Business resource retrieval method and device based on AI large model, computer equipment and readable storage medium

Abstract

The invention relates to a business resource retrieval method and device based on an AI large model, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a business file, and determining business file content in the business file; the business file content comprises at least two content elements with different content formats; matching the business file content with a preset business keyword according to the matching priority of the content format corresponding to each content element; key information matched with the business keyword in the business file content is extracted, and the key information is stored in a business resource question and answer database; in response to an input business resource search text, outputting a business resource search result used for responding to the business resource search text based on business file content corresponding to the target key information; the target key information is key information matched with the business resource search text in the business resource question and answer database. By adopting the method, the retrieval result accuracy of the business resources can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a business resource retrieval method, device, computer equipment, computer-readable storage medium and computer program product based on an AI big model. Background Art

[0002] As enterprises continue to grow, a large amount of information has accumulated in their business resource libraries, including reports, contracts, instructions, training materials, etc. Most companies organize these resources by file type. When they need to find specific information, they rely heavily on the organizers, which is time-consuming and labor-intensive. In addition, they cannot accurately search based on the document content, and the search results are too complicated and require further manual screening.

[0003] Therefore, there is a problem in the related art that the accuracy of the retrieval results of business resources is low. Summary of the invention

[0004] Based on this, it is necessary to provide a business resource retrieval method, device, computer equipment, computer-readable storage medium and computer program product based on an AI big model that can improve the accuracy of business resource retrieval results in response to the above technical problems.

[0005] In a first aspect, the present application provides a business resource retrieval method based on an AI big model, comprising:

[0006] Acquire a business file, and determine the business file content in the business file; the business file content includes at least two content elements with different content formats;

[0007] Matching the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element;

[0008] Extract key information matching the preset business keywords from the business document content through the AI ​​big model, and store the key information in the business resource question and answer database;

[0009] In response to input business resource search text, a business resource search result for responding to the business resource search text is output based on the business file content corresponding to the target key information; the target key information is the key information in the business resource question and answer database that matches the business resource search text.

[0010] In one embodiment, extracting key information matching the preset business keywords from the business document content by using the AI ​​big model includes:

[0011] Extracting target content matching the preset business keyword from the business document content;

[0012] The key information in the target content is extracted through the AI ​​big model as the key information matching the preset business keywords.

[0013] In one embodiment, the content elements of the business document content include at least a title and a body content, and matching the business document content with preset business keywords according to the matching priority of the content format corresponding to each of the content elements includes:

[0014] Matching the title with the preset business keywords;

[0015] When the preset business keyword is not matched in the title, the body content is matched with the preset business keyword.

[0016] In one embodiment, extracting target content matching the preset business keyword from the business file content includes:

[0017] When the preset business keyword is matched in the title, extracting the text content belonging to the title;

[0018] The extracted text content belonging to the title is used as the target content.

[0019] In one of the embodiments, when the preset business keyword is not matched in the title, extracting target content matching the preset business keyword in the business file content includes:

[0020] When the preset business keyword is matched in the main text content, the main text content is extracted and the extracted main text content is used as the target content.

[0021] In one embodiment, the content elements of the business file content include a title, a body content, and a file text identifier. When the preset business keyword is not matched in the title and the body content, the method further includes:

[0022] Extract key information from the document text identifier by using the AI ​​big model;

[0023] The key information in the file text identifier is stored in the business resource question and answer database.

[0024] In a second aspect, the present application also provides a business resource retrieval device based on an AI big model, comprising:

[0025] An acquisition module, used to acquire a business file and determine the business file content in the business file; the business file content includes at least two content elements with different content formats;

[0026] A matching module, used to match the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element;

[0027] An extraction module, used to extract key information matching the preset business keywords from the business document content, and store the key information in a business resource question and answer database;

[0028] An output module is used to respond to an input business resource search text and output a business resource search result that responds to the business resource search text based on the business file content corresponding to the target key information; the target key information is the key information in the business resource question and answer database that matches the business resource search text.

[0029] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0031] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0032] The above-mentioned business resource retrieval method, device, computer equipment, computer-readable storage medium and computer program product based on the AI ​​big model determine the business file content in the business file by acquiring the business file; the business file content includes at least two content elements with different content formats; the business file content is matched with the preset business keywords according to the matching priority of the content format corresponding to each content element; the key information matching the preset business keywords in the business file content is extracted, and the key information is stored in the business resource question and answer database; in response to the input business resource search text, based on the business file content corresponding to the target key information, the business resource search result for responding to the business resource search text is output; the target key information is the key information matching the business resource search text in the business resource question and answer database.

[0033] In this way, the business document content is matched with the preset business keywords according to the matching priority of the content format corresponding to each content element in the business document content, and key information of the business document content is extracted through the preset business keywords. The priority setting makes the matching of business keywords more accurate, so that when parsing the key information of the business document content based on the business keywords, the key information related to the preset business keywords in the business document content can be more accurately parsed to store the key information in the business resource question and answer database. When the user enters a business resource search text, the business resource search results related to the search text can be quickly and accurately output based on the key information stored in the database, which effectively improves the accuracy and reliability of the retrieval results of business resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions 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 related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A flowchart of a business resource retrieval method based on an AI big model in one embodiment;

[0036] Figure 2 A flowchart of a business resource retrieval method based on an AI big model in another embodiment;

[0037] Figure 3 A flowchart of another business resource retrieval method based on an AI big model in one embodiment;

[0038] Figure 4 It is a structural block diagram of a business resource retrieval device based on an AI big model in one embodiment;

[0039] FIG. 5 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0042] In one embodiment, Figure 1 As shown, a business resource retrieval method based on an AI big model is provided. This embodiment uses the method applied to a computer device as an example. It can be understood that the computer device can be a terminal, a server, or a system including a terminal and a server. In this embodiment, the method includes the following steps:

[0043] Step S110, obtaining a business file and determining the business file content in the business file.

[0044] The business document content includes at least two content elements with different content formats.

[0045] Among them, business files can be files that record business-related information. Exemplarily, business files can be files that record and manage internal and external information of the enterprise, including contracts, reports, plans, policies, manuals, standard operating procedures, etc. Business files can be collected through file upload, API interface, etc.

[0046] After acquiring the business file, the computer device can determine the business file content in the business file by parsing the business file.

[0047] The file format of the business file may be determined first, and the business file may be parsed according to a parsing method corresponding to the file format to determine the business file content in the business file.

[0048] Among them, the file format of the business file can be an editable format or a non-editable format. That is, the business file can be an editable file or a non-editable file. In the case where the business file is an editable file, such as an editable text file, the business file content can be directly extracted from the editable file from the computer device; in the case where the business file is a non-editable file, for example, the business file is a scanned version of the file (such as a file obtained by scanning a paper file), the business file content in the business file can be parsed through OCR (Optical Character Recognition) recognition technology.

[0049] Step S120 , matching the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element.

[0050] Among them, content elements with different content formats have different matching priorities. For example, a content element may include a title and a body content with different content formats, and the matching priorities of the title and the body content are different.

[0051] The preset business keywords may be business-related keywords related to the business document.

[0052] In a specific implementation, the computer device may match the business file content with the preset business keywords according to the matching priority of the content format corresponding to each content element.

[0053] Step S130, extract key information matching preset business keywords from the business document content through the AI ​​big model, and store the key information in the business resource question and answer database.

[0054] In a specific implementation, if a computer device matches content in a business file that matches preset business keywords, the key information in the business file that matches the preset business keywords can be extracted through the AI ​​big model to store the key information in the business resource question and answer database.

[0055] The extracted key information may be vectorized and stored in a business resource question and answer database, thereby realizing vectorized storage of key information in the business document content.

[0056] In this way, for a large number of business files, the computer device can store the key information in each business file into the business resource question and answer database according to the above steps S110 to S130.

[0057] Step S140 , in response to the inputted business resource search text, outputting business resource search results for responding to the business resource search text based on the business file content corresponding to the target key information.

[0058] The business resource search text is a business-related query text entered by the user, which is intended to find specific information or resources. This text may contain keywords, questions or topics, and the user hopes to obtain relevant business documents, data or knowledge through the search.

[0059] The target key information is the key information in the business resource question and answer database that matches the business resource search text.

[0060] In a specific implementation, when a user needs to find a business file, he or she can input a business resource search text into the question-and-answer interactive interface. The computer device can query the business resource question-and-answer database for key information that matches the business resource search text as the target key information, and output a business resource search result that responds to the business resource search text based on the business file content corresponding to the target key information.

[0061] Among them, according to the keywords of the input business resource search text, key information matching the keywords of the business resource search text in the business resource question and answer database can be determined as the target key information.

[0062] In some embodiments, the business resource question and answer database may be a database of a business resource question and answer system, and the business resource question and answer system runs in a computer device. A user may input a business resource search text through a question and answer interactive interface provided by the business resource question and answer system, and the business resource question and answer system may query key information matching the key information in the business resource question and answer database according to the key words in the business resource search text, and use the key information as the target key information, so that the business resource search results may be output based on the business file content corresponding to the target key information.

[0063] In other embodiments, the user can input business resource search text in the interactive interface of the question and answer big model, and the computer device queries the business resource question and answer database for key information that matches the keywords of the business resource search text based on the input business resource search text, and uses the key information as the target key information. According to the business file content corresponding to the target key information and the preset prompt word template, a target question and answer prompt is obtained, and the target question and answer prompt is used to instruct the question and answer big model to output business resource search results for responding to the business resource search text by referring to the business file content corresponding to the target key information.

[0064] In this way, users can query business files through dialogue, which improves the flexibility of business resource query and makes the query more convenient and quick.

[0065] In the above-mentioned business resource retrieval method based on AI big model, the business file content in the business file is determined by obtaining the business file; the business file content includes at least two content elements with different content formats; the business file content is matched with preset business keywords according to the matching priority of the content format corresponding to each content element; the key information matching the preset business keywords in the business file content is extracted, and the key information is stored in the business resource question and answer database; in response to the input business resource search text, based on the business file content corresponding to the target key information, the business resource search result for responding to the business resource search text is output; the target key information is the key information matching the business resource search text in the business resource question and answer database.

[0066] In this way, the business document content is matched with the preset business keywords according to the matching priority of the content format corresponding to each content element in the business document content, and key information of the business document content is extracted through the preset business keywords. The priority setting makes the matching of business keywords more accurate, so that when parsing the key information of the business document content based on the business keywords, the key information related to the preset business keywords in the business document content can be more accurately parsed to store the key information in the business resource question and answer database. When the user enters a business resource search text, the business resource search results related to the search text can be quickly and accurately output based on the key information stored in the database, which effectively improves the accuracy and reliability of the retrieval results of business resources.

[0067] In one embodiment, key information matching preset business keywords in business document content is extracted through the AI ​​big model, including: extracting target content matching preset business keywords in business document content; extracting key information in the target content through the AI ​​big model as key information matching preset business keywords.

[0068] In a specific implementation, when a computer device is extracting key information from a business document that matches preset business keywords, the computer device can extract the content from the business document that matches the preset business keywords as the target content, and then extract the key information from the target content as the key information that matches the preset business keywords.

[0069] Among them, AI big models can be used to extract key information from the target content, which may include keywords, summaries, highlights and other elements.

[0070] In actual applications, computer devices can store the mapping relationship between key information and target content, so that in the process of outputting business resource search results for responding to business resource search texts based on the business file content corresponding to the target key information, the computer device can determine the target content corresponding to the target key information based on the mapping relationship between the key information and the target content, and thus obtain the business file content corresponding to the target key information based on the target content corresponding to the target key information.

[0071] Among them, the business file content corresponding to the target key information may include a file link of the business file corresponding to the target business key information (the file link is used for users to access the corresponding business file), and may also include the business file content containing the target key information, such as a title containing the target key information and the text content under the title, as well as the text content containing the target key information, and the page numbers and specific locations of these titles and text contents in the corresponding business files.

[0072] The technical solution of this embodiment is to extract the target content that matches the preset business keywords from the business file content; and extract the key information from the target content through the AI ​​big model as the key information that matches the preset business keywords. In this way, the target content that matches the business keywords in the business file content is screened out according to the business keywords, and then the key information in the target content is extracted, so that the extracted key information is strongly related to the preset business keywords, so that more accurate business resource retrieval can be achieved based on the extracted key information.

[0073] In one embodiment, the content elements of the business document content include at least a title and body content. The business document content is matched with preset business keywords according to the matching priority of the content format corresponding to each content element, including: matching the title with the preset business keywords; if the preset business keywords are not matched in the title, matching the body content with the preset business keywords.

[0074] The content elements of the business document content include at least a title and a body content.

[0075] The main content may include text, tables, etc.

[0076] Among them, the matching priority of the title is higher than the matching priority of the text content.

[0077] In a specific implementation, when a computer device matches the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element, the computer device may match each title with the preset business keyword. If the preset business keyword is not matched in the title, the body content will be matched with the preset business keyword.

[0078] The process of matching the main text content with the preset business keywords refers to matching the main text content under all titles with the preset business keywords.

[0079] The technical solution of this embodiment is to match the title with the preset business keywords; when the preset business keywords are not matched in the title, the main text content is matched with the preset business keywords. In this way, since the title is usually concise and clear and can quickly indicate the subject, the priority matching of the title can quickly filter out the content related to the preset business keywords, thereby improving the matching efficiency. If the title has matched the preset business keywords, further matching of the main text content can be avoided, thereby saving computing resources and time. In addition, the title can usually reflect the business information of the file more accurately. Prioritizing the matching of the title can improve the relevance and accuracy of the final extraction results. When the business keywords are not matched in the title, the main text content is matched. The matching of the main text content can supplement the relevant information that may not be clearly stated in the title, forming a more comprehensive search result.

[0080] In one embodiment, target content matching preset business keywords in business document content is extracted, including: when the preset business keywords are matched in the title, the body content belonging to the title is extracted; and the extracted body content belonging to the title is used as the target content.

[0081] In a specific implementation, when a computer device is extracting target content that matches preset business keywords from business file content, if the preset business keywords are matched in the title, the main text content under the title that matches the preset business keywords can be extracted, and the extracted main text content under the title can be used as the target content that matches the preset business keywords, thereby extracting key information from the target content as key information that matches the preset business keywords.

[0082] Among them, in the process of determining the text content under the title that matches the preset business keyword, the computer device can use the title that matches the preset business keyword as the target title, and the computer device can determine the next title at the same level as the target title based on the hierarchical structure of the outline directory of the business file, and the text content between the target title and the next title is used as the target content that matches the preset business keyword.

[0083] The technical solution of this embodiment is to extract the text content under the title when the preset business keywords are matched in the title; and use the text content under the title as the target content. In this way, since the title can usually more accurately reflect the business information of the text content under it, when the preset business keywords are matched in the title, the text content under the title that matches the preset business keywords is also the content that has a strong association with the preset business keywords. The text content under the title that matches the preset business keywords is extracted and used as the target content that matches the preset business keywords to extract key information, so that the key information that matches the preset business keywords can be accurately extracted from the business file, which is conducive to improving the accuracy of the business resource retrieval results.

[0084] In one embodiment, when the preset business keywords are not matched in the title, the target content matching the preset business keywords in the business file content is extracted, including: when the preset business keywords are matched in the body content, the body content is extracted and the extracted body content is used as the target content.

[0085] In a specific implementation, when the preset business keywords are not matched in the title, the computer device may match the text content under each title with the preset business keywords. In this way, when the computer device is extracting the target content that matches the preset business keywords in the business file content, when the computer device matches the preset business keywords in the text content, it may extract the text content that matches the preset business keywords, and use the extracted text content as the target content that matches the preset business keywords, thereby extracting key information of the target content.

[0086] The technical solution of this embodiment is that when the preset business keywords are not matched in the title, the body content is extracted by matching the preset business keywords in the body content, and the extracted body content is used as the target content to extract the key information of the target content. In this way, when the preset business keywords are not matched in the title, since the body content of the business file can also reflect the business information of the business file, when the preset business keywords are matched in the body content, the body content that matches the preset business keywords is extracted, and the extracted body content is used as the target content that matches the preset business keywords, so as to extract the key information of the target content, and the key information associated with the preset business keywords in the business file can also be accurately and comprehensively extracted.

[0087] In one embodiment, the content elements of the business file content include a title, body content, and a file text identifier. When neither the title nor the body content matches the preset business keywords, the method further includes: extracting key information in the file text identifier through an AI big model; storing the key information in the file text identifier in a business resource question and answer database.

[0088] The file text identifier may be the file name of the business file.

[0089] In the specific implementation, when the preset business keywords are not matched in the title of the business file and the text content does not match the preset business keywords, since the file text identifier can also contain the business information of the business file, the key information in the file text identifier can be extracted through the AI ​​big model and stored in the business resource question and answer database.

[0090] In actual applications, taking the file text identifier as the file name as an example, the computer device can extract key information in the file name, such as keywords, summaries and highlights, through the AI ​​big model, and store it in the business resource question and answer database.

[0091] This step-by-step key information extraction method effectively reduces the omission of key information. Even if the title and body content fail to match the preset business keywords, key information can still be obtained from the file name.

[0092] In the technical solution of this embodiment, the content elements of the business file content also include a file text identifier. When the preset business keywords are not matched in the main text content, the key information in the file text identifier is extracted through the AI ​​large model; the key information in the file text identifier is stored in the business resource question and answer database. In this way, when the title and main text content of the business file do not match the preset business keywords, since the file text identifier of the business file can also reflect certain business information of the business file, by extracting the key information in the file text identifier and storing the key information in the file text identifier in the business resource question and answer database, the key information can be effectively supplemented to ensure that the key information of the business file is extracted.

[0093] In another embodiment, Figure 2 As shown, a business resource retrieval method based on an AI big model is provided, which is described by taking the method applied to a computer device as an example, and includes the following steps:

[0094] Step S202, obtaining a business file, and determining the business file content in the business file.

[0095] Step S204, matching the title with preset business keywords.

[0096] Step S206, when the preset business keyword is matched in the title, the text content belonging to the title is extracted as the target content.

[0097] Step S208: When the preset business keywords are not matched in the title, the body content is matched with the preset business keywords.

[0098] Step S210: When the preset business keywords are matched in the main text content, the main text content is used as the target content.

[0099] Step S212, extracting key information from the target content through the AI ​​big model as key information matching the preset business keywords.

[0100] Step S214, when the preset business keywords are not matched in the title and the text content, the key information in the file text identifier is extracted through the AI ​​big model.

[0101] Step S216, storing the key information in the business resource question and answer database.

[0102] Step S218 , in response to the inputted business resource search text, outputting business resource search results for responding to the business resource search text based on the business file content corresponding to the target key information.

[0103] It should be noted that the specific limitations of the above steps can be found in the specific limitations of a business resource retrieval method based on an AI big model mentioned above.

[0104] In one embodiment, Figure 3 As shown, a flowchart of another business resource retrieval method based on the AI ​​big model is provided. Take the business file as a contract file as an example for explanation. In contract management, faced with a large number of existing contracts and new contracts, you can obtain scanned versions of non-editable contract files (such as contract file images). First, upload the contract files in batches, and then perform OCR analysis one by one. If the parsed file is a compressed package containing contract file images, you need to reorder the content returned by OCR (after OCR parsing, rearrange the order according to the numbers in the footer of the page). If it is not a compressed package, there is no need to reorder. The content after OCR analysis will be divided into title, body content, contract name, etc. according to the format of the content. There are three different steps to obtain key information for different situations:

[0105] 1. Match the title with the preset business keywords. If a title that matches the business keywords is selected, extract the main content under the title, and use the AI ​​big model to extract keywords, summaries, and highlights from the extracted content.

[0106] 2. When the business keywords are not matched in the title, the business keywords are matched with the main text content under all titles. If the business keywords are matched, the main text content is extracted and the keywords, summaries and highlights are extracted from the extracted content through the AI ​​big model.

[0107] 3. When the target keywords are not matched in the title and body content, because the contract name also contains the business information of the contract document, the contract name will be extracted with keywords, summaries and highlights through the AI ​​big model.

[0108] After obtaining the key information of the contract document through the above steps, the key information will be stored in the vector database (business resource question and answer database). In this way, users can find the required contract documents through dialogue, making the file search method more diversified and intelligent.

[0109] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiment of the present application also provides a business resource retrieval device based on an AI big model for implementing the business resource retrieval method based on an AI big model involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the business resource retrieval device based on an AI big model provided below can be referred to the limitations of the business resource retrieval method based on an AI big model above, and will not be repeated here.

[0111] In an exemplary embodiment, Figure 4 As shown, a business resource retrieval device based on an AI big model is provided, including: an acquisition module 410, a matching module 420, an extraction module 430 and an output module 440, wherein:

[0112] The acquisition module 410 is used to acquire a business file and determine the business file content in the business file; the business file content includes at least two content elements with different content formats.

[0113] The matching module 420 is used to match the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element.

[0114] The extraction module 430 is used to extract key information matching the preset business keywords in the content of the business document through the AI ​​big model, and store the key information in the business resource question and answer database.

[0115] The output module 440 is used to respond to the input business resource search text and output the business resource search results that respond to the business resource search text based on the business file content corresponding to the target key information; the target key information is the key information in the business resource question and answer database that matches the business resource search text.

[0116] In one of the embodiments, the extraction module 430 is specifically used to extract target content in the business document content that matches the preset business keyword; and extract key information in the target content through the AI ​​big model as key information that matches the preset business keyword.

[0117] In one embodiment, the content elements of the business document content include at least a title and a body content, and the matching module 420 is specifically used to match the title with the preset business keywords; if the preset business keywords are not matched in the title, the body content is matched with the preset business keywords.

[0118] In one of the embodiments, the extraction module 430 is specifically used to extract the text content belonging to the title when the preset business keyword is matched in the title; and use the extracted text content belonging to the title as the target content.

[0119] In one of the embodiments, when the preset business keyword is not matched in the title, the extraction module 430 is specifically used to extract the body content when the preset business keyword is matched in the body content, and use the extracted body content as the target content.

[0120] In one embodiment, the content elements of the business file content include a title, body content and a file text identifier. When the preset business keywords are not matched in the title and the body content, the extraction module 430 is further used to extract key information in the file text identifier through the AI ​​big model; and store the key information in the file text identifier in the business resource question and answer database.

[0121] Each module in the above-mentioned business resource retrieval device based on AI big model can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0122] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a business resource retrieval method based on an AI large model is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0123] Those skilled in the art will understand that the structure shown in FIG. 5 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0125] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0126] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0128] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0129] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0130] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A business resource retrieval method based on AI big model, characterized in that: The method comprises: Acquire a business file, and determine the business file content in the business file; the business file content includes at least two content elements with different content formats; Matching the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element; Extract key information matching the preset business keywords from the business document content through the AI ​​big model, and store the key information in the business resource question and answer database; In response to input business resource search text, a business resource search result for responding to the business resource search text is output based on the business file content corresponding to the target key information; the target key information is the key information in the business resource question and answer database that matches the business resource search text.

2. The method according to claim 1, characterized in that The extracting key information matching the preset business keywords from the business document content by using the AI ​​big model includes: Extracting target content matching the preset business keyword from the business document content; The key information in the target content is extracted through the AI ​​big model as the key information matching the preset business keywords.

3. The method according to claim 2, characterized in that The content elements of the business document content include at least a title and a body content, and the business document content is matched with preset business keywords according to the matching priority of the content format corresponding to each of the content elements, including: Matching the title with the preset business keywords; When the preset business keyword is not matched in the title, the body content is matched with the preset business keyword.

4. The method according to claim 3, characterized in that The extracting target content matching the preset business keyword from the business file content includes: When the preset business keyword is matched in the title, extracting the text content belonging to the title; The extracted text content belonging to the title is used as the target content.

5. The method according to claim 3, characterized in that: In the case that the preset business keyword is not matched in the title, extracting target content matching the preset business keyword in the business file content includes: When the preset business keyword is matched in the main text content, the main text content is extracted and the extracted main text content is used as the target content.

6. The method according to claim 1, characterized in that The content elements of the business file content include a title, a body content, and a file text identifier. When the preset business keyword is not matched in the title and the body content, the method further includes: Extract key information from the document text identifier by using the AI ​​big model; The key information in the file text identifier is stored in the business resource question and answer database.

7. A business resource retrieval device based on AI big model, characterized in that: The device comprises: An acquisition module, used to acquire a business file and determine the business file content in the business file; the business file content includes at least two content elements with different content formats; A matching module, used to match the business file content with preset business keywords according to the matching priority of the content format corresponding to each content element; An extraction module, used to extract key information matching the preset business keywords from the business file content through the AI ​​big model, and store the key information in a business resource question and answer database; An output module is used to respond to an input business resource search text and output a business resource search result that responds to the business resource search text based on the business file content corresponding to the target key information; the target key information is the key information in the business resource question and answer database that matches the business resource search text.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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