Bidding deposit intelligent management method and system based on dynamic risk portrait

Abstract

The invention discloses a bidding margin intelligent management method and system based on a dynamic risk portrait, and the method comprises the steps: building a dynamic risk portrait through obtaining project attribute data and supplier multi-source heterogeneous behavior data, and inputting a pre-trained bidding risk prediction model to calculate a dynamic risk index; and synchronously executing cash deposit payment state rule verification and dynamic risk index evaluation at the bid opening verification node, generating a comprehensive verification report, calculating a real-time commercial credit score through a credit evaluation engine, generating a differentiated cash deposit management strategy according to the score, and outputting a final state scheme. According to the invention, the conversion of bidding margin management from formal review to risk intelligent early warning is realized, the bidding risk is effectively identified, the risk prevention and control accuracy is improved, and the safety and intelligent level of government purchase are significantly improved based on a differentiation strategy of real-time credit scoring.

Description

Technical Field

[0001] This invention relates to the field of bid bond management technology, specifically to an intelligent management method and system for bid bonds based on dynamic risk profiling. Background Technology

[0002] In the field of government procurement, bid bond management is a crucial link in ensuring the fairness and impartiality of procurement activities. Currently, the payment, verification, and refund processes for bid bonds have gradually become electronic, with basic fund status verification and automated refunds achieved through integration with bank systems and guarantee platforms. However, the existing management model primarily relies on preset static rules for formal review of bid bond payments, lacking the ability to identify and analyze dynamic risks in supplier behavior. Due to the failure to effectively integrate multi-dimensional behavioral data of suppliers during the bidding process, the system struggles to provide effective early warnings of potential violations at the bid opening stage. Furthermore, bid bond management strategies typically employ uniform standards, failing to differentiate based on suppliers' real-time credit status. This not only affects the participation enthusiasm of high-credit suppliers but also increases overall compliance costs. Establishing an intelligent management mechanism that integrates with dynamic risk assessment, extending beyond formal verification to risk warning and credit management, has become a key issue that needs to be addressed in the current bid bond management field. Summary of the Invention

[0003] In view of the above problems, the present invention provides a method and system for intelligent management of bid security based on dynamic risk profiles. By constructing dynamic risk profiles of suppliers and integrating rule verification and intelligent assessment, the present invention solves the problem that the existing management model lacks the ability to dynamically identify risks and differentiate credit processing.

[0004] To achieve the above objectives, in a first aspect, this application provides a method for intelligent management of bid bonds based on dynamic risk profiling, comprising:

[0005] Obtain project attribute data for the target procurement project and multi-source heterogeneous behavioral data of participating supplier groups. The multi-source heterogeneous behavioral data should include at least historical fund flow records, guarantee application sequences, and bidding behavior time sequence characteristics.

[0006] Based on multi-source heterogeneous behavioral data, a dynamic risk profile of the supplier group is constructed through correlation network analysis algorithm and time series pattern mining. The dynamic risk profile includes at least the characteristics of the financial correlation network, the consistency characteristics of bidding behavior, and the abnormal pattern characteristics of the use of guarantee.

[0007] The dynamic risk profile is input into the pre-trained bid-rigging risk prediction model to calculate the dynamic risk index of each supplier for the target procurement project and generate a risk correlation map.

[0008] At the bid opening verification node of the target procurement project, the rule verification of the deposit payment status and the intelligent assessment of the dynamic risk index are executed simultaneously to generate a comprehensive verification report that integrates the rule verification results and the risk warning level.

[0009] Based on the comprehensive verification report, the real-time business credit score of each supplier is calculated through the credit assessment engine, and differentiated margin management strategies are generated based on the real-time business credit score. The differentiated margin management strategies include at least a virtual credit line scheme for high-credit suppliers.

[0010] Based on the comprehensive verification report and differentiated bid bond management strategies, generate and output the final intelligent management solution for bid bonds of the target procurement project.

[0011] Furthermore, based on multi-source heterogeneous behavioral data, a dynamic risk profile of the supplier group is constructed through correlation network analysis algorithms and time series pattern mining, including:

[0012] By analyzing historical fund flow records, account entities and tracking fund flows, a fund transfer network is constructed with supplier legal entities, shareholders, and payment accounts as nodes;

[0013] A community detection algorithm is used to perform topological analysis on the fund transfer network, identify potential bid-rigging groups with close fund connections, and extract the network density and node centrality indices of the fund transfer network as features of the fund connection network.

[0014] Periodic analysis and outlier detection are performed on the temporal characteristics of bidding behavior to extract collaborative behavior patterns of suppliers in terms of bid deadline distribution, bidding geographical concentration and similarity of bid targets, and generate consistency characteristics of bidding behavior.

[0015] Frequent pattern mining and sequence alignment analysis were performed on the guarantee application sequence to identify anomalies in the concentration of guarantee issuing institutions, the synchronicity of guarantee expiration time, and the dispersion of guarantee amount, and to construct a feature of abnormal guarantee usage patterns.

[0016] By fusing multimodal features such as fund-related network characteristics, consistency characteristics of bidding behavior, and abnormal patterns of guarantee usage, a dynamic risk profile with timestamps is generated.

[0017] Furthermore, periodic analysis and outlier detection are performed on the temporal characteristics of bidding behavior to extract collaborative behavior patterns among suppliers in terms of bid deadline distribution, geographical concentration of bids, and similarity of bid targets, generating consistency characteristics of bidding behavior, including:

[0018] Construct a bidding behavior time series matrix. The matrix dimension of the bidding behavior time series matrix shall include at least the bidding timestamp sequence, the bidding geographical code sequence, and the bidding target classification sequence.

[0019] The dynamic time warping algorithm is used to calculate the similarity distance between different suppliers in the bidding time distribution, and to identify the supplier group with synchronous bidding behavior;

[0020] Spatial distribution patterns of bidding region coding sequences are mined using spectral clustering algorithm to detect bidding groups with abnormally consistent regional selection.

[0021] Anomaly detection is performed on the classification sequence of bidding targets based on the isolated forest algorithm to identify supplier combinations with unconventional similarities in business domain selection.

[0022] A correlation analysis is conducted on synchronous bidding behavior, consistency in abnormal regions, and similarity of unconventional business to generate a bidding behavior synergy index.

[0023] The consistency characteristics of bidding behavior are obtained based on the coordination index of bidding behavior.

[0024] Furthermore, the dynamic risk profile is input into the pre-trained bid-rigging risk prediction model to calculate the dynamic risk index of each supplier for the target procurement project, and to generate a risk correlation map, including:

[0025] The dynamic risk profile is processed by weighting the importance of features, and the contribution of each feature dimension to the risk of collusion is calculated by the gradient boosting decision tree algorithm to obtain the weighted feature vector.

[0026] The weighted feature vectors are input into the pre-trained isolated forest anomaly detection model to calculate the anomaly score of each supplier in the feature space, and the anomaly score is normalized into a primary risk index.

[0027] Based on the characteristics of the funding network, a graph neural network model is used to learn node embedding representations to obtain the structural relationships and potential association strengths of suppliers in the funding network.

[0028] The primary risk index is concatenated with the node embedding vector output by the graph neural network model, and then input into a multilayer perceptron classifier for joint training to output the dynamic risk index of each supplier for the target procurement project.

[0029] Based on the node similarity and association strength learned by the graph neural network model, a risk propagation link between suppliers is constructed, and a risk association map is generated.

[0030] Furthermore, the primary risk index is concatenated with the node embedding vectors output by the graph neural network model, and then fed into a multilayer perceptron classifier for joint training. This process outputs a dynamic risk index for each supplier for the target procurement project, including:

[0031] A deep neural network architecture for a multilayer perceptron classifier is constructed. The deep neural network architecture includes a feature fusion layer, multiple hidden layers, and a risk index output layer.

[0032] In the feature fusion layer, an attention-weighted feature splicing mechanism is used to adaptively allocate weights and deeply fuse the primary risk index and the node embedding vector to obtain the spliced ​​node embedding vector.

[0033] By using nonlinear transformation of the hidden layer, deep pattern features of high-risk scrambling behavior are extracted layer by layer from the spliced ​​node embedding vector.

[0034] The Sigmoid activation function is used in the risk index output layer to map the final output value to a dynamic risk index in the range of 0 to 1.

[0035] Furthermore, at the bid opening verification stage of the target procurement project, rule verification of the bid bond payment status and intelligent assessment of the dynamic risk index are performed simultaneously, generating a comprehensive verification report that integrates rule verification results and risk warning levels, including:

[0036] The automatic verification engine for margin deposits is invoked to retrieve real-time deposit data from the banking system and financial guarantee platform through standardized interfaces.

[0037] The preset rule verification process is executed. The preset rule verification process includes at least verification of the adequacy of the margin amount, verification of the consistency of the payment account name, and review of the validity of the guarantee, so as to obtain the rule verification result.

[0038] A binary verification conclusion is generated based on the rule verification results, and abnormal verification data is marked as a level 1 risk event.

[0039] The intelligent risk assessment process is initiated in parallel, and the dynamic risk index is input into the risk level classification function;

[0040] Based on preset dual threshold ranges, suppliers are divided into three intelligent early warning levels: low-risk range, medium-risk range, and high-risk range;

[0041] For suppliers in medium-risk and high-risk ranges, extract their association paths and risk cluster information from the risk association map;

[0042] Establish a mapping and association rule between rule verification results and intelligent early warning levels;

[0043] When a supplier meets both the rule verification requirements and is in a low-risk zone, it is marked as a safe pass.

[0044] When a supplier passes the rule verification but is in a medium-to-high risk range, a risk tracing explanation is generated based on its associated path characteristics.

[0045] When the supplier rule verification fails, the corresponding Level 1 risk event will be correlated with the intelligent early warning level for analysis.

[0046] By integrating the binary verification conclusions, intelligent early warning levels, risk tracing explanations, and correlation analysis results, a structured comprehensive verification report is generated. The comprehensive verification report includes an overview of the supplier verification status, a list of high-risk groups, and a visual display of the correlation paths.

[0047] Furthermore, establish mapping rules between rule verification results and intelligent early warning levels, including:

[0048] Construct an association rule engine based on fuzzy reasoning, and use rule verification results and intelligent early warning levels as input variables;

[0049] Define the membership function of the rule verification result, including three fuzzy sets: fully compliant, conditionally compliant, and explicitly non-compliant;

[0050] Establish a two-way correlation matrix between intelligent early warning levels and rule verification results to obtain comprehensive risk assessment values ​​under different combinations;

[0051] Evidence theory is used to fuse the uncertainty information of rule verification and intelligent assessment to calculate the final risk confidence of each supplier;

[0052] Construct a multi-level decision tree classification rule to classify suppliers into three disposal levels: automatic approval, manual review, and veto, based on the final risk confidence level.

[0053] The decision boundary of the association rule is adaptively optimized based on historical verification accuracy and risk event occurrence rate;

[0054] When rule verification and intelligent assessment reach contradictory conclusions, an expert intervention process is initiated based on the principle of maximizing risk.

[0055] Furthermore, based on the comprehensive verification report, a credit assessment engine calculates the real-time business credit score for each supplier, and generates differentiated margin management strategies based on these real-time business credit scores, including:

[0056] Construct a multi-dimensional credit assessment indicator system, which should include at least historical performance integrity, financial stability indicators, and risk profile health.

[0057] The entropy weight method is used to calculate the weight of each indicator, and the qualitative and quantitative indicators are normalized by the fuzzy comprehensive evaluation model to obtain the normalized result.

[0058] Based on the indicator weights and normalization results, a weighted aggregation algorithm is used to generate real-time business credit scores for each supplier.

[0059] Establish a rule base for mapping real-time business credit scoring to margin management strategies;

[0060] When a supplier’s real-time business credit score is higher than a first preset threshold, a virtual margin credit limit is automatically granted, and a suggested plan for the use of the credit limit is generated.

[0061] When a supplier’s real-time business credit score is within the second preset threshold range, a guarantee priority recommendation strategy and rate optimization scheme are generated.

[0062] When a supplier’s real-time business credit score falls below the third preset threshold, an enhanced margin supervision strategy is activated, which includes increasing the required payment ratio and extending the refund review period.

[0063] The differentiated margin management strategy is dynamically adjusted based on the risk warning level in the comprehensive verification report;

[0064] For suppliers in high-risk areas, the credit score reassessment mechanism will be triggered immediately and their virtual credit line eligibility will be suspended.

[0065] The finalized differentiated margin management strategy will be linked and updated with the supplier credit profile.

[0066] Furthermore, based on the comprehensive verification report and differentiated bid bond management strategies, a final intelligent management solution for the bid bond of the target procurement project is generated and output, including:

[0067] Based on the overall supplier verification status overview in the comprehensive verification report, a summary table of security deposit verification conclusions is generated.

[0068] Based on the high-risk group identification list and the visualization of related paths, a special analysis report on bid-rigging risks is generated;

[0069] By combining differentiated deposit management strategies, a supplier tiered management list and corresponding deposit disposal solutions are formed;

[0070] Integrate the suggested schemes for the use of credit limits, the priority recommendation strategy for letters of guarantee, and the enhanced margin supervision strategy to generate detailed rules for margin management.

[0071] The updated information in the supplier credit profile will be linked and stored with the project management data.

[0072] Based on the preset report template, the summary table of deposit verification conclusions, the special analysis report on bid rigging risk, the supplier hierarchical management list, and the deposit management implementation rules are structured and integrated.

[0073] The output includes a final solution for intelligent management of bid security deposits, comprising text reports, visual charts, and machine-readable data interfaces.

[0074] In a second aspect, the present invention also provides an intelligent management system for bid security deposits based on dynamic risk profiling, applicable to the method described in the first aspect. The system includes a data acquisition module, a risk profiling construction module, a risk prediction module, an intelligent verification module, a credit assessment module, a strategy generation module, and a solution output module. The data acquisition module is configured to acquire project attribute data of the target procurement project and multi-source heterogeneous behavioral data of the participating supplier group. The risk profiling construction module is configured to construct a dynamic risk profile of the supplier group through correlation network analysis algorithms and time series pattern mining. The risk prediction module is configured to calculate the dynamic risk index of each supplier through a pre-trained collusion risk prediction model and generate a risk correlation graph. The intelligent verification module is configured to simultaneously perform rule verification of the bid security deposit payment status and intelligent assessment of the dynamic risk index at the bid opening verification node. The credit assessment module is configured to calculate the real-time commercial credit score of each supplier through a credit assessment engine. The strategy generation module is configured to generate differentiated bid security deposit management strategies based on the real-time commercial credit scores. The solution output module is configured to generate and output the final state solution for intelligent management of bid security deposits.

[0075] Unlike existing technologies, the above-mentioned technical solution provides a method and system for intelligent management of bid bonds based on dynamic risk profiling. It acquires project attribute data and multi-source heterogeneous behavioral data from suppliers, and constructs a dynamic risk profile based on correlation network analysis algorithms and time series pattern mining. The profile is then input into a pre-trained model for predicting bid rigging risks to calculate a dynamic risk index and generate a risk correlation graph. At the bid opening verification node, rule verification of bid bond payment status and intelligent assessment of the dynamic risk index are performed simultaneously, generating a comprehensive verification report that integrates rule verification results and risk warning levels. Based on the report, a real-time business credit score is calculated using a credit assessment engine, and differentiated bid bond management strategies are generated based on the score, ultimately outputting a final intelligent management solution for bid bonds. This invention realizes the transformation of bid bond management from formal review to intelligent risk warning, effectively identifying bid rigging risks; improves the accuracy of risk prevention and control through the synergy of rule verification and risk index assessment; and optimizes management efficiency through differentiated strategies based on real-time credit scoring, significantly improving the security and intelligence level of government procurement.

[0076] The above description of the invention is merely an overview of the technical solution of this application. In order to enable those skilled in the art to better understand the technical solution of this application and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of this application easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of this application. Attached Figure Description

[0077] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on this application.

[0078] In the accompanying drawings of the instruction manual:

[0079] Figure 1 This is a schematic diagram illustrating steps S101 to S106 of the method described in the specific implementation embodiment;

[0080] Figure 2 This is a schematic diagram illustrating steps S201 to S205 of the method described in a specific implementation.

[0081] Figure 3 This is a schematic diagram illustrating steps S301 to S306 of the method described in a specific implementation.

[0082] Figure 4 This is a schematic diagram illustrating steps S401 to S405 of the method described in a specific embodiment;

[0083] Figure 5 This is a flowchart illustrating the method described in a specific implementation. Detailed Implementation

[0084] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this application in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this application and are therefore intended to limit the scope of protection of this application.

[0085] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0086] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit this application.

[0087] In the description of this application, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" logical relationship.

[0088] In this application, terms such as “first” and “second” are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy or order relationship between these entities or operations.

[0089] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this application is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0090] As understood in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceeding" are understood to exclude the stated number; expressions such as "above," "below," and "within" are understood to include the stated number. Furthermore, in the description of the embodiments in this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise explicitly specified.

[0091] In the description of the embodiments of this application, the space-related expressions used, such as "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "vertical," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," indicate the orientation or positional relationship based on the orientation or positional relationship shown in the specific embodiments or drawings. They are only for the purpose of describing the specific embodiments of this application or for the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0092] The processor described in the embodiments of this application can be implemented by hardware, firmware, software, or a combination thereof. It can be a circuit, one or more of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, or a microprocessor. It also includes other physical, biological, or chemical structures that can implement the same or equivalent functions as the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of this application, or any combination of the steps mentioned therein.

[0093] The computer program involved in the embodiments can be stored in a computer device readable storage medium, which includes, but is not limited to, disks, magnetic tapes, magnetic cards, floppy disks, flash memory, optical disks, optical cards, read-only memory (ROM), random access memory (RAM), erasable programmable ROM (EPROM), and electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical, or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, proteins, and other units with information storage capabilities. In specific embodiments, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiments can be centrally stored in a single medium, or distributed and stored in multiple media. The memory containing the computer device readable storage medium can be non-volatile memory or random access memory. These computer device readable storage media can be built into the device, or can be connected to the device involved in the embodiments as an external device or part of an external device. In some embodiments, the memory having a computer device readable storage medium is deployed locally; in other embodiments, the memory may be deployed remotely from the processor, for example, as a network-attached memory accessed via RF circuitry or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), or a suitable combination thereof, as long as computer device access to the memory is enabled. Furthermore, the computer program involved in the embodiments may be stored in plaintext / ciphertext form, or it may be designed as training data, integrated and recombined through model training and implicitly stored in the parameter states of a deep neural network or other machine learning model.

[0094] Please see Figure 1 In a first aspect, this embodiment provides a method for intelligent management of bid security deposits based on dynamic risk profiling, including:

[0095] S101. Obtain project attribute data of the target procurement project and multi-source heterogeneous behavior data of participating supplier groups. The multi-source heterogeneous behavior data shall at least include historical fund flow records, guarantee application sequence and bidding behavior time sequence characteristics.

[0096] S102. Based on multi-source heterogeneous behavioral data, construct a dynamic risk profile of the supplier group through correlation network analysis algorithm and time series pattern mining. The dynamic risk profile shall include at least the characteristics of the financial correlation network, the characteristics of the consistency of bidding behavior, and the characteristics of the abnormal pattern of the use of the guarantee.

[0097] S103. Input the dynamic risk profile into the pre-trained bid-rigging risk prediction model, calculate the dynamic risk index of each supplier for the target procurement project, and generate a risk correlation map.

[0098] S104. At the bid opening verification node of the target procurement project, simultaneously perform rule verification of the bid security payment status and intelligent assessment of dynamic risk index to generate a comprehensive verification report that integrates rule verification results and risk warning level.

[0099] S105. Based on the comprehensive verification report, calculate the real-time business credit score of each supplier through the credit assessment engine, and generate differentiated margin management strategies based on the real-time business credit scores. The differentiated margin management strategies shall include at least a virtual credit line scheme for high-credit suppliers.

[0100] S106. Based on the comprehensive verification report and differentiated bid bond management strategies, generate and output the final intelligent management solution for the bid bond of the target procurement project.

[0101] In step S101, project attribute data includes basic information such as the procurement project's budget amount, procurement method, and industry classification. This data can be obtained through the project management system of the government procurement platform. Historical fund flow records in the multi-source heterogeneous behavioral data come from the transaction data interface of the banking system, recording the supplier's bid security deposit payment time, amount, and account information in historical bids. The guarantee application sequence is obtained from the financial guarantee platform, containing serialized data such as guarantee issuance time, amount, and validity period. Bidding behavior time-series characteristics are generated by analyzing the supplier's historical bidding records on the procurement platform, including bidding time distribution and bidding frequency. This data is collected and cleaned through standardized data interfaces to form a structured dataset. This step establishes a quantitative foundation for supplier behavior analysis by integrating multi-source data, providing data support for subsequent risk identification.

[0102] In step S102, the correlation network analysis algorithm identifies abnormal fund-related patterns by constructing a fund transfer network among suppliers. This includes analyzing network features such as transfer relationships and fund flows between suppliers. Time series pattern mining employs sliding window analysis to perform periodic analysis and trend detection on supplier bidding behavior sequences. Fund-related network features reflect the potential degree of correlation between suppliers, while consistent bidding behavior features are used to identify abnormal collaborative bidding patterns. Abnormal guarantee usage patterns are detected by analyzing the timing and amount changes of guarantee applications. Dynamic risk profiling integrates these features to form a comprehensive representation of the supplier's risk status. This step, through multi-dimensional feature analysis, achieves a shift from single-source data analysis to multi-source data fusion-based risk assessment.

[0103] In step S103, the pre-trained collusion risk prediction model is trained based on historical collusion case data, using machine learning algorithms to learn the mapping relationship between risk characteristics and collusion behavior. After the dynamic risk profile is input into the model, feature extraction and pattern recognition are used to output a dynamic risk index characterizing the supplier's risk level. This index comprehensively considers the supplier's current behavioral characteristics and historical risk performance. The risk association graph uses visualization technology to display the risk propagation path and association strength among suppliers, facilitating intuitive identification of risk groups. This step transforms qualitative risk characteristics into quantifiable risk indices, providing a standardized basis for subsequent risk assessment.

[0104] In step S104, the bid opening verification node refers to the key time point for verifying the bid bond during the bid opening of the procurement project. Rule-based verification is based on preset bid bond payment rules, including basic verification items such as the accuracy of the amount and the consistency of the payment account. Intelligent assessment combines a dynamic risk index to conduct in-depth risk analysis, identifying potential risks that traditional rule-based verification may fail to detect. The comprehensive verification report integrates the binary results of rule-based verification with multi-level risk warning assessments, forming a structured report containing verification conclusions, risk levels, and handling recommendations. This step, through the parallel execution of rule-based verification and intelligent assessment, achieves an improvement from formal review to substantive risk identification.

[0105] In step S105, the credit assessment engine constructs an assessment model based on multi-dimensional data such as the supplier's historical performance records and financial status. Real-time business credit scores are derived through weighted calculation of various credit indicators. Differentiated margin management strategies are formulated according to credit score grading. Virtual credit line schemes for high-credit suppliers allow them to waive margin requirements within a specific limit, while suppliers with lower credit scores are subject to stricter margin management measures. These strategies are automatically triggered by preset scoring thresholds, ensuring the accuracy and efficiency of management. This step, through the linkage between credit assessment and margin management, achieves optimized resource allocation and precise risk control.

[0106] In step S106, the final state solution for intelligent bid bond management integrates all outputs from the preceding steps, including comprehensive verification conclusions, risk warning information, and differentiated strategy recommendations. The final state solution adopts a standardized report format, including text descriptions, data tables, and visualization charts, and can be directly output to relevant management personnel through the procurement platform. The final state solution not only provides the current verification results but also includes subsequent management suggestions and risk handling guidelines, forming a complete management loop. This step provides comprehensive support for bid bond management decisions by systematically integrating the outputs of each stage.

[0107] This embodiment achieves intelligent and precise management of bid security deposits by constructing a complete management process from data collection to solution output. It overcomes the limitations of traditional single-data source analysis by employing multi-source data fusion analysis technology. A dynamic risk profiling mechanism enables a comprehensive characterization of risk features. Parallel execution of rule verification and intelligent assessment ensures the accuracy and timeliness of risk identification. The linkage between credit assessment and differentiated strategies improves resource utilization efficiency. This embodiment upgrades traditional bid security deposit management, which is primarily based on formal review, to data-driven intelligent risk management, effectively enhancing the risk prevention and control capabilities and management efficiency of government procurement.

[0108] Please see Figure 2 In some embodiments, a dynamic risk profile of a supplier group is constructed based on multi-source heterogeneous behavioral data through correlation network analysis algorithms and time series pattern mining, including:

[0109] S201. Perform account entity analysis and fund flow tracking on historical fund flow records to construct a fund flow network with supplier legal persons, shareholders and payment accounts as nodes;

[0110] S202. The community detection algorithm is used to perform topological analysis on the fund transfer network, identify potential bid-rigging groups with close fund associations, and extract the network density and node centrality indices of the fund transfer network as fund association network features.

[0111] S203. Perform periodic analysis and anomaly detection on the temporal characteristics of bidding behavior, extract the collaborative behavior patterns of suppliers in terms of bid deadline distribution, bidding geographical concentration and similarity of bid targets, and generate consistency characteristics of bidding behavior.

[0112] S204. Perform frequent pattern mining and sequence alignment analysis on the guarantee application sequence to identify abnormal concentration of guarantee issuing institutions, abnormal synchronization of guarantee expiration time, and abnormal dispersion of guarantee amount, and construct abnormal guarantee usage pattern features.

[0113] S205. Multimodal feature fusion is performed on the characteristics of fund-related networks, the consistency characteristics of bidding behavior, and the abnormal pattern characteristics of guarantee usage to generate a dynamic risk profile with timestamps.

[0114] In step S201, account entity parsing uses natural language processing technology to identify account entity information in the fund flow records, associating different accounts with entities such as supplier legal representatives and shareholders. Fund flow tracking uses graph database technology to record the flow path of funds between accounts, constructing a network structure with entities as nodes and fund flow as edges. The fund flow network uses visualization technology to display the fund relationships between suppliers, providing basic data support for subsequent network analysis. This step transforms scattered fund flow records into structured network data through entity identification and flow tracking.

[0115] In step S202, the community detection algorithm preferably employs the Louvain algorithm, which identifies tightly connected groups in the network through modularity optimization. Network density is obtained by calculating the ratio of the actual number of edges to the maximum possible number of edges, reflecting the intensity of financial transactions. Node centrality indicators include degree centrality, proximity centrality, and eigenvector centrality, representing the number of direct connections between nodes, network accessibility, and influence, respectively. These indicators collectively constitute the characteristics of the financial association network, used to quantitatively assess the importance and association strength of suppliers in the financial network. This step, through network topology analysis, achieves a shift from intuitive observation to quantitative identification.

[0116] In step S203, periodic analysis uses Fourier transform to detect the temporal patterns of bidding behavior, and outlier detection uses the isolated forest algorithm to identify behaviors that deviate from the normal pattern. The bid deadline distribution analysis analyzes the temporal patterns of supplier bid submissions, the geographical concentration of bids is calculated using geographic information aggregation to determine regional distribution characteristics, and the similarity of bids is analyzed based on text mining techniques to determine the degree of correlation between procurement content. Collaborative behavior patterns are identified through multi-dimensional correlation analysis to identify abnormally consistent bidding behavior characteristics. This step effectively identifies potential bid-rigging behavior characteristics through temporal pattern mining.

[0117] In step S204, frequent pattern mining uses the Apriori algorithm to discover common combination patterns in guarantee applications, and sequence alignment analysis compares the similarity of guarantee application sequences using dynamic time warping technology. Anomalies in the concentration of guarantee issuing institutions are identified by statistically analyzing the proportion of guarantees issued by the same institution. Anomalies in the synchronicity of guarantee expiration times are detected by identifying abnormal concentrations in guarantee maturity dates. Anomalies in the dispersion of guarantee amounts are analyzed by examining abnormal fluctuations in the distribution of guarantee amounts. These anomaly patterns collectively constitute the characteristics of abnormal guarantee usage patterns. This step, through sequence analysis technology, delves into the potential risk signals in the use of guarantees.

[0118] In step S205, multimodal feature fusion employs a feature-level fusion strategy, normalizing feature vectors from different sources before concatenation. A timestamp records the specific time of feature extraction, ensuring the timeliness of the risk profile. The dynamic risk profile maintains feature freshness through a periodic update mechanism; preferably, a daily or weekly update cycle is set. This step, through feature fusion and timestamps, constructs a comprehensive and real-time supplier risk characterization system.

[0119] This embodiment constructs a dynamic profile that comprehensively reflects the supplier's risk status through multi-level feature extraction and fusion analysis. Network analysis technology is used to deeply mine financial relationship characteristics, time series analysis methods are used to identify abnormal behavioral patterns, and sequence mining technology is combined to discover patterns in the use of guarantees. Finally, a comprehensive risk assessment is formed through multi-modal fusion. This embodiment overcomes the limitations of traditional single-dimensional risk assessment, improving the accuracy and comprehensiveness of risk identification through collaborative analysis of multi-source data, providing a reliable data foundation for subsequent risk prediction and control.

[0120] Please see Figure 3 In some embodiments, periodic analysis and anomaly detection are performed on the temporal characteristics of bidding behavior to extract collaborative behavior patterns among suppliers in terms of bid deadline distribution, bidding geographical concentration, and similarity of bid targets, generating consistency characteristics of bidding behavior, including:

[0121] S301. Construct a bidding behavior time series matrix. The matrix dimension of the bidding behavior time series matrix shall include at least the bidding timestamp sequence, the bidding geographical code sequence, and the bidding target classification sequence.

[0122] S302. Use the dynamic time warping algorithm to calculate the similarity distance between different suppliers in the bidding time distribution and identify the supplier group with synchronous bidding behavior.

[0123] S303. Spatial distribution pattern mining is performed on the bidding region coding sequence using spectral clustering algorithm to detect bidding groups with abnormally consistent regional selection.

[0124] S304. Based on the isolated forest algorithm, perform anomaly detection on the classification sequence of bidding targets to identify supplier combinations with unconventional similarity in business domain selection.

[0125] S305. Conduct correlation analysis on synchronous bidding behavior, consistency of abnormal regions, and similarity of unconventional business to generate a bidding behavior synergy index;

[0126] S306. Obtain the consistency characteristics of bidding behavior based on the coordination index of bidding behavior.

[0127] In step S301, the bidding behavior time series matrix is ​​constructed by arranging the historical bidding records of each supplier in chronological order. The bidding timestamp sequence records the specific time point of the bidding operation, the bidding location code sequence uses administrative division codes to identify the location of the bidding project, and the bidding item classification sequence categorizes the bidding content based on the procurement item classification standards. The bidding behavior time series matrix, through the combination of time, space, and business dimensions, forms a multi-dimensional quantitative representation of supplier bidding behavior. This step, by constructing a multi-dimensional time series matrix, provides a structured data foundation for subsequent collaborative behavior analysis.

[0128] In step S302, the dynamic time warping algorithm overcomes the limitation of traditional Euclidean distance in its sensitivity to time axis scaling by calculating the minimum alignment distance between two time series. A smaller similarity distance indicates greater synchronization in the suppliers' bidding time selection; when the distance is below a preset threshold, it is identified as synchronized bidding behavior. Preferably, the dynamic time warping algorithm uses a sliding window mechanism to process time series of different lengths, ensuring the accuracy and efficiency of the calculation. This step effectively identifies potential collaborative bidding time patterns through time series alignment analysis.

[0129] In step S303, the spectral clustering algorithm transforms the geographic coding sequence into a graph structure and identifies natural clusters in the data through eigenvalue decomposition. First, a similarity matrix is ​​constructed to characterize the degree of association between suppliers in their geographic choices. Then, low-dimensional embeddings are obtained through Laplace matrix eigenvalue decomposition. Finally, the K-means algorithm is used for clustering. Anomalies are identified by detecting statistical outliers in the geographic distribution within clusters, with the Z-score method preferably used to identify bidding groups that significantly deviate from the normal distribution. This step, through spatial clustering analysis, reveals potential correlation patterns in bidding behavior across geographic dimensions.

[0130] In step S304, the Isolation Forest algorithm constructs multiple isolation trees by randomly partitioning the feature space. The unusual similarity in business domain selection is quantified by calculating the supplier's anomaly score in the target classification sequence. The algorithm randomly selects features and segmentation values ​​to construct a binary tree; anomaly samples, due to their significantly different feature values ​​from normal samples, are isolated to leaf nodes more quickly. The anomaly score of supplier combinations is calculated using the average path length; a higher score indicates a more anomalous similarity in business domain selection. This step, through unsupervised anomaly detection, identifies supplier groups exhibiting suspicious consistency in business domain selection.

[0131] In step S305, the correlation analysis employs a multi-dimensional evidence fusion strategy, weighting and integrating abnormal indicators from three dimensions: time, region, and business. The bidding behavior coordination index is calculated using linear weighting or rule-based fusion methods. The weights of each dimension can be adjusted based on historical risk cases, with entropy weighting being the preferred method for determining initial weights. Higher index values ​​indicate a more abnormal degree of coordination among suppliers, suggesting a greater likelihood of bid-rigging. This step, through multi-dimensional evidence fusion, achieves a comprehensive quantitative assessment of coordinated bidding behavior.

[0132] In step S306, the consistency feature of bidding behavior is standardized to map the coordination index to a fixed range, facilitating subsequent model use. The feature values ​​are preferably transformed to the 0-1 range using the min-max normalization method, where higher values ​​indicate more significant behavioral consistency features. This feature, as an important component of the dynamic risk profile, together with other risk features, constitutes a comprehensive risk assessment of the supplier. This step, through feature standardization, ensures the comparability between different risk features and the standardization of model input.

[0133] This embodiment utilizes multi-dimensional time-series analysis technology to deeply explore potential collaborative patterns in supplier bidding behavior. Algorithms such as dynamic time warping, spectral clustering, and isolated forest are employed to identify abnormal consistency from temporal, spatial, and business dimensions, respectively. Finally, a comprehensive collaboration index is formed through multi-evidence fusion. This embodiment overcomes the limitations of traditional single-dimensional analysis, significantly improving the accuracy and reliability of bid-rigging behavior identification through multi-angle collaborative detection, providing crucial technical support for constructing accurate dynamic risk profiles.

[0134] Please see Figure 4 In some embodiments, a dynamic risk profile is input into a pre-trained bid-rigging risk prediction model to calculate the dynamic risk index of each supplier for the target procurement project and generate a risk correlation map, including:

[0135] S401. Perform feature importance weighting on the dynamic risk profile, and calculate the contribution of each feature dimension to the risk of collusion through the gradient boosting decision tree algorithm to obtain the weighted feature vector.

[0136] S402. Input the weighted feature vector into the pre-trained isolated forest anomaly detection model, calculate the anomaly score of each supplier in the feature space, and normalize the anomaly score into a primary risk index.

[0137] S403. Based on the characteristics of the capital relationship network, a graph neural network model is used to learn the node embedding representation to obtain the structural relationship and potential relationship strength of suppliers in the capital network.

[0138] S404. The primary risk index and the node embedding vector output by the graph neural network model are concatenated for feature concatenation and input into the multilayer perceptron classifier for joint training, and the dynamic risk index of each supplier for the target procurement project is output.

[0139] S405. Based on the node similarity and association strength learned by the graph neural network model, construct the risk propagation link between suppliers and generate a risk association map.

[0140] In step S401, the feature importance weighting process is implemented using the gradient boosting decision tree algorithm. This algorithm automatically calculates the information gain of each feature at the splitting node during training, and normalizes the information gain value as the feature weight. Features with higher contribution receive greater weight in the weighted feature vector, ensuring that key risk features play a dominant role in subsequent analysis. This step, through adaptive weight allocation, highlights the feature dimensions most discriminative for identifying collusion and fraud risks.

[0141] In step S402, the pre-trained isolated forest anomaly detection model constructs a set of binary trees that randomly partition the feature space and calculates the path length from a sample to a leaf node as an anomaly score. Shorter path lengths indicate a more anomalous sample. These raw scores are then converted into a primary risk index in the 0-1 range through min-max normalization. This step effectively identifies outliers in the feature space by leveraging the characteristics of unsupervised learning.

[0142] In step S403, the graph neural network model employs a graph convolutional network architecture, learning the embedded representation of suppliers in the funding network by aggregating node neighbor information. The node embedding vectors capture the structural relationship features of the suppliers, including direct association strength and indirect association paths, with the potential association strength quantified by the cosine similarity of the embedding vectors. This step transforms the network topology information into numerical features that can be processed by machine learning.

[0143] In step S404, feature concatenation connects the primary risk index and the node embedding vector along the feature dimension to form an enhanced feature representation. The multilayer perceptron classifier learns the complex mapping relationship between features and the risk index through fully connected layers and nonlinear activation functions, and the output layer uses the sigmoid function to constrain the predicted values ​​to the 0-1 range. This step, through feature fusion and deep learning, achieves effective integration of multi-source information and accurate risk quantification.

[0144] In step S405, node similarity is obtained by calculating the inner product of the embedding vectors, and the association strength is calculated based on a combination of the frequency and amount of financial transactions. The risk propagation chain is constructed by identifying supplier pairs with similarity exceeding a threshold and existing financial transactions, using directed edges to represent the direction of risk propagation. The risk association graph uses a graph structure to visually represent the risk association network between suppliers. This step transforms abstract association relationships into an intuitive visual analysis tool.

[0145] This embodiment constructs a precise risk prediction system for collusion and bid-rigging through the fusion of multi-level machine learning models. Feature importance weighting ensures the prominent role of key risk features, isolated forest anomaly detection provides basic risk identification, graph neural networks mine network structure information, and multilayer perceptrons achieve effective fusion of multi-source features. This embodiment upgrades traditional rule-based risk assessment to data-driven intelligent prediction, visually displaying risk propagation paths through risk association graphs, significantly improving the accuracy and interpretability of collusion and bid-rigging risk identification.

[0146] In some embodiments, the primary risk index is concatenated with the node embedding vectors output by the graph neural network model, and then input into a multilayer perceptron classifier for joint training. The output is a dynamic risk index for each supplier for the target procurement project, including:

[0147] A deep neural network architecture for a multilayer perceptron classifier is constructed. The deep neural network architecture includes a feature fusion layer, multiple hidden layers, and a risk index output layer.

[0148] In the feature fusion layer, an attention-weighted feature splicing mechanism is used to adaptively allocate weights and deeply fuse the primary risk index and the node embedding vector to obtain the spliced ​​node embedding vector.

[0149] By using nonlinear transformation of the hidden layer, deep pattern features of high-risk scrambling behavior are extracted layer by layer from the spliced ​​node embedding vector.

[0150] The Sigmoid activation function is used in the risk index output layer to map the final output value to a dynamic risk index in the range of 0 to 1.

[0151] In this embodiment, the deep neural network architecture of the multilayer perceptron classifier adopts a layered design. The feature fusion layer is responsible for receiving and processing feature inputs from different sources, multiple hidden layers realize deep transformation of features through a fully connected structure, and the risk index output layer generates the final risk assessment result. This layered architecture effectively extracts complex patterns from the input features through layer-by-layer abstraction and transformation.

[0152] The feature fusion layer employs an attention-weighted mechanism that dynamically allocates importance weights for each feature dimension by calculating the correlation score between the primary risk index and the node embedding vector. Specifically, an attention score is calculated using a learnable weight matrix, and then the feature vectors are weighted and summed to achieve adaptive weight allocation. The deep fusion process concatenates the weighted features dimensionally to form a unified feature representation. This mechanism ensures that key risk features receive sufficient attention during the fusion process.

[0153] Hidden layers achieve hierarchical transformation of features through non-linear activation functions. Each hidden unit linearly transforms the input using a weight matrix and bias vector before introducing non-linearity through the activation function. By stacking multiple hidden layers, the network can extract abstract representations from local features to global patterns layer by layer, effectively capturing deep feature patterns of high-risk spoofing behavior. The choice of the number of hidden layers needs to balance model complexity and expressive power.

[0154] The risk index output layer uses the Sigmoid activation function to map the final output value to the range of 0 to 1. This function converts any real number into a probabilistic output through exponential operations. The closer the dynamic risk index is to 1, the higher the risk of supplier collusion; the closer it is to 0, the lower the risk. This output format facilitates subsequent risk level classification and decision support.

[0155] This embodiment achieves intelligent fusion and accurate prediction of multi-source risk features through a deep neural network architecture. An attention-weighted mechanism ensures the prominent role of key features, a multi-layered hidden structure effectively extracts deep risk patterns, and a sigmoid output layer provides intuitive risk quantification indicators. This embodiment overcomes the limitations of traditional linear models in recognizing complex risk patterns and significantly improves the accuracy and reliability of risk prediction for collusion through deep learning technology.

[0156] In some embodiments, at the bid opening verification node of the target procurement project, rule verification of the bid security payment status and intelligent assessment of the dynamic risk index are performed simultaneously, generating a comprehensive verification report that integrates the rule verification results and risk warning levels, including:

[0157] The automatic verification engine for margin deposits is invoked to retrieve real-time deposit data from the banking system and financial guarantee platform through standardized interfaces.

[0158] The preset rule verification process is executed. The preset rule verification process includes at least verification of the adequacy of the margin amount, verification of the consistency of the payment account name, and review of the validity of the guarantee, so as to obtain the rule verification result.

[0159] A binary verification conclusion is generated based on the rule verification results, and abnormal verification data is marked as a level 1 risk event.

[0160] The intelligent risk assessment process is initiated in parallel, and the dynamic risk index is input into the risk level classification function;

[0161] Based on preset dual threshold ranges, suppliers are divided into three intelligent early warning levels: low-risk range, medium-risk range, and high-risk range;

[0162] For suppliers in medium-risk and high-risk ranges, extract their association paths and risk cluster information from the risk association map;

[0163] Establish a mapping and association rule between rule verification results and intelligent early warning levels;

[0164] When a supplier meets both the rule verification requirements and is in a low-risk zone, it is marked as a safe pass.

[0165] When a supplier passes the rule verification but is in a medium-to-high risk range, a risk tracing explanation is generated based on its associated path characteristics.

[0166] When the supplier rule verification fails, the corresponding Level 1 risk event will be correlated with the intelligent early warning level for analysis.

[0167] By integrating the binary verification conclusions, intelligent early warning levels, risk tracing explanations, and correlation analysis results, a structured comprehensive verification report is generated. The comprehensive verification report includes an overview of the supplier verification status, a list of high-risk groups, and a visual display of the correlation paths.

[0168] In this embodiment, the automatic margin verification engine establishes data connections with the bank's core system and the financial guarantee platform through pre-configured standardized interface protocols to obtain the supplier's margin deposit status and guarantee information in real time. Preferably, these standardized interfaces employ encrypted transmission to ensure data security and use timed polling or event-triggered mechanisms to ensure data timeliness. This step achieves automated collection of multi-source data, providing an accurate data foundation for subsequent verification.

[0169] In the pre-defined rule verification process, the adequacy of the security deposit amount is verified by comparing the difference between the actual deposited amount and the amount due. The consistency of the payment account name is verified using a string matching algorithm to compare the payer's name with the supplier's registered name. The validity of the guarantee is reviewed by verifying the guarantee's status, validity period, and the issuing institution's qualifications. These rules are set based on procurement regulations and business requirements, forming a systematic verification standard. This step ensures the compliance of the security deposit payment through multi-dimensional rule verification.

[0170] The binary verification conclusion simplifies complex verification results into two states: "pass" or "fail," facilitating unified management of subsequent processing. Verification anomaly data is categorized and marked according to anomaly type, with Level 1 risk events specifically referring to serious anomalies suspected of bid-rigging. This step, through result simplification and categorization, improves the efficiency and accuracy of anomaly identification.

[0171] The risk level classification function employs a piecewise linear function or a quantile-based method based on statistical distribution to map continuous risk indices to discrete risk levels. The preset dual threshold intervals are determined by analyzing historical risk data distribution, preferably using ternary quantiles or thresholds based on business experience. This step achieves quantitative classification of risk levels, providing a basis for differentiated processing.

[0172] Linkage path extraction is achieved by traversing the connecting edges in the risk association graph, and risk community information is used to identify closely related supplier groups based on graph clustering algorithms. This information reveals risk propagation paths and potential networks of vested interests, providing a visual basis for risk tracing. This step, through graph structure analysis, delves into the inherent patterns of risk associations.

[0173] The mapping and association rules establish a collaborative mechanism for rule verification and intelligent assessment, enabling multi-condition combination judgments through decision tables or rule engines. Security is assessed by indicating that the supplier meets requirements in both formal review and substantive risk assessment; risk tracing details the characteristics and correlation paths of abnormal behavior; and correlation analysis explores the intrinsic link between rule anomalies and risk indices. This step, through multi-dimensional evidence fusion, forms a comprehensive risk assessment system.

[0174] The structured comprehensive verification report uses a standardized document format. The supplier verification status overview presents the overall verification results in tabular form. The high-risk group identification list highlights suppliers requiring special attention, and the network diagram visually illustrates the risk propagation relationships. The report content is structured to facilitate managers' quick understanding of core information. This step, through systematic integration, results in a complete verification conclusion.

[0175] This embodiment constructs a multi-layered risk identification system through the parallel execution and collaborative analysis of rule verification and intelligent assessment. Automated data collection ensures verification efficiency, the dual verification mechanism enhances the comprehensiveness of risk identification, and structured reports provide clear decision support, effectively improving the risk control capabilities of bid bond management.

[0176] In some embodiments, establishing a mapping association rule between rule verification results and intelligent early warning levels includes:

[0177] Construct an association rule engine based on fuzzy reasoning, and use rule verification results and intelligent early warning levels as input variables;

[0178] Define the membership function of the rule verification result, including three fuzzy sets: fully compliant, conditionally compliant, and explicitly non-compliant;

[0179] Establish a two-way correlation matrix between intelligent early warning levels and rule verification results to obtain comprehensive risk assessment values ​​under different combinations;

[0180] Evidence theory is used to fuse the uncertainty information of rule verification and intelligent assessment to calculate the final risk confidence of each supplier;

[0181] Construct a multi-level decision tree classification rule to classify suppliers into three disposal levels: automatic approval, manual review, and veto, based on the final risk confidence level.

[0182] The decision boundary of the association rule is adaptively optimized based on historical verification accuracy and risk event occurrence rate;

[0183] When rule verification and intelligent assessment reach contradictory conclusions, an expert intervention process is initiated based on the principle of maximizing risk.

[0184] In this embodiment, the association rule engine based on fuzzy reasoning introduces fuzzy logic to handle the uncertain correlation between rule verification results and intelligent early warning levels. This engine converts precise input values ​​into fuzzy sets, obtains fuzzy outputs through fuzzy rule reasoning, and finally obtains a precise comprehensive risk assessment value through defuzzification, effectively addressing the boundary ambiguity problem in risk assessment.

[0185] The membership function of the rule verification results is defined using a triangular or trapezoidal function. Full compliance corresponds to the case where the verification result fully meets the requirements, conditional compliance indicates a state with slight deviations but still acceptable, and explicit violation refers to a case of serious violation of the rules. Each fuzzy set quantifies its degree of belonging to that set through the membership function, realizing the transformation from binary judgment to continuous measurement.

[0186] The bidirectional association matrix is ​​constructed with the three fuzzy sets of rule verification results as rows and the intelligent early warning level as columns. The matrix elements represent the risk impact weights under specific combinations. The comprehensive risk assessment value is obtained by combining matrix operations with membership values, reflecting the combined impact of rule verification and intelligent assessment, and facilitating an intuitive understanding of the relationships between different factors.

[0187] Evidence theory processes uncertainties from rule verification and intelligent evaluation using a basic probability assignment function, and fuses information from multiple evidence sources using Dempster's combination rule. The final risk confidence calculation considers the degree of conflict and credibility among the evidence, providing a more reliable probabilistic basis for decision-making and effectively addressing the limitations of traditional methods in handling uncertain information.

[0188] The multi-level decision tree classification rule sets two thresholds based on the final risk reliability, dividing suppliers into three handling levels. Automatic approval is granted for suppliers with low risk reliability, manual review is applicable to medium-risk reliability cases, and a veto is applied to high-risk reliability suppliers. This tiered handling mechanism achieves a balance between risk control and efficiency.

[0189] The decision boundary optimization dynamically adjusts the classification threshold by analyzing the correlation between historical verification accuracy and risk event occurrence rates. When historical data indicates that the original boundary settings are not reasonable enough, the system automatically calculates new optimal boundary values ​​to ensure the accuracy and timeliness of risk assessment.

[0190] The expert intervention process is initiated when there is a conflict between the rule verification and intelligent evaluation conclusions, prioritizing the conclusion with higher risk based on the principle of maximizing risk. Experts review detailed information through a manual review interface, make a final judgment based on their professional experience, and provide feedback data for system optimization.

[0191] This embodiment constructs an intelligent risk assessment fusion mechanism through fuzzy reasoning and evidence theory, effectively addressing the uncertainties in rule verification and intelligent assessment. Multi-level decision-making and adaptive optimization ensure the accuracy and adaptability of risk assessment, while expert intervention provides ultimate assurance for complex situations. This embodiment significantly improves the scientific rigor and reliability of risk assessment, providing more precise decision support for bid bond management.

[0192] In some embodiments, based on a comprehensive verification report, a credit assessment engine calculates real-time business credit scores for each supplier, and generates differentiated margin management strategies based on these real-time business credit scores, including:

[0193] Construct a multi-dimensional credit assessment indicator system, which should include at least historical performance integrity, financial stability indicators, and risk profile health.

[0194] The entropy weight method is used to calculate the weight of each indicator, and the qualitative and quantitative indicators are normalized by the fuzzy comprehensive evaluation model to obtain the normalized result.

[0195] Based on the indicator weights and normalization results, a weighted aggregation algorithm is used to generate real-time business credit scores for each supplier.

[0196] Establish a rule base for mapping real-time business credit scoring to margin management strategies;

[0197] When a supplier’s real-time business credit score is higher than a first preset threshold, a virtual margin credit limit is automatically granted, and a suggested plan for the use of the credit limit is generated.

[0198] When a supplier’s real-time business credit score is within the second preset threshold range, a guarantee priority recommendation strategy and rate optimization scheme are generated.

[0199] When a supplier’s real-time business credit score falls below the third preset threshold, an enhanced margin supervision strategy is activated, which includes increasing the required payment ratio and extending the refund review period.

[0200] The differentiated margin management strategy is dynamically adjusted based on the risk warning level in the comprehensive verification report;

[0201] For suppliers in high-risk areas, the credit score reassessment mechanism will be triggered immediately and their virtual credit line eligibility will be suspended.

[0202] The finalized differentiated margin management strategy will be linked and updated with the supplier credit profile.

[0203] In this embodiment, the multi-dimensional credit assessment indicator system constructs a comprehensive assessment framework by integrating the supplier's historical performance, financial status, and risk characteristics. Historical performance integrity is calculated based on the supplier's past contract execution records and default situations; financial stability indicators are derived by analyzing the solvency and cash flow status in financial statements; and risk profile health is assessed by comprehensively evaluating various risk characteristics in the dynamic risk profile. These indicators reflect the supplier's creditworthiness from different dimensions.

[0204] The entropy weight method determines the dispersion of each indicator by calculating its information entropy. Indicators with lower information entropy are considered to contain more effective information and thus receive higher weights. The fuzzy comprehensive evaluation model converts qualitative indicators into quantitative values ​​using membership functions, while quantitative indicators eliminate the influence of dimensions through methods such as range normalization. Normalization ensures the comparability of indicators with different dimensions and types.

[0205] The weighted aggregation algorithm linearly combines the normalized indicator values ​​with weights determined by the entropy weight method to generate a real-time business credit score ranging from 0 to 100. A higher score indicates a better supplier creditworthiness. The score update frequency can be set according to business needs, with daily or weekly updates being preferred. This dynamic scoring mechanism can promptly reflect changes in supplier creditworthiness.

[0206] The mapping rule base associates credit scores with specific management strategies through preset scoring thresholds. Preferably, the first preset threshold is set above 80 points, corresponding to suppliers with excellent credit; the second preset threshold ranges from 60 to 80 points, corresponding to suppliers with average credit; and the third preset threshold is set below 60 points, corresponding to suppliers whose credit requires close monitoring. These scoring thresholds can be adjusted according to business policies and risk appetite.

[0207] Virtual margin credit limits are determined based on the supplier's credit score and historical transaction records. The suggested usage plan includes specific terms such as the scope of application, usage period, and repayment requirements. The letter of guarantee priority recommendation strategy guides suppliers to choose lower-cost guarantee methods, while the rate optimization plan negotiates more favorable rate terms with guarantee institutions.

[0208] Enhanced margin supervision strategies strengthen risk control by increasing margin requirements, while extended refund review periods provide more time for reviewing fund security. These measures, while ensuring procurement security, also provide incentives for credit improvement.

[0209] The dynamic adjustment mechanism based on risk warning levels ensures timely adjustments to management strategies upon the detection of new risk signals. The credit score reassessment mechanism activates immediately upon detecting high-risk signals, ensuring the accuracy of the assessment results by recalculating the credit score. The suspension of virtual credit line eligibility prevents the spread of risk and can be restored after review and approval once the risk has been eliminated.

[0210] The linked updates to supplier credit profiles ensure that management strategies are synchronized with credit status. All strategy adjustments and credit changes are recorded, forming a complete credit management track. This mechanism provides a historical basis for subsequent credit assessments and strategy formulation.

[0211] This embodiment achieves precise and differentiated margin management by constructing a scientific credit assessment system and a flexible strategy mapping mechanism. Multi-dimensional indicator evaluation ensures the comprehensiveness of credit scoring, entropy weighting reflects the differences in indicator importance, and a dynamic adjustment mechanism guarantees timely risk response. This embodiment establishes a credit-based refined management model, which not only improves risk control effectiveness but also optimizes the supplier service experience.

[0212] In some embodiments, based on a comprehensive verification report and a differentiated bid bond management strategy, a final intelligent management solution for the bid bond of a target procurement project is generated and output, including:

[0213] Based on the overall supplier verification status overview in the comprehensive verification report, a summary table of security deposit verification conclusions is generated.

[0214] Based on the high-risk group identification list and the visualization of related paths, a special analysis report on bid-rigging risks is generated;

[0215] By combining differentiated deposit management strategies, a supplier tiered management list and corresponding deposit disposal solutions are formed;

[0216] Integrate the suggested schemes for the use of credit limits, the priority recommendation strategy for letters of guarantee, and the enhanced margin supervision strategy to generate detailed rules for margin management.

[0217] The updated information in the supplier credit profile will be linked and stored with the project management data.

[0218] Based on the preset report template, the summary table of deposit verification conclusions, the special analysis report on bid rigging risk, the supplier hierarchical management list, and the deposit management implementation rules are structured and integrated.

[0219] The output includes a final solution for intelligent management of bid security deposits, comprising text reports, visual charts, and machine-readable data interfaces.

[0220] In this embodiment, the summary table of security deposit verification results extracts key verification results from the comprehensive verification report and clearly displays the security deposit payment status, verification conclusions, and anomaly explanations for each supplier in tabular form. The table is sorted by supplier name and includes core fields such as verification time, verification result, and anomaly type, facilitating managers to quickly grasp the overall verification situation.

[0221] This special analysis report on bid-rigging risks, based on supplier information from a high-risk group identification list and combined with a visualized risk propagation network, provides an in-depth analysis of potential bid-rigging patterns. The report includes risk group characteristic analysis, explanation of association paths, and risk level assessment, providing professional support for risk prevention and control decisions.

[0222] The supplier tiered management list categorizes suppliers into different management levels based on differentiated security deposit management strategies, and clearly defines the security deposit handling requirements for each level. The list includes the basis for supplier tiering, management level identifiers, and specific handling measures, forming a systematic tiered management system.

[0223] The detailed rules for margin management integrate specific implementation plans for various strategies to form operable management standards. The suggested scheme for the use of virtual credit lines clarifies the application process and usage rules; the priority recommendation strategy for letters of guarantee explains the preferential conditions and processing procedures for selecting letters of guarantee; and the enhanced margin supervision strategy specifies in detail the implementation standards and time limits for regulatory measures.

[0224] The updated supplier credit profiles are linked to project management data, enabling the analysis of historical credit records and current project performance through the establishment of a data correlation index. This linkage mechanism provides comprehensive data support for credit assessment and strategy formulation, ensuring the continuity and accuracy of management decisions.

[0225] The pre-set report template adopts a standardized document structure, ensuring the logic and readability of each component through chapter division and formatting specifications. The structured integration process organizes the content according to business logic, forming a complete report system with clear hierarchy and highlighting key points.

[0226] The intelligent bid bond management solution meets diverse usage needs through multiple output formats: text reports provide detailed explanations, visual charts intuitively display key data and relationship networks, and machine-readable data interfaces support data exchange and subsequent processing between systems. This diversified output approach ensures the solution's practical usability and scalability.

[0227] This embodiment constructs a complete intelligent management closed loop for bid security deposits through systematic solution integration and diversified output formats. A standardized report structure ensures the integrity and standardization of information, while visual presentation enhances the understandability of the information, and a machine-readable interface supports subsequent automated processing. This embodiment integrates scattered management information into a unified decision support tool, significantly improving the systematic nature and operability of bid security deposit management.

[0228] In a second aspect, this embodiment also provides an intelligent management system for bid security deposits based on dynamic risk profiles, applicable to the method described in the first aspect. The system includes a data acquisition module, a risk profile construction module, a risk prediction module, an intelligent verification module, a credit assessment module, a strategy generation module, and a solution output module. The data acquisition module is configured to acquire project attribute data of the target procurement project and multi-source heterogeneous behavioral data of the participating supplier group. The risk profile construction module is configured to construct a dynamic risk profile of the supplier group through correlation network analysis algorithms and time series pattern mining. The risk prediction module is configured to calculate the dynamic risk index of each supplier through a pre-trained collusion risk prediction model and generate a risk correlation graph. The intelligent verification module is configured to simultaneously perform rule verification of the bid security deposit payment status and intelligent assessment of the dynamic risk index at the bid opening verification node. The credit assessment module is configured to calculate the real-time commercial credit score of each supplier through a credit assessment engine. The strategy generation module is configured to generate differentiated bid security deposit management strategies based on the real-time commercial credit scores. The solution output module is configured to generate and output the final state solution for intelligent management of bid security deposits.

[0229] In this embodiment, the data acquisition module obtains project attribute data and multi-source heterogeneous behavioral data from the banking system, financial guarantee platform, and procurement business system through standardized interfaces, providing a data foundation for subsequent analysis. The risk profile construction module uses correlation network analysis algorithms and time series pattern mining techniques to process the acquired data and construct a dynamic risk profile that includes financial correlation network characteristics, consistency characteristics of bidding behavior, and abnormal pattern characteristics of guarantee usage. The risk prediction module inputs the dynamic risk profile into a pre-trained bid-rigging risk prediction model, calculates the dynamic risk index of each supplier, and generates a risk correlation graph to achieve quantitative risk assessment.

[0230] The intelligent verification module performs rule-based verification of the bid bond payment status and intelligent assessment of the dynamic risk index in parallel at the bid opening verification node, ensuring the comprehensiveness of the verification through a dual verification mechanism. The credit assessment module calculates a real-time business credit score based on the comprehensive verification report using a credit assessment engine, providing a basis for differentiated strategy formulation. The strategy generation module generates differentiated bid bond management strategies, including virtual credit line schemes, based on the credit score, achieving precise management. The solution output module integrates the output results from each stage, generating a final intelligent management solution for bid bonds that includes text reports, visual charts, and data interfaces.

[0231] This system achieves intelligent management of the entire bid security deposit process through a modular architecture. The modules work collaboratively, integrating traditionally fragmented manual operations into an automated closed-loop management process. This not only improves the efficiency of bid security deposit verification but also enhances risk control capabilities through dynamic risk identification and credit assessment, providing a safe and efficient bid security deposit management solution for government procurement.

[0232] Furthermore, the above technical solution can be understood in conjunction with the following example:

[0233] Please see Figure 5 The process of the intelligent management method for bid security deposits based on dynamic risk profiling is as follows:

[0234] (1) When preparing the procurement documents, the agency shall set the amount of the bid security to be paid by the supplier and the optional payment channels. When submitting (responding to) the bid, the supplier shall select the payment channel and pay the bid security. When the payment channel is a virtual security, the following steps shall be taken:

[0235] ① The bidding service of the business system transmits project information, package information, supplier ID, and other information to the clearing service of the business system;

[0236] ② The clearing service automatically generates corresponding payment orders;

[0237] ③ After generation, the clearing service transmits the order number to the bank system, which then generates the corresponding virtual account.

[0238] ④ The bank system provides the clearing service with the association between the order number and the virtual account;

[0239] ⑤ The clearing service is returned to the bidding service provider, and the supplier can independently pay the deposit to the corresponding bank using the virtual account;

[0240] This process ensures the confidentiality of supplier bidding information when the business system interacts with third-party systems.

[0241] When the payment method is an electronic guarantee, the following steps should be followed:

[0242] ① The bidding service of the business system transmits project information, package information, supplier ID, and other information to the clearing service of the business system;

[0243] ② The clearing service automatically generates corresponding guarantee payment orders;

[0244] (2) When the bid opening time arrives, the clearing service of the business system determines the retrieval channel based on the payment method of the payment order. It can simultaneously retrieve data from the bank system and the financial platform guarantee under a single procurement package, ensuring normal interaction between channels. After automatically retrieving the data, it performs automatic verification. When the payment channel is a virtual deposit, the following steps are implemented:

[0245] ① The clearing service draws down the bank's margin funds through payment orders;

[0246] ② After successfully retrieving the data, the order information is automatically compared with the transaction record, and the deposit payment status is verified according to the pre-configured deposit verification rules;

[0247] ③ During the bid opening process, the bid opening service retrieves the verification results from the clearing service;

[0248] This process automatically verifies the supplier's bid security deposit payment status, improves the efficiency of bid opening, and ensures the legality of the payee and the payment process;

[0249] When the payment method is an electronic guarantee, the following steps should be followed:

[0250] ① The clearing service retrieves guarantee data through payment orders;

[0251] ② After successfully retrieving the guarantee information, the order information is automatically compared with the guarantee, and the guarantee status is verified according to the pre-configured deposit verification rules;

[0252] ③ During the bid opening process, the bid opening service retrieves the verification results from the clearing service;

[0253] This process automatically verifies the status of supplier guarantees, improving bid opening efficiency and ensuring the validity of guarantees;

[0254] (3) When the bid bond verification fails due to account verification (payment by other suppliers, payment by the same entity [not participating in the bidding], or payment account name not matching the bidder's name), record the abnormal information to provide analysis and early warning of bid collusion, and ensure the compliance of the bidding;

[0255] (4) Non-successful bidders can receive automatic refunds after the results announcement, while successful bidders can receive automatic refunds after the contract is signed. At this time, the clearing service requests the bank to initiate a refund application, and the status changes to "Refund in Progress" until the end-of-day reconciliation is completed, at which point the status changes to "Refund Completed".

[0256] (5) After the clearing service initiates a refund request, it queries the order information currently in the refund process, initiates a reconciliation request, obtains bank reconciliation data, and matches it with local business data. If the amount and reconciliation information are normal, the order status is adjusted to "refund completed." If there are any abnormalities in the reconciliation, the abnormal information is recorded. For records of virtual accounts in the current order that are not closed but have a balance of 0, a closure request is initiated.

[0257] By adopting the above technical solutions, this invention differs from existing technologies and possesses the following beneficial effects: It transforms bid bond management from traditional formal review to intelligent risk early warning by constructing a dynamic risk profile, effectively solving the problem that existing technologies struggle to identify potential bid-rigging risks. Through the fusion analysis of multi-source heterogeneous behavioral data, combined with correlation network algorithms and time series pattern mining, a dynamic risk profile is constructed, encompassing fund correlation network characteristics, consistency characteristics of bidding behavior, and abnormal pattern characteristics of guarantee usage, significantly improving the accuracy and comprehensiveness of risk identification. The dynamic risk profile is input into a pre-trained bid-rigging risk prediction model to calculate a dynamic risk index, and rule verification and intelligent assessment are simultaneously executed at the bid opening verification node, achieving dual protection of formal review and substantive risk analysis. Based on the comprehensive verification report, a real-time commercial credit score is generated through a credit assessment engine, and differentiated bid bond management strategies are formulated accordingly, ensuring both the convenience of participation for high-credit suppliers and strengthening the control over risky suppliers. The final intelligent bid bond management solution integrates verification conclusions, risk early warnings, and disposal strategies, forming a complete intelligent management closed loop. The above-mentioned technical solutions have effectively improved the risk control capabilities and operational efficiency of bid security management in government procurement, realizing the transformation from passive compliance review to proactive risk control.

[0258] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for intelligent management of bid security deposits based on dynamic risk profiling, characterized in that, include: Obtain project attribute data of the target procurement project and multi-source heterogeneous behavior data of participating supplier groups. The multi-source heterogeneous behavior data includes at least historical fund flow records, guarantee application sequence and bidding behavior time sequence characteristics. Based on the multi-source heterogeneous behavioral data, a dynamic risk profile of the supplier group is constructed through the correlation network analysis algorithm and time series pattern mining. The dynamic risk profile includes at least the characteristics of the financial correlation network, the consistency characteristics of bidding behavior, and the abnormal pattern characteristics of the use of guarantee. The dynamic risk profile is input into a pre-trained bid-rigging risk prediction model to calculate the dynamic risk index of each supplier for the target procurement project and generate a risk correlation map. At the bid opening verification node of the target procurement project, the rule verification of the deposit payment status and the intelligent assessment of the dynamic risk index are performed simultaneously to generate a comprehensive verification report that integrates the rule verification results and the risk warning level. Based on the comprehensive verification report, the real-time business credit score of each supplier is calculated through the credit assessment engine, and a differentiated margin management strategy is generated based on the real-time business credit score. The differentiated margin management strategy includes at least a virtual credit line scheme for high-credit suppliers. Based on the comprehensive verification report and the differentiated bid bond management strategy, generate and output the final intelligent management solution for the bid bond of the target procurement project.

2. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 1, characterized in that, Based on the aforementioned multi-source heterogeneous behavioral data, a dynamic risk profile of the supplier group is constructed using correlation network analysis algorithms and time series pattern mining, including: The historical fund flow records are analyzed for account entities and fund flow is tracked to construct a fund flow network with supplier legal persons, shareholders and payment accounts as nodes; A community detection algorithm is used to perform topological analysis on the fund transfer network to identify potential bid-rigging groups with close fund associations, and the network density and node centrality indices of the fund transfer network are extracted as fund association network features. Periodic analysis and anomaly detection are performed on the temporal characteristics of the bidding behavior to extract the collaborative behavior patterns of suppliers in terms of bid deadline distribution, bid geographical concentration and similarity of bid targets, and generate consistency characteristics of bidding behavior. Frequent pattern mining and sequence alignment analysis were performed on the guarantee application sequence to identify anomalies in the concentration of guarantee issuing institutions, the synchronicity of guarantee expiry time, and the dispersion of guarantee amount, and to construct abnormal guarantee usage pattern features. The fund-related network characteristics, bidding behavior consistency characteristics, and abnormal guarantee usage pattern characteristics are fused using multimodal features to generate a dynamic risk profile with timestamps.

3. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 2, characterized in that, Periodic analysis and outlier detection are performed on the temporal characteristics of the bidding behavior to extract collaborative behavior patterns among suppliers in terms of bid deadline distribution, bidding geographical concentration, and similarity of bid targets, generating consistency characteristics of bidding behavior, including: Construct a bidding behavior time series matrix, wherein the matrix dimension of the bidding behavior time series matrix includes at least the bidding timestamp sequence, the bidding geographical code sequence, and the bidding target classification sequence; The dynamic time warping algorithm is used to calculate the similarity distance between different suppliers in the bidding time distribution, and to identify the supplier group with synchronous bidding behavior; Spatial distribution patterns of bidding region coding sequences are mined using spectral clustering algorithm to detect bidding groups with abnormally consistent regional selection. Anomaly detection is performed on the classification sequence of bidding targets based on the isolated forest algorithm to identify supplier combinations with unconventional similarities in business domain selection. The correlation analysis of the synchronous bidding behavior, the consistency of abnormal regions, and the similarity of unconventional business is performed to generate a bidding behavior coordination index. The consistency characteristics of bidding behavior are obtained based on the aforementioned bidding behavior coordination index.

4. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 1, characterized in that, The dynamic risk profile is input into a pre-trained bid-rigging risk prediction model to calculate the dynamic risk index of each supplier for the target procurement project, and a risk correlation graph is generated, including: The dynamic risk profile is processed by weighting the features based on their importance, and the contribution of each feature dimension to the risk of collusion is calculated by the gradient boosting decision tree algorithm to obtain the weighted feature vector. The weighted feature vectors are input into a pre-trained isolated forest anomaly detection model to calculate the anomaly score of each supplier in the feature space, and the anomaly score is normalized into a primary risk index. Based on the characteristics of the funding network, a graph neural network model is used to learn node embedding representations to obtain the structural relationships and potential association strengths of suppliers in the funding network. The primary risk index is concatenated with the node embedding vector output by the graph neural network model, and then input into a multilayer perceptron classifier for joint training to output the dynamic risk index of each supplier for the target procurement project. Based on the node similarity and association strength learned by the graph neural network model, a risk propagation link between suppliers is constructed, and a risk association map is generated.

5. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 4, characterized in that, The primary risk index is concatenated with the node embedding vector output by the graph neural network model, and then input into a multilayer perceptron classifier for joint training. The result is a dynamic risk index for each supplier for the target procurement project, including: A deep neural network architecture for a multilayer perceptron classifier is constructed, the deep neural network architecture comprising a feature fusion layer, multiple hidden layers, and a risk index output layer; In the feature fusion layer, an attention-weighted feature splicing mechanism is used to adaptively allocate weights and deeply fuse the primary risk index and the node embedding vector to obtain the spliced ​​node embedding vector. By using nonlinear transformation of the hidden layer, deep pattern features of high-risk scrambling behavior are extracted layer by layer from the spliced ​​node embedding vector. The Sigmoid activation function is used in the risk index output layer to map the final output value to a dynamic risk index in the range of 0 to 1.

6. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 1, characterized in that, At the bid opening verification node of the target procurement project, the rule verification of the bid bond payment status and the intelligent assessment of the dynamic risk index are performed simultaneously, generating a comprehensive verification report that integrates the rule verification results and risk warning levels, including: The automatic verification engine for margin deposits is invoked to retrieve real-time deposit data from the banking system and financial guarantee platform through standardized interfaces. The preset rule verification process is executed. The preset rule verification process includes at least the verification of the adequacy of the margin amount, the verification of the consistency of the payment account name, and the review of the validity of the guarantee, so as to obtain the rule verification result. A binary verification conclusion is generated based on the rule verification results, and abnormal verification data is marked as a level 1 risk event. The intelligent risk assessment process is initiated in parallel, and the dynamic risk index is input into the risk level classification function; Based on preset dual threshold ranges, suppliers are divided into three intelligent early warning levels: low-risk range, medium-risk range, and high-risk range; For suppliers in medium-risk and high-risk ranges, their association paths and risk cluster information are extracted from the risk association map; Establish a mapping and association rule between rule verification results and intelligent early warning levels; When a supplier meets both the rule verification requirements and is in a low-risk zone, it is marked as a safe pass. When a supplier passes the rule verification but is in a medium-to-high risk range, a risk tracing explanation is generated based on its associated path characteristics. When the supplier rule verification fails, the corresponding Level 1 risk event will be correlated with the intelligent early warning level for analysis. By integrating the binary verification conclusions, intelligent early warning levels, risk tracing explanations, and correlation analysis results, a structured comprehensive verification report is generated. The comprehensive verification report includes an overview of the supplier verification status, a list of high-risk groups, and a visual display of the correlation paths.

7. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 6, characterized in that, Establish a mapping and association rule between rule verification results and intelligent early warning levels, including: Construct an association rule engine based on fuzzy reasoning, and use rule verification results and intelligent early warning levels as input variables; Define the membership function of the rule verification result, including three fuzzy sets: fully compliant, conditionally compliant, and explicitly non-compliant; Establish a two-way correlation matrix between intelligent early warning levels and rule verification results to obtain comprehensive risk assessment values ​​under different combinations; Evidence theory is used to fuse the uncertainty information of rule verification and intelligent assessment to calculate the final risk confidence of each supplier; Construct a multi-level decision tree classification rule to classify suppliers into three disposal levels: automatic approval, manual review, and veto, based on the final risk confidence level. The decision boundary of the association rule is adaptively optimized based on historical verification accuracy and risk event occurrence rate; When rule verification and intelligent assessment reach contradictory conclusions, an expert intervention process is initiated based on the principle of maximizing risk.

8. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 1, characterized in that, Based on the comprehensive verification report, a real-time business credit score for each supplier is calculated using a credit assessment engine, and a differentiated margin management strategy is generated based on the real-time business credit score, including: Construct a multi-dimensional credit assessment indicator system, which includes at least historical performance integrity, financial stability indicators, and risk profile health. The entropy weight method is used to calculate the weight of each indicator, and the qualitative and quantitative indicators are normalized by the fuzzy comprehensive evaluation model to obtain the normalized result. Based on the indicator weights and normalization results, a weighted aggregation algorithm is used to generate real-time business credit scores for each supplier. Establish a rule base for mapping real-time business credit scoring to margin management strategies; When a supplier’s real-time business credit score is higher than a first preset threshold, a virtual margin credit limit is automatically granted, and a suggested plan for the use of the credit limit is generated. When a supplier’s real-time business credit score is within the second preset threshold range, a guarantee priority recommendation strategy and rate optimization scheme are generated. When a supplier’s real-time business credit score falls below a third preset threshold, an enhanced margin supervision strategy is activated, which includes increasing the payment ratio requirement and extending the refund review period. The differentiated margin management strategy is dynamically adjusted based on the risk warning level in the comprehensive verification report. For suppliers in high-risk areas, the credit score reassessment mechanism will be triggered immediately and their virtual credit line eligibility will be suspended. The finalized differentiated margin management strategy will be linked and updated with the supplier credit profile.

9. The intelligent management method for bid security deposits based on dynamic risk profiling according to claim 8, characterized in that, Based on the comprehensive verification report and the differentiated bid bond management strategy, generate and output the final intelligent management solution for the bid bond of the target procurement project, including: Based on the overall supplier verification status overview in the comprehensive verification report, a summary table of security deposit verification conclusions is generated. Based on the high-risk group identification list and the visualization of related paths, a special analysis report on bid-rigging risks is generated; Based on the aforementioned differentiated deposit management strategies, a supplier tiered management list and corresponding deposit disposal solutions are formed; By integrating the aforementioned suggested use of credit limits, priority recommendation strategy for letters of guarantee, and enhanced margin supervision strategy, detailed rules for margin management are generated. The updated information in the supplier credit profile will be linked and stored with the project management data. Based on the preset report template, the summary table of the deposit verification conclusions, the special analysis report on bid rigging risks, the supplier hierarchical management list, and the deposit management implementation rules are structured and integrated. The output includes a final solution for intelligent management of bid security deposits, comprising text reports, visual charts, and machine-readable data interfaces.

10. A bid bond intelligent management system based on dynamic risk profiling, characterized in that, The system applicable to the method of any one of claims 1 to 9 comprises: The data acquisition module is configured to acquire project attribute data of the target procurement project and multi-source heterogeneous behavioral data of participating supplier groups; The risk profile building module is configured to construct dynamic risk profiles of supplier groups through correlation network analysis algorithms and time series pattern mining. The risk prediction module is configured to calculate the dynamic risk index of each supplier through a pre-trained bid-rigging risk prediction model and generate a risk correlation map. The intelligent verification module is configured to simultaneously perform rule-based verification of the deposit payment status and intelligent assessment of the dynamic risk index at the bid opening verification node. The credit assessment module is configured to calculate real-time business credit scores for each supplier through the credit assessment engine. The strategy generation module is configured to generate differentiated margin management strategies based on real-time business credit scores. The solution output module is configured to generate and output the final state solution for intelligent management of bid security deposits.

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