Bid invitation file generation method, device and equipment based on pattern analysis and readable storage medium

Abstract

The invention relates to the field of automatic generation of files, and particularly provides a bid invitation file generation method, device and equipment based on pattern analysis and a readable storage medium, the method comprises the following steps: performing model semantic analysis according to item description input by a user to obtain key information, the key information comprising title contents corresponding to a plurality of keywords; key bid invitation content corresponding to the key information is matched from a preset vector library, and the vector library is obtained by splitting historical bid invitation files in a historical bid invitation file set and then conducting vector labeling; and replacing corresponding content in a preset bid invitation file case template with the key bid invitation content to generate a target bid invitation file. Through the method, the effect of accurately and efficiently generating the bid inviting file can be achieved.

Description

Technical Field

[0001] The present application relates to the field of automatic document generation, and in particular, to a method, device, equipment and readable storage medium for generating bidding documents based on pattern analysis. Background Art

[0002] The bidding document generation system and medium based on pattern analysis is a work of automating the writing of bidding documents through large model capabilities. At present, the traditional bidding document generation method usually requires users to manually adjust the directory structure and content according to project requirements.

[0003] The existing bidding document processing has obvious deficiencies in intelligence, flexibility and content quality, and it is difficult to meet the needs of increasingly complex bidding projects. At present, the existing bidding writing mainly relies on template filling, and users need to manually select and fill in a lot of information. Since the self-written content is based on a fixed template, users must carefully check the generated documents to ensure that they meet the actual needs of the specific project; at the same time, they also need to check the details one by one to ensure accuracy and compliance. This step is not only time-consuming, but also prone to errors.

[0004] Therefore, how to generate bidding documents accurately and efficiently is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method for generating bidding documents based on pattern analysis. The technical solution of the embodiments of the present application can achieve the effect of accurately and efficiently generating bidding documents.

[0006] In a first aspect, an embodiment of the present application provides a method for generating a bidding document based on pattern analysis, comprising: performing a model semantic analysis based on a project description input by a user to obtain key information, wherein the key information includes title content corresponding to multiple keywords; matching key bidding content corresponding to the key information from a preset vector library, wherein the vector library is obtained by splitting historical bidding documents in a historical bidding document collection and then performing vector annotation; replacing the key bidding content with the corresponding content in a preset bidding document case template to generate a target bidding document.

[0007] In the above embodiment of the present application, by splitting the user's project description and matching it with the bidding content in the pre-built vector library, the key bidding content corresponding to the user's project description can be quickly obtained, and then the key bidding content is presented through the screened bidding document case template to generate the target bidding document, which can achieve the effect of accurately and efficiently generating the bidding document.

[0008] In some embodiments, before performing model semantic analysis based on the project description input by the user to obtain key information, it also includes: obtaining a set of historical bidding documents; splitting the historical bidding documents in the set of historical bidding documents according to chapters to obtain a set of bidding content; annotating the bidding content in the set of bidding content with key information vectors to obtain a vector library, wherein the set of bidding content includes key bidding content.

[0009] In the above embodiment of the present application, by splitting the historical bidding documents and vector marking the key information, a vector library with the bidding content, the corresponding vectors of the bidding content and the corresponding relationship between the key information can be constructed, and the bidding content corresponding to the user description information can be quickly matched through the vector library.

[0010] In some embodiments, after replacing the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document, it also includes: regularly collecting bidding documents, and dividing the collected bidding documents by chapters and keywords and storing them in the vector library; automatically labeling and classifying the data in the bidding documents in the vector library through a machine learning algorithm.

[0011] In the above embodiment of the present application, by regularly collecting bidding documents and automatically labeling and classifying the bidding documents in the vector library through the machine learning algorithm, the effect of regularly updating the bidding content in the vector library can be achieved, thereby ensuring the accuracy of the bidding content.

[0012] In some embodiments, before performing model semantic analysis based on the project description input by the user to obtain key information, it also includes: selecting a template in the bidding document case template set whose number of keywords meets preset requirements according to the bidding standards to obtain a bidding document case template, wherein the bidding standards include the project title and the project standards corresponding to the project title.

[0013] In the above-mentioned embodiment of the present application, the bidding document case template that can more accurately generate the target bidding document can be screened out according to the bidding document case templates in different bidding document case templates that match the number of keywords in the user description information.

[0014] In some embodiments, a model semantic analysis is performed based on the project description input by the user to obtain key information, including: constructing a small bidding model according to a preset title configuration; and analyzing the project description through the small bidding model to obtain key information.

[0015] In the above-mentioned embodiment of the present application, by constructing a small bidding model, key information in the user description information can be quickly obtained according to the user's description information, so that the corresponding bidding content can be accurately matched according to the key information later.

[0016] In some embodiments, matching key bidding contents corresponding to key information from a preset vector library includes: converting the key information into a vector to obtain multiple vectors; and matching key bidding contents corresponding to the multiple vectors according to a cosine similarity matching method.

[0017] In the above-mentioned embodiments of the present application, the key bidding contents corresponding to the key information can be matched by means of cosine similarity to ensure the accuracy of the acquisition of the bidding contents.

[0018] In some embodiments, the key bidding content replaces the corresponding content in the preset bidding document case template to generate a target bidding document, including: matching the title corresponding to the key bidding content and the corresponding title in the bidding document case template to obtain the corresponding relationship; according to the corresponding relationship, replacing the key bidding content with the corresponding content in the bidding document case template to obtain the target bidding document.

[0019] In the above-mentioned embodiment of the present application, the key bidding content can be replaced into the bidding document case template according to the title relationship between the bidding content and the title in the bidding document case template, so as to quickly generate the target bidding document.

[0020] In a second aspect, an embodiment of the present application provides a bidding document generation device based on pattern analysis, comprising:

[0021] An analysis module is used to perform model semantic analysis based on the project description input by the user to obtain key information, wherein the key information includes title content corresponding to multiple keywords;

[0022] A matching module is used to match key bidding contents corresponding to key information from a preset vector library, wherein the vector library is obtained by splitting historical bidding documents in a historical bidding document set and then performing vector annotation;

[0023] The generation module is used to replace the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document.

[0024] Optionally, the device also includes:

[0025] A construction module is used in the analysis module to obtain a collection of historical bidding documents before performing a model semantic analysis based on the project description input by the user and obtaining key information;

[0026] Split the historical bidding documents in the historical bidding document set according to chapters to obtain a bidding content set;

[0027] Key information vectors are annotated on the bidding contents in the bidding content set to obtain a vector library, wherein the bidding content set includes key bidding contents.

[0028] Optionally, the device further comprises:

[0029] An updating module, used for the generation module to replace the corresponding content in the preset bidding document case template with the key bidding content, and after generating the target bidding document, regularly collect bidding documents, and divide the collected bidding documents into chapters and keywords and store them in the vector library;

[0030] Automatically annotate and classify data in tender documents in the vector library through machine learning algorithms.

[0031] Optionally, the device further comprises:

[0032] The screening module is used for the analysis module to perform model semantic analysis based on the project description input by the user, and before obtaining key information, select a template whose number of keywords in the bidding document case template set meets the preset requirements according to the bidding standards to obtain the bidding document case template, wherein the bidding standards include the project title and the project standards corresponding to the project title.

[0033] Optionally, the analysis module is specifically used for:

[0034] Construct a small bidding model according to the preset title configuration;

[0035] Analyze project descriptions through a small bidding model to obtain key information.

[0036] Optionally, the matching module is specifically used for:

[0037] Convert key information into vectors to obtain multiple vectors;

[0038] According to the cosine similarity matching method, the key bidding contents corresponding to multiple vectors are matched.

[0039] Optionally, the generating module is specifically used for:

[0040] Match the title corresponding to the key bidding content with the corresponding title in the bidding document case template to obtain the corresponding relationship;

[0041] According to the corresponding relationship, the key bidding content is replaced with the corresponding content in the bidding document case template to obtain the target bidding document.

[0042] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are performed.

[0043] In a fourth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are performed.

[0044] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flowchart of a method for generating bidding documents based on pattern analysis provided in an embodiment of the present application;

[0047] Figure 2 An implementation method for updating bidding data in a vector library provided in an embodiment of the present application;

[0048] Figure 3 A schematic block diagram of a bidding document generation device based on pattern analysis provided in an embodiment of the present application;

[0049] Figure 4 A schematic diagram of the structure of a bidding document generation device based on pattern analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0051] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0052] First, some terms involved in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0053] Pattern analysis is a pattern recognition method that finds out the components of a complex pattern, the relationships between the components, and the corresponding symbolic descriptions according to the purpose of the analysis.

[0054] This application is applied to the scenario of automatic generation of bidding documents. The specific scenario is to analyze the key information in the user description information, match it with the bidding content in the pre-built vector library, present the obtained bidding content to the bidding document case template that best matches the user description information, and generate the target bidding document.

[0055] The bidding document generation system and medium based on pattern analysis is a work of automating the writing of bidding documents through large model capabilities. At present, the traditional way of generating bidding documents usually requires users to manually adjust the directory structure and content according to project requirements. The existing bidding document processing has obvious deficiencies in intelligence, flexibility and content quality, and it is difficult to meet the needs of increasingly complex bidding projects. At present, the existing bidding writing mainly relies on template filling, and users need to manually select and fill in a lot of information. Since the self-written content is based on a fixed template, users must carefully check the generated documents to ensure that they meet the actual needs of the specific project; at the same time, they also need to check the details one by one to ensure accuracy and compliance. This step is not only time-consuming, but also prone to errors.

[0056] To this end, this application performs a model semantic analysis based on the project description input by the user to obtain key information, wherein the key information includes the title content corresponding to multiple keywords; matches the key bidding content corresponding to the key information from the preset vector library, wherein the vector library is obtained by splitting the historical bidding documents in the historical bidding document collection and then performing vector annotation; replaces the key bidding content with the corresponding content in the preset bidding document case template to generate the target bidding document. By splitting the user's project description and matching it with the bidding content in the pre-built vector library, the key bidding content corresponding to the user's project description can be quickly obtained, and then the key bidding content is presented through the screened bidding document case template to generate the target bidding document, which can achieve the effect of accurately and efficiently generating bidding documents.

[0057] In an embodiment of the present application, the executing entity may be a bidding document generation device based on pattern analysis in a bidding document generation system based on pattern analysis. In actual applications, the bidding document generation device based on pattern analysis may be electronic devices such as terminal devices and servers, which are not limited here.

[0058] Combine the following Figure 1The bidding document generation method based on pattern analysis in an embodiment of the present application is described in detail.

[0059] Please see Figure 1 , Figure 1 A flowchart of a method for generating bidding documents based on pattern analysis provided in an embodiment of the present application is shown in FIG. Figure 1 The method for generating bidding documents based on pattern analysis shown includes:

[0060] Step 110: Perform model semantic analysis based on the project description input by the user to obtain key information.

[0061] Among them, key information includes title content corresponding to multiple keywords. Keywords can also be keywords, key phrases and key sentences, etc. The project description can be a text or a voice. If it is a voice, the voice recognition model can be used to recognize the voice and then get the text. The project description can generally include the keywords, industry, name and project scope of the target project, etc. The target project can be any project that can be bid.

[0062] In some embodiments of the present application, before performing model semantic analysis based on the project description input by the user to obtain key information, Figure 1 The method shown also includes: obtaining a set of historical bidding documents; splitting the historical bidding documents in the set of historical bidding documents according to chapters to obtain a set of bidding contents; annotating the bidding contents in the set of bidding contents with key information vectors to obtain a vector library, wherein the set of bidding contents includes key bidding contents.

[0063] In the above process, this application can construct a vector library with bidding content, corresponding vectors of bidding content and corresponding relationships between key information by splitting historical bidding documents and vector marking of key information. The vector library can be used to quickly match the bidding content corresponding to the user description information.

[0064] Among them, the historical bidding document set can be composed of the historical bidding data files of the target project. The bidding content set includes the titles of different chapters and the corresponding chapter contents. The key information vector annotation can be the vectorized annotation of the chapter title or the vectorized annotation of the words, phrases or texts extracted from the chapter content. Specifically, the key information corresponding to the key words such as project name, project address, bidding scope, subject matter, funding requirements, qualification conditions, bidding requirements, tenderer, registration start and end time, business conditions, technical requirements and contract terms can be identified and extracted for annotation to form a multi-dimensional vector query library. In addition, the bidding content, the corresponding vector of the bidding content and the key information in the vector library have a certain mapping relationship.

[0065] In some embodiments of the present application, before performing model semantic analysis based on the project description input by the user to obtain key information, Figure 1 The method shown also includes: selecting a template whose number of keywords in the bidding document case template set meets preset requirements according to the bidding standards to obtain a bidding document case template, wherein the bidding standards include a project title and a project standard corresponding to the project title.

[0066] In the above process, the present application can screen out a bidding document case template that can more accurately generate a target bidding document based on the bidding document case templates in different bidding document case templates that match the number of keywords in the user description information.

[0067] The preset requirements may be set according to the requirements, such as the number and type of keywords, etc. The project standards may be set according to the requirements, such as the number and type of project keywords, etc.

[0068] Optionally, a template whose number of keywords meets preset requirements is selected from the bidding document case template set according to the bidding standards, and the bidding document case template containing the largest number of key information can be searched in the bidding document case template set according to the analyzed key information as the target bidding document case template.

[0069] For example, according to the standards of "project name 60%, subject matter 90%, bidding scope 90%" marked in the vector library, one of the cases with the most keywords corresponding to the key information can be selected as a template to use. In this way, the system can accurately understand user needs and match relevant cases more accurately.

[0070] In some embodiments of the present application, a model semantic analysis is performed based on the project description input by the user to obtain key information, including: constructing a small bidding model according to a preset title configuration; and analyzing the project description through the small bidding model to obtain key information.

[0071] In the above process, the present application can quickly obtain key information in the user description information according to the user's description information by constructing a small bidding model, so as to accurately match the corresponding bidding content according to the key information in the subsequent process.

[0072] Among them, the title configuration can be set according to needs, and may include one or more of the project name, project address, bidding scope, subject matter, funding requirements, qualification conditions, bidding requirements, tenderer, registration start and end time, business conditions, technical requirements and contract terms.

[0073] Optionally, the project description can be analyzed through the bidding mini-model to obtain key information. The key information can be obtained by matching the keywords in the description information that match the title configuration according to the configuration set in the bidding mini-model.

[0074] Step 120: Match the key bidding content corresponding to the key information from the preset vector library.

[0075] The vector library is obtained by splitting the historical bidding documents in the historical bidding document collection and then performing vector annotation.

[0076] In some embodiments of the present application, matching key bidding content corresponding to key information from a preset vector library includes: converting the key information into a vector to obtain multiple vectors; and matching the key bidding content corresponding to the multiple vectors according to a cosine similarity matching method.

[0077] In the above process, the present application can match the key bidding contents corresponding to the key information by means of cosine similarity to ensure the accuracy of the acquisition of the bidding contents.

[0078] Optionally, according to the cosine similarity matching method, key bidding contents corresponding to multiple vectors are matched, including matching the multiple vectors with vectors in the vector library respectively, and taking the bidding contents corresponding to the vector with the largest similarity value as the key bidding contents.

[0079] Step 130: Replace the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document.

[0080] In some embodiments of the present application, the key bidding content replaces the corresponding content in the preset bidding document case template to generate a target bidding document, including: matching the title corresponding to the key bidding content and the corresponding title in the bidding document case template to obtain the corresponding relationship; according to the corresponding relationship, replacing the key bidding content with the corresponding content in the bidding document case template to obtain the target bidding document.

[0081] In the above process, this application can replace the key bidding content into the bidding document case template according to the title of the bidding content and the title relationship in the bidding document case template, and quickly generate the target bidding document.

[0082] For example, according to the corresponding content of "project name, project address, bidding scope, subject matter, funding requirements, qualification conditions, bidding requirements, tenderer, registration start and end time, business conditions, technical requirements, contract terms, etc." marked in the key bidding content, the corresponding field response marked in the bidding document case template is replaced with "the key information content after semantic analysis of the bidding model described by the user". The replaced target and text are the "catalog" of the generated "bidding document", and the target bidding document is obtained.

[0083] In some embodiments of the present application, after replacing the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document, Figure 1The method shown also includes: regularly collecting bidding documents, and dividing the collected bidding documents by chapters and keywords and storing them in a vector library; automatically labeling and classifying the data in the bidding documents in the vector library through a machine learning algorithm.

[0084] In the above process, this application can achieve the effect of regular updating of the tender content in the vector library by regularly collecting tender documents and automatically labeling and classifying the tender documents in the vector library through machine learning algorithms, thereby ensuring the accuracy of the tender content.

[0085] Specifically, the bidding documents are collected regularly, and the collected bidding documents are segmented by chapters and keywords and stored in the vector library; the data in the bidding documents in the vector library are automatically annotated and classified by machine learning algorithms. Figure 2 The vector library bidding data update method shown.

[0086] Please see Figure 2 , Figure 2 An implementation method for updating bidding data in a vector library provided by this application, such as Figure 2 The methods shown include:

[0087] The semantic decomposition of a certain bidding document requirement "Example - **** Building's Property Service Bidding Document" can be specifically achieved by selecting the bidding case that needs to be updated for semantic decomposition and vector algorithm implementation, generating the directory and chapter text corresponding to the bidding case that needs to be updated, and putting them into the vector library of the bidding document case. The data in the bidding document in the vector library is automatically labeled and classified through the machine learning algorithm, and the intelligent document output is used to obtain the bidding content that needs to be filled in the vector library: "**** Building's Property Service Bidding Document Document", and then the bidding content is corrected through the revised existing document reverse correction model algorithm and finally stored in the vector library.

[0088] Among them, the algorithm implemented by selecting the bidding cases that need to be updated for semantic decomposition and vector algorithm is as follows:

[0089] Vector algorithm: f(y) = ∫(x(n1,n2,n3...nn)dx) / ∫(yn)dy*∫(sum(zn))dx;

[0090] X is the key information split after the user demand description, y is the key content information in the vector library, z is the chapter number in the bidding document case; n is the bidding document case number (n1 is the bidding document case numbered 1, n2 is the bidding document case numbered 2, and n3 is the bidding document case numbered 3) n is a positive integer.

[0091] also, Figure 2 The specific methods and steps shown can be found in Figure 1 The method shown will not be described in detail here.

[0092] In the above Figure 1 In the process shown, the application performs a model semantic analysis based on the project description input by the user to obtain key information, wherein the key information includes the title content corresponding to multiple keywords; matches the key bidding content corresponding to the key information from the preset vector library, wherein the vector library is obtained by splitting the historical bidding documents in the historical bidding document collection and then performing vector annotation; replaces the key bidding content with the corresponding content in the preset bidding document case template to generate the target bidding document. By splitting the user's project description and matching it with the bidding content in the pre-built vector library, the key bidding content corresponding to the user's project description can be quickly obtained, and then the key bidding content is presented through the screened bidding document case template to generate the target bidding document, which can achieve the effect of accurately and efficiently generating bidding documents.

[0093] Previous article Figure 1 The method of generating bidding documents based on pattern analysis is described below. Figure 3-Figure 4 A bidding document generation device based on pattern analysis is described.

[0094] Please refer to Figure 3 , is a schematic block diagram of a bidding document generation device 300 based on pattern analysis provided in an embodiment of the present application. The device 300 may be a module, program segment or code on an electronic device. The device 300 is similar to the above Figure 1 The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device 300 can be found in the description below. To avoid repetition, the detailed description is appropriately omitted here.

[0095] Optionally, the device 300 includes:

[0096] The analysis module 310 is used to perform model semantic analysis according to the project description input by the user to obtain key information, wherein the key information includes title content corresponding to multiple keywords;

[0097] A matching module 320 is used to match key bidding contents corresponding to key information from a preset vector library, wherein the vector library is obtained by splitting historical bidding documents in a historical bidding document set and then performing vector annotation;

[0098] The generation module 330 is used to replace the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document.

[0099] Optionally, the device also includes:

[0100] A construction module is used for the analysis module, which performs model semantic analysis according to the project description input by the user to obtain a historical bidding document set before obtaining key information; splits the historical bidding documents in the historical bidding document set according to chapters to obtain a bidding content set; and annotates the bidding contents in the bidding content set with key information vectors to obtain a vector library, wherein the bidding content set includes key bidding contents.

[0101] Optionally, the device further comprises:

[0102] The updating module is used for the generation module to replace the corresponding content in the preset bidding document case template with the key bidding content, generate the target bidding document, regularly collect bidding documents, and store the collected bidding documents into the vector library after segmenting them according to chapters and keywords; automatically annotate and classify the data in the bidding documents in the vector library through a machine learning algorithm.

[0103] Optionally, the device further comprises:

[0104] The screening module is used for the analysis module to perform model semantic analysis based on the project description input by the user, and before obtaining key information, select a template whose number of keywords in the bidding document case template set meets the preset requirements according to the bidding standards to obtain the bidding document case template, wherein the bidding standards include the project title and the project standards corresponding to the project title.

[0105] Optionally, the analysis module is specifically used for:

[0106] Construct a small bidding model according to the preset title configuration; analyze the project description through the small bidding model to obtain key information.

[0107] Optionally, the matching module is specifically used for:

[0108] The key information is converted into a vector to obtain multiple vectors; and the key bidding contents corresponding to the multiple vectors are matched according to the cosine similarity matching method.

[0109] Optionally, the generating module is specifically used for:

[0110] Match the title corresponding to the key bidding content with the corresponding title in the bidding document case template to obtain the corresponding relationship; according to the corresponding relationship, replace the key bidding content with the corresponding content in the bidding document case template to obtain the target bidding document.

[0111] Please refer to Figure 4 4 is a schematic block diagram of a bidding document generation device based on pattern analysis provided in an embodiment of the present application. The device may include a memory 410 and a processor 420. Optionally, the device may also include: a communication interface 430 and a communication bus 440. The device and the above Figure 1The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device can be found in the description below.

[0112] Specifically, the memory 410 is used to store computer-readable instructions.

[0113] Processor 420 is used to process the readable instructions stored in the memory and can execute Figure 1 The steps in the method.

[0114] The communication interface 430 is used for signaling or data communication with other node devices, for example, for communication with a server or a terminal, or for communication with other device nodes, but the embodiments of the present application are not limited thereto.

[0115] The communication bus 440 is used to realize direct connection and communication among the above components.

[0116] The communication interface 430 of the device in the embodiment of the present application is used to communicate signals or data with other node devices. The memory 410 can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The memory 410 can also be at least one storage device located away from the aforementioned processor. The memory 410 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 420, the electronic device executes the aforementioned Figure 1 The method process shown. The processor 420 can be used on the device 300 and is used to perform the functions in the present application. Exemplarily, the above-mentioned processor 420 can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the embodiments of the present application are not limited thereto.

[0117] The embodiment of the present application also provides a readable storage medium, when the computer program is executed by a processor, Figure 1 The method process in the method embodiment shown is executed by the electronic device.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0119] In summary, the embodiment of the present application provides a method, device, equipment and readable storage medium for generating bidding documents based on pattern analysis, the method comprising: performing model semantic analysis according to the project description input by the user to obtain key information, wherein the key information includes title content corresponding to multiple keywords; matching key bidding content corresponding to the key information from a preset vector library, wherein the vector library is obtained by splitting the historical bidding documents in the historical bidding document collection and then performing vector annotation; replacing the key bidding content with the corresponding content in the preset bidding document case template to generate the target bidding document. This method can achieve the effect of accurately and efficiently generating bidding documents.

[0120] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0122] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0123] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0124] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0125] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for generating bidding documents based on pattern analysis, characterized in that: include: Performing semantic analysis on the model according to the project description input by the user to obtain key information, wherein the key information includes title content corresponding to multiple keywords; Matching the key bidding content corresponding to the key information from a preset vector library, wherein the vector library is obtained by splitting the historical bidding documents in the historical bidding document set and then performing vector annotation; The key bidding content replaces the corresponding content in the preset bidding document case template to generate a target bidding document.

2. The method according to claim 1, characterized in that Before performing model semantic analysis according to the project description input by the user to obtain key information, the method further includes: Obtaining the historical bidding document set; Splitting the historical bidding documents in the historical bidding document set according to chapters to obtain a bidding content set; The bidding contents in the bidding content set are annotated with key information vectors to obtain the vector library, wherein the bidding content set includes the key bidding contents.

3. The method according to claim 2, characterized in that After replacing the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document, the method further includes: regularly collecting bidding documents, and dividing the collected bidding documents by chapters and keywords and storing them in the vector library; The data in the bidding document in the vector library is automatically labeled and classified by a machine learning algorithm.

4. The method according to any one of claims 1 to 3, characterized in that: Before performing model semantic analysis according to the project description input by the user to obtain key information, the method further includes: A template whose number of keywords meets preset requirements is selected from a set of case templates for bidding documents according to bidding standards to obtain the case template for bidding documents, wherein the bidding standards include a project title and a project standard corresponding to the project title.

5. The method according to any one of claims 1 to 3, characterized in that The model semantic analysis is performed based on the project description input by the user to obtain key information, including: Construct a small bidding model according to the preset title configuration; The project description is analyzed through the small bidding model to obtain the key information.

6. The method according to any one of claims 1 to 3, characterized in that The matching of the key bidding content corresponding to the key information from the preset vector library includes: Convert the key information into a vector to obtain multiple vectors; The key bidding contents corresponding to the multiple vectors are matched according to a cosine similarity matching method.

7. The method according to any one of claims 1 to 3, characterized in that: The step of replacing the corresponding content in the preset bidding document case template with the key bidding content to generate the target bidding document includes: Match the title corresponding to the key bidding content with the corresponding title in the bidding document case template to obtain a corresponding relationship; According to the corresponding relationship, the key bidding content replaces the corresponding content in the bidding document case template to obtain the target bidding document.

8. A bidding document generation device based on pattern analysis, characterized in that: include: An analysis module, used to perform model semantic analysis based on the project description input by the user to obtain key information, wherein the key information includes title content corresponding to multiple keywords; A matching module, used for matching the key bidding content corresponding to the key information from a preset vector library, wherein the vector library is obtained by splitting the historical bidding documents in the historical bidding document set and then performing vector annotation; The generation module is used to replace the corresponding content in the preset bidding document case template with the key bidding content to generate a target bidding document.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium, characterized in that: include: A computer program, when the computer program is run on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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