Bridge steel structure technical file intelligent auxiliary generation method and device based on large model

Abstract

The invention relates to the technical field of natural language processing, in particular to a bridge steel structure technical file intelligent auxiliary generation method and device based on a large model, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining demand information of a target object in response to a generation request of the target object; according to the demand information, performing retrieval comparison in a pre-constructed database to obtain material data matched with the demand information; calling a pre-constructed large language model to process the material data to obtain a target technical file generated based on the material data; and returning the target technical file to respond to the generation request of the target object. By adopting the method, the quality of the intelligently generated technical file can be improved.

Description

Technical Field

[0001] The present application relates to the field of natural language processing technology, and in particular to a method, device, computer equipment, storage medium and computer program product for intelligently assisting the generation of technical documents for bridge steel structures based on a large model. Background Art

[0002] Bridge steel structure is a widely used structural form in modern bridge construction. It is mainly made of steel and has the advantages of high strength, light weight, good seismic resistance and short construction period. The technical documents of bridge steel structure usually include professional documents in many aspects, which specify in detail the technical requirements of each stage such as design, construction, acceptance and maintenance. The technical documents of bridge steel structure usually include: design documents, construction technical documents, quality control documents, safety management documents, maintenance and management documents, post-evaluation documents and various reference standard documents.

[0003] In related technologies, as the complexity of engineering projects increases, traditional manual writing of technical documents is not only time-consuming but also prone to errors. Therefore, automated document generation technology has gradually attracted attention. In recent years, technologies based on large language models have developed rapidly, providing new possibilities for intelligent generation and processing of technical documents. Combined with the integration of domain knowledge bases, automatically generated documents can better meet actual needs, thereby improving the standardization and consistency of documents.

[0004] However, the current intelligent assisted generation method of technical documents has the following technical problems:

[0005] Existing technologies mainly include template-based document generation systems and traditional natural language generation technologies. These technologies usually rely on fixed formats and preset templates, which are difficult to meet the dynamic needs of complex fields. In addition, although some automated tools use NLP technology for text processing, most of them lack deep integration of professional domain knowledge, resulting in insufficient accuracy and professionalism in the generated documents. Summary of the invention

[0006] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for intelligently assisted generation of bridge steel structure technical documents based on large models, which can improve the quality of intelligently generated technical documents in response to the above-mentioned technical problems.

[0007] In a first aspect, the present application provides a method for intelligently assisting the generation of technical documents for bridge steel structures based on a large model. The method comprises:

[0008] In response to a request for generating a target object, obtaining demand information of the target object;

[0009] According to the demand information, searching and comparing are performed in a pre-built database to obtain material data matching the demand information;

[0010] Calling a pre-built large language model to process the material data to obtain a target technical file generated based on the material data;

[0011] The target technology file is returned in response to the generation request of the target object.

[0012] In one embodiment, calling a pre-built large language model to process the material data to obtain a target technical file generated based on the material data includes:

[0013] Determine the association relationship between the material data, and generate a local technical file based on the association relationship;

[0014] The local technical files are integrated to obtain the target technical files.

[0015] In one embodiment, calling a pre-built large language model to process the material data to obtain a target technical file generated based on the material data includes:

[0016] Preprocessing the material data, wherein the preprocessing includes data correction;

[0017] The target technology file is generated based on the preprocessed material data.

[0018] In one embodiment, the method comprises:

[0019] Obtaining raw data from several sources, parsing the raw data, and extracting structured information from the raw data;

[0020] The structured information is aligned and reconstructed, and the database is constructed based on the reconstructed structured information.

[0021] In one embodiment, after returning the target technology file in response to the generation request of the target object, the method further includes:

[0022] In response to the interactive input of the target object, acquiring an interactive instruction corresponding to the interactive input information;

[0023] The target technical file is adjusted based on the interactive instruction.

[0024] In one embodiment, adjusting the target technology file based on the interactive instruction includes:

[0025] Based on the interactive instruction, obtaining externally imported non-conventional materials, wherein the non-conventional materials include tables, formulas, and pictures;

[0026] The unconventional material is inserted into the technical file to form the target technical file.

[0027] In a second aspect, the present application also provides a device for intelligently assisting the generation of technical documents for bridge steel structures based on a large model. The device comprises:

[0028] A demand information module, configured to obtain demand information of a target object in response to a request for generating the target object;

[0029] A material data module, used to search and compare in a pre-built database according to the demand information, and obtain material data matching the demand information;

[0030] A file generation module, used to call a pre-built large language model to process the material data to obtain a target technical file generated based on the material data;

[0031] The file response module is used to return the target technology file to respond to the generation request of the target object.

[0032] In one embodiment, the file generation module includes:

[0033] A local generation module, used to determine the association relationship between the material data, and generate a local technical file based on the association relationship;

[0034] The file integration module is used to integrate the local technical files to obtain the target technical files.

[0035] In one embodiment, the file generation module includes:

[0036] A data fine-calibration module, used for pre-processing the material data, wherein the pre-processing includes data fine-calibration;

[0037] The integration generation module is used to generate the target technology file based on the pre-processed material data.

[0038] In one embodiment, the device comprises:

[0039] A raw data module, used to obtain raw data from several sources, parse the raw data, and extract structured information from the raw data;

[0040] A reconstruction module is used to align and reconstruct the structured information, and build the database based on the reconstructed structured information.

[0041] In one embodiment, after the file response module, the following is also included:

[0042] An interaction control module, configured to obtain an interaction instruction corresponding to the interaction input information in response to the interaction input of the target object;

[0043] The file adjustment module is used to adjust the target technical file based on the interactive instruction.

[0044] In one embodiment, the file adjustment module includes:

[0045] A material acquisition module, used to acquire externally imported unconventional materials based on the interactive instructions, wherein the unconventional materials include tables, formulas, and pictures;

[0046] The material insertion module is used to insert the unconventional material into the technical file to form the target technical file.

[0047] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the intelligent auxiliary generation method of bridge steel structure technical documents based on a large model as described in any embodiment of the first aspect when executing the computer program.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the method for intelligently assisting generation of technical documents of bridge steel structures based on a large model as described in any one of the embodiments in the first aspect are implemented.

[0049] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, which, when executed by a processor, implements the steps in the method for intelligently assisting generation of technical documents of bridge steel structures based on a large model as described in any one of the embodiments in the first aspect.

[0050] The above-mentioned method, device, computer equipment, storage medium and computer program product for intelligently assisting the generation of technical documents of bridge steel structures based on a large model can achieve the beneficial effects of the technical problems in the corresponding background technology by deducing the technical features in the claims:

[0051] The present application provides a method for intelligently assisting the generation of technical documents of bridge steel structures based on a large model, including: in response to a generation request of a target object, obtaining the demand information of the target object; searching and comparing in a pre-built database according to the demand information, obtaining material data matching the demand information; calling a pre-built large language model to process the material data, obtaining a target technical document generated based on the material data; returning the target technical document to respond to the generation request of the target object. In implementation, by integrating a database of domain knowledge, the data material required for the technical document is retrieved, which helps to intelligently and automatically collect materials, thereby helping to improve the richness of the materials and content of the technical documents finally generated, and then realizing the integration of materials and the intelligent generation of technical documents through a large language model, which helps to improve the generation efficiency of technical documents, so that when facing the generation requirements of complex projects, it can reduce the time consumption compared with conventional methods and achieve the purpose of rapid response. On the other hand, compared with the manual input method, the integration of materials through a large language model helps to reduce input errors, thereby improving the document quality of technical documents. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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.

[0053] Figure 1 An application environment diagram of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in one embodiment;

[0054] Figure 2 It is a first flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in one embodiment;

[0055] Figure 3 A second flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in another embodiment;

[0056] Figure 4 A third flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in another embodiment;

[0057] Figure 5 A fourth flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in another embodiment;

[0058] Figure 6 A fifth flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in another embodiment;

[0059] Figure 7 A sixth flow chart of a method for intelligently assisting generation of technical documents of a bridge steel structure based on a large model in another embodiment;

[0060] Figure 8 It is a structural block diagram of a device for intelligently assisting generation of technical documents of bridge steel structures based on a large model in one embodiment;

[0061] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] 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.

[0063] In related technologies, as the complexity of engineering projects increases, traditional manual writing of technical documents is not only time-consuming but also prone to errors. Therefore, automated document generation technology has gradually attracted attention. In recent years, technologies based on large language models have developed rapidly, providing new possibilities for intelligent generation and processing of technical documents. Combined with the integration of domain knowledge bases, automatically generated documents can better meet actual needs, thereby improving the standardization and consistency of documents.

[0064] However, the current intelligent assisted generation method of technical documents has the following technical problems:

[0065] Existing technologies mainly include template-based document generation systems and traditional natural language generation technologies. These technologies usually rely on fixed formats and preset templates, which are difficult to meet the dynamic needs of complex fields. In addition, although some automated tools use NLP technology for text processing, most of them lack deep integration of professional domain knowledge, resulting in insufficient accuracy and professionalism in the generated documents.

[0066] Based on this, the embodiment of the present application provides a method for intelligently assisting the generation of technical documents of bridge steel structures based on a large model, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0067] In one embodiment, Figure 2 As shown in the figure, a method for intelligent auxiliary generation of bridge steel structure technical documents based on a large model is provided. Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0068] Step 202: In response to a request for generating a target object, obtaining demand information of the target object;

[0069] Step 204: searching and comparing in a pre-built database according to the demand information to obtain material data matching the demand information;

[0070] Step 206: calling a pre-built large language model to process the material data to obtain a target technical file generated based on the material data;

[0071] Step 208: Return the target technology file in response to the generation request of the target object.

[0072] In the above-mentioned intelligent auxiliary generation method of bridge steel structure technical documents based on a large model, reasonable deduction is performed in combination with the technical features in the embodiment to achieve the beneficial effect of solving the technical problems raised in the background technology:

[0073] The present application provides a method for intelligently assisting the generation of technical documents of bridge steel structures based on a large model, including: in response to a generation request of a target object, obtaining the demand information of the target object; searching and comparing in a pre-built database according to the demand information, obtaining material data matching the demand information; calling a pre-built large language model to process the material data, obtaining a target technical document generated based on the material data; returning the target technical document to respond to the generation request of the target object. In implementation, by integrating a database of domain knowledge, the data material required for the technical document is retrieved, which helps to intelligently and automatically collect materials, thereby helping to improve the richness of the materials and content of the technical documents finally generated, and then realizing the integration of materials and the intelligent generation of technical documents through a large language model, which helps to improve the generation efficiency of technical documents, so that when facing the generation requirements of complex projects, it can reduce the time consumption compared with conventional methods and achieve the purpose of rapid response. On the other hand, compared with the manual input method, the integration of materials through a large language model helps to reduce input errors, thereby improving the document quality of technical documents.

[0074] In one embodiment, if Figure 3 As shown, step 206 includes:

[0075] Step 302: determining the association relationship between the material data, and generating a local technical file based on the association relationship;

[0076] Step 304: Integrate the local technical files to obtain the target technical files.

[0077] In this embodiment, various functional modules provided in the large language model, including a local generation module and a whole-article generation module, help to realize the segmented generation of technical documents with the help of the local generation module, help to realize the convenience of modifying and adjusting the technical documents, and on the other hand, help to improve the flexibility of technical document generation.

[0078] In one embodiment, if Figure 4 As shown, step 206 includes:

[0079] Step 402: pre-processing the material data, wherein the pre-processing includes data fine-tuning;

[0080] Step 404: Generate the target technology file based on the preprocessed material data.

[0081] In this embodiment, after the retrieval is completed, the obtained material data is preprocessed, which helps to improve the quality of the material data used to generate the technical document through the preprocessing step, thereby improving the overall quality of the technical document.

[0082] In one embodiment, if Figure 5 As shown, the method includes:

[0083] Step 502: obtaining raw data from a plurality of sources, parsing the raw data, and extracting structured information from the raw data;

[0084] Step 504: align and reconstruct the structured information, and build the database based on the reconstructed structured information.

[0085] In this embodiment, by parsing and reconstructing unstructured data, valid data in the original data, that is, knowledge data in the field, can be extracted, thereby realizing the construction of a domain knowledge database, and then using the domain knowledge database to achieve efficient technical document generation.

[0086] In one embodiment, if Figure 6 As shown, after step 208, the following further includes:

[0087] Step 602: In response to the interactive input of the target object, acquiring an interactive instruction corresponding to the interactive input information;

[0088] Step 604: adjusting the target technical file based on the interactive instruction.

[0089] In this embodiment, after the technical file is generated, the user can adjust the technical file through interaction with the terminal, which helps to improve the flexibility and operability of the technical file generation.

[0090] In one embodiment, if Figure 7 As shown, step 604 includes:

[0091] Step 702: based on the interactive instruction, obtaining externally imported non-conventional materials, wherein the non-conventional materials include tables, formulas, and pictures;

[0092] Step 704: inserting the unconventional material into the technical file to form the target technical file.

[0093] In this embodiment, through interactive instructions, unconventional types of material data, such as tables, formulas, pictures, etc., can be inserted into the technical document, which helps to increase the richness of the technical document and improve the quality of the generated technical document.

[0094] 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.

[0095] Based on the same inventive concept, the embodiment of the present application also provides a device for intelligently assisting the generation of technical documents for bridge steel structures based on a large model, which is used to implement the above-mentioned method for intelligently assisting the generation of technical documents for bridge steel structures based on a large model. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the device for intelligently assisting the generation of technical documents for bridge steel structures based on a large model provided below can be referred to the limitations of the method for intelligently assisting the generation of technical documents for bridge steel structures based on a large model above, and will not be repeated here.

[0096] In one embodiment, Figure 8 As shown, a device for intelligently assisting the generation of technical documents of bridge steel structures based on a large model is provided, comprising: a demand information module, a material data module, a file generation module and a file response module, wherein:

[0097] A demand information module, configured to obtain demand information of a target object in response to a request for generating the target object;

[0098] A material data module, used to search and compare in a pre-built database according to the demand information, and obtain material data matching the demand information;

[0099] A file generation module, used to call a pre-built large language model to process the material data to obtain a target technical file generated based on the material data;

[0100] The file response module is used to return the target technology file to respond to the generation request of the target object.

[0101] In one embodiment, the file generation module includes:

[0102] A local generation module, used to determine the association relationship between the material data, and generate a local technical file based on the association relationship;

[0103] The file integration module is used to integrate the local technical files to obtain the target technical files.

[0104] In one embodiment, the file generation module includes:

[0105] A data fine-calibration module, used for pre-processing the material data, wherein the pre-processing includes data fine-calibration;

[0106] The integration generation module is used to generate the target technology file based on the pre-processed material data.

[0107] In one embodiment, the device comprises:

[0108] A raw data module, used to obtain raw data from several sources, parse the raw data, and extract structured information from the raw data;

[0109] A reconstruction module is used to align and reconstruct the structured information, and build the database based on the reconstructed structured information.

[0110] In one embodiment, after the file response module, the following is also included:

[0111] An interaction control module, configured to obtain an interaction instruction corresponding to the interaction input information in response to the interaction input of the target object;

[0112] The file adjustment module is used to adjust the target technical file based on the interactive instruction.

[0113] In one embodiment, the file adjustment module includes:

[0114] A material acquisition module, used to acquire externally imported unconventional materials based on the interactive instructions, wherein the unconventional materials include tables, formulas, and pictures;

[0115] The material insertion module is used to insert the unconventional material into the technical file to form the target technical file.

[0116] Each module in the above-mentioned intelligent auxiliary generation device for bridge steel structure technical documents based on a large model can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0117] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9 As shown. 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 through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through 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 the 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 can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for intelligently assisting the generation of technical documents of bridge steel structures based on a large model is realized. The display unit of the computer device is used to form a visually visible picture, which can 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.

[0118] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 laws, regulations and standards of relevant countries and regions.

[0123] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 this application can include at least one of non-volatile 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), magnetoresistive 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, etc., but are not limited to this.

[0124] 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 specification.

[0125] 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 method for intelligently assisting the generation of technical documents of bridge steel structures based on a large model, characterized in that: The method comprises: In response to a request for generating a target object, obtaining demand information of the target object; According to the demand information, searching and comparing are performed in a pre-built database to obtain material data matching the demand information; Calling a pre-built large language model to process the material data to obtain a target technical file generated based on the material data; The target technology file is returned in response to the generation request of the target object.

2. The method according to claim 1, characterized in that The calling of the pre-built large language model to process the material data to obtain a target technical file generated based on the material data includes: Determine the association relationship between the material data, and generate a local technical file based on the association relationship; The local technical files are integrated to obtain the target technical files.

3. The method according to claim 2, characterized in that The calling of the pre-built large language model to process the material data to obtain a target technical file generated based on the material data includes: Preprocessing the material data, wherein the preprocessing includes data correction; The target technology file is generated based on the preprocessed material data.

4. The method according to claim 1, characterized in that: The method comprises: Obtaining raw data from several sources, parsing the raw data, and extracting structured information from the raw data; The structured information is aligned and reconstructed, and the database is constructed based on the reconstructed structured information.

5. The method according to any one of claims 1 to 4, characterized in that: After returning the target technology file in response to the generation request of the target object, the method further includes: In response to the interactive input of the target object, acquiring an interactive instruction corresponding to the interactive input information; The target technical file is adjusted based on the interactive instruction.

6. The method according to claim 5, characterized in that The adjusting of the target technical file based on the interactive instruction includes: Based on the interactive instruction, obtaining externally imported non-conventional materials, wherein the non-conventional materials include tables, formulas, and pictures; The unconventional material is inserted into the technical file to form the target technical file.

7. An intelligent auxiliary generation device for bridge steel structure technical documents based on a large model, characterized in that: The device comprises: A demand information module, configured to obtain demand information of a target object in response to a request for generating the target object; A material data module, used to search and compare in a pre-built database according to the demand information, and obtain material data matching the demand information; A file generation module, used to call a pre-built large language model to process the material data to obtain a target technical file generated based on the material data; The file response module is used to return the target technology file to respond to the generation request of the target object.

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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