A method and system for guarantee fund whole life cycle intelligent management and control

Abstract

This invention discloses a method and system for intelligent management and control of the entire lifecycle of margin deposits, primarily relating to the field of intelligent decision-making technology in enterprise fund management. It includes: collecting margin deposit business data and financial processing data; constructing independent intelligent agents from the data sources; and having each agent collaborate through a declarative HTTP client. Within each agent, margin deposit processing rules are encapsulated using a strategy pattern. Upon triggering a business event, each agent analyzes the data in parallel, generating analysis results including actions, amounts, and risk levels. A collaborative engine summarizes the results, performs normalization, conflict identification and adjudication, generates a final decision, and executes it synchronously, automatically calling the financial agent interface to generate vouchers. The beneficial effects of this invention are: through multi-agent collaboration and automatic conflict adjudication, it achieves the integration of business and financial aspects of margin deposit management and proactive risk control.

Description

Technical Field

[0001] This invention relates to the field of intelligent decision-making technology for enterprise fund management, specifically a method and system for intelligent management and control of margin deposits throughout their entire lifecycle based on multiple agents. Background Technology

[0002] In existing technologies, the management of margin deposits largely relies on independent systems for each business module. For example, procurement management systems, contract management systems, or financial systems each maintain their own margin deposit ledgers and trigger maturity reminders through simple scheduled tasks. This approach, as the closest existing technology, has significant shortcomings in practical applications: First, the strategies of each module are fragmented, and the margin deposit status of the same supplier in different modules cannot be monitored in a coordinated manner, creating information silos. Second, the warning method is limited, only providing alerts based on fixed dates, and failing to provide differentiated warnings based on business stages, risk levels, guarantee validity periods, etc. Third, there is a lack of systematic support for the registration, authenticity verification, near-expiration reminders, and claim processing of bank guarantees, mainly relying on manual offline operations.

[0003] The root cause of the above defects is that the existing technical architecture lacks an intelligent engine that can integrate strategies of multiple business modules, collaboratively analyze various risk factors, and automatically execute cross-system decisions. This results in margin management being in a passive response state for a long time, unable to achieve proactive risk prevention and control and automatic closed-loop flow of business and financial data.

[0004] Therefore, there is an urgent need for a multi-agent-based intelligent management method and system for the entire lifecycle of margin deposits to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent management and control of margin deposits throughout their entire lifecycle based on multiple agents. Through multi-agent collaboration and automatic conflict resolution, it achieves the integration of business and finance in margin deposit management and proactive risk prevention and control.

[0006] To achieve the above objectives, the present invention employs the following technical solution: On the one hand, the present invention provides a method for intelligent management and control of margin deposits throughout their entire lifecycle, comprising the following steps: Step S1: Collect margin business data and financial processing data, and construct each data source into an independent intelligent agent. Each intelligent agent encapsulates the business logic of the corresponding module. The agents communicate and collaborate remotely through a declarative HTTP client. The data sources include: procurement module, contract module, engineering module, and financial module. Step S2: Within each business intelligence agent, the margin processing rules of the corresponding business module are encapsulated using the strategy pattern. A corresponding strategy class is configured for each margin type and payment method, and the strategy class encapsulates differentiated business rules. Step S3: When a preset business event is triggered, each business intelligent agent receives the event in parallel and calls the corresponding internal strategy class for analysis, generating analysis results including suggested actions, amounts, time limits, and risk levels; Step S4: The collaboration engine summarizes the analysis results of each business intelligent agent, normalizes the analysis results, and then performs consistency checks and conflict identification to identify at least one of the following: action conflict, amount conflict, timeliness conflict, or status conflict. Step S5: The collaborative engine uses the built-in conflict resolution rule engine to adjudicate the identified conflicts, generate the final decision result, and push the final decision result to each business intelligent agent to update the margin status. Step S6: Based on the final decision, perform subsequent processing of the deposit, which includes any one of refund, confiscation, transfer or partial refund, and automatically generate corresponding financial vouchers by calling the standardized interface of the financial intelligence agent through a declarative HTTP client.

[0007] Preferably, in step S2, configuring a corresponding strategy class for each type of margin and payment method specifically involves: Configure the procurement module with bid cash security strategy, bid bond strategy, performance cash security strategy, and performance bond strategy; Configure the contract module with quality cash deposit strategy, advance payment cash deposit strategy, quality guarantee strategy, advance payment guarantee strategy, and performance guarantee strategy; Configure the following strategies for the engineering module: cash deposit for migrant workers' wages, cash deposit for progress, guarantee for migrant workers' wages, guarantee for progress, and guarantee for quality assurance. The differentiated business rules encapsulated in the guarantee-related strategies include: accepting bank-wide verification, ensuring that the guarantee validity period is not shorter than the corresponding business validity period plus a preset number of days, calling bank interfaces for authenticity verification, and determining the redemption conditions.

[0008] Preferably, in step S4, the normalization process for the analysis results specifically involves: The analysis results output by each business intelligence agent and financial intelligence agent adopt a unified data structure, which includes the following fields: Action field, with enumerated values ​​including refund, confiscation, transfer, partial refund, and extension; Action amount field; Execution time field; Risk level field, with enumerated values ​​including low, medium, and high; Reason code field, used to record the business reason that triggered the action; Supporting document ID field, used to associate contracts or bidding projects.

[0009] Preferably, in step S5, the collaborative engine uses its built-in conflict resolution rule engine to adjudicate the identified conflicts, specifically as follows: Rule 1: Adjudication is based on Agent priority. The priority order is set as follows: Contract Agent has higher priority than Engineering Agent, Engineering Agent has higher priority than Procurement Agent, and Procurement Agent has higher priority than Finance Agent. In the event of action conflicts or monetary conflicts, the analysis results output by the Agent with higher priority shall be adopted. Rule 2: Adjudicate based on business stage. If a conflict occurs in different business stages, the analysis results of the Agent corresponding to the later business stage shall be adopted. Rule 3: Decisions are made based on risk level weighting. A risk-weighted score is calculated for each Agent's analysis results. The formula is: Risk-weighted score equals risk level weight multiplied by Agent weight. The analysis result with the highest risk-weighted score is adopted.

[0010] Preferably, in step S6, the subsequent processing of the security deposit, for a transfer scenario, specifically includes: Step S61: Receive the final decision result and determine which type of transfer is full transfer, partial transfer, guarantee conversion, or payment deduction; Step S62: Calculate the processing amount according to the transfer type, mark the status as "refund in progress" in the original deposit record, and lock the record; Step S63: Automatically create a new business document and establish a two-way association between the original deposit record and the new business document. The two-way association supports multi-level traceability and can trace back to the original deposit record from the final state. Step S64: Determine if there is a difference after the transfer. If it needs to be paid, generate a payment notice and push it to the supplier. If it needs to be refunded, automatically initiate a partial refund process and call the financial agent interface to generate a refund instruction. Step S65: After the transfer is completed, update the original deposit status and record the completion time, and send a notification to the preset notification recipients through the message center.

[0011] Preferably, in step S62, calculating the processing amount based on the transfer type specifically involves: If the transfer type is full transfer, the processing amount is equal to the original margin amount; If the transfer type is partial transfer, the processing amount is equal to the transfer amount specified by the user in advance or calculated by the business rules; If the transfer type is a guarantee conversion, the original guarantee is marked as pending cancellation and the new guarantee is marked as being opened. If the transfer type is payment deduction, the processing amount is the smaller of the original deposit amount and the accounts payable balance.

[0012] Preferably, in step S63, the automatic creation of new business documents specifically includes: New business documents include performance bond records, new guarantee records, or accounts payable write-offs; A "Transfer to Document ID" field has been added to the original margin deposit record to record the unique identifier of the new document; The new business document adds a Source Margin ID field to record the unique identifier of the original margin record.

[0013] Preferably, if an anomaly occurs during the transfer process, the following anomaly handling steps are executed: If the call to the financial agent interface fails, the system will automatically retry a preset number of times; if it still fails after retrying, a transfer / refund exception work order will be generated and pushed to the manual processing queue. If the amount of transfer calculated by the system is inconsistent with the amount agreed in the related contract, the system will trigger an alert for the inconsistency and suspend the automatic transfer and refund process. If the target business document is missing, the system will automatically create a pending order task and push it to the system.

[0014] On the other hand, the present invention also provides an intelligent management system for the entire lifecycle of margin deposits, used to implement the aforementioned intelligent management method for the entire lifecycle of margin deposits, comprising: The multi-Agent collaboration engine module is responsible for the registration, communication, task distribution, and result aggregation of each agent. At least one business intelligence agent module, including a procurement agent module, a contract agent module, and an engineering agent module. Each business intelligence agent module encapsulates margin processing rules organized using a strategy pattern, which are used to perform parallel analysis on different business events and generate analysis results. The Financial Intelligence Agent module is used to automatically generate financial vouchers based on the final decision results and provide a standardized RESTful API interface to the outside world. The early warning and decision-making center module is used to summarize the analysis results of each agent and generate multi-level early warning information. This module has a built-in collaboration engine, which is used to perform result normalization, conflict identification and conflict adjudication functions.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs a multi-Agent collaborative architecture, which builds the procurement, contract, engineering and finance modules into independent intelligent agents. Each agent performs parallel analysis and collaborative decision-making, fundamentally breaking down the management barriers of isolated margin information among business modules in traditional solutions, and realizing cross-module linked monitoring of margin status. 2. This invention encapsulates margin processing rules within each Agent using the strategy pattern, so that each margin type corresponds to an independent strategy class. Adding or adjusting margin rules only requires modifying or adding the corresponding strategy class, without changing the Agent's main logic, which greatly improves the system's scalability and maintainability. 3. The collaborative engine of this invention normalizes the analysis results of multiple agents and automatically resolves conflicts. Through built-in priority rules, business stage rules and risk weighting rules, it can automatically resolve most cross-module conflicts, avoid the inefficiency of manual comparison and coordination, and ensure the uniqueness and traceability of decision results. 4. This invention fully covers both cash and bank guarantee payment methods in the life cycle management of margin deposits. It not only supports the registration, authenticity verification, and near-expiration warning of guarantees, but also automates operations such as guarantee conversion and demand condition determination, filling the functional gaps of traditional systems in this regard. 5. This invention enables the automatic triggering of business actions and the generation of financial vouchers through standardized interfaces, and incorporates complex transfer and refund scenarios such as deposit refund, confiscation, transfer, and partial refund into automated closed-loop processing. It is supplemented by abnormal retry and manual backup mechanisms for transfer and refund failures, which improves the efficiency of business and financial data flow while effectively reducing the error risk caused by manual operation. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the transfer scenario operation of the present invention; Figure 3 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0017] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0018] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0019] Example: like Figure 1 As shown, the method in this embodiment specifically includes the following steps: Step S1, Data Collection and Agent Construction: First, data on margin deposit transactions are collected from the procurement, contract, and engineering modules, as well as financial processing data from the finance module. Margin deposit transaction data includes, but is not limited to: bidding project information, contract performance status information, project progress and quality acceptance information, supplier basic information, margin deposit payment records, and bank guarantee ledger information. Financial processing data includes: accounts receivable and payable information, general ledger account information, and cash account balance information. After data collection is completed, each data source is constructed into an independent intelligent agent: Procurement Agent, Contract Agent, Engineering Agent, and Finance Agent. Each intelligent agent encapsulates the core business logic and data processing capabilities of the corresponding business module. For example, the Procurement Agent encapsulates the bidding process management logic and the rules for the payment and refund of bid bonds and performance bonds; the Contract Agent encapsulates the management rules for contract approval, contract performance tracking, and quality bonds and advance payment bonds; the Engineering Agent encapsulates the rules for project progress management and the payment and refund of migrant worker wage bonds; and the Finance Agent encapsulates the core logic for accounts receivable management, accounts payable management, general ledger processing, and fund allocation. Each agent is deployed independently under the microservice architecture, and remote calls and data interaction between services are realized through the Feign declarative HTTP client, which ensures loose coupling at the technical architecture level and high cohesion at the business collaboration level.

[0020] Step S2: Configure the strategy mode for margin processing rules: Within each business intelligence agent, the margin processing rules for the corresponding business module are encapsulated using the strategy pattern. First, a unified margin processing strategy interface is defined, which declares common processing methods, including rule matching methods, amount calculation methods, timeliness calculation methods, and risk level assessment methods. Then, specific strategy classes are implemented for each margin type and payment method, and each strategy class encapsulates differentiated business rules. Taking a procurement agent as an example, its internally configured strategy classes include: bid cash deposit strategy, bid bond strategy, performance cash deposit strategy, and performance bond strategy. Among them, the bid cash deposit strategy encapsulates the following business rules: (1) The payment ratio is set at two percent of the target amount; (2) The time limit for refunds to unsuccessful bidders is set at five working days; (3) The logic of automatically converting the bid into a performance bond after winning the bid; The bid bond strategy encapsulates the following business rules: (1) The scope of accepting banks is limited to large state-owned commercial banks; (2) The validity period of the guarantee shall not be less than the validity period of the bid plus thirty days; (3) The authenticity of the guarantee needs to be verified online by calling the bank's interface; (4) The conditions for demanding payment of the guarantee are that the successful bidder refuses to sign the contract or refuses to submit the performance bond; The performance guarantee strategy encapsulates the following business rules: (1) An automatic warning is triggered sixty days before the expiry date of the guarantee; (2) The conditions for demanding payment are set as follows: final acceptance fails and the supplier fails to complete rectification within thirty days; Similarly, the contract agent is configured with quality cash deposit strategy, advance payment cash deposit strategy, quality guarantee strategy, advance payment guarantee strategy, and performance guarantee strategy. The project agent is internally configured with strategies for cash deposits for migrant workers' wages, cash deposits for progress, guarantees for migrant workers' wages, guarantees for progress, and guarantees for quality assurance. When the business agent runs, it dynamically retrieves the corresponding strategy class instance from the strategy factory and executes it based on the input margin type field and payment method field. There is no need to write lengthy conditional branch statements. When a new margin type needs to be added, only a new strategy class needs to be added and registered in the strategy factory. There is no need to modify the main logic of the agent, which realizes the extensibility, configurability and easy maintenance of the rules.

[0021] Step S3, Business Event Triggering and Multi-Agent Parallel Analysis: When the system detects the occurrence of a preset business event, it automatically initiates a multi-agent collaborative analysis process. Preset business events include: bidding completion, announcement of bid opening results, contract signing completion, project completion and acceptance, arrival of a preset number of days before the expiration of the guarantee, and confirmation of supplier default behavior. Each business intelligent agent receives the same business event in parallel and independently initiates its internal analysis. For a business event where "the bidding process has ended and the supplier has not won the bid," the procurement agent, upon receiving the event, calls its internal bid cash deposit strategy or bid bond strategy to analyze the situation, determines whether a refund should be issued, calculates the refund amount as the full amount of the bid bond actually paid by the supplier, sets the execution deadline to within five working days, and assesses the risk level as low. Upon receiving the same event, the contract agent calls its internal strategy to check if there are any other performance bond records related to the supplier. If there are any outstanding records, this information is output as well. The engineering agent checks the event information to see if there are any engineering-related performance bonds related to the supplier. Each agent encapsulates its analysis results into a unified data structure and sends them to the collaboration engine through the Feign client.

[0022] Step S4: Result Normalization and Conflict Identification After the collaborative engine aggregates the analysis results output by each business agent and financial agent, it first performs normalization processing on the analysis results. In this embodiment, the unified data structure used for normalization processing includes the following fields: Action field, whose enumerated values ​​include refund, confiscation, transfer, partial refund, and postponement; Action amount field, in yuan; Execution time field; Risk level field, whose enumerated values ​​include low, medium, and high; Reason code field, used to record the specific business reason that triggered the action, such as "failed to win the bid", "acceptance passed", "construction period delayed", etc.; Supporting document ID field, used to associate the corresponding contract ID or bidding project ID; After normalization, the collaboration engine compares the outputs of all agents pairwise, performing consistency checks and conflict identification. This embodiment supports the identification of four types of conflicts: The first category is action conflict, which refers to mutually exclusive actions suggested by different agents for the same margin deposit. For example, the purchasing agent suggests "confiscation" while the contract agent suggests "refund". The second category is monetary conflict, which refers to inconsistent action amounts suggested by different agents. For example, a procurement agent suggests a full confiscation of 100,000 yuan, while an engineering agent suggests only a partial deduction of 20,000 yuan. The third category is time-sensitive conflicts, which refers to conflicts in the execution time suggested by different agents. For example, the procurement agent suggests immediate refund, while the contract agent suggests refund after thirty days. The fourth category is status conflict, which refers to the inconsistent status of the same deposit in the ledgers of different agents. For example, the purchasing agent has marked it as "refunded", while the finance agent still shows it as "not cleared".

[0023] Step S5, Conflict Resolution and Final Decision Generation: When the collaboration engine identifies a conflict, it automatically adjudicates the conflict resolution rule engine using its built-in rules. In this embodiment, the conflict resolution rule engine contains four adjudication rules, which are executed sequentially according to priority: Rule 1: Adjudication based on Agent priority. The priority order set in this embodiment is as follows: Contract Agent has the highest priority, followed by Engineering Agent, then Procurement Agent, and Finance Agent has the lowest priority. When there is a conflict of action or amount, the analysis result output by the Agent with the higher priority is adopted. For example, if there is a conflict of action between Contract Agent and Procurement Agent, the suggestion of Contract Agent shall prevail. Rule 2: Adjudication based on business stage: When conflicts occur at different business stages, the analysis results of the agent corresponding to the later business stage shall be adopted. For example, if a bid bond has entered the contract performance stage, and the procurement agent recommends a refund based on the bidding stage rules, while the contract agent recommends a transfer based on the performance stage rules, then the contract agent's recommendation shall prevail, because the performance stage is a subsequent stage of the bidding stage. Rule 3: Weighted decision based on risk level: A risk-weighted score is calculated for each Agent's analysis results. The formula for calculating the risk-weighted score is: Risk-weighted score equals risk level weight multiplied by Agent weight. The risk level weights are set as follows: high risk level weight is 1.0, medium risk level weight is 0.6, and low risk level weight is 0.2. The Agent weights correspond to the priorities in Rule 1: contract Agent weight is 0.4, engineering Agent weight is 0.3, procurement Agent weight is 0.2, and financial Agent weight is 0.1. The system adopts the analysis result with the highest risk-weighted score as the final decision-making basis. This embodiment also incorporates a new rule, namely, manual intervention as a fallback: For conflicts that cannot be automatically resolved by the above three rules, such as when the analysis results of the three agents are completely contradictory and the risk weighting scores cannot be significantly different, the system automatically generates a conflict work order and pushes it to the preset cross-departmental approval workflow. The designated approval personnel make the final decision in the system, and the decision is automatically recorded and used as the basis for subsequent execution. At the same time, it is fed back to the rule engine for reference in similar scenarios in the future. Once the conflict is resolved, the collaborative engine generates a final decision, which includes four core elements: the final action taken, the final execution amount, the final execution time, and a description of the decision's basis. The description of the basis must fully record the rules or human judgment opinions used in this ruling. This decision is locked once generated and cannot be modified by any person or module during the lifecycle of the security deposit unless a specific unlocking event is triggered. Specific unlocking events include, but are not limited to, court judgments and approved contract amendment agreements. Any modification to the locked decision requires a change approval process, and the system retains a complete historical version record to ensure the traceability of the decision-making process. The final decision is pushed synchronously to all relevant business agents via the Feign client. Each agent updates the margin account status within its module accordingly to ensure the consistency of the global status.

[0024] Step S6: Subsequent processing of margin deposits and automatic linkage between business and finance: Based on the final decision, the system automatically executes the subsequent processing of the deposit. If the decision is to refund, confiscate, or partially refund, the system directly calls the standardized RESTful API interface of the financial agent through the Feign client to generate the corresponding financial voucher. The financial agent automatically determines the financial sub-module to be pushed to based on the action type: a refund action triggers the accounts payable module to generate a payment voucher; a confiscation action triggers the general ledger module to generate a non-operating income voucher; and a transfer action triggers the linkage processing between the accounts receivable and accounts payable modules. For transfer scenarios, this embodiment provides a more detailed automated processing flow, such as... Figure 2 As shown, the processing of transfer scenarios specifically includes the following sub-steps: Step S61: After receiving the final decision result, the system first determines the specific type of this transfer. This embodiment supports four types of transfers: full transfer, which means that the original deposit is transferred in full to another amount, such as the bid bond being automatically converted into a performance bond after winning the bid; partial transfer, which means that part of the original deposit is transferred to another amount, and the remaining part is returned to the original payer; bank guarantee conversion, which means that the original bank guarantee is cancelled and a new bank guarantee is issued at the same time, such as the bid bond being converted into a performance bond; and payment deduction, which means that the balance of the deposit is directly used to offset the payment due to the supplier. Step S62: The system calculates the processing amount based on the transfer type, marks the original deposit record as "transfer in progress" and locks the record to prevent it from being processed repeatedly by other processes. The specific calculation rules are as follows: When transferring the full amount, the processing amount is equal to the original deposit amount; when transferring the partial amount, the processing amount is equal to the amount specified by the user in advance or the transfer amount automatically calculated by the business rules; when converting a guarantee, no cash amount is involved, but the original guarantee record needs to be marked as "pending cancellation" and the new guarantee record needs to be marked as "opening in progress"; when offsetting payments, the processing amount is the smaller of the original deposit amount and the current accounts payable balance. Step S63: The system automatically creates corresponding new business documents based on the transfer type. If it is a full or partial transfer to the performance bond, a performance bond record is created; if it is a guarantee conversion, a new guarantee record is created; if it is a payment deduction, an accounts payable offsetting document is created. While creating new documents, the system establishes a two-way association between the original bond record and the new business document: the "Transfer to Document ID" field is written into the original bond record to record the unique identifier of the new document; the "Source Bond ID" field is written into the new business document to record the unique identifier of the original bond record. Through this two-way association, the system supports tracing back from the final business status along the association chain to the most original bond payment record, realizing full-chain auditability and traceability. Step S64: The system determines whether there is a difference in amount after the transfer. If the target amount is greater than the original deposit amount, the supplier needs to make up the difference. The system automatically generates a "Supplementary Payment Notice", pushes it to the corresponding supplier through the message center, and creates a supplementary payment task in the internal system for business personnel to follow up and collect. If the target amount is less than the original deposit amount, the difference needs to be refunded to the supplier. The system automatically initiates a partial refund process, calls the refund interface of the financial agent to generate a refund instruction, and refunds the difference to the supplier's designated account. Step S65: After the entire transfer process is completed, the system will update the status of the original security deposit record to "Refunded (Full Amount)" or "Refunded (Partial)" and record the exact timestamp of the transfer completion. At the same time, the system will automatically send a notification to the preset notification recipients through the message center. The notification recipients include internal business operators and financial processing personnel. For external suppliers, the system will decide whether to push a notification based on the system configuration. If this transfer involves the cancellation or reissue of the guarantee, the system will also send the guarantee cancellation application or reissue application to the corresponding issuing bank through the interface or email. In the subsequent processing of margin deposits, this embodiment also sets up a comprehensive exception handling mechanism to ensure the robustness of the system, specifically including the handling of the following three exception scenarios: 1. When the system fails to call the standardized interface of the financial agent, such as due to network timeout or parameter verification error, the system will automatically perform a retry operation. The number of retries is preset to three, and the interval between each retry increases. If it still fails after three retries, the system will stop the automatic processing flow, generate a "return exception work order", and push the work order to the manual processing queue of the financial processing position, so that the human will intervene to check the reason for the failure and complete the subsequent operations. 2. When the transfer amount automatically calculated by the system is inconsistent with the amount explicitly agreed in the associated contract text, the system triggers an "amount inconsistency warning" and suspends the current automatic transfer and refund process. This warning information is pushed to the contract management position and business handlers through the message center, requiring them to check whether the contract terms match the system configuration rules. The automatic transfer and refund process will be resumed after the correct amount is manually confirmed. 3. When the target business document that needs to be associated with the transfer process does not exist in the system, such as when no corresponding accounts payable document is found in the case of payment deduction, the system will not terminate the process directly. Instead, it will automatically create a "pending document creation" task, clearly indicating the missing document type and associated business information, and push the task to the person in charge of the corresponding business position. After the person in charge fills in the relevant document, the system will automatically resume the previously suspended transfer process.

[0025] Through the above steps S1 to S6, the method of this embodiment realizes closed-loop management of the entire lifecycle of the security deposit from the occurrence of the business to the financial processing. Throughout the process, the multi-Agent collaborative architecture ensures real-time sharing and linkage analysis of cross-module information, the strategy mode ensures the configurability and scalability of business rules, the conflict resolution rule engine ensures the automation and standardization of cross-module conflict adjudication, and the refined processing of the transfer scenario, supplemented by the exception fallback mechanism, ensures the automatic closed loop of complex business.

[0026] like Figure 3 As shown in the figure, this embodiment also provides a margin deposit full lifecycle intelligent management and control system for implementing the above method. The system includes a multi-Agent collaborative engine module, at least one business intelligent agent module, a financial intelligent agent module, and an early warning and decision-making center module.

[0027] The multi-Agent collaboration engine module is responsible for the registration management, service communication, task distribution, and analysis result aggregation of each agent. In this embodiment, each agent registers with the collaboration engine at startup and reports the types of business events and policy categories that it supports processing. The collaboration engine maintains a global service directory accordingly. When a business event is triggered, it can accurately distribute the event to the agent with the corresponding processing capability. The Business Intelligence Agent module includes the Procurement Agent module, Contract Agent module, and Engineering Agent module. Each Business Intelligence Agent module encapsulates the margin processing rules organized using the strategy pattern as described above. When it receives an event distributed by the Collaboration Engine, each module executes the analysis task in parallel, independently completes the strategy matching and result calculation, and returns the analysis results to the Collaboration Engine. The Financial Intelligence Agent module receives the final decision results and automatically matches the corresponding financial sub-modules based on the action instructions in the results to generate the corresponding financial vouchers. This module provides a standardized RESTful API interface for other agents to call through the Feign client. The interface design follows a unified request and response format to ensure the standardization and consistency of business and financial data flow. The early warning and decision-making center module is responsible for two aspects. First, it summarizes the analysis results of each agent and generates hierarchical early warning information according to preset multi-dimensional early warning rules. The early warning types include early warning of guarantee near expiration, early warning of abnormal refund, early warning of excess amount, early warning of conflict, early warning of guarantee authenticity, early warning of fulfillment of redemption conditions, and early warning of overdue refund. The early warning levels are divided into prompt level, warning level, and severe level. Second, the module has a built-in collaboration engine, which performs result normalization processing, consistency check and conflict identification, automatic conflict adjudication, and the generation and distribution of the final decision results.

[0028] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for intelligent management and control of margin deposits throughout their entire lifecycle, characterized in that, Includes the following steps: Step S1: Collect margin business data and financial processing data, and construct each data source into an independent intelligent agent. Each intelligent agent encapsulates the business logic of the corresponding module. The agents communicate and collaborate remotely through a declarative HTTP client. The data sources include: procurement module, contract module, engineering module, and financial module. Step S2: Within each business intelligence agent, the margin processing rules of the corresponding business module are encapsulated using the strategy pattern. A corresponding strategy class is configured for each margin type and payment method, and the strategy class encapsulates differentiated business rules. Step S3: When a preset business event is triggered, each business intelligent agent receives the event in parallel and calls the corresponding internal strategy class for analysis, generating analysis results including suggested actions, amounts, time limits, and risk levels; Step S4: The collaboration engine summarizes the analysis results of each business intelligent agent, normalizes the analysis results, and then performs consistency checks and conflict identification to identify at least one of the following: action conflict, amount conflict, timeliness conflict, or status conflict. Step S5: The collaborative engine uses the built-in conflict resolution rule engine to adjudicate the identified conflicts, generate the final decision result, and push the final decision result to each business intelligent agent to update the margin status. Step S6: Based on the final decision, perform subsequent processing of the deposit, which includes any one of refund, confiscation, transfer or partial refund, and automatically generate corresponding financial vouchers by calling the standardized interface of the financial intelligence agent through a declarative HTTP client.

2. The intelligent management method for the entire lifecycle of margin deposits according to claim 1, characterized in that, In step S2, the configuration of corresponding strategy classes for each type of margin and payment method is specifically as follows: Configure the procurement module with bid cash security strategy, bid bond strategy, performance cash security strategy, and performance bond strategy; Configure the contract module with quality cash deposit strategy, advance payment cash deposit strategy, quality guarantee strategy, advance payment guarantee strategy, and performance guarantee strategy; Configure the following strategies for the engineering module: cash deposit for migrant workers' wages, cash deposit for progress, guarantee for migrant workers' wages, guarantee for progress, and guarantee for quality assurance. The differentiated business rules encapsulated in the guarantee-related strategies include: accepting bank-wide verification, ensuring that the guarantee validity period is not shorter than the corresponding business validity period plus a preset number of days, calling bank interfaces for authenticity verification, and determining the redemption conditions.

3. The intelligent management method for the entire lifecycle of margin deposits according to claim 1, characterized in that, In step S4, the normalization process for the analysis results specifically involves: The analysis results output by each business intelligence agent and financial intelligence agent adopt a unified data structure, which includes the following fields: Action field, with enumerated values ​​including refund, confiscation, transfer, partial refund, and extension; Action amount field; Execution time field; Risk level field, with enumerated values ​​including low, medium, and high; Reason code field, used to record the business reason that triggered the action; Supporting document ID field, used to associate contracts or bidding projects.

4. The intelligent management method for the entire lifecycle of margin deposits according to claim 1, characterized in that, In step S5, the collaborative engine uses its built-in conflict resolution rule engine to adjudicate the identified conflicts, specifically as follows: Rule 1: Adjudication is based on Agent priority. The priority order is set as follows: Contract Agent has higher priority than Engineering Agent, Engineering Agent has higher priority than Procurement Agent, and Procurement Agent has higher priority than Finance Agent. In the event of action conflicts or monetary conflicts, the analysis results output by the Agent with higher priority shall be adopted. Rule 2: Adjudicate based on business stage. If a conflict occurs in different business stages, the analysis results of the Agent corresponding to the later business stage shall be adopted. Rule 3: Decisions are made based on risk level weighting. A risk-weighted score is calculated for each Agent's analysis results. The formula is: Risk-weighted score equals risk level weight multiplied by Agent weight. The analysis result with the highest risk-weighted score is adopted.

5. The intelligent management method for the entire lifecycle of margin deposits according to claim 1, characterized in that, In step S6, the subsequent processing of the security deposit, for the transfer scenario, specifically includes: Step S61: Receive the final decision result and determine which type of transfer is full transfer, partial transfer, guarantee conversion, or payment deduction; Step S62: Calculate the processing amount according to the transfer type, mark the status as "refund in progress" in the original deposit record, and lock the record; Step S63: Automatically create a new business document and establish a two-way association between the original deposit record and the new business document. The two-way association supports multi-level traceability and can trace back to the original deposit record from the final state. Step S64: Determine if there is a difference after the transfer. If it needs to be paid, generate a payment notice and send it to the supplier. If a refund is required, a partial refund process will be automatically initiated and the financial agent interface will be called to generate a refund instruction; Step S65: After the transfer is completed, update the original deposit status and record the completion time, and send a notification to the preset notification recipients through the message center.

6. The intelligent management method for the entire lifecycle of margin deposits according to claim 5, characterized in that, In step S62, calculating the processing amount based on the transfer type specifically involves: If the transfer type is full transfer, the processing amount is equal to the original margin amount; If the transfer type is partial transfer, the processing amount is equal to the transfer amount specified by the user in advance or calculated by the business rules; If the transfer type is a guarantee conversion, the original guarantee is marked as pending cancellation and the new guarantee is marked as being opened. If the transfer type is payment deduction, the processing amount is the smaller of the original deposit amount and the accounts payable balance.

7. The intelligent management method for the entire lifecycle of margin deposits according to claim 5, characterized in that, In step S63, the automatic creation of new business documents specifically involves: New business documents include performance bond records, new guarantee records, or accounts payable write-offs; A "Transfer to Document ID" field has been added to the original margin deposit record to record the unique identifier of the new document; The new business document adds a Source Margin ID field to record the unique identifier of the original margin record.

8. The intelligent management method for the entire lifecycle of margin deposits according to claim 5, characterized in that, If an exception occurs during the transfer process, the following exception handling steps will be executed: If the call to the financial agent interface fails, the system will automatically retry a preset number of times; if it still fails after retrying, a transfer / refund exception work order will be generated and pushed to the manual processing queue. If the amount of transfer calculated by the system is inconsistent with the amount agreed in the related contract, the system will trigger an alert for the inconsistency and suspend the automatic transfer and refund process. If the target business document is missing, the system will automatically create a pending order task and push it to the system.

9. A margin deposit full lifecycle intelligent management and control system, used to implement the margin deposit full lifecycle intelligent management and control method as described in any one of claims 1-8, characterized in that, include: The multi-Agent collaboration engine module is responsible for the registration, communication, task distribution, and result aggregation of each agent. At least one business intelligence agent module, including a procurement agent module, a contract agent module, and an engineering agent module. Each business intelligence agent module encapsulates margin processing rules organized using a strategy pattern, which are used to perform parallel analysis on different business events and generate analysis results. The Financial Intelligence Agent module is used to automatically generate financial vouchers based on the final decision results and provide a standardized RESTful API interface to the outside world. The Early Warning and Decision Center module is used to summarize the analysis results of each Agent and generate multi-level early warning information; This module has a built-in collaboration engine, which is used to perform result normalization, conflict identification, and conflict adjudication functions.

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