Information display method and apparatus, and electronic device and storage medium

Abstract

Provided are an information display method and apparatus, and an electronic device and a storage medium. The method comprises: displaying table data, wherein the table data comprises at least one data object and at least one piece of basic statistical information corresponding to the data object (S101); and in response to receiving a trigger instruction, generating conclusion information corresponding to the table data, and displaying same, wherein the conclusion information comprises text information and corresponding chart information, the chart information is a result of graphical processing corresponding to the text information, and the chart information at least comprises a chart and / or a pivot table (S102).

Description

Information display method, device, electronic device and storage medium

[0001] This application claims priority to Chinese Patent Application No. 202311559430.2 filed on November 21, 2023, and the contents of the above-mentioned Chinese patent application disclosure are hereby incorporated by reference in their entirety as a part of this application. Technical Field

[0002] Embodiments of the present disclosure relate to an information display method, device, electronic device, and storage medium. Background Art

[0003] A spreadsheet is a commonly used data carrier that records information in the form of rows and columns. The resulting tabular data is widely used in various scenarios such as data management and data analysis.

[0004] Currently, the processing and analysis of tabular data is usually implemented based on user experience, which has a high operational threshold and leads to problems such as low data processing efficiency and poor accuracy of analysis results.

[0005] Summary of the Invention

[0006] The embodiments of the present disclosure provide an information display method, device, electronic device, and storage medium to overcome the problems of low data processing efficiency and poor accuracy of analysis results.

[0007] The present disclosure provides an information display method, including:

[0008] Displaying tabular data, the tabular data including at least one data object and at least one basic statistical information corresponding to the data object; generating and displaying conclusion information corresponding to the tabular data in response to receiving a trigger instruction, wherein the conclusion information includes text information and corresponding chart information, the chart information is a result of graphical processing corresponding to the text information, and the chart information includes at least a chart and / or a pivot table.

[0009] An embodiment of the present disclosure provides an information display device, comprising:

[0010] An interactive unit, configured to display tabular data, wherein the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object;

[0011] a processing unit, configured to generate conclusion information corresponding to the table data in response to receiving a trigger instruction, wherein the conclusion information includes text information and corresponding chart information, the chart information being a result of graphical processing corresponding to the text information, and the chart information at least including a chart and / or a pivot table;

[0012] Wherein, the interaction unit is further used to display the conclusion information.

[0013] An embodiment of the present disclosure provides an electronic device, including: a processor and a memory;

[0014] The memory stores computer-executable instructions;

[0015] The processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the information display method described in the various possible designs above.

[0016] An embodiment of the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the information display method described in the various possible designs above is implemented.

[0017] An embodiment of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the information display method described in the various possible designs above. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, a brief introduction will be given below to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] FIG1 is a diagram of an application scenario of the information display method provided by an embodiment of the present disclosure;

[0020] FIG2 is a flow chart of the information display method according to an embodiment of the present disclosure;

[0021] FIG3 is a schematic diagram of an interactive process of a user inputting a trigger instruction according to an embodiment of the present disclosure;

[0022] FIG4 is a schematic diagram of displaying text information and chart information provided by an embodiment of the present disclosure;

[0023] FIG5 is a schematic diagram of a process for generating extended reasoning according to an embodiment of the present disclosure;

[0024] FIG6 is a second flow chart of the information display method provided by an embodiment of the present disclosure;

[0025] FIG7 is a flowchart of a specific implementation of step S203 in the embodiment shown in FIG6 ;

[0026] FIG8 is a schematic diagram of a system structure for implementing the method of the present embodiment provided by the present disclosure;

[0027] FIG9 is a flowchart of a specific implementation of step S204 in the embodiment shown in FIG6 ;

[0028] FIG10 is a flowchart of a specific implementation of step S2041 in the embodiment shown in FIG9 ;

[0029] FIG11 is a schematic structural diagram of a first intelligent agent provided by an embodiment of the present disclosure;

[0030] FIG12 is a flowchart of a specific implementation method of step S2042 in the embodiment shown in FIG9 ;

[0031] FIG13 is a structural block diagram of an information display device provided by an embodiment of the present disclosure;

[0032] FIG14 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure; and

[0033] FIG15 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0035] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0036] The following explains the application scenarios of the embodiments of the present disclosure:

[0037] Figure 1 illustrates an application scenario for the information display method provided by an embodiment of the present disclosure. The information display method provided by an embodiment of the present disclosure can be applied to applications with table data processing capabilities, and more specifically, to applications based on online spreadsheet data processing. The execution entity of this embodiment can be a terminal device running the aforementioned application with online spreadsheet functionality and program, a server hosting the server corresponding to the aforementioned application, or other electronic devices that perform similar functions. Taking a terminal device as an example, an application with an online spreadsheet function is running in the terminal device. As shown in Figure 1, a table data sheet_1 is displayed in the interactive interface of the application. The table data sheet_1 has, for example, N data rows and M data columns (N and M are positive integers), where each data row corresponds to a data object. As shown in the reference figure, the first column in the table data sheet_1 is used to record the object identifier of the data object, for example, the object identifier of the data object corresponding to the first row is "Material Data #1"; the object identifier of the data object corresponding to the second row is "Material Data #2"; in other data columns except the first column, the statistical information corresponding to each data object is recorded respectively, for example, the first column records the statistical quantity of the data object, the second column records the statistical time of the data object, and so on.

[0038] For the above table data, while displaying the table data, the terminal device can process the above table data by running a manually written data processing program to obtain data processing results, thereby achieving purposes such as data analysis and information mining.

[0039] The analysis process for tabular data like the one above is typically based on user experience, meaning users write data processing programs to process and analyze the data. However, data processing and analysis have a high barrier to entry, and the accuracy of the analysis results depends primarily on the user's data analysis skills. This leads to problems such as low data processing efficiency and poor analysis accuracy during the analysis and processing of tabular data.

[0040] The disclosed embodiment provides an information display method to solve the above-mentioned problem. The information display method provided in this embodiment displays tabular data, wherein the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object; in response to receiving a trigger instruction, the conclusion information corresponding to the tabular data is generated and displayed; wherein the conclusion information includes text information and corresponding chart information, the chart information is the result of graphical processing corresponding to the text information, and the chart information includes at least a chart and / or a pivot table. By processing the tabular data based on a language model, conclusion information including text information and corresponding chart information is generated, thereby realizing automatic analysis and information insight of the tabular data, and improving data processing efficiency and data analysis accuracy.

[0041] Referring to FIG2 , FIG2 is a flow chart of an information display method according to an embodiment of the present disclosure. The method of this embodiment can be applied in a terminal device, and the information display method includes:

[0042] Step S101: displaying table data, where the table data includes at least one data object and at least one basic statistical information corresponding to the data object.

[0043] Exemplarily, for example, tabular data is displayed in an interactive interface, and the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object. Referring to the application scenario diagram shown in FIG1 , the execution subject of the information display method provided by the embodiment of the present disclosure may be a terminal device, such as a personal computer, etc. An application with a spreadsheet function is running in the terminal device, and the terminal device provides an operation entry for the user by displaying the interactive interface of the application. The user operates the application through the operation entry, and can load and open the tabular data in the application, and display the tabular data in the interactive interface of the application. Among them, the application may have an online electronic function table, that is, the user can edit the tabular data online and synchronize multiple terminals. Therefore, the tabular data can be stored locally on the terminal device and directly loaded locally by the terminal device based on user instructions; it can also be stored in the cloud and obtained by the terminal device accessing the cloud server, which will not be described in detail here.

[0044] Furthermore, referring to the table data in the application scenario diagram shown in FIG1 , the data table includes at least one data object and at least one basic statistical information corresponding to the data object, that is, the data table is a data storage carrier for data storage based on the data object. Each data object corresponds to the same amount of basic statistical information. Specifically, for example, taking the application scenario of storing "book information" in table data as an example, the data object corresponds to the name of the book, such as "XX Autobiography" and "XXX Adventures". The basic statistical information corresponding to each data object includes, for example, the author of the book, the time of publication of the book, the sales volume of the book, etc., that is, in the table data, the author of the book, the time of publication of the book, and the sales volume of the book "XX Autobiography" and "XXX Adventures" are included respectively.

[0045] Among them, the way of displaying tabular data in the interactive interface, in one possible implementation method, can be to display data objects by row, that is, each row displays one data object, and corresponding one or more basic statistical information, which is the display method of tabular data shown in Figure 1; in another possible implementation method, can also be to display data objects by column, that is, each column displays one data object, and corresponding one or more basic statistical information. The specific implementation method can be set as needed and is not specifically limited here.

[0046] Step S102: In response to receiving a trigger instruction, generate and display conclusion information corresponding to the table data, wherein the conclusion information includes text information and corresponding chart information, the chart information is the result of graphical processing corresponding to the text information, and the chart information includes at least charts and / or pivot tables.

[0047] Exemplarily, for example, in response to a trigger instruction input by a user, conclusion information corresponding to the table data is obtained and displayed in an interactive interface, wherein the conclusion information is generated based on a language model, the conclusion information represents the target data object, and the processing rules corresponding to the target data object, the processing rules represent the logic of determining the target data object using advanced statistical information, and the advanced statistical information is generated based on basic statistical information. After the table data is displayed in the interactive interface, the terminal device automatically processes the table data in response to the trigger instruction input by the user, achieving information insight, reasoning and analysis of the table data, thereby obtaining conclusion information corresponding to the table data. The conclusion information includes text information and corresponding chart information, wherein the text information is a textual representation of the conclusion of the language model processing; the chart information is the result of the graphical processing corresponding to the text information, and the chart information includes at least charts and / or pivot tables. In the content dimension represented by it, the generated conclusion information includes description information of the target data object and description information of the processing rules corresponding to the target data object using advanced statistical information generated based on basic statistical information. In one possible implementation, the content of the text corresponding to the text information in the conclusion information is: "The book with the fastest sales growth rate is "The Autobiography of XX", where "sales growth rate" is advanced statistical information, and the advanced statistical information "sales growth rate" is calculated based on the basic statistical information "sales volume". And "the fastest sales growth rate" is a processing rule, that is, the target data object is determined from multiple data objects through the "sales growth rate", and the specific determination logic is "determine the book with the fastest sales growth rate as the target data object". Furthermore, the conclusion information also includes chart information, and the chart information is the result of graphical processing corresponding to the text information, such as charts and / or pivot tables. The content represented by the above text information is expressed in the form of charts through the chart information, thereby achieving a more detailed information expression effect.

[0048] Furthermore, the conclusion information can be implemented in a variety of ways, depending on its content. For example, the conclusion information can represent at least one of the following: the average value of the advanced statistical information corresponding to the target data object within the target time interval; the change in the advanced statistical information corresponding to the target data object within the target time interval; the increase and / or decrease in the advanced statistical information corresponding to the target data object within the target time interval; or the trend of change in the advanced statistical information corresponding to the target data object within the target time interval. In other words, the target data object represented by the conclusion information, as well as the processing rules corresponding to the target data object, can be implemented in a variety of ways, meaning that insights and analysis of tabular data can be conducted from multiple dimensions, thereby obtaining effective and high-value data analysis results.

[0049] Furthermore, the process of generating conclusion information is generated based on a language model, which is a model for task processing based on natural language, such as a large language model (LLM). In this embodiment, the language model can be combined with a preset target prompt word to generate a corresponding action instruction to call an external data analysis module, thereby realizing automated analysis and data insight of the above-mentioned table data, thereby generating corresponding conclusion information containing text information and corresponding chart information (in a combination of text and graphics). The generation of the above-mentioned conclusion information is based on the language model combined with the prompt word. The generation control of the above-mentioned conclusion information, including the generation control of text information and the generation control of chart information, can be achieved by setting the prompt word template; by default, the terminal device can use the system default prompt word, that is, the system prompt word, to call the language model to analyze the above-mentioned table data, thereby obtaining the optimal conclusion information. The system prompt word can be set by the user based on business prior information, and is not limited here.

[0050] Furthermore, the trigger instruction can be triggered based on the user's operation on the functional components in the interactive interface. Figure 3 is a schematic diagram of the interactive process of a user inputting a trigger instruction provided by an embodiment of the present disclosure. As shown in Figure 3, after the table data is displayed in the interactive interface of the terminal device, when the user clicks the interactive component #1 in the interactive interface, a function list containing multiple function items pops up in the interactive interface, and the function list includes the "Data Insight" function. After that, the user clicks on the "Data Insight" (the corresponding trigger component) or enters the word "Data Insight" in the editable text box above the function list (this situation is not shown in the figure). The terminal device obtains the trigger instruction corresponding to the "Data Insight" function, and then executes the steps of the above embodiment to obtain the conclusion information corresponding to the table data and displays it in the interactive interface.

[0051] Furthermore, with respect to the chart information in the conclusion information, in a possible implementation method, the chart information is used to characterize the proportional relationship between the advanced statistical information corresponding to at least two target data objects. The chart information is generated based on the tabular data, that is, the tabular data is processed by a language model to generate the chart information. The specific implementation process is similar to the process of generating text information and will not be repeated here. For example, after displaying the chart information, a corresponding data report can be further generated based on the chart information and the text information. Specifically, after obtaining the conclusion information, the terminal device can extract one or more statistical information from the corresponding advanced statistical information or multiple basic statistical information according to the target data object represented by the conclusion information, generate the chart information directly or after calculation, and generate the corresponding picture for display. Furthermore, with respect to the display process of the chart information, after generating the conclusion information, the terminal device can pop up a second interactive interface in addition to the first interactive interface for displaying the tabular data, and display the text corresponding to the conclusion information and the picture corresponding to the chart information in the second interactive interface.

[0052] Figure 4 is a schematic diagram of displaying text information and chart information provided by an embodiment of the present disclosure. As shown in Figure 4, tabular data is displayed in the first interactive interface of the terminal device, and after responding to the trigger instruction (triggered by clicking the interactive component #1), the tabular data is processed based on the language model to obtain text information and chart information, and displayed in the second interactive interface. Specifically, with reference to Figure 4, the text content corresponding to the text information includes: "1. The top three books with the highest average sales in the past week are "XX Autobiography", "XXX Adventures", and "X Story"." Correspondingly, based on the text information, the terminal device generates chart information. Specifically, the terminal device obtains the average sales of the target objects: "XX Autobiography", "XXX Adventures", and "X Story" in the past week, and the sum of the average sales of all data objects in the past week (that is, the total sales of all books), and obtains the proportion of the average sales of "XX Autobiography", "XXX Adventures", and "X Story" in the past week, that is, the chart information, and then displays the chart information in the form of a picture. For example, as shown in the figure, the proportion of the average sales of "XX Autobiography" in the past week is 20%; the proportion of the average sales of "XXX Adventures" in the past week is 12%; the proportion of the average sales of "X Story" in the past week is 9%, and the sum of the average sales of other data objects in the past week accounts for 59%.

[0053] In this embodiment, by further generating and displaying chart information corresponding to the text information after generating the text information, the information display dimension for the table data is improved, and the information display efficiency and display effect after data analysis are improved.

[0054] Furthermore, optionally, after the conclusion information is displayed in the interactive interface, it also includes: receiving a requirement text input by the user, the requirement text is used to represent the intention to adjust the content of the conclusion information; generating extended conclusion information based on the requirement text, the extended conclusion information including extended text information generated based on the requirement text, and extended chart information conclusion information corresponding to the extended text information.

[0055] Specifically, in conjunction with the embodiment shown in FIG4 , an editable text box for entering a request text is further provided in the first or second interactive interface. Following the terminal device's text information and chart information, the user can further receive the request text entered through the editable text box to adjust the text information, thereby generating extended text information and extended chart information corresponding to the extended text information. FIG5 is a schematic diagram of a process for generating extended inferences provided by an embodiment of the present disclosure. As shown in FIG5 , the terminal device has an editable text box in the second interactive interface. After displaying the text corresponding to the text information and the image corresponding to the chart information, the user enters the request text "Count more best-selling books" into the editable text box in the second interactive interface. The terminal device then generates corresponding prompt words based on the request text and further invokes a language model to generate extended text information. For example, as shown in the figure, the text content corresponding to the extended text information includes: "1. The top five books with the highest average sales in the past week are XX Autobiography, XXX Adventures, X Story, X Sports, and X Movie." Accordingly, the terminal device further updates the chart information based on the extended text information and generates a picture corresponding to the extended chart information for display. For example, as shown in the figure, the average sales volume of "XX Autobiography" in the past week accounts for 20%; the average sales volume of "XXX Adventures" in the past week accounts for 12%; the average sales volume of "X Story" in the past week accounts for 9%; the average sales volume of "X Sports" in the past week accounts for 8%; the average sales volume of "X Movie" in the past week accounts for 7%; and the average sales volume of other data objects in the past week accounts for 44%.

[0056] In the steps of this embodiment, by receiving the required text input by the user, the generated text information and the corresponding chart information are quickly adjusted, so that the conclusion information can better meet the user's needs and improve the efficiency and accuracy of data analysis.

[0057] In this embodiment, tabular data is displayed within an interactive interface. The tabular data includes at least one data object and at least one basic statistical information corresponding to the data object. In response to a trigger instruction input by a user, conclusion information corresponding to the tabular data is obtained and displayed within the interactive interface. The conclusion information is generated based on a language model and includes a target data object and a processing rule corresponding to the target data object. The processing rule represents the logic for determining the target data object using advanced statistical information. The advanced statistical information is generated based on the basic statistical information. By processing the tabular data based on the language model and generating the advanced statistical information and corresponding processing rules corresponding to the tabular data, automatic analysis and information insight of the tabular data are achieved, thereby improving data processing efficiency and data analysis accuracy.

[0058] Referring to FIG6 , FIG6 is a second flow chart of the information display method provided by the embodiment of the present disclosure. Based on the embodiment shown in FIG2 , this embodiment further refines step S102 , and the information display method includes:

[0059] Step S201: displaying table data in an interactive interface, where the table data includes at least one data object and at least one basic statistical information corresponding to the data object.

[0060] Step S202: According to the trigger instruction input by the user, a target prompt word is obtained, where the target prompt word is used to describe the intention of generating conclusion information based on a natural language description.

[0061] Exemplarily, after receiving the trigger instruction input by the user, the terminal device obtains the corresponding prompt word (prompt) template by running a preset function program or accessing a preset service, and then obtains the target prompt word. The target prompt word is used to describe the intention of generating conclusion information based on natural language. The target prompt word can be recognized by the language model used subsequently and guide the output content of the language model. The trigger instruction is an instruction with a clear meaning (i.e., performing insight analysis on the table data to obtain the corresponding conclusion information). Therefore, the trigger instruction corresponds to a specified target prompt word for the intention of generating conclusion information based on the natural language description, that is, the mapping relationship between the trigger instruction and the target prompt word is pre-configured. There is no specific restriction on the specific implementation method of the target prompt word.

[0062] Step S203: Process the target prompt word through the preset table data service to generate a data inference request. The table data service is used to obtain table data and generate a data inference request based on the table data and the target prompt word.

[0063] Exemplarily, afterward, by accessing a preset table data service and processing the target prompt word, the table data service is used to respond to the above intention of generating conclusion information, identify and convert the table data required to generate the conclusion information, and combine it with the target prompt word to generate a data inference request that can be processed by the language model.

[0064] For example, as shown in FIG7 , the specific implementation of step S203 includes:

[0065] Step S2031: identifying the table data structure of the table data through the table data service;

[0066] Step S2032: Based on the table data structure, load the table data and convert the table data into parsed data that can be used to input and execute the plug-in;

[0067] Step S2033: Generate a data inference request based on the parsed data and the target prompt word.

[0068] Exemplarily, the terminal device sends the target prompt word to the table data service in an intention distribution manner. The table data service can be deployed as a server-side of an online spreadsheet function or deployed locally on the terminal device. After receiving the target prompt word, the table data service identifies the table data structure of the table data, the number of rows and columns of the table data, and the position of the data labels in the table data (for example, the first column is the data label), etc., through the table data service, so as to load the table data into the memory and convert it into data with a specific data format that can be processed by the subsequent execution plug-in, that is, parsed data. Afterwards, the parsed data and prompt word information are packaged into a data inference request. Among them, the prompt word information is generated based on the target prompt word, which can be the target prompt word itself, or it can be text, logo or other information generated after processing the target prompt word.

[0069] Step S204: Based on the data reasoning request, the execution plug-in is called to obtain conclusion information corresponding to the table data. The execution plug-in is used to call the external knowledge base to process the table data to generate conclusion information.

[0070] Exemplarily, after a data inference request is generated through a table data service, an execution plug-in is called to process the inference request to generate conclusion information. In one possible implementation, the execution plug-in is a functional plug-in implemented based on a language model, i.e., the execution plug-in includes a language model or can implement the language model's functionality by accessing or calling an external model. After the data inference request is input into the execution plug-in, the execution plug-in processes the prompt word information and parsed data included or indicated in the data inference request, leveraging the capabilities of the language model to achieve the purpose of generating conclusion information.

[0071] FIG8 is a schematic diagram of a system structure for implementing the method of the present embodiment. Referring to FIG8 , first, after receiving a target prompt word or intent information containing a target prompt word, the table data service processes it, obtains table data, parses it, and generates a data inference request. The table data service then sends the data inference request to an execution plug-in. The execution plug-in, which includes a large language model or is capable of implementing a language model, processes the data inference request and generates conclusion information, shown as conclusion information Info_1, which is returned to the table data service. Specifically, the execution plug-in includes a first agent implemented based on the large language model. The first agent invokes an external knowledge base to obtain conclusion information output by the external knowledge base and sends it to the table data service. The table data service then formats the conclusion information returned by the execution plug-in, generating conclusion information in a specific target data format that can be displayed on the terminal device's interactive interface, shown as conclusion information Info_2. The result is returned to the terminal device, which displays it on the interactive interface.

[0072] In the steps of this embodiment, by decoupling the table data service and the execution plug-in, the process of processing the table data to generate conclusion information is decomposed into two independent steps: data processing and language model processing, thereby achieving the decoupling of the business data processing flow. Subsequently, the table data service and the execution plug-in can be easily adjusted independently to improve the scalability and maintainability of the program.

[0073] For example, a first agent based on a reason-action (ReAct) model is deployed in the execution plug-in, as shown in FIG9 , and a specific implementation of step S204 includes:

[0074] Step S2041: Process the data reasoning request through the first agent to obtain a first result, wherein the first agent is used to call the first external interface of the external knowledge base to obtain the first result returned by the external knowledge base.

[0075] First, a brief introduction to the reason-action model: The ReAct (reason, act) model integrates reasoning and action capabilities and is a language model that uses natural language reasoning to solve complex tasks. The ReAct model is designed to be used for tasks that allow language models to perform certain operations. For example, in the MRKL system, the language model can interact with an external knowledge base by calling an external interface to retrieve information. When a question is asked, the language model can choose to perform operations to retrieve information, and then answer the question based on the retrieved information. The intelligent agent (Agent, also called an agent) based on the ReAct model can receive data reasoning requests and retrieve the content of the external knowledge base by calling the functional interface of the external knowledge base, or use the capabilities provided by the external knowledge base to obtain the first result returned by the external knowledge base.

[0076] For example, as shown in FIG10 , a possible implementation of step S2041 includes:

[0077] Step S2041A: Process the data reasoning request through the first agent and generate the reasoning target.

[0078] Step S2041B: Generate a first action instruction based on the inference target.

[0079] Step S2041C: By executing the first action instruction, the first module interface of the code processing module is called to generate a first program code corresponding to the first action instruction and applied to the external knowledge base.

[0080] Step S2041D: By executing the first program code, the first external interface of the external knowledge base is called to obtain the first result returned by the external knowledge base.

[0081] Exemplarily, Figure 11 is a structural diagram of a first intelligent agent provided by an embodiment of the present disclosure. The above steps are described in detail below in conjunction with Figure 11. As shown in the figure, a language model (shown as LLM in the figure) is included in the first intelligent agent. First, after the data reasoning request is input into the first intelligent agent, a system prompt word (target prompt word) is obtained based on the data reasoning request, and the language model (shown as LLM+system prompt word in the figure) is input. The language model generates an inference target. Then, a first action instruction is generated based on the inference target, and by executing the first action instruction, the first module interface (i.e., action) of the code processing module is called to generate a first program code, wherein the code processing module is a functional module for generating a program code matching an external knowledge base. By calling the first module interface of the code processing module, the program code corresponding to the first action instruction, i.e., the first program code, can be obtained. Access to the external knowledge base can be achieved through the first program code; then, by executing the first program code, the first external interface of the external knowledge base is called to obtain the first result output by the external knowledge base, and the first result is returned to the language model as an observation result (observation). Optionally, the code processing module may further include a second code module, and the specific function of the second code module will be introduced in the subsequent embodiment steps.

[0082] Furthermore, in one possible implementation, in conjunction with the above example, after obtaining the first result, the language model can directly use the first result as conclusion information. In another possible implementation, conclusion information can also be generated by repeating the above reason-action cycle multiple times. That is, based on the obtained first result, a new first action instruction is generated, and the above steps are repeated. After multiple cycles of reasoning, a final, multiple-updated first result is obtained, and conclusion information is then generated based on the first result. Specifically, after step S2041D, the following is also included:

[0083] Step S2041E: If the preset conditions are not met, the first result is processed by the first agent to generate an updated reasoning target, and return to step S2041B; if the preset conditions are met, the first result is output.

[0084] Specifically, after returning to step S2041B, the execution steps include:

[0085] Based on the updated reasoning target, a second action instruction is generated; by executing the second action instruction, the first module interface of the code processing module is called to generate a second program code corresponding to the second action instruction and applied to the external knowledge base; by executing the second program code, the second external interface of the external knowledge base is called to obtain the second result returned by the external knowledge base (i.e., the updated first result). Afterwards, conclusion information can be generated based on the second result, or, based on the second result, an updated reasoning target is obtained, and the above steps are further repeated based on the updated reasoning target again until the reasoning target or the obtained reasoning result (such as the third result, the fourth result, etc.) meets the requirements. Afterwards, conclusion information is generated based on the reasoning result (such as the second result) to realize chain processing of multiple steps of tabular data.

[0086] Step S2042: Generate conclusion information based on the first result.

[0087] For example, as shown in FIG12 , the specific implementation of step S2042 includes:

[0088] Step S2042A: Send the first result to the table data service.

[0089] Step S2042B: Process the first result through the table data service to generate conclusion information in the target data format.

[0090] For example, the first result can be the first result obtained in the initial reason-action cycle in the steps of the above embodiment, or it can be an updated first result obtained after multiple reason-action cycles. The first result generated by the first agent is sent to the table data service, which processes the first result to generate conclusion information in the target data format, so that the conclusion information can be displayed in the interactive interface.

[0091] Optionally, before step S2041, the method further includes:

[0092] Step S2040: Acquire historical request information received before the trigger instruction.

[0093] Correspondingly, the specific implementation method of step S2041 includes: processing the data inference request and historical request information through the first intelligent agent to obtain the first result.

[0094] Exemplarily, on the other hand, the terminal device can record historical request information input by the user, thereby enabling data analysis based on multiple conversations. Specifically, before the user inputs the trigger command, the user also inputs other commands into the terminal device. For example, the user enters the text command "Count the book sales volume in the past week" into the terminal device through the intelligent assistant. The terminal device will save this text command as historical request information. In one possible implementation, the execution plug-in is provided with a new memory module for caching these historical requests. Thereafter, before the terminal device processes the data reasoning request through the first agent (i.e., before executing step S2041), the terminal device loads at least one historical request information through this memory module. The historical request information and the data reasoning request are then processed by the language model (i.e., the historical request information and the data reasoning request are simultaneously used as inputs to the first agent), and the reason-action loop described in the above embodiment is executed to obtain the first result. In this embodiment, by combining the historical request information, the language model can be combined with contextual information, thereby obtaining more accurate data analysis results (conclusion information in a combined graphic and text format) that meet the user's needs in deep questioning scenarios.

[0095] Optionally, after step S2041, the method further includes:

[0096] Step S2043: Through the first intelligent agent, call the second module interface of the code processing module to generate a correction code corresponding to the first program code, and by executing the correction code, call the first external interface of the external knowledge base to regain the first result returned by the external knowledge base.

[0097] Exemplarily, since the first result is generated by executing the first program code, when the first program code contains an exception or error, it will cause an error in calling the external knowledge base, which in turn causes an error in the generated first result. Therefore, in this embodiment, the first result can be tested, and when the test result is abnormal, the first agent calls the second module interface of the code processing module to repair the generated first program code and obtain the corresponding correction code. Afterwards, the first external interface of the external knowledge base is called by executing the correction code to re-obtain the first result returned by the external knowledge base. Optionally, after re-obtaining the first result returned by the external knowledge base, the first result can be further tested. If the test result of the first result is normal, the subsequent steps are executed, that is, conclusion information is generated based on the first result; and if the first result is still abnormal, the above-mentioned code correction steps can be repeated to try to correct the program code (correction code) again and re-obtain the first result returned by the external knowledge base again until the test result of the first result is normal or a preset condition is met, such as reaching a target number of cycles.

[0098] In this embodiment, the first intelligent agent calls the second module interface of the code processing module to generate the modification code corresponding to the first program code, thereby realizing automatic detection and repair of the external call code created in the process of generating conclusion information without human intervention, thereby improving the stability of program operation and the efficiency of data analysis.

[0099] Step S205: Display the conclusion information in the interactive interface. The conclusion information includes text information and corresponding chart information. The chart information is the result of graphical processing corresponding to the text information. The chart information at least includes a chart and / or a pivot table.

[0100] In this embodiment, the implementation of step S201 and step S205 is the same as the implementation of the corresponding parts of step S101 and step S102 in the embodiment shown in Figure 2 of the present disclosure, and will not be repeated here.

[0101] Corresponding to the information display method of the above embodiment, FIG13 is a structural block diagram of the information display device provided by the embodiment of the present disclosure. For the sake of convenience, only the parts related to the embodiment of the present disclosure are shown. Referring to FIG13, the information display device 3 includes:

[0102] The interactive unit 31 is configured to display table data, where the table data includes at least one data object and at least one basic statistical information corresponding to the data object;

[0103] The processing unit 32 is configured to generate conclusion information corresponding to the table data in response to receiving the trigger instruction;

[0104] The interactive unit 31 is also used to display conclusion information.

[0105] In one embodiment of the present disclosure, conclusion information is generated based on a language model, the conclusion information represents a target data object and a processing rule corresponding to the target data object, the processing rule represents the logic of determining the target data object using advanced statistical information, and the advanced statistical information is generated based on basic statistical information.

[0106] In one embodiment of the present disclosure, the processing unit 32 is specifically used to: obtain a target prompt word according to a trigger instruction input by a user, the target prompt word is used to describe the intention of generating conclusion information based on a natural language; process the target prompt word through a preset table data service to generate a data inference request, the table data service is used to obtain table data, and generate a data inference request based on the table data and the target prompt word; based on the data inference request, call an execution plug-in to obtain conclusion information corresponding to the table data, and the execution plug-in is used to call an external knowledge base to process the table data to generate conclusion information.

[0107] In one embodiment of the present disclosure, when the processing unit 32 processes the target prompt word through a preset table data service and generates a data inference request, it is specifically used to: identify the table data structure of the table data through the table data service; load the table data based on the table data structure, and convert the table data into parsed data that can be used to input the execution plug-in; generate a data inference request based on the parsed data and the target prompt word.

[0108] In one embodiment of the present disclosure, a first intelligent agent based on a reason-action model is deployed in the execution plug-in. When the processing unit 32 calls the execution plug-in based on a data reasoning request to obtain conclusion information corresponding to the tabular data, it is specifically used to: process the data reasoning request through the first intelligent agent to obtain a first result, wherein the first intelligent agent is used to call a first external interface of an external knowledge base to obtain a first result returned by the external knowledge base; and generate conclusion information based on the first result.

[0109] In one embodiment of the present disclosure, when the processing unit 32 processes a data reasoning request through a first intelligent agent and obtains a first result, it is specifically used to: process the data reasoning request through the first intelligent agent to generate an reasoning target; generate a first action instruction based on the reasoning target; call the first module interface of the code processing module by executing the first action instruction to generate a first program code corresponding to the first action instruction and applied to an external knowledge base; call the first external interface of the external knowledge base by executing the first program code to obtain the first result returned by the external knowledge base.

[0110] In one embodiment of the present disclosure, after obtaining the first result returned by the external knowledge base, the processing unit 32 is also used to: process the first result through the first intelligent agent to generate an updated reasoning target; when the processing unit 32 generates conclusion information based on the first result, it is specifically used to: generate a second action instruction based on the updated reasoning target; by executing the second action instruction, call the first module interface of the code processing module to generate a second program code corresponding to the second action instruction applied to the external knowledge base; by executing the second program code, call the second external interface of the external knowledge base to obtain the second result returned by the external knowledge base; and generate conclusion information based on the second result.

[0111] In one embodiment of the present disclosure, after obtaining the first result returned by the external knowledge base, the processing unit 32 is also used to: call the second module interface of the code processing module through the first intelligent agent to generate a correction code corresponding to the first program code; and call the first external interface of the external knowledge base by executing the correction code to regain the first result returned by the external knowledge base.

[0112] In one embodiment of the present disclosure, when generating conclusion information based on the first result, the processing unit 32 is specifically configured to: send the first result to a table data service; and process the first result through the table data service to generate conclusion information in a target data format.

[0113] In one embodiment of the present disclosure, the processing unit 32 is also used to: obtain historical request information received before the triggering instruction; when the processing unit 32 processes the data inference request through the first intelligent agent and obtains the first result, it is specifically used to: process the data inference request and historical request information through the first intelligent agent to obtain the first result.

[0114] In one embodiment of the present disclosure, after obtaining the conclusion information corresponding to the tabular data, the processing unit 32 is further used to: generate chart information based on the conclusion information, and the chart information is used to characterize the proportional relationship between the advanced statistical information corresponding to at least two target data objects; the interaction unit 31 is further used to: display the chart information, and / or generate a corresponding data report based on the conclusion information.

[0115] In one embodiment of the present disclosure, after the conclusion information is displayed in the interactive interface, the interaction unit 31 is also used to: receive the requirement text input by the user, the requirement text is used to represent the intention to adjust the content of the conclusion information; the processing unit 32 is also used to: generate extended conclusion information based on the requirement text, the extended conclusion information includes extended text information generated based on the requirement text, and extended chart information conclusion information corresponding to the extended text information.

[0116] In one embodiment of the present disclosure, the conclusion information includes at least one of the following: the average value of the advanced statistical information corresponding to the target data object within the target time interval; the change in the advanced statistical information corresponding to the target data object within the target time interval; the rising value and / or falling value of the advanced statistical information corresponding to the target data object within the target time interval; and the changing trend of the advanced statistical information corresponding to the target data object within the target time interval.

[0117] The interaction unit 31 is connected to the processing unit 32. The information display device 3 provided in this embodiment can implement the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0118] FIG14 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. As shown in FIG14 , the electronic device 4 includes:

[0119] A processor 41, and a memory 42 communicatively connected to the processor 41;

[0120] Memory 42 stores computer-executable instructions;

[0121] The processor 41 executes the computer-executable instructions stored in the memory 42 to implement the information display method in the embodiments shown in FIG. 2 to FIG. 12 .

[0122] Optionally, the processor 41 and the memory 42 are connected via a bus 43 .

[0123] The relevant explanations can be understood by referring to the relevant descriptions and effects corresponding to the steps in the embodiments corresponding to Figures 2 to 12, and no further details will be given here.

[0124] An embodiment of the present disclosure provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the information display method provided in any of the embodiments corresponding to Figures 2 to 12 of the present disclosure.

[0125] In order to implement the above embodiment, the embodiment of the present disclosure further provides an electronic device.

[0126] Referring to FIG15 , there is shown a schematic diagram of the structure of an electronic device 900 suitable for implementing an embodiment of the present disclosure. The electronic device 900 may be a terminal device or a server. The terminal device may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (Portable Android Devices, PADs), portable multimedia players (PMPs), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in FIG15 is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0127] As shown in FIG15 , the electronic device 900 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the electronic device 900 are also stored in the RAM 903. The processing device 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0128] Typically, the following devices can be connected to the I / O interface 905: input devices 906 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 908 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 909. The communication device 909 can allow the electronic device 900 to communicate with other devices wirelessly or by wire to exchange data. Although FIG15 shows an electronic device 900 with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0129] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 909, or installed from the storage device 908, or installed from the ROM 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0130] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0131] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0132] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0133] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession 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 flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0135] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0136] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0137] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] In a first aspect, according to one or more embodiments of the present disclosure, there is provided an information display method, comprising:

[0139] Displaying tabular data, the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object; generating and displaying conclusion information corresponding to the tabular data in response to receiving a trigger instruction.

[0140] According to one or more embodiments of the present disclosure, the conclusion information is generated based on a language model, the conclusion information represents the target data object and the processing rules corresponding to the target data object, the processing rules represent the logic of determining the target data object using advanced statistical information, and the advanced statistical information is the processing rules generated based on the basic statistical information.

[0141] According to one or more embodiments of the present disclosure, the conclusion information corresponding to the table data is obtained in response to a trigger instruction input by a user, including: obtaining a target prompt word according to the trigger instruction input by the user, the target prompt word being used to describe the intention of generating the conclusion information based on a natural language; processing the target prompt word through a preset table data service to generate a data inference request, the table data service being used to obtain the table data and generate the data inference request based on the table data and the target prompt word; calling an execution plug-in based on the data inference request to obtain the conclusion information corresponding to the table data, the execution plug-in being used to call an external knowledge base to process the table data to generate the conclusion information.

[0142] According to one or more embodiments of the present disclosure, the target prompt word is processed through a preset table data service to generate a data inference request, including: identifying the table data structure of the table data through the table data service; loading the table data based on the table data structure, and converting the table data into parsed data that can be used to input the execution plug-in; and generating the data inference request based on the parsed data and the target prompt word.

[0143] According to one or more embodiments of the present disclosure, a first agent based on a reason-action model is deployed in the execution plug-in, and based on the data reasoning request, the execution plug-in is called to obtain conclusion information corresponding to the tabular data, including: processing the data reasoning request through the first agent to obtain a first result, wherein the first agent is used to call the first external interface of the external knowledge base to obtain the first result returned by the external knowledge base; and generating the conclusion information based on the first result.

[0144] According to one or more embodiments of the present disclosure, the data reasoning request is processed by the first intelligent agent to obtain a first result, including: processing the data reasoning request by the first intelligent agent to generate an inference target; generating a first action instruction based on the inference target; calling the first module interface of the code processing module by executing the first action instruction to generate a first program code corresponding to the first action instruction and applied to the external knowledge base; calling the first external interface of the external knowledge base by executing the first program code to obtain the first result returned by the external knowledge base.

[0145] According to one or more embodiments of the present disclosure, after obtaining the first result returned by the external knowledge base, it also includes: processing the first result through the first intelligent agent to generate an updated reasoning target; generating the conclusion information based on the first result includes: generating a second action instruction based on the updated reasoning target; calling the first module interface of the code processing module by executing the second action instruction to generate a second program code corresponding to the second action instruction applied to the external knowledge base; calling the second external interface of the external knowledge base by executing the second program code to obtain the second result returned by the external knowledge base; generating the conclusion information based on the second result.

[0146] According to one or more embodiments of the present disclosure, after obtaining the first result returned by the external knowledge base, it also includes: calling the second module interface of the code processing module through the first intelligent agent to generate a correction code corresponding to the first program code; calling the first external interface of the external knowledge base by executing the correction code to re-obtain the first result returned by the external knowledge base.

[0147] According to one or more embodiments of the present disclosure, generating the conclusion information based on the first result includes: sending the first result to the table data service; processing the first result through the table data service to generate conclusion information in a target data format.

[0148] According to one or more embodiments of the present disclosure, the method further includes: obtaining historical request information received before the trigger instruction; processing the data inference request through the first intelligent agent to obtain a first result, including: processing the data inference request and the historical request information through the first intelligent agent to obtain the first result.

[0149] According to one or more embodiments of the present disclosure, the chart information is used to represent the proportional relationship of the advanced statistical information corresponding to at least two target data objects; and further includes: generating a corresponding data report based on the conclusion information.

[0150] According to one or more embodiments of the present disclosure, after the conclusion information is displayed in the interactive interface, it also includes: receiving a requirement text input by a user, wherein the requirement text is used to represent the intention to adjust the content of the text information; generating extended conclusion information based on the requirement text, wherein the extended conclusion information includes extended text information generated based on the requirement text, and extended chart information corresponding to the extended text information.

[0151] According to one or more embodiments of the present disclosure, the conclusion information includes at least one of the following: the average value of the advanced statistical information corresponding to the target data object within the target time interval; the change in the advanced statistical information corresponding to the target data object within the target time interval; the rising value and / or falling value of the advanced statistical information corresponding to the target data object within the target time interval; and the changing trend of the advanced statistical information corresponding to the target data object within the target time interval.

[0152] In a second aspect, according to one or more embodiments of the present disclosure, there is provided an information display device, comprising:

[0153] An interactive unit, configured to display tabular data, wherein the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object;

[0154] a processing unit, configured to generate conclusion information corresponding to the table data in response to receiving a trigger instruction, wherein the conclusion information includes text information and corresponding chart information, the chart information being a result of graphical processing corresponding to the text information, and the chart information at least including a chart and / or a pivot table;

[0155] The interaction unit is further configured to display the conclusion information.

[0156] According to one or more embodiments of the present disclosure, the conclusion information is generated based on a language model, the conclusion information represents the target data object and the processing rules corresponding to the target data object, the processing rules represent the logic of determining the target data object using advanced statistical information, and the advanced statistical information is generated based on the basic statistical information.

[0157] According to one or more embodiments of the present disclosure, the processing unit is specifically used to: obtain a target prompt word according to a trigger instruction input by a user, and the target prompt word is used to describe the intention of generating the conclusion information based on a natural language; process the target prompt word through a preset table data service to generate a data inference request, and the table data service is used to obtain the table data, and generate the data inference request based on the table data and the target prompt word; based on the data inference request, call an execution plug-in to obtain conclusion information corresponding to the table data, and the execution plug-in is used to call an external knowledge base to process the table data to generate the conclusion information.

[0158] According to one or more embodiments of the present disclosure, when the processing unit processes the target prompt word through a preset table data service and generates a data inference request, it is specifically used to: identify the table data structure of the table data through the table data service; load the table data based on the table data structure, and convert the table data into parsed data that can be used to input the execution plug-in; and generate the data inference request based on the parsed data and the target prompt word.

[0159] According to one or more embodiments of the present disclosure, a first agent based on a reason-action model is deployed in the execution plug-in. When the processing unit calls the execution plug-in based on the data reasoning request and obtains conclusion information corresponding to the tabular data, it is specifically used to: process the data reasoning request through the first agent to obtain a first result, wherein the first agent is used to call the first external interface of the external knowledge base to obtain the first result returned by the external knowledge base; and generate the conclusion information based on the first result.

[0160] According to one or more embodiments of the present disclosure, when the processing unit processes the data reasoning request through the first intelligent agent and obtains the first result, it is specifically used to: process the data reasoning request through the first intelligent agent to generate an inference target; generate a first action instruction based on the inference target; call the first module interface of the code processing module by executing the first action instruction to generate a first program code corresponding to the first action instruction and applied to the external knowledge base; call the first external interface of the external knowledge base by executing the first program code to obtain the first result returned by the external knowledge base.

[0161] According to one or more embodiments of the present disclosure, after obtaining the first result returned by the external knowledge base, the processing unit is further used to: process the first result through the first intelligent agent to generate an updated reasoning target; when the processing unit generates the conclusion information based on the first result, it is specifically used to: generate a second action instruction based on the updated reasoning target; by executing the second action instruction, call the first module interface of the code processing module to generate a second program code corresponding to the second action instruction applied to the external knowledge base; by executing the second program code, call the second external interface of the external knowledge base to obtain the second result returned by the external knowledge base; and generate the conclusion information based on the second result.

[0162] According to one or more embodiments of the present disclosure, after obtaining the first result returned by the external knowledge base, the processing unit is also used to: call the second module interface of the code processing module through the first intelligent agent to generate a correction code corresponding to the first program code; and call the first external interface of the external knowledge base by executing the correction code to regain the first result returned by the external knowledge base.

[0163] According to one or more embodiments of the present disclosure, when the processing unit generates the conclusion information based on the first result, it is specifically used to: send the first result to the table data service; process the first result through the table data service to generate conclusion information in a target data format.

[0164] According to one or more embodiments of the present disclosure, the processing unit is further used to: obtain historical request information received before the trigger instruction; when the processing unit processes the data inference request through the first intelligent agent to obtain the first result, it is specifically used to: process the data inference request and the historical request information through the first intelligent agent to obtain the first result.

[0165] According to one or more embodiments of the present disclosure, the chart information is used to represent the proportional relationship of the advanced statistical information corresponding to at least two target data objects; the interaction unit is further used to generate a corresponding data report based on the conclusion information.

[0166] According to one or more embodiments of the present disclosure, after the conclusion information is displayed in the interactive interface, the interactive unit is further used to: receive a requirement text input by a user, wherein the requirement text is used to represent the intention to adjust the content of the text information; the processing unit is further used to: generate extended conclusion information based on the requirement text, wherein the extended conclusion information includes extended text information generated based on the requirement text, and extended chart information corresponding to the extended text information.

[0167] According to one or more embodiments of the present disclosure, the conclusion information includes at least one of the following: the average value of the advanced statistical information corresponding to the target data object within the target time interval; the change in the advanced statistical information corresponding to the target data object within the target time interval; the rising value and / or falling value of the advanced statistical information corresponding to the target data object within the target time interval; and the changing trend of the advanced statistical information corresponding to the target data object within the target time interval.

[0168] In a third aspect, according to one or more embodiments of the present disclosure, there is provided an electronic device, comprising: at least one processor and a memory;

[0169] The memory stores computer-executable instructions;

[0170] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the information display method described in the first aspect and various possible designs of the first aspect.

[0171] In a fourth aspect, according to one or more embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the information display method described in the first aspect and various possible designs of the first aspect is implemented.

[0172] In a fifth aspect, according to one or more embodiments of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the information display method as described in the first aspect and various possible designs of the first aspect.

[0173] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0174] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0175] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. An information display method, comprising: Displaying tabular data, wherein the tabular data includes at least one data object and at least one basic statistical information corresponding to the data object; and In response to receiving a trigger instruction, conclusion information corresponding to the table data is generated and displayed; wherein the conclusion information includes text information and corresponding chart information, the chart information is the result of graphical processing corresponding to the text information, and the chart information includes at least a chart and / or a pivot table.

2. The method according to claim 1, wherein: The conclusion information is generated based on a language model, the conclusion information represents a target data object and a processing rule corresponding to the target data object, the processing rule represents logic for determining the target data object using advanced statistical information, and the advanced statistical information is generated based on the basic statistical information.

3. The method according to claim 1 or 2, wherein: In response to a trigger instruction input by a user, conclusion information corresponding to the table data is obtained, including: Obtaining a target prompt word according to the trigger instruction input by the user, wherein the target prompt word is used to describe the intention of generating the conclusion information based on a natural language description; Processing the target prompt word through a preset table data service to generate a data inference request, wherein the table data service is used to obtain the table data and generate the data inference request based on the table data and the target prompt word; Based on the data reasoning request, an execution plug-in is called to obtain conclusion information corresponding to the table data. The execution plug-in is used to call an external knowledge base to process the table data to generate the conclusion information.

4. The method according to claim 3, wherein: The process of processing the target prompt word through a preset table data service to generate a data inference request includes: identifying, by the table data service, a table data structure of the table data; Based on the table data structure, loading the table data and converting the table data into parsed data for inputting into the execution plug-in; The data inference request is generated based on the parsed data and the target prompt word.

5. The method according to claim 3 or 4, wherein: The execution plug-in has a first agent based on a reason-action model deployed therein, and the execution plug-in is called based on the data reasoning request to obtain conclusion information corresponding to the table data, including: Processing the data reasoning request through the first agent to obtain a first result, wherein the first agent is used to call a first external interface of the external knowledge base to obtain the first result returned by the external knowledge base; The conclusion information is generated based on the first result.

6. The method according to claim 5, wherein: The step of processing the data reasoning request through the first agent to obtain a first result includes: Processing the data reasoning request through the first agent to generate a reasoning target; Based on the inference target, generating a first action instruction; By executing the first action instruction, a first module interface of a code processing module is called to generate a first program code corresponding to the first action instruction and applied to the external knowledge base; By executing the first program code, the first external interface of the external knowledge base is called to obtain the first result returned by the external knowledge base.

7. The method according to claim 6, wherein: After obtaining the first result returned by the external knowledge base, the method further includes: Processing the first result through the first agent to generate an updated reasoning target; The generating the conclusion information based on the first result includes: Based on the updated reasoning target, generating a second action instruction; By executing the second action instruction, the first module interface of the code processing module is called to generate a second program code corresponding to the second action instruction and applied to the external knowledge base; By executing the second program code, a second external interface of the external knowledge base is called to obtain a second result returned by the external knowledge base; The conclusion information is generated according to the second result.

8. The method according to claim 6, wherein: After obtaining the first result returned by the external knowledge base, the method further includes: Calling, by the first agent, a second module interface of the code processing module to generate a correction code corresponding to the first program code; By executing the correction code, the first external interface of the external knowledge base is called to retrieve the first result returned by the external knowledge base.

9. The method according to any one of claims 5 to 8, wherein: The generating the conclusion information based on the first result includes: sending the first result to the table data service; The first result is processed through the table data service to generate conclusion information in a target data format.

10. The method according to any one of claims 5 to 9, further comprising: Acquire historical request information received before the trigger instruction; The step of processing the data reasoning request through the first agent to obtain a first result includes: The first agent processes the data inference request and the historical request information to obtain the first result.

11. The method according to claim 2, wherein: The chart information is used to represent the proportional relationship of the advanced statistical information corresponding to at least two of the target data objects; The method further comprises: Generate a corresponding data report based on the conclusion information.

12. The method according to claim 1, further comprising: Receiving a demand text input by a user, wherein the demand text is used to represent an intention to adjust the content of the text information; According to the requirement text, extended conclusion information is generated, wherein the extended conclusion information includes extended text information generated based on the requirement text and extended chart information corresponding to the extended text information.

13. The method according to claim 1, wherein: The conclusion information includes at least one of the following: The average value of the advanced statistics corresponding to the target data object within the target time interval; The amount of change in the advanced statistical information corresponding to the target data object within the target time interval; An increase value and / or a decrease value of the advanced statistical information corresponding to the target data object within the target time interval; The change trend of the advanced statistical information corresponding to the target data object within the target time interval.

14. An information display device, comprising: an interaction unit, configured to display table data, wherein the table data includes at least one data object and at least one basic statistical information corresponding to the data object; and A processing unit, configured to generate conclusion information corresponding to the table data in response to receiving a trigger instruction, wherein the conclusion information includes text information and corresponding chart information, the chart information is a result of graphical processing corresponding to the text information, and the chart information at least includes a chart and / or a pivot table; Wherein, the interaction unit is further configured to display the conclusion information.

15. An electronic device, comprising: Processor and memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the information display method according to any one of claims 1 to 13.

16. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the information display method according to any one of claims 1 to 13 is implemented.

17. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the information display method according to any one of claims 1 to 13 is implemented.

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