A method and system for enterprise file data management interaction based on large language model

Abstract

The present application relates to an enterprise file data management interaction method and system based on a large language model, including sending acquisition instructions to a pre-trained file acquisition model at intervals; after the file acquisition model receives the acquisition instruction, it downloads the corresponding file data based on a preset acquisition path; the downloaded file data is sent to a pre-trained file analysis model; the file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information; the file analysis model calls the preset matching rules, and matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information; based on the transmission path, the file data and the prompt information are packaged and sent to the user end of the corresponding enterprise customer. The present application has the effect of improving the efficiency of file data transmission interaction for different enterprise customers and optimizing the management of file data for different enterprises.

Description

Technical Field

[0001] The present application relates to the technical field of data management, and in particular to an enterprise file data management interaction method and system based on a large language model. Background Art

[0002] Currently, some companies and agencies have the need to manage different customer files and interact with different customers through electronic file transfers. In particular, when agents receive file data from third parties, they need to provide timely feedback to the corresponding customers so that the customers can respond to the files in a timely manner.

[0003] Currently, third-party file data is generally downloaded regularly by dedicated personnel and entered into the corresponding customer management system. After receiving the prompt information, the customer checks the relevant file data to know the file type and makes corresponding feedback. The entire operation process is relatively lengthy, the prompt effect is poor, and the reception and entry of files rely on manpower, which is inefficient. Overall, it leads to poor management of corporate file data, so it needs to be improved. Summary of the invention

[0004] In order to improve the efficiency of file data transmission interaction for different enterprise customers and optimize the management effect of file data of different enterprises, the present application provides an enterprise file data management interaction method and system based on a large language model.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] An enterprise file data management interaction method based on a large language model comprises the following steps:

[0007] Send acquisition instructions to the pre-trained file acquisition model at intervals;

[0008] After receiving the acquisition instruction, the file acquisition model downloads the corresponding file data based on the preset acquisition path;

[0009] Sending the downloaded file data to a pre-trained file analysis model;

[0010] The file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information;

[0011] The file analysis model calls the preset matching rules, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information;

[0012] Based on the transmission path, the file data and prompt information are packaged and sent to the user end of the corresponding enterprise customer.

[0013] By adopting the above technical solution, the file acquisition model pre-trained by the neural network can realize automatic interval acquisition of file data of each customer of the third-party platform, and further send the downloaded file data to the file analysis model to realize intelligent format naming and classification of file data of different customers, and then pre-match the transmission channels of file data belonging to different customers through matching rules, and send them to the user end in combination with prompt messages. The entire operation process is realized through a large language model, eliminating the steps of manual operation, and the naming of file data is unified through file name information, and sent to the user end of the corresponding corporate customer in combination with prompt messages, so that corporate customers can know the type of file and the feedback to be made, thereby improving user experience, thereby improving the efficiency of file data transmission and interaction for different corporate customers, and optimizing the management effect of file data of different enterprises.

[0014] Optionally, the file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information; including:

[0015] The file analysis model scans text fields in the file data to determine the file type of the file data;

[0016] Call the corresponding text extraction framework based on the file type;

[0017] Based on the text extraction framework, extract the corresponding key information in the text field, and the key information includes the key fields;

[0018] The key fields are simplified and sorted as preset to obtain the file name information and associate it with the file data.

[0019] By adopting the above technical solution, the file analysis model can determine the type of file by scanning the text fields in the file data, and through semantic analysis and the meaning of the characters in the text fields. Furthermore, different file types correspond to different text extraction frameworks. The text extraction framework extracts the key information in the text field, that is, the representative information in the file data, to achieve automatic recognition and acquisition of file name information in the text data. Then, by simplifying and sorting the key fields and unifying the naming habits, the user-side personnel can quickly and clearly know the type and approximate content of the file data.

[0020] Optionally, the file analysis model calls a preset matching rule, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information; including:

[0021] Decompose the simplified and sorted name fields in the file name information;

[0022] Identify and obtain the user end of the file data to be received, the transmission and storage paths available to the user end of the file data to be received, and the number of files to be received by the same user end based on the name field;

[0023] Based on the number of files received by the user end, the transmission and storage paths of the user end for receiving the file data are screened from the transmission and storage paths available to the user end.

[0024] By adopting the above technical solution, the file name information is disassembled to obtain several name fields for determining and identifying the transmission path of the user terminal to receive the file data. In order to improve the timeliness of file data transmission, multiple transmission and storage paths are set. The file data transmission and storage path depends on the amount of file data to be received by the user terminal at one time. A transmission channel with higher communication efficiency is used to transmit the user terminal that needs to receive file data in large quantities, so as to improve the efficiency and timeliness of all file data transmission.

[0025] Optionally, the file analysis model calls a preset matching rule, matches a corresponding transmission path according to the file name information, and matches prompt information according to the key information; and further includes:

[0026] Identify all key fields in the key information and retrieve prompt factors matching the key fields;

[0027] All prompt factors are sorted and input into the preset AI question-answering model;

[0028] The AI ​​question-answering model generates prompt information based on a combination of prompt factors.

[0029] By adopting the above technical solution, for the automatic generation of prompt messages, it is necessary to extract the key fields in the key information, that is, to retrieve the field characters that can represent the content to be informed by the file data. Furthermore, not only the key fields are used as the notification content of the prompt message, but also the prompt factors that match the key fields are matched through the model, so that the corporate customers on the user side can clearly know how to feedback the file data, and the prompt factors are combined through the AI ​​question-and-answer model to enhance the reading experience of the corporate customers on the user side.

[0030] Optionally, the step of identifying all key fields in the key information and retrieving prompt factors matching the key fields may include:

[0031] The file analysis model identifies key fields and determines whether risk assessment is required for key fields;

[0032] If the key field does not need to perform risk judgment, the only prompt factor matching the key field is directly retrieved;

[0033] If a key field needs to perform risk judgment, the corresponding prompt factors are retrieved based on the result of the risk judgment. The risk judgment results include two results: existing risks and potential risks.

[0034] By adopting the above technical solution, in addition to matching the prompt factors, it is also necessary to perform risk judgment on the key fields in the file data. The results of the risk judgment include two results: potential risks and existing risks. By matching different prompt factors with risk judgment, different prompt information is formed, which can accurately and clearly notify the user end when the file data of corporate customers is at risk or has risks, so that corporate customers on the user end can give feedback on the risk situation more promptly.

[0035] Optionally, after the file data and the prompt information are packaged and sent to the user end of the corresponding enterprise customer based on the transmission path, the method includes:

[0036] The sending records of file data and prompt information are stored and recorded;

[0037] Screen out the prompt information with potential risks, and based on the preset interval length, generate additional prompt information at intervals and send it to the corresponding user end before the user end responds;

[0038] Filter out the risky prompt information, generate a pending message and send it to the customer service terminal associated with the user end.

[0039] By adopting the above technical solution, different prompt settings will be made in the sending method of prompt messages for the two types of file data, namely, file data with potential risks and file data that are at risk. For file data with potential risks, the corresponding prompt message will be sent at intervals before the user end gives feedback, so as to achieve repeated prompts of the risk situation and ensure that the corporate customers on the user end can understand the risk situation. For file data that are at risk, after the prompt message is sent, a to-be-processed message is also generated to the customer service terminal of the associated user end, that is, the customer service personnel corresponding to the corporate customer of the user end are notified, so that the file data that are at risk can be efficiently processed through the customer service personnel's special connection with the corporate customer.

[0040] Optionally, after the file data and the prompt information are packaged and sent to the user end of the corresponding enterprise customer based on the transmission path, the method further includes:

[0041] When receiving a search request from a user terminal for obtaining a file associated with the file data, identifying the file data associated with the search request;

[0042] The document analysis model scans the document data and obtains the text to be retrieved in the document data based on the extraction rules;

[0043] Sending the text to be searched to the search system, and obtaining a preset number of search texts based on the search results;

[0044] The search text is sent to the user end that issued the search request.

[0045] By adopting the above technical solution, through the associated retrieval system, when the user terminal issues a retrieval request for obtaining associated files related to the file data, it is possible to identify the text to be retrieved in the file data and automatically find a preset number of retrieval texts in the retrieval system and send them to the user terminal. For example, for other files mentioned in the file data, automatic retrieval can be achieved and the retrieval texts can be sent to the user terminal through a channel that interacts with the user terminal, thereby improving the user experience.

[0046] The second object of the invention is achieved by the following technical solutions:

[0047] An enterprise file data management interactive system based on a large language model, comprising:

[0048] An acquisition module, used for sending acquisition instructions to the pre-trained file acquisition model at intervals;

[0049] The download module is used to download the corresponding file data based on the preset acquisition path after the file acquisition model receives the acquisition instruction;

[0050] An analysis and sending module, used for sending the downloaded file data to a pre-trained file analysis model;

[0051] An analysis module, used for extracting and identifying key information in the file data by a file analysis model, and generating file name information associated with the file data based on the key information;

[0052] The prompt matching module is used for the file analysis model to call the preset matching rules, match the corresponding transmission path according to the file name information, and match the prompt information according to the key information;

[0053] The transmission module is used to package the file data and prompt information based on the transmission path and send them to the user end of the corresponding enterprise customer.

[0054] The third objective of the present application is achieved through the following technical solutions:

[0055] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the enterprise file data management interaction method based on a large language model are implemented.

[0056] The fourth objective of the present application is achieved through the following technical solutions:

[0057] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the enterprise file data management interaction method based on a large language model.

[0058] In summary, the present application includes at least one of the following beneficial technical effects:

[0059] 1. Send the downloaded file data to the file analysis model to realize intelligent format naming and classification of file data of different customers, and then pre-match the transmission channels of file data belonging to different customers through matching rules, and send them to the user end in combination with prompt messages. The entire operation process is realized through the large language model, eliminating the steps of manual operation, and the file data is named uniformly through file name information, and sent to the user end of the corresponding corporate customer in combination with prompt messages, so that corporate customers can know the type of files and the feedback to be made, improve user experience, and thus improve the efficiency of file data transmission interaction for different corporate customers, and optimize the management effect of file data of different enterprises;

[0060] 2. Extract key information from text fields through the text extraction framework to automatically identify and obtain file name information in text data. Then, simplify and sort key fields and unify naming conventions so that users can quickly and clearly understand the type and general content of file data.

[0061] 3. Not only does it use the key fields as the notification content of the prompt message, but it also uses the model to match the prompt factors that match the key fields, so that the enterprise customers on the user side can clearly know how to feedback the file data;

[0062] 4. Through the associated retrieval system, when the user terminal issues a retrieval request for associated files related to the file data, it can identify the text to be retrieved in the file data and automatically find a preset number of retrieval texts in the retrieval system and send them to the user terminal. For example, for other files mentioned in the file data, it can realize automatic retrieval and send the retrieval text to the user terminal through the channel of interaction with the user terminal, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flowchart of an implementation of an enterprise file data management interaction method embodiment based on a large language model of the present application;

[0064] Figure 2 This is a flowchart for implementing step S40 in an embodiment of an enterprise file data management interaction method based on a large language model of the present application;

[0065] Figure 3This is a flowchart for implementing step S50 in an embodiment of an enterprise file data management interaction method based on a large language model of the present application;

[0066] Figure 4 It is a schematic diagram of a computer device of the present application. DETAILED DESCRIPTION

[0067] The following is combined with Figure 1-4 This application is described in further detail.

[0068] In the following embodiments, Figure 1 As shown, the present application discloses an enterprise file data management interaction method based on a large language model, which specifically includes the following steps:

[0069] S10: sending acquisition instructions to the pre-trained file acquisition model at intervals;

[0070] In this embodiment, the interval duration is a custom setting, which is set to 12 hours. The file acquisition model is a pre-trained model for automating operation login and issuing file selection instructions in the specified path and page. The acquisition instruction is used to start the file acquisition model to download file data.

[0071] S20: After receiving the acquisition instruction, the file acquisition model downloads the corresponding file data based on the preset acquisition path;

[0072] In this embodiment, the file data includes official notification documents, pending files and messages of different corporate clients downloaded by the agency from a third-party platform, including text, pictures, videos and other files.

[0073] Specifically, the file acquisition model is started after receiving the acquisition instruction, and the automated program steps are started to operate the login steps at the specified web page address and download the file on the specified interface.

[0074] S30: Send the downloaded file data to a pre-trained file analysis model;

[0075] In this embodiment, the downloaded file data is classified by format and then sent to the file analysis model through different transmission channels. Different file sizes correspond to different sending paths.

[0076] The file analysis model is used to name the downloaded files and classify them according to the content of the notification, and can also convert the format of the files when necessary.

[0077] S40: The file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information;

[0078] In this embodiment, the key information comes from the content of the file data, including text, pattern, video and other information, and the key information is extracted based on preset text, image recognition and semantic analysis functions. The file name information is the name of the text data, which is used to identify the information of different notification contents.

[0079] Specifically, refer to Figure 2 , step S40 comprises the steps of:

[0080] S41: The file analysis model scans the text field in the file data to determine the file type of the file data;

[0081] S42: Calling a corresponding text extraction framework based on the file type;

[0082] S43: extracting corresponding key information from the text field based on a text extraction framework, where the key information includes key fields;

[0083] S44: Perform preset simplification and sorting processing on the key fields to obtain the file name information and associate it with the file data.

[0084] Among them, the file analysis model also scans the images and video information in the file data, and determines the text type through the content in the file data, that is, determines the type of notification content, including but not limited to notifications reminding corporate customers about payment, notifications about statement feedback, and notifications about time limits.

[0085] After the text type is determined, the corresponding text extraction framework is matched to accurately extract the key information in different file data. After the key information is extracted, only the text field is obtained as the file name information. At this time, the extracted text field is relatively chaotic and lengthy. Unnecessary text is deleted through preset simplification processing, and multiple simplified key fields are sorted to unify the naming format of the file name, and the user's reading experience is prompted through standardization.

[0086] S50: the file analysis model calls a preset matching rule, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information;

[0087] In this embodiment, by presetting transmission paths for multiple different channels and establishing a matching database for prompt messages, the transmission channel and prompt information are matched before file data transmission. The matching rules are used to identify the amount of file data to be received by the user end and the current occupancy rate and bandwidth size of each transmission channel.

[0088] The prompt information includes key information of the text data and content for prompting the user to give feedback. The content of the feedback includes the text, method and time node of the feedback.

[0089] Specifically, refer to Figure 3 , step S50 comprises the steps of:

[0090] S51: Decomposing the simplified and sorted name fields in the file name information;

[0091] S52: obtaining a user terminal of the to-be-received file data, a transmission and storage path available to the user terminal of the to-be-received file data, and the number of to-be-received files of the same user terminal based on the name field identification;

[0092] S53: Based on the number of files received by the user end, filter the transmission and storage paths of the user end for receiving the file data from the transmission and storage paths available to the user end.

[0093] In this embodiment, the name address of the user terminal to be received, as well as the available transmission and storage paths and file data to be received for the user terminal are identified by disassembling the name field. The selected transmission channel and storage path are determined by the number of files to be received. In order to improve the transmission efficiency of file data, all file data to be received by the same user terminal can be transmitted through different transmission paths.

[0094] Regarding the storage of file data, the storage action is performed while sending. When the storage space of any user terminal is less than the preset storage space, a prompt message is generated to remind the background staff to increase the storage space.

[0095] Further, step S50 also includes the steps of:

[0096] S55: identifying all key fields in the key information, and retrieving prompt factors matching the key fields;

[0097] S56: sorting all prompt factors and inputting them into a preset AI question-answering model;

[0098] S57: The AI ​​question-answering model generates prompt information based on a combination of prompt factors.

[0099] In this embodiment, the key field and the prompt factor are pre-associated, and the pre-associated prompt factor can be matched by semantically analyzing the content represented by the key field, and the prompt factor includes a text field, a picture or video data. The picture and video data include a guide picture and a guide video for prompting the user terminal how to feedback the file data.

[0100] After being sorted, the prompt factors are sent to the AI ​​question-answering model to generate prompt messages. This can also unify the description format of the prompt messages and enhance the reading experience of corporate customers on the user side.

[0101] Wherein, step S55 further includes:

[0102] The file analysis model identifies key fields and determines whether risk assessment is required for the key fields. Risk assessment is based on the meaning expressed by the key fields to determine whether the file data on the user side has preset risks. Preset risks include but are not limited to time limit risks, rejection, expiration risks, unclear file risks, warning risks, and penalty risks.

[0103] After completing the risk assessment, the sending records of the file data and prompt information are stored and recorded;

[0104] If the key field does not need to perform risk judgment, the only prompt factor matching the key field is directly retrieved;

[0105] If a key field needs to perform risk judgment, the corresponding prompt factors are retrieved based on the result of the risk judgment. The risk judgment results include two results: existing risks and potential risks.

[0106] The processing methods for the two risk judgment results include: screening out prompt information with potential risks, and based on a preset interval length, generating additional prompt information at intervals and sending it to the corresponding user end before the user end provides feedback; when the user provides feedback, the method of adding prompts at intervals is changed to sending additional prompt information to the user end once when the processing period of the potential risk reaches the preset time.

[0107] For the risky prompt information, a to-be-processed message is generated and sent to the customer service terminal associated with the user end, and the customer service terminal assists the corporate customer of the user end to process the risky file data.

[0108] S60: Packing the file data and the prompt information based on the transmission path and sending them to the user end of the corresponding enterprise customer.

[0109] Specifically, after step S60, the method further includes the following steps:

[0110] S61: When receiving a search request for obtaining files associated with file data from a user terminal, identifying the file data associated with the search request;

[0111] S62: The file analysis model scans the file data and obtains the text to be retrieved in the file data based on the extraction rule;

[0112] S63: sending the text to be searched to the search system, and obtaining a preset number of search texts based on the search results;

[0113] S64: Send the search text to the user end that issued the search request.

[0114] In this embodiment, the search request includes but is not limited to searching for files mentioned in the file data and searching for file names automatically input by the user end. The search request also includes file data associated with this search. The extraction rule is different from the text extraction box. The extraction rule is a recognition rule that is activated only when the user end issues a search request, and is used to identify the associated files mentioned in the text data.

[0115] The retrieval system includes different types of public documents. After the retrieval is completed, a preset number of retrieval documents will be sent to the user terminal based on the retrieval requirements. The user terminal can issue a switching instruction to re-receive different preset numbers of retrieval documents.

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0117] In one embodiment, a large language model-based enterprise file data management interaction system is provided, and the large language model-based enterprise file data management interaction system corresponds to the large language model-based enterprise file data management interaction method in the above embodiment. The large language model-based enterprise file data management interaction system includes:

[0118] An acquisition module, used for sending acquisition instructions to the pre-trained file acquisition model at intervals;

[0119] The download module is used to download the corresponding file data based on the preset acquisition path after the file acquisition model receives the acquisition instruction;

[0120] An analysis and sending module, used for sending the downloaded file data to a pre-trained file analysis model;

[0121] An analysis module, used for extracting and identifying key information in the file data by a file analysis model, and generating file name information associated with the file data based on the key information;

[0122] The prompt matching module is used for the file analysis model to call the preset matching rules, match the corresponding transmission path according to the file name information, and match the prompt information according to the key information;

[0123] A transmission module, used to package the file data and prompt information and send them to the user end of the corresponding enterprise customer based on the transmission path;

[0124] A retrieval module, configured to, when receiving a retrieval request from a user end for obtaining associated files of file data, identify the file data associated with the retrieval request;

[0125] A retrieval and extraction module is used for scanning file data using a file analysis model and obtaining text to be retrieved in the file data based on extraction rules;

[0126] A search execution module, used to send the text to be searched to the search system, and obtain a preset number of search texts based on the search results;

[0127] The search sending module is used to send the search text to the user end that issued the search request.

[0128] Optional analysis modules include:

[0129] A type identification submodule is used for the file analysis model to scan the text field in the file data and determine the file type of the file data;

[0130] The extraction framework submodule is used to call the corresponding text extraction framework based on the file type;

[0131] The key information submodule is used to extract corresponding key information from the text field based on the text extraction framework, and the key information includes key fields;

[0132] The simplified sorting submodule is used to perform preset simplified processing and sorting processing on key fields, obtain file name information and associate it with file data.

[0133] Optionally, the prompt matching module includes:

[0134] The disassembly submodule is used to disassemble the name field after simplification and sorting in the file name information;

[0135] A name field identification submodule, used for obtaining the user end of the to-be-received file data, the transmission and storage paths available to the user end of the to-be-received file data, and the number of to-be-received files of the same user end based on the name field identification;

[0136] A path submodule, used for selecting the transmission and storage path of the user end for receiving the file data from the transmission and storage paths available to the user end based on the number of files received by the user end;

[0137] The factor submodule is used to identify all key fields in the key information and retrieve the prompt factors matching the key fields;

[0138] The prompt input submodule is used to sort all prompt factors and input them into the preset AI question-answering model;

[0139] The prompt generation submodule is used by the AI ​​question-answering model to generate prompt information based on a combination of prompt factors.

[0140] Optionally, factor submodules include:

[0141] The risk identification unit is used to identify key fields in the file analysis model and determine whether risk judgment needs to be performed on the key fields;

[0142] A first execution unit is used for directly retrieving a unique prompt factor matching the key field if the key field does not need to perform risk judgment;

[0143] The second execution unit is used to retrieve corresponding prompt factors based on the results of risk judgment if risk judgment is required for the key fields, and the risk judgment results include two results: the existence of risks and potential risks; store the sending records of file data and prompt information and form record information; screen out prompt information with potential risks, and based on a preset interval, generate additional prompt information at intervals and send it to the corresponding user terminal before the user terminal provides feedback; screen out prompt information with risks, generate a message to be processed and send it to the customer service terminal associated with the user terminal.

[0144] For the specific definition of an enterprise file data management interaction system based on a large language model, please refer to the definition of an enterprise file data management interaction method based on a large language model above, which will not be repeated here. Each module in the above-mentioned enterprise file data management interaction system based on a large language model can be implemented in whole or in part through software, hardware and a combination thereof. 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 of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0145] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an enterprise file data management interaction method based on a large language model is implemented.

[0146] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, an enterprise file data management interaction method based on a large language model is implemented.

[0147] 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, an enterprise file data management interaction method based on a large language model is implemented.

[0148] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0150] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An enterprise file data management interaction method based on a large language model, characterized in that: Send acquisition instructions to the pre-trained file acquisition model at intervals; After receiving the acquisition instruction, the file acquisition model downloads the corresponding file data based on the preset acquisition path; Sending the downloaded file data to a pre-trained file analysis model; The file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information; The file analysis model calls the preset matching rules, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information; Based on the transmission path, the file data and prompt information are packaged and sent to the user end of the corresponding enterprise customer; The file analysis model extracts and identifies key information in the file data, and generates file name information associated with the file data based on the key information; including: The file analysis model scans text fields in the file data to determine the file type of the file data; Call the corresponding text extraction framework based on the file type; Based on the text extraction framework, corresponding key information is extracted from the text field, and the key information includes key fields; The key fields are simplified and sorted by default to obtain the file name information and associate it with the file data; The file analysis model calls preset matching rules, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information; and also includes: Identify all key fields in the key information and retrieve prompt factors matching the key fields; All prompt factors are sorted and input into the preset AI question-answering model; The AI ​​question-answering model generates prompt information based on the combination of prompt factors; the key fields and prompt factors are pre-associated, and the pre-associated prompt factors can be matched by semantic analysis of the content represented by the key fields. The prompt factors are sorted and then sent to the AI ​​question-answering model to generate prompt messages; The step of identifying all key fields in the key information and retrieving prompt factors matching the key fields includes: The file analysis model identifies key fields and determines whether risk assessment is required for key fields; If the key field does not need to perform risk judgment, the only prompt factor matching the key field is directly retrieved; If a key field needs to perform risk judgment, the corresponding prompt factors are retrieved based on the result of the risk judgment. The risk judgment results include two results: existing risks and potential risks.

2. According to claim 1, a method for enterprise file data management interaction based on a large language model is characterized in that: The file analysis model calls preset matching rules, matches the corresponding transmission path according to the file name information, and matches the prompt information according to the key information; including: Decompose the simplified and sorted name fields in the file name information; Identify and obtain the user end of the file data to be received, the transmission and storage paths available to the user end of the file data to be received, and the number of files to be received by the same user end based on the name field; Based on the number of files received by the user end, the transmission and storage paths of the user end for receiving the file data are screened from the transmission and storage paths available to the user end.

3. The enterprise file data management interaction method based on a large language model according to claim 1 is characterized in that: After the file data and the prompt information are packaged and sent to the user end of the corresponding enterprise customer based on the transmission path, it includes: The sending records of file data and prompt information are stored and recorded; Screen out the prompt information with potential risks, and based on the preset interval length, generate additional prompt information at intervals and send it to the corresponding user end before the user end responds; Filter out the risky prompt information, generate a pending message and send it to the customer service terminal associated with the user end.

4. The enterprise file data management interaction method based on a large language model according to claim 1 is characterized in that: After the file data and the prompt information are packaged and sent to the user end of the corresponding enterprise customer based on the transmission path, the method further includes: When receiving a search request from a user terminal for obtaining a file associated with the file data, identifying the file data associated with the search request; The document analysis model scans the document data and obtains the text to be retrieved in the document data based on the extraction rules; Sending the text to be searched to the search system, and obtaining a preset number of search texts based on the search results; The search text is sent to the user end that issued the search request.

5. An enterprise file data management interactive system based on a large language model, characterized by: An acquisition module, used for sending acquisition instructions to a pre-trained file acquisition model at intervals; The download module is used to download the corresponding file data based on the preset acquisition path after the file acquisition model receives the acquisition instruction; An analysis and sending module, used for sending the downloaded file data to a pre-trained file analysis model; An analysis module, used for extracting and identifying key information in the file data by a file analysis model, and generating file name information associated with the file data based on the key information; The prompt matching module is used for the file analysis model to call the preset matching rules, match the corresponding transmission path according to the file name information, and match the prompt information according to the key information; A transmission module, used to package the file data and prompt information and send them to the user end of the corresponding enterprise customer based on the transmission path; The analysis modules include: A type identification submodule is used for the file analysis model to scan the text field in the file data and determine the file type of the file data; The extraction framework submodule is used to call the corresponding text extraction framework based on the file type; The key information submodule is used to extract corresponding key information from the text field based on the text extraction framework, and the key information includes key fields; The simplified sorting submodule is used to perform preset simplified processing and sorting processing on key fields, obtain file name information and associate it with file data; The factor submodule is used to identify all key fields in the key information and retrieve the prompt factors matching the key fields; The prompt input submodule is used to sort all prompt factors and input them into the preset AI question-answering model; The prompt generation submodule is used by the AI ​​question-answering model to generate prompt information based on the combination of prompt factors; Optionally, factor submodules include: The risk identification unit is used to identify key fields in the file analysis model and determine whether risk judgment needs to be performed on the key fields; A first execution unit is used for directly retrieving a unique prompt factor matching the key field if the key field does not need to perform risk judgment; The second execution unit is used to retrieve corresponding prompt factors based on the result of risk judgment if the key field needs to perform risk judgment. The risk judgment result includes two results: existing risk and potential risk.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the enterprise file data management interaction method based on a large language model as described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the enterprise document data management interaction method based on a large language model as described in any one of claims 1 to 4 are implemented.

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