Electronic bidding and tendering intelligent bid evaluation method and system based on artificial intelligence

Abstract

The invention relates to the technical field of bidding and tendering, in particular to an electronic bidding and tendering intelligent bid evaluation method and system based on artificial intelligence, and the method comprises the steps: obtaining a bid invitation announcement of a to-be-bid invitation project, and carrying out the structural extraction, and obtaining the project parameters, budget amount, qualification requirements and bid evaluation standards of the to-be-bid invitation project; obtaining an electronic bidding file, and performing feature extraction to obtain bidding feature parameters of a bidding unit; screening and scoring the bidding units based on the project parameters and the bidding characteristic parameters to obtain a comprehensive score of the target bidding unit; and based on the comprehensive score, performing sorting and enterprise credit checking on the target bidding units, and if the checking is passed, generating a bid evaluation result according to the sorting. The bid evaluation work in the electronic bidding and tendering process can be automatically and intelligently completed, and the bid evaluation efficiency and accuracy are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of bidding technology, and in particular to an intelligent evaluation method and system for electronic bidding based on artificial intelligence. Background Technology

[0002] With the development of information technology, electronic bidding has been widely used in commercial activities. However, traditional electronic bidding methods mainly rely on manual review or simple rule-based judgment, which suffers from problems such as low efficiency, strong subjectivity, and susceptibility to interference. For example, the reliance of evaluation experts on experience can easily lead to scoring biases, affecting the fairness and consistency of the evaluation; the lack of a unified and objective credit evaluation system makes it difficult to accurately reflect the performance capabilities and historical performance of bidding companies; the processing of large amounts of paper materials and multiple rounds of manual review result in high time costs and low efficiency; and it is difficult to detect violations such as bid rigging and collusion in a timely manner. Therefore, a more intelligent, efficient, and accurate bidding method is needed to improve the quality and fairness of electronic bidding. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent evaluation method and system for electronic bidding based on artificial intelligence, which solves the problems of low efficiency, strong subjectivity, and susceptibility to interference in traditional electronic bidding methods that mainly rely on manual review or simple rule judgment.

[0004] This invention provides an intelligent evaluation method for electronic bidding based on artificial intelligence, comprising:

[0005] Obtain the tender notice for the project to be tendered, and use natural language processing technology to extract the structured information from the tender notice to obtain the project parameters, including budget amount, qualification requirements and evaluation criteria;

[0006] The electronic bid documents received by the bidding platform are obtained, and the features of the electronic bid documents are extracted using optical character recognition technology to obtain the bidding feature parameters of the bidding unit.

[0007] Based on the budget amount and qualification requirements, bidders are screened according to the bidding characteristic parameters to obtain target bidders; based on the evaluation criteria, the target bidders are scored according to the bidding characteristic parameters to obtain the comprehensive score of the target bidders;

[0008] Based on the comprehensive score, the target bidding units are ranked, and the corporate credit of the target bidding units is verified according to the ranking results. If the verification is successful, the evaluation results are generated according to the ranking results.

[0009] Preferably, natural language processing technology is used to extract the structured parameters of the tender notice to obtain the project parameters of the project to be tendered, including:

[0010] The tender notice is preprocessed to convert it into a format suitable for natural language processing. The preprocessing includes word segmentation, stop word removal, and punctuation processing.

[0011] Named entity recognition technology is used to extract entity information, and key fields are extracted based on keyword matching or rule matching. Relationship extraction technology is used to identify the relationships between key fields.

[0012] The extracted entity information, key fields, and relationships between key fields are converted into a structured format to obtain the project parameters for the project to be tendered.

[0013] The project parameters include: project name, project type, tendering entity, project location, budget amount, construction period requirements, qualification requirements, evaluation criteria, and other terms.

[0014] Preferably, optical character recognition (OCR) technology is used to extract features from the electronic bid document to obtain the bidder's bid feature parameters, including:

[0015] The electronic tender documents are preprocessed, including noise reduction, binarization, and layout analysis.

[0016] The text information in the electronic tender document is identified using optical character recognition technology, and the recognition results are corrected based on context information.

[0017] Extract the feature information from the electronic bid documents, including the basic information of the bidding unit, qualification certificates, price list, technical responsiveness, completion status of past projects, and project plan of the project to be tendered;

[0018] The characteristic information is standardized to obtain the bidding characteristic parameters of the bidding units. Preferably, based on the budget amount and qualification requirements, the bidding units are screened according to the bidding characteristic parameters to obtain target bidding units, including:

[0019] The bid amount of the bidding unit is determined according to the price list in the bid characteristic parameters;

[0020] The bid amount is compared with the budget amount. If the bid amount is greater than or equal to the budget amount, the corresponding bidder is determined to pass the screening.

[0021] The qualification level and category of bidding units that pass the first screening are determined based on their qualification certificates;

[0022] Based on the qualification requirements, the qualified bidders who passed the first screening are further screened according to their qualification level and qualification category to obtain the target bidders.

[0023] Preferably, the evaluation criteria include: monetary weighting, technical weighting, qualification weighting, and service quality weighting.

[0024] Preferably, based on the evaluation criteria, the target bidder is scored according to the bidding characteristic parameters to obtain a comprehensive score for the target bidder, including:

[0025] Determine the difference between the bid amount and the budget amount, and determine the target bidder's score based on the difference and the amount weighting index;

[0026] The technical score of the target bidding unit is determined based on the aforementioned technical responsiveness and technical weighting indicators.

[0027] The qualification score of the target bidding unit is determined based on the aforementioned qualification requirements, qualification level, and qualification weighting indicators;

[0028] The service score of the target bidding unit is determined based on the past project completion rate and service quality weighting indicators.

[0029] The comprehensive score of the target bidding unit is determined based on the aforementioned monetary score, technical score, qualification score, and service score.

[0030] Preferably, the target bidding entities are ranked based on the comprehensive score, including:

[0031] Based on the comprehensive scores of the target bidders, the target bidders are ranked in descending order;

[0032] If the overall scores are the same, the target bidders with the same overall scores will be ranked in order of qualification level, technical responsiveness, and past project completion rate.

[0033] Preferably, the creditworthiness of the target bidding entities is verified based on the ranking. If the verification is successful, the evaluation results are generated according to the ranking, including:

[0034] A credit check will be conducted on the top-ranked bidders after ranking. The check will include: whether the bidders have any negative records, whether they are involved in tax issues, whether they are involved in legal disputes, and whether they have the ability to perform their obligations.

[0035] If the verification results show that the target bidding unit has good credit and meets the bidding requirements, the winning bidder will be determined according to the ranking results, and an evaluation report will be generated.

[0036] If the bidder does not meet the bidding requirements, the next highest-ranking bidder will be subject to a credit check until the winning bidder is determined.

[0037] The evaluation report includes: the evaluation process, the evaluation criteria, the comprehensive scores of each bidding unit, the winning bidder and the reasons.

[0038] Preferably, the process of verifying the corporate credit of target bidding entities based on their ranking also includes:

[0039] Obtain the target bidder's historical bid documents, analyze the historical bidding behavior, and determine whether the target bidder has engaged in bid rigging or collusion.

[0040] If such a target bidding entity exists, it will be added to the manual review queue, and the abnormal behavior log will be recorded.

[0041] This invention also discloses an intelligent evaluation system for electronic bidding based on artificial intelligence, used to apply the above-mentioned intelligent evaluation method for electronic bidding based on artificial intelligence, including:

[0042] The bidding parameter determination module is configured to obtain the bidding announcement of the project to be bid on, and use natural speech processing technology to extract the structured bidding announcement to obtain the project parameters of the project to be bid on, including budget amount, qualification requirements and evaluation criteria;

[0043] The bidding parameter determination module is configured to acquire the electronic bidding documents received by the bidding platform, and use optical character recognition technology to extract features from the electronic bidding documents to obtain the bidding feature parameters of the bidding unit.

[0044] The bidder scoring module is configured to filter bidders based on the budget amount and qualification requirements, according to the bid characteristic parameters, to obtain target bidders; and to score the target bidders based on the evaluation criteria, according to the bid characteristic parameters, to obtain a comprehensive score for the target bidders.

[0045] The bid evaluation result determination module is configured to rank the target bidders based on the comprehensive score, and verify the corporate credit of the target bidders according to the ranking result. If the verification is successful, the bid evaluation result is generated based on the ranking result.

[0046] Compared with existing technologies, the advantages of this invention lie in its ability to extract key project parameters such as budget amount, qualification requirements, and evaluation criteria quickly and accurately from tender notices through natural language processing technology. This avoids the tediousness and errors of manual extraction, laying a solid and accurate data foundation for subsequent bid evaluation. Furthermore, optical character recognition technology can rapidly extract bid feature parameters from a large number of documents, greatly improving the speed of bid document processing, reducing the time cost and error risk of manual operation, and enhancing the efficiency of the entire bid evaluation process.

[0047] The process involves screening bidders based on clear budget amounts and qualification requirements, and scoring target bidders according to evaluation criteria. The entire process is driven by pre-set rules and algorithms, reducing interference from human factors and making the evaluation results more fair and objective, reflecting the actual level of bidders and their suitability for the project more accurately.

[0048] First, the target bidders are ranked based on their comprehensive scores, and then their credit is checked. If the check fails, adjustments can be made in a timely manner. This process avoids wasting too much time and energy on bidders with poor credit, making the entire evaluation process more scientific and reasonable. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the intelligent evaluation method for electronic bidding based on artificial intelligence, as described in this invention.

[0051] Figure 2 This is a functional block diagram of the intelligent evaluation system for electronic bidding based on artificial intelligence, as described in this invention. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0053] like Figure 1 As shown, the present invention provides an intelligent evaluation method for electronic bidding based on artificial intelligence, including: obtaining the bidding announcement of the project to be bid, using natural speech processing technology to perform structured extraction of the bidding announcement to obtain the project parameters of the project to be bid, wherein the project parameters include budget amount, qualification requirements and evaluation criteria.

[0054] The electronic bid documents received by the bidding platform are obtained, and the features of the electronic bid documents are extracted using optical character recognition technology to obtain the bidding feature parameters of the bidding unit.

[0055] Based on the budget amount and qualification requirements, bidders are screened according to the bidding characteristic parameters to obtain target bidders; based on the evaluation criteria, the target bidders are scored according to the bidding characteristic parameters to obtain the comprehensive score of the target bidders.

[0056] Based on the comprehensive score, the target bidding units are ranked, and the corporate credit of the target bidding units is verified according to the ranking results. If the verification is successful, the evaluation results are generated according to the ranking results.

[0057] This invention automates and intelligently completes the bid evaluation process in electronic bidding, significantly improving efficiency and accuracy. Through natural language processing and optical character recognition technologies, it accurately extracts key information from bidding announcements and tender documents, avoiding errors and omissions that may occur with manual input. Furthermore, by screening and scoring bidders based on budget amounts, qualification requirements, and evaluation criteria, it ensures the fairness and scientific rigor of the evaluation process. In addition, verifying the corporate credit of target bidders further guarantees the quality and reliability of the bidding project.

[0058] In some embodiments of this application, natural language processing technology is used to extract structured data from the tender notice to obtain project parameters for the project to be tendered. This includes: preprocessing the tender notice to convert it into a format suitable for natural language processing, wherein the preprocessing includes word segmentation, stop word removal, and punctuation mark processing; extracting entity information using named entity recognition technology, extracting key fields based on keyword matching or rule matching, and identifying the relationships between key fields based on relation extraction technology; converting the extracted entity information, key fields, and relationships between key fields into a structured format to obtain project parameters for the project to be tendered; the project parameters include: project name, project type, tendering unit, project location, budget amount, construction period requirements, qualification requirements, evaluation criteria, and other terms.

[0059] In this embodiment, a preprocessing step ensures that the content of the bidding announcement can be accurately identified and processed by natural language processing technology, improving the accuracy and efficiency of information extraction. The application of named entity recognition technology can automatically identify key entities in the bidding announcement, providing a foundation for subsequent information extraction. Simultaneously, by combining keyword matching or rule matching with relationship extraction technology, key fields and their relationships can be further extracted, thereby constructing structured project parameters.

[0060] In this embodiment, named entity recognition technology is used to extract entity information (such as project name, location, time, amount, etc.). Keyword matching or rule matching is used to extract specific fields (such as "budget amount", "bid deadline", etc.). Relationship extraction technology is used to identify the relationships between fields (such as the association between "project location" and "project name").

[0061] In some embodiments of this application, optical character recognition (OCR) technology is used to extract features from the electronic bid document to obtain the bidding feature parameters of the bidding entity. This includes: preprocessing the electronic bid document, including denoising, binarization, and layout analysis; recognizing text information in the electronic bid document using OCR technology and correcting the recognition results based on context information; extracting feature information from the electronic bid document, including basic information of the bidding entity, qualification certificates, quotation list, technical responsiveness, past project completion rate, and project plan for the project to be tendered; and standardizing the feature information to obtain the bidding feature parameters of the bidding entity.

[0062] In this embodiment, by preprocessing the electronic bid document, such as denoising, binarization, and layout analysis, the present invention ensures the recognition effect of optical character recognition technology and improves the accuracy of text information extraction. The application of optical character recognition technology can quickly identify text information in electronic bid documents, greatly shortening the information extraction time. Simultaneously, by combining contextual information to correct the recognition results, recognition errors can be further reduced, improving the accuracy of the information.

[0063] In some embodiments of this application, the process of screening bidding entities based on the budget amount and qualification requirements, according to the bidding characteristic parameters, to obtain target bidding entities includes: determining the bid amount of the bidding entity based on the price list in the bidding characteristic parameters; comparing the bid amount with the budget amount, and if the bid amount is greater than or equal to the budget amount, determining that the corresponding bidding entity has passed the first screening; determining the qualification level and qualification category of the bidding entities that have passed the first screening based on qualification certificates; and performing a second screening on the qualification level and qualification category of the bidding entities that have passed the first screening according to the qualification requirements to obtain target bidding entities.

[0064] In this embodiment, by comparing the bid amount with the budget amount, the present invention can quickly screen out bidders with reasonable prices, avoiding the risk of exceeding the budget. Simultaneously, a second screening is conducted based on the qualification certificates of the bidders to determine their qualification level and category, ensuring that the bidders possess the necessary qualifications and capabilities to complete the bidding project. This two-step screening method improves screening efficiency and guarantees the accuracy of the screening results.

[0065] In some embodiments of this application, the evaluation criteria include: monetary weighting, technical weighting, qualification weighting, and service quality weighting.

[0066] In some embodiments of this application, based on the evaluation criteria, the target bidder is scored according to the bidding characteristic parameters to obtain a comprehensive score for the target bidder. This includes: determining the difference between the bid amount and the budget amount; determining the bidder's bid amount score based on the bid amount difference and the bid amount weighting index; determining the target bidder's technical score based on the technical responsiveness and technical weighting index; determining the target bidder's qualification score based on the qualification requirements, qualification level, and qualification weighting index; determining the target bidder's service score based on the past project completion rate and service quality weighting index; and determining the target bidder's comprehensive score based on the bid amount score, technical score, qualification score, and service score.

[0067] In this embodiment, the difference between the bid amount and the budget amount is determined. The bid amount score of the target bidder is determined based on the difference and a weighted index. The weighted index reflects the importance of the bid amount in the evaluation, and the bid amount score is based on the closeness of the bid amount to the budget. The technical score of the target bidder is determined based on technical responsiveness and a weighted index. Technical responsiveness refers to the degree to which the bidder meets the technical requirements, and the weighted index indicates the proportion of the technical score in the overall score. The qualification score of the target bidder is determined based on qualification requirements, qualification level, and qualification weighted index. The qualification score considers whether the bidder's qualification level meets the project requirements and the importance of qualifications in the evaluation. The service score of the target bidder is determined based on past project completion and service quality weighted indexes. The service score focuses on the bidder's performance in previous projects and the quality of services provided. Finally, the bid amount score, technical score, qualification score, and service score are weighted and averaged according to their respective weights to obtain the overall score of the target bidder. The overall score is the most crucial score in the evaluation process; it integrates all scoring factors and reflects the overall competitiveness of the bidder.

[0068] In this embodiment, by comprehensively considering multiple aspects such as amount, technology, qualifications, and service quality, the present invention can comprehensively evaluate the overall strength of bidding units. The amount score reflects the reasonableness of the bidding unit's price; the technology score reflects the bidding unit's technical level and innovation capability; the qualifications score ensures that the bidding unit possesses the qualifications and capabilities to complete the project; and the service score reflects the bidding unit's service quality and reputation. This multi-dimensional scoring system not only improves the scientific rigor and fairness of the evaluation process but also provides the bidding party with more comprehensive and accurate information about the bidding units, helping the bidding party make more informed decisions.

[0069] In some embodiments of this application, ranking the target bidding entities based on the comprehensive score includes: ranking the target bidding entities in descending order of their comprehensive scores; if the comprehensive scores are the same, the target bidding entities with the same comprehensive scores are ranked sequentially according to their qualification level, technical responsiveness, and past project completion rate.

[0070] In this embodiment, by ranking the comprehensive scores of the target bidders, the present invention can clearly demonstrate the order of merit of each bidder, providing the bidding party with an intuitive evaluation result. In the case of identical comprehensive scores, further ranking is conducted based on qualification level, technical responsiveness, and past project completion rate, ensuring the comprehensiveness and accuracy of the ranking results. This meticulous ranking method not only helps the bidding party better understand the comprehensive strength of the bidders but also provides more reference points for the bidding party when selecting the winning bidder.

[0071] In some embodiments of this application, the corporate credit of target bidding units is verified according to the ranking. If the verification is successful, the evaluation results are generated according to the ranking, including: verifying the corporate credit of the first-ranked target bidding unit, including whether the target bidding unit has any bad records, whether it is involved in tax issues, whether it is involved in legal disputes, and whether it has the ability to perform the contract; if the verification results show that the target bidding unit has good credit and meets the bidding requirements, the winning bidder is determined according to the ranking results, and an evaluation report is generated; if it does not meet the bidding requirements, the corporate credit of the next-ranked target bidding unit is verified according to the ranking results until the winning bidder is determined; the evaluation report includes: the evaluation process, the evaluation criteria, the comprehensive score of each bidding unit, the winning bidder and the reasons.

[0072] In this embodiment, by verifying the corporate credit of the target bidding units, the present invention ensures the reliability and integrity of the winning bidder. The verification covers multiple aspects, including negative records, tax issues, legal disputes, and performance capabilities, providing the bidding party with a comprehensive credit assessment. If the verification is successful, the winning bidder is determined according to the ranking results, and a detailed evaluation report is generated. This not only ensures the transparency and fairness of the evaluation process but also provides the bidding party with a complete evaluation record. If the first-ranked target bidding unit does not meet the bidding requirements, the next-ranked bidding units are verified sequentially until a qualified winning bidder is found. This flexible verification method ensures both the quality and reliability of the bidding project and provides the bidding party with more choices.

[0073] In some embodiments of this application, the verification of the corporate credit of the target bidding unit according to the ranking also includes: obtaining the historical bidding documents of the target bidding unit, analyzing the historical bidding behavior, and determining whether the target bidding unit has engaged in bid rigging or collusion; if so, the corresponding target bidding unit is included in the manual review queue and the abnormal behavior log is recorded.

[0074] In this embodiment, by analyzing the historical bid documents of the target bidders, the present invention can promptly detect and address unfair bidding behaviors such as bid rigging and collusion. This tracing and verification of historical bidding behavior not only helps maintain fair competition in the electronic bidding market but also improves the impartiality and transparency of the evaluation process. Including target bidders with alleged unfair bidding behavior in the manual review queue and recording abnormal behavior logs provides strong evidentiary support for subsequent supervision and processing.

[0075] like Figure 2 As shown, the present invention also discloses an intelligent evaluation system for electronic bidding based on artificial intelligence, which is used to apply the above-mentioned intelligent evaluation method for electronic bidding based on artificial intelligence. The system includes: a bidding parameter determination module, configured to obtain the bidding announcement of the project to be bid, and to extract the project parameters of the project to be bid by using natural speech processing technology. The project parameters include budget amount, qualification requirements and evaluation criteria.

[0076] The bidding parameter determination module is configured to acquire the electronic bidding documents received by the bidding platform, and use optical character recognition technology to extract features from the electronic bidding documents to obtain the bidding characteristic parameters of the bidding unit.

[0077] The bidding unit scoring module is configured to filter bidding units based on the budget amount and qualification requirements and according to the bidding characteristic parameters to obtain target bidding units; and to score the target bidding units based on the evaluation criteria and according to the bidding characteristic parameters to obtain the comprehensive score of the target bidding units.

[0078] The bid evaluation result determination module is configured to rank the target bidders based on the comprehensive score, and verify the corporate credit of the target bidders according to the ranking result. If the verification is successful, the bid evaluation result is generated based on the ranking result.

[0079] This invention significantly improves the efficiency and accuracy of electronic bidding through automation and intelligent methods. The bidding parameter determination module accurately extracts key information from the bidding announcement, providing accurate foundational data for subsequent steps. The bid parameter determination module efficiently extracts characteristic parameters of bidding units from electronic bid documents, greatly reducing the workload of manual review. The bid unit scoring module comprehensively considers budget amount, qualification requirements, and bid characteristic parameters to conduct a comprehensive evaluation of bidding units, ensuring the fairness and scientific nature of the evaluation. The bid result determination module further sorts and verifies based on the comprehensive score and corporate credit, resulting in a more reliable and reasonable final bid evaluation result.

[0080] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent evaluation method for electronic bidding based on artificial intelligence, characterized in that, include: Obtain the tender notice for the project to be tendered, and use natural language processing technology to extract the structured information from the tender notice to obtain the project parameters, including budget amount, qualification requirements and evaluation criteria; The electronic bid documents received by the bidding platform are obtained, and the features of the electronic bid documents are extracted using optical character recognition technology to obtain the bidding feature parameters of the bidding unit. Based on the budget amount and qualification requirements, the bidding units are screened according to the bidding characteristic parameters to obtain the target bidding units; Based on the evaluation criteria, the target bidder is scored according to the bid characteristic parameters to obtain the comprehensive score of the target bidder; Based on the comprehensive score, the target bidding units are ranked, and the corporate credit of the target bidding units is verified according to the ranking results. If the verification is successful, the evaluation results are generated according to the ranking results.

2. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 1, characterized in that, The bidding announcement was structured and extracted using natural language processing technology to obtain the project parameters for the project to be tendered, including: The tender notice is preprocessed to convert it into a format suitable for natural language processing. The preprocessing includes word segmentation, stop word removal, and punctuation processing. Named entity recognition technology is used to extract entity information, and key fields are extracted based on keyword matching or rule matching. Relationship extraction technology is used to identify the relationships between key fields. The extracted entity information, key fields, and relationships between key fields are converted into a structured format to obtain the project parameters for the project to be tendered. The project parameters include: project name, project type, tendering entity, project location, budget amount, construction period requirements, qualification requirements, evaluation criteria, and other terms.

3. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 1, characterized in that, The electronic bid document is used to extract features to obtain the bidding entity's bid feature parameters, including: The electronic tender documents are preprocessed, including noise reduction, binarization, and layout analysis. The text information in the electronic tender document is identified using optical character recognition technology, and the recognition results are corrected based on context information. Extract the feature information from the electronic bid documents, including the basic information of the bidding unit, qualification certificates, price list, technical responsiveness, completion status of past projects, and project plan of the project to be tendered; The feature information is standardized to obtain the bidding feature parameters of the bidding unit.

4. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 3, characterized in that, Based on the budget amount and qualification requirements, bidding entities are screened according to the bidding characteristic parameters to obtain target bidding entities, including: The bid amount of the bidding unit is determined according to the price list in the bid characteristic parameters; The bid amount is compared with the budget amount. If the bid amount is greater than or equal to the budget amount, the corresponding bidder is determined to pass the screening. The qualification level and category of bidding units that pass the first screening are determined based on their qualification certificates; Based on the qualification requirements, the qualified bidders who passed the first screening are further screened according to their qualification level and qualification category to obtain the target bidders.

5. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 4, characterized in that, The evaluation criteria include: monetary weighting, technical weighting, qualification weighting, and service quality weighting.

6. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 5, characterized in that, Based on the evaluation criteria, the target bidders are scored according to the bidding characteristic parameters to obtain a comprehensive score for each target bidder, including: Determine the difference between the bid amount and the budget amount, and determine the target bidder's score based on the difference and the amount weighting index; The technical score of the target bidding unit is determined based on the aforementioned technical responsiveness and technical weighting indicators. The qualification score of the target bidding unit is determined based on the aforementioned qualification requirements, qualification level, and qualification weighting indicators; The service score of the target bidding unit is determined based on the past project completion rate and service quality weighting indicators. The comprehensive score of the target bidding unit is determined based on the aforementioned monetary score, technical score, qualification score, and service score.

7. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 6, characterized in that, Based on the comprehensive score, the target bidding entities are ranked, including: Based on the comprehensive scores of the target bidders, the target bidders are ranked in descending order; If the overall scores are the same, the target bidders with the same overall scores will be ranked in order of qualification level, technical responsiveness, and past project completion rate.

8. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 7, characterized in that, The creditworthiness of the target bidding entities is verified based on the ranking. If the verification is successful, the evaluation results are generated according to the ranking, including: A corporate credit check will be conducted on the top-ranked target bidders. The check will include: whether the target bidders have any negative records, whether they are involved in tax issues, whether they are involved in legal disputes, and whether they have the ability to perform their contractual obligations. If the verification results show that the target bidding unit has good credit and meets the bidding requirements, the winning bidder will be determined according to the ranking results, and an evaluation report will be generated. If the bidder does not meet the bidding requirements, the next highest-ranking bidder will be subject to a credit check until the winning bidder is determined. The evaluation report includes: the evaluation process, the evaluation criteria, the comprehensive scores of each bidding unit, the winning bidder and the reasons.

9. The intelligent evaluation method for electronic bidding based on artificial intelligence according to claim 8, characterized in that, The creditworthiness of the target bidding entities is verified based on the ranking, which also includes: Obtain the target bidder's historical bid documents, analyze the historical bidding behavior, and determine whether the target bidder has engaged in bid rigging or collusion. If such a target bidding entity exists, it will be added to the manual review queue, and the abnormal behavior log will be recorded.

10. An intelligent evaluation system for electronic bidding based on artificial intelligence, used to apply the intelligent evaluation method for electronic bidding based on artificial intelligence as described in any one of claims 1-9, characterized in that, include: The bidding parameter determination module is configured to obtain the bidding announcement of the project to be bid on, and use natural speech processing technology to extract the structured bidding announcement to obtain the project parameters of the project to be bid on, including budget amount, qualification requirements and evaluation criteria; The bidding parameter determination module is configured to acquire the electronic bidding documents received by the bidding platform, and use optical character recognition technology to extract features from the electronic bidding documents to obtain the bidding feature parameters of the bidding unit. The bidder scoring module is configured to filter bidders based on the budget amount and qualification requirements, according to the bid characteristic parameters, to obtain target bidders; Based on the evaluation criteria, the target bidder is scored according to the bid characteristic parameters to obtain the comprehensive score of the target bidder; The bid evaluation result determination module is configured to rank the target bidders based on the comprehensive score, and verify the corporate credit of the target bidders according to the ranking result. If the verification is successful, the bid evaluation result is generated based on the ranking result.

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