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

Abstract

Embodiments of the present disclosure provide a task processing method, apparatus, device, and computer-readable storage medium. The method includes parsing 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. For a subtask request among the multiple subtask requests, the data requirements of the subtask request are determined. In response to obtaining target data based on the data requirements, a specified function is called to process the subtask request, thereby completing task processing from different problem-solving perspectives.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to task processing methods, apparatuses, devices, and computer-readable storage media. Background Art

[0002] With the development of information technology, various terminal devices can provide people with a variety of services in work and life. Applications that provide these services can be deployed on these devices. These applications present content and interact with users through their user interfaces, meeting their needs. In some cases, users may initiate task processing requests within applications. Therefore, how to best achieve the goals of these task processing requests is a key concern. Summary of the Invention

[0003] In a first aspect of the present disclosure, a task processing method is provided. The method includes parsing 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. For a subtask request among the multiple subtask requests, data requirements are determined for the subtask request. In response to obtaining target data based on the data requirements, a specified function is invoked to process the subtask request.

[0004] In a second aspect of the present disclosure, a task processing apparatus is provided, comprising: a parsing module 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. A query instruction generation module configured to determine the data requirements of a subtask request among the multiple subtask requests. An analysis instruction production module configured to, in response to obtaining target data based on the data requirements, call a specified function to process the subtask request.

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

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

[0007] It should be understood that the content described in this section is not intended to limit the key features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0009] Figure 1 A schematic diagram illustrating an example environment in which embodiments of the present disclosure can be implemented;

[0010] Figure 2 A block diagram illustrating a task processing process according to some embodiments of the present disclosure is shown;

[0011] Figure 3 A schematic diagram illustrating a task processing process according to some embodiments of the present disclosure is shown;

[0012] Figure 4 A schematic diagram showing a page displaying results according to some embodiments of the present disclosure is shown;

[0013] Figures 5A to 5C A schematic diagram showing an example interface for displaying charts according to some embodiments of the present disclosure is shown;

[0014] Figure 6 shows a schematic structural block diagram of a task processing device according to some embodiments of the present disclosure;

[0015] Figure 7 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. DETAILED DESCRIPTION

[0016] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0017] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below.

[0018] Herein, unless explicitly stated otherwise, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.

[0019] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0020] It is understandable that before using the technical solutions disclosed in the various embodiments of the present disclosure, the type, scope of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and authorization should be obtained from relevant users in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

[0021] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to the software or hardware such as the electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.

[0022] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message may be sent to the relevant user in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.

[0023] It is understandable that the above notification and user authorization process are merely illustrative and do not limit the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure. The activation of the digital assistant-related functions of the embodiment of the present disclosure, the data obtained, the processing and storage of the data, etc., shall all obtain the prior authorization of the user and other rights holders associated with the user, and shall comply with the provisions of relevant laws and regulations and the rules of agreement between rights holders.

[0024] As used herein, the term "model" can learn the association between corresponding inputs and outputs from training data, so that after training is completed, corresponding outputs can be generated for given inputs. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. A neural network model is an example of a model based on deep learning. In this article, "model" may also be referred to as "machine learning model", "learning model", "machine learning network" or "learning network", and these terms are used interchangeably in this article.

[0025] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. Environment 100 includes an application management platform 110, which can support the creation and / or execution of applications. In some embodiments, the portion of application management platform 110 that supports application creation can also be referred to as an application creation portion. In some embodiments, the portion of application management platform 110 that supports application execution can also be referred to as an application execution portion.

[0026] As shown in the figure, the application creation part can provide a user 105 with an environment for creating and publishing applications. User 105 can be referred to as an application creation user or creator. In some embodiments, the application creation part can be a low-code platform that provides a collection of tools for application creation. The application creation part can support visual development of various types of applications, allowing developers to skip the manual coding process and speed up the application development cycle and cost. The application creation part can support any appropriate platform for users to develop one or more types of applications, for example, it can include a platform based on Application Platform as a Service (aPaaS). Such a platform can support users to efficiently develop applications and implement operations such as application creation and application function adjustment.

[0027] The application creation part can be deployed locally on the terminal device of user 105, and / or can be supported by a server-side device. For example, the terminal device of user 105 can run a client of the application creation part, which can support the interaction between the user and the application creation part provided by the server. In the case where the application creation part runs locally on the user's terminal device, user 105 can directly use the terminal device to interact with the local application creation part. In the case where the application creation part runs on the server-side device, the server-side device can realize the service provision of the client running in the terminal device based on the communication connection between the terminal device. The application creation part can present a corresponding page 130 to the user 105 based on the operation of the user 105 to output and / or receive information related to application creation to the user 105 and / or from the user 105.

[0028] In some embodiments, the application creation portion can be associated with a corresponding database, which stores the data or information required for the application creation process supported by the application creation portion. For example, the database can store the code and description information corresponding to each functional module used to make up the application. The application creation portion can also perform operations such as calling, adding, deleting, and updating the functional modules in the database. The database can also store operations that can be performed on different functional blocks. For example, in a scenario where an application is to be created, the application creation portion can call the corresponding functional blocks from the database to build the application.

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

[0030] The digital assistant 122 can be configured to have the ability of intelligent dialogue. In the example shown in the figure, the digital assistant 122 can be integrated into the target application 120 and assist in executing task processing within the target application 120 as part of the target application 120. In other examples, the digital assistant 122 can be configured as an independently running application, such as a web application or other types of applications. In such an example, the digital assistant 122 and the target application 120 can be regarded as the same application. The digital assistant 122 is provided to assist users with various task processing needs in different applications and scenarios. During the interaction with the digital assistant 122, the user inputs an interactive message, and the digital assistant 122 provides a reply message in response to the user input. Generally, the digital assistant 122 can support users to input questions in natural language, and perform tasks and provide replies based on the understanding of natural language input and logical reasoning ability.

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

[0032] For each terminal user 145, the client of the application running portion can present an interaction window 142 of the target application 120 or the digital assistant 122 in the client interface, such as a conversation window with the digital assistant 122. The terminal user 145 can enter a conversation message in the conversation window, and the target application 120 can determine a reply message of the digital assistant 122 based on the created configuration information and present it 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 multimodal forms, such as text messages (e.g., natural language text), voice messages, image messages, video messages, and the like.

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

[0034] In some embodiments, at least some of the functionality of the target application 120 and / or at least some of the functionality of the digital assistant 122 in the target application 120 can be implemented based on a model. During the creation or execution of the target application 120, one or more models 155, such as the capabilities of the models 155, can be invoked. In the target application 120, the digital assistant 122 can utilize the models 155 to understand user input and provide responses to the user based on the output of the models 155.

[0035] During the creation process, the application management platform 110 tests the target application 120 using the model 155 to ensure that the target application 120's operating results meet expectations. During operation, in response to various user operation requests of the target application 120, the application execution component may need to use the model 155 to determine the user's response results.

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

[0038] It should be understood that the structure and functionality of the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. For example, although the figure shows a single user interacting with the application creation portion and a single user interacting with the application execution portion, in reality, multiple users can access the application management platform 110 to each create a digital assistant, and each digital assistant can be used to interact with multiple users.

[0039] At present, user input is usually received in the target application, that is, a task execution request for the target application. Taking the case where the user input includes data analysis requirements as an example, when the user input is a clear instruction, it can usually be completed well. For example, if the user input is "use a line chart and a bar chart to reflect last month's sales", it contains clear line chart and bar chart instructions, so there will be a good degree of completion during execution. However, if the user input is more vague, the relevant technology will be more difficult to complete. For example, if the user input is "analyze last month's sales", then the relevant technology will usually only get a sales data. In other words, the relevant technology lacks sufficient recognition capabilities and multi-dimensional problem-solving capabilities. For the target application 120, how to accurately understand the user's desired goals and provide solutions from different perspectives is worth exploring.

[0040] In an embodiment of the present disclosure, an improved task processing scheme is provided. In this scheme, received user input is parsed to obtain a parsed result, indicating that the user input is targeted for a target application. For a subtask request among multiple subtask requests, the data requirements of the subtask request are determined. In response to obtaining the target data based on the data requirements, a designated function is invoked to process the subtask request. In this manner, user input is first parsed to determine multiple subtask requests, each of which corresponds to a solution to the user input. Secondly, personalized data requirements are determined for each subtask request. This means that the data required for each subtask can be the same or different from that required for the other subtasks. For example, if there are 5,000 pieces of data, the solution for the first subtask may select 1,000 of those pieces of data, while the solution for the second subtask may involve 1,000 of those pieces of data. The data between the two subtasks may be the same data, or they may or may not overlap. For example, the second subtask may require the maximum, minimum, or average value of the 1,000 pieces of data required by the first subtask. It is also possible that the data involved in the second subtask is other data other than the 1,000 pieces of data required for the first subtask. In other words, the acquisition of data is for the subtask to better solve the problem corresponding to the user input. After each subtask request obtains the target data, it can perform the task according to the specified function called. The specified function can be the different functions of the target application 120, such as the function of editing data charts, the function of generating data analysis broadcasts, etc. Ultimately, the embodiment of the present application can generate multiple solutions based on user input, and each solution will have adapted data. This can better solve the problem corresponding to the user input.

[0041] Some example embodiments of the present disclosure will be described in detail below with reference to the examples in the accompanying drawings. It should be understood that the pages shown in the accompanying drawings are merely examples, and a variety of page designs may exist. The various graphical elements in a page may have different arrangements and different visual representations, one or more elements may be omitted or replaced, and one or more other elements may also be present. The embodiments of the present disclosure are not limited in this respect.

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

[0043] Figure 2 FIG2 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. Figure 1 To describe Figure 2 The task processing process is shown.

[0044] like Figure 2 As shown, in block 201 , the application management platform 110 parses the received user input to obtain a parsing result, where 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.

[0045] End user 145 can perform interactive actions in interaction window 142, such as text input and voice input. User input can be natural language received in interaction window 142. User input is a task execution request for target application 120. For example, user input can be "Analyze the sales of product A last quarter" or "Predict the market performance of product B."

[0046] Figure 3 A schematic diagram of a task processing process 300 according to some embodiments of the present disclosure is shown, Figure 3 As shown, at block 301, after receiving user input 301, application management platform 110 may parse the user input. Exemplarily, the parsing may be accomplished by application management platform 110 invoking model 155 and leveraging model 155's natural language processing capabilities. At block 302, model 155 parses the user input to obtain a parsing result. The parsing result may be used to indicate multiple subtask requests corresponding to the task execution request.

[0047] Taking the example of user input 301 being "Analyze the sales of product A last quarter," the parsing results can include presenting sales data in the form of a bar chart, presenting sales percentages in the form of a pie chart, and presenting month-on-month sales change data in the form of a bar chart combined with a line chart. Different formats can correspond to subtask requests. That is, in the current example, the parsing results can conclude that the goal of analyzing the sales of product A last quarter can be completed in three ways. The three methods can be independent of each other, that is, all three methods can achieve the goal of analyzing the sales of product A last quarter from their own independent problem-solving perspectives. Taking the example of the parsing result being the goal of analyzing the sales of product A last quarter in three ways, the parsing result indicates that there are three subtask requests corresponding to the task execution request. The above user input and parsing results are merely illustrative, and actual situations are not limited to this.

[0048] like Figure 2 As shown, in block 202 , the application management platform 110 determines the data requirement of a subtask request among a plurality of subtask requests.

[0049] For each of the multiple subtask requests, the data requirements can usually be different, which are referred to as subtasks in the following text. Still taking the above user input of "analyzing the sales of product A in the last quarter" as an example, for the first subtask corresponding to the bar chart, combined with Figure 3 As shown, in box 303, the application management platform 110 can determine that the data requirement for the first subtask is the sales data of product A for the past several quarters. For the second subtask corresponding to the pie chart format, the application management platform 110 can determine that the data requirement for the second subtask is the sales data of product A for the previous quarter, as well as the sales data of several other products (from the same manufacturer) in the previous quarter. For the third subtask corresponding to the bar chart superimposed on the line chart format, the application management platform 110 can determine that the data requirement for the third subtask is the sales data of product A for the past several quarters, as well as the sales data of several other products (from the same manufacturer) in the past several quarters.

[0050] The application management platform 110 can use the code compilation instructions of the model 155 to combine the user input as the token of the query instruction to be generated. In addition, the management platform 110 can input the parsing results and the content of the interaction with the user as context information to the model 155. Finally, the management platform 110 can also input database information, such as the database name and database access address, to the model 155. Thus, the query instruction for obtaining the target data can be generated with the help of the model 155. For example, the query instruction can be a Structured Query Language (SQL) query statement.

[0051] Through the above process, it is not difficult to find that even for the same database, the data requirements of different subtask requests may be different. The data requirements of subtask requests and the goals of the subtasks are strongly correlated. Therefore, in the current embodiment, even if the source data is the same database, the goals corresponding to different subtask requests can be achieved based on different slices of the source data.

[0052] like Figure 2 As shown, in block 203 , in response to obtaining target data based on data requirements, the application management platform 110 calls a designated function to process the subtask request.

[0053] Using the example above where the user input is "Analyze the sales of product A last quarter," the application management platform 110 can obtain three sets of target data for each of the three subtask requests. After obtaining the three sets of target data, the application management platform 110 can call a specified function to process each subtask request.

[0054] The three subtask requests in the above example are requests for generating charts based on user input, so the application management platform 110 can call the function of generating editable data charts when calling the specified function. Figure 3 As shown, in block 304, the function of generating an editable data chart may include generating a pie chart, a bar chart, a line chart, or a combination chart, etc. It is easy to understand that the above charts are only exemplary and do not limit the actual situation.

[0055] Use the function of generating editable data charts to process subtask requests. Finally, combine Figure 3As shown, in block 305, the final editable data chart is generated by rendering the editable data chart. It is not difficult to understand that although the designated function in the current embodiment is a function related to chart generation to generate an editable data chart, in actual scenarios, the application management platform 110 can call various functions related to user input based on user input. For example, if the user input is "predict the market performance of product B", then, for example, the designated function determined by the application management platform 110 can be a function of editing a data chart, a digital human broadcasting function, an editable text report generation function, etc.

[0056] Through the embodiments of the present application, the single-dimensional interpretation of user input can be overcome, and the multiple subtask requests corresponding to the task execution request can be determined by parsing the user input. For different subtask requests, target data suitable for the subtask request can be selected. Finally, the specified function is called to complete the execution of multiple subtasks. In this way, a diversified approach to solving the problem can be provided, and the data used by each approach to solving the problem is more suitable. Through the above solution, the problem corresponding to the user input can be better solved.

[0057] In one embodiment, the application management platform 110 parses the received user input to include: determining multiple solutions to the task execution request based on the user input; adding a reasoning identifier to each solution, where the reasoning identifier is used to indicate the reason for determining the solution; and generating subtask requests corresponding to each solution.

[0058] After the calling model 155 completes understanding of the text input by the user, the application management platform 110 may generate multiple solutions to the task execution request based on the task execution request for the target application.

[0059] Let's use the example of the user input "Analyze the sales of product A last quarter" as an example. The analysis results show three ways to complete the goal of analyzing the sales of product A last quarter. These three ways correspond to three solutions.

[0060] For each solution approach, the application management platform 110 may add a reasoning identifier, thereby using the reasoning identifier to explain why the application management platform 110 selects the solution approach.

[0061] Taking the example of presenting sales data in the form of a bar chart, the inference indicator added by the application management platform 110 may be "comparing the sales of the previous quarter with the sales of the two previous quarters to show the sales changes."

[0062] Taking the example of presenting sales share in the form of a pie chart, the inference indicator added by the application management platform 110 may be "comparing the sales of the same type of product B and product C, etc., to display the market share of product A."

[0063] Taking the example of displaying the month-on-month sales change data in the form of a bar chart combined with a line chart, the reasoning identifier added by the application management platform 110 can be "on the one hand, intuitively compare the sales of the previous quarter, and on the other hand, introduce the sales change trend for dynamic comparison."

[0064] Furthermore, the application management platform 110 may also add a word limit to the reasoning identifier. If the word count of the generated reasoning identifier exceeds a preset word count threshold, the application management platform 110 may also summarize the generated reasoning identifier, thereby completing word count compression of the reasoning identifier.

[0065] Finally, the application management platform 110 can generate subtask requests based on the solution path after adding the inference identifier, that is, splitting the single task execution request corresponding to the user input into multiple subtask execution requests.

[0066] In one embodiment, the application management platform 110 can also display the solution path with added reasoning identification in a designated display area; in response to the received adjustment instruction, execute the adjustment action corresponding to the adjustment instruction, and the adjustment instruction is for at least one solution path with added reasoning identification.

[0067] refer to Figure 4 , Figure 4 An example of a page 400 displaying data information according to some embodiments of the present disclosure is shown. User input can be displayed in the query content interaction area 411. In the solution path interaction area 412, multiple solution paths determined based on the user input, along with the reasoning identifiers for the paths, can be displayed. Furthermore, the solution path interaction area 412 can serve as an interactive window for receiving instructions from the end user 145 regarding adjustment of the solution paths.

[0068] Exemplarily, the adjustment instruction may include a delete instruction. For example, if the terminal user 145 determines that the solution path corresponding to the recommended chart 1 is inappropriate and has no future prospects for modification, they may directly issue an adjustment instruction to "delete recommended chart 1." Thus, the application management platform 110 may directly delete the solution path according to the delete instruction.

[0069] Exemplarily, the adjustment instruction may include a modification instruction. For example, the reasoning identifier of the recommendation chart 3 records "Compare the sales of product B and product C of the same type, and analyze the market share of product A." The modification instruction may be "No need to compare product B", "Add the product with the highest current market sales for comparison", "In addition to market share, can a new comparison dimension be added?", etc. Thus, the application management platform 110 can update the solution corresponding to the recommendation chart 3 according to the modification instruction.

[0070] Exemplarily, adjustment instructions may also include addition instructions. For example, if end user 145 has come up with a better solution for an existing solution, they can directly issue a request such as, "Can we combine recommended chart 1 and recommended chart 2?" or "Add a deviation chart to include the increase amount and growth rate?" Application management platform 110 can then expand the solution based on the addition instruction.

[0071] The application management platform 110 can generate an adjustment action corresponding to the adjustment instruction and finally determine the adjustment object of the adjustment action, thereby applying the adjustment action to the corresponding adjustment object, thereby applying the adjustment action to the matching solution and optimizing the solution.

[0072] Through the above process, the solution approach can be improved by interacting with the terminal user 145 to optimize the execution effect of the task execution request.

[0073] In one embodiment, the application management platform 110 determines the data requirements for the subtask request in the following manner: determining data screening conditions based on the obtained target of the subtask request; generating data query instructions based on the data screening conditions; and using the results obtained by executing the data query instructions as target data.

[0074] Each subtask request has a corresponding target. For example, if sales data is presented in the form of a bar chart, the target is to display the sales data for the previous quarter. The application management platform 110 can then determine that the corresponding filtering condition is to query the database for the sales data for product A for the previous quarter. Based on this, the application management platform 110 generates a query instruction to obtain the sales data for product A for the previous quarter based on the known database, the tables in the known database, and the columns in the tables.

[0075] Similarly, taking the example of presenting the sales percentage in the form of a pie chart, the goal is to show the sales percentage of product A. Then the application management platform 110 can determine that the screening condition corresponding to the goal is to query the sales data of product A from the database, as well as the sales data of other products (same type or competing products) except product A. The sales data of other products except product A can be the total number (for example, the sum of the sales of product B, product C and product D), or it can be obtained in sequence according to the category of the product (for example, the sales data of product B, product C and product D are obtained in sequence).

[0076] After the query instruction is generated, the result obtained by executing the query instruction can be used as a response to the target data obtained based on the data demand.

[0077] Through the above process, personalized data acquisition for different subtask requests can be completed. The ultimate goal is to ensure that each subtask request can achieve the corresponding goal.

[0078] In one embodiment, the application management platform 110 may also determine the data requirements for subtask requests in the following manner: in response to the result obtained from the data query instruction being unable to achieve the goal, a data analysis instruction is generated, and the data analysis instruction is configured to perform data analysis and processing on the result obtained from executing the data query instruction to obtain an analysis and processing result; and the analysis and processing result is used as the target data.

[0079] Still taking the example of presenting sales data in the form of a bar chart, its goal is to display the sales data of the previous quarter. Then the application management platform 110 can determine that the result obtained by the data query instruction corresponding to the target is to query the sales data of product A in the previous quarter from the database. After obtaining the result, the application management platform 110 can judge whether the obtained target data can complete the goal of displaying the sales data of the previous quarter. If it is judged that the target can be achieved, then the sales data of product A in the previous quarter queried from the database can be used as the target data. On the contrary, if the judgment result is that the target cannot be achieved, then the application management platform 110 can generate an analysis instruction. The result obtained by executing the data query instruction is subjected to data analysis processing through the analysis instruction to obtain the analysis processing result, and the analysis processing result is used as the target data.

[0080] Taking the example of presenting the sales share in the form of a pie chart, the goal is to show the sales share of product A. Then the application management platform 110 can determine that the filtering condition corresponding to the goal is to query the sales data of product A and the sales data of other products except product A from the database. After obtaining the result, the application management platform 110 can judge whether the obtained data can achieve the goal of showing the sales share of product A. Obviously, showing the sales share requires not only sales data but also a share calculation. Therefore, the application management platform 110 can determine that the result obtained by the data query instruction cannot achieve the goal of showing the sales share of product A. In response to the result obtained by the data query instruction being unable to achieve the goal, 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 product A and the total sales of other products including product A. If the result of the ratio operation can achieve the goal of showing the sales share of product A, then the result of the ratio operation can be used as the target data. Conversely, if the result of the ratio calculation still fails to achieve the goal of displaying the sales volume share of product A, the application management platform 110 can adjust the analysis instructions or add new analysis instructions so that the analysis processing results obtained by executing the adjusted analysis instructions can achieve the goal of the subtask request. In other words, if the result of the ratio calculation still fails to achieve the goal of displaying the sales volume share of product A, the application management platform 110 can set multiple rounds of analysis instructions, each of which can refer to the results of the previous round and the reasons for the failure to achieve the goal, thereby ultimately achieving the goal of the subtask request through multiple rounds of analysis instructions.

[0081] Through the above process, the application management platform 110 can simultaneously use analysis instructions and query instructions to complete the goals of subtask requests. If the advantage of query instructions is to quickly obtain data, then the advantage of analysis instructions is that complex calculations can be performed on the data. For example, proportion problems, year-on-year problems, quarter-on-quarter problems, etc. can all correspond to complex calculations. Analysis instructions can be compiled using a suitable programming language. The basic statements of the programming language contain arithmetic operators, logical operators, etc., so complex problems such as ratio, quarter-on-quarter, and proportion can be easily dealt with. In the current embodiment, the code compilation for query instructions and analysis instructions can be completed by the application management platform 110 calling model 155. Utilizing the code compilation capability of model 155 can save the process of manual code compilation.

[0082] In one embodiment, the application management platform 110 may further perform data optimization processing on the results obtained by executing the data query instruction, where the data optimization processing includes at least one of data cleaning and data bit number adjustment.

[0083] 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 adjustment.

[0084] 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. For example, the meaningless data can be unrecognizable data, null value data, or data with a value of 0.

[0085] Taking the adjustment of the number of data bits as an example, the application management platform 110 can adjust the number of bits of the result obtained by the data query instruction. For example, the adjustment may include rounding the data to an integer, retaining one decimal place, etc.

[0086] Through the above process, the query results can be optimized. It is not difficult to understand that the results of the analysis command can also be adjusted by adjusting the number of data bits, so that the data can be simplified when generating editable charts or performing data broadcasting functions.

[0087] In one embodiment, the application management platform 110 may further add a data source identifier to the target data, where the data source identifier is used to indicate the source of the target data.

[0088] Taking the results of a query command as the target data as an example, after obtaining the query command results, the application management platform 110 may also add a data source identifier to the query command results. The data source identifier is used to indicate the source of the results. For example, the data source identifier can be used to indicate the database name, table in the database, column in the table, and other information of the database from which the query command results originated.

[0089] Taking the result obtained by the analysis instruction as the target data as an example, since the analysis instruction is the result obtained by analyzing and calculating the result obtained by the query instruction, the application management platform 110 can also use 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.

[0090] The significance of adding data source identification is that it can mark the authenticity of the data, thereby avoiding false data without a source.

[0091] In one embodiment, the application management platform 110 calls a specified function to process each subtask request, which may specifically be: calling a data chart generation function to generate a data chart based on each subtask request and the target data corresponding to the subtask request, and the presentation style of the data chart is editable.

[0092] In the current embodiment, the function of generating an editable data chart is used as an example. The application management platform 110 can generate an editable data chart based on the subtask request and the target data corresponding to the subtask request by calling the function of the editable data chart supported by the target application 120. The function of the editable data chart can be implemented based on a domain-specific language (DSL). DSL can be used as a scripting language dedicated to chart generation, which 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. Figure 5A , Figure 5A An example of a page 500 for calling a specified function to process a bar chart result corresponding to a subtask request according to some embodiments of the present disclosure is shown. Figure 5A In the figure, the sales data of product A in the past three quarters are shown in the form of a bar chart. Figure 5B , Figure 5B An example of a page 510 showing a pie chart result corresponding to a request to process a subtask by calling a specified function according to some embodiments of the present disclosure is shown. Figure 5B In the figure, a pie chart is shown showing the sales volume of product A in the previous quarter. Figure 5C , Figure 5C An example of a page 520 showing a bar chart and a line chart result corresponding to a request to process a subtask by calling a specified function according to some embodiments of the present disclosure is shown. Figure 5C , a bar chart combined with a line chart is shown to show the month-on-month change in sales of product A compared to other products. For example, the presentation style of the data chart on page 520 is editable. For example, the presentation style of the font, font size, color, etc. in the data chart are all editable.

[0093] In one embodiment, the application management platform 110 may further store the target data in a designated storage area, and calling a designated function to process the subtask request includes providing the address of the designated storage area to the designated function.

[0094] After the application management platform 110 uses the query instruction to perform a data query and obtains the query result, it can store the result obtained by the query instruction in a designated storage area. The designated area can be a cache area in the programming language sandbox. After the storage is completed, the application management platform 110 can generate a storage identifier based on the address where the result obtained by the query instruction is stored. The storage identifier can be used to characterize the data. Therefore, when the application management platform 110 calls a designated function to process a subtask request to generate a new query instruction or the generated analysis instruction needs to access data, it can first generate a storage address identifier reading instruction, thereby using the storage identifier to read the query result stored in the designated storage area. Similarly, the result obtained by executing the analysis instruction can also be stored in the designated storage area, and a storage identifier is generated based on the address where the result of the analysis instruction is stored.

[0095] Through the above process, data access is represented solely through storage identifiers, thereby reducing the data size required to compile tokens when the application management platform 110 invokes the model. In other words, when compiling a token, there's no need to list all the data to be retrieved; instead, only instructions need to be generated to read the storage area address. This significantly reduces model resource overhead when the model context window has limited data reception capacity.

[0096] Figure 6 FIG2 shows a schematic structural block diagram of a task processing apparatus 600 according to some embodiments of the present disclosure. The apparatus 600 may be implemented in or included in the application management platform 110. Each module / component in the apparatus 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0097] As shown, apparatus 600 includes a parsing module 601 configured to parse received user input and obtain a parsing result. 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. A data requirement determination module 602 is configured to determine the data requirements of a subtask request among the multiple subtask requests. A subtask request processing module 603 is configured to, in response to obtaining target data based on the data requirements, invoke a specified function to process the subtask request.

[0098] In some embodiments, the parsing module 601 may also include a solution path determination submodule configured to determine multiple solution paths for the task execution request based on user input; an identifier addition submodule configured to add a reasoning identifier to each solution path, the reasoning identifier being used to indicate the reason for determining the solution path; and a subtask request generation module configured to generate a subtask request corresponding to each solution path.

[0099] In some embodiments, the device 600 is further configured to include a solution display module, which is configured to display the solution with added reasoning identification in a designated display area; and a solution adjustment module, which is configured to execute an adjustment action corresponding to the adjustment instruction in response to a received adjustment instruction, and the adjustment instruction is for at least one solution with added reasoning identification.

[0100] In some embodiments, the data requirement determination module 602 is configured to include a filtering condition determination submodule, which is configured to determine the data filtering condition based on the target of the acquired subtask request; a data query instruction generation submodule, which is configured to generate a data query instruction based on the data filtering condition; and use the result obtained by executing the data query instruction as the target data.

[0101] In some embodiments, the data requirement determination module 602 is configured to further include an analysis instruction generation submodule, which is configured to generate a data analysis instruction in response to the result obtained by the data query instruction being unable to achieve the target. 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 use the analysis processing result as the target data.

[0102] In some embodiments, the data requirement determination module 602 is configured to perform data optimization processing on the results obtained by executing the data query instruction, and the data optimization processing includes at least one of data cleaning and data bit number adjustment.

[0103] In some embodiments, the apparatus 600 further includes a data source identifier adding module, which is configured to add a data source identifier to the target data, where the data source identifier is used to indicate the source of the target data.

[0104] In some embodiments, the subtask request processing module 603 is specifically configured to call a data chart generation function to 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 is editable.

[0105] 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 calling a designated function to process the subtask request includes providing an address of the designated storage area to the designated function.

[0106] Figure 7 1 shows a block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 7 The illustrated electronic device 700 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 7The electronic device 700 shown may include or be implemented as Figure 1 application management platform 110, or Figure 6 device 600.

[0107] like Figure 7 As shown, electronic device 700 is in the form of a general electronic device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a real or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of electronic device 700.

[0108] The electronic device 700 typically includes a plurality of computer storage media. Such media can be any accessible media that can be obtained by the electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 720 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 730 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk, or any other medium that can be used to store information and / or data and can be accessed within the electronic device 700.

[0109] The electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 7 As shown in FIG, a magnetic 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 may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 720 may include a computer program product 725 having one or more program modules configured to perform the various methods or actions of various embodiments of the present disclosure.

[0110] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functions of the components of the electronic device 700 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 700 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0111] Input device 750 may be one or more input devices, such as a mouse, keyboard, or trackball. Output device 760 may be one or more output devices, such as a display, a speaker, or a printer. Electronic device 700 may also communicate with one or more external devices (not shown) via communication unit 740 as needed, such as storage devices, display devices, or the like, with one or more devices that allow a user to interact with electronic device 700, or with any device that allows electronic device 700 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0112] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0113] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0114] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0115] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0116] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0117] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, not exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A task processing method, comprising: Parsing the 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 subtask requests corresponding to the task execution request, each of the plurality of subtask requests corresponding to one of a plurality of solution paths for the task execution request, the plurality of solution paths being capable of independently completing a goal of the task execution request; For each of the plurality of subtask requests, Determining data requirements for the subtask request, wherein the data requirements are associated with a goal of a solution approach corresponding to the subtask request; as well as In response to acquiring target data based on the data requirement, a data chart generation function is called to generate a data chart based on the subtask request and the target data corresponding to the subtask request.

2. The method according to claim 1, wherein parsing the received user input comprises: determining the plurality of solutions to the task execution request based on the user input; Adding a reasoning identifier to each of the solution paths, wherein the reasoning identifier is used to indicate the reason for determining the solution path; as well as The subtask request corresponding to each of the solution paths is generated respectively.

3. The method according to claim 2, further comprising: Display the solution with added reasoning mark in the designated display area; as well as In response to the received adjustment instruction, an adjustment action corresponding to the adjustment instruction is executed, wherein the adjustment instruction is for at least one solution path to which the reasoning identifier has been added.

4. The method according to claim 1, wherein determining the data requirements of the subtask request comprises: Determining data screening conditions based on the obtained target of the subtask request; Based on the data screening conditions, generating a data query instruction; as well as The result obtained by executing the data query instruction is used as the target data.

5. The method according to claim 4, wherein determining the data requirement of the subtask request further comprises: In response to a result obtained by the data query instruction failing to achieve the goal, generating a data analysis instruction, wherein 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; as well as The analysis result is used as the target data.

6. The method according to claim 4, further comprising: Data optimization processing is performed 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.

7. The method according to claim 1, 4 or 5, further comprising: A data source identifier is added to the target data, where the data source identifier is used to indicate the source of the target data. The method according to claim 1 , wherein the presentation style of the data chart is editable.

9. The method according to claim 1, further comprising: storing the target data in a designated storage area, and The calling of the specified function to process the subtask request includes: and The address of the designated storage area is provided to the designated function.

10. A task processing device comprising: a parsing module 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 a plurality of subtask requests corresponding to the task execution request, each of the plurality of subtask requests corresponding to one of a plurality of solution paths for the task execution request, the plurality of solution paths being capable of independently completing a goal of the task execution request; a data requirement determination module configured to determine, for each subtask request among the plurality of subtask requests, a data requirement of the subtask request, wherein the data requirement is associated with a target of a solution approach corresponding to the subtask request; as well as The subtask request processing module is configured to, in response to acquiring target data based on the data requirement, call a data chart generating function and generate a data chart based on the subtask request and the target data corresponding to the subtask request.

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

12. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 9.

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