Online bidding and tendering system based on AI large model
By introducing an online bidding system based on large AI models, the bidding process has been made intelligent and automated, solving the problems of low efficiency, insufficient transparency, and high cost in traditional systems. This has improved process efficiency and fairness, and provided accurate data support.
Patent Information
- Application Number
- CN202511075459.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-01
- Publication Date
- 2025-11-21
AI Technical Summary
Traditional online bidding systems are inefficient, lack transparency, have limited data analysis capabilities, and are costly. Manual operation is prone to errors, which affects fairness.
The system employs large-scale AI models for generating tender documents, reviewing bids, and evaluating bids. By combining data analysis and prediction, it achieves intelligent and automated processes, including modules for user management, tender document generation, bid review, intelligent bid evaluation, and data analysis, ensuring data security and privacy.
It improves the efficiency and transparency of the bidding process, reduces human intervention, provides accurate data analysis support, ensures fairness and security, and reduces operating costs.
Smart Images
Figure CN120996895A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of big data processing, artificial intelligence, and e-commerce, specifically to an online bidding system based on an AI big data model, which aims to improve the intelligence and automation of the bidding process through the AI big data model. Background Technology
[0002] Tender documents are crucial supporting documents in the project bidding and procurement process. Traditional online bidding and tendering mainly rely on manual operation, which has the following problems:
[0003] 1. Inefficiency: The preparation of bidding documents, review of bid documents, and evaluation of bids are time-consuming and prone to errors due to manual operation.
[0004] 2. Insufficient transparency: The evaluation process may be subject to human intervention, affecting fairness.
[0005] 3. Limited data analysis capabilities: It is difficult to conduct in-depth analysis of historical data and market trends, and thus cannot provide effective support for decision-making.
[0006] 4. High cost: Manual operation and review require a lot of human resources, which increases operating costs.
[0007] With the development of artificial intelligence technology, large-scale AI models have demonstrated powerful capabilities in fields such as natural language processing, data analysis, and automated decision-making. Therefore, combining large-scale AI models with online bidding and tendering systems has become an effective way to solve the aforementioned problems. Summary of the Invention
[0008] To address the aforementioned problems, the purpose of this invention is to provide an online bidding and tendering system based on an AI large model, which, by introducing AI technology, achieves intelligent, automated, and efficient bidding and tendering processes.
[0009] To achieve the above objectives, the present invention adopts the following technical solution:
[0010] An online bidding and tendering system based on an AI large-scale model includes:
[0011] The user management module is used to manage the account information and permissions of the bidding party, bidders, and evaluation experts;
[0012] The tender document generation module is used to automatically generate tender documents that comply with relevant specifications based on the tender requirements input by the tendering party using a large AI model.
[0013] The tender document review module uses AI big data models to perform compliance checks and content analysis on the tender documents submitted by the bidders, and generates a review report.
[0014] The intelligent evaluation module is used to automatically score and rank each bid document based on preset evaluation rules and the analysis results of the corresponding suppliers in the bid documents, and generate an evaluation report using a large AI model.
[0015] The data analysis and forecasting module is used to analyze historical bidding data and generate market trend forecasts and supplier credit assessment reports.
[0016] The AI large model engine is used for pre-training AI large models and provides natural language processing, data analysis, intelligent recommendation, and intelligent early warning functions for the bidding document generation module, bid document review module, intelligent bid evaluation module, and data analysis and prediction module.
[0017] The security and privacy protection module is used to ensure the security and privacy of user data in the system.
[0018] Furthermore, the tender document generation module includes:
[0019] The input module provides a system interface for the bidding party to input their bidding requirements.
[0020] The automatic tender document generation module uses an AI big data model to automatically parse the tender requirements input by the tendering party, and generates tender documents that meet the tendering requirements and compliance checks based on historical tender document templates and industry standards.
[0021] Furthermore, the tender document review module includes:
[0022] The content extraction module is used by large AI models to extract content from tender documents using optical character recognition technology.
[0023] The analysis and identification module is used to analyze the extracted content of the bid documents using natural language processing technology to identify key bidding information;
[0024] The audit report generation module is used to compare the identified key bidding information according to the requirements of the bidding documents and generate a compliance audit report based on the comparison results.
[0025] Furthermore, the intelligent evaluation module includes:
[0026] The bid evaluation rule setting module is used to preset bid evaluation rules;
[0027] The bid scoring module is used to automatically score each bid document according to preset evaluation rules using a large AI model and generate ranking results;
[0028] The report generation module is used to generate visual reports based on the scoring and ranking results, showing the scoring results and comparisons of key indicators.
[0029] Furthermore, the preset evaluation rules refer to setting corresponding weights for different indicators, and the different indicators include at least price, technical capability, delivery time, and credit assessment.
[0030] Furthermore, the data analysis and prediction module includes:
[0031] The market trend prediction module is used by AI large models to analyze historical bidding data using machine learning algorithms, identify market trends, and combine them with external data to generate prediction results.
[0032] The credit assessment report generation module is used to assess the creditworthiness of suppliers based on their historical performance data and generate supplier credit assessment reports.
[0033] Furthermore, the AI large model engine includes at least DeepSeek.
[0034] Furthermore, the security and privacy protection layer module employs the following security and privacy protection mechanisms to ensure the security and privacy of user data in the system:
[0035] An encrypted storage mechanism is used to encrypt and store sensitive data using the AES encryption algorithm;
[0036] An encrypted transmission mechanism is used to ensure data transmission security by employing the SSL / TLS protocol during data transmission.
[0037] The de-identification mechanism is used to de-identify user privacy data.
[0038] Furthermore, the system also establishes and maintains a bidding database, a historical database, and external data sources;
[0039] The bidding database stores bidding data, bid data, bid opening data, bid evaluation data, and bid award data.
[0040] The historical database stores completed bidding data, bid data, bid opening data, bid evaluation data, and bid award data;
[0041] The external data sources include supplier information and offline bidding data.
[0042] The present invention has the following advantages due to the adoption of the above technical solutions:
[0043] 1. High efficiency: This invention uses a large AI model to realize the intelligent and automated bidding process, accelerate the bidding process, and significantly improve efficiency.
[0044] 2. Fairness: This invention uses an AI-powered large-scale model for bid evaluation, reducing human intervention, ensuring the fairness of the evaluation process, and enhancing the transparency of the bidding process.
[0045] 3. Intelligentization: Based on data analysis and market trend prediction provided by AI big data models, this invention provides more accurate recommendation services for bidding parties and bidders, improves the success rate of bidding, and provides scientific decision-making for evaluation experts.
[0046] 4. Security: This invention ensures the security of bidding data through symmetric data encryption and privacy protection technologies.
[0047] Therefore, this invention can be widely applied in the fields of big data processing, artificial intelligence, and e-commerce. Attached Figure Description
[0048] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings:
[0049] Figure 1 This is an architecture diagram of an online bidding system based on a large AI model provided by an embodiment of the present invention. Detailed Implementation
[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0051] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0052] In some embodiments of the present invention, an online bidding system based on an AI large-scale model is provided, comprising: a user layer, an application layer, an AI layer, a data layer, an infrastructure layer, and a security and privacy protection layer. By pre-training an AI large-scale model in the AI layer, bidding documents are automatically generated in the application layer, and bids are automatically reviewed and evaluated, greatly improving the efficiency of bidding.
[0053] Example 1
[0054] like Figure 1 As shown, this invention provides an online bidding system based on an AI large-scale model, which includes:
[0055] The user management module is used to manage the account information and permissions of the bidding party, bidders, and evaluation experts;
[0056] The tender document generation module is used to automatically generate tender documents that comply with the tendering law based on the tendering requirements input by the tendering party and using a large AI model.
[0057] The tender document review module uses AI big data models to perform compliance checks and content analysis on the tender documents submitted by the bidders, and generates a review report.
[0058] The intelligent evaluation module is used to automatically score and sort each bid document based on the analysis results of preset evaluation rules and the supplier's qualifications, quotations and other information corresponding to the bid documents, and generate an evaluation report.
[0059] The data analysis and forecasting module is used to analyze historical bidding data and generate market trend forecasts and supplier credit assessment reports.
[0060] The AI large model engine is used to pre-train large AI models (such as DeepSeek), thereby providing natural language processing, data analysis, intelligent recommendation, and intelligent early warning functions for the bidding document generation module, bid document review module, intelligent bid evaluation module, and data analysis and prediction module.
[0061] The security and privacy protection module is used to ensure the security and privacy of user data in the system through symmetric encryption and data anonymization technologies.
[0062] Furthermore, the tender document generation module includes:
[0063] The input module provides a system interface for the bidding party to input their bidding requirements.
[0064] The automatic tender document generation module uses an AI large model to automatically parse the tender requirements input by the tendering party through natural language processing technology, and generate tender documents that meet the tendering requirements and compliance checks based on historical tender document templates and industry standards.
[0065] Furthermore, the tender document review module includes:
[0066] The content extraction module is used by the AI large model to extract content from the tender documents using OCR (Optical Character Recognition) technology;
[0067] The analysis and identification module is used to analyze the extracted bid document content using natural language processing technology to identify key bid information (specifications, price, technical parameters, delivery time, etc.).
[0068] The audit report generation module is used to compare the identified key bidding information according to the requirements of the bidding documents and generate a compliance audit report based on the comparison results.
[0069] Furthermore, the intelligent evaluation module includes:
[0070] The bid evaluation rule setting module is used to preset bid evaluation rules, such as price weighting of 40%, technical capability weighting of 30%, delivery time weighting of 20%, and credit assessment weighting of 10%.
[0071] The bid scoring module is used to automatically score each bid document according to preset evaluation rules using a large AI model and generate ranking results;
[0072] The report generation module is used to generate visual reports based on the scoring and ranking results, showing the scoring results and comparisons of key indicators.
[0073] Furthermore, the data analysis and prediction module includes:
[0074] The market trend prediction module uses machine learning algorithms to analyze historical bidding data, identify market trends, and combine them with external data (such as industry reports and economic indicators) to generate prediction results.
[0075] The credit assessment report generation module is used to assess the creditworthiness of suppliers based on their historical performance data and generate supplier credit assessment reports.
[0076] Furthermore, the security and privacy protection mechanisms set in the security and privacy protection layer module include:
[0077] An encrypted storage mechanism is used to encrypt and store sensitive data using the AES encryption algorithm;
[0078] An encrypted transmission mechanism is used to ensure data security during data transmission by employing the SSL / TLS protocol.
[0079] The de-identification mechanism is used to de-identify user privacy data to prevent information leakage.
[0080] Furthermore, the system also includes infrastructure to provide infrastructure resources such as computing, storage, and networking to support upper-layer applications and services, and to achieve efficient resource utilization and stable system operation through virtualization, automation, and elastic management technologies.
[0081] Furthermore, the system also establishes and maintains a bidding database, a historical database, and external data sources. The bidding database stores bidding data, tender data, bid opening data, bid evaluation data, and bid award data. The historical database stores completed bidding data, tender data, bid opening data, bid evaluation data, and bid award data. External data sources include supplier information and offline bidding data.
[0082] 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 online bidding and tendering system based on an AI large-scale model, characterized in that, include: The user management module is used to manage the account information and permissions of the bidding party, bidders, and evaluation experts; The tender document generation module is used to automatically generate tender documents that comply with relevant specifications based on the tender requirements input by the tendering party using a large AI model. The tender document review module uses AI big data models to perform compliance checks and content analysis on the tender documents submitted by the bidders, and generates a review report. The intelligent evaluation module is used to automatically score and rank each bid document based on preset evaluation rules and the analysis results of the corresponding suppliers in the bid documents, and generate an evaluation report using a large AI model. The data analysis and forecasting module is used to analyze historical bidding data and generate market trend forecasts and supplier credit assessment reports. The AI large model engine is used for pre-training AI large models and provides natural language processing, data analysis, intelligent recommendation, and intelligent early warning functions for the bidding document generation module, bid document review module, intelligent bid evaluation module, and data analysis and prediction module. The security and privacy protection module is used to ensure the security and privacy of user data in the system.
2. The online bidding system based on an AI large model as described in claim 1, characterized in that, The tender document generation module includes: The input module provides a system interface for the bidding party to input their bidding requirements. The automatic tender document generation module uses an AI big data model to automatically parse the tender requirements input by the tendering party, and generates tender documents that meet the tendering requirements and compliance checks based on historical tender document templates and industry standards.
3. The online bidding system based on an AI large model as described in claim 1, characterized in that, The tender document review module includes: The content extraction module is used by large AI models to extract content from tender documents using optical character recognition technology. The analysis and identification module is used to analyze the extracted content of the bid documents using natural language processing technology to identify key bidding information; The audit report generation module is used to compare the identified key bidding information according to the requirements of the bidding documents and generate a compliance audit report based on the comparison results.
4. The online bidding system based on an AI large model as described in claim 1, characterized in that, The intelligent evaluation module includes: The bid evaluation rule setting module is used to preset bid evaluation rules; The bid scoring module is used to automatically score each bid document according to preset evaluation rules using a large AI model and generate ranking results; The report generation module is used to generate visual reports based on the scoring and ranking results, showing the scoring results and comparisons of key indicators.
5. The online bidding system based on an AI large model as described in claim 4, characterized in that, The preset evaluation rules refer to setting corresponding weights for different indicators, which include at least price, technical capability, delivery time, and credit assessment.
6. The online bidding system based on an AI large model as described in claim 1, characterized in that, The data analysis and prediction module includes: The market trend prediction module is used by AI large models to analyze historical bidding data using machine learning algorithms, identify market trends, and combine them with external data to generate prediction results. The credit assessment report generation module is used to assess the creditworthiness of suppliers based on their historical performance data and generate supplier credit assessment reports.
7. The online bidding system based on an AI large model as described in claim 1, characterized in that, The AI large model engine includes at least DeepSeek.
8. The online bidding system based on an AI large model as described in claim 1, characterized in that, The security and privacy protection layer module employs the following security and privacy protection mechanisms to ensure the security and privacy of user data in the system: An encrypted storage mechanism is used to encrypt and store sensitive data using the AES encryption algorithm; An encrypted transmission mechanism is used to ensure data transmission security by employing the SSL / TLS protocol during data transmission. The de-identification mechanism is used to de-identify user privacy data.
9. The online bidding system based on an AI large model as described in claim 1, characterized in that, The system also establishes and maintains a bidding database, a historical database, and external data sources; The bidding database stores bidding data, bid data, bid opening data, bid evaluation data, and bid award data. The historical database stores completed bidding data, bid data, bid opening data, bid evaluation data, and bid award data; The external data sources include supplier information and offline bidding data.