Information display method and device, electronic equipment, storage medium and program product

Abstract

The invention provides an information display method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of computer technology, big data technology and data processing, in particular to data analysis and visualization. The information display method comprises the steps that in response to a received query request for a target theme, a data set for the target theme is queried according to a target intention indicated by the query request, target mapping information is obtained, the target mapping information represents the mapping relation between data mapping and data items, and the target mapping information represents the mapping relation between the data mapping and the data items; the data mapping and the data item are both related to the target intention; according to a preset verification rule, an initial statement generated based on the target mapping information is verified to obtain a verification result, and the preset verification rule defines respective query permissions of data mapping and data items; determining a query statement based on the initial statement according to the verification result; and displaying a query result obtained by executing the query statement.

Description

Technical Field

[0001] The present disclosure relates to the fields of computer technology, big data technology and data processing technology, and in particular to data analysis and visualization, and more specifically to an information display method and device, electronic equipment, storage medium and program product. Background Art

[0002] With the advent of the big data era, the needs for data management and analysis are becoming increasingly complex. The amount and types of data involved in various industries are constantly increasing, and the coupling complexity of data is also increasing. For non-R&D users, it is extremely challenging to complete data analysis independently. Summary of the invention

[0003] The present disclosure provides an information display method and device, an electronic device, a storage medium, and a program product.

[0004] According to one aspect of the present disclosure, there is provided an information display method, comprising: in response to receiving a query request for a target subject, querying a data set for the target subject according to the target intent indicated by the query request to obtain target mapping information, wherein the target mapping information represents a mapping relationship between a data mapping and a data item, and both the data mapping and the data item are related to the target intent; verifying an initial statement generated based on the target mapping information according to a preset verification rule to obtain a verification result, wherein the preset verification rule defines the query permissions of the data mapping and the data item respectively; determining a query statement based on the initial statement according to the verification result; and displaying the query result obtained by executing the query statement.

[0005] According to another aspect of the present disclosure, there is provided an information display device, including: a query module, for responding to receiving a query request for a target subject, querying a data set for the target subject according to the target intent indicated by the query request, and obtaining target mapping information, wherein the target mapping information represents a mapping relationship between a data mapping and a data item, and both the data mapping and the data item are related to the target intent; a verification module, for verifying an initial statement generated based on the target mapping information according to a preset verification rule, and obtaining a verification result, wherein the preset verification rule defines the query authority of each of the data mapping and the data item; a first determination module, for determining a query statement based on the initial statement according to the verification result; and a display module, for displaying the query result obtained by executing the query statement.

[0006] According to another aspect of the present disclosure, an artificial intelligence agent is provided, which is configured to execute an information presentation method.

[0007] According to another aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0008] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0009] According to another aspect of the present disclosure, a computer program product is provided, including a computer program or instructions, and the steps of the above method are implemented when the computer program or instructions are executed by a processor.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0012] Figure 1 The system architecture to which the information display method can be applied according to an embodiment of the present disclosure is schematically shown;

[0013] Figure 2 A flowchart of an information display method according to an embodiment of the present disclosure is schematically shown;

[0014] Figure 3 An example schematic diagram schematically shows a process of constructing a data set for a target subject according to an embodiment of the present disclosure;

[0015] Figure 4 An example schematic diagram schematically shows a process of obtaining a target intention according to an embodiment of the present disclosure;

[0016] Figure 5 An example schematic diagram of a process of querying a data set for a target subject and obtaining target mapping information according to a target intent indicated by a query request according to an embodiment of the present disclosure is schematically shown;

[0017] Figure 6 An example schematic diagram schematically shows an information display process according to an embodiment of the present disclosure;

[0018] Fig. 7AAn example schematic diagram of an information display interface for data query according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 7B An example schematic diagram of an information display interface for data query according to another embodiment of the present disclosure is schematically shown;

[0020] Figure 7C An example schematic diagram of an information display interface for topic management according to an embodiment of the present disclosure is schematically shown;

[0021] Fig.7D An example schematic diagram of an information display interface for topic management according to another embodiment of the present disclosure is schematically shown;

[0022] Figure 8 A block diagram of an information display device according to an embodiment of the present disclosure is schematically shown;

[0023] Fig. 9 A block diagram schematically shows a structure of an intelligent agent of a large model according to an embodiment of the present disclosure; and

[0024] Fig.10 A block diagram of an electronic device suitable for implementing an information display method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] The data management platform carries a large amount of enterprise data resources. Every day, a large number of users use the data management platform to search for data, apply for permissions, and use data. During the operation of the data management platform, there are a large number of non-R&D users such as product, operation, and sales who come to consult about data query and data analysis. For non-R&D users, it is extremely challenging to complete data analysis independently.

[0030] Although the NL2SQL (Natural Language to SQL) field has made significant progress in converting natural language instructions into executable SQL (Structured Query Language) scripts for data query and processing, achieving full automation in the broader data science pipeline (i.e., including data query, analysis, visualization, and reporting) remains a complex challenge. For example, current data analysis tasks still require human participation, that is, they are completed through R&D paradigms such as NL2SQL+toolset call+manual task splicing, which is time-consuming and labor-intensive.

[0031] In one example, the data analysis method may adopt at least one of the following: implementing data analysis based on a fine-tuned NL2SQL dedicated model and implementing data analysis based on a general large model or a data analysis agent-type product.

[0032] Implementing data analysis based on a fine-tuned NL2SQL dedicated model means improving the accuracy of NL2SQL tasks by fine-tuning a proprietary model. However, since this method requires collecting a sufficiently large cross-domain generalized dataset in order to adapt to each data domain (for example, databases of grocery stores, wholesalers, corporate finance, etc.), it consumes a lot of resources. At the same time, improper fine-tuning may lead to catastrophic forgetting, affecting the adaptability of LLM (Large Language Model).

[0033] Implementing data analysis based on general big models or data analysis agent products means generating executable SQL through natural language interaction with data analysis agent products such as instruction engineering, general big models or code interpreters. However, due to general big models or code interpreters, scripts need to be generated under user supervision, or uploaded data sets need to be operated for potential data analysis. Between each interactive step, manual intervention is still required (for example, checking the correctness of the generated script, copying and pasting the script to the local execution environment for further data operations), and the data science pipeline is not fully automated.

[0034] To this end, the disclosed embodiment proposes an information display scheme. For example, in response to receiving a query request for a target subject, according to the target intent indicated by the query request, a query is performed on the data set for the target subject to obtain target mapping information, wherein the target mapping information represents the mapping relationship between the data mapping and the data item, and both the data mapping and the data item are related to the target intent; according to a preset verification rule, an initial statement generated based on the target mapping information is verified to obtain a verification result, wherein the preset verification rule defines the query authority of each data mapping and data item; according to the verification result, a query statement is determined based on the initial statement; and the query result obtained by executing the query statement is displayed.

[0035] According to the embodiments of the present disclosure, by responding to query requests for target topics and performing precise queries in relevant data sets according to the target intent indicated by the query requests, it is possible to quickly locate and obtain target mapping information, thereby ensuring the accuracy and relevance of data retrieval. On this basis, the initial statement is checked for permissions through preset verification rules, thereby enhancing the security of data access, and the query statements generated according to the verification results can be accurately executed, displaying query results that meet the user's intent, thereby improving the user experience and data processing efficiency.

[0036] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0037] In the technical solution of the present invention, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0038] Figure 1 The system architecture to which the information display method can be applied according to the embodiment of the present disclosure is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0039] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0040] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0041] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0042] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0043] It should be noted that the information display method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the information display device provided in the embodiment of the present disclosure can generally be set in the server 105. The information display method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the information display device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0044] Alternatively, the information display method provided in the embodiment of the present disclosure may also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the information display device provided in the embodiment of the present disclosure may also be provided in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or may be provided in other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0046] It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely in the order shown.

[0047] Figure 2 The flowchart of the information display method according to the embodiment of the present disclosure is schematically shown.

[0048] like Figure 2 As shown, the information display method 200 includes operations S210 to S240.

[0049] In operation S210, in response to receiving a query request for a target subject, a query is performed on the data set used for the target subject according to the target intent indicated in the query request to obtain target mapping information, wherein the target mapping information represents a mapping relationship between data mapping and data items, and both the data mapping and the data items are related to the target intent.

[0050] In operation S220, the initial statement generated based on the target mapping information is verified according to a preset verification rule to obtain a verification result, wherein the preset verification rule defines the query authority of the data mapping and the data item.

[0051] In operation S230 , according to the verification result, a query statement is determined based on the initial statement.

[0052] In operation S240 , the query result obtained by executing the query statement is displayed.

[0053] A query request refers to a query operation initiated by a user for a specific target topic. After receiving the query request, the query request can be subjected to intent analysis to determine the target intent indicated by the query request. Target intent refers to the specific purpose or demand expressed in the query request, which is used to guide the direction and scope of the query. The specific method of intent analysis can be configured according to actual business needs and is not limited here. For example, the specific method of intent analysis may include at least one of the following: a rule-based method, a context-based method, a machine learning-based method, a deep learning-based method, and a large model-based method, etc.

[0054] The rule-based approach refers to using predefined rules to identify keywords or phrases in query requests and map them to specific intents. The context-based approach refers to considering the contextual information of the query request, such as the content of previous conversations, the user's historical behavior, etc., to more accurately judge the intent. The machine learning-based approach refers to using a well-labeled training data set to extract relevant features through feature engineering, and then using machine learning algorithms (such as logistic regression, random forest, support vector machine, etc.) to classify intent. The deep learning-based approach refers to using deep neural networks to automatically extract features and classify intent. The large model-based approach refers to using a pre-trained language model to identify the intent of query requests.

[0055] A data set refers to a collection of all data related to a target topic, and each data in the data set can be used for query operations. The specific form of the data set can be configured according to actual business needs and is not limited here. For example, the form of the data set may include at least one of the following: a distributed file system, a database, a data lake, and a cloud storage service.

[0056] In an embodiment of the present disclosure, before querying the data set for the target subject, the user's consent or authorization may be obtained. For example, before operation S210, a request for querying the data set for the target subject may be issued to the user. When the user agrees or authorizes that the data set for the target subject may be queried, operation S210 is performed.

[0057] After obtaining the target intent, the data set used for the target subject can be queried according to the target intent to obtain the target mapping information. The target mapping information is used to characterize the mapping relationship between the data mapping and the data item, and may include at least one data mapping related to the target intent, and at least one data item related to the target intent in each data mapping. Data mapping refers to a representation used to reflect the corresponding relationship between data. The specific form of the data mapping can be configured according to actual business needs and is not limited here. For example, the form of the data mapping may include at least one of the following: list form, dictionary form, and array form, etc. A data item refers to a single data element or field in a data mapping, that is, the smallest data unit. For example, when the data mapping is in list form, the data item may be a row or column. Alternatively, when the data mapping is in dictionary form, the data item may be a key-value pair. Alternatively, when the data mapping is in array form, the data item may be an element.

[0058] After obtaining the target mapping information, the target mapping information can be converted into an executable initial statement to realize the automatic interaction between the natural language question and answer and the data set. For example, when the data set is in the form of a database, the initial statement can be an SQL statement. After obtaining the initial statement, the initial statement can be verified according to the preset verification rules to obtain the verification result. The preset verification rules define the query permissions of the data mapping and the data items, respectively, to ensure that the query operation meets the predetermined permission requirements.

[0059] After obtaining the verification result, the query statement can be determined based on the initial statement according to the verification result. The verification result indicates whether the initial statement complies with the query permissions of the data mapping and the data item. For example, if the verification result indicates that the initial statement complies with the query permissions of the data mapping and the data item, the initial statement can be determined as the query statement. Alternatively, if the verification result indicates that the initial statement does not comply with the query permissions of the data mapping and the data item, the initial statement can be processed again to obtain the query statement.

[0060] After obtaining the query statement, the query statement can be executed and the query results can be displayed. The display form of the query results can be configured according to actual business needs and is not limited here. For example, the display form of the query results may include at least one of the following: table form, chart form, and text form. The table form can be used to display structured query results. The chart form may include a bar chart, a line chart, a pie chart, and a scatter chart. The bar chart is used to show the comparison of data of different categories. The line chart is used to show the trend of data changes over time. The pie chart is used to show the proportion of each part to the whole. The scatter chart is used to show the relationship between two variables. The text form is used to summarize the query results.

[0061] This application can be applied in the field of data analysis, and can automate end-to-end data science tasks such as data query, data analysis, chart visualization, and report generation according to the target intent indicated by the query request.

[0062] According to the embodiments of the present disclosure, by responding to query requests for target topics and performing precise queries in relevant data sets according to the target intent indicated by the query requests, it is possible to quickly locate and obtain target mapping information, thereby ensuring the accuracy and relevance of data retrieval. On this basis, the initial statement is checked for permissions through preset verification rules, thereby enhancing the security of data access, and the query statements generated according to the verification results can be accurately executed, displaying query results that meet the user's intent, thereby improving the user experience and data processing efficiency.

[0063] Figure 3 An example schematic diagram of a process of constructing a data set for a target topic according to an embodiment of the present disclosure is schematically shown.

[0064] like Figure 3 As shown, in 300, the data collection for the target subject can be understood as an offline stage, which is equivalent to a pre-processing link and only needs to be executed once.

[0065] The candidate set for the target subject includes at least one first candidate data mapping 301, and the structural information 303 can be obtained by semantically modeling the metadata 302 of at least one first candidate data mapping 301. Semantic modeling includes building indicators, dimensions and views. The first candidate data mapping 301 can refer to a data table, and the metadata 302 can refer to the schema of the data table. For example, the metadata 302 can include at least one of the following: table name, table description, data source connection information, SQL engine type, database, field name, field type, Chinese name, field description. The structural information 303 can refer to a view, which represents the description information of each first candidate data mapping 301 for the target subject, and the association relationship between each first candidate data mapping 301.

[0066] By performing semantic modeling on each metadata 302 to obtain structural information 303, multiple reuses can be achieved through one definition, and the data caliber is easy to change. It can also simplify SQL code generation, thereby solving the problem of large differences in results caused by different semantics or semantic ambiguity when users inquire, solidifying the data caliber definition at the semantic layer, and simplifying user semantic expression.

[0067] The initial object 304 may refer to a Query-SQL sample pair, and the initial object 304 may include a question text 304_1 and a history statement 304_2. In one example, the specific source of the initial object 304 may be configured according to actual business needs, which is not limited here. For example, the initial object 304 may be provided by the business side, in which case the input is a natural language question and the output is SQL. Alternatively, the initial object 304 may be provided by user feedback and SQL correction, in which case the input is a natural language question and an incorrect SQL, and the output is a natural language question and a correct SQL.

[0068] Alternatively, the initial object 304 may be obtained through SQL2NL, in which case the input is SQL and the output is a natural language question. It should be noted that the seed data is added through reverse engineering based on SQL2NL, as the initial object 304, and reverse engineering of SQL2NL refers to the process of generating natural language based on SQL and Schema information.

[0069] Compared with not providing seed data, the execution accuracy of seed data generated by SQL2NL in NL2SQL tasks is about 40% higher. The business effect achieved by this solution is that users no longer need to provide expensive seed data after creating a new subject domain. We will reversely generate natural language based on the user's use of SQL, and then build seed data, greatly reducing the cost of user access.

[0070] By performing semantic enhancement on the question text 304_1, an enhanced text 305_1 is obtained, that is, based on the table-based Schema information, the semantics of the user's question is data enhanced, and the enhanced result is added to the memory of the large model. Therefore, by adding the incorrect SQL sample and its data enhancement result to the model memory, the accuracy of the NL2SQL task can be effectively improved.

[0071] By performing syntax tree parsing on the historical statement 304_2, candidate mapping information 305_2 is obtained. The candidate mapping information 305_2 may include at least one second candidate data mapping and candidate data items of each second candidate data mapping. In an example, the candidate mapping information 305_2 refers to schema_linking information. For example, the candidate mapping information 305_2 may be [{"FIELD":["shop_id","profits","product_type","cities","year"],"TABLE":"product"}], which indicates the candidate data items "shop_id", "profits", "product_type", "cities", and "year" in the second candidate data mapping "product".

[0072] On this basis, the enhanced text 305_1, the historical sentence 304_2 and the candidate mapping information 305_2 can be determined as candidate objects 305, and a data set 306 for the target topic is constructed according to the structural information 303 and the plurality of candidate objects 305. In addition, the structural information 303 can also have a corresponding vectorized representation, and the enhanced text 305_1 can also have a corresponding vectorized representation.

[0073] According to the embodiments of the present disclosure, by semantically modeling the metadata of each first candidate data mapping in the candidate set, the intrinsic structure and semantic relationship of the data can be deeply understood, thereby generating accurate structural information. In addition, the generation of enhanced text is obtained by semantically enhancing the question text of the initial object in the target subject, ensuring the semantic richness and accuracy of the text. By parsing the historical sentences through the syntax tree to obtain the candidate mapping information, the analysis accuracy of the sentence structure is improved, which helps to improve the accuracy and efficiency of data processing.

[0074] Figure 4 An example schematic diagram schematically illustrates a process of obtaining a target intent according to an embodiment of the present disclosure.

[0075] like Figure 4 As shown, in 400, in response to receiving a query request 401, a large model 404 can be used to understand the intent of the query request 401 to obtain an initial intent 402. Intent understanding refers to the process of analyzing the query request 401 to determine the purpose or intent of the object. After obtaining the initial intent 402, operation S410 can be performed.

[0076] In operation S410, does the initial intent 402 satisfy the query condition? In one example, the query condition can be configured according to actual business needs and is not limited here. For example, the query condition can include whether the initial intent 402 is related to the subject domain. Alternatively, the query request can also include whether the initial intent 402 includes a drawing type.

[0077] If yes, the initial intent 402 can be directly determined as the target intent 403. If no, the large model 404 can be used to conduct multiple rounds of interactive dialogues with the object to guide the object to supplement the intent so that the query request 401 meets the query conditions. Multiple rounds of interactive dialogues refer to using the large model to conduct multiple rounds of dialogues with the object to gradually collect the required information and make appropriate responses according to the context.

[0078] On this basis, the target intent 403 can be determined based on the query request 401 and the dialogue information 405 obtained through multiple rounds of interactive dialogue. In another example, the object can also supplement the intent by asking follow-up questions. On this basis, the target intent 403 can be determined based on the pre-questioning question, the follow-up question, the vectorization of the pre-questioning question + the follow-up question, and the integrated question.

[0079] According to the embodiments of the present disclosure, through the big model technology, when the initial intent does not meet the query conditions, multiple rounds of interactive dialogues can be conducted with the user to guide the user to supplement the intent, thereby ensuring that the query request meets the conditions. This not only improves the accuracy of the query, but also optimizes the user interaction experience and enhances the pertinence and effectiveness of the query.

[0080] Figure 5 An example schematic diagram of a process of querying a data set for a target subject according to a target intent indicated by a query request to obtain target mapping information according to an embodiment of the present disclosure is schematically shown.

[0081] like Figure 5 As shown, in 500 , in response to receiving a query request, a query may be performed in a data set 520 for a target subject according to a target intent 510 indicated in the query request to obtain target mapping information 560 .

[0082] The data set 520 refers to a collection of all data related to the target subject, and each data in the data set 520 can be used for query operations. The data set 520 may include structure information 521 and multiple candidate objects. The process of obtaining the target mapping information 560 may include a multi-way recall phase and a pattern matching phase. The multi-way recall phase may be used to recall at least one data mapping 523 that matches the target intent 510. The pattern matching phase may be used to associate the at least one data mapping 523 to a specific target data mapping and the target data items of each target data mapping to obtain the target mapping information 560.

[0083] In the multi-way recall stage, the structural information 521 may be recalled according to the target intent 510 to obtain at least one data mapping 523. Recall refers to the process of retrieving structural information related to the target intent from a data set, and the purpose of recall is to obtain at least one data mapping 523.

[0084] According to the embodiments of the present disclosure, by dividing the data set into structural information and multiple candidate objects, the system can quickly recall relevant structural information according to the target intent, and then obtain at least one data mapping, thereby improving the pertinence and speed of data retrieval. On this basis, by deeply matching multiple candidate objects based on these data mappings to determine the final target mapping information, the unnecessary data retrieval scope is reduced, the matching accuracy is improved, and the relevance and reliability of the query results are ensured.

[0085] In one example, the structure information 521 represents the description information of each first candidate data mapping for the target subject, and the association relationship between the first candidate data mappings. The association relationship refers to the logical or semantic connection between the first candidate data mappings.

[0086] In the process of recalling at least one data mapping 523 from the structural information 521, an indicator 522 may be extracted from the target intent 510, and based on the association relationship, the description information of each first candidate data mapping may be matched according to the indicator 522 to obtain a first number of data mappings 523. The indicator 522 refers to a specific standard or parameter extracted from the target intent 510, and is used to evaluate the relevance of the description information of each first candidate data mapping.

[0087] For example, if the query request is "What is the closed-loop rate of online bugs in business line C of group B of department A in 2023", the query request is closer to "Revenue of business line C of group B of department A in 2023" than "What is the closed-loop rate of online bugs in business line C of group B of department A in 2022", so it is easy for data mapping with key indicators to be ignored. Therefore, the indicator 522 of the target intent 510 is extracted by using the large model, and the indicator 522 is used to recall the examples most similar to the target intent 510.

[0088] In the process of recalling at least one data mapping 523 from the structural information 521, the intent feature 524 of the target intent 510 can be determined, and based on the association relationship, the description information of each first candidate data mapping is matched according to the intent feature to obtain a second number of data mappings 523. The intent feature 524 refers to a specific attribute or feature of the target intent 510.

[0089] By merging the first number of data mappings 523 recalled based on the indicator 522 and the second number of data mappings 523 recalled based on the intent feature 524 , the accuracy of subsequent query statement generation can be improved.

[0090] It should be noted that, in the multi-channel recall stage, each channel of the recall needs to be as independent as possible, so as to ensure that the target data mapping can appear in at least one data mapping 523. In one example, after obtaining at least one data mapping 523, the effect of the multi-channel recall can also be evaluated. The specific evaluation method can be configured according to actual business needs and is not limited here. For example, the evaluation method can include determining whether the target data mapping is in at least one data mapping 523. Alternatively, the evaluation method can also include that the position of the target data mapping in at least one data mapping 523 is as forward as possible.

[0091] According to the embodiments of the present disclosure, by matching the description information based on the association relationship and the indicators in the target intent, the first number of data mappings can be accurately identified, ensuring the pertinence and accuracy of data processing. By matching the description information using the intent features of the target intent, the second number of data mappings is obtained, which not only optimizes the data retrieval process, but also improves the accuracy of the matching, ensuring the relevance and reliability of the query results.

[0092] When querying a database using natural language, the model input includes natural language query statements and description information. The description information can be understood as database schema description information, which refers to a string text that organizes all data mappings in the database in a certain serialization format.

[0093] Considering that actual natural language queries are often only related to partial data mappings and partial data items, the remaining schema information of redundant input may have a negative impact. Therefore, a new pattern matching stage can be added as a precursor to query statement generation to alleviate the problems caused by redundant schema input.

[0094] In the pattern matching phase, multiple candidate objects may be matched based on at least one data mapping 523 to determine target mapping information 560. For example, the capability of the large model interface may be used to complete the pattern link matching relationship from natural language to partial data mapping and partial data items.

[0095] In one example, the candidate object may include enhanced text and candidate mapping information 530. The candidate mapping information 530 may include at least one second candidate data mapping 540 and candidate data items 550 for each second candidate data mapping 540. The second candidate data mapping 540 refers to a data mapping in the candidate mapping information, and each second candidate data mapping 540 has at least one candidate data item. The candidate data item refers to a data item associated with each second candidate data mapping 540.

[0096] After obtaining at least one data mapping 523, multiple enhanced texts can be matched according to the target intent 510 to obtain at least one target object. According to the candidate mapping information of at least one data mapping and at least one target object, M target data mappings and N target data items of each target data mapping are determined. For example, the mapping relationship between the target data mapping 540_1 and the data item 550_2, the mapping relationship between the data item 550_3 and the data item 550_X-1, and the mapping relationship between the target data mapping 540_2 and the data item 550_1 and the data item 550_2. On this basis, the target mapping information 560 can be determined according to the M target data mappings and the N target data items of each target data mapping.

[0097] According to the embodiments of the present disclosure, by matching the enhanced text according to the target intent, at least one target object is quickly identified, which not only improves the accuracy of data retrieval, but also optimizes the efficiency of data processing. On this basis, by determining the target data mapping and its respective target data items according to the candidate mapping information of at least one data mapping and the target object, this multi-level matching and determination mechanism improves the accuracy and flexibility of data mapping, and provides strong technical support for the efficient management and retrieval of complex data sets.

[0098] Figure 6 An example schematic diagram of an information display process according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 6As shown, in 600, in response to receiving a query request for a target subject from object 610, an intent analysis is performed on the query request to obtain an initial intent, and operation S610 is performed.

[0100] In operation S610, is it determined whether the initial intent satisfies the query condition? If so, the initial intent can be determined as the target intent 620. If not, the large model can be used to conduct multiple rounds of interactive dialogues with the object 610 to guide the object 610 to supplement the intent so that the query request satisfies the query condition, and the target intent 620 is determined based on the query request and the dialogue information obtained through multiple rounds of interactive dialogues.

[0101] After obtaining the target intent 620, a multi-way recall phase may be performed, that is, the structural information in the data set is recalled according to the target intent 620 to obtain at least one data mapping 630. After obtaining at least one data mapping 630, a pattern matching phase may be performed, that is, multiple candidate objects in the data set are matched based on at least one data mapping 630 to determine the target mapping information 640.

[0102] In one example, the target mapping information 640 may include M target data mappings and N target data items for each target data mapping. The M target data mappings and the N target data items for each target data mapping may be translated into an initial statement 650. The initial statement 650 refers to a preliminary query statement obtained by integrating the M target data mappings and the N target data item information for each target data mapping. At the same time, in order to alleviate potential model illusions, the data analysis agent may perform self-reflection and error correction on the generated initial statement 650.

[0103] In response to the initial statement 650 passing the syntax tree check, the initial statement 650 is subjected to a permission check according to the query permissions of each target data mapping and each target data item, and a verification result is obtained. Syntax tree checking refers to performing a grammatical analysis on the initial statement 650 to ensure the correctness of its grammatical structure. Each target data mapping and each target data item each have a preset query permission, which is used to determine whether the initial statement 650 meets the predetermined security and permission requirements. On this basis, the query statement 660 can be determined based on the initial statement 650 according to the verification result.

[0104] In one example, when the verification result indicates that the initial statement 650 passes the verification, the initial statement 650 is determined as the query statement 660. Alternatively, when the verification result indicates that the initial statement 650 fails the verification, the initial statement 650 is corrected to obtain the query statement 660.

[0105] For example, the problem with the initial statement 650 may include that the relationship between the initial statement 650 and the target mapping information 640 does not match due to the existence of illusions in the model. In view of this, a self-reflection instruction can be set, that is, when it is detected that the generated initial statement 650 and the target mapping information 640 do not match, the large model can be made to reflect and regenerate the query statement 660 to improve the execution accuracy.

[0106] Alternatively, the problem with the initial statement 650 may include that the generated initial statement 650 cannot be executed correctly due to a syntax error. In view of this, the table dimension of each target data mapping can be authenticated, and the field dimension of each target data item can be authenticated. After the authentication is passed, different SQL engines are called to execute, so that the entire query process is safe and controllable.

[0107] According to the embodiments of the present disclosure, by generating an initial statement based on M target data mappings and their respective N target data items, the system can ensure the preliminary accuracy and relevance of the query statement. Through syntax tree checking and permission verification, not only the grammatical correctness of the initial statement is verified, but also the query permission of each data mapping and data item is ensured to comply with the preset rules, thereby enhancing the security of data access. On this basis, by determining the query statement based on the initial statement according to the verification results, the efficiency and accuracy of query statement generation are improved.

[0108] After obtaining the query statement 660, the query result 670 obtained by executing the query statement 660 can be displayed. The display form of the query result 670 can be configured according to actual business needs and is not limited here. For example, the display form of the query result 670 can include at least one of the following: table form, chart form, and text form.

[0109] In one example, the chart type used for the query chart can be determined based on the target intent 620 of the query request and the execution result obtained by executing the query statement 660. Execute a script for generating a chart of the chart type to display the query chart. For example, a table format 671 can be used to display structured query results. Chart formats can include bar charts, line charts, pie charts, and scatter charts. Bar charts 672 can be used to display data comparisons of different categories. Line charts 673 can be used to display trends of data changes over time. Pie charts 674 can be used to display the proportion of each part to the whole. Scatter charts are used to display the relationship between two variables.

[0110] In another example, a query text may be generated and displayed according to the execution result. The text form 675 may be used to summarize the query result.

[0111] According to the embodiments of the present disclosure, based on the target intent of the query request and the execution results obtained by executing the query statement, the system intelligently determines the chart type used for the query chart, ensures the matching degree between the chart form and the data content, and improves the efficiency of information communication. By executing the script that generates the chart type, the system can automatically display the query chart, enhancing the intuitiveness and comprehensibility of the data. In addition, by generating and displaying the query text according to the execution results, the system ensures a detailed description and explanation of the data, meets the user's query needs for data details, and not only optimizes the diversity and accuracy of data display, but also improves the user experience and the flexibility of data processing.

[0112] Fig. 7A An example schematic diagram of an information display interface for data query according to an embodiment of the present disclosure is schematically shown.

[0113] like Fig. 7A As shown, in 700A, the process of data query is explained by taking the target subject as chain supermarket 702 as an example.

[0114] In the process of executing data query, the user can select the data query control 701 of the information display interface, enter a natural language question for the target topic in the input box 704, and click the query button 705 to execute the data query operation.

[0115] It should be noted that the display box 703 may display data dimensions for the target topic to help the user select any dimension to input a natural language question.

[0116] Figure 7B An example schematic diagram of an information display interface for data query according to another embodiment of the present disclosure is schematically shown.

[0117] like Figure 7B As shown, in 700B, the process of information display is explained by taking the target subject as chain supermarket 702 as an example.

[0118] The natural language question 706 input by the user may be “Which store among all stores has the highest total profit in “daily necessities” in 2022?” The agent may execute the information display method provided by the present disclosure, obtain a query statement, and display the query result 704 obtained by executing the query statement.

[0119] Figure 7C An example schematic diagram of an information display interface for topic management according to an embodiment of the present disclosure is schematically shown.

[0120] like Figure 7C As shown, in 700C, the process of theme management is explained by taking the target theme of chain supermarket 702 as an example.

[0121] In the process of executing theme management, the user can select theme management control 708 in the information display interface, and continue to select data management control 709 in the information display interface to implement management for data mapping 710. Management can include deletion, addition and modification.

[0122] Fig.7D An example schematic diagram of an information display interface for topic management according to another embodiment of the present disclosure is schematically shown.

[0123] like Fig.7D As shown, in 700D, the sample management control 711 in the information display interface can be selected to implement management of candidate objects.

[0124] The above are merely exemplary embodiments, but are not limited thereto and may also include other information display methods known in the art, as long as they can display query results that meet the user's intent, thereby improving data processing efficiency.

[0125] Figure 8 A block diagram of an information display device according to an embodiment of the present disclosure is schematically shown.

[0126] like Figure 8 As shown, the information display device 800 may include a query module 810 , a verification module 820 , a first determination module 830 and a display module 840 .

[0127] The query module 810 is used to respond to receiving a query request for a target subject, query the data set used for the target subject according to the target intent indicated in the query request, and obtain target mapping information, wherein the target mapping information represents the mapping relationship between the data mapping and the data items, and both the data mapping and the data items are related to the target intent.

[0128] The verification module 820 is used to verify the initial statement generated based on the target mapping information according to the preset verification rules to obtain the verification result, wherein the preset verification rules define the query permissions of the data mapping and the data items respectively.

[0129] The first determination module 830 is used to determine the query statement based on the initial statement according to the verification result.

[0130] The display module 840 is used to display the query results obtained by executing the query statement.

[0131] According to an embodiment of the present disclosure, a data set includes structure information and a plurality of candidate objects.

[0132] According to an embodiment of the present disclosure, the query module 810 may include a recall submodule and a matching submodule.

[0133] The recall submodule is used to recall the structural information according to the target intent to obtain at least one data mapping.

[0134] The matching submodule is used to match multiple candidate objects based on at least one data mapping to determine target mapping information.

[0135] According to an embodiment of the present disclosure, the structural information represents the description information of each first candidate data mapping for the target subject, and the association relationship between the first candidate data mappings.

[0136] According to an embodiment of the present disclosure, the recall submodule may include a first matching unit and a second matching unit.

[0137] The first matching unit is used to match the description information of each first candidate data mapping based on the association relationship and according to the indicators extracted from the target intent to obtain a first number of data mappings.

[0138] The second matching unit is used to match the description information of each first candidate data mapping based on the association relationship and according to the intention characteristics of the target intention, so as to obtain a second number of data mappings.

[0139] According to an embodiment of the present disclosure, the candidate object includes enhanced text and candidate mapping information, and the candidate mapping information includes at least one second candidate data mapping and respective candidate data items of each second candidate data mapping.

[0140] According to an embodiment of the present disclosure, the matching submodule may include a third matching unit, a first determining unit, and a second determining unit.

[0141] The third matching unit is used to match the multiple enhanced texts according to the target intent to obtain at least one target object.

[0142] The first determination unit is used to determine M target data mappings and N target data items of each target data mapping according to candidate mapping information of at least one data mapping and at least one target object, wherein M and N are both positive integers.

[0143] The second determining unit is used to determine the target mapping information according to the M target data mappings and the N target data items of each target data mapping.

[0144] According to an embodiment of the present disclosure, the verification module 820 may include a generation submodule and a verification submodule.

[0145] The generating submodule is used to generate an initial statement according to the M target data mappings and the respective N target data items of each target data mapping.

[0146] The verification submodule is used to respond to the initial statement through the syntax tree check, and perform permission verification on the initial statement according to the query permissions of each target data mapping and each target data item to obtain a verification result.

[0147] According to an embodiment of the present disclosure, the first determining module 830 may include a first determining submodule and a second determining submodule.

[0148] The first determination submodule is configured to determine the initial statement as a query statement when the verification result indicates that the initial statement passes the verification.

[0149] The second determination submodule is used to correct the initial statement to obtain a query statement when the verification result indicates that the initial statement fails the verification.

[0150] According to an embodiment of the present disclosure, the query result includes a query graph and a query text.

[0151] According to an embodiment of the present disclosure, the display module 840 may include a third determination submodule, a first display submodule, and a second display submodule.

[0152] The third determination submodule is used to determine the chart type used for querying the chart according to the target intent of the query request and the execution result obtained by executing the query statement.

[0153] The first display submodule is used to execute a script for generating a chart of a chart type to display a query chart.

[0154] The second display submodule is used to generate and display the query text according to the execution result.

[0155] According to an embodiment of the present disclosure, the structural information is obtained by semantically modeling the metadata of each first candidate data mapping in the data set for the target subject. The candidate object includes enhanced text, historical sentences and candidate mapping information, the enhanced text is obtained by semantically enhancing the question text in the initial object in the target subject, and the candidate mapping information is obtained by parsing the historical sentences through a syntax tree.

[0156] According to an embodiment of the present disclosure, the information display device 800 may further include an interactive dialogue module and a second determination module.

[0157] The interactive dialogue module is used to respond to the initial intent obtained by performing intent analysis on the query request not meeting the query conditions, and use the large model to conduct multiple rounds of interactive dialogues with the object to guide the object to supplement the intent so that the query request meets the query conditions.

[0158] The second determination module is used to determine the target intention based on the query request and the dialogue information obtained through multiple rounds of interactive dialogues.

[0159] Fig. 9 The structural block diagram of the intelligent agent of the large model according to the embodiment of the present disclosure is schematically shown.

[0160] In the embodiments of the present disclosure, inspired by the von Neumann structure in modern computer theory, such as Fig. 9 As shown, the AI ​​agent 900 may include five core modules: an input module 910 , a control module 920 , a storage module 930 , a calculation module 940 and an output module 950 .

[0161] The input module 910 is responsible for receiving or sensing information such as queries, requests, instructions, signals or data from the outside world (such as users or external environment), and converting them into a format that can be understood and processed by the AI ​​agent 900. The input module 910 is the primary link for the AI ​​agent 900 to interact with the outside world. It enables the AI ​​agent 900 to efficiently and accurately obtain the necessary "sensory" information from the outside world and respond to this information.

[0162] In an example, the input module 910 may input the query request described above.

[0163] The control module 920 is the core support for the AI ​​agent 900 to handle complex tasks. The control module 920 can execute the information display method described above.

[0164] In the example, the control module 920 will continuously interact with the storage module 930, the computing module 940 and / or the output module 950 during operation. However, it should be noted that in the embodiment of the present disclosure, the control module 920 acts as a single initiator to initiate communication with the storage module 930, the computing module 940 and / or the output module 950, and there is no communication coupling between the storage module 930, the computing module 940 and the output module 950.

[0165] In the example, the performance of the control module 920 may be closely related to the large model on which the AI ​​agent 900 is based. In order to give full play to the capabilities of the large language model, the internal structure of the control module 920 may be designed to be highly configurable and scalable to cope with various types of tasks and requirements in real scenarios.

[0166] The storage module 930 may be responsible for memorizing information such as historical conversations, event streams, etc. The candidate set as above may be included in the storage module 930 .

[0167] In the example, after receiving the query request, the AI ​​agent 900 can query the data set for the target subject according to the target intent indicated by the query request to obtain the target mapping information. The target mapping information can be stored in the storage module 930. The AI ​​agent 900 can obtain the target mapping information from the storage module 930 and feed it back to the control module 920. Then, the control module 920 can further process the fed-back target mapping information to obtain the query result, and pass the query result to the output module 950.

[0168] The operation module 940 may be viewed as a predefined tool library, and tools for semantic modeling, semantic enhancement, and syntax tree parsing, etc., as described above, may be included in the operation module 940 .

[0169] In the example, when the AI ​​agent 900 needs to perform semantic modeling, the relevant tool for semantic modeling can be called from the operation module 940 and fed back to the control module 920. Then, the control module 920 can use the tool for semantic modeling fed back to perform semantic modeling on the metadata of each first candidate data mapping, obtain structural information, and pass the structural information to the output module 950. It can be understood that although the large language model has excellent language understanding and generation capabilities, it is like a human being, without the help of any tools, the tasks that can be solved are very limited. When the AI ​​agent 900 is given the ability to call tools, it can achieve, for example, by using tools for semantic modeling.

[0170] The output module 950 can output the query results described above.

[0171] The AI ​​agent 900 according to the embodiment of the present disclosure can simply and effectively improve the level of intelligence, and improve flexibility and versatility.

[0172] Fig.10 A block diagram of an electronic device suitable for implementing an information display method according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0173] like Fig.10As shown, the device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0174] A number of components in the device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0175] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as the information display method. For example, in some embodiments, the information display method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the information display method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to perform the information display method in any other appropriate manner (e.g., by means of firmware).

[0176] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0177] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0178] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may 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.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0180] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0181] A computer system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises through computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server combined with a blockchain.

[0182] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0183] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An information display method, comprising: In response to receiving a query request for a target subject, querying a data set for the target subject according to a target intent indicated by the query request to obtain target mapping information, wherein the target mapping information represents a mapping relationship between a data mapping and a data item, and both the data mapping and the data item are related to the target intent; According to a preset verification rule, the initial statement generated based on the target mapping information is verified to obtain a verification result, wherein the preset verification rule defines the query authority of the data mapping and the data item; According to the verification result, determining a query statement based on the initial statement; and The query results obtained by executing the query statement are displayed.

2. The method according to claim 1, wherein: The data set includes structural information and a plurality of candidate objects; The querying in the data set for the target subject according to the target intent indicated by the query request to obtain the target mapping information includes: Recalling the structure information according to the target intent to obtain at least one data mapping; and Based on the at least one data mapping, the plurality of candidate objects are matched to determine the target mapping information.

3. The method according to claim 2, wherein: The structural information represents the description information of each first candidate data mapping for the target subject, and the association relationship between the first candidate data mappings; The recalling of the structural information according to the target intent to obtain at least one data mapping comprises: Based on the association relationship, and according to the index extracted from the target intent, the description information of each first candidate data mapping is matched to obtain a first number of the data mappings; as well as Based on the association relationship and according to the intent feature of the target intent, the description information of each first candidate data mapping is matched to obtain a second number of the data mappings.

4. The method according to claim 2, wherein: The candidate object includes enhanced text and candidate mapping information, wherein the candidate mapping information includes at least one second candidate data mapping and a candidate data item of each second candidate data mapping; The matching the plurality of candidate objects based on the at least one data mapping to determine the target mapping information includes: According to the target intent, the multiple enhanced texts are matched to obtain at least one target object; Determine, according to the candidate mapping information of each of the at least one data mapping and the at least one target object, M target data mappings and N target data items of each target data mapping, wherein M and N are both positive integers; and The target mapping information is determined based on the M target data mappings and the N target data items of each target data mapping.

5. The method according to claim 4, wherein: The initial statement generated based on the target mapping information is verified according to the preset verification rules to obtain the verification result, which includes: generating an initial statement according to the M target data mappings and the N target data items of each of the target data mappings; and In response to the initial statement passing the syntax tree check, performing permission verification on the initial statement according to the query permission of each target data mapping and each target data item, and obtaining the verification result; Determining the query statement based on the initial statement according to the verification result includes: If the verification result indicates that the initial statement passes the verification, determining the initial statement as the query statement; and When the verification result indicates that the initial statement fails the verification, the initial statement is corrected to obtain the query statement.

6. The method according to any one of claims 1 to 5, wherein: The query result includes a query graph and a query text; The display of the query result obtained by executing the query statement includes: Determining a chart type for the query chart according to a target intent of the query request and an execution result obtained by executing the query statement; executing a script for generating a chart of the chart type to display the query chart; and According to the execution result, the query text is generated and displayed.

7. The method according to claim 2, wherein: The structural information is obtained by semantically modeling metadata of each first candidate data mapping in the candidate set for the target subject; The candidate objects include enhanced text, historical sentences and candidate mapping information. The enhanced text is obtained by semantically enhancing the question text in the initial object within the target subject. The candidate mapping information is obtained by parsing the historical sentences through a syntax tree.

8. The method according to claim 1 further comprises, before querying the data set for the target subject according to the target intent indicated by the query request to obtain the target mapping information: In response to the initial intent obtained by performing intent analysis on the query request not satisfying the query condition, using the big model, conducting multiple rounds of interactive dialogues with the subject to guide the subject to supplement the intent so that the query request satisfies the query condition; and The target intention is determined based on the query request and dialogue information obtained through multiple rounds of interactive dialogues.

9. An information display device, comprising: A query module, configured to, in response to receiving a query request for a target subject, query a data set for the target subject according to a target intent indicated by the query request to obtain target mapping information, wherein the target mapping information represents a mapping relationship between a data mapping and a data item, and both the data mapping and the data item are related to the target intent; A verification module, configured to verify the initial statement generated based on the target mapping information according to a preset verification rule to obtain a verification result, wherein the preset verification rule defines the query authority of the data mapping and the data item; A first determination module, configured to determine a query statement based on the initial statement according to the verification result; and The display module is used to display the query results obtained by executing the query statement.

10. The device according to claim 9, wherein: The data set includes structural information and a plurality of candidate objects; The query module includes: a recall submodule, configured to recall the structural information according to the target intent to obtain at least one data mapping; and A matching submodule is used to match the multiple candidate objects based on the at least one data mapping to determine the target mapping information.

11. The device according to claim 10, wherein: The structural information represents the description information of each first candidate data mapping for the target subject, and the association relationship between the first candidate data mappings; The recall submodule includes: A first matching unit, configured to match the description information of each of the first candidate data mappings based on the association relationship and according to the index extracted from the target intent, to obtain a first number of the data mappings; as well as The second matching unit is used to match the description information of each of the first candidate data mappings based on the association relationship and according to the intention characteristics of the target intention, so as to obtain a second number of the data mappings.

12. The device according to claim 10, wherein: The candidate object includes enhanced text and candidate mapping information, wherein the candidate mapping information includes at least one second candidate data mapping and a candidate data item of each second candidate data mapping; The matching submodule includes: A third matching unit, configured to match the plurality of enhanced texts according to the target intent to obtain at least one target object; A first determining unit is configured to determine M target data mappings and N target data items of each target data mapping according to the candidate mapping information of each of the at least one data mapping and the at least one target object, wherein both M and N are positive integers; and The second determining unit is used to determine the target mapping information according to the M target data mappings and the N target data items of each target data mapping.

13. The device according to claim 12, wherein: The verification module comprises: a generating submodule, configured to generate an initial statement according to the M target data mappings and the N target data items of each target data mapping; and A verification submodule, configured to, in response to the initial statement passing the syntax tree check, perform permission verification on the initial statement according to the query permissions of each target data mapping and each target data item, and obtain the verification result; The first determining module comprises: A first determination submodule is configured to determine the initial statement as the query statement if the verification result indicates that the initial statement passes the verification; and The second determination submodule is configured to, if the verification result indicates that the initial statement fails the verification, correct the initial statement to obtain the query statement.

14. The device according to any one of claims 9 to 13, wherein: The query result includes a query graph and a query text; The display module comprises: A third determination submodule, configured to determine a chart type for the query chart according to a target intent of the query request and an execution result obtained by executing the query statement; a first display submodule, configured to execute a script for generating a chart of the chart type to display the query chart; and The second display submodule is used to generate and display the query text according to the execution result.

15. The device according to claim 10, wherein: The structural information is obtained by semantically modeling metadata of each first candidate data mapping in the candidate set for the target subject; The candidate objects include enhanced text, historical sentences and candidate mapping information. The enhanced text is obtained by semantically enhancing the question text in the initial object within the target subject. The candidate mapping information is obtained by parsing the historical sentences through a syntax tree.

16. The apparatus according to claim 9, further comprising: An interactive dialogue module, configured to, in response to the initial intent obtained by performing intent analysis on the query request not satisfying the query condition, conduct multiple rounds of interactive dialogues with the object using a large model to guide the object to supplement the intent so that the query request satisfies the query condition; as well as The second determination module is used to determine the target intention based on the query request and the dialogue information obtained through multiple rounds of interactive dialogues.

17. An artificial intelligence agent, configured to execute the method according to any one of claims 1 to 8.

18. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

19. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

20. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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