Intelligent bid response method, system, apparatus, and storage medium

Abstract

The present disclosure relates to the technical field of artificial intelligence, and specifically discloses an intelligent bidding method, system, device and storage medium, wherein the method comprises: analyzing an intelligent agent based on a demand, analyzing a bidding document to obtain bidding demand information corresponding to the bidding document and generating a bidding document directory, each bidding section in the bidding document directory corresponding to one or more bidding demand information; generating a bidding template corresponding to each bidding section based on a bidding generation intelligent agent, and determining the required bidding data content of the bidding template according to an enterprise bidding database; and generating a bidding document corresponding to the bidding document based on the bidding data content, the bidding template and the bidding specification requirements in the bidding document. Through the technical solution provided by the present disclosure, the full-process automatic generation and management of the bidding process can be realized by utilizing the intelligent agent and the enterprise bidding database, and a large amount of manpower and time cost is saved while obtaining a bidding document that can reflect the bidding advantages of the enterprise.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and in particular to an intelligent bidding method, system, device and storage medium. Background Technology

[0002] Tendering is the process by which a procuring entity issues tender documents and invites multiple suppliers to compete based on its own procurement needs; while bidding is the process by which suppliers respond to tender documents, prepare and submit bid documents to compete. In the fields of wind turbine equipment and energy storage, bidding is one of the core revenue-generating methods that companies focus on.

[0003] In existing technologies, the preparation of bid documents for tenders primarily relies on manual operation. This involves interpreting the tender documents, collecting and organizing relevant company information, developing corresponding bid strategies, and ultimately creating a complete bid document to participate in the subsequent bidding process. However, due to the large size of the tender documents and bid documents, which cover both business and technical aspects, multiple personnel from different departments are often required to collaborate on various stages, including interpreting the tender documents and preparing bid materials, resulting in significant time and manpower costs. Furthermore, because bid documents have high compliance review requirements, even after collecting all necessary company data and materials, relying solely on manual operation to create a final bid document that meets review requirements will still consume substantial time and manpower, and there is even a risk that bid documents for complex projects may not be completed within the bidding deadline. Summary of the Invention

[0004] The purpose of this disclosure is to provide an intelligent bidding method, system, device, and storage medium that can automate the entire bidding process by utilizing intelligent agents and enterprise bidding databases, thereby saving significant manpower and time costs while obtaining bidding documents that reflect the enterprise's bidding advantages.

[0005] The first aspect of this disclosure provides an intelligent bidding method, which may include the following steps: based on a preset demand parsing agent, parsing the tender document to obtain the tender demand information corresponding to the tender document and generating a bid document directory, wherein each bid section in the bid document directory corresponds to one or more tender demand information; based on a preset bid generation agent, generating a bid template corresponding to each bid section, and determining the bid data content required for the bid template according to the enterprise's bid database; generating bid documents corresponding to the tender document based on the bid data content, bid template, and bid specification requirements in the tender document; wherein the demand parsing agent is obtained by training on the parsing process of historical tender documents; and the bid generation agent is obtained by training on the generation process of historical bid documents.

[0006] In one possible implementation of the first aspect above, the process of obtaining the bidding requirements information corresponding to the bidding documents and generating the bid document catalog includes: performing semantic understanding of the bidding documents based on the requirements parsing intelligent agent to obtain the bidding requirements information of the bidding documents; and / or retrieving the bidding requirements information from the bidding documents based on the requirements identification tags preset by the requirements parsing intelligent agent; and generating the bid document catalog through the requirements parsing intelligent agent based on the bidding requirements information.

[0007] In one possible implementation of the first aspect above, after generating the bid document directory, the process includes: identifying the differences between the bid document directory and the preset standard bid document directory, and determining the bidding requirements information corresponding to the differences; displaying the bid document directory, the differences, and the bidding requirements information corresponding to the differences; adjusting the order of the bid sections in the bid document directory based on a manual update instruction; and / or adding and / or deleting at least some bid sections in the bid document directory. In one possible implementation of the first aspect above, the process of generating the bid template corresponding to each bid section in the bid document directory includes: using the matched preset template as the bid template based on the bidding requirements information corresponding to the bid section; or extracting the template from the bidding documents as the bid template; or selecting the matched preset template based on the bidding requirements information corresponding to the bid section, and adjusting the preset template according to the bid specification requirements to use it as the bid template.

[0008] In one possible implementation of the first aspect above, the process of determining the bidding data content required for the bidding template includes: identifying placeholder information in the bidding template based on the bidding template; if the type of placeholder information is data retrieval type, generating a corresponding data retrieval instruction based on the placeholder information, and obtaining the data content corresponding to the data retrieval instruction from the enterprise bidding database as the bidding data content; if the type of placeholder information is data generation type, generating a data generation instruction based on the bidding requirements information corresponding to the bidding chapter and the placeholder information; and obtaining the data content required for the data generation instruction from the enterprise bidding database and generating placeholder content, so as to use the generated placeholder content as the bidding data content.

[0009] In one possible implementation of the first aspect mentioned above, the intelligent bidding method further includes: generating dynamic review standards for bidding documents through a requirements parsing intelligent agent based on the tender documents; reviewing the bidding documents based on the dynamic review standards and generating corresponding review risk warnings.

[0010] In one possible implementation of the first aspect above, the intelligent bidding method may further include: displaying the bidding file and updating the bidding file based on the received manual optimization instructions; generating a manual optimization record of the bidding file based on the manual optimization instructions, and updating the demand parsing agent and / or the bidding generation agent based on the manual optimization record.

[0011] The second aspect of this disclosure provides an intelligent bidding system, which specifically includes: a requirements parsing unit, used to parse the tender document to generate a bid document catalog based on a preset requirements parsing agent; a bid generation unit, used to generate bid templates corresponding to each bid section in the bid document catalog based on a preset bid generation agent, and determine the bid data content required for the bid templates according to the enterprise's bid database; and a content integration unit, used to generate bid documents corresponding to the tender document based on the bid data content, bid templates, and bid specification requirements in the tender document; wherein, the requirements parsing agent is obtained by training on the parsing process of historical tender documents; and the bid generation agent is obtained by training on the generation process of historical bid documents.

[0012] A third aspect of this disclosure provides an intelligent bidding apparatus, which may include: at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the steps of the intelligent bidding method provided in the first aspect.

[0013] A fourth aspect of this disclosure provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the smart tagging method provided in the first aspect.

[0014] The technical solution disclosed herein enables fully automated generation and management of the entire bidding process by utilizing intelligent agents and enterprise bidding databases. This saves significant manpower and time costs while obtaining bidding documents that showcase the enterprise's competitive advantages. Furthermore, the technical solution can select appropriate data retrieval or generation methods based on the different types of placeholder information in the bidding template to obtain bidding data content that conforms to the tender documents. Moreover, the technical solution supports continuous iterative updates to the pre-set intelligent agent based on each bidding document generation / optimization process, ensuring the agent can adapt to constantly changing bidding scenarios. Furthermore, the technical solution also supports setting different dynamic review standards for different tender documents, effectively detecting potential risks in bidding documents and helping to improve the overall generation efficiency of bidding documents. Attached Figure Description

[0015] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0016] Figure 1 This is a flowchart illustrating an intelligent bidding method provided according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of a process for generating a bid file directory according to an embodiment of this disclosure; Figure 3 This is a flowchart illustrating an optimized bid document directory according to an embodiment of this disclosure; Figure 4 This is a flowchart illustrating a process for determining the bidding data content required for a bidding template, according to an embodiment of this disclosure. Figure 5 This is a schematic diagram of a process for intelligently reviewing bid documents according to an embodiment of this disclosure; Figure 6 This is a schematic diagram of the structure of an intelligent bidding system provided according to an embodiment of the present disclosure; Figure 7 This is a structural diagram of an intelligent labeling device provided according to an embodiment of the present disclosure. Detailed Implementation

[0017] As can be understood from the background description, due to the large size of the tender documents and bid submissions, which cover both business and technical aspects, the bidding process often requires multiple personnel from different departments to collaborate on various stages, such as interpreting the tender documents and preparing bid materials, resulting in significant time and manpower costs. To overcome these problems, some embodiments of this disclosure provide an intelligent bidding method, system, device, and storage medium. By utilizing intelligent agents and a corporate bidding database, the entire bidding process can be automated in its generation and management, saving substantial manpower and time costs while obtaining bid documents that reflect the company's competitive advantages.

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the various embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are provided in the embodiments of this disclosure to facilitate a better understanding of the disclosure. However, the technical solutions claimed in this disclosure can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this disclosure. The various embodiments can be combined with and referenced by each other without contradiction.

[0019] In some embodiments of this disclosure, Figure 1 A flowchart of an intelligent bidding method is shown, such as... Figure 1 As shown, process 100 may specifically include the following steps: Step 110: Based on a preset demand parsing intelligent agent, parse the tender document to obtain the corresponding tender demand information and generate a bid document directory. In some embodiments, the intelligent agent is an entity capable of making autonomous decisions and taking actions based on perceived information. The demand parsing intelligent agent provided in this disclosure can parse the received tender document and autonomously generate a bid document directory. For example, when a large language model is used as the core of decision-making and cognition, the above-mentioned demand parsing intelligent agent can be an intelligent agent based on natural language understanding and generation, capable of acquiring, understanding, and parsing tender documents presented in natural language. In some embodiments, the demand parsing intelligent agent may specifically include an artificial intelligence analysis and processing model and a demand reusable business component library, wherein the demand reusable business component library may include interpretation information of historical tender documents and support demand slicing in the tender documents, enabling the structuring of tender demands in the tender documents, which is not limited here. In some embodiments, the demand parsing intelligent agent can be trained based on the parsing process of historical tender documents, which is not limited here. In some embodiments, the bid document directory may include multiple bid sections, each bid section corresponding to one or more tender demand information, which is not limited here. The acquisition of bidding requirements information and the specific generation of the bid document catalog will be explained in detail later, and will not be repeated here.

[0020] Step 120: Based on the preset bid generation agent, generate bid templates corresponding to each bid section, and determine the bid data content required for the bid templates according to the enterprise's bid database. In some embodiments, the bid generation agent may also include an artificial intelligence analysis and processing model and a reusable bid business component library. This reusable component library supports determining the required data retrieval for each bid section and generating bid data content based on the bidding requirements information in the tender documents. Specifically, this may involve text calls, table calls, image calls, and interface calls. As a key part of receiving requirements and generating bid data content, the reusable component library also supports obtaining the original reference format from the tender documents for bid template generation, which is not limited here. In some embodiments, the bid generation agent may be trained based on the generation process of historical bid documents, which is not limited here. The specific determination of bid data content will be explained in detail later and will not be elaborated here.

[0021] Step 130: Based on the bid data content, bid template, and bid specification requirements in the tender documents, generate the bid documents corresponding to the tender documents. In some embodiments, the bid data content and bid template can be concatenated to the corresponding bid chapters through engineering means, and the bid chapters can be sorted according to the bid document directory to generate one or more bid documents. The generated bid documents need to meet the bid specification requirements in the tender documents, which is not limited here. In some embodiments, the generated bid documents can be further displayed and previewed to the user, and updated according to the user's manual optimization instructions. During the process of updating the bid documents by the user through manual optimization instructions, manual optimization records of the bid documents can be generated based on the manual optimization instructions, and the requirement analysis agent and / or bid generation agent can be updated based on the manual optimization records, thereby realizing the iterative update of the agents. All manual optimization traces will be recorded and formed into new training data packages, which are fed back to the core artificial intelligence analysis and execution model through reinforcement learning to achieve closed-loop optimization. It is understandable that, based on the aforementioned process 100, the entire bidding process can be automated and managed by utilizing intelligent agents and enterprise bidding databases, saving significant manpower and time costs while obtaining bidding documents that reflect the enterprise's bidding advantages. The specific implementation of process 100 will be further explained and illustrated below with specific embodiments.

[0022] In some embodiments of this disclosure, further, Figure 2 A flowchart illustrating the process of generating a bid file directory is shown, such as... Figure 2 As shown, process 200 may specifically include the following steps: Step 210: Based on the demand parsing intelligent agent, perform semantic understanding of the bidding documents to obtain the bidding demand information. In some embodiments, the bidding demand information may specifically include the specific bidding requirements corresponding to the bidding documents. For example, in the bidding documents for energy storage systems, the bidding demand information may include the total energy storage capacity of the energy storage system, the safety management system, etc., which are not limited here. In some embodiments, the obtained bidding demand information can be further matched with the demand reusable business component library provided in the aforementioned embodiments to identify the parts of the bidding demand that match the company's own needs, which are not limited here.

[0023] Step 220: Based on the preset requirement identification tags in the requirement parsing agent, retrieve bidding requirement information from the bidding documents. In some embodiments, steps 210 and 220 can be executed simultaneously or one of them can be executed; when steps 210 and 220 are executed simultaneously, the order of execution of steps 210 and 220 is not limited here. In some embodiments, some keywords appearing in the bidding documents can be retrieved and identified according to the preset requirement identification tags, such as the bidding project name, bidding code, bid section number, invalid items, etc., and used as bidding requirement information, which is not limited here.

[0024] Step 230: Based on the bidding requirements information, generate a bid document catalog through the requirements parsing intelligent agent. In some embodiments, when obtaining bidding requirements information, the corresponding bid sections can be obtained based on the bidding requirements, and the required bid document catalog can be generated according to the bid catalog requirements included in the bidding requirements information; this is not limited here.

[0025] It is understood that the bid document directory obtained based on the foregoing embodiments can be adaptively adjusted according to the user's actual needs, specifically including adjusting the hierarchical relationship of bid chapters, adjusting the order of bid chapters, adding or removing bid chapters, etc. In some embodiments of this disclosure, further... Figure 3 A flowchart illustrating an optimized bid file directory is shown, such as... Figure 3 As shown, process 300 may specifically include the following steps; Step 310: Identify the differences between the bid document directory and the preset standard bid directory, and determine the bidding requirements information corresponding to the differences. In some embodiments, the standard bid directory can be the best practice directory selected from historical bid documents. By marking the differences between the bid document directory and the preset standard bid directory, users can quickly locate the differences that need further review and confirmation during subsequent review processes, and learn which specific bidding requirement in the bidding document the difference corresponds to. This is not limited here.

[0026] Step 320: Display the bid document directory, the differences, and the corresponding bidding requirements information. In some embodiments, the bid document directory generated by the agent can be presented to the user for confirmation before generating subsequent bid data. During the user confirmation process, the differences and corresponding bidding requirements information can be simultaneously provided to the user while displaying the bid document directory. Since the parts consistent with the standard bid document directory usually do not require further adjustment or optimization by the user, the above-mentioned marked presentation method allows the user to quickly locate the parts of the bid document directory that need to be reviewed, facilitating the user's rapid review of the bid document directory.

[0027] Step 330: Based on the manual update command, adjust the order of the bidding chapters in the bidding document directory, and / or add and / or delete at least some bidding chapters in the bidding document directory. In some embodiments, users can flexibly adjust the bidding document directory dynamically through manual update commands. For example, a visual drag-and-drop directory arrangement interface can be provided to users, allowing them to freely drag and drop bidding chapters in the bidding document directory for hierarchical arrangement, and to further customize the structure of the bidding document directory. Users can add and / or delete some bidding chapters generated based on the intelligent agent according to the needs of the bidding documents, laying the framework foundation for the subsequent specific generation of bidding documents. This is not limited here. In some embodiments, especially in bidding scenarios related to energy storage systems, the bidding requirements in the bidding documents are often progressive, that is, the bidding requirements of newly appearing bidding documents are often based on the bidding requirements of previous bidding documents and are generated by superimposing them. Adaptively, the original specification bidding directory can be updated based on the bidding document directory updated by the manual update command. This is not limited here.

[0028] In some embodiments of this disclosure, further, during the process of generating the bid template, a pre-defined template can be matched based on the bidding requirements information corresponding to the bid section, and used as the bid template. That is, the bid generation agent can match bid templates based on multiple pre-defined templates. When the bidding requirements information corresponding to the bid section is appearing for the first time, if there is no matching pre-defined template, the bid generation agent can also autonomously generate the bid template by referring to the pre-defined template and the bidding requirements information, or use the standard reference template given in the bidding document, which is not limited here. In some embodiments, further, during the process of generating the bid template, a matching pre-defined template can be selected first based on the bidding requirements information corresponding to the bid section, and then the pre-defined template can be adjusted according to the bid specification requirements to serve as the bid template. The matching pre-defined template can be the pre-defined target with the highest matching degree among multiple pre-defined templates; the bid specification requirements can be the specific text format specified in the bidding document, etc., which is not limited here.

[0029] In some embodiments of this disclosure, when the bid template corresponding to the bid section is a preset template, the preset template contains preset placeholder information, and the subsequent bid data content determination process can be directly performed based on the placeholder information. In other embodiments, when the bid template corresponding to the bid section is extracted and / or generated based on the content in the tender document, it is necessary to fill in the placeholder information in the extracted and / or generated template, and perform the subsequent bid data content determination process based on the filled placeholder information; this is not limited here. In some embodiments of this disclosure, further... Figure 4 A flowchart illustrating the process of determining the required bid data content for a bid template is shown, such as... Figure 4 As shown, process 400 may specifically include the following steps: Step 410: Identify placeholder information in the bidding template based on the bidding template. In some embodiments, during the process of obtaining bidding data content, the selected bidding template can be traversed to obtain various placeholder information contained in the bidding template. In some embodiments, the placeholder information includes the placeholder type corresponding to the placeholder, which may specifically include data retrieval type and data generation type. The data retrieval type means that the data corresponding to the placeholder can be directly obtained from the enterprise bidding database without secondary processing; the data generation type means that the data corresponding to the placeholder cannot be directly obtained and needs to be generated by secondary processing of the data content obtained from the enterprise bidding database using an intelligent agent. This is not limited here.

[0030] Step 420: If the placeholder information is of the data retrieval type, generate the corresponding data retrieval instruction based on the placeholder information, and retrieve the data content corresponding to the data retrieval instruction from the enterprise bidding database as the bidding data content. In some embodiments, specifically, if the placeholder information is of the data retrieval type, it is not necessary to call the artificial intelligence model to generate the bidding data content. The corresponding bidding data content can be directly obtained through text retrieval, image retrieval, table retrieval, third-party interface retrieval, etc., and filled into the corresponding placeholder position. This is not limited here.

[0031] Step 430: When the placeholder information is of the data generation type, generate a data generation instruction based on the bidding requirements information corresponding to the bidding chapter and the placeholder information; and obtain the data content required for the data generation instruction from the enterprise bidding database and generate placeholder content, so as to use the generated placeholder content as the bidding data content. In some embodiments, when the placeholder information is of the data generation type, it means that the content to be filled in the placeholder area corresponding to the placeholder information cannot be directly obtained from the enterprise bidding database. The bidding data content can be processed and generated by the bidding generation agent: Specifically, a data generation instruction can be generated based on the bidding requirements information corresponding to the bidding chapter and the placeholder information, and the required data content can be extracted from the enterprise bidding database according to the data generation instruction to generate placeholder content, and the generated placeholder content can be used as the bidding data content. For example, when generating a parameter table for a product, the required technical parameters can be categorized by identifying placeholder information. Then, the required parameter types can be determined based on these categories, and specific values ​​for the corresponding parameter types can be extracted from the product model. This allows for the automatic generation and drawing of the table: the technical parameter categories can be filled into the table header, and the specific values ​​for the parameter types can be filled into the corresponding positions in the table. A unified unit conversion is then performed, ultimately generating the required tabularized bidding data. In some embodiments, such as for bidding sections like "Letter of Tender" and "Letter of Authorization," the data can be automatically generated according to the latest format requirements stipulated in the tender documents, and accurate content filling (including text filling and image insertion) can be completed. For complex tables such as financial information and bidder basic information in the bidding documents, the corresponding table template can be parsed and read from the tender documents, and the content filling positions can be automatically located. Then, the company's knowledge base can be called for automatic matching and filling.

[0032] Understandably, since the bid generation intelligence provided in this disclosure is trained based on specific bidding scenario data and enterprise bid data, it can accurately understand and automatically generate complex structured data and table content as bid data content. It can also intelligently extract the required data content from the enterprise bid database (such as financial information database, company personnel information database, etc.), fill the generated bid data content into the corresponding positions in the bid template and form an accurate format. This not only saves a lot of manpower and time costs, but also ensures the accuracy and professionalism of the bid content data. Nearly 80% of the technical solution text and 90% of the table content can be automatically generated by the intelligence. Users only need to focus on the review and optimization of the core architecture and strategy, reducing writing time by 60%. At the same time, the generated bid data content can be set according to predefined format specifications, so that each bid section, each data table, each filled image or each text paragraph in the bid document can achieve automatic format application, so that the enterprise's bid documents can have a unified style and conform to the enterprise's specifications.

[0033] It is understood that the bid documents generated based on the aforementioned embodiments can basically meet the various bidding requirements of the tender documents. To further ensure that the generated bid documents meet strict compliance requirements and avoid defects such as missing materials or omissions in responding to key bidding requirements affecting the entire bidding process, the AI ​​agent provided in this disclosure can be used for further intelligent review of the bid documents. In some embodiments of this disclosure, further... Figure 5 A flowchart illustrating an intelligent review process for bid documents is shown, such as... Figure 5 As shown, process 500 may specifically include the following steps: Step 510: Based on the tender documents, generate dynamic review standards for bid documents through a requirements parsing agent. In some embodiments, dynamic review standards may specifically include general standards and customized standards that change dynamically according to the tender documents. General standards may specifically include basic spell checking, etc., and are not limited here. In some embodiments, dynamic review standards can be generated based on the tender requirements information in the tender documents obtained in the foregoing embodiments to review whether the bid documents meet all the tender requirements. In some embodiments, further, in the process of generating dynamic review standards, in addition to being based on the various tender requirements information in the tender documents, dynamic review standards can also be generated based on the enterprise risk database clauses stored in the enterprise bid database. For example, whether there is a risk of leakage of trade secrets if certain core parameters of enterprise equipment are filled into the bid documents, etc., is not limited here. In some embodiments, dynamic review criteria may specifically include format integrity review, such as whether there are any missing signatures, seals, etc. appearing in various parts of the bid documents, and whether they are consistent; content consistency review, such as whether the name of the tendering party, the name of the authorized person, etc. in the bid documents are consistent, and whether there are any contradictions or inconsistencies in the product parameters in different parts of the bid documents; compliance review, such as whether the validity period of the certificates appearing in the bid documents has expired, and whether the bid bond has been added, etc., which are not limited here.

[0034] Step 520: Review the bid documents based on the dynamic review standards and generate corresponding review risk warnings. In some embodiments, the bid documents can be fully scanned and reviewed according to the dynamic review standards obtained in Step 510, and corresponding review risk warnings can be generated based on the review results. For example, when content that does not comply with the dynamic review standards is found during the review, the problematic pages in the bid documents can be marked and corresponding review risk warnings can be generated to guide users to quickly make targeted repairs to the problematic pages in the bid documents. This can effectively avoid the huge risk of being rejected due to failure to pass the formal review, and further ensure the validity of the bid documents and their corresponding bidding results.

[0035] In some embodiments of this disclosure, Figure 6 The diagram illustrates the structure of an intelligent bidding system, such as... Figure 6 As shown, this intelligent bidding system may specifically include a demand analysis unit 610, a bid generation unit 620, and a content integration unit 630.

[0036] In some embodiments, the requirement parsing unit 610 can be used to parse the tender document to generate a bid document catalog based on a preset requirement parsing agent; in some embodiments, the bid generation unit 620 can be used to generate a bid template corresponding to each bid section in the bid document catalog based on a preset bid generation agent, and determine the bid data content required for the bid template according to the enterprise bid database; in some embodiments, the content integration unit 630 can be used to generate bid documents corresponding to the tender document based on the bid data content, bid template, and bid specification requirements in the tender document. In the above embodiments, the requirement parsing agent can be obtained by training on the parsing process of historical tender documents; the bid generation agent can be obtained by training on the generation process of historical bid documents, and no limitation is made here.

[0037] In some embodiments, further, such as Figure 6 As shown, this intelligent bidding system may further include a bid review unit 640. The bid review unit 640 can generate dynamic review standards for bid documents based on the tender documents, and review the bid documents based on these dynamic review standards to generate corresponding review risk warnings. In some embodiments, the specific functional implementation of the data acquisition unit 610 to the bid review unit 640 can be achieved by referring to the various steps in the intelligent bidding method provided in the foregoing embodiments, and will not be elaborated here.

[0038] Some embodiments of this disclosure also relate to an intelligent labeling device, specifically... Figure 7 A schematic diagram of a smart labeling device is shown, such as... Figure 7 As shown, the smart tagging device includes at least one processor 710 and a memory 720 communicatively connected to the at least one processor. The memory 720 stores instructions that can be executed by the at least one processor 710. The instructions are executed by the at least one processor 710 to enable the at least one processor 710 to perform the steps of the smart tagging method provided in the foregoing embodiments.

[0039] The memory 720 and processor 710 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 710 and memory 720 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 710 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to the processor.

[0040] In some embodiments, the processor 710 may be responsible for managing the bus and general processing, and may also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions, while the memory 720 may be used to store data used by the processor when performing operations, without limitation.

[0041] Some embodiments of this disclosure also relate to a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the smart tagging method provided in the foregoing embodiments. In some embodiments, the computer-readable storage medium may include flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of a computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., provided on the computer device. Of course, the computer-readable storage medium may also include both internal storage units and external storage devices of a computer device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the computer device, such as the program code of the smart tagging method in this embodiment. Furthermore, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.

[0042] Some embodiments of this disclosure also relate to a computer program product, including a computer program that, when executed by a processor, implements the steps of the smart bidding method provided in the foregoing embodiments.

[0043] In some embodiments, the computer program product may involve only a computer program, which may be carried on a storage medium or a processing device. In other embodiments, the computer program product may also be a storage medium or processing device containing the aforementioned computer program. The processing device may include one or more processors, and the storage medium. Those skilled in the art will understand that all or part of the steps in the smart tagging method provided in the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the smart tagging method provided in the various embodiments of this disclosure.

[0044] The basic concepts have been described above. It is obvious that the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are taught in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

Claims

1. A smart bidding method, characterized in that, include: Based on a preset demand parsing intelligent agent, the tender document is parsed to obtain the tender demand information corresponding to the tender document and generate a bid document directory. Each bid section in the bid document directory corresponds to one or more of the tender demand information. Based on a preset bid generation intelligent agent, a bid template corresponding to each bid chapter is generated, and the bid data content required for the bid template is determined according to the enterprise bid database; Based on the bid data content, the bid template, and the bid specification requirements in the bidding documents, a bid document corresponding to the bidding documents is generated; The demand parsing agent is obtained by training on the parsing process of historical bidding documents; The target generation agent is obtained by training on the generation process of historical target documents.

2. The intelligent bidding method according to claim 1, characterized in that, The process of obtaining the bidding requirements information corresponding to the bidding documents and generating the bid submission directory includes: Based on the semantic understanding of the tender document by the aforementioned demand parsing agent, the tender demand information of the tender document is obtained; and / or Based on the pre-set requirement identification tags of the requirement parsing intelligent agent, the bidding requirement information is retrieved from the bidding documents; Based on the bidding requirements information, the bid submission document directory is generated by the requirements parsing intelligent agent.

3. The intelligent bidding method according to claim 1 or 2, characterized in that, After generating the target file directory, it includes: Identify the differences between the bid document directory and the preset standard bid directory, and determine the bidding requirements information corresponding to the differences; Displays the bid document directory, the differences, and the bidding requirements information corresponding to the differences; Based on manual update instructions, adjust the order of the bidding chapters in the bidding file directory; and / or Add and / or delete at least some of the bid sections in the bid document directory.

4. The intelligent bidding method according to claim 1, characterized in that, The process of generating the bid template for each bid section in the bid document directory includes: Based on the bidding requirements information corresponding to the bidding section, the matched preset template will be used as the bidding template; or Based on the bidding requirements information corresponding to the bidding section, select the matching preset template, and adjust the preset template according to the bidding specification requirements to serve as the bidding template.

5. The intelligent bidding method according to claim 1 or 4, characterized in that, The process of determining the bid data content required for the bid template includes: Based on the bid template, identify the placeholder information in the bid template; When the type of the placeholder information is data call type, a corresponding data call instruction is generated based on the placeholder information, and the data content corresponding to the data call instruction is obtained from the enterprise bidding database as the bidding data content; When the placeholder information is of type data generation, a data generation instruction is generated based on the bidding requirements information corresponding to the bidding section and the placeholder information; and The system obtains the data content required by the data generation instruction from the enterprise bidding database and generates placeholder content, which is then used as the bidding data content.

6. The intelligent bidding method according to claim 1, characterized in that, Also includes: Based on the tender documents, the dynamic review criteria for the bid documents are generated by the requirement parsing agent. The bid documents are reviewed based on the dynamic review criteria, and corresponding review risk warnings are generated.

7. The intelligent bidding method according to claim 1, characterized in that, Also includes: Display the bid document and update the bid document based on the received manual optimization instructions; as well as Based on the manual optimization instructions, a manual optimization record for the bid application file is generated, and the requirement parsing agent and / or the bid application generation agent are updated based on the manual optimization record.

8. An intelligent bidding system, characterized in that, include: The requirement parsing unit is used to parse the tender documents based on a preset requirement parsing agent to generate a bid submission document directory. The bid generation unit is used to generate bid templates corresponding to each bid chapter in the bid file directory based on a preset bid generation intelligent agent, and to determine the bid data content required for the bid templates according to the enterprise bid database; The content integration unit is used to generate a bid document corresponding to the tender document based on the bid data content, the bid template, and the bid specification requirements in the tender document; The demand parsing agent is obtained by training on the parsing process of historical bidding documents; The target generation agent is obtained by training on the generation process of historical target documents.

9. A smart labeling device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor; The memory stores instructions executable by at least one of the processors, which are executed by at least one of the processors to enable the at least one processor to implement the steps of the smart bidding method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the steps of the smart bidding method according to any one of claims 1 to 7.

Images