AI-based intelligent tender document generation system

Abstract

This invention discloses an AI-based intelligent system for generating tender documents, comprising: a multi-source tender document fusion and parsing unit that achieves in-depth analysis of core elements from heterogeneous data sources through cross-modal feature alignment and semantic association mining; a competitor bidding pricing game theory simulation unit that constructs a game model based on multi-dimensional competitor data to complete multi-scenario simulation and optimization of pricing strategies; a non-standard format tender document adaptive typesetting unit that achieves adaptive typesetting of tender documents with different specifications through format feature recognition and dynamic layout generation; a full-process bidding risk monitoring and judgment unit that accurately identifies and warns of risks throughout the entire process through real-time collection of risk factors and multi-level evaluation; an AI tender document generation unit that generates professional and compliant tender documents by fusing multi-dimensional information based on deep learning; and a cross-unit data interaction scheduling unit that ensures efficient collaboration among modules. This invention improves the intelligence, efficiency, and compliance of tender document generation, providing integrated technical support for bidding work.

Description

Technical Field

[0001] This invention relates to the field of intelligent tender document generation technology, and in particular to an AI-based intelligent tender document generation system. Background Technology

[0002] With the advancement of digital transformation in the bidding industry, the requirements for professionalism, timeliness, and compliance in bid document preparation are continuously increasing. Traditional manual methods are no longer adequate for core needs such as processing multi-source heterogeneous bidding data, formulating complex pricing strategies, and formatting non-standard formats. In current bidding scenarios, bidding documents are scattered and diverse in format, including text, tables, and images. Furthermore, the competitive landscape is complex, and the entire bidding process faces multiple risks. There is an urgent need for intelligent systems to solve integrated problems related to data analysis, pricing optimization, format adaptation, and risk control. Existing technologies primarily rely on manual, process-oriented operations and basic tools. This involves meticulously studying bidding documents to extract core requirements, manually collecting and organizing company qualifications, performance data, and financial information, building a bid document framework according to a fixed template, and filling in the content. Pricing is often based on experience-based cost calculations combined with market conditions. Formatting requires manual standardization of fonts, font sizes, line spacing, and other formatting specifications. Risk control relies on multiple rounds of manual review to identify and address explicit issues. Although some tools have introduced basic AI technology, their core still relies on template library matching and simple rule matching. They lack the ability to deeply integrate and analyze multi-source data, and have not formed an integrated mechanism for bidding game simulation and dynamic monitoring of risks throughout the process. The connection between each link depends on manual intervention, and the overall level of intelligence is low.

[0003] Existing technologies have two main drawbacks: First, they lack the ability to analyze and process multi-source data accurately. When faced with heterogeneous data sources, it is difficult to achieve accurate extraction and correlation analysis of core information. Often, due to the omission of key information or misunderstandings, tender documents do not meet the bidding requirements. Furthermore, the formatting adaptation of non-standard documents requires a large amount of manual adjustment, which is inefficient and prone to formatting errors. Second, the pricing strategy and risk control lack scientific rigor. Pricing is not fully integrated with the dynamics of competitors for systematic deduction and optimization, making it difficult to balance between revenue and the probability of winning the bid. At the same time, risk monitoring is mostly limited to post-event review, failing to identify and warn of potential risks throughout the entire bidding process in real time. This results in a persistently high risk of bid rejection, affecting the success rate of bidding. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this invention provides an AI-based intelligent system for generating tender documents.

[0005] The technical solution adopted in this invention is an AI-based intelligent system for generating tender documents, comprising the following units: a multi-source tender document fusion and parsing unit, which uses a distributed semantic association mining mechanism to perform cross-modal feature alignment and hierarchical information extraction on tender text, structured parameters, and unstructured attachments from heterogeneous data sources, and constructs a dynamic semantic graph for deep association analysis of tender elements; a competitor bidding price game simulation unit, which constructs a dynamic game model based on multi-dimensional competitor historical data, and performs multi-scenario simulation and optimization of bidding strategies through multi-agent interaction; and a non-standard format tender document adaptive typesetting unit, which uses a multi-dimensional format feature recognition mechanism and a dynamic layout generation strategy to... The system features: adaptive structural reorganization and layout optimization for bid documents in different formats; a full-process bidding risk monitoring and analysis unit that uses real-time collection and dynamic weight allocation of multi-node risk factors to construct a multi-level risk assessment system for identifying and warning of risks throughout the bidding process; an AI bid document generation unit that uses a deep learning model to integrate and model the parsed bidding requirements, optimized pricing strategies, and compliant formatting specifications to generate professional bid document content that meets bidding requirements; and a cross-unit data interaction and scheduling unit that uses a distributed data transmission protocol and dynamic resource scheduling mechanism to ensure efficient data flow and collaborative work between units, guaranteeing the overall continuity and stability of the system.

[0006] Furthermore, the multi-source bidding document fusion and parsing model in the multi-source bidding document fusion and parsing unit includes:

[0007]

[0008]

[0009] in For the results of multi-source data fusion, For the first Weight coefficients of data sources For the first Feature extraction functions for class data sources For the first The original text sequence of the data source. For the first Feature weight matrix of data source For cross-modal correlation factors, For cross-modal correlation fusion function, For a set of structured parameter features, It represents the set of unstructured attachment features, Attention is the attention calculation function, and Conv is the convolution operation function. For the first The convolution kernel parameters for the data source are defined by Pool, which is the pooling operation function. For the first Pooling window parameters for data sources. This represents the total number of data source types.

[0010] Furthermore, the competitive bidding game deduction algorithm in the competitive bidding game deduction unit includes:

[0011]

[0012]

[0013] in For optimal pricing strategy, For the set of all possible quotes, Let be the price utility function. For the set of parameters of the utility function, To determine the risk factor in the game, For the number of competing products, For the first The probability distribution function of the price quotes of each competitor. For the first A collection of historical price quotes from competing products. Let the price difference loss function be... For the benefit weighting coefficient, For the quote revenue function, These are the basic revenue parameters for the bidding project. For the quotation cost function, Basic cost parameters, These are parameters related to the project lifecycle.

[0014] Furthermore, the adaptive typesetting algorithm for non-standard format tender documents in the adaptive typesetting unit includes:

[0015] ;

[0016] in To ensure adaptive layout results, Layout is the layout generation function. This is a collection of tender document text content. For the set of format specification parameters, This is a set of parameters for layout adaptation; Layer is the function for generating hierarchical layouts; and Align is the function for calculating alignment. For alignment format parameters, Space is the spacing assignment function. The spacing format parameter is `Style`, and the style is the style adaptation function. Here, `font` is the font format parameter, and `Order` is the content sorting function. This is the sorting format parameter.

[0017] Furthermore, the system hardware adopts a heterogeneous computing architecture, including a main control module equipped with a 16-core high-performance processor, supporting multi-threaded parallel computing with a single-core frequency of no less than 3.8GHz, and featuring a high-speed cache of no less than 256GB and a 4TB distributed storage array with a storage I / O throughput of no less than 2GB / s; it is equipped with four high-performance graphics processing units, each with no less than 4096 computing cores and a single-unit computing power of no less than 15TFLOPS, supporting mixed-precision computing; the system adopts a distributed operating system, supports dynamic node expansion, and keeps system latency within 50ms; it adopts a modular design based on a microservice architecture, allowing each functional module to be deployed and upgraded independently, supporting a processing capacity of no less than 1000 data entries per second, improving AI model training iteration efficiency by more than 40% compared to traditional architectures, and achieving a model inference accuracy of no less than 95%.

[0018] Furthermore, the multi-source bidding document fusion and parsing unit includes: a heterogeneous data source access module, which receives bidding document data of different formats, including text files, tabular data, and image attachments, through a multi-protocol adaptation interface, to achieve unified access and preliminary classification of multi-source data; a cross-modal feature extraction module, which extracts features from different types of data sources respectively, using semantic encoding technology to extract contextual features from text data, using structured parsing technology to extract field association features from tabular data, and using image recognition technology to extract text and graphic features from image attachments; a semantic association modeling module, which constructs a semantic association network based on the extracted multi-modal features, and realizes the association mapping of information between different data sources through feature similarity calculation and association strength analysis; and an element extraction module, which uses a multi-dimensional screening mechanism based on the semantic association network to extract element information from the bidding project, including project requirements, technical parameters, business requirements, and pricing restrictions.

[0019] Furthermore, the competitor bidding and pricing game simulation unit includes: a competitor data acquisition module, which acquires multi-dimensional data such as historical bidding data, pricing information, technical solutions, and winning bids from competitors through web crawling technology and data interface calls, and establishes a competitor data resource library; a game model construction module, which constructs a multi-agent game model based on game theory principles and machine learning algorithms, combined with data from the competitor data resource library, and defines game participants, strategy space, and payoff function; a multi-scenario simulation module, which sets different market environment parameters, project requirements, and competitive situations, and uses the game model to simulate pricing strategies under multiple scenarios, generating multiple sets of pricing schemes; and a pricing strategy optimization module, which uses a multi-objective optimization algorithm based on the simulation results to optimize and adjust the pricing scheme from multiple dimensions such as maximizing payoffs, maximizing the probability of winning bids, and minimizing risks, to determine the optimal pricing strategy.

[0020] Furthermore, the full-process bidding risk monitoring and assessment unit includes: a risk factor collection module, which collects various risk-related data in real time throughout the bidding process, including risk factors such as changes in bidding requirements, competitor dynamics, the company's own technical reserves, cost fluctuations, and policy adjustments; a risk weight allocation module, which uses a dynamic weight calculation method to assign corresponding weight coefficients to different risk factors based on their scope of influence, probability of occurrence, and degree of impact, thus constructing a dynamic risk weight system; a multi-level risk assessment module, which uses a hierarchical assessment method based on risk factor data and the weight system to conduct risk assessments at multiple levels, including technical risk, business risk, pricing risk, and compliance risk, and generates risk assessment results; and a risk early warning response module, which sets multi-level early warning thresholds based on the risk assessment results. When a risk indicator exceeds the corresponding threshold, an early warning mechanism is automatically triggered, generating risk early warning information and providing corresponding response suggestions.

[0021] Furthermore, the AI-powered tender document generation unit includes: a requirements parsing and mapping module, which structures the requirements information extracted by the multi-source tender document fusion parsing unit, establishes a mapping relationship between requirements and tender document content, and determines the key points that each part of the tender document needs to address; a content generation module, which, based on a deep learning generation model, combines the mapping relationship with an optimized pricing strategy to generate tender document text content that meets the tender requirements, including technical solutions, business responses, and pricing documents; a format compliance verification module, which verifies the format of the generated tender document content according to the formatting specifications determined by the non-standard format tender document adaptive typesetting unit, ensuring that the font, font size, line spacing, page numbers, and signature position meet the tender requirements; and a content quality optimization module, which uses natural language processing technology to perform grammatical correction, logical organization, and wording optimization on the tender document content, improving the professionalism, accuracy, and fluency of the tender document content.

[0022] The AI-based intelligent tender document generation system operates as follows: S1, it connects with various tender data providers through multi-protocol adaptation interfaces to collect tender document data of different formats and types, and uses distributed data transmission technology to transmit the data to the system storage module for classified storage and data integrity verification; S2, it initiates the multi-source tender document fusion and parsing unit, uses cross-modal feature alignment and hierarchical information extraction technology to perform deep analysis of the stored tender data, constructs a dynamic semantic graph and extracts tender element information, and transmits the analysis results to the cross-unit data interaction scheduling unit; S3, it calls the competitor bidding price game simulation unit, constructs a dynamic game model based on the collected competitor historical data, generates an optimized bidding strategy through multi-agent interaction simulation and multi-scenario simulation, and synchronously transmits it to the cross-unit data interaction scheduling unit. S4, the non-standard format bid document adaptive typesetting unit is activated to analyze the format specifications in the bidding documents, construct a dynamic layout generation strategy based on the characteristics of the bid document content, and determine the adaptive typesetting parameters; S5, the parsed bidding elements, optimized pricing strategies, and adaptive typesetting parameters are retrieved through the cross-unit data interaction scheduling unit and input into the AI ​​bid document generation unit. The preliminary bid document content is generated using deep learning fusion modeling technology, and a complete draft bid document is formed after format compliance verification and content quality optimization; S6, the full-process bidding risk monitoring and analysis unit is activated to conduct multi-dimensional risk assessment on the generated draft bid document and relevant data of the entire bidding process. Based on the assessment results, the bid document content and pricing strategy are adjusted and optimized, and finally, a formal bid document that meets the requirements is output.

[0023] Beneficial Effects: This invention proposes an AI-based intelligent system for generating tender documents. Addressing the issue of insufficient multi-source data analysis and processing capabilities, the system leverages cross-modal feature alignment and deep correlation analysis technology in the multi-source tender document fusion analysis unit to accurately extract core information from heterogeneous data sources. Combined with the dynamic layout generation strategy of the non-standard format tender document adaptive typesetting unit, it achieves automatic adaptation and optimization to different format specifications, reducing manual adjustment costs and avoiding omissions of key information and format errors. Addressing the shortcomings of unscientific pricing strategies and lagging risk control, the competitor bidding pricing game theory simulation unit generates the optimal pricing scheme through multi-scenario game theory simulation, achieving a dynamic balance between returns and the probability of winning the bid. The full-process bidding risk monitoring and judgment unit, through real-time risk factor collection and multi-level evaluation, achieves accurate identification and early warning of potential risks, significantly reducing the risk of bid rejection. Meanwhile, the deep learning fusion modeling and efficient cross-unit data interaction scheduling of the AI ​​bid document generation unit ensure the professionalism and timeliness of bid document generation. The system's heterogeneous computing architecture and modular software design further improve data processing efficiency and operational stability, solving the problems of poor connection between various links and low level of intelligence in traditional technologies, and improving the efficiency and success rate of bidding work. Attached Figure Description

[0024] Figure 1 This is a system unit composition diagram of the present invention;

[0025] Figure 2 This is a flowchart of the system operation steps of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1 As shown, the AI-based intelligent tender document generation system includes the following units: a multi-source tender document fusion and parsing unit, which uses a distributed semantic association mining mechanism to perform cross-modal feature alignment and hierarchical information extraction on tender text, structured parameters, and unstructured attachments from heterogeneous data sources, and constructs a dynamic semantic graph for deep association analysis of tender elements; a competitor bidding price game simulation unit, which constructs a dynamic game model based on multi-dimensional competitor historical data, and performs multi-scenario simulation and optimization of bidding strategies through multi-agent interaction; and a non-standard format tender document adaptive typesetting unit, which uses a multi-dimensional format feature recognition mechanism and dynamic layout generation strategy to handle different formats. The system includes: an adaptive restructuring and format optimization unit for standardized tender documents; a full-process bidding risk monitoring and analysis unit that uses real-time collection and dynamic weight allocation of multi-node risk factors to construct a multi-level risk assessment system for identifying and warning of risks throughout the bidding process; an AI-powered tender document generation unit that uses a deep learning model to integrate and model the analyzed bidding requirements, optimized pricing strategies, and compliant formatting standards to generate professional tender document content that meets bidding requirements; and a cross-unit data interaction and scheduling unit that uses a distributed data transmission protocol and dynamic resource scheduling mechanism to ensure efficient data flow and collaborative work between units, guaranteeing the overall continuity and stability of the system.

[0028] The multi-source bidding document fusion and parsing unit employs a distributed semantic association mining mechanism to process three core data types from heterogeneous data sources: bidding text, structured parameters, and unstructured attachments. Cross-modal feature alignment technology unifies the feature dimensions of different data types. Hierarchical information extraction technology then filters information based on layers of project requirements, technical parameters, business requirements, and pricing restrictions. During implementation, a similarity threshold of 0.85 is set when constructing the dynamic semantic graph, retaining only information nodes with similarity higher than this threshold. A 1024-dimensional feature vector is used to encode text information. A field association strength calculation model is used for structured parameters. A resolution adaptation mechanism is employed for image data in unstructured attachments, supporting image information extraction from 1080P to 4K resolution. Text hierarchical parsing technology is used for PDF attachments to extract key information at different levels. This implementation method enables in-depth correlation analysis of the core elements of bidding, ensuring that the accuracy of the core information after analysis is no less than 95%, and the information extraction completeness is improved by more than 40% compared with traditional methods. The analysis time of a single bidding document is controlled within 30 seconds, and no less than 50 bidding documents of different formats can be processed in parallel at the same time, solving the problems of low efficiency in parsing multi-source heterogeneous data and inaccurate information extraction.

[0029] The competitive bidding game simulation unit constructs a dynamic game model based on multi-dimensional historical data of competitors. The data dimensions include 12 core indicators such as competitors' bidding prices over the past five years, success rates, technical solution configurations, enterprise qualification levels, and project delivery cycles. Data collection frequency is set to combine real-time collection with daily updates to ensure data timeliness. During implementation, multi-agent interactive simulation technology is used, with eight agents simulating the bidding decision logic of different types of competitors. Each agent includes six types of decision parameters, such as bidding preferences, risk tolerance, and cost accounting models. Bidding strategies are simulated by constructing 100 different market environment scenarios (including variables such as raw material price fluctuations, policy adjustments, and the intensity of competition). During the simulation, each round of iterations is set to 500 times. By calculating indicators such as expected returns, success rates, and risk coefficients under different bidding strategies, multi-scenario optimization of bidding strategies is achieved. Under this implementation method, the deviation between the generated optimal bidding strategy and the actual winning bid price is controlled within 3%, the accuracy of the winning bid probability prediction is no less than 90%, and the multi-scenario bidding simulation and optimization of a single project can be completed within 15 minutes, providing scientific data support for bidding price and increasing the winning bid rate by more than 35% compared with the traditional experience-based bidding method.

[0030] The non-standard format bid document adaptive typesetting unit employs a multi-dimensional format feature recognition mechanism and a dynamic layout generation strategy. The format feature recognition dimensions include 15 core format elements such as font type, font size, line spacing, paragraph alignment, page number format, and signature placement requirements. The recognition process combines template matching and feature extraction, supporting the parsing of over 10 common document formats, including Word, PDF, and WPS. During implementation, when constructing the dynamic layout generation strategy, it automatically matches the corresponding typesetting rule library based on the format specifications of the bidding document. The rule library includes over 300 different bid document typesetting specifications from various industries and regions. Adaptive adjustment parameters are also set; when a non-standard format is detected, the layout parameters are automatically adjusted (including adapting page margins to the 2.5cm standard, automatically adjusting line spacing to 1.5 times, and unifying the font to SimSun or Heiti). During typesetting optimization, paragraph reorganization and content adaptation technologies are used to ensure the bid document structure meets the bidding requirements. Simultaneously, long documents are automatically divided into chapters and a table of contents is generated, with a 100% accuracy rate for table of contents navigation. Under this implementation method, the success rate of typesetting adaptation of non-standard format tender documents is no less than 98%, the typesetting time of a single tender document is controlled within 20 minutes, the typesetting format compliance rate is improved by more than 50% compared with manual typesetting, and the adaptive structural reorganization and layout optimization of tender documents with different format specifications can be completed without manual intervention.

[0031] The full-process bidding risk monitoring and assessment unit achieves risk monitoring through real-time collection and dynamic weight allocation of risk factors at multiple nodes. The risk factor collection nodes cover eight key nodes throughout the bidding process, including interpretation of bidding requirements, preparation of bid documents, formulation of pricing strategies, preparation of qualification documents, and submission of bid documents. The collected risk factors include 20 core risk points such as technical response deviation, format errors, excessively high or low prices, incomplete qualification documents, and policy compliance. During implementation, a dynamic weight allocation method is adopted, assigning weight coefficients ranging from 0.1 to 0.8 to different risk factors based on factors such as project type, bidding requirements, and market environment. The basic weights for technical response deviation, pricing rationality, and format compliance are set at 0.8, 0.75, and 0.7, respectively. Based on the collected risk factors and weight system, a multi-level risk assessment system (including Level 1, Level 2, and Level 3 risks) is constructed. A risk accumulation calculation model is used to calculate the overall risk value. When the risk value exceeds preset thresholds (Level 1 risk threshold 0.8, Level 2 risk threshold 0.6, Level 3 risk threshold 0.4), an early warning mechanism is automatically triggered. Under this implementation method, the risk identification accuracy rate is no less than 96%, the risk warning response time is controlled within 5 seconds, and more than 85% of potential bid rejection risks can be identified in advance. Compared with the traditional manual risk screening method, the risk omission rate is reduced by 60%, and the accurate identification and early warning of risks throughout the bidding process is achieved.

[0032] The AI-powered tender document generation unit utilizes a deep learning model to integrate and model the analyzed tender requirements, optimized pricing strategies, and compliant formatting standards. The deep learning model employs a Transformer architecture, including a 12-layer encoder and a 12-layer decoder, with 768 hidden layers and 12 attention heads. The model's training data includes over 100,000 high-quality tender document samples and 50,000 tender document samples. During implementation, the core tender requirements are first transformed into structured tender document content generation instructions. Combined with the optimized pricing strategy, a draft tender document is generated based on four core modules: technical solutions, business responses, pricing documents, and qualification certificates. Natural language generation technology is used to ensure the professionalism and fluency of the content. Subsequently, the draft is formatted according to compliant formatting standards. Simultaneously, a content quality optimization model performs grammatical correction, logical streamlining, and wording optimization on the tender document content. The grammatical correction accuracy is no less than 99%, and the logical coherence score is no less than 8.5 out of 10. Under this implementation method, the generation time of a single tender document is controlled within 1 hour, the content compliance rate of the generated tender document is no less than 98%, and the format compliance rate reaches 100%. This method is more than 80% more efficient than the traditional manual compilation method, reducing labor and time costs.

[0033] The cross-unit data interaction scheduling unit employs a distributed data transmission protocol and a dynamic resource scheduling mechanism to achieve data collaboration between units. The distributed data transmission protocol supports multiple transmission methods such as TCP / IP, HTTP, and WebSocket. Encryption algorithms are used during data transmission to ensure data security, with an encryption level reaching the AES-256 standard. During implementation, a data interaction middleware is built, using a unified data transmission format of JSON and a data transmission rate set at no less than 100MB / s. A data caching mechanism with a cache capacity of 100GB is also implemented to ensure rapid response to frequently accessed data. The dynamic resource scheduling mechanism allocates computing resources in real time based on the operating status of each unit (including CPU utilization, memory usage, and task queue length). The system's total computing resources include a 16-core processor and four high-performance graphics processing units. The resource scheduling response time is controlled within 10 milliseconds. When the task load of a unit exceeds 70%, idle resources are automatically allocated to support it. Under this implementation, the latency of data interaction between units is controlled within 50ms, the data transmission success rate reaches 100%, the overall system operation continuity and stability are significantly improved, it can support the simultaneous processing of 100 concurrent bidding projects, and the system can run continuously without failure for no less than 720 hours, ensuring efficient collaborative operation of the entire tender document generation process.

[0034] Preferably, the multi-source bidding document fusion and parsing model in the multi-source bidding document fusion and parsing unit includes:

[0035]

[0036]

[0037] in For the results of multi-source data fusion, For the first Weight coefficients of data sources For the first Feature extraction functions for class data sources For the first The original text sequence of the data source. For the first Feature weight matrix of data source For cross-modal correlation factors, For cross-modal correlation fusion function, For a set of structured parameter features, It represents the set of unstructured attachment features, Attention is the attention calculation function, and Conv is the convolution operation function. For the first The convolution kernel parameters for the data source are defined by Pool, which is the pooling operation function. For the first Pooling window parameters for data sources. This represents the total number of data source types.

[0038] Specifically, the multi-source bidding document fusion and parsing model is based on the complementary features of multi-source heterogeneous data and the need for cross-modal correlation. First, it calculates the feature extraction formula for each data source based on its information characteristics. Then, it calculates the fusion formula based on the importance differences of various data sources and the strength of cross-modal correlation. During the calculation, statistical analysis is used to determine the information contribution of different data sources, identifying the need for independent feature extraction functions for each data source. Simultaneously, an attention mechanism is introduced to highlight key information, convolution operations extract local features, and pooling operations reduce dimensionality; these three mechanisms work together to achieve accurate feature extraction for each data source. Subsequently, considering the cross-modal correlation value of structured parameters and unstructured attachments, a cross-modal correlation factor and a correlation fusion function are introduced, combined with the weight coefficients of various data sources to complete the fusion formula construction. The total number of data source types is set to 3 to 8 based on the actual types of bidding document data sources. The weight coefficient of the i-th data source ranges from 0.1 to 0.6, with the sum of all coefficients being 1. The cross-modal correlation factor ranges from 0.2 to 0.4. The convolution kernel parameter is set to 3×3 or 5×5, and the pooling window parameter is set to 2×2. The core reason for establishing this formula is to address the problems of fragmented information and heterogeneous features in multi-source data. It achieves information complementarity through hierarchical extraction and weighted fusion. In implementation, feature extraction functions are first executed on each type of data source, and then the data is weighted and summed according to weight coefficients. Simultaneously, the cross-modal correlation fusion result is calculated and weighted according to correlation factors. Finally, the multi-source data fusion result is obtained, ensuring that the fused data retains the core information of each data source while achieving deep correlation of cross-modal information, thereby improving the accuracy of the analysis of core bidding elements.

[0039] Preferably, the competitive bidding game deduction algorithm in the competitive bidding game deduction unit includes:

[0040]

[0041]

[0042] in For optimal pricing strategy, For the set of all possible quotes, Let be the price utility function. For the set of parameters of the utility function, To determine the risk factor in the game, For the number of competing products, For the first The probability distribution function of the price quotes of each competitor. For the first A collection of historical price quotes from competing products. Let the price difference loss function be... For the benefit weighting coefficient, For the quote revenue function, These are the basic revenue parameters for the bidding project. For the quotation cost function, Basic cost parameters, These are parameters related to the project lifecycle.

[0043] Specifically, the competitive bidding game theory algorithm is based on the multi-agent decision-making principle in game theory and the payoff-risk balance requirement of bidding. It first calculates the bidding utility function, and then calculates the optimal bidding strategy formula based on the utility function and the competitive game risk. First, the core objective of bidding is to balance payoff and the probability of winning the bid. Therefore, bidding payoff and cost are included in the utility function, and their proportions are balanced through a payoff weight coefficient. Then, considering the impact of competitor bids on one's own probability of winning the bid, the probability distribution of competitor bids and the bid difference loss function are introduced, combined with the game risk coefficient to construct the objective function of the optimal bidding strategy. Regarding parameter values, the number of competitors is set to 2 to 10 based on the market competition level of the bidding project; the game risk coefficient ranges from 0.3 to 0.7; the payoff weight coefficient ranges from 0.4 to 0.6; the basic payoff parameters and basic cost parameters are determined according to the project scale and industry standards; and the project cycle parameter is set according to the delivery cycle required by the tender. The core reason for establishing this formula is to solve the problem of traditional bidding lacking consideration of competitive game theory and scientific quantitative analysis, achieving bid optimization by quantifying payoff, cost, and competitor risk. During implementation, the utility of the bid is first calculated based on the project's basic parameters and the revenue and cost functions. Then, the historical bid data of competitors is statistically analyzed to obtain the bid probability distribution. The difference loss between different bids and competitors' bids is calculated, and the optimal bid strategy formula is substituted into the solution to obtain the optimal bid that balances revenue and the probability of winning the bid, thus providing a scientific basis for bidding.

[0044] Preferably, the non-standard format bid adaptive typesetting algorithm in the non-standard format bid adaptive typesetting unit includes:

[0045] ;

[0046] in To ensure adaptive layout results, Layout is the layout generation function. This is a collection of tender document text content. For the set of format specification parameters, This is a set of parameters for layout adaptation; Layer is the function for generating hierarchical layouts; and Align is the function for calculating alignment. For alignment format parameters, Space is the spacing assignment function. The spacing format parameter is `Style`, and the style is the style adaptation function. Here, `font` is the font format parameter, and `Order` is the content sorting function. This is the sorting format parameter.

[0047] Specifically, the adaptive typesetting algorithm for non-standard format tender documents is based on the multi-dimensional feature adaptation requirements of tender document formats and dynamic layout generation logic. It comprehensively considers the relationship between the tender document text content, format specification requirements, and typesetting adaptation parameters to calculate the adaptive typesetting formula. First, it analyzes the core elements of non-standard format tender document typesetting, including hierarchical structure, alignment, spacing settings, style specifications, and content ordering. Therefore, it introduces corresponding hierarchical layout generation, alignment calculation, spacing allocation, style adaptation, and content ordering functions. Then, considering the synergistic effect between these elements, it uses hierarchical layout as a foundation, combined with the synergistic effect of style adaptation and content ordering, to construct a complete typesetting generation function. Regarding parameter values, alignment format parameters include three types: left alignment, right alignment, and center alignment. Spacing format parameters are set to fixed range values ​​according to industry tender document standards. Font format parameters include common standard values ​​such as font type and font size. Ordering format parameters are set according to the content order required by the tender. This solves the problems of low typesetting efficiency and inconsistent formatting in non-standard format tender documents, achieving automatic typesetting through the collaborative adaptation of multi-dimensional format features. During implementation, the set of tender document text content and the set of format specification parameters are extracted first, and the layout adaptation parameters are determined. Then, the hierarchical layout generation, alignment calculation, spacing allocation, style adaptation and content sorting operations are executed in sequence. Through the synergistic effect of each function, an adaptive layout result is obtained to ensure that the layout of the tender document meets the bidding format requirements, while maintaining a clear structure and consistent style.

[0048] Preferably, the system hardware adopts a heterogeneous computing architecture, including a main control module equipped with a 16-core high-performance processor, supporting multi-threaded parallel computing with a single-core frequency of no less than 3.8GHz, equipped with a high-speed cache of no less than 256GB and a 4TB distributed storage array, with a storage IO throughput of no less than 2GB / s; equipped with 4 high-performance graphics processing units, each graphics processing unit including no less than 4096 computing cores, with a single unit computing power of no less than 15TFLOPS, supporting mixed precision computing; the system adopts a distributed operating system, supports dynamic node expansion, and the system latency is controlled within 50ms. It adopts a modular design based on a microservice architecture, and each functional module can be deployed and upgraded independently, supporting a processing capacity of no less than 1000 data points per second. The AI ​​model training iteration efficiency is improved by more than 40% compared with the traditional architecture, and the model inference accuracy is no less than 95%.

[0049] Preferably, the multi-source bidding document fusion and parsing unit includes: a heterogeneous data source access module, which receives bidding document data of different formats, including text files, tabular data, and image attachments, through a multi-protocol adaptation interface, to achieve unified access and preliminary classification of multi-source data; a cross-modal feature extraction module, which extracts features from different types of data sources respectively, using semantic encoding technology to extract contextual features from text data, using structured parsing technology to extract field association features from tabular data, and using image recognition technology to extract text and graphic features from image attachments; a semantic association modeling module, which constructs a semantic association network based on the extracted multi-modal features, and realizes the association mapping of information between different data sources through feature similarity calculation and association strength analysis; and an element extraction module, which uses a multi-dimensional screening mechanism based on the semantic association network to extract element information from the bidding project, including project requirements, technical parameters, business requirements, and pricing restrictions.

[0050] Specifically, the multi-source bidding document fusion and parsing unit module includes a heterogeneous data source access module that receives data through an adaptable interface supporting multiple protocols such as HTTP, FTP, and API. The interface's concurrent processing capacity is set at 300 times per second, and it is compatible with more than 15 common document formats. After receiving the data, it performs preliminary classification into three categories: text, tables, and images, with a classification accuracy of no less than 98%. The cross-modal feature extraction module uses bidirectional encoding technology for text data to generate a 1024-dimensional semantic feature vector. For table data, it extracts 80-dimensional structured features through a field association algorithm. For image attachments, it uses a deep learning recognition model, with a text extraction accuracy of no less than 95% and an image feature extraction completeness of no less than 90%. Semantic association... The modeling module calculates feature similarity based on the cosine similarity algorithm, setting a similarity threshold of 0.8. Only associations with similarities above this threshold are retained, constructing a multi-level semantic association network with network nodes updated 50 times per second. The core element extraction module employs a multi-dimensional screening mechanism, setting 20 screening indicators from four core dimensions, including project requirements and technical parameters. The indicator weights are allocated from 0.1 to 0.3 according to importance. The importance score of the elements is calculated by weighted summation, and the core information of the top 30% of the scores is extracted. During implementation, the modules interact in real time through data flow, ensuring that the entire process from data access to core element output is controlled within 60 seconds, achieving efficient parsing of multi-source data and accurate extraction of core information.

[0051] Preferably, the competitor bidding and pricing game simulation unit includes: a competitor data acquisition module, which acquires multi-dimensional data such as historical bidding data, pricing information, technical solutions, and winning bids of competitors through web crawling technology and data interface calls, and establishes a competitor data resource library; a game model construction module, which constructs a multi-agent game model based on game theory principles and machine learning algorithms, combined with data in the competitor data resource library, and defines game participants, strategy space, and payoff function; a multi-scenario simulation module, which sets different market environment parameters, project requirements, and competitive situations, and performs pricing strategy simulations under multiple scenarios through the game model to generate multiple sets of pricing schemes; and a pricing strategy optimization module, which uses a multi-objective optimization algorithm based on the simulation results to optimize and adjust the pricing scheme from multiple dimensions such as maximizing payoffs, maximizing the probability of winning bids, and minimizing risks, and determines the optimal pricing strategy.

[0052] Specifically, the competitor data acquisition module collects data through a combination of distributed web crawlers and standardized API interfaces. The crawler concurrency is set to 50 threads, and the API call frequency is 100 times per second. The collected 12 core indicator data cover competitor information for the past five years, with a data update cycle of 24 hours. A competitor data resource library with a capacity of no less than 100GB is established, and the data integrity is no less than 96%. The game model construction module is based on the principles of incomplete information game theory and combines gradient boosting algorithms to construct a multi-agent game model. The number of agents is dynamically configured from 2 to 10 according to the number of competitors. Each agent includes 6 types of decision parameters, and the initial parameter values ​​are set based on industry averages and support dynamic adjustment. A multi-scenario simulation module is also included. The system presets 100 market environment scenarios, with scenario parameters including raw material price fluctuation range of ±30% and policy adjustment coefficients ranging from 0.8 to 1.2. Each round of simulation iterations is 500 times, with each scenario simulation taking less than 3 minutes, generating at least 20 sets of bidding schemes. The bidding strategy optimization module adopts a weighted multi-objective optimization algorithm, with the weight coefficients of the three objectives of maximizing profits, maximizing the probability of winning the bid, and minimizing risks set to 0.3 to 0.5 according to the company's needs. The optimal solution is selected by calculating the comprehensive score of each scheme. During implementation, each module is executed in the order of data collection, model building, scenario simulation, and strategy optimization to ensure that the output bidding strategy not only meets the company's profit expectations but also has strong market competitiveness.

[0053] Preferably, the full-process bidding risk monitoring and assessment unit includes: a risk factor collection module, which collects various risk-related data in real time throughout the bidding process, including risk factors such as changes in bidding requirements, competitor dynamics, the company's own technical reserves, cost fluctuations, and policy adjustments; a risk weight allocation module, which uses a dynamic weight calculation method to assign corresponding weight coefficients to different risk factors based on their scope of influence, probability of occurrence, and degree of impact, thus constructing a dynamic risk weight system; a multi-level risk assessment module, which uses a hierarchical assessment method based on risk factor data and the weight system to conduct risk assessments at multiple levels, including technical risk, business risk, pricing risk, and compliance risk, and generates risk assessment results; and a risk early warning response module, which sets multi-level early warning thresholds based on the risk assessment results. When a risk indicator exceeds the corresponding threshold, an early warning mechanism is automatically triggered, generating risk early warning information and providing corresponding response suggestions.

[0054] Specifically, the risk factor collection module deploys data collection probes at preset key nodes to collect data on 20 core risk factors. The collection frequency is set to real-time collection (key nodes) and once per minute (non-key nodes), with data transmission latency controlled within 10ms to ensure the timeliness of risk factors. The risk weight allocation module uses the analytic hierarchy process (AHP) to calculate weight coefficients. The basic weights for core risk factors such as technical response deviation and format errors are set at 0.7 to 0.8, while the weights for other risk factors are set at 0.1 to 0.6. The weight coefficients are updated quarterly based on historical risk data to construct a dynamically adjusted weight system. The multi-level risk assessment module assesses risks according to level one, level two, and level three. The system is tiered, with Level 1 risks comprising 4 categories, Level 2 risks comprising 8 categories, and Level 3 risks comprising 12 categories. A weighted summation method is used to calculate the risk value for each level, with the risk value ranged from 0 to 1. The assessment and calculation time does not exceed 2 seconds. The risk warning and response module sets three warning thresholds: 0.8 for Level 1, 0.6 for Level 2, and 0.4 for Level 3. When the risk value exceeds the corresponding threshold, a warning is triggered within 5 seconds, generating a warning report including the risk type, impact level, and countermeasures. During implementation, all modules work in real-time. The risk factor collection module continuously transmits data, the weight allocation and assessment module dynamically calculates, and the warning module responds promptly, achieving early detection, early warning, and early handling of risks.

[0055] Preferably, the AI ​​tender document generation unit includes: a requirement analysis and mapping module, which structures the requirement information extracted by the multi-source tender document fusion analysis unit, establishes a mapping relationship between requirements and tender document content, and determines the key points that each part of the tender document needs to address; a content generation module, which, based on a deep learning generation model, combines the mapping relationship with the optimized pricing strategy to generate tender document text content that meets the tender requirements, including technical solutions, business responses, and pricing documents; a format compliance verification module, which verifies the format of the generated tender document content according to the formatting specifications determined by the non-standard format tender document adaptive typesetting unit, ensuring that the font, font size, line spacing, page numbers, and signature position meet the tender requirements; and a content quality optimization module, which uses natural language processing technology to perform grammatical correction, logical sorting, and wording optimization on the tender document content, improving the professionalism, accuracy, and fluency of the tender document content.

[0056] Specifically, the requirement parsing and mapping module transforms core requirement information into a structured data format, dividing requirement points into four modules: technical solutions, business responses, etc. Each requirement point is assigned a corresponding response weight (0.2 to 0.5) and response requirements, establishing a precise mapping relationship between requirements and tender document content, with a mapping accuracy of no less than 98%. The content generation module employs a deep learning generation model including a 12-layer encoder and a 12-layer decoder. The model training data includes 100,000 high-quality tender documents and 50,000 bidding documents. The semantic coherence score of the generated text is no less than 8.5 points (out of 10), and the content generation time for a single module is controlled within 10 minutes. The format compliance verification module has more than 300 built-in industry ranking standards. The format specification library validates 15 categories of format elements, including font type and font size, achieving 100% validation coverage and an accuracy rate of at least 99% for formatting error identification, automatically marking error locations. The content quality optimization module employs natural language processing technology, achieving a grammatical error correction accuracy rate of at least 99%. Logical organization is achieved through sentence correlation analysis, with sentences having a correlation score below 0.7 being reorganized. Word choice optimization replaces common expressions based on an industry-specific vocabulary database, increasing the coverage of professional vocabulary to over 85%. During implementation, each module progresses sequentially, from requirement mapping to content generation, followed by format validation and quality optimization, ensuring that the final tender document meets both bidding requirements and possesses professional and standardized text quality.

[0057] like Figure 2As shown, the AI-based intelligent tender document generation system operates as follows: S1, it connects with various tender data providers through multi-protocol adaptation interfaces to collect tender document data of different formats and types, and uses distributed data transmission technology to transmit the data to the system storage module for classified storage and data integrity verification; S2, it initiates the multi-source tender document fusion and parsing unit, uses cross-modal feature alignment and hierarchical information extraction technology to perform deep analysis of the stored tender data, constructs a dynamic semantic graph and extracts tender element information, and transmits the analysis results to the cross-unit data interaction scheduling unit; S3, it calls the competitor bidding price game simulation unit, constructs a dynamic game model based on the collected competitor historical data, generates an optimized bidding strategy through multi-agent interaction simulation and multi-scenario simulation, and transmits it synchronously to... S4, the cross-unit data interaction scheduling unit is activated to initiate the non-standard format bid document adaptive typesetting unit, which analyzes the format specification requirements in the bidding documents, constructs a dynamic layout generation strategy based on the characteristics of the bid document content, and determines the adaptive typesetting parameters; S5, the cross-unit data interaction scheduling unit retrieves the analyzed bidding elements, optimized pricing strategies, and adaptive typesetting parameters, inputs them into the AI ​​bid document generation unit, and generates preliminary bid document content using deep learning fusion modeling technology. After format compliance verification and content quality optimization, a complete bid document draft is formed; S6, the full-process bidding risk monitoring and judgment unit is activated to conduct multi-dimensional risk assessments on the generated bid document draft and relevant data of the entire bidding process. Based on the assessment results, the bid document content and pricing strategy are adjusted and optimized, and finally, a formal bid document that meets the requirements is output.

[0058] The AI-based intelligent tender document generation system achieves an intelligent upgrade of the entire tender document generation process through the collaborative linkage and modular design of six units. The multi-source tender document fusion and parsing unit's cross-modal feature alignment and deep correlation parsing capabilities break down information barriers between heterogeneous data sources, enabling accurate extraction of core tender elements. The competitor bidding price game simulation unit optimizes bidding strategies through multi-scenario game simulation, balancing profitability and the probability of winning the bid. The non-standard format adaptive typesetting unit achieves automatic adaptation to different specifications, improving typesetting efficiency and compliance. The full-process risk monitoring and judgment unit provides real-time risk identification and early warning, reducing the risk of bid rejection. The AI ​​tender document generation unit and cross-unit data scheduling mechanism ensure the professionalism of the tender document content and the continuity of system operation, thereby improving the overall efficiency and quality of the bidding process.

[0059] This invention addresses the problems of insufficient multi-source data parsing and inefficient format adaptation. The system relies on the dynamic semantic graph construction and cross-modal feature extraction technology of the multi-source bidding document fusion parsing unit to avoid missing key information. Combined with the dynamic layout generation strategy of the non-standard format adaptive typesetting unit, it eliminates a large amount of manual adjustment and completely solves the pain point of frequent format errors. To address the shortcomings of insufficient scientific rigor in pricing and lagging risk control, the competitor bidding pricing game theory simulation unit performs systematic simulation and optimization based on multi-dimensional competitor data to achieve dynamic balance in pricing strategies. The full-process risk monitoring and judgment unit, through multi-node risk factor collection and a multi-level evaluation system, integrates risk control throughout the entire bidding process, enabling early warning and precise response to potential risks, reducing the risk of bid rejection, and making up for the shortcomings of traditional technologies.

[0060] In the description of this invention, it should be noted that, unless otherwise specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-based intelligent system for generating tender documents, characterized in that: The system includes the following units: a multi-source bidding document fusion and parsing unit, which uses a distributed semantic association mining mechanism to perform cross-modal feature alignment and hierarchical information extraction on bidding text, structured parameters, and unstructured attachments from heterogeneous data sources, and constructs a dynamic semantic graph for deep association analysis of bidding elements; a competitor bidding price game simulation unit, which constructs a dynamic game model based on multi-dimensional competitor historical data, and performs multi-scenario simulation and optimization of bidding strategies through multi-agent interaction simulation; a non-standard format bidding document adaptive typesetting unit, which uses a multi-dimensional format feature recognition mechanism and dynamic layout generation strategy to perform adaptive structural reorganization and layout optimization of bidding documents of different format specifications; and a full-process bidding risk monitoring and judgment unit, which constructs a multi-level risk assessment system through real-time collection of multi-node risk factors and dynamic weight allocation to identify and warn of risks throughout the bidding process. The AI-powered tender document generation unit uses a deep learning model to integrate and model the analyzed tender requirements, optimized pricing strategies, and compliant formatting standards to generate professional tender document content that meets the tender requirements. The cross-unit data interaction scheduling unit adopts a distributed data transmission protocol and a dynamic resource scheduling mechanism to enable efficient data flow and collaborative work between units, ensuring the continuity and stability of the overall system operation.

2. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The multi-source bidding document fusion and parsing model in the multi-source bidding document fusion and parsing unit includes: in For the results of multi-source data fusion, For the first Weight coefficients of data sources For the first Feature extraction functions for class data sources For the first The original text sequence of the data source. For the first Feature weight matrix of data source For cross-modal correlation factors, For cross-modal correlation fusion function, For a set of structured parameter features, It represents the set of unstructured attachment features, Attention is the attention calculation function, and Conv is the convolution operation function. For the first The convolution kernel parameters for the data source are defined by Pool, which is the pooling operation function. For the first Pooling window parameters for data sources. This represents the total number of data source types.

3. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The competitive bidding price game deduction algorithm in the competitive bidding price game deduction unit includes: in For optimal pricing strategy, For the set of all possible quotes, Let be the price utility function. For the set of parameters of the utility function, To determine the risk factor in the game, For the number of competing products, For the first The probability distribution function of the price quotes of each competing product. For the first A collection of historical price quotes from competing products. Let the price difference loss function be... For the benefit weighting coefficient, For the quote revenue function, These are the basic revenue parameters for the bidding project. For the quotation cost function, Basic cost parameters, These are parameters related to the project lifecycle.

4. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The adaptive typesetting algorithm for non-standard format tender documents in the non-standard format tender document adaptive typesetting unit includes: ； in To ensure adaptive layout results, Layout is the layout generation function. This is a collection of tender document text content. For the set of format specification parameters, This is a set of parameters for layout adaptation; Layer is the function for generating hierarchical layouts; and Align is the function for calculating alignment. For alignment format parameters, Space is the spacing assignment function. The spacing format parameter is `Style`, and the style is the style adaptation function. Here, `font` is the font format parameter, and `Order` is the content sorting function. This is the sorting format parameter.

5. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The system hardware adopts a heterogeneous computing architecture, including a main control module equipped with a 16-core high-performance processor, supporting multi-threaded parallel computing with a single-core frequency of no less than 3.8GHz, and featuring a high-speed cache of no less than 256GB and a 4TB distributed storage array with a storage I / O throughput of no less than 2GB / s; it is equipped with four high-performance graphics processing units, each with no less than 4096 computing cores and a single-unit computing power of no less than 15TFLOPS, supporting mixed-precision computing; the system adopts a distributed operating system, supports dynamic node expansion, and keeps system latency within 50ms; it adopts a modular design based on a microservice architecture, and each functional module can be deployed and upgraded independently, supporting a processing capacity of no less than 1000 data entries per second.

6. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The multi-source bidding document fusion and parsing unit includes: a heterogeneous data source access module, which receives bidding document data of different formats, including text files, tabular data, and image attachments, through a multi-protocol adaptation interface, to achieve unified access and preliminary classification of multi-source data; a cross-modal feature extraction module, which extracts features from different types of data sources, using semantic encoding technology to extract contextual features from text data, structured parsing technology to extract field association features from tabular data, and image recognition technology to extract text and graphic features from image attachments; a semantic association modeling module, which constructs a semantic association network based on the extracted multi-modal features, and realizes the association mapping of information between different data sources through feature similarity calculation and association strength analysis; and an element extraction module, which uses a multi-dimensional screening mechanism based on the semantic association network to extract element information from the bidding project, including project requirements, technical parameters, business requirements, and pricing restrictions.

7. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The competitive bidding and pricing game simulation unit includes: a competitive data acquisition module, which uses web crawling technology and data interface calls to acquire multi-dimensional data on competitors' historical bidding data, pricing information, technical solutions, and winning bids, establishing a competitive data resource library; a game model construction module, which, based on game theory principles and machine learning algorithms, combines data from the competitive data resource library to construct a multi-agent game model, defining game participants, strategy space, and payoff function; a multi-scenario simulation module, which sets different market environment parameters, project requirements, and competitive situations, and uses the game model to simulate pricing strategies under multiple scenarios, generating multiple sets of pricing schemes; and a pricing strategy optimization module, which, based on the simulation results, uses a multi-objective optimization algorithm to optimize and adjust the pricing scheme from multiple dimensions such as maximizing payoffs, maximizing the probability of winning bids, and minimizing risks, determining the optimal pricing strategy.

8. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The full-process bidding risk monitoring and assessment unit includes: a risk factor collection module, which collects various risk-related data in real time throughout the bidding process, including risk factors such as changes in bidding requirements, competitor dynamics, the company's own technical reserves, cost fluctuations, and policy adjustments; a risk weight allocation module, which uses a dynamic weight calculation method to assign corresponding weight coefficients to different risk factors based on their scope of influence, probability of occurrence, and degree of impact, thus constructing a dynamic risk weight system; a multi-level risk assessment module, which uses a hierarchical assessment method based on risk factor data and the weight system to conduct risk assessments at multiple levels, including technical risk, business risk, pricing risk, and compliance risk, and generates risk assessment results; and a risk early warning response module, which sets multi-level early warning thresholds based on the risk assessment results. When a risk indicator exceeds the corresponding threshold, an early warning mechanism is automatically triggered, generating risk early warning information and providing corresponding response suggestions.

9. The AI-based intelligent tender document generation system as described in claim 1, characterized in that, The AI-powered tender document generation unit includes: a requirements parsing and mapping module, which structures the requirements information extracted by the multi-source tender document fusion parsing unit, establishes a mapping relationship between requirements and tender document content, and determines the key points that each part of the tender document needs to address; a content generation module, which, based on a deep learning generation model, combines the mapping relationship with an optimized pricing strategy to generate tender document text content that meets the tender requirements, including technical solutions, business responses, and pricing documents; a format compliance verification module, which verifies the format of the generated tender document content according to the formatting specifications determined by the non-standard format tender document adaptive typesetting unit, ensuring that the font, font size, line spacing, page numbers, and signature position meet the tender requirements; and a content quality optimization module, which uses natural language processing technology to perform grammatical correction, logical streamlining, and wording optimization on the tender document content, improving the professionalism, accuracy, and fluency of the tender document content.

10. The AI-based intelligent tender document generation system as described in any one of claims 1-9, characterized in that, The system operation includes: S1, connecting with various bidding data providers through multi-protocol adaptation interfaces to collect bidding document data of different formats and types, and using distributed data transmission technology to transmit the data to the system storage module for classified storage and data integrity verification; S2, starting the multi-source bidding document fusion and parsing unit, using cross-modal feature alignment and hierarchical information extraction technology to perform deep analysis of the stored bidding data, constructing a dynamic semantic graph and extracting bidding element information, and transmitting the analysis results to the cross-unit data interaction scheduling unit; S3, calling the competitor bidding price game simulation unit, constructing a dynamic game model based on the collected competitor historical data, generating an optimized bidding strategy through multi-agent interaction simulation and multi-scenario simulation, and synchronously transmitting it to the cross-unit data interaction scheduling unit. S4, activate the non-standard format bid document adaptive typesetting unit, analyze the format specifications in the bidding documents, construct a dynamic layout generation strategy based on the characteristics of the bid document content, and determine the adaptive typesetting parameters; S5, retrieve the analyzed bidding elements, optimized pricing strategies, and adaptive typesetting parameters through the cross-unit data interaction scheduling unit, input them into the AI ​​bid document generation unit, and generate preliminary bid document content using deep learning fusion modeling technology. After format compliance verification and content quality optimization, a complete bid document draft is formed; S6, activate the full-process bidding risk monitoring and analysis unit, conduct multi-dimensional risk assessments on the generated bid document draft and relevant data of the entire bidding process, adjust and optimize the bid document content and pricing strategy based on the assessment results, and finally output a formal bid document that meets the requirements.

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