, there are 5000 pieces of data, and the solution corresponding to the first subtask can select 1000 pieces of data, and the solution corresponding to the second subtask can involve 1000 pieces of data. The data between the two subtasks can be the same data, or there can be an intersection, or there can be no intersection. For example, the second subtask can involve the maximum value, the minimum value, or the average value of the 1000 pieces of data required by the first subtask. Or the data involved by the second subtask is other data besides the 1000 pieces of data required by the first subtask. That is, the data is obtained to better solve the problem corresponding to the user input by the subtask. After obtaining the target data, each subtask request can perform a task according to the called specified function. The specified function can be different functions of the target application 120, such as a function of editing a data chart, a function of generating a data analysis report, etc. Finally, the embodiments of the present application can generate a plurality of solutions according to the user input, and each solution will have adaptive data. Thus, the problem corresponding to the user input can be better solved.

[0043] Some example embodiments of the present disclosure will be described in detail below with reference to examples of the accompanying drawings. It should be understood that the pages shown in the accompanying drawings are merely examples, and various page designs can actually exist. Various graphical elements in the page can have different arrangements and different visual representations, one or more elements of which can be omitted or replaced, and one or more other elements can also exist. The embodiments of the present disclosure are not limited in this respect.

[0044] The task management process described by 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 an application management platform, such as the application management platform 110 shown in FIG. 1, is described. The user interface presented by the application management platform 110 can be presented via a terminal device of the end user 145, and the application management platform 110 can receive user input via the terminal device of the end user 145. In some embodiments of the present disclosure, the end user 145 is an end 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 a 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.

[0045] FIG. 2 shows a block diagram of a task processing process 200, according to some embodiments of the present disclosure. The task processing process can be implemented in the application management platform 110. The task processing process shown in FIG. 2 is described below in conjunction with FIG. 1.

[0046] As shown in FIG. 2, at block 201, the application management platform 110 parses the received user input to obtain a parsing result, the user input indicating a task execution request for a target application, and the parsing result indicating a plurality of sub-task requests corresponding to the task execution request.

[0047] The end user 145 can perform interactive actions in the interactive window 142, which can include text input, voice input, and the like. The user input can be natural language received in the interactive window 142. The user input is a task execution request for the target application 120. For example, the user input can be “analyze the sales situation of A product in the last quarter”, “predict the market performance of B product”, and the like.

[0048] FIG. 3 shows a schematic diagram of a task processing process 300, according to some embodiments of the present disclosure. In conjunction with FIG. 3, at block 301, after the application management platform 110 receives the user input 301, the application management platform 110 can parse the user input. Illustratively, the parsing can be accomplished by the application management platform 110 invoking the model 155, with the aid of the natural language processing capability of the model 155. At block 302, the model 155 can obtain a parsing result by parsing the user input. The parsing result can be used to indicate a plurality of sub-task requests corresponding to the task execution request.

[0049] Taking the user input 301 of "analyze the sales of product A in the last quarter" as an example, the analysis result can include presenting the sales data in the form of a column chart, presenting the sales proportion in the form of a pie chart, and presenting the sales change data in the form of a column chart combined with a line chart. Different forms can correspond to subtask requests. That is, in the current example, the analysis result can complete the goal of analyzing the sales of product A in the last quarter in 3 ways. The 3 ways can be independent of each other, that is, the 3 ways can each complete the goal of analyzing the sales of product A in the last quarter from an independent problem-solving perspective. Taking the analysis result of completing the goal of analyzing the sales of product A in the last quarter in 3 ways as an example, the analysis result indicates that the subtask requests corresponding to the task execution request are 3. The above user input and analysis result are only exemplary and the actual situation is not limited thereto.

[0050] As shown in FIG. 2, at block 202, the application management platform 110 determines, for a subtask request in the plurality of subtask requests, a data requirement of the subtask request.

[0051] For each subtask request in the plurality of subtask requests, the data requirement can be different, hereinafter referred to as a subtask. Still taking the analysis result of the user input of "analyze the sales of product A in the last quarter" as an example. For the first subtask corresponding to the form of a column chart, as shown in FIG. 3, at block 303, the application management platform 110 can determine that the data requirement of the first subtask is the sales data of product A in the past several quarters. For the second subtask corresponding to the form of a pie chart, the application management platform 110 can determine that the data requirement of the second subtask is the sales data of product A in the last quarter and the sales data of other several products (of the same manufacturer) in the last quarter. For the third subtask corresponding to the form of a column chart combined with a line chart, the application management platform 110 can determine that the data requirement of the third subtask is the sales data of product A in the past several quarters and the sales data of other several products (of the same manufacturer) in the past several quarters.

[0052] The application management platform 110 can use the code compilation instructions of the model 155 to combine the user input as a token of a query instruction to be generated. In addition, the management platform 110 can input the analysis result and the interaction content with the user, etc. as context information to the model 155. Finally, the management platform 110 can also input the database information, such as the database name, the database access address, etc. to the model 155. In this way, the model 155 can generate a query instruction to obtain the target data. Exemplarily, the query instruction can be a structured query language (SQL) query statement.

[0053] It can be found through the above process that even if the same database, the data requirements of different sub-task requests can also be different. The data requirements of sub-task requests have a strong correlation with the goals of sub-tasks. Therefore, in the current embodiment, even if the source data is the same database, different slices of the source data can be used to achieve the goals corresponding to different sub-task requests.

[0054] As shown in FIG. 2, at block 203, the application management platform 110 calls a specified function to process the sub-task request in response to obtaining the target data based on the data requirement.

[0055] Still taking the example that the user input is "analyze the sales situation of product A in the last quarter" as described above, the application management platform 110 can obtain three pieces of target data for the three sub-task requests. After obtaining the three pieces of target data, the application management platform 110 can call a specified function to process each sub-task request.

[0056] The three sub-task requests in the foregoing example are requests that can generate a chart according to the user input, so the application management platform 110 can correspondingly call a function of generating an editable data chart when calling the specified function. Still in combination with FIG. 3, at block 304, the function of generating an editable data chart can include functions such as generating a pie chart, a column chart, a line chart, or a combination chart. It can be understood that the above charts are only illustrative and do not limit the actual situation.

[0057] The function of generating an editable data chart is used to process the sub-task request. Finally, in combination with FIG. 3, at block 305, the final editable data chart is generated by executing the rendering of the editable data chart. It can be understood that although the specified function in the current embodiment is the function of generating an editable data chart related to chart generation. In actual scenarios, the application management platform 110 can call various functions related to user input based on user input. For example, the user input is "predict the market performance of product B", so the specified function determined by the application management platform 110 can be a function of generating an editable data chart, a digital person broadcasting function, a function of generating an editable text report, etc.

[0058] Through the embodiments of the present application, the single-dimensional interpretation of the user input can be overcome, and multiple sub-task requests corresponding to the task execution request can be determined by analyzing the user input. For different sub-task requests, target data suitable for the sub-task request can be selected. Finally, the execution of multiple sub-tasks is completed by calling a specified function. Therefore, diversified ways to solve problems can be provided, and the data used by each way to solve problems is more suitable. Through the above scheme, the problem corresponding to the user input can be better solved.

[0059] In some embodiments, the application management platform 110 parsing the received user input comprises: based on the user input, determining a plurality of solution approaches for the task execution request; adding an inference identifier to each solution approach, the inference identifier being used to indicate the reason for determining the solution approach; and respectively generating a sub-task request corresponding to each solution approach.

[0060] After the application management platform 110 completes the textual understanding of the user input by calling the model 155, the application management platform 110 can generate a plurality of solution approaches for the task execution request based on the task execution request for the target application.

[0061] Still taking the parsing result of the aforementioned user input “analyze the sales situation of product A in the last quarter” as an example, the parsing result concludes that the target of analyzing the sales situation of product A in the last quarter can be achieved in 3 ways. The 3 ways correspond to 3 solution approaches.

[0062] For each solution approach, the application management platform 110 can add an inference identifier, so as to explain the reason why the application management platform 110 selects the solution approach by using the inference identifier.

[0063] Taking the form of presenting the sales data in a column chart as an example, the inference identifier added by the application management platform 110 can be “compare the sales of the last quarter with the sales of the previous two quarters to show the sales change”.

[0064] Taking the form of presenting the sales proportion in a pie chart as an example, the inference identifier added by the application management platform 110 can be “compare the sales of product B and product C of the same type with the sales of product A to show the market share of product A”.

[0065] Taking the form of presenting the sales change data in a column chart combined with a line chart as an example, the inference identifier added by the application management platform 110 can be “compare the sales of the last quarter directly, and introduce the sales trend to make a dynamic comparison”.

[0066] Furthermore, the application management platform 110 can also add a word limit to the inference identifier. If the word number of the generated inference identifier exceeds the preset word threshold, the application management platform 110 can also summarize the generated inference identifier to compress the word number of the inference identifier.

[0067] Finally, the application management platform 110 can generate a sub-task request based on the solution approach after adding the inference identifier. That is, the single task execution request corresponding to the user input is split into a plurality of sub-task execution requests.

[0068] In some embodiments, the application management platform 110 can also execute the solution path with the added reasoning identifier in the designated display area; in response to the received adjustment instruction, execute the adjustment action corresponding to the adjustment instruction, which is for at least one solution path with the added reasoning identifier.

[0069] Referring to FIG. 4, FIG. 4 shows an example of a page 400 of display data information according to some embodiments of the present disclosure. The query content interaction area 411 can display the user input. In the solution path interaction area 412, on the one hand, a plurality of solution paths determined according to the user input can be displayed, as well as the reasoning identifier of the path. On the other hand, the solution path interaction area 412 can be an interactive window to receive the adjustment instruction of the terminal user 145 to the solution path.

[0070] For example, the adjustment instruction can include a deletion instruction. For example, the terminal user 145 determines that the solution path corresponding to the recommended chart 1 is not suitable and has no modification prospect, and can directly issue an adjustment instruction of “deleting the recommended chart 1”. Thus, the application management platform 110 can directly delete the solution path according to the deletion instruction.

[0071] For example, the adjustment instruction can include a modification instruction. For example, the reasoning identifier of the recommended chart 3 records “comparing the sales of B and C products of the same type to analyze the market share of A product”. The modification instruction can be “not comparing B product”, “adding the product with the highest market sales for comparison”, “adding new comparison dimensions in addition to market share”, etc. Thus, the application management platform 110 can update the solution path corresponding to the recommended chart 3 according to the modification instruction.

[0072] For example, the adjustment instruction can also include an addition instruction. For example, the terminal user 145 thinks of a better solution path for the existing solution path, and can directly issue an instruction such as “can you combine the recommended chart 1 and the recommended chart 2”, or “add a deviation chart to also include the growth amount and the growth rate”. Thus, the application management platform 110 can expand the solution path according to the addition instruction.

[0073] The application management platform 110 can generate an adjustment action corresponding to the adjustment instruction for the adjustment instruction. Finally, the adjustment object of the adjustment action is determined, so that the adjustment action is applied to the corresponding adjustment object, that is, the adjustment action is applied to the solution path matched therewith, and the solution path is optimized and adjusted.

[0074] Through the above process, the solution path can be perfected through interaction with the terminal user 145 to optimize the execution effect of the task execution request.

[0075] In some embodiments, the application management platform 110 determines the data requirement of the subtask request in the following manner: based on the target of the obtained subtask request, a data filtering condition is determined; based on the data filtering condition, a data query instruction is generated; and the result obtained by executing the data query instruction is taken as the target data.

[0076] Each subtask request has its corresponding target. Taking the example of presenting the sales data in the form of a column chart, the target is to show the sales data of the last quarter. Then the application management platform 110 can determine that the filtering condition corresponding to the target is to query the sales data of the A product in the last quarter from the database. Based on this, the application management platform 110 generates a query instruction to obtain the sales data of the A product in the last quarter according to the known database, the table in the known database, and the column in the table, and the like.

[0077] Similarly, taking the example of presenting the sales proportion in the form of a pie chart, the target is to show the sales proportion of the A product. Then the application management platform 110 can determine that the filtering condition corresponding to the target is to query the sales data of the A product and the sales data of other (same type or competitive product) products except the A product from the database. The sales data of other products except the A product can be the total (for example, the total sales of the B product, the C product and the D product), or the sales data of other products can be obtained according to the category of the product (for example, the sales data of the B product, the sales data of the C product and the sales data of the D product are obtained in sequence).

[0078] After the query instruction is generated, the result obtained by executing the query instruction can be taken as the target data obtained in response to the data requirement.

[0079] Through the above process, the personalized data acquisition for different subtask requests can be completed. The ultimate goal is to enable each subtask request to complete the corresponding target.

[0080] In some embodiments, the application management platform 110 can also determine the data requirement of the subtask request in the following manner: in response to the result obtained by the data query instruction being unable to complete the target, a data analysis instruction is generated, the data analysis instruction is configured to perform data analysis processing on the result obtained by executing the data query instruction to obtain an analysis processing result; and the analysis processing result is taken as the target data.

[0081] Still taking the column chart as an example, the target is to show the sales data of the last quarter. Then the application management platform 110 can determine that the data query instruction corresponding to the target is to query the sales data of the A product in the last quarter from the database. After obtaining the result, the application management platform 110 can determine whether the obtained target data can complete the target of showing the sales data of the last quarter. If the determination is that the target can be completed, the sales data of the A product in the last quarter from the database can be used as the target data. Otherwise, if the determination is that the target cannot be completed, the application management platform 110 can generate an analysis instruction. The result of the data analysis processing on the result of executing the data query instruction is obtained through the analysis instruction, and the analysis processing result is used as the target data.

[0082] Taking the pie chart as an example, the target is to show the sales proportion of the A product. Then the application management platform 110 can determine that the filtering condition corresponding to the target is to query the sales data of the A product and the sales data of other products except the A product from the database. After obtaining the result, the application management platform 110 can determine whether the obtained data can complete the target of showing the sales proportion of the A product. Obviously, showing the sales proportion not only needs the sales data, but also needs to calculate the proportion. Therefore, the application management platform 110 can determine that the result of the data query instruction cannot complete the target of showing the sales proportion of the A product. In response to the result of the data query instruction being unable to complete the target, the application management platform 110 needs to generate a data analysis instruction. In the current embodiment, the data analysis instruction is configured to perform a ratio operation on the sales of the A product and the total sales of other products including the A product. If the result of the ratio operation can complete the target of showing the sales proportion of the A product, the result of the ratio operation can be used as the target data. Otherwise, if the result of the ratio operation still cannot complete the target of showing the sales proportion of the A product, the application management platform 110 can adjust the analysis instruction or add a new analysis instruction, so that the analysis processing result obtained by executing the adjusted analysis instruction can complete the target of the sub-task request. That is, if the result of the ratio operation still cannot complete the target of showing the sales proportion of the A product, the application management platform 110 can set multiple rounds of analysis instructions, each round of analysis instruction can refer to the result of the previous round and the information of the determined reason that the target cannot be completed, so as to finally complete the target of the sub-task request through multiple rounds of analysis instructions.

[0083] Through the above process, the application management platform 110 can complete the target of the subtask request by using the analysis instruction and the query instruction at the same time. If the advantage of the query instruction is to quickly obtain data, the advantage of the analysis instruction is that the data can be calculated complexly. Exemplarily, the proportion problem, the same problem, the ring problem, etc. can correspond to complex calculation. The analysis instruction can be compiled by using a suitable programming language. The basic statements of the programming language include arithmetic operators, logical operators, etc., so that the complex problems such as the same, the ring, the proportion, etc. can be easily solved. In the current embodiment, the code compilation of the query instruction and the analysis instruction can be completed by the application management platform 110 calling the model 155. By using the code compilation capability of the model 155, the process of manually compiling the code can be saved.

[0084] In some embodiments, the application management platform 110 can also perform data optimization processing on the result obtained by executing the data query instruction, and the data optimization processing includes at least one of data cleaning and data bit number adjustment.

[0085] After executing the data query instruction to obtain the query result, the application management platform 110 can also perform data optimization processing on the query result. The data optimization processing can include at least one of data cleaning and data bit number adjustment.

[0086] Taking data cleaning as an example, if the application management platform 110 determines that there is meaningless data in the query result, the meaningless data can be cleaned. Exemplarily, the meaningless data can be data that cannot be recognized, null data, or data with a value of 0, etc.

[0087] Taking data bit number adjustment as another example, the application management platform 110 can adjust the bit number of the result obtained by the data query instruction. For example, the adjustment can include data rounding, data retaining one digit after the decimal point, etc.

[0088] Through the above process, the optimization processing of the result obtained by the query instruction can be completed. It is not difficult to understand that the result obtained by the analysis instruction can also be processed, such as data bit number adjustment, so that the data can be simplified when generating an editable chart or executing a data broadcast function in the subsequent process.

[0089] In some embodiments, the application management platform 110 can also add a data source identifier to the target data, and the data source identifier is used to indicate the source of the target data.

[0090] Taking the result obtained by the query instruction as an example, after obtaining the result obtained by the query instruction, the application management platform 110 can also add a data source identifier to the result obtained by the query instruction. The data source identifier is used to indicate the origin of the result. For example, the data source identifier can be used to indicate that the result obtained by the query instruction is derived from the database name of the database, the table in the database, the column in the table, and the like.

[0091] Taking the result obtained by the analysis instruction as an example, since the analysis instruction is the result obtained by analyzing and calculating the result obtained by the query instruction. Therefore, for the result obtained by the analysis instruction, the application management platform 110 can also take the data source identifier of the object of the analysis and calculation as the data source identifier corresponding to the result of the analysis and calculation.

[0092] The significance of adding the data source identifier is that the authenticity of the data can be marked, thereby avoiding false data without provenance.

[0093] In some embodiments, the application management platform 110 can call a specified function to process each sub-task request, which can be: calling a data chart generation function, generating a data chart based on each sub-task request and the target data corresponding to the sub-task request, and the presentation style of the data chart is editable.

[0094] In the current embodiment, the function of generating an editable data chart is taken as an example of the specified function called. The application management platform 110 can generate an editable data chart by calling the function of the editable data chart supported by the target application 120 based on the subtask request and the target data corresponding to the subtask request. The function of the editable data chart can be implemented based on a domain-specific language (DSL). The DSL can be used as a script language specific to chart generation, and can complete the generation and rendering of the chart corresponding to the subtask according to the target of the subtask and the target data of the subtask. Referring to FIG. 5A, FIG. 5A shows an example of a page 500 of calling the specified function to process the column chart result corresponding to the subtask request according to some embodiments of the present disclosure. In FIG. 5A, the sales data of the A product in the past three quarters is shown in the form of a column chart. Referring to FIG. 5B, FIG. 5B shows an example of a page 510 of calling the specified function to process the pie chart result corresponding to the subtask request according to some embodiments of the present disclosure. In FIG. 5B, the sales proportion of the A product in the last quarter is shown in the form of a pie chart. Referring to FIG. 5C, FIG. 5C shows an example of a page 520 of calling the specified function to process the column chart combined with the line chart result corresponding to the subtask request according to some embodiments of the present disclosure. In FIG. 5C, the change of the sales of the A product compared with the sales of other products is shown in the form of a column chart combined with a line chart. Exemplarily, the data chart presentation style in the page 520 is editable, for example, the font, font size, color, and other presentation styles in the data chart are in an editable state.

[0095] In some embodiments, the application management platform 110 can also store the target data in a specified storage area. And wherein calling the specified function to process the subtask request comprises: providing the address of the specified storage area to the specified function.

[0096] After the application management platform 110 obtains the query result by querying the data using the query instruction, the application management platform 110 can store the result obtained by the query instruction in a specified storage area. The specified area can be a cache area in the programming language sandbox. After storage is completed, the application management platform 110 can generate a storage identifier according to the address where the result obtained by the query instruction is stored. The storage identifier can be used to represent the data. Thus, when the application management platform 110 calls the specified function to process the subtask request to generate a new query instruction or an analysis instruction, the application management platform 110 can first generate a storage address identifier reading instruction to read the query result stored in the specified storage area using the storage identifier. Similarly, the result obtained by executing the analysis instruction can also be stored in the specified storage area, and a storage identifier can be generated according to the address where the result of the analysis instruction is stored.

[0097] Through the above process, the access between data is only represented by the storage identifier, so that the data dimension reduction of the token compiled by the application management platform 110 when calling the model can be completed. That is, when the token is compiled, all the data to be obtained does not need to be listed, and only a read instruction for the address of the storage area needs to be generated. In the case of limited model context window data receiving capacity, the resource overhead of the model can be greatly reduced.

[0098] FIG. 6 shows a schematic structural block diagram of a task processing apparatus 600 according to some embodiments of the present disclosure. The apparatus 600 may, for example, be implemented in or included in the application management platform 110. Various modules / components in the apparatus 600 can be implemented by hardware, software, firmware, or any combination thereof.

[0099] As shown, the apparatus 600 includes a parsing module 601 configured to parse a received user input to obtain a parsing result, the user input indicating a task execution request for a target application, and the parsing result indicating a plurality of sub-task requests corresponding to the task execution request. A data requirement determination module 602 is configured to determine, for a sub-task request in the plurality of sub-task requests, a data requirement of the sub-task request. A sub-task request processing module 603 is configured to, in response to obtaining target data based on the data requirement, invoke a specified function to process the sub-task request.

[0100] In some embodiments, the parsing module 601 can further include a solution approach determination submodule configured to determine a plurality of solution approaches of the task execution request based on the user input, an identifier adding submodule configured to add an inference identifier to each solution approach, the inference identifier being used to indicate a reason for determining the solution approach, and a sub-task request generation module configured to generate a sub-task request corresponding to each solution approach, respectively.

[0101] In some embodiments, the apparatus 600 can further include a solution approach display module configured to display the solution approach with the added inference identifier in a specified display area, and a solution approach adjustment module configured to, in response to receiving an adjustment instruction, perform an adjustment action corresponding to the adjustment instruction, the adjustment instruction being for at least one solution approach with the added inference identifier.

[0102] In some embodiments, the data requirement determination module 602 can include a screening condition determination submodule configured to determine a data screening condition based on the target of the obtained sub-task request, a data query instruction generation submodule configured to generate a data query instruction based on the data screening condition, and a result obtained by executing the data query instruction as the target data.

[0103] In some embodiments, the data requirement determination module 602 is configured to further include an analysis instruction generation submodule configured to, in response to the result of the data query instruction failing to achieve the target, generate a data analysis instruction, the data analysis instruction being configured to perform data analysis processing on the result of executing the data query instruction to obtain an analysis processing result; and take the analysis processing result as the target data.

[0104] In some embodiments, the data requirement determination module 602 is configured to perform data optimization processing on the result of executing the data query instruction, the data optimization processing including at least one of data cleaning and data bit number adjustment.

[0105] In some embodiments, the apparatus 600 further includes a data source identifier adding module configured to add a data source identifier to the target data, the data source identifier being used to indicate the origin of the target data.

[0106] In some embodiments, the subtask request processing module 603 is specifically configured to invoke a data chart generation function, and generate a data chart based on each subtask request and the target data corresponding to the subtask request, the presentation style of the data chart being editable.

[0107] In some embodiments, the apparatus 600 further includes a target data representation module configured to store the target data in a designated storage area. And invoking the designated function to process the subtask request includes: providing an address of the designated storage area to the designated function.

[0108] FIG. 7 illustrates a block diagram of an electronic device 700 in which one or more embodiments of the disclosure can be implemented. It should be understood that the electronic device 700 illustrated in FIG. 7 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. The electronic device 700 illustrated in FIG. 7 can include or be implemented as the application management platform 110 of FIG. 1, or the apparatus 600 of FIG. 6.

[0109] As shown in FIG. 7, the electronic device 700 is in the form of a general electronic device. The components of the electronic device 700 can include, but are not limited to, one or more processors 710 or processing units, a memory 720, a storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. The processor 710 can be an actual or virtual processor and is capable of performing various processing according to programs stored in the memory 720. In a multi-processor system, multiple processors perform computer-executable instructions in parallel to improve the parallel processing capability of the electronic device 700.

[0110] The electronic device 700 typically includes a plurality of computer storage media. Such media can be volatile and / or nonvolatile, removable and / or non-removable, and can be implemented in any method or technology for storage of information and / or data. The memory 720 can be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, or any other memory technology), or some combination of the multiple memory types. The storage device 730 can be a removable storage medium or a non-removable storage medium implemented in any method or technology for storage of information and / or data.

[0111] The electronic device 700 can further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 7, a disk drive or other computer-readable media drive can be provided for reading from or writing to a removable, non-removable, volatile, or non-volatile computer-readable medium. In these instances, each drive can be connected to the bus (not shown) by one or more data media interfaces. The memory 720 can include a computer program product 725 having one or more program modules configured to carry out the various methods or actions of the various embodiments of the present disclosure.

[0112] The communication unit 740 enables communications with other electronic devices over a communication medium. Additionally, the functionality of the components of the electronic device 700 can be implemented in a single computing cluster or a plurality of computer machines that are capable of communicating with one another over a communication connection. As such, the electronic device 700 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network nodes.

[0113] The input device 750 can be one or more input devices, such as a mouse, a keyboard, a trackball, etc. The output device 760 can be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 700 can also communicate with one or more external devices (not shown) such as a storage device, a display device, etc. through the communication unit 740, as needed, one or more devices that enable a user to interact with the electronic device 700, or any device (e.g., a network card, a modem, etc.) that enables the electronic device 700 to communicate with one or more other electronic devices. Such communication can be carried out via an input / output (I / O) interface (not shown).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Having described several implementations of the present disclosure, it will be clear to those skilled in the art that many modifications, additions, and substitutions are possible without departing from the scope and spirit of the described implementations. Many modifications and variations of the present disclosure are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims, the present disclosure can be practiced otherwise than as specifically described. While the present disclosure has been described with reference to the above examples, it is to be understood that modifications and variations of the examples can be configured and that it is therefore intended to cover any and all modifications and variations.

Claims

1. A task processing method, comprising: The received user input is parsed to obtain a parsing result. The user input indicates a task execution request for the target application, and the parsing result indicates multiple subtask requests corresponding to the task execution request. For each of the multiple subtask requests, determine the data requirements of that subtask request; as well as In response to obtaining the target data based on the data requirement, the specified function is invoked to process the subtask request.

2. The method according to claim 1, wherein parsing the received user input comprises: Based on the user input, multiple solutions to the task execution request are determined; Add a reasoning identifier to each of the solutions, the reasoning identifier being used to indicate the reason for determining the solution; as well as Each subtask request corresponding to one of the aforementioned solutions is generated.

3. The method according to claim 2, further comprising: Display the solutions with added reasoning tags in the designated display area; as well as In response to a received adjustment instruction, an adjustment action corresponding to the adjustment instruction is performed, the adjustment instruction being for at least one of the solutions for which an inference identifier has been added.

4. The method according to claim 1, wherein determining the data requirements of the subtask request includes: Based on the obtained target of the subtask request, determine the data filtering conditions; Based on the data filtering conditions, a data query instruction is generated; as well as The result obtained by executing the data query instruction shall be used as the target data.

5. The method according to claim 4, wherein determining the data requirements for the subtask request further includes: If the result obtained from the data query instruction cannot achieve the objective, a data analysis instruction is generated. The data analysis instruction is configured to perform data analysis processing on the result obtained from executing the data query instruction to obtain the analysis processing result. as well as The analysis and processing results are used as the target data.

6. The method according to claim 4, further comprising: The results obtained from executing the data query command are subjected to data optimization processing, which includes at least one of data cleaning and data bit adjustment.

7. The method according to claim 1, 4 or 5, further comprising: Add a data source identifier to the target data, the data source identifier being used to indicate the origin of the target data.

8. The method of claim 1, wherein invoking a specified function to process each of the subtask requests includes: The data chart generation function is invoked to generate a data chart based on each subtask request and the target data corresponding to that subtask request. The presentation style of the data chart is editable.

9. The method according to claim 1, further comprising: The target data is stored in a designated storage area, and The invocation of a specified function to handle the subtask request includes: and Provide the address of the specified storage area to the specified function.

10. A task processing apparatus, comprising: The parsing module is configured to parse received user input to obtain a parsing result, wherein the user input indicates a task execution request for a target application, and the parsing result indicates multiple subtask requests corresponding to the task execution request. The data requirement determination module is configured to determine the data requirement of a subtask request for the plurality of subtask requests. as well as The subtask request processing module is configured to call a specified function to process the subtask request in response to obtaining the target data based on the data requirement.

11. An electronic device, comprising: At least one processor; as well as At least one memory, coupled to at least one processor and storing instructions for execution by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the method according to any one of claims 1 to 9.

12. 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 9.

13. 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 to 9.

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