Fund seal full-process online management and control tool based on software and hardware integration

Abstract

The invention discloses a fund seal full-process online management and control tool based on software and hardware integration, relates to the technical field of seal management and control, and aims to solve the technical problem that existing fund seal management and control lacks software and hardware integration full-process linkage and causes high-amount fund payment safety and compliance risks. The hardware terminal layer is in communication connection with the online management and control platform layer through an encrypted channel; the hardware terminal layer integrates a risk prediction processing module and a seal management and control execution module, and the online management and control platform layer deploys a risk data integration module and a linkage management and control engine; the risk data integration module collects multi-dimensional risk data and transmits the multi-dimensional risk data to the hardware terminal layer, the risk prediction processing module analyzes and predicts the data to obtain a potential risk level, and the linkage management and control engine generates a management and control instruction according to the risk level and issues the management and control instruction to the seal management and control execution module. The method has the advantages that software and hardware are integrated, whole-process linkage is achieved, and high-amount fund payment safety and compliance are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of seal management technology, and more specifically, to an online management tool for the entire process of fund seals based on integrated hardware and software. Background Technology

[0002] In the process of globalization, multinational corporations employ complex and diverse scenarios for the use of official seals for large sums of money. These scenarios encompass core businesses such as cross-border trade settlement, overseas project investment disbursements, and cross-regional fund transfers within the group. They involve collaboration across multiple entities and timeframes, including headquarters risk control departments, overseas branch finance teams, and business initiating departments. Traditional official seal management models are ill-suited to such complex scenarios: identity verification often relies on a single hardware key or password, failing to deeply bind it to specific business types and operational permissions, making it susceptible to identity theft or unauthorized operations; risk assessment is limited to internal financial data, failing to effectively integrate multi-dimensional external risk information such as global macroeconomic fluctuations, overseas policy changes, counterparty cross-border operations, and international public opinion, leading to biased risk predictions; seal management instructions are often fixed settings, lacking dynamic adaptation to business scenarios, transaction amounts, and counterparty credit ratings, and the entire process operation records are mostly stored on centralized servers, posing risks of tampering and loss, resulting in insufficient traceability reliability. These problems, when combined, pose multiple threats to the security of funds during large-scale fund payments by multinational corporations, including identity theft, missed risk assessment, and lagging management. At the same time, compliance audits are difficult to conduct effectively due to the lack of a reliable traceability link, which seriously affects the security and standardization of corporate fund operations. In view of this, we propose an online control tool for the entire process of fund signature based on integrated hardware and software. Summary of the Invention

[0003] The purpose of this invention is to provide an online control tool for the entire process of fund signature based on integrated hardware and software, so as to solve the technical problem that the existing fund signature control lacks the integration of hardware and software throughout the entire process, which leads to the risk of security and compliance of large-amount fund payments.

[0004] To solve the above technical problems, the present invention provides the following technical solution: a software and hardware integrated online management tool for the entire process of fund seals, including a hardware terminal layer and an online management platform layer, wherein the hardware terminal layer and the online management platform layer are connected through an encrypted channel. The hardware terminal layer integrates a risk prediction and processing module and a seal control and execution module, while the online control platform layer deploys a risk data integration module and a linkage control engine. The risk data integration module collects multi-dimensional risk data and transmits it to the hardware terminal layer. The risk prediction and processing module analyzes and predicts the data to obtain the potential risk level. The linkage control engine generates control instructions based on the risk level and issues them to the seal control execution module. The seal control execution module performs the corresponding seal locking, permission adjustment or unlocking operations.

[0005] Preferably, the hardware terminal layer further includes an identity authentication module, which establishes a linkage communication with the seal control execution module and the risk prediction processing module to form a front-end verification link of "identity verification - risk prediction - seal control". The identity authentication module adopts a two-factor cross-verification mechanism to simultaneously complete the identification of the operator's biometric features and the verification of the business permission hardware key. During the verification process, it automatically associates the business type corresponding to the current seal application. Only when the biometric features, hardware key and business permission match can the predictive analysis process of the risk prediction processing module be started. The identity authentication module also has an abnormal verification behavior monitoring function, which marks the behavior of continuous verification failure and verification information that deviates too much from the historical record in real time, and synchronizes it to the risk control terminal of the online management and control platform layer.

[0006] Preferably, the risk prediction and processing module includes an AI inference chip, a local encrypted storage unit, and a model redundancy backup unit; The AI ​​inference chip is used to run a lightweight risk prediction model and realize edge computing analysis of potential risks. The local encrypted storage unit adopts a layered encryption architecture, which independently encrypts and stores the risk prediction model parameters, multi-dimensional risk data and prediction results, and the stored data is bound to the unique identifier of the hardware terminal. The model redundancy backup unit pre-stores a complete backup version of the currently running model. When the AI ​​inference chip performs a model update, it automatically switches to the backup model to continue risk prediction and analysis, ensuring that the control process is not interrupted. After the model update is completed, it smoothly switches back to the updated model. Specifically, risk prediction and analysis are achieved through the following algorithm formula: ; After the AI ​​inference chip calculates the basic risk value using this formula, it makes a preliminary determination of the risk level based on the preset risk level classification rules. in, As the baseline value for potential risks, The number of multi-dimensional risk characteristics, For the first The weighting coefficients of each risk characteristic For the first Standardized values ​​for each risk characteristic This is the model bias term.

[0007] Preferably, the AI ​​inference chip also integrates a model adaptation and optimization unit, which can dynamically adjust the inference accuracy and computing resource allocation ratio of the lightweight risk prediction model according to the real-time computing power status of the hardware terminal layer and the complexity of the current business to be predicted. When insufficient computing power of the hardware terminal is detected, the core feature-focused inference mode is automatically activated, prioritizing the completion of predictive analysis based on key risk features to ensure prediction efficiency. When computing power is sufficient, activate the full-dimensional feature reasoning mode to improve prediction accuracy; The model adaptation and optimization unit also records the prediction effect data under different adaptation modes and synchronizes it to the online management and control platform layer for model iteration and optimization. The specific algorithm formula for model adaptation optimization is as follows: ; ; in, To adapt to the optimized final risk prediction value, This is the computing power adaptation coefficient. As the baseline value for risk, Risk calculation values ​​are based on the core feature-focused reasoning model. Provides real-time available computing power for hardware terminals. This represents the maximum computing power of the hardware terminal.

[0008] Preferably, the risk data integration module of the online management and control platform layer includes a multi-source data interface unit, a data preprocessing unit, and a data conflict arbitration unit; The multi-source data interface unit adopts a standardized interface adaptation architecture, which is compatible with different types of data sources such as macroeconomic data platforms, industry data centers, counterparty business information systems and policy public opinion release platforms, so as to realize the real-time collection and dynamic access of multi-dimensional risk data; The data preprocessing unit sequentially completes the cleaning, desensitization, format standardization, and feature extraction of the raw data, removing invalid data, duplicate data, and interfering information to generate structured risk data; The data conflict arbitration unit is used to identify conflicting data for the same risk indicator from different data sources. Based on the preset data source credibility weight and data timeliness priority, it automatically determines the optimal data and marks the conflict information. The conflict information is synchronized to the data analysis terminal of the online management and control platform for manual review. The specific algorithm formula for data conflict arbitration is as follows: ; in, The optimal data for the same risk indicator. The number of conflicting data sources. For the first The credibility weight of each data source For the first The timeliness coefficient of each data source.

[0009] Preferably, the linkage control engine includes a risk level determination unit, a dynamic instruction generation unit, and a control strategy adaptation unit; The risk level determination unit receives the prediction results output by the risk prediction processing module, combines them with the preset basic risk level classification rules, and associates the current large-amount payment business type, transaction amount range and counterparty credit rating to dynamically adjust the risk level determination threshold and determine the final risk level. The risk level includes three levels: low risk, medium risk and high risk. The dynamic instruction generation unit generates differentiated control instructions based on different risk levels and current business scenarios. High-risk levels correspond to a seal-forcibly-locking instruction, with an accompanying risk blocking explanation; medium-risk levels correspond to a seal-use permission restriction instruction, including but not limited to adjusting the seal-use approval process nodes and reducing the single-use limit; low-risk levels correspond to a seal-unlocking instruction, with an accompanying risk warning. The control strategy adaptation unit pre-stores a special control strategy library for different industries and business types, and automatically matches the corresponding control strategy according to the business attributes of the seal application to improve the accuracy of control. The specific algorithm formula for dynamically determining the risk level is as follows: ; when Greater than At that time, it was determined to be high risk; In When the range is within a certain range, it is judged as medium risk; when Less than At that time, it was determined to be low risk; in, The threshold for determining the risk level is dynamically adjusted. Basic risk threshold, Risk coefficient for business type This refers to the counterparty's credit rating coefficient. The coefficient representing the impact of transaction amount. This is the relative size coefficient of the transaction amount.

[0010] Preferably, the linkage control engine also includes an early warning push unit and an emergency response unit. When the risk level is determined to be medium risk or high risk, the early warning push unit pushes risk warning information to the enterprise risk control management terminal and the corresponding business person in charge terminal through an encrypted channel. The early warning information includes the basis for risk prediction, potential risk type, risk impact scope and suggested handling measures, and records the early warning push time, reception status and read status. After receiving the high-risk level assessment result, the emergency response unit automatically triggers the emergency control process. In addition to issuing a locking instruction to the seal control execution module, it also simultaneously freezes the transaction permissions of the corresponding large-amount payment account until the risk control department completes the risk verification and issues an unlocking instruction.

[0011] Preferably, the seal control execution module includes a physical locking unit, an access control unit, and an operation status feedback unit; The physical locking unit adopts a dual protection mechanism of mechanical structure and electronic lock. When it receives the seal locking command, it physically fixes the seal through the mechanical limit structure and simultaneously activates the electronic lock. The permission control unit is used to dynamically adjust the permission parameters such as the usage limit of the seal, the length of the approval process, and the available time period when receiving permission adjustment instructions. The permission adjustment record is synchronized to the online management platform layer in real time to form a permission change trajectory. The operation status feedback unit collects the operation status data of the seal control execution module in real time, including instruction reception status, execution progress, and execution result, and feeds it back to the linkage control engine through an encrypted channel. If instruction execution failure or abnormality is detected, a retry mechanism is immediately triggered and an abnormal alarm message is pushed.

[0012] Preferably, it also includes a full-process traceability module, which establishes bidirectional communication connections with the hardware terminal layer and the online management and control platform layer respectively, and adopts an "event-driven + data association" traceability architecture to record data of the entire process of risk data collection, risk prediction and analysis, management and control instruction generation, and seal operation execution. The full-process traceability module marks the recorded data with correlation, and uses a unique business number to link the traceability data of different links to form a complete traceability chain. The recorded data is stored in a distributed manner using a blockchain notarization mechanism to ensure that the data is tamper-proof and traceable.

[0013] A method for online management of the entire fund signature process based on integrated hardware and software includes the following steps: S1. The identity authentication module performs two-factor cross-verification on the operator who initiates the seal application, and associates the verification business permissions. After the verification is passed, the risk data collection process is triggered. S2. The risk data integration module collects multi-dimensional risk data through the multi-source data interface unit. After being processed by the data preprocessing unit, the data conflict arbitration unit handles potential data conflicts, generates structured risk data, and transmits it to the local encrypted storage unit of the hardware terminal layer. The S3 AI inference chip calls the local lightweight risk prediction model to perform edge computing analysis on structured risk data. During the model update process, the model redundancy backup unit ensures that the prediction process is not interrupted, and the potential risk level is obtained. S4, the risk level determination unit of the linkage control engine dynamically adjusts the determination threshold according to the business scenario to determine the final risk level, and the dynamic instruction generation unit generates differentiated control instructions, which are sent to the seal control execution module through an encrypted channel. S5, the seal control execution module executes control commands through the physical locking unit or the permission control unit, and the operation status feedback unit feeds back the execution results to the linkage control engine; S6. The full-process traceability module records the above full-process data, performs correlation marking and blockchain notarization, and when the risk level is medium or high, the early warning push unit pushes risk warning information, and when the risk is high, the emergency response unit is triggered to execute the emergency control process.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a closed-loop management link of "two-factor identity verification - multi-dimensional risk AI prediction - precise seal execution" through encrypted communication between the hardware terminal layer and the online management platform layer. It strongly binds identity verification with business permissions and business types, and integrates internal and external multi-source data for risk prediction, thereby fundamentally solving the problems of fund security risks and compliance risks caused by the lack of full-process collaboration in existing tools.

[0015] 2. This invention also utilizes the model adaptation and optimization mechanism of the AI ​​inference chip to dynamically adjust the risk prediction mode based on the real-time computing power of the hardware and the complexity of the business. When the computing power is insufficient, the prediction efficiency is guaranteed, and when the computing power is sufficient, the prediction accuracy is improved, thereby ensuring the stability and accuracy of risk prediction.

[0016] 3. This invention also selects the optimal risk data through a multi-source data conflict arbitration mechanism and combines it with a blockchain-based end-to-end traceability architecture, which not only ensures the reliability of risk prediction input data, but also achieves the immutability and traceability of data throughout the entire operation chain, thus meeting the audit compliance requirements of multinational corporations. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0018] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0019] Example 1, such as Figure 1 and Figure 2 As shown, the present invention provides an online management tool for the entire process of fund seal based on hardware and software integration, including a hardware terminal layer and an online management platform layer, which are connected to each other through an encrypted channel. The hardware terminal layer integrates a risk prediction and processing module and a seal control and execution module, while the online control platform layer deploys a risk data integration module and a linkage control engine. The risk data integration module collects multi-dimensional risk data and transmits it to the hardware terminal layer. The risk prediction and processing module analyzes and predicts the data to obtain the potential risk level. The linkage control engine generates control instructions based on the risk level and issues them to the seal control execution module. The seal control execution module performs the corresponding seal locking, permission adjustment or unlocking operations, realizing a closed-loop linkage between risk prediction and seal control throughout the entire process, ensuring the security of large-amount fund payments.

[0020] In an embodiment of the present invention, the hardware terminal layer further includes an identity authentication module. The identity authentication module establishes linkage communication with the seal control execution module and the risk prediction processing module to form a front-end verification link of "identity verification - risk prediction - seal control". The identity authentication module adopts a two-factor cross-verification mechanism to simultaneously complete the biometric identification of operators and the verification of business permissions hardware keys. During the verification process, it automatically associates the business type corresponding to the current seal application. Only when the biometrics, hardware keys and business permissions match can the predictive analysis process of the risk prediction processing module be started. The identity authentication module also has an abnormal verification behavior monitoring function, which marks the behavior of continuous verification failure and verification information that deviates too much from the historical record in real time, and synchronizes it to the risk control terminal of the online management and control platform layer.

[0021] In an embodiment of the present invention, the risk prediction processing module includes an AI inference chip, a local encrypted storage unit, and a model redundancy backup unit; AI inference chips are used to run lightweight risk prediction models, enabling edge computing analysis of potential risks and avoiding delays in risk prediction caused by network transmission latency. The local encrypted storage unit adopts a layered encryption architecture, which independently encrypts and stores the risk prediction model parameters, multi-dimensional risk data and prediction results, and binds the stored data to the unique identifier of the hardware terminal to prevent data tampering and illegal migration. The model redundancy backup unit pre-stores a complete backup version of the currently running model. When the AI ​​inference chip performs a model update, it automatically switches to the backup model to continue risk prediction and analysis, ensuring that the control process is not interrupted. After the model update is completed, it smoothly switches back to the updated model. Specifically, risk prediction and analysis are achieved through the following algorithm formula: ; After the AI ​​inference chip calculates the basic risk value using this formula, it makes a preliminary determination of the risk level based on the preset risk level classification rules. in, As a baseline value for potential risks, it is the core intermediate quantitative result for risk prediction, used to initially reflect the degree of potential risks faced by large-value payment transactions; The number of multi-dimensional risk characteristics covers various characteristic dimensions related to the risk of large-sum fund payments, such as macroeconomics, industry dynamics, counterparty operations, and policy and public opinion. For the first The weighting coefficients for each risk feature are set according to the degree of impact of different risk features on the security of fund payments. The higher the degree of impact, the larger the weighting coefficient, which is used to distinguish the risk contribution of each feature. For the first The standardized value of each risk feature is obtained by the risk data integration module after cleaning, desensitizing and format standardizing the original collected data, ensuring that risk features of different dimensions and ranges can be uniformly weighted. This is a model bias term used to correct systematic errors in the weighted summation result, adapt to the differences in risk prediction baselines for enterprises of different industries and sizes, and improve the accuracy of basic risk value calculation. This algorithm provides a precise quantitative calculation basis for risk prediction. Through the combination of weighted summation and bias correction logic, it can effectively integrate multi-dimensional risk information and avoid the one-sidedness of prediction caused by a single risk feature. At the same time, its lightweight operation logic is adapted to the edge computing requirements of AI inference chips, ensuring that risk prediction can be completed quickly, providing support for the real-time control of seals, and initially realizing the quantitative correlation between risk features and seal control, laying a reliable foundation for subsequent risk level determination and control instruction generation.

[0022] In an embodiment of the present invention, the AI ​​inference chip also integrates a model adaptation and optimization unit, which can dynamically adjust the inference accuracy and computing resource allocation ratio of the lightweight risk prediction model according to the real-time computing power status of the hardware terminal layer and the complexity of the current business to be predicted. When insufficient computing power of the hardware terminal is detected, the core feature-focused inference mode is automatically activated, prioritizing the completion of predictive analysis based on key risk features to ensure prediction efficiency. When computing power is sufficient, activate the full-dimensional feature reasoning mode to improve prediction accuracy; The model adaptation and optimization unit also records the prediction performance data under different adaptation modes and synchronizes it to the online management and control platform layer for model iteration and optimization. The specific algorithm formula for model adaptation optimization is as follows: ; ; in, To adapt to the optimized final risk prediction value, which is the optimal risk quantification result that takes into account both hardware computing power status and business complexity, and to provide accurate input for subsequent dynamic determination of risk level; The computing power adaptation coefficient is used to quantify the matching degree between the real-time computing power and the maximum computing power of the hardware terminal. Its value range is [0,1], which directly determines the weight distribution of the full-dimensional reasoning and core feature reasoning results. As the risk baseline value, i.e. the risk prediction result under the full-dimensional feature reasoning mode, it has high prediction accuracy; Risk calculations based on core feature-focused reasoning models are performed only on the core features most critical to financial risk, resulting in high computational efficiency but slightly lower accuracy. ; The computing power available to the hardware terminal in real time refers to the computing resources that the hardware terminal can use for risk prediction calculations at the current moment. The maximum computing power of the hardware terminal is the upper limit of the maximum computing resources that the hardware terminal can provide under ideal conditions. The accuracy and efficiency of risk prediction are dynamically balanced based on the real-time computing power status of the hardware terminal. This is first achieved through the computing power adaptation coefficient. The calculation quantifies the current computing power redundancy of the hardware terminal; then based on... The system dynamically adjusts the weights of the results from full-dimensional feature reasoning and core feature-focused reasoning, achieving a smooth fusion of the two reasoning modes. When computing power is sufficient, The value approaches 1, retaining more of the full-dimensional reasoning results to improve accuracy; when computing power is insufficient, The value approaches 0, relying more on the inference results of core features to ensure efficiency, while ensuring that the prediction process is not interrupted; This set of algorithm formulas effectively resolves the contradiction between hardware computing power fluctuations and the accuracy and efficiency of risk prediction. By using a computing power adaptation coefficient, it achieves dynamic fusion of two inference modes, which not only avoids interruption or delay in the prediction process when computing power is insufficient, but also maximizes the prediction accuracy when computing power is sufficient. At the same time, this logic can automatically adapt to the prediction needs of different business complexities without manual intervention, improving the system's adaptability and ensuring that risk prediction can still provide stable and accurate support for seal management in complex hardware operating environments.

[0023] In an embodiment of the present invention, the risk data integration module of the online management and control platform layer includes a multi-source data interface unit, a data preprocessing unit, and a data conflict arbitration unit; The multi-source data interface unit adopts a standardized interface adaptation architecture, which is compatible with different types of data sources such as macroeconomic data platforms, industry data centers, counterparty business information systems and policy and public opinion release platforms, so as to realize the real-time collection and dynamic access of multi-dimensional risk data; The data preprocessing unit sequentially completes the cleaning, desensitization, format standardization, and feature extraction of the raw data, removing invalid data, duplicate data, and interfering information to generate structured risk data; The data conflict arbitration unit is used to identify conflicting data for the same risk indicator from different data sources. Based on the preset data source credibility weight and data timeliness priority, it automatically determines the optimal data and marks the conflict information. The conflict information is synchronized to the data analysis terminal of the online management and control platform for manual review. The specific algorithm formula for data conflict arbitration is as follows: ; in, The optimal data for the same risk indicator is the risk characteristic value (i.e., determined after conflict arbitration) used as input to the risk prediction model. ), to ensure the reliability of input data; The number of conflicting data sources, i.e., the total number of inconsistent data sources collected from different data platforms for the same risk indicator; For the first The credibility weight of each data source is set based on the data source's authority, historical accuracy, and other factors. The higher the authority and the better the historical accuracy, the greater the weight. For the first The timeliness coefficient of each data source is set according to the interval between the data collection time and the current time. The shorter the interval (the newer the data), the larger the coefficient value, ensuring that the latest data is used first. This algorithm effectively solves the data conflict problem in the process of multi-source data collection. Through dual quantitative evaluation of reliability and timeliness, it can accurately screen out the optimal risk data, ensuring the reliability of the input data of the risk prediction model from the source and avoiding risk prediction deviations caused by erroneous data. At the same time, the conflict information marking function provides a clear direction for manual review, improves the standardization of data management, further enhances the accuracy of risk prediction, and lays a solid data foundation for the scientific nature of subsequent seal management decisions.

[0024] In an embodiment of the present invention, the linkage control engine includes a risk level determination unit, a dynamic instruction generation unit, and a control strategy adaptation unit. The risk level determination unit receives the prediction results output by the risk prediction processing module, combines them with the preset basic risk level classification rules, and also associates the current large-amount payment business type, transaction amount range and counterparty credit rating to dynamically adjust the risk level determination threshold and determine the final risk level. The risk level includes three levels: low risk, medium risk and high risk. The dynamic instruction generation unit generates differentiated control instructions based on different risk levels and current business scenarios. High-risk levels correspond to mandatory seal locking instructions, with risk blocking explanations added. Medium-risk levels correspond to seal usage permission restriction instructions, including but not limited to adjusting seal approval process nodes and reducing the single seal usage limit. Low-risk levels correspond to seal unlocking instructions, with risk warning prompts added. The control strategy adaptation unit pre-stores a special control strategy library for different industries and business types, and automatically matches the corresponding control strategy according to the business attributes of the seal application to improve the accuracy of control. The specific algorithm formula for dynamically determining the risk level is as follows: ; when Greater than At that time, it was determined to be high risk; In When the range is within a certain range, it is judged as medium risk; when Less than At that time, it was determined to be low risk; in, The risk level determination threshold is dynamically adjusted and is a personalized risk threshold adapted to the current business scenario, used to accurately distinguish between high risk and medium risk; The basic risk threshold is a benchmark risk threshold set based on common business scenarios, used to distinguish between medium and low risk. This is the business type risk coefficient, which is set according to the inherent risk level of different business types. The higher the inherent risk, the larger the coefficient value. The counterparty credit rating coefficient is set based on the counterparty's credit rating results. The lower the credit rating (the worse the credit status), the larger the coefficient value. This is the transaction amount impact coefficient, used to quantify the degree of influence of transaction amount on risk level determination; This is the relative scale coefficient of the transaction amount, which is set according to the ratio of the current transaction amount to the scale of large-amount daily fund payments of the enterprise. The higher the ratio, the larger the coefficient value. This algorithm enables dynamic and accurate risk level determination. By introducing multi-dimensional business scenario correlation coefficients, it adapts risk thresholds to differences in business types, counterparty credit status, and transaction amount, avoiding misjudgments of risk levels caused by universal thresholds. At the same time, the clear level classification logic provides a clear basis for generating differentiated seal control instructions, ensuring that high-risk transactions can have their seals accurately locked, medium-risk transactions can be subject to targeted permission restrictions, and low-risk transactions can be efficiently unlocked and used. This achieves a balance between risk control and business efficiency, significantly improving the scientific and accurate nature of seal control for large-value payments.

[0025] In an embodiment of the present invention, the linkage control engine further includes an early warning push unit and an emergency response unit. When the risk level is determined to be medium risk or high risk, the early warning push unit pushes risk warning information to the enterprise risk control management terminal and the corresponding business person in charge terminal through an encrypted channel. The early warning information includes the basis for risk prediction, potential risk type, risk impact scope and suggested handling measures. At the same time, the early warning push time, reception status and read status are recorded. After receiving the high-risk level assessment result, the emergency response unit automatically triggers the emergency control process. In addition to issuing a locking instruction to the seal control execution module, it also simultaneously freezes the transaction permissions of the corresponding large-amount payment account until the risk control department completes the risk verification and issues an unlocking instruction.

[0026] In an embodiment of the present invention, the seal control execution module includes a physical locking unit, a permission control unit, and an operation status feedback unit. The physical locking unit adopts a dual protection mechanism of mechanical structure and electronic lock. When it receives the seal locking command, it physically fixes the seal through the mechanical limit structure and simultaneously activates the electronic lock to lock. The dual protection ensures that the seal cannot be operated or used. The permission control unit is used to dynamically adjust the permission parameters such as the usage limit of the seal, the length of the approval process, and the available time period when receiving permission adjustment instructions. The permission adjustment record is synchronized to the online management platform layer in real time to form a permission change trajectory. The operation status feedback unit collects operation status data from the seal control execution module in real time, including instruction reception status, execution progress, and execution results. It feeds this data back to the linkage control engine through an encrypted channel. If instruction execution failure or abnormality is detected, a retry mechanism is immediately triggered and an abnormal alarm message is pushed.

[0027] In embodiments of the present invention, a full-process traceability module is also included. The full-process traceability module establishes bidirectional communication connections with the hardware terminal layer and the online management and control platform layer respectively. It adopts an "event-driven + data association" traceability architecture to record data of the entire process of risk data collection, risk prediction and analysis, management and control instruction generation, and seal operation execution, including but not limited to data collection time, collection source, prediction results, instruction content, operator, operation time, operation location, and equipment identification. The end-to-end traceability module marks the recorded data with correlation and connects the traceability data of different links through a unique business number to form a complete traceability chain. The recorded data is stored in a distributed manner using a blockchain notarization mechanism to ensure that the data is tamper-proof and traceable. The end-to-end traceability module also features traceability data retrieval and visualization capabilities, supporting the retrieval of traceability data by business number, time range, risk level, and other dimensions, and generating traceability reports.

[0028] Example 2: A method for online management of the entire process of fund signature based on integrated hardware and software, comprising the following steps: S1. The identity authentication module performs two-factor cross-verification on the operator who initiates the seal application, and associates the verification business permissions. After the verification is passed, the risk data collection process is triggered. S2. The risk data integration module collects multi-dimensional risk data through the multi-source data interface unit. After being processed by the data preprocessing unit, the data conflict arbitration unit handles potential data conflicts, generates structured risk data, and transmits it to the local encrypted storage unit of the hardware terminal layer. The S3 AI inference chip calls the local lightweight risk prediction model to perform edge computing analysis on structured risk data. During the model update process, the model redundancy backup unit ensures that the prediction process is not interrupted, and the potential risk level is obtained. S4, the risk level determination unit of the linkage control engine dynamically adjusts the determination threshold according to the business scenario to determine the final risk level, and the dynamic instruction generation unit generates differentiated control instructions, which are sent to the seal control execution module through an encrypted channel. S5, the seal control execution module executes control commands through the physical locking unit or the permission control unit, and the operation status feedback unit feeds back the execution results to the linkage control engine; S6. The full-process traceability module records the above full-process data, performs correlation marking and blockchain notarization, and when the risk level is medium or high, the early warning push unit pushes risk warning information, and when the risk is high, the emergency response unit is triggered to execute the emergency control process.

[0029] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A software-hardware integrated online management tool for the entire process of fund signature verification, characterized in that: It includes a hardware terminal layer and an online management and control platform layer, which are connected via an encrypted channel. The hardware terminal layer integrates a risk prediction and processing module and a seal control and execution module, while the online control platform layer deploys a risk data integration module and a linkage control engine. The risk data integration module collects multi-dimensional risk data and transmits it to the hardware terminal layer. The risk prediction and processing module analyzes and predicts the data to obtain the potential risk level. The linkage control engine generates control instructions based on the risk level and issues them to the seal control execution module. The seal control execution module performs the corresponding seal locking, permission adjustment or unlocking operations.

2. The online management tool for the entire process of fund signature based on integrated hardware and software as described in claim 1, characterized in that, The hardware terminal layer also includes an identity authentication module, which establishes a linkage communication with the seal control execution module and the risk prediction processing module to form a front-end verification link of "identity verification - risk prediction - seal control". The identity authentication module adopts a two-factor cross-verification mechanism to simultaneously complete the identification of the operator's biometric features and the verification of the business permission hardware key. During the verification process, it automatically associates the business type corresponding to the current seal application. Only when the biometric features, hardware key and business permission match can the predictive analysis process of the risk prediction processing module be started. The identity authentication module also has an abnormal verification behavior monitoring function, which marks the behavior of continuous verification failure and verification information that deviates too much from the historical record in real time, and synchronizes it to the risk control terminal of the online management and control platform layer.

3. The online management tool for the entire process of fund signature based on integrated hardware and software as described in claim 1, characterized in that, The risk prediction and processing module includes an AI inference chip, a local encrypted storage unit, and a model redundancy backup unit. The AI ​​inference chip is used to run a lightweight risk prediction model and realize edge computing analysis of potential risks. The local encrypted storage unit adopts a layered encryption architecture, which independently encrypts and stores the risk prediction model parameters, multi-dimensional risk data and prediction results, and the stored data is bound to the unique identifier of the hardware terminal. The model redundancy backup unit pre-stores a complete backup version of the currently running model. When the AI ​​inference chip performs a model update, it automatically switches to the backup model to continue risk prediction and analysis, ensuring that the control process is not interrupted. After the model update is completed, it smoothly switches back to the updated model. Specifically, risk prediction and analysis are achieved through the following algorithm formula: ； After the AI ​​inference chip calculates the basic risk value using this formula, it makes a preliminary determination of the risk level based on the preset risk level classification rules. in, As the baseline value for potential risks, The number of multi-dimensional risk characteristics, For the first The weighting coefficients of each risk characteristic For the first Standardized values ​​for each risk characteristic This is the model bias term.

4. The online management tool for the entire process of fund signature based on integrated hardware and software as described in claim 3, characterized in that, The AI ​​inference chip also integrates a model adaptation and optimization unit, which can dynamically adjust the inference accuracy and computing resource allocation ratio of the lightweight risk prediction model according to the real-time computing power status of the hardware terminal layer and the complexity of the current business to be predicted. When insufficient computing power of the hardware terminal is detected, the core feature-focused inference mode is automatically activated, prioritizing the completion of predictive analysis based on key risk features to ensure prediction efficiency. When computing power is sufficient, activate the full-dimensional feature reasoning mode to improve prediction accuracy; The model adaptation and optimization unit also records the prediction effect data under different adaptation modes and synchronizes it to the online management and control platform layer for model iteration and optimization. The specific algorithm formula for model adaptation optimization is as follows: ； ； in, To adapt to the optimized final risk prediction value, This is the computing power adaptation coefficient. As the baseline value for risk, Risk calculation values ​​are based on the core feature-focused reasoning model. Provides real-time available computing power for hardware terminals. This represents the maximum computing power of the hardware terminal.

5. The online management tool for the entire process of fund signature based on integrated hardware and software as described in claim 1, characterized in that, The risk data integration module of the online management and control platform layer includes a multi-source data interface unit, a data preprocessing unit, and a data conflict arbitration unit. The multi-source data interface unit adopts a standardized interface adaptation architecture, which is compatible with different types of data sources such as macroeconomic data platforms, industry data centers, counterparty business information systems and policy public opinion release platforms, so as to realize the real-time collection and dynamic access of multi-dimensional risk data; The data preprocessing unit sequentially completes the cleaning, desensitization, format standardization, and feature extraction of the raw data, removing invalid data, duplicate data, and interfering information to generate structured risk data; The data conflict arbitration unit is used to identify conflicting data for the same risk indicator from different data sources. Based on the preset data source credibility weight and data timeliness priority, it automatically determines the optimal data and marks the conflict information. The conflict information is synchronized to the data analysis terminal of the online management and control platform for manual review. The specific algorithm formula for data conflict arbitration is as follows: ； in, The optimal data for the same risk indicator. The number of conflicting data sources. For the first The credibility weight of each data source For the first The timeliness coefficient of each data source.

6. The online management tool for the entire process of fund signature based on hardware and software integration as described in claim 4, characterized in that, The linkage control engine includes a risk level determination unit, a dynamic instruction generation unit, and a control strategy adaptation unit. The risk level determination unit receives the prediction results output by the risk prediction processing module, combines them with the preset basic risk level classification rules, and associates the current large-amount payment business type, transaction amount range and counterparty credit rating to dynamically adjust the risk level determination threshold and determine the final risk level. The risk level includes three levels: low risk, medium risk and high risk. The dynamic instruction generation unit generates differentiated control instructions based on different risk levels and current business scenarios. High-risk levels correspond to a seal-forcibly-locking instruction, with a risk blocking explanation attached. Medium-risk levels correspond to a seal usage permission restriction instruction, including but not limited to adjusting the seal approval process nodes and reducing the single seal usage limit. Low-risk levels correspond to the seal unlocking and usage instructions, with additional risk warning prompts; The control strategy adaptation unit pre-stores a special control strategy library for different industries and business types, and automatically matches the corresponding control strategy according to the business attributes of the seal application to improve the accuracy of control. The specific algorithm formula for dynamically determining the risk level is as follows: ； when Greater than At that time, it was determined to be high risk; In When the range is within a certain range, it is judged as medium risk; when Less than At that time, it was determined to be low risk; in, The threshold for determining the risk level is dynamically adjusted. Basic risk threshold, Risk coefficient for business type This refers to the counterparty's credit rating coefficient. The coefficient representing the impact of transaction amount. This is the relative size coefficient of the transaction amount.

7. The online management tool for the entire process of fund signature based on hardware and software integration as described in claim 6, characterized in that, The linkage control engine also includes an early warning push unit and an emergency response unit. When the risk level is determined to be medium or high risk, the early warning push unit pushes risk warning information to the enterprise risk control management terminal and the corresponding business person in charge terminal through an encrypted channel. The warning information includes the basis for risk prediction, potential risk type, risk impact scope and suggested handling measures. At the same time, the early warning push time, reception status and read status are recorded. After receiving the high-risk level assessment result, the emergency response unit automatically triggers the emergency control process. In addition to issuing a locking instruction to the seal control execution module, it also simultaneously freezes the transaction permissions of the corresponding large-amount payment account until the risk control department completes the risk verification and issues an unlocking instruction.

8. The online management tool for the entire process of fund signature based on hardware and software integration as described in claim 1, characterized in that, The seal control execution module includes a physical locking unit, an access control unit, and an operation status feedback unit. The physical locking unit adopts a dual protection mechanism of mechanical structure and electronic lock. When it receives the seal locking command, it physically fixes the seal through the mechanical limit structure and simultaneously activates the electronic lock. The permission control unit is used to dynamically adjust the permission parameters such as the usage limit of the seal, the length of the approval process, and the available time period when receiving permission adjustment instructions. The permission adjustment record is synchronized to the online management platform layer in real time to form a permission change trajectory. The operation status feedback unit collects the operation status data of the seal control execution module in real time, including instruction reception status, execution progress, and execution result, and feeds it back to the linkage control engine through an encrypted channel. If instruction execution failure or abnormality is detected, a retry mechanism is immediately triggered and an abnormal alarm message is pushed.

9. The online management tool for the entire process of fund signature based on hardware and software integration as described in claim 1, characterized in that, It also includes a full-process traceability module, which establishes bidirectional communication connections with the hardware terminal layer and the online management and control platform layer respectively. It adopts an "event-driven + data association" traceability architecture to record data of the entire process of risk data collection, risk prediction and analysis, management and control instruction generation, and seal operation execution. The full-process traceability module marks the recorded data with correlation, and uses a unique business number to link the traceability data of different links to form a complete traceability chain. The recorded data is stored in a distributed manner using a blockchain notarization mechanism to ensure that the data is tamper-proof and traceable.

10. A method for applying to the online management tool for the entire process of fund signature based on hardware and software integration as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. The identity authentication module performs two-factor cross-verification on the operator who initiates the seal application, and associates the verification business permissions. After the verification is passed, the risk data collection process is triggered. S2. The risk data integration module collects multi-dimensional risk data through the multi-source data interface unit. After being processed by the data preprocessing unit, the data conflict arbitration unit handles potential data conflicts, generates structured risk data, and transmits it to the local encrypted storage unit of the hardware terminal layer. The S3 AI inference chip calls the local lightweight risk prediction model to perform edge computing analysis on structured risk data. During the model update process, the model redundancy backup unit ensures that the prediction process is not interrupted, and the potential risk level is obtained. S4, the risk level determination unit of the linkage control engine dynamically adjusts the determination threshold according to the business scenario to determine the final risk level, and the dynamic instruction generation unit generates differentiated control instructions, which are sent to the seal control execution module through an encrypted channel. S5, the seal control execution module executes control commands through the physical locking unit or the permission control unit, and the operation status feedback unit feeds back the execution results to the linkage control engine; S6. The full-process traceability module records the above full-process data, performs correlation marking and blockchain notarization, and when the risk level is medium or high, the early warning push unit pushes risk warning information, and when the risk is high, the emergency response unit is triggered to execute the emergency control process.

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