Data processing method and device, computer, storage medium and program product

Abstract

The embodiment of the invention discloses a data processing method and device, a computer, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that a business processing request text sent by a first business object is obtained in an intelligent session page, and the business processing request text is analyzed to obtain a business processing request text; service semantic information and intention demand information are obtained; determining a business database associated with the first business object; obtaining a first target type indicated by the business semantic information, obtaining a first business data set belonging to the first target type from classified data corresponding to N business types of a business database, performing information retrieval in the first business data set, obtaining retrieval content associated with a first target component, and sending the retrieval content to the business database; and generating a service result for the service processing request text based on the intention demand information and the retrieval content, and displaying the service result in the intelligent session page. By adopting the method, the data retrieval efficiency can be improved, and the labor cost can be saved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data processing method, device, computer, storage medium and program product. Background Art

[0002] With the advancement of science and technology, information management technology has developed rapidly. The transformation of file storage from paper archives to electronic file storage has realized the evolution from traditional to digital. Electronic file storage has introduced computer technology, scanning and OCR (Optical Character Recognition) technology, making file storage, management and retrieval more efficient and flexible. The traditional storage method is to store files through key-value pairs. When searching, the key value must be completely consistent to retrieve the corresponding content in the database. This is inefficient and has a high query threshold. Summary of the invention

[0003] The embodiments of the present application provide a data processing method, device, computer, storage medium and program product, which can reduce the search volume and improve the search efficiency through classified storage, and can perform targeted data search by identifying the semantics of the business processing request text, thereby lowering the search threshold.

[0004] On the one hand, an embodiment of the present application provides a data processing method, including:

[0005] Obtaining a business processing request text sent by the first business object in the smart conversation page, parsing the business processing request text, and obtaining business semantic information and intention requirement information; the smart conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box includes the business processing request text;

[0006] Obtain a first object identifier of a first business object, and determine a business database associated with the first object identifier based on the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer;

[0007] Acquire a first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to N business types in the business database, and determine a first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type;

[0008] Information retrieval is performed in a first business data set to obtain retrieval content associated with a first target component, business results for a business processing request text are generated based on the intention requirement information and the retrieval content, and a smart avatar and a smart message text box containing the business results associated with the smart avatar are displayed in a smart conversation page; the smart avatar is used to conduct conversational interaction with the object avatar in the smart conversation page.

[0009] An embodiment of the present application provides a data processing device, the device comprising:

[0010] A data parsing module is used to obtain a business processing request text sent by the first business object in the intelligent conversation page, parse the business processing request text, and obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text;

[0011] a data determination module, configured to obtain a first object identifier of the first business object, and determine a business database associated with the first object identifier based on the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer;

[0012] a data acquisition module, configured to acquire a first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine a first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type;

[0013] A data retrieval module is used to perform information retrieval in a first business data set, obtain retrieval content associated with a first target component, generate business results for a business processing request text based on intention requirement information and the retrieval content, and display an intelligent avatar and an intelligent message text box containing business results associated with the intelligent avatar in an intelligent conversation page; the intelligent avatar is used to conduct conversational interaction with an object avatar in an intelligent conversation page.

[0014] In a possible implementation, the data processing device further includes a data storage module, and the data storage module is specifically configured to perform the following operations:

[0015] Acquire virtual asset association data for an aircraft sent by the first business object; the aircraft is composed of at least two components;

[0016] Obtain asset data classification rules, and classify the virtual asset associated data of the aircraft according to the N business types in the asset data classification rules to obtain N types of classification data; each type of classification data corresponds to one business type;

[0017] Generate storage data according to the N types of classification data, and store the storage data in a business database associated with the first business object.

[0018] In a possible implementation, the N types of business types include asset business data types, basic business data types, and transaction business data types; the N types of classified data include first classified data, second classified data, and third classified data; the data storage module is used to classify the virtual asset associated data of the aircraft according to the N types of business types in the asset data classification rule, and when the N types of classified data are obtained, the data storage module is specifically used to perform the following operations:

[0019] Obtain the data key fields corresponding to the asset business data type; the data key fields include the usage record key fields, maintenance record key fields, and impairment record key fields;

[0020] In the virtual asset associated data of the aircraft, historical use records corresponding to the use record key field, historical maintenance records corresponding to the maintenance record key field, and historical damage records corresponding to the damage record key field are obtained, and the historical use records, historical maintenance records, and historical damage records are determined as first classification data corresponding to the asset business data type;

[0021] Obtain the factory key field corresponding to the basic business data type, obtain the factory associated data corresponding to the factory key field in the virtual asset associated data of the aircraft, and determine the factory associated data as the second classification data corresponding to the basic business data type;

[0022] Obtain the transaction key field corresponding to the transaction business data type, obtain the transaction related data corresponding to the transaction key field in the virtual asset related data of the aircraft, and determine the transaction related data as the third classification data corresponding to the transaction business data type.

[0023] In a possible implementation, the N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j , j is a positive integer; classification data S i A components include component asset data corresponding to each of A components; A components belong to at least two components in the aircraft; A components include component M j, A is a positive integer; the data storage module is used to generate storage data according to the N types of classification data, and when the storage data is stored in the business database associated with the first business object, the data storage module is specifically used to perform the following operations:

[0024] For the classification data S i The corresponding business type is vectorized to obtain a type vectorization result;

[0025] Perform vectorization processing on A components respectively to obtain vectorization results of A components;

[0026] Get Part M j In the classification data S i The target component asset data under the target component asset data is analyzed based on the file type corresponding to the target component asset data to obtain the data analysis result corresponding to the target component asset data;

[0027] Perform vectorization processing on the data analysis results to obtain the data vectorization results corresponding to the asset data of the target component;

[0028] The type vectorization result, the component vectorization result and the data vectorization result are determined as storage data, and the storage data is stored in a business database associated with the first business object.

[0029] In a possible implementation, the N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j , j is a positive integer; classification data S i A components include component asset data corresponding to each of A components; A components belong to at least two components in the aircraft; A components include component M j , A is a positive integer; the data storage module is used to generate storage data according to the N types of classification data, and when the storage data is stored in the business database associated with the first business object, the data storage module is specifically used to perform the following operations:

[0030] The classified data S i The corresponding business type is determined as the initial root node, and A components are determined as A parent nodes under the initial root node;

[0031] Get Part M j In the classification data S i The target component asset data under the target component asset data are format-converted for P component asset sub-data in the target component asset data to obtain P structured component asset sub-data; P is a positive integer;

[0032] Assign P structured component asset sub-data to component M respectivelyj In the child node under the corresponding parent node;

[0033] When generating child nodes under the parent nodes corresponding to the A components respectively, the initial root node, the A parent nodes and the child nodes corresponding to the A parent nodes respectively are determined as the child nodes corresponding to the classification data S i A graph subtree of; a child node is used to allocate a structured component asset sub-data;

[0034] When graph subtrees corresponding to N types of classification data are obtained, the aircraft is determined as the graph root node, and an object knowledge graph corresponding to the aircraft is generated based on the N graph subtrees and the graph root node. The object knowledge graph is determined as storage data, and the storage data is stored in a business database associated with the first business object.

[0035] In a possible implementation, the data determination module is used to obtain the first target type indicated by the business semantic information, obtain the first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information. The data determination module is specifically used to perform the following operations:

[0036] Extract keywords from the business semantic information to obtain B business keywords, perform vectorization processing on each business keyword to obtain B keyword vectors, perform vector similarity matching based on the B keyword vectors and the type vectorization results corresponding to the N business types, and determine the business type corresponding to the matching type vectorization results as the first target type; B is a positive integer;

[0037] Acquire a first business data set belonging to a first target type from the classification data corresponding to the N business types in the business database;

[0038] Based on the keyword vectors corresponding to the remaining business keywords, vector similarity matching is performed with the component vectorization results corresponding to each component under the first business data set, and the component corresponding to the matching component vectorization result is determined as the first target component associated with the business semantic information; the remaining business keywords are the business keywords among the B business keywords, except for the business keywords that match the type vectorization results corresponding to the first target type.

[0039] In a possible implementation, the data processing device further includes a first data updating module, and the first data updating module is specifically configured to perform the following operations:

[0040] When a data update request for a second target component sent by the first business object is obtained in the intelligent conversation page, based on the data update request, to-be-matched asset data belonging to the second target component are respectively obtained from N types of classified data; at least two components include the second target component; the data update request includes component update data of the second target component; and the to-be-matched asset data includes component asset data corresponding to the second target component in the N types of classified data;

[0041] Match the to-be-matched asset data with the component update data; if the to-be-matched asset data does not contain the component update data, vectorize the component update data to obtain vectorized update data; store the vectorized update data in a business database associated with the first business object; generate a data update success notification; and display the data update success notification in a smart message text box on the smart conversation page;

[0042] If there is component update data in the asset data to be matched, a data duplication notification is generated and displayed in the smart message text box on the smart conversation page.

[0043] In a possible implementation, the data determination module is used to obtain the first target type indicated by the business semantic information, obtain the first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information. The data determination module is specifically used to perform the following operations:

[0044] Extract keywords from the business semantic information to obtain B business keywords; B is a positive integer;

[0045] Traversing the item knowledge graph based on B business keywords, when traversing to a type node matching the B business keywords, determining the business type corresponding to the matching type node as the first target type;

[0046] From the item knowledge graph, a first graph subtree with the first target type as an initial root node is obtained, and the first graph subtree is determined as a first business data set; the N graph subtrees in the item knowledge graph include the first graph subtree;

[0047] Traversing the first business data set based on the first keyword, when traversing to a component node matching the first keyword, determining the matching component node as a first target component associated with the business semantic information; the first keyword is a business keyword for indicating a component among the B business keywords;

[0048] The data retrieval module is used to perform information retrieval in the first business data set. When obtaining the retrieval content associated with the first target component, the data retrieval module is specifically used to perform the following operations:

[0049] Determine the business keywords other than the business keywords indicating the business type and the business keywords indicating the component among the B business keywords as keywords to be matched;

[0050] In the first business data set, the keyword to be matched is matched with each sub-node under the first target component, and the corresponding structured component asset sub-data in the matched data sub-node is determined as the search content associated with the first target component.

[0051] In a possible implementation, the data processing device further includes a second data updating module, and the second data updating module is specifically configured to perform the following operations:

[0052] When a data update request for a second target component sent by the first business object is obtained in the smart session page, a second target type corresponding to the component update data is determined based on the component update data of the second target component in the data update request; at least two components include the second target component; and N business types include the second target type;

[0053] Based on the second target type and the item knowledge graph, determine a second graph subtree with the second target type as the initial root node, and in the second graph subtree, obtain a to-be-matched data set of all child nodes with the second target component as the parent node;

[0054] If there is no component update data in the to-be-matched data set, then in the second graph subtree, an update child node is added to the parent node corresponding to the second target component, and the component update data is assigned to the update child node to obtain a graph update subtree, and the item knowledge graph is updated based on the graph update subtree to obtain an item update knowledge graph, and the item update knowledge graph is stored in the business database associated with the first business object, and a data update success notification is generated, and the data update success notification is displayed in the smart message text box in the smart conversation page;

[0055] If there is component update data in the to-be-matched data set, a data duplication notification is generated and displayed in the smart message text box in the smart conversation page.

[0056] In a possible implementation, when the data retrieval module is used to generate a business result for the business processing request text based on the intention requirement information and the retrieval content, the data retrieval module is specifically used to perform the following operations:

[0057] Identify key information in search results and obtain key data;

[0058] If the intention requirement information indicates a data analysis requirement, data analysis is performed on the key data based on the intention requirement information to obtain a data analysis result, and the data analysis result is determined as a business result for the business processing request text;

[0059] If the intention requirement information indicates a drawing processing requirement, an image is drawn based on the intention requirement information and key data to obtain a drawing data graph, and the drawing data graph is determined as a business result for the business processing request text.

[0060] In a possible implementation, the data retrieval module is used to perform data analysis on key data based on the intention requirement information. When the data analysis result is obtained, the data retrieval module is specifically used to perform the following operations:

[0061] Obtain Q search data for key data and intent requirement information, perform feature extraction on the Q search data respectively through the large language model, and obtain Q first extracted features; Q is a positive integer;

[0062] The key data is feature extracted through a large language model to obtain a second extracted feature, and cross-attention processing is performed on the Q first extracted features and the second extracted features to obtain the attention scores corresponding to the Q first extracted features respectively;

[0063] The search data associated with the attention scores greater than or equal to the attention threshold among the Q attention scores are determined as the data analysis results.

[0064] In a possible implementation, the data retrieval module is used to perform image drawing based on the intention requirement information and key data. When the drawing data graph is obtained, the data retrieval module is specifically used to perform the following operations:

[0065] Identify the type of drawn image according to the intention requirement information, obtain an image template with the type of drawn image in the image template library, generate a Gaussian noise image according to the image template and the initial noise data, and input the Gaussian noise image, key data and intention requirement information into the Wensheng graph model;

[0066] The feature extraction of Gaussian noise image is performed through the Wensheng graph model to obtain Gaussian noise features, and the Gaussian noise features are forward diffused to obtain forward noise vectors;

[0067] The key data is feature-encoded through the text graph model to obtain data encoding features, the intention requirement information is feature-encoded to obtain intention encoding features, and the data encoding features and the intention encoding features are feature-concatenated to obtain concatenated encoding features;

[0068] According to the forward noise vector and the concatenated coding features, the Gaussian noise image is denoised to obtain the plotted data graph.

[0069] On the one hand, an embodiment of the present application provides a computer device, including a processor, a memory, and an input and output interface;

[0070] The processor is connected to the memory and the input / output interface respectively, wherein the input / output interface is used to receive and output data, the memory is used to store a computer program, and the processor is used to call the computer program so that a computer device including the processor executes the method in one aspect of an embodiment of the present application.

[0071] On the one hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded and executed by a processor so that a computer device having the processor executes the method in one aspect of the embodiment of the present application.

[0072] In one aspect, an embodiment of the present application provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional ways in one aspect of the embodiment of the present application. In other words, when the computer instruction is executed by the processor, the method provided in various optional ways in one aspect of the embodiment of the present application is implemented.

[0073] Implementing the embodiments of the present application will have the following beneficial effects:

[0074] In an embodiment of the present application, a business processing request text sent by a first business object is obtained in an intelligent conversation page, and the business processing request text is parsed to obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text; the first object identifier of the first business object is obtained, and based on the first object identifier, a business database associated with the first object identifier is determined; the business database includes virtual asset association data corresponding to N business types respectively; the virtual asset association data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer; The first target type indicated by the business semantic information is obtained, and the first business data set belonging to the first target type is obtained from the classification data corresponding to the N business types in the business database, and the first target component associated with the business semantic information is determined based on the business semantic information; the N business types include the first target type; information retrieval is performed in the first business data set, and the retrieval content associated with the first target component is obtained, and the business result for the business processing request text is generated based on the intention requirement information and the retrieval content, and the smart avatar and the smart message text box containing the business result associated with the smart avatar are displayed in the smart conversation page; the smart avatar is used to interact with the object avatar in the smart conversation page. Through the above process, the classified storage of files based on business types is realized, the retrieval amount during retrieval is reduced, and the retrieval efficiency is improved. And the intelligent management of the file system is realized through the intelligent object. The business requirements (business processing request text) of the first business object are obtained through the intelligent object, and the business semantic information of the business processing request text is parsed. Based on the business semantic information, a targeted intelligent search is performed in the file system, and the business results are quickly determined and displayed in the intelligent conversation page. The data is classified and stored based on N types of business types. In the subsequent data reading process for a certain target component (for example, the first target component) of the aircraft under a certain specified business type (for example, the first target type), it is only necessary to query the data corresponding to the first target component under the first target type, so as to reduce the search threshold, save data query time, and improve data reading and search efficiency. And the entire search process is completed by the intelligent object, which can save labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0076] Figure 1 This is a network interaction architecture diagram provided by an embodiment of the present application;

[0077] Figure 2 It is a scenario schematic diagram of a data processing method provided in an embodiment of the present application;

[0078] Figure 3 This is a data processing method provided by the embodiment of the present application. Figure 1 ;

[0079] Figure 4 This is a data processing method provided by the embodiment of the present application. Figure 2 ;

[0080] Figure 5 This is a data classification diagram provided in an embodiment of the present application;

[0081] Figure 6 is a schematic diagram of a data processing device provided in an embodiment of the present application;

[0082] Figure 7 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0084] Among them, if it is necessary to collect object (such as user, etc.) data in this application, a prompt interface or pop-up window will be displayed before or during the collection. The prompt interface or pop-up window is used to prompt the user that certain data is currently being collected. Only after the user confirms the prompt interface or pop-up window, the relevant steps of data acquisition will be started, otherwise the process will end. Moreover, the acquired user data will be used in reasonable and legal scenarios or purposes. Optionally, in some scenarios where user data needs to be used but has not been authorized by the user, authorization can be requested from the user, and the user data can be used when the authorization is passed.

[0085] It is understandable that in the specific implementation of this application, the user data involved, when the following embodiments of this application are applied to specific products or technologies, needs to obtain user permission or consent, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of the relevant regions.

[0086] In the examples of this application, see Figure 1 , Figure 1 is a network interaction architecture diagram provided by an embodiment of the present application, such as Figure 1 As shown, the network interaction architecture diagram may include a business server 101 and a terminal device cluster, and the terminal device cluster may include terminal device 102a, terminal device 102b, terminal device 102c, ..., terminal device 102n, wherein there may be a communication connection between the terminal device clusters, for example, there is a communication connection between terminal device 102a and terminal device 102b, and there is a communication connection between terminal device 102a and terminal device 102c. At the same time, any terminal device in the terminal device cluster may have a communication connection with the business server 101, for example, there is a communication connection between terminal device 102a and business server 101, wherein the above communication connection does not limit the connection method, and may be directly or indirectly connected through a wired communication method, or directly or indirectly connected through a wireless communication method, or through other methods, and this application does not make any restrictions here.

[0087] It should be understood that Figure 1 Each terminal device in the terminal device cluster shown in FIG. 1 may be installed with an application client having a data processing function. When the application client runs in each terminal device, an intelligent conversation page may be displayed and the above-mentioned Figure 1 The business servers 101 shown in the figure interact with each other so that the business server 101 can receive business data from each terminal device. Among them, the application client can be a social application, instant messaging application, live broadcast application, short video application, video application, music application, shopping application, novel application, browser, etc., which has the function of displaying text, images, audio, video and other data information. Among them, the application client can be an independent client or an embedded sub-client (for example, application applet, browser web page access, etc.) integrated in a client (for example, instant messaging client, social client, video client, etc.), which is not limited here.

[0088] like Figure 1As shown, any terminal device in the terminal device cluster can send a business processing request text to the business server 101 through the intelligent conversation page. Here, the terminal device 102a is taken as the terminal device corresponding to the first business object as an example. The business server 101 can obtain the business processing request text sent by the first business object in the intelligent conversation page, that is, the first business object can send the business processing request text in the intelligent conversation page of the terminal device 102a, and the terminal device 102a displays the object avatar corresponding to the first business object and the object message text box associated with the object avatar in the intelligent conversation page, wherein the object message text box contains the business processing request text. The terminal device 102a can send the business processing request text to the business server 101 through the intelligent conversation page, so that the business server 101 receives the business processing request text and performs subsequent operations on the business processing request text. The business server 101 can parse the business processing request text through natural language processing technology (Natural Language Processing, NLP) to obtain business semantic information and intention demand information. For example, the text of a business processing request may be "Help me query the lease contract for XX aircraft, and generate a rental change curve chart for XX aircraft based on the query content." The business semantic information obtained after the business server parses it may include "Query the lease contract for XX aircraft under the transaction business data type, and generate a rental change curve chart for XX aircraft based on the lease contract." The intent requirement information may be indicated as a query requirement and a drawing processing requirement.

[0089] The business server 101 can obtain the first object identifier of the first business object, and determine the business database associated with the first object identifier based on the first object identifier, that is, a database for storing various data uploaded by the first business object. The business database includes virtual asset association data corresponding to N types of business types of the aircraft, and the virtual asset association data under each business type includes component asset data of at least one component of the heading item, and N is a positive integer. Among them, the aircraft can be a transportation equipment for navigation (for example, an airplane, etc.), or it can be a related component that constitutes the transportation equipment. For example, when the aircraft is an airplane, the related components that constitute the airplane may include an engine, a fuselage, a landing gear, etc., and the engine, fuselage, landing gear and other components can all be used as the aircraft of this application. The business server 101 can obtain the first target type indicated by the business semantic information, and obtain the first business data set belonging to the first target type from the classification data corresponding to the N types of business types in the business database. Further, the business server 101 can determine the first target component associated with the business semantic information based on the business semantic information. Among them, the N types of business types include the first target type.

[0090] The business server 101 can perform information retrieval in the first business data set, obtain the retrieval content associated with the first target component, and generate a business result for the business processing request text based on the intention requirement information and the retrieval content. The business server 101 can send the business result to the terminal device 102a, so that the terminal device 102a displays the smart avatar in its smart conversation page, and the smart message text box associated with the smart avatar containing the business result, wherein the smart avatar is used to interact with the object avatar in the smart conversation page. Among them, the processes performed by the above-mentioned business server 101 can all be completed by the smart object (e.g., Artificial Intelligence object, AI object) associated with the smart avatar.

[0091] Through the above process, the classified storage of files (virtual asset-related data) based on N types of business types is realized, which reduces the amount of retrieval during retrieval and improves the retrieval efficiency. In addition, the intelligent management of the file system is realized through the intelligent object. The business requirements (business processing request text) of the first business object are obtained through the intelligent object, and the business semantic information of the business processing request text is parsed. Based on the business semantic information, a targeted intelligent search is performed in the file system, and the business results are quickly determined and displayed in the intelligent session page. The data is classified and stored based on N types of business types. In the subsequent data reading process for a certain target component (for example, the first target component) of the aircraft under a certain specified business type (for example, the first target type), it is only necessary to query the data corresponding to the first target component under the first target type, so as to reduce the retrieval threshold, save data query time, and improve data reading and retrieval efficiency. In addition, the entire retrieval process is completed by the intelligent object, which can save labor costs.

[0092] For details, see Figure 2 , Figure 2 Schematic diagram of a data processing method provided in an embodiment of the present application. Figure 2As shown, in the smart conversation page 201 corresponding to the terminal device 102a, an object avatar corresponding to the first business object and an object message text box 2011 associated with the object avatar are displayed, and the object message text box 2011 includes a business processing request text sent by the first business object. The smart object 202 corresponding to the terminal device 102a can obtain the business processing request text and send the business processing request text to the business server 101. It can be understood that the smart object 202 can be provided by the service party corresponding to the business server 101, and the smart object 202 processes the business processing request text through the server 101 and obtains the corresponding result. The business server 101 can parse the business processing request text to obtain business semantic information and intention requirement information. The business server 101 can obtain the first object identifier of the first business object, wherein the first object identifier can be a unique indicative identifier created and allocated by the business server 101 for the first business object when the first business object uses the application client provided by the business server 101 to create an object account. The business server 101 can determine the business database associated with the first object identifier based on the first object identifier. The business database includes virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer. That is, when the first business object creates an account in the application client and uploads data, the business server 101 can classify the uploaded data (divided into data corresponding to N business types respectively) and store it in the business database corresponding to the first business object. The business server 101 can obtain the first target type indicated by the business semantic information, obtain the first business data set belonging to the first target type from the classified data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information, wherein the N business types include the first target type. The business server 101 can perform information retrieval in the first business data set, obtain the retrieval content associated with the first target component, and generate a business result for the business processing request text based on the intention requirement information and the retrieval content. Further, the business server 101 sends the generated business result to the terminal device 102a, and the smart object corresponding to the terminal device 102a displays the business result in the smart conversation page. As shown in the smart conversation page 203, it includes an object avatar, an object message text box 2011 (displaying the business processing request text), a smart avatar corresponding to the smart object 202, and a smart message text box 2031 (displaying the business result).The smart avatar is used to interact with the object avatar in the smart conversation page, that is, the first business object can consult a certain content (business processing request text) in the smart conversation page, the smart object obtains the text of the consultation content for intelligent processing, and obtains the final business result to reply to the consultation of the first business object.

[0093] Through the above process, the classified storage of files (virtual asset-related data) based on N types of business types is realized, which reduces the amount of retrieval during retrieval and improves the retrieval efficiency. In addition, the intelligent management of the file system is realized through the intelligent object. The business requirements (business processing request text) of the first business object are obtained through the intelligent object, and the business semantic information of the business processing request text is parsed. Based on the business semantic information, a targeted intelligent search is performed in the file system, and the business results are quickly determined and displayed in the intelligent session page. The data is classified and stored based on N types of business types. In the subsequent data reading process for a certain target component (for example, the first target component) of the aircraft under a certain specified business type (for example, the first target type), it is only necessary to query the data corresponding to the first target component under the first target type, so as to reduce the retrieval threshold, save data query time, and improve data reading and retrieval efficiency. In addition, the entire retrieval process is completed by the intelligent object, which can save labor costs.

[0094] It is understandable that the terminal device mentioned in the embodiments of the present application may also be a computer device, and the computer device in the embodiments of the present application includes but is not limited to a terminal device or a server. In other words, the computer device may be a server or a terminal device, or it may be a system composed of a server and a terminal device. Among them, the terminal device mentioned above may be an electronic device, including but not limited to mobile phones, tablet computers, desktop computers, laptop computers, PDAs, vehicle-mounted devices, augmented reality / virtual reality (AR / VR) devices, helmet displays, smart TVs, wearable devices, smart speakers, digital cameras, cameras and other mobile Internet devices (mobile internet device, MID) with network access capabilities, or terminal devices in scenarios such as trains, ships, and flights. As Figure 1 As shown in , the terminal device may be a mobile phone (as shown in terminal device 102a), a desktop computer (as shown in terminal device 102b), a tablet computer (as shown in terminal device 102c) or a laptop computer (as shown in terminal device 102n), etc. Figure 1Only some of the devices are listed. The servers mentioned above can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0095] Optionally, the data involved in the embodiments of the present application can be stored in a computer device, or the data can be stored based on cloud storage technology or a blockchain network, which is not limited here.

[0096] For further information, see Figure 3 , Figure 3 This is a data processing method provided by the embodiment of the present application. Figure 1 The data processing method may be executed by a computer device, which may be Figure 1 The business server 101 shown or any terminal device in the terminal device cluster. The following will take the data processing method executed by a computer device as an example for explanation. The data processing method may at least include the following steps S301-S304:

[0097] Step S301, obtain the business processing request text sent by the first business object in the intelligent conversation page, parse the business processing request text, and obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text.

[0098] In an embodiment of the present application, a computer device can obtain a business processing request text sent by a first business object on an intelligent conversation page, parse the business processing request text through natural language processing technology, and obtain the business semantic information and intention requirement information indicated by the business processing request text. Optionally, the computer device can also parse the business processing request text through a pre-trained language model (Pre-trained Language Models, PLMs) to obtain the business semantic information and intention requirement information indicated by the business processing request text. Among them, the pre-trained language model has a strong context understanding and semantic capture ability, and can improve the accuracy and adaptability of retrieval through dynamic embedding and context perception mechanisms. The pre-trained language model can be a bidirectional transformer encoder (Bidirectional Encoder Representations from Transformers, BERT), a generative pre-trained transformer (Generative Pre-trained Transformer, GPT), a text-to-text converter (Text-to-Text Transfer Transformer, T5), etc., without limitation here. And PLMs can use a self-supervised learning method to predict masked words or predict the next sentence, so that higher retrieval performance and wider application scenario coverage can be achieved through less annotated data. Among them, the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains a business processing request text. Business semantic information refers to the specific execution content indicated by the business processing request text, that is, information extracted from the business processing request text that can reflect the business content and logic, and the intention requirement information refers to the specific intention or requirement expressed by the business processing request. For example, the business processing request text may be "Help me query the lease contract of XX aircraft, and generate a rental change curve chart for XX aircraft based on the query content", then the business semantic information obtained by the computer device after parsing it may include "Query the lease contract of XX aircraft under the transaction business data type, and generate a rent change curve chart for XX aircraft based on the lease contract", and the intention requirement information can be indicated as query requirements and drawing processing requirements.

[0099] Step S302, obtain the first object identifier of the first business object, and based on the first object identifier, determine the business database associated with the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under the business type includes component asset data for at least one component of the aircraft; N is a positive integer.

[0100] In an embodiment of the present application, a computer device can obtain a first object identifier of a first business object. When the computer device obtains the business processing request text sent by the first business object, the account information of the first business object can be obtained at the same time, and the first object identifier of the first business object can be obtained from the account information. Wherein, the first object identifier is a unique indicative identifier obtained when the first business object creates an object account in the application client corresponding to the smart conversation page. The computer device can determine the business database associated with the first object identifier based on the first object identifier; wherein the business database includes virtual asset association data corresponding to N types of business types respectively; the virtual asset association data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer. The business database is a database for storing data allocated by the application client to the first business object in a data repository or in the cloud when the first business object uploads data in the application client. Taking the aircraft as an engine, for example, the virtual asset-related data of the aircraft may include the usage data of the components of the engine (for example, the components are installed in the engine, and the engine is installed in the aircraft. The flight-related data, such as the flight time, the flight area, etc.), the accident certification data of the components of the engine (for example, the certification data of the components having experienced fire, water ingress, falling, etc.), the asset impairment data of the components of the engine (for example, the different flight discount rates of the flight area when the components are installed in the engine and the engine is installed in the aircraft, because the worse the environment, the more it affects the performance of the engine, so different flight areas will have different flight discount rates). Different flight discount rates), historical maintenance data of engine components (such as component maintenance time, maintenance location and recovery performance ratio after maintenance), property certificate data of engine components (such as factory license certificate, production license certificate, manufacturer, component flight permit certificate, etc.), transaction data of engine components (for example, component leasing related data: such as component leasing time, leasing amount and component return status after leasing; for example, component buying and selling related data: such as component buying and selling amount, buying and selling time, etc.; for example, component modification related data: such as component modification cost, modification time, etc.). The virtual asset related data of engine components (including usage data, accident proof data, asset depreciation data, historical maintenance data, property certificate data, transaction data, etc.) together constitute the virtual asset related data of the engine.

[0101] Step S303, obtain the first target type indicated by the business semantic information, obtain the first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type.

[0102] In an embodiment of the present application, a computer device can obtain a first target type indicated by business semantic information. For example, the N business types existing in a business database are asset business data types, basic business data types, and transaction business data types, respectively. The keyword indication in the business semantic information matches the basic business data type, and the first target type is the basic business data type. The computer device can obtain a first business data set belonging to the first target type from the classified data corresponding to the N business types in the business database. When the first target type is the basic business data type, the first business data set obtained by the computer device includes all virtual asset-related data whose business type is the basic business data type. The computer device can determine the first target component associated with the business semantic information based on the business semantic information, that is, continue to match the specific components of the aircraft under the basic business data type based on the keywords in the business semantic information, and determine the matched components as the first target components. Among them, the N business types include the first target type.

[0103] Step S304, perform information retrieval in the first business data set, obtain the retrieval content associated with the first target component, generate a business result for the business processing request text based on the intention requirement information and the retrieval content, display the smart avatar in the smart conversation page, and the smart message text box containing the business result associated with the smart avatar; the smart avatar is used to interact with the object avatar in the smart conversation page.

[0104] In an embodiment of the present application, a computer device can perform information retrieval in a business data set to obtain retrieval content associated with a first target component, that is, after determining that the business type is the first target type and the specific component is the first target component, the computer device needs to retrieve the specific virtual asset-related data associated with the first target component indicated by the business processing request text in the first business data set. For example, the first business data set includes virtual asset-related data 1, virtual asset-related data 2, virtual asset-related data 3 of component 1, virtual asset-related data 4 (lease contract), virtual asset-related data 5 (invoice), and virtual asset-related data 6 of component 2. If the business semantic information indicates that the lease contract for component 2 needs to be obtained, the final retrieval content is virtual asset-related data 4; if the business semantic information indicates that the invoice information for component 2 needs to be obtained, the final retrieval content is virtual asset-related data 5.

[0105] Furthermore, the computer device can generate a business result for the business processing request text based on the intention demand information and the search content, that is, when the intention demand information is a drawing demand (for example, drawing a rent change curve), the computer device can draw a rent change curve for the search content (lease contract, including the rent field), and jointly determine the rent change curve and the search content as the business result for the business processing request text. Furthermore, the computer device can display an intelligent avatar in an intelligent conversation page, and an intelligent message text box associated with the intelligent avatar containing the business result. Among them, the intelligent avatar is used to conduct a conversation interaction with the object avatar in the intelligent conversation page, that is, the intelligent object conducts a conversation interaction with the first business object.

[0106] It can be understood that all operations performed by the computer device in the above process can be completed by the smart object calling the computer device after receiving the business processing request text sent by the first business object.

[0107] Through the above process, the classified storage of files (virtual asset-related data) based on N types of business types is realized, which reduces the amount of retrieval during retrieval and improves the retrieval efficiency. In addition, the intelligent management of the file system is realized through the intelligent object. The business requirements (business processing request text) of the first business object are obtained through the intelligent object, and the business semantic information of the business processing request text is parsed. Based on the business semantic information, a targeted intelligent search is performed in the file system, and the business results are quickly determined and displayed in the intelligent session page. The data is classified and stored based on N types of business types. In the subsequent data reading process for a certain target component (for example, the first target component) of the aircraft under a certain specified business type (for example, the first target type), it is only necessary to query the data corresponding to the first target component under the first target type, so as to reduce the retrieval threshold, save data query time, and improve data reading and retrieval efficiency. In addition, the entire retrieval process is completed by the intelligent object, which can save labor costs.

[0108] For further information, see Figure 4 , Figure 4 This is a data processing method provided by the embodiment of the present application. Figure 2 The data processing method can be executed by a computer device, which can be Figure 1 The business server 101 shown or any terminal device in the terminal device cluster. The following will take the data processing method executed by a computer device as an example for description. The data processing method may at least include the following steps S401-S406:

[0109] Step S401, obtaining virtual asset associated data for the aircraft sent by the first business object; obtaining asset data classification rules, and classifying the virtual asset associated data of the aircraft according to N business types in the asset data classification rules to obtain N types of classified data; generating storage data according to the N types of classified data, and storing the storage data in a business database associated with the first business object; the aircraft is composed of at least two components; each type of classification data corresponds to a business type.

[0110] In an embodiment of the present application, a computer device may obtain virtual asset associated data for an aircraft sent by a first business object. The virtual asset associated data of the aircraft may include aircraft usage data, aircraft accident certification data, asset depreciation data, historical maintenance data, property certificate data, transaction data (such as lease associated data), etc. The aircraft may be composed of at least two components, and the virtual asset associated data of the aircraft may include component usage data, component accident certification data, component asset depreciation data, component historical maintenance data, component property certificate data, component transaction data, etc. of each component. The computer device can obtain asset data classification rules. The N business types included in the asset data classification rules for data classification can be manually defined. For example, the N business types can include asset business data types, basic business data types and transaction business data types, wherein the data corresponding to the asset business data type can be data used to record aircraft losses, such as the above-mentioned aircraft component usage data, component accident certificate data, component asset depreciation data, component historical maintenance data, etc.; the data corresponding to the basic business data type can be the aircraft's property certificate data, such as the above-mentioned aircraft's property certificate data (such as component factory license certificate, component flight permit certificate, etc.); the data corresponding to the transaction business data type can be the aircraft's transaction-related data (such as aircraft leasing-related data, aircraft purchase and sale-related data, aircraft modification-related data, aircraft component leasing-related data, aircraft component purchase and sale-related data, aircraft component modification-related data, etc.).

[0111] The computer device can classify the virtual asset associated data of the aircraft according to the N business types in the asset data classification rules to obtain N types of classified data. For example, the virtual asset associated data of the aircraft belonging to the same business type can be classified into one category. If there are N business types, the virtual asset associated data of the aircraft can be classified into N categories. Among them, the N types of classified data can include first classified data, second classified data and third classified data. The computer device classifies the virtual asset associated data of the aircraft to obtain N types of classified data. The specific implementation process may be: obtaining the data key field corresponding to the asset business data type; wherein the data key field includes the usage record key field, the maintenance record key field and the depreciation record key field; then, in the virtual asset associated data of the aircraft, the historical usage records corresponding to the usage record key field, the historical maintenance records corresponding to the maintenance record key field and the historical depreciation records corresponding to the depreciation record key field may be obtained, and the historical usage records, historical maintenance records and historical depreciation records may be determined as the first classified data corresponding to the asset business data type; the factory key field corresponding to the basic business data type may be obtained, and in the virtual asset associated data of the aircraft, the factory associated data corresponding to the factory key field may be obtained, and the factory associated data may be determined as the second classified data corresponding to the basic business data type; the transaction key field corresponding to the transaction business data type may be obtained, and in the virtual asset associated data of the aircraft, the transaction associated data corresponding to the transaction key field may be obtained, and the transaction associated data may be determined as the third classified data corresponding to the transaction business data type.

[0112] It should be understood that each business type may correspond to different key fields, and each key field may correspond to a type of data. Then, data belonging to different business types may be obtained in the aircraft's virtual asset-related data according to different key fields. For example, the asset business data type may correspond to the usage record key field, the maintenance record key field, and the depreciation record key field. Then, according to the usage record key field, the historical usage record (such as the historical usage record of each component of the aircraft) may be obtained in the aircraft's virtual asset-related data. According to the maintenance record key field, the historical maintenance record (such as the historical maintenance record of each component of the aircraft) may be obtained in the aircraft's virtual asset-related data. According to the depreciation record key field, the historical depreciation record (such as the historical accident record of each component of the aircraft) may be obtained in the aircraft's virtual asset-related data. And the historical usage record, historical maintenance record, and historical depreciation record may all be used as data corresponding to the asset business type. If the basic business data type can correspond to the factory key field, then the factory-related data (such as factory permit certificate, production permit certificate, etc.) can be obtained in the aircraft's virtual asset-related data based on the factory key field; if the transaction business data type can correspond to the transaction key field, then the transaction-related data (such as rent, lease term, return status after lease, etc.) can be obtained in the aircraft's virtual asset-related data based on the transaction key field.

[0113] For further information, see Figure 5 , Figure 5 is a data classification diagram provided in an embodiment of the present application. Figure 5 As shown, the terminal device 501 may be the terminal device 102a mentioned above, and the object a may be the first business object mentioned above. The object a may send the virtual asset associated data 502 of the aircraft (composed of component 1 and component 2) to the computer device 503, wherein the virtual asset associated data 502 of the aircraft may include virtual asset associated data 1, virtual asset associated data 2, virtual asset associated data 3, virtual asset associated data 4, virtual asset associated data 5, and virtual asset associated data 6. After receiving the virtual asset associated data 502 of the aircraft, the computer device 503 may obtain an asset data classification rule, which includes N business types (including business type 1, business type 2, and business type 3) for data classification; further, the virtual asset associated data 502 of the aircraft may be classified according to the business type in the asset data classification rule. Figure 5As shown, in the virtual asset association data 502 (including virtual asset association data 1, virtual asset association data 2, virtual asset association data 3, virtual asset association data 4, virtual asset association data 5 and virtual asset association data 6) of the aircraft, it is possible to obtain virtual asset association data whose business type is the business type 1 (including virtual asset association data 1 and virtual asset association data 3), obtain virtual asset association data whose business type is the business type 2 (including virtual asset association data 2 and virtual asset association data 4), and obtain virtual asset association data whose business type is the business type 3 (including virtual asset association data 5 and virtual asset association data 6).

[0114] like Figure 5 As shown, the virtual asset associated data under one business type can be used as a classification data. Then, according to business type 1, business type 2 and business type 3, the virtual asset associated data of the item can be divided into three classification data, and the three classification data can include the classification data under business type 1 (including virtual asset associated data 1 and virtual asset associated data 3), the classification data under business type 2 (including virtual asset associated data 2 and virtual asset associated data 4), and the classification data under business type 3 (including virtual asset associated data 5 and virtual asset associated data 6). Optionally, the computer device can further classify the virtual asset associated data under each classification data according to the parts of the aircraft (including part 1 and part 2). For example, for the classified data under business type 1, since virtual asset associated data 1 is the virtual asset associated data of component 1 and virtual asset associated data 3 is the virtual asset associated data of component 2, the classified data under business type 1 can be further divided into two types of data (one is virtual asset associated data 1 belonging to component 1, and the other is virtual asset associated data 3 belonging to component 2); similarly, for the classified data under business type 2, since virtual asset associated data 2 and virtual asset associated data 4 are both virtual asset associated data of component 2, the classified data under business type 2 can be further divided into one type of data (virtual asset associated data 2 and virtual asset associated data 4 belonging to component 2); similarly, for the classified data under business type 3, since virtual asset associated data 5 and virtual asset associated data 6 are both virtual asset associated data of component 1, the classified data under business type 3 can be further divided into one type of data (virtual asset associated data 5 and virtual asset associated data 6 belonging to component 1).

[0115] Furthermore, the computer device may generate storage data based on the N types of classification data, and store the storage data in a business database associated with the first business object. Specifically, the N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j, j is a positive integer; classification data S i A components include component asset data corresponding to each of A components; A components belong to at least two components in the aircraft; A components include component M j , A is a positive integer. The following will be a stored procedure S for classified data i The computer device can store the classified data S i The corresponding business type is vectorized to obtain the type vectorization result; that is, the computer device processes the classified data S i The text of the corresponding business type is vectorized to obtain a numerical form that can be processed by a machine learning algorithm. The computer device can perform word segmentation on the text of the business type to obtain a word segmentation result, and further perform data cleaning on the word segmentation result to obtain a data cleaning result. That is, the text is divided into words, phrases or other meaningful units (called word segmentation, "tokens"), and punctuation marks or stop words in the text that do not carry much meaning are removed. For example, there is a business type "The AssetBusiness Data Type", and the word segmentation result obtained after word segmentation processing can be "["The", "Asset", "Business", "Data", "Type"]", and the data cleaning result obtained after data cleaning can be "["Asset", "Business", "Data", "Type"]". Further, the data cleaning result can be mapped to the vector space by word embedding to obtain a type vectorization result. Further, the computer device can perform vectorization processing on A components respectively to obtain A component vectorization results; the computer device can obtain component M j In the classification data S iThe target component asset data under the target component asset data is analyzed for text based on the file type corresponding to the target component asset data to obtain the data analysis result corresponding to the target component asset data. The text analysis can be OCR analysis. For example, the file type corresponding to the target component asset data is an image type (for example, a PDF file). The computer device can identify the text information from the image type file through OCR analysis and convert it into editable text, that is, obtain the data analysis result. Optionally, the computer device can also directly call a multimodal large model (which can process text, pictures or videos) to perform text analysis on the target component asset data, for example, a large language model (LLM, Large Language Model). The computer device can use the OCR capability of the LLM to perform text analysis on the target component asset data to obtain the data analysis result corresponding to the target component asset data. Since LLM can not only process text, but also support the processing of multimodal data such as images and videos, it can also be combined with the text-to-image technology to more naturally process files containing text and pictures. Thus, the computer device does not need to first determine the file type corresponding to the target component asset data, and then further perform OCR analysis through different file types, but can directly obtain the data analysis results corresponding to the target component asset data through LLM, so that file analysis becomes more efficient. And through technologies such as Federated Learning or Transfer Learning, the LLM model can be self-updated as data grows and business needs change, maintaining efficient processing capabilities. Optionally, the computer device (or smart object) can combine causal inference technology to realize the judgment of causal relationships through contextual data, improve the intelligence of business processing and the reasoning ability of complex problems. Further, the computer device vectorizes the data analysis results to obtain the data vectorization results corresponding to the target component asset data; the type vectorization results, the component vectorization results and the data vectorization results are determined as storage data, and the storage data is stored in the business database associated with the first business object. It can be understood that in this vectorized data storage method, the computer device can distinguish different business types corresponding to different data through different areas indicated by the coordinate positions of the vectorization information of the data (for example, different data vectorization results) in the vector space.

[0116] Optionally, the computer device may classify the data S i The corresponding business type is determined as the initial root node, and A components are determined as A parent nodes under the initial root node, and component M is obtained. j In the classification data S iThe target component asset data under the target component asset data is format-converted for each of the P component asset sub-data in the target component asset data to obtain P structured component asset sub-data; P is a positive integer. Among them, a component asset sub-data is a virtual asset associated data corresponding to a component under a business type. The specific implementation process of a computer device formatting a component asset sub-data to obtain a structured component asset sub-data can be: the computer device uses a library in a programming language (such as the Python-docx library in the Python language) to read the component asset sub-data, traverse the content in the component asset sub-data, that is, traverse the paragraphs, tables, pictures and other elements in the component asset sub-data, extract the required important information (that is, the key content in the component asset sub-data), construct a structured object based on the key content, and write the constructed structured object into a structured file to obtain structured component asset sub-data. Among them, the structured component asset sub-data can be a structured file with a data format of Json. The computer device can assign P structured component asset sub-data to component M respectively. j When generating child nodes under the parent nodes corresponding to the A components, the computer device may determine the initial root node, the A parent nodes, and the child nodes corresponding to the A parent nodes as the child nodes corresponding to the classification data S i ; wherein a sub-node is used to allocate a structured component asset sub-data. When the graph sub-trees corresponding to N types of classification data are obtained, the computer device can determine the aircraft as the graph root node, generate the item knowledge graph corresponding to the aircraft based on the N graph sub-trees and the graph root node, determine the item knowledge graph as storage data, and store the storage data in the business database associated with the first business object. Wherein, the graph root node is an entity node in the item knowledge graph, and the computer device can associate the N graph sub-trees with the entity node to obtain the item knowledge graph corresponding to the aircraft. Optionally, the computer device can realize the construction of the item knowledge graph through multi-scale graph embedding technology, that is, the entity in the item knowledge graph can have different representations in different contexts and scales. For example, the entity corresponding to a certain business type in the item knowledge graph is an asset business data type, and its representation at another scale can be historical use records, historical maintenance records, and historical depreciation records. That is, the concept of the entity is represented by the asset business data type, and the specific details of the entity are represented by historical use records, historical maintenance records, and historical depreciation records. It is understandable that in this structured data storage method, the computer device can distinguish different components or different business types corresponding to different data through the branch relationship between different nodes.

[0117] It is understandable that the data stored in the business database may be the vectorization results or item knowledge graphs corresponding to the various data mentioned in step S302; or may be the various tag sequences (i.e., token tag sequences) obtained after word segmentation of the various data mentioned above, and the corresponding sequence vectorization results or sequence item knowledge graphs. The process of obtaining the sequence vectorization results and the sequence item knowledge graph is the same as that of obtaining the vectorization results or item knowledge graphs corresponding to the various data.

[0118] It should be noted that in other data storage methods, that is, the computer device directly stores the virtual asset-related data after classifying it by business type, when subsequently retrieving information based on business semantic information, the computer device can adopt a multimodal model based on contrastive learning (Contrastive Language-Image Pre-training, CLIP), which can not only process text, but also images, videos and other formats of content, thereby realizing multimodal retrieval and enhancing the diversity and depth of retrieval.

[0119] Step S402, obtaining the business processing request text sent by the first business object in the intelligent conversation page, parsing the business processing request text, and obtaining business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text.

[0120] In the embodiment of the present application, the specific implementation process of step S402 can be referred to as follows. Figure 3 The specific description of step S301 shown in FIG. 1 is not repeated here.

[0121] Step S403, obtain the first object identifier of the first business object, and determine the business database associated with the first object identifier based on the first object identifier; the business database includes virtual asset association data corresponding to N business types respectively; the virtual asset association data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer.

[0122] In the embodiment of the present application, the specific implementation process of step S403 can be referred to as follows. Figure 3 The specific description of step S302 shown in FIG. 1 is not repeated here.

[0123] Step S404, obtain the first target type indicated by the business semantic information, obtain the first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type.

[0124] In the embodiment of the present application, for the data vectorization classification storage method when storing data, that is, the first data storage method mentioned in the above step S401, when the computer device obtains the first target type indicated by the business semantic information, it can extract keywords from the business semantic information to obtain B business keywords, vectorize each business keyword to obtain B keyword vectors, perform vector similarity matching based on the type vectorization results corresponding to the B keyword vectors and N business types, and determine the business type corresponding to the matching type vectorization results as the first target type; B is a positive integer. A possible implementation method for the computer device to perform vector similarity matching can be seen in formula ①:

[0125]

[0126] As shown in formula ①, cosine_similarity(A,B) is used to represent the cosine similarity between vector A and vector B, that is, the computer device can use a keyword vector as vector A and a type vectorization result as vector B to calculate the cosine similarity between a keyword vector and a type vectorization result, that is, vector similarity. Wherein "·" represents the dot product, A·B represents the dot product result between vector A and vector B, ‖A‖ represents the modulus of vector A, and ‖B‖ represents the modulus of vector B. The computer device performs vector similarity matching based on B keyword vectors and the type vectorization results corresponding to N business types. When calculating the vector similarity between the keyword vector and the type vectorization result, it can also be determined by calculating the Euclidean distance, Jaccard similarity, Manhattan distance, Pearson Correlation Coefficient, etc. between the keyword vector and the type vectorization result, which is not limited here. The value range of vector similarity is [-1, 1]. The closer the value of vector similarity is to 1, the more similar the two vectors are.

[0127] The computer device can obtain a first business data set belonging to the first target type from the classified data corresponding to N business types in the business database. Based on the keyword vectors corresponding to the remaining business keywords, the vector similarity matching is performed with the component vectorization results corresponding to each component under the first business data set, and the component corresponding to the matched component vectorization result is determined as the first target component associated with the business semantic information. Among them, the remaining business keywords are business keywords among the B business keywords, except for the business keywords that match the type vectorization results corresponding to the first target type. For example, the B business keywords include business keywords b, business keywords c, business keywords d, and business keywords f. There may be a business keyword (for example, business keyword b) among the B keywords that matches the type vectorization results corresponding to one of the N business types, and the remaining business keywords include business keywords c, business keywords d, and business keywords f. The computer device can perform vector similarity matching with the component vectorization results corresponding to each component under the first business data set based on the keyword vectors corresponding to the business keywords c, business keywords d, and business keywords f.

[0128] Optionally, for the data structured classification storage method when storing data, that is, the second data storage method mentioned in the above step S401, when the computer device obtains the first target type indicated by the business semantic information, it can extract keywords from the business semantic information to obtain B business keywords; B is a positive integer. The computer device can traverse the item knowledge graph based on the B business keywords, and when traversing to the type node that matches the B business keywords, the business type corresponding to the matching type node is determined as the first target type. That is, if one of the B business keywords is highly similar or identical to an initial root node representing a business type under the graph root node in the item knowledge graph, the initial root node representing the business type is considered to be a type node that matches the B business keywords, and the computer device can determine the business type corresponding to the type node as the first target type. For example, if the B business keywords include business keywords indicating the basic business data type, the first target type matched by the computer device is the basic business data type. Optionally, the computer device can also implement retrieval in the item knowledge graph through the Graph Neural Networks (GNNs) technology to determine the type node matching the B business keywords. Specifically, the computer device can map the B business keywords to the nodes in the item knowledge graph to generate business keyword embedding. And call a suitable GNN model to perform feature learning on the nodes in the item knowledge graph to generate node embedding. Among them, the GNN model can include different GNN models with different characteristics, such as the Graph Sample and Aggregate (GraphSAGE) model and the Graph Attention Networks (GAT), which are not limited here. Among them, GraphSAGE is suitable for large-scale graph data, and GAT can capture the different importance between nodes. It can be understood that node embedding can capture the semantic information of the node and the structural information of the node in the item knowledge graph. Further, the computer device can calculate the similarity between the business keyword embedding and the node embedding in the knowledge graph, determine the node associated with the maximum similarity value as the type node, and determine the business type corresponding to the type node as the first target type. Furthermore, the computer device may obtain a first graph subtree with the first target type as the initial root node from the item knowledge graph, and determine the first graph subtree as the first business data set, that is, the computer device retrieves the graph subtree with the first target type as the root node from the item knowledge graph, and analyzes the related entities and relationships corresponding to the nodes in the graph subtree, thereby constructing and determining the content of the first business data set. Among them, the N graph subtrees in the item knowledge graph include the first graph subtree.That is, the first graph subtree includes an initial root node (the first target type at this time), and all branch nodes under the initial root node. The computer device can traverse the first business data set based on the first keyword. When traversing to a component node that matches the first keyword, the matched component node is determined as the first target component associated with the business semantic information. Among them, the first keyword is a business keyword used to indicate a component among B business keywords; that is, the computer device uses graph query and reasoning algorithms to match subnodes (including component nodes) that are identical or highly similar to the first keyword in the subnodes under the initial root node representing the first target type in the first graph subtree, and determines the matched component node as the first target component associated with the business semantic information. For example, the first target type includes subnodes corresponding to component 1, component 2, and component 3, respectively. If the first keyword matches component 2, component 2 is determined as the first target component. Optionally, the computer device may query the item knowledge graph using a semantic search and matching algorithm based on B business keywords, and when querying type nodes that match the semantics of the B business keywords, determine the business types corresponding to all the matched type nodes as potential target types, and further match the business types included in the potential target types with the B business keywords, and determine the business types that match the B business keywords as the first target type. The first target type may be the business type corresponding to the virtual asset associated data that needs to be retrieved as indicated by the business semantic information.

[0129] It should be noted that when the computer device matches nodes based on business keywords and the item knowledge graph or the first graph subtree (first business data set), it can construct a query statement (for example, a SPARQL query statement) based on the business keywords, and perform query matching in the item knowledge graph or the first graph subtree based on the query statement. When performing query matching, the matching degree between the node and the business keyword can be determined. The way to determine the matching degree can be exact matching (i.e. checking whether the label or attribute of the node is completely consistent with the business keyword), fuzzy matching (determining the similarity between the node and the business keyword) and semantic matching (using natural language processing technology to understand the semantics of the business keyword and compare it with the semantic features of the node), etc., which are not limited here.

[0130] Step S405, perform information retrieval in the first business data set, obtain the retrieval content associated with the first target component, generate a business result for the business processing request text based on the intention requirement information and the retrieval content, display the smart avatar in the smart conversation page, and the smart message text box associated with the smart avatar containing the business result; the smart avatar is used to interact with the object avatar in the smart conversation page.

[0131] In an embodiment of the present application, with respect to the data vectorization classification storage method when storing data, after the computer device determines the first target component, if in the classification data corresponding to the first target type, the number of data vectorization results under the first target component is 1, that is, the target component asset data corresponding to the data vectorization result only includes one virtual asset-related data, then the computer device can directly determine the virtual asset-related data corresponding to this data vectorization result as the search content associated with the first target component. If the number of data vectorization results under the first target component is multiple, that is, the target component asset data corresponding to the data vectorization result includes multiple virtual asset-related data, then the computer device can determine the business keywords of the remaining keywords except the business keywords that match the component vectorization results corresponding to the first target component as the keywords to be searched, and match them with the multiple data vectorization results under the first target component under the first business data set based on the keyword vectors corresponding to the keywords to be searched, and determine the virtual asset-related data corresponding to the matched data vectorization results as the search content associated with the first target component. Optionally, the computer device may also determine the data analysis result corresponding to the virtual asset associated data as the search content, wherein the data analysis result is an editable text obtained by text analysis of the virtual asset associated data based on the file type corresponding to the virtual asset associated data. It is understandable that there is a unique mapping relationship between the data vectorization result and the virtual asset associated data, and the computer device can directly obtain the virtual asset associated data corresponding to the data vectorization result through the data vectorization result.

[0132] Optionally, for the data structured classification storage method when storing data, after the computer device determines the first target component, the business keywords other than the business keywords for indicating the business type and the business keywords for indicating the component among the B business keywords can be determined as the keywords to be matched. In the first business data set, the keywords to be matched can be matched with each sub-node under the first target component, and the corresponding structured component asset sub-data in the matched data sub-nodes can be determined as the search content associated with the first target component.

[0133] Furthermore, the computer device can generate a business result for the business processing request text based on the intention requirement information and the search content. Specifically, the computer device can identify key information of the search results and obtain key data. If the intention requirement information indicates a data analysis requirement, the computer device can perform data analysis on the key data based on the intention requirement information to obtain a data analysis result, and determine the data analysis result as the business result for the business processing request text. Specifically, the computer device can obtain Q search data for key data and intention requirement information, and perform feature extraction on the Q search data respectively through a large language model to obtain Q first extracted features; Q is a positive integer. Among them, the Q search data can come from the search results obtained by the computer device based on the key data and intention requirement information through one or more different search engines. The computer device can perform feature extraction on the key data through a large language model to obtain a second extracted feature, and perform cross-attention processing on the Q first extracted features and the second extracted features to obtain the attention scores corresponding to the Q first extracted features. Among them, a possible cross-attention processing method can be seen in formula ②:

[0134]

[0135] As shown in formula ②, Attention(Q,K,V) represents the cross attention function, Q is used to represent the query vector query, K is used to represent the key vector key, and V is used to represent the value vector value; T represents transposition, K T Representing the transposed matrix of K, the computer device may determine the first extracted feature as a query vector, and the second extracted feature as a key vector and a value vector, d k Used to indicate the number of dimensions corresponding to the second extracted feature, The calculation result of is used to express as the cross attention score.

[0136] The computer device may determine the search data associated with the attention score greater than or equal to the attention threshold among the Q attention scores as the data analysis result. The attention threshold may be manually set. Optionally, the computer device may determine the search data corresponding to the maximum score among the Q attention scores as the data analysis result.

[0137] Optionally, if the intention requirement information indicates a drawing processing requirement, the computer device can perform image drawing based on the intention requirement information and key data to obtain a drawing data graph, and determine the drawing data graph as a business result for the business processing request text. Specifically, the computer device can identify the type of drawing image according to the intention requirement information, obtain an image template with the drawing image type in the image template library, generate a Gaussian noise image according to the image template and the initial noise data, and input the Gaussian noise image, key data and intention requirement information into the Vincent graph model. Among them, the image template library includes various types of image modules, such as statistical graphs, histograms, line graphs, etc., which are image templates for mathematical statistical analysis. The computer device can extract features from the Gaussian noise image through the Vincent graph model to obtain the mean vector and variance vector of the Gaussian noise image. Among them, the mean vector can be the average value of all pixel values ​​of the Gaussian noise image on each channel, that is, the first-order statistic of the Gaussian noise image, and the variance vector can be the average value of the variance of the pixel values ​​of each channel. That is, the second-order statistic of the Gaussian noise image. The computer device can randomly sample the mean vector and variance vector of the Gaussian noise image to obtain the potential mean vector and potential variance vector, and generate Gaussian noise features based on the potential mean vector and potential variance vector. Furthermore, the computer device obtains the latent variable distribution in the forward diffusion network layer of the Vincent graph model, wherein the latent variable distribution is the conceptual distribution of added noise, for example, it can be a Gaussian distribution. The computer device can continuously add random noise vectors to the Gaussian noise feature in T time steps to obtain a forward noise vector. Wherein, T is a positive integer, the time step refers to the amplitude of adding noise to the Gaussian noise feature based on the latent variable distribution, and the noise can refer to unnecessary or redundant interference information present in the image data. When T is large enough, the forward noise vector can be used to represent an image that is completely noise-containing.

[0138] The computer device can encode the key data in the text encoding layer in the text graph model to obtain data encoding features; and perform feature encoding on the intention demand information through the text encoding layer to obtain the intention encoding features. The data encoding features and the intention encoding features can be feature spliced ​​to obtain spliced ​​encoding features. The computer device can perform denoising on the Gaussian noise image according to the forward noise vector and the spliced ​​encoding features to obtain a drawing data graph. That is, the computer device continuously performs noise prediction on the forward noise vector through the spliced ​​encoding features in T time steps to obtain a predicted noise vector, and performs denoising on the forward noise vector through the predicted noise vector to obtain a target latent vector. The computer device can reconstruct the target latent vector through the decoder in the text graph model to obtain a target predicted image, and determine the target predicted image as a drawing data graph for the intention demand information and key data. Among them, the target latent vector is used to characterize the features of the target predicted image, that is, the potential representation of the target predicted image after noise removal in the latent space. Further, the computer device can display an intelligent avatar in the intelligent conversation page, and an intelligent message text box containing business results associated with the intelligent avatar. Among them, the smart avatar is used to interact with the object avatar in the smart conversation page.

[0139] Step S406: when a data update request for the second target component sent by the first business object is obtained in the intelligent conversation page, the data in the business database is updated based on the data update request.

[0140] In an embodiment of the present application, when a computer device obtains a data update request for a second target component sent by a first business object in a smart conversation page, if the data storage method in the business database is a vectorized classification storage method, the computer device can obtain the asset data to be matched belonging to the second target component in N types of classified data based on the data update request. Among them, at least two components constituting an aircraft include a second target component; the data update request includes component update data of the second target component; and the asset data to be matched includes component asset data corresponding to the second target component in N types of classified data. For example, there are component asset data 1a (including virtual asset associated data a and virtual asset associated data b) of the second target component in classified data 1, there are component asset data 1b (including virtual asset associated data c) of the second target component in classified data 2, and there are component asset data 1c (including virtual asset associated data d and virtual asset associated data f) of the second target component in classified data 3, then the asset data to be matched includes asset associated data a, virtual asset associated data b, virtual asset associated data c, virtual asset associated data d, and virtual asset associated data f.

[0141] The computer device can match the asset data to be matched with the component update data. If the component update data does not exist in the asset data to be matched, the component update data is vectorized to obtain vectorized update data, and the vectorized update data is stored in the business database associated with the first business object, and a data update success notification is generated, and the data update success notification is displayed in the smart message text box in the smart conversation page. For example, the component asset data is virtual asset associated data g, that is, there is no virtual asset associated data g in the asset data to be matched (including asset associated data a, virtual asset associated data b, virtual asset associated data c, virtual asset associated data d, and virtual asset associated data f). The computer device can vectorize the virtual asset associated data g to obtain vectorized update data, and store the vectorized update data in the business database associated with the first business object. It should be noted that after the virtual asset associated data under different business types are vectorized, the data vectorization results obtained have very different coordinate positions in the vector space. For example, the data vectorization result corresponding to the virtual asset associated data under classification data 1 has a vector position in the vector space located in the spatial area A. 1 , the data vectorization result corresponding to the virtual asset associated data under classification data 2, the vector position in the vector space is located in the space area A 2 , the data vectorization result corresponding to the virtual asset associated data under classification data 3, the vector position in the vector space is located in the space area A 3 , the space region A in the space vector 1 、Space region A 2 、Space region A 3 Belonging to three different areas, the computer equipment can directly divide the virtual asset related data under different business types through different data vectorization results, and realize the data classification storage in the business database.

[0142] Optionally, if there is component update data in the asset data to be matched, a data duplication notification is generated and displayed in the smart message text box in the smart conversation page. For example, if the component update data is the same as the virtual asset associated data c, that is, the component update data exists in the asset data to be matched (including asset associated data a, virtual asset associated data b, virtual asset associated data c, virtual asset associated data d, virtual asset associated data f), the computer device does not need to update the data in the business database.

[0143] If the data storage method in the business database is a structured classification storage method, the computer device can determine the second target type corresponding to the component update data based on the component update data of the second target component in the data update request. Among them, at least two components constituting the aircraft include the second target component; N types of business types include the second target type. Based on the second target type and the item knowledge graph, a second graph subtree with the second target type as the initial root node can be determined, and in the second graph subtree, a set of data to be matched of all child nodes with the second target component as the parent node is obtained.

[0144] If there is no component update data in the to-be-matched data set, that is, the structured component asset sub-data in the child node in the to-be-matched data set does not match the component update data, then the computer device can add an update child node for the parent node corresponding to the second target component in the second graph subtree, convert the format of the component update data, obtain structured component update data, and assign the structured component update data to the update child node, that is, determine the structured component update data as the content of the update child node, and obtain the graph update subtree. Further, the item knowledge graph can be updated based on the graph update subtree to obtain the item update knowledge graph, store the item update knowledge graph in the business database associated with the first business object, generate a data update success notification, and display the data update success notification in the smart message text box in the smart conversation page. Specifically, the computer device can detect the influx of new data (structured component update data) in the second graph subtree through incremental learning technology (i.e., the process of adding an update child node to the parent node corresponding to the second target component in the second graph subtree, and determining the structured component update data as the content of the update child node), and further identify changes in data in the second graph subtree, such as the addition of new entities (entities corresponding to the structured component update data), the update of existing entity attributes, or the formation of new relationships. Entity alignment is achieved based on the changes in data in the second graph subtree, so as to add the structured component update data to the item knowledge graph and obtain the graph update subtree, thereby ensuring that the final item knowledge graph can maintain consistency after being updated to obtain the item update knowledge graph, without contradictory or redundant information. Optionally, if there is component update data in the data set to be matched, a data duplication notification is generated, and the data duplication notification is displayed in the smart message text box in the smart conversation page.

[0145] It is understandable that all operations performed by the computer device in the above process can be completed by the smart object calling the computer device after receiving the business processing request text or data update request sent by the first business object.

[0146] Through the above process, two different ways (vectorized classified storage and structured classified storage) of files (virtual asset associated data) based on N types of business types of classified storage are realized, which reduces the amount of retrieval during retrieval and improves the retrieval efficiency. In addition, the intelligent object is used to realize intelligent management of the file system (including the business database). The business requirements (business processing request text) of the first business object are obtained through the intelligent object, and the business semantic information of the business processing request text is parsed. Targeted intelligent retrieval can be performed in the file system. In addition, the business database is based on vectorized classified storage or structured classified storage, which can further improve the matching efficiency of business semantic information and classified data in the business database, determine the business results more quickly, and display them in the intelligent conversation page. Based on N types of business types, data is classified and stored. In the subsequent data reading process for a target component (for example, the first target component) of the aircraft under a specified business type (for example, the first target type), it is only necessary to query the data corresponding to the first target component under the first target type, so as to reduce the retrieval threshold, save data query time, and improve data reading and retrieval efficiency. In addition, the entire retrieval process is completed by the intelligent object, which can improve the retrieval accuracy and save labor costs. When a data update request is received from the first business object, the data can also be updated through the smart object without manual operation and searching the business database to determine whether to update the data, thereby further saving labor costs and improving business processing efficiency.

[0147] For further information, see Figure 6 , Figure 6 600 is a schematic diagram of a data processing device provided in an embodiment of the present application. The data processing device 600 may be a computer program (including program code, etc.) running in a computer device. For example, the data processing device 600 may be an application software. The data processing device 600 may be used to execute the corresponding steps in the method provided in an embodiment of the present application. Figure 6 As shown, the data processing device 600 can be used to Figure 3 and Figure 4 The computer device in the corresponding embodiment, specifically, the device may include: a data storage module 11, a data analysis module 12, a data determination module 13, a data acquisition module 14, a data retrieval module 15, a first data update module 16, and a second data update module 17.

[0148] The data parsing module 12 is used to obtain the business processing request text sent by the first business object in the intelligent conversation page, parse the business processing request text, and obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text;

[0149] The data determination module 13 is used to obtain a first object identifier of a first business object, and determine a business database associated with the first object identifier based on the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer;

[0150] The data acquisition module 14 is used to acquire the first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type;

[0151] The data retrieval module 15 is used to perform information retrieval in the first business data set, obtain the retrieval content associated with the first target component, generate a business result for the business processing request text based on the intention requirement information and the retrieval content, and display the smart avatar and the smart message text box containing the business result associated with the smart avatar in the smart conversation page; the smart avatar is used to interact with the object avatar in the smart conversation page.

[0152] In a possible implementation, the data processing device 600 further includes a data storage module 11, and the data storage module 11 is specifically configured to perform the following operations:

[0153] Acquire virtual asset association data for an aircraft sent by the first business object; the aircraft is composed of at least two components;

[0154] Obtain asset data classification rules, and classify the virtual asset associated data of the aircraft according to the N business types in the asset data classification rules to obtain N types of classification data; each type of classification data corresponds to one business type;

[0155] Generate storage data according to the N types of classification data, and store the storage data in a business database associated with the first business object.

[0156] In a possible implementation, the N types of business types include asset business data types, basic business data types, and transaction business data types; the N types of classified data include first classified data, second classified data, and third classified data; the data storage module 11 is used to classify the virtual asset associated data of the aircraft according to the N types of business types in the asset data classification rule, and when the N types of classified data are obtained, the data storage module 11 is specifically used to perform the following operations:

[0157] Obtain the data key fields corresponding to the asset business data type; the data key fields include the usage record key fields, maintenance record key fields, and impairment record key fields;

[0158] In the virtual asset associated data of the aircraft, historical use records corresponding to the use record key field, historical maintenance records corresponding to the maintenance record key field, and historical damage records corresponding to the damage record key field are obtained, and the historical use records, historical maintenance records, and historical damage records are determined as first classification data corresponding to the asset business data type;

[0159] Obtain the factory key field corresponding to the basic business data type, obtain the factory associated data corresponding to the factory key field in the virtual asset associated data of the aircraft, and determine the factory associated data as the second classification data corresponding to the basic business data type;

[0160] Obtain the transaction key field corresponding to the transaction business data type, obtain the transaction related data corresponding to the transaction key field in the virtual asset related data of the aircraft, and determine the transaction related data as the third classification data corresponding to the transaction business data type.

[0161] In a possible implementation, the N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j , j is a positive integer; classification data S i A components include component asset data corresponding to each of A components; A components belong to at least two components in the aircraft; A components include component M j , A is a positive integer; the data storage module 11 is used to generate storage data according to N types of classified data, and when the storage data is stored in a business database associated with the first business object, the data storage module 11 is specifically used to perform the following operations:

[0162] For the classification data S i The corresponding business type is vectorized to obtain a type vectorization result;

[0163] Perform vectorization processing on A components respectively to obtain vectorization results of A components;

[0164] Get Part M j In the classification data S i The target component asset data under the target component asset data is analyzed based on the file type corresponding to the target component asset data to obtain the data analysis result corresponding to the target component asset data;

[0165] Perform vectorization processing on the data analysis results to obtain the data vectorization results corresponding to the asset data of the target component;

[0166] The type vectorization result, the component vectorization result and the data vectorization result are determined as storage data, and the storage data is stored in a business database associated with the first business object.

[0167] In a possible implementation, the N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j , j is a positive integer; classification data S i A components include component asset data corresponding to each of A components; A components belong to at least two components in the aircraft; A components include component M j , A is a positive integer; the data storage module 11 is used to generate storage data according to N types of classified data, and when the storage data is stored in a business database associated with the first business object, the data storage module 11 is specifically used to perform the following operations:

[0168] The classified data S i The corresponding business type is determined as the initial root node, and A components are determined as A parent nodes under the initial root node;

[0169] Get Part M j In the classification data S i The target component asset data under the target component asset data are format-converted for P component asset sub-data in the target component asset data to obtain P structured component asset sub-data; P is a positive integer;

[0170] Assign P structured component asset sub-data to component M respectively j In the child node under the corresponding parent node;

[0171] When generating child nodes under the parent nodes corresponding to the A components respectively, the initial root node, the A parent nodes and the child nodes corresponding to the A parent nodes respectively are determined as the child nodes corresponding to the classification data S i A graph subtree of; a child node is used to allocate a structured component asset sub-data;

[0172] When graph subtrees corresponding to N types of classification data are obtained, the aircraft is determined as the graph root node, and an object knowledge graph corresponding to the aircraft is generated based on the N graph subtrees and the graph root node. The object knowledge graph is determined as storage data, and the storage data is stored in a business database associated with the first business object.

[0173] In a possible implementation, the data determination module 13 is used to obtain the first target type indicated by the business semantic information, obtain a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information. The data determination module 13 is specifically used to perform the following operations:

[0174] Extract keywords from the business semantic information to obtain B business keywords, perform vectorization processing on each business keyword to obtain B keyword vectors, perform vector similarity matching based on the B keyword vectors and the type vectorization results corresponding to the N business types, and determine the business type corresponding to the matching type vectorization results as the first target type; B is a positive integer;

[0175] Acquire a first business data set belonging to a first target type from the classification data corresponding to the N business types in the business database;

[0176] Based on the keyword vectors corresponding to the remaining business keywords, vector similarity matching is performed with the component vectorization results corresponding to each component under the first business data set, and the component corresponding to the matching component vectorization result is determined as the first target component associated with the business semantic information; the remaining business keywords are the business keywords among the B business keywords, except for the business keywords that match the type vectorization results corresponding to the first target type.

[0177] In a possible implementation, the data processing device 600 further includes a first data updating module 16, and the first data updating module 16 is specifically configured to perform the following operations:

[0178] When a data update request for a second target component sent by the first business object is obtained in the intelligent conversation page, based on the data update request, to-be-matched asset data belonging to the second target component are respectively obtained from N types of classified data; at least two components include the second target component; the data update request includes component update data of the second target component; and the to-be-matched asset data includes component asset data corresponding to the second target component in the N types of classified data;

[0179] Match the to-be-matched asset data with the component update data; if the to-be-matched asset data does not contain the component update data, vectorize the component update data to obtain vectorized update data; store the vectorized update data in a business database associated with the first business object; generate a data update success notification; and display the data update success notification in a smart message text box on the smart conversation page;

[0180] If there is component update data in the asset data to be matched, a data duplication notification is generated and displayed in the smart message text box on the smart conversation page.

[0181] In a possible implementation, the data determination module 13 is used to obtain the first target type indicated by the business semantic information, obtain a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine the first target component associated with the business semantic information based on the business semantic information. The data determination module 13 is specifically used to perform the following operations:

[0182] Extract keywords from the business semantic information to obtain B business keywords; B is a positive integer;

[0183] Traversing the item knowledge graph based on B business keywords, when traversing to a type node matching the B business keywords, determining the business type corresponding to the matching type node as the first target type;

[0184] From the item knowledge graph, a first graph subtree with the first target type as an initial root node is obtained, and the first graph subtree is determined as a first business data set; the N graph subtrees in the item knowledge graph include the first graph subtree;

[0185] Traversing the first business data set based on the first keyword, when traversing to a component node matching the first keyword, determining the matching component node as a first target component associated with the business semantic information; the first keyword is a business keyword for indicating a component among the B business keywords;

[0186] The data retrieval module 15 is used to perform information retrieval in the first business data set. When obtaining the retrieval content associated with the first target component, the data retrieval module 15 is specifically used to perform the following operations:

[0187] Determine the business keywords other than the business keywords indicating the business type and the business keywords indicating the component among the B business keywords as keywords to be matched;

[0188] In the first business data set, the keyword to be matched is matched with each sub-node under the first target component, and the corresponding structured component asset sub-data in the matched data sub-node is determined as the search content associated with the first target component.

[0189] In a possible implementation, the data processing device 600 further includes a second data updating module 17, and the second data updating module 17 is specifically configured to perform the following operations:

[0190] When a data update request for a second target component sent by the first business object is obtained in the smart session page, a second target type corresponding to the component update data is determined based on the component update data of the second target component in the data update request; at least two components include the second target component; and N business types include the second target type;

[0191] Based on the second target type and the item knowledge graph, determine a second graph subtree with the second target type as the initial root node, and in the second graph subtree, obtain a to-be-matched data set of all child nodes with the second target component as the parent node;

[0192] If there is no component update data in the to-be-matched data set, then in the second graph subtree, an update child node is added to the parent node corresponding to the second target component, and the component update data is assigned to the update child node to obtain a graph update subtree, and the item knowledge graph is updated based on the graph update subtree to obtain an item update knowledge graph, and the item update knowledge graph is stored in the business database associated with the first business object, and a data update success notification is generated, and the data update success notification is displayed in the smart message text box in the smart conversation page;

[0193] If there is component update data in the to-be-matched data set, a data duplication notification is generated and displayed in the smart message text box in the smart conversation page.

[0194] In a possible implementation, when the data retrieval module 15 is used to generate a business result for the business processing request text based on the intention requirement information and the retrieval content, the data retrieval module 15 is specifically used to perform the following operations:

[0195] Identify key information in search results and obtain key data;

[0196] If the intention requirement information indicates a data analysis requirement, data analysis is performed on the key data based on the intention requirement information to obtain a data analysis result, and the data analysis result is determined as a business result for the business processing request text;

[0197] If the intention requirement information indicates a drawing processing requirement, an image is drawn based on the intention requirement information and key data to obtain a drawing data graph, and the drawing data graph is determined as a business result for the business processing request text.

[0198] In a possible implementation, the data retrieval module 15 is used to perform data analysis on key data based on the intention demand information. When the data analysis result is obtained, the data retrieval module 15 is specifically used to perform the following operations:

[0199] Obtain Q search data for key data and intent requirement information, perform feature extraction on the Q search data respectively through the large language model, and obtain Q first extracted features; Q is a positive integer;

[0200] The key data is feature extracted through a large language model to obtain a second extracted feature, and cross-attention processing is performed on the Q first extracted features and the second extracted features to obtain the attention scores corresponding to the Q first extracted features respectively;

[0201] The search data associated with the attention scores greater than or equal to the attention threshold among the Q attention scores are determined as the data analysis results.

[0202] In a possible implementation, the data retrieval module 15 is used to perform image drawing based on the intention requirement information and key data. When the drawing data graph is obtained, the data retrieval module 15 is specifically used to perform the following operations:

[0203] Identify the type of drawn image according to the intention requirement information, obtain an image template with the type of drawn image in the image template library, generate a Gaussian noise image according to the image template and the initial noise data, and input the Gaussian noise image, key data and intention requirement information into the Wensheng graph model;

[0204] The feature extraction of Gaussian noise image is performed through the Wensheng graph model to obtain Gaussian noise features, and the Gaussian noise features are forward diffused to obtain forward noise vectors;

[0205] The key data is feature-encoded through the text graph model to obtain data encoding features, the intention requirement information is feature-encoded to obtain intention encoding features, and the data encoding features and the intention encoding features are feature-concatenated to obtain concatenated encoding features;

[0206] According to the forward noise vector and the concatenated coding features, the Gaussian noise image is denoised to obtain the plotted data graph.

[0207] See also Figure 7 , Figure 7 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Figure 7As shown, the computer device in the embodiment of the present application may include: a processor 701, a network interface 704 and a memory 705. In addition, the above-mentioned computer device 700 may also include: a user interface 703, and at least one communication bus 702. Among them, the communication bus 702 is used to realize the connection and communication between these components. Among them, the user interface 703 may include a display screen (Display), a keyboard (Keyboard), and the user interface 703 may optionally include a standard wired interface and a wireless interface. The network interface 704 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 705 may be a high-speed RAM memory, or it may be a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 705 may optionally also be at least one storage device located away from the aforementioned processor 701. As Figure 7 As shown, the memory 705 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.

[0208] The network interface 704 can provide a network communication network element; the user interface 703 is mainly used to provide an input interface for the user; and the processor 701 can be used to call the device control application stored in the memory 705 to perform the following operations:

[0209] Obtaining a business processing request text sent by the first business object in the smart conversation page, parsing the business processing request text, and obtaining business semantic information and intention requirement information; the smart conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box includes the business processing request text;

[0210] Obtain a first object identifier of a first business object, and determine a business database associated with the first object identifier based on the first object identifier; the business database includes virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer;

[0211] Acquire a first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to N business types in the business database, and determine a first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type;

[0212] Information retrieval is performed in a first business data set to obtain retrieval content associated with a first target component, business results for a business processing request text are generated based on the intention requirement information and the retrieval content, and a smart avatar and a smart message text box containing the business results associated with the smart avatar are displayed in a smart conversation page; the smart avatar is used to conduct conversational interaction with the object avatar in the smart conversation page.

[0213] In addition, it should be noted that: the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program is suitable for being loaded and executed by the processor Figure 3 or Figure 4 For details on the methods provided in each step, please refer to the Figure 3 or Figure 4 The implementation methods provided in each step are not repeated here. In addition, the description of the beneficial effects of adopting the same method is not repeated. For technical details not disclosed in the computer-readable storage medium embodiment involved in this application, please refer to the description of the method embodiment of this application. As an example, the computer program can be deployed to execute on one computer device, or on multiple computer devices located in one place, or on multiple computer devices distributed in multiple locations and interconnected by a communication network.

[0214] The computer-readable storage medium may be the device provided in any of the aforementioned embodiments or the internal storage unit of the computer device, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (smart media card, SMC), a secure digital (secure digital, SD) card, a flash card (flash card), etc. equipped on the computer device. Further, the computer-readable storage medium may also include both the internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0215] The present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes Figure 3 or Figure 4 The methods provided in the various optional methods are therefore not described in detail here.

[0216] The terms "first", "second", etc. in the description, claims, and drawings of the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product, or equipment that includes a series of steps or units is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other step units inherent to these processes, methods, devices, products, or equipment.

[0217] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0218] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in this description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0219] The method and related apparatus provided by the embodiment of the present application are described with reference to the method flow chart and / or structural diagram provided by the embodiment of the present application. Specifically, each process and / or box in the method flow chart and / or structural diagram, as well as the combination of the processes and / or boxes in the flow chart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable device to generate a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the process in the process. Figure 1 A process or multiple processes and / or structures Figure 1The computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including the instruction device, or are transmitted through a computer-readable storage medium. Computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The instruction device is implemented in the process Figure 1 A process or multiple processes and / or structures Figure 1 These computer program instructions can also be loaded onto a computer or other programmable device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 A flow or multiple flows and / or structures illustrate the steps of the functions specified in one block or multiple blocks.

[0220] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs.

[0221] The modules in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0222] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A data processing method, characterized in that: include: Acquire a business processing request text sent by a first business object in an intelligent conversation page, parse the business processing request text, and obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text; Obtaining a first object identifier of the first business object, and determining a business database associated with the first object identifier based on the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer; Acquire a first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to N business types in the business database, and determine a first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type; Performing information retrieval in the first business data set, acquiring retrieval content associated with the first target component, generating a business result for the business processing request text based on the intention requirement information and the retrieval content, and displaying an intelligent avatar and an intelligent message text box associated with the intelligent avatar and containing the business result in the intelligent conversation page; The smart avatar is used to perform conversation interaction with the object avatar in the smart conversation page.

2. The method according to claim 1, characterized in that The method further comprises: Acquire virtual asset association data for an aircraft sent by a first business object; the aircraft is composed of at least two components; Acquire asset data classification rules, and classify virtual asset-related data of the aircraft according to N types of business types in the asset data classification rules to obtain N types of classification data; each type of classification data corresponds to one business type; Generate storage data according to the N types of classification data, and store the storage data in a business database associated with the first business object.

3. The method according to claim 2, characterized in that The N types of business types include asset business data types, basic business data types, and transaction business data types; the N types of classified data include first classified data, second classified data, and third classified data; The virtual asset associated data of the aircraft is classified according to the N business types in the asset data classification rule to obtain N types of classified data, including: Acquire the data key fields corresponding to the asset business data type; the data key fields include the usage record key field, the maintenance record key field and the depreciation record key field; In the virtual asset associated data of the aircraft, historical usage records corresponding to the usage record key field, historical maintenance records corresponding to the maintenance record key field, and historical damage records corresponding to the damage record key field are obtained, and the historical usage records, the historical maintenance records, and the historical damage records are determined as first classification data corresponding to the asset business data type; Acquire a factory key field corresponding to the basic business data type, acquire factory-related data corresponding to the factory key field in the virtual asset-related data of the aircraft, and determine the factory-related data as the second classification data corresponding to the basic business data type; Obtain a transaction key field corresponding to the transaction business data type, obtain transaction-related data corresponding to the transaction key field in the virtual asset-related data of the aircraft, and determine the transaction-related data as third classification data corresponding to the transaction business data type.

4. The method according to claim 2, characterized in that The N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft components include components M j , j is a positive integer; the classification data S i The component asset data includes A components respectively corresponding to the A components; the A components belong to at least two components in the aircraft; the A components include the component M j , A is a positive integer; generating storage data according to the N types of classification data, and storing the storage data in a business database associated with the first business object, comprises: For the classification data S i The corresponding business type is vectorized to obtain a type vectorization result; Performing vectorization processing on the A components respectively to obtain vectorization results of the A components; Get the component M j In the classification data S i The target component asset data under the target component asset data, based on the file type corresponding to the target component asset data, performs text parsing on the target component asset data to obtain a data parsing result corresponding to the target component asset data; Performing vectorization processing on the data analysis result to obtain a data vectorization result corresponding to the target component asset data; The type vectorization result, the component vectorization result and the data vectorization result are determined as storage data, and the storage data is stored in a business database associated with the first business object.

5. The method according to claim 2, characterized in that: The N types of classification data include classification data S i , i is a positive integer less than or equal to N; the aircraft parts include parts M j , j is a positive integer; the classification data S i The component asset data includes A components respectively corresponding to the A components; the A components belong to at least two components in the aircraft; the A components include the component M j , A is a positive integer; generating storage data according to the N types of classification data, and storing the storage data in a business database associated with the first business object, comprises: The classification data S i The corresponding business type is determined as an initial root node, and the A components are determined as A parent nodes under the initial root node; Get the component M j In the classification data S i The target component asset data under the target component asset data are format-converted for P component asset sub-data in the target component asset data to obtain P structured component asset sub-data; P is a positive integer; The P structured component asset sub-data are respectively assigned to the component M j In the child node under the corresponding parent node; When generating child nodes under the parent nodes corresponding to the A components respectively, the initial root node, the A parent nodes and the child nodes corresponding to the A parent nodes respectively are determined as the child nodes corresponding to the classification data S i A graph subtree of; a child node is used to allocate a structured component asset sub-data; When graph subtrees corresponding to N types of classification data are obtained, the aircraft is determined as a graph root node, and an object knowledge graph corresponding to the aircraft is generated based on the N graph subtrees and the graph root node. The object knowledge graph is determined as storage data, and the storage data is stored in a business database associated with the first business object.

6. The method according to claim 4, characterized in that The step of obtaining the first target type indicated by the business semantic information, obtaining a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determining a first target component associated with the business semantic information based on the business semantic information includes: Perform keyword extraction on the business semantic information to obtain B business keywords, perform vectorization processing on each business keyword to obtain B keyword vectors, perform vector similarity matching based on the B keyword vectors and the type vectorization results corresponding to the N business types, and determine the business type corresponding to the matching type vectorization results as the first target type; B is a positive integer; Acquire a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database; Based on the keyword vectors corresponding to the remaining business keywords, vector similarity matching is performed with the component vectorization results corresponding to each component under the first business data set, and the component corresponding to the matching component vectorization result is determined as the first target component associated with the business semantic information; the remaining business keywords are the business keywords among the B business keywords, except for the business keywords that match the type vectorization results corresponding to the first target type.

7. The method according to claim 4, characterized in that The method further comprises: When a data update request for a second target component sent by the first business object is obtained in the smart conversation page, based on the data update request, to-be-matched asset data belonging to the second target component are respectively obtained from the N types of classified data; the at least two components include the second target component; the data update request includes component update data of the second target component; the to-be-matched asset data includes component asset data corresponding to the second target component in the N types of classified data; Matching the to-be-matched asset data with the component update data; if the component update data does not exist in the to-be-matched asset data, vectorizing the component update data to obtain vectorized update data; storing the vectorized update data in a business database associated with the first business object; generating a data update success notification; and displaying the data update success notification in the smart message text box in the smart conversation page; If the component update data exists in the asset data to be matched, a data duplication notification is generated and displayed in the smart message text box in the smart conversation page.

8. The method according to claim 5, characterized in that The step of obtaining the first target type indicated by the business semantic information, obtaining a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determining a first target component associated with the business semantic information based on the business semantic information includes: Extract keywords from the business semantic information to obtain B business keywords, where B is a positive integer; Traversing the item knowledge graph based on the B business keywords, when a type node matching the B business keywords is traversed, determining the business type corresponding to the matching type node as the first target type; From the item knowledge graph, a first graph subtree with the first target type as an initial root node is obtained, and the first graph subtree is determined as a first business data set; the N graph subtrees in the item knowledge graph include the first graph subtree; Traversing the first business data set based on a first keyword, when traversing to a component node matching the first keyword, determining the matching component node as a first target component associated with the business semantic information; the first keyword is a business keyword for indicating a component among the B business keywords; The performing information retrieval in the first business data set to obtain retrieval content associated with the first target component includes: Determining the business keywords other than the business keywords indicating the business type and the business keywords indicating the component among the B business keywords as keywords to be matched; In the first business data set, the to-be-matched keyword is matched with each sub-node under the first target component, and the corresponding structured component asset sub-data in the matched data sub-node is determined as the search content associated with the first target component.

9. The method according to claim 5, characterized in that The method further comprises: When a data update request for a second target component sent by the first business object is obtained in the smart conversation page, based on the component update data of the second target component in the data update request, a second target type corresponding to the component update data is determined; the at least two components include the second target component; and the N business types include the second target type; Based on the second target type and the item knowledge graph, determine a second graph subtree with the second target type as an initial root node, and in the second graph subtree, obtain a to-be-matched data set of all child nodes with the second target component as a parent node; If the component update data does not exist in the to-be-matched data set, then in the second graph subtree, an update child node is added to the parent node corresponding to the second target component, and the component update data is assigned to the update child node to obtain a graph update subtree, and the item knowledge graph is updated based on the graph update subtree to obtain an item update knowledge graph, and the item update knowledge graph is stored in the business database associated with the first business object, a data update success notification is generated, and the data update success notification is displayed in the smart message text box in the smart conversation page; If the component update data exists in the to-be-matched data set, a data duplication notification is generated and displayed in the smart message text box in the smart conversation page.

10. The method according to claim 1, characterized in that The generating of a business result for the business processing request text based on the intention requirement information and the search content includes: Identify key information of the search results to obtain key data; If the intention requirement information indicates a data analysis requirement, performing data analysis on the key data based on the intention requirement information to obtain a data analysis result, and determining the data analysis result as a business result for the business processing request text; If the intention requirement information indicates a drawing processing requirement, image drawing is performed based on the intention requirement information and the key data to obtain a drawing data graph, and the drawing data graph is determined as a business result for the business processing request text.

11. The method according to claim 10, characterized in that The performing data analysis on the key data based on the intention requirement information to obtain a data analysis result includes: Acquire Q search data for the key data and the intention demand information, and perform feature extraction on the Q search data respectively through a large language model to obtain Q first extracted features; Q is a positive integer; Performing feature extraction on the key data through the large language model to obtain a second extracted feature, performing cross attention processing on the Q first extracted features and the second extracted features to obtain attention scores corresponding to the Q first extracted features respectively; The search data associated with the attention scores greater than or equal to the attention threshold among the Q attention scores are determined as the data analysis results.

12. The method according to claim 10, characterized in that The image drawing is performed based on the intention requirement information and the key data to obtain a drawing data graph, including: Identify the type of drawn image according to the intention requirement information, obtain an image template having the type of drawn image in an image template library, generate a Gaussian noise image according to the image template and initial noise data, and input the Gaussian noise image, the key data and the intention requirement information into a Vincent graph model; Extracting features of the Gaussian noise image using the Vincent graph model to obtain Gaussian noise features, and performing forward diffusion processing on the Gaussian noise features to obtain a forward noise vector; Performing feature encoding on the key data through the document graph model to obtain data encoding features, performing feature encoding on the intention requirement information to obtain intention encoding features, and performing feature splicing on the data encoding features and the intention encoding features to obtain spliced ​​encoding features; The Gaussian noise image is denoised according to the forward noise vector and the concatenated coding feature to obtain a drawing data graph.

13. A data processing device, characterized in that: include: a data parsing module, configured to obtain a business processing request text sent by a first business object in an intelligent conversation page, parse the business processing request text, and obtain business semantic information and intention requirement information; the intelligent conversation page includes an object avatar corresponding to the first business object and an object message text box associated with the object avatar, and the object message text box contains the business processing request text; a data determination module, configured to obtain a first object identifier of the first business object, and determine a business database associated with the first object identifier based on the first object identifier; the business database includes storage data associated with virtual asset associated data corresponding to N business types respectively; the virtual asset associated data under each business type includes component asset data for at least one component of the aircraft; N is a positive integer; a data acquisition module, configured to acquire a first target type indicated by the business semantic information, acquire a first business data set belonging to the first target type from the classification data corresponding to the N business types in the business database, and determine a first target component associated with the business semantic information based on the business semantic information; the N business types include the first target type; a data retrieval module, configured to perform information retrieval in the first business data set, obtain retrieval content associated with the first target component, generate a business result for the business processing request text based on the intention requirement information and the retrieval content, and display an intelligent avatar and an intelligent message text box associated with the intelligent avatar containing the business result in the intelligent conversation page; The smart avatar is used to perform conversation interaction with the object avatar in the smart conversation page.

14. A computer device, characterized in that: Includes processor, memory, input and output interfaces; The processor is connected to the memory and the input / output interface respectively, wherein the input / output interface is used to receive and output data, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded and executed by a processor, so that a computer device having the processor executes the method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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