Supply chain finance post-loan risk early warning method, system and device and readable storage medium

Abstract

The embodiment of the invention provides a supply chain financial post-loan risk early warning method, system and device and a readable storage medium. The method comprises the steps of data acquisition, block chain storage, data acquisition, risk analysis, early warning generation and early warning interaction. And the data acquisition and block chain storage step realizes distributed storage of data. In the risk analysis step, the risk state is judged based on the difference value range of the behavior data and the historical data, and a risk early warning index is generated. And the intelligent contract verifies the early warning index, if the early warning index exceeds a preset risk threshold, an early warning signal is generated, and the early warning signal is pushed to the target user in real time through an event monitoring mechanism of the block chain network. According to the invention, the problems of low information processing efficiency, early warning lagging and data unreliability of post-loan risk early warning are solved, and an innovative post-loan risk early warning solution is provided for the field of supply chain finance.

Description

Technical Field

[0001] The present invention relates to the field of supply chain finance risk control, and in particular to a supply chain finance post-loan risk early warning method, system, device and readable storage medium. Background Art

[0002] With the rapid development of financial technology, more and more technological innovations are being applied to the financial field, especially in the risk control of supply chain finance. As an important financing model, supply chain finance can effectively solve the financing difficulties of upstream and downstream enterprises in the supply chain, but its post-loan risk warning has always been one of the key challenges faced by lending institutions.

[0003] In the existing technology, the post-loan risk warning of supply chain finance mainly relies on manual collection and processing of information related to the borrowing enterprise. This method requires screening, sorting and analyzing data from a huge amount of information sources, which is a huge workload and inefficient. Due to the complexity of information processing and the limitations of manual operations, lending institutions often find it difficult to obtain accurate risk assessment results in a timely manner, resulting in a lag in risk warning. This lag makes it impossible for relevant personnel and institutions to trigger the risk handling process in a timely manner, thereby increasing the risk exposure of lending institutions and may affect their fund security and business stability.

[0004] In addition, relying on manual processing of information also results in insufficient data accuracy. Manual operations are easily affected by subjective factors, which may lead to information omissions or errors, thereby affecting the accuracy of the risk warning model. At the same time, as the scale of supply chain finance business continues to expand, traditional manual processing methods can no longer meet the needs of efficient and accurate risk warnings. Summary of the invention

[0005] The present invention provides a supply chain finance post-loan risk warning method, system, device and readable storage medium to solve the problems of low information processing efficiency, delayed warning and insufficient data accuracy in the supply chain finance post-loan risk warning in the related technology.

[0006] According to one aspect of the present invention, a supply chain finance post-loan risk warning method is provided, comprising: a data collection step, collecting historical data and behavioral data of each supply chain finance event of a target user, wherein the supply chain finance event is an integer greater than or equal to one; a blockchain storage step, uploading the historical data and behavioral data of each supply chain finance event to the blockchain network, and distributing and storing the data in multiple nodes of the blockchain through smart contracts; a data acquisition step, acquiring the historical data and behavioral data stored in the blockchain storage step; a risk analysis step, judging whether the difference range between the behavioral data and the historical data is within a normal range, and generating a risk warning indicator if the difference range is not within the normal range; a warning generation step, verifying the risk warning indicator through a smart contract, and generating a warning signal if the risk warning indicator is greater than or equal to a risk threshold; a warning interaction step, pushing the warning signal to the target user in real time through the event monitoring mechanism of the blockchain network.

[0007] Furthermore, the historical data includes the target user's basic information, interest calculation stage, interest-bearing principal, daily interest rate, interest accrual date, repayment date, interest calculation days, payable interest and repayable amount; the behavioral data includes the target user's transaction details data, fund flow data and repayment record.

[0008] Furthermore, the difference range between the behavioral data and the historical data includes a first difference range and a second difference range. The first difference calculation formula is: first difference = amount repaid in the behavioral data - amount to be repaid in the historical data; if the first difference is greater than or equal to zero, the difference range is in a normal range; if the first difference is less than zero, the difference range is in an abnormal range. The second difference calculation formula is: second difference = number of days interest has been accrued in the behavioral data - number of days interest should be accrued in the historical data; if the second difference is less than or equal to zero, the difference range is in a normal range; if the second difference is greater than zero, the difference range is in an abnormal range.

[0009] Furthermore, the smart contract verifies the risk warning indicator, including: when receiving the risk warning indicator, triggering the verification instruction, and executing the verification of the risk warning indicator. If the risk warning indicator meets the verification rules, a verification pass result is generated, and the comparison of the risk warning indicator with the risk threshold is triggered; if the risk warning indicator does not meet the preset verification rules, a verification failure result is generated, and the verification failure result is sent to the target user as an alarm.

[0010] Furthermore, the warning generation and warning interaction include: when the risk warning indicator is greater than or equal to the risk threshold, generating a warning signal; pushing the warning signal to the target user in real time through a message push interface, and the message push interface is implemented based on the event monitoring mechanism of the blockchain network.

[0011] According to a second aspect of the present invention, a supply chain finance post-loan risk early warning system is provided, comprising: a data collection module, for collecting historical data and behavioral data of each supply chain finance event of a target user, and uploading the data to a blockchain network; a blockchain storage module, communicatively connected to the data collection module, for storing the data collected by the data collection module; a data acquisition module, communicatively connected to the blockchain storage module, for acquiring historical data and behavioral data stored in the blockchain storage module; a risk analysis module, communicatively connected to the data acquisition module, for calculating the data acquired by the data acquisition module to generate a risk early warning indicator; an early warning generation module, communicatively connected to the risk analysis module, for verifying the risk early warning indicator generated by the risk analysis module, and generating an early warning signal by comparing the verification result with the risk threshold; an early warning interaction module, communicatively connected to the early warning generation module, for sending the early warning signal generated by the early warning generation module to the target user.

[0012] Furthermore, the risk analysis module includes: determining whether the difference range between the behavior data and the historical data is within a normal range, and generating a risk warning indicator if the difference range does not fall within the normal range. The difference range includes a first difference range and a second difference range. The first difference range includes the difference between the repayment amount in the behavior data and the amount to be repaid in the historical data; if the first difference is greater than or equal to zero, the difference range is within a normal range. The second difference range includes the difference between the number of interest-bearing days in the behavior data and the number of interest-bearing days in the historical data; if the second difference is less than or equal to zero, the difference range is within a normal range.

[0013] Furthermore, the warning generation module includes: an event monitoring unit for monitoring changes in risk warning indicators; and a smart contract verification unit for verifying the risk warning indicators. If the risk warning indicators pass the verification, the smart contract generates a verification pass result; if the risk warning indicators fail the verification, the smart contract generates a verification failure result. The threshold judgment unit receives the risk warning indicators that have passed the verification, and judges whether to generate a warning signal according to the risk threshold. If the risk warning indicator is compared with the risk threshold, if the risk warning indicator is less than the risk threshold, there will be no operation instructions and no warning signal will be generated; if the risk warning indicator is greater than or equal to the risk threshold, a warning signal will be automatically generated.

[0014] According to a third aspect of the present invention, a supply chain finance post-loan risk warning device is provided, comprising: a processor; a memory storing a supply chain finance post-loan risk warning program. When the processor executes the supply chain finance post-loan risk warning program, the method steps provided in the first aspect are implemented.

[0015] According to a fourth aspect of the present invention, a readable storage medium is provided, comprising: a supply chain finance post-loan risk warning program is stored on the readable storage medium, and the supply chain finance post-loan risk warning program implements the method steps provided in the first aspect when executed by a processor.

[0016] The present invention provides a supply chain finance post-loan risk warning method, system, device and readable storage medium to solve the problems of low information processing efficiency, delayed warning response and insufficient data accuracy in the existing supply chain finance post-loan risk warning. By introducing blockchain technology and smart contract mechanism, the present invention realizes data distributed storage and risk analysis, and significantly improves the real-time and efficiency of information processing. At the same time, based on the preset risk threshold and difference calculation model, the system can accurately identify abnormal behavior data, and realize the real-time push of warning signals through the event monitoring mechanism preset by the blockchain network, thereby effectively improving the timeliness and data reliability of post-loan risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of method steps according to an embodiment of the present invention.

[0019] Figure 2 It is a flow chart of the steps for verifying risk warning indicators by a smart contract according to an embodiment of the present invention.

[0020] Figure 3 It is a flow chart of warning generation and warning interaction steps according to an embodiment of the present invention.

[0021] Figure 4 is a schematic diagram of system modules according to an embodiment of the present invention.

[0022] Figure 5 2 is a schematic diagram of an early warning generation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the present invention described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, product or device that includes a series of steps or units is not necessarily limited to the steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Supply chain finance is a model that relies on core enterprises in the supply chain, focuses on the financing needs of upstream and downstream small and medium-sized enterprises, and provides comprehensive financial services to all supply chain participants by integrating logistics, capital flow and information flow based on real trade background.

[0027] Blockchain is a new application model that integrates computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism and encryption algorithm. In essence, blockchain is a decentralized distributed database consisting of a series of data blocks that are interconnected through cryptographic methods. Each data block records a batch of network transaction information, and encryption technology is used to ensure that the data cannot be tampered with and can be verified. Each block contains not only transaction data, but also the hash value of the previous block, thus forming a chain structure to ensure the continuity and integrity of the data.

[0028] Risk threshold refers to a critical value set in advance in risk management, which is used to measure and judge whether the risk has reached a level that requires countermeasures. When the risk indicator exceeds the threshold, the system or manager will trigger the corresponding early warning or intervention mechanism to prevent the risk from further expanding or causing losses.

[0029] The event monitoring mechanism of the blockchain network is a technology based on smart contracts and network nodes, which is used to monitor and respond to specific events or status changes occurring on the blockchain in real time. Its core function is to automatically trigger predefined operations or notifications by monitoring transactions, contract executions or data updates in the blockchain network. In an embodiment of the present invention, the event monitoring mechanism refers to when a repayment behavior or overdue event occurs, the monitoring mechanism triggers the generation of a risk warning indicator instruction, and when the risk warning indicator is greater than or equal to the preset risk threshold, a warning signal is generated to push the message to the target user.

[0030] A smart contract is a contract code written in a specific programming language (such as Ethereum's Solidity) to define the rules and logic of the contract; the contract code is then deployed to the blockchain network to generate a unique contract address; when the preset conditions (such as the occurrence of a repayment or overdue event) are met, the contract begins to execute; the nodes in the blockchain network verify the execution results of the contract and record them on the blockchain; the results of the contract execution (such as generating an early warning signal and pushing it to the target user) are written to the blockchain and visible to all participants.

[0031] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] refer to Figure 1 The supply chain finance post-loan risk warning method of an embodiment of the present invention includes: step S102, collecting historical data and behavior data of each supply chain finance loan event of the target user, including goods information, total price of goods, unit price, amount to be repaid, number of days to accrue interest, interest to be repaid and logistics information, capital transaction flow, and repayment record; the supply chain finance loan event is an integer greater than or equal to one, that is, the target user may have multiple loan orders.

[0033] Step S104, uploading the historical data and behavioral data of each supply chain finance loan event to the blockchain network, and distributing and storing the data in multiple nodes of the blockchain through smart contracts. Once the data is written into the blockchain, it cannot be tampered with or deleted.

[0034] Step S106, obtaining the historical data and behavior data stored in the blockchain storage step for risk analysis.

[0035] Step S108, determining whether the difference range between the behavior data and the historical data is within a normal range, and generating a risk warning indicator if the difference range is not within a normal range.

[0036] Specifically, the two characteristic data for calculating the difference range are preset as a repayment amount and interest-bearing days that can be standardized and calculated. The difference range between the behavior data and the historical data includes a first difference range and a second difference range.

[0037] The calculation formula of the first difference is: first difference (repayment amount difference) = amount repaid in the behavior data - amount to be repaid in the historical data. If the first difference is greater than or equal to zero, that is, the full or excess repayment is made, then the difference range is within the normal range; if the first difference is less than zero, that is, the full repayment is not made, then the difference range is within the abnormal range.

[0038] The calculation formula of the second difference is: second difference (difference of interest-bearing days) = number of days with interest accrued in the behavior data - number of days with interest accrued in the historical data. If the second difference is less than or equal to zero, that is, repayment is made on time or in advance, then the difference range is within the normal range; if the second difference is greater than zero, that is, overdue payment caused by failure to repay on time, then the difference range is within the abnormal range.

[0039] Step S110, verifying the risk warning indicator through a smart contract, and generating a warning signal if the risk warning indicator is greater than or equal to the risk threshold.

[0040] Step S112: Push the warning signal to the target user in real time through the event monitoring mechanism of the blockchain network.

[0041] refer to Figure 2 , in operation step S110, the smart contract verifies the risk warning indicator including the following steps: Step S202, when the risk warning indicator is received. Step S204, trigger the verification instruction. Step S206, execute the verification of the risk warning indicator. Step S208, if the risk warning indicator meets the verification rules, a verification pass result is generated, triggering the comparison of the risk warning indicator with the risk threshold. Step S210, if the risk warning indicator does not meet the preset verification rules, a verification failure result is generated, and the verification failure result is sent to the target user as an alarm.

[0042] Specifically, the data source is obtained from step S106, including two characteristic data (repayment amount and interest-bearing days). The difference range is calculated according to the preset first difference and second difference calculation formulas, that is, the repayment amount difference (repaid amount - amount to be repaid) and the interest-bearing days difference (interest-bearing days - interest-bearing days) are calculated. According to the calculation result, it is judged whether the difference exceeds the normal range, and a preliminary risk warning indicator is generated; through the event monitoring mechanism of the blockchain network, the generation of the risk warning indicator is monitored, the verification instruction is triggered, and the verification process is started; the smart contract checks the risk warning indicator according to the preset verification rules, and the verification rules include that the input data has not been tampered with or omitted and the indicator calculation conforms to the preset logic. If the risk warning indicator meets the verification rules, a verification pass result is generated, and the comparison of the risk warning indicator with the risk threshold is triggered; if the risk warning indicator does not meet the preset verification rules, a verification failure result is generated, and the verification failure result is sent to the target user as an alarm.

[0043] refer to Figure 3 In step S112, the warning generation and warning interaction include the following steps: S302, when the risk warning indicator is greater than or equal to the risk threshold. S304, generating a warning signal in response to step S302. S306, pushing the warning signal to the target user in real time through the message push interface.

[0044] Specifically, the risk threshold is preset as the deviation ratio of the repayment amount or the number of overdue days. For the verified risk warning indicators, the smart contract compares them with the preset risk threshold. If the generated risk warning indicator is the first difference (repayment amount difference), it is compared with the preset deviation ratio of the repayment amount; if the generated risk warning indicator is the second difference (interest-bearing days difference), it is compared with the preset number of overdue days. If the risk warning indicator is less than the risk threshold, it is considered to be low risk and no warning signal is triggered; if the risk warning indicator is greater than or equal to the risk threshold, it is considered to be high risk and triggers the generation of a warning signal. The generation of warning signals is monitored in real time through the event monitoring mechanism of the blockchain network. Once the warning signal is generated, the warning signal is sent to the target user or related party in real time through the message push interface (such as email, SMS, etc.). After receiving the warning signal, the target user and related party can take corresponding risk response measures according to the prompt.

[0045] The following introduces the system embodiment of the present application, which can be used to execute the supply chain finance post-loan risk warning method in the above embodiment of the present application. For details not disclosed in the system embodiment of the present application, please refer to the above embodiment of the supply chain finance post-loan risk warning method of the present application.

[0046] refer to Figure 4 The supply chain finance post-loan risk warning system of the embodiment of the present invention includes: a data acquisition module, which is used to collect historical data and behavioral data of each supply chain finance loan event of the target user, and upload the data to the blockchain network; a blockchain storage module, which is communicated with the data acquisition module and is used to store the data collected by the data acquisition module; a data acquisition module, which is communicated with the blockchain storage module and is used to acquire the historical data and behavioral data stored in the blockchain storage module; a risk analysis module, which is communicated with the data acquisition module and is used to calculate the data acquired by the data acquisition module to generate a risk warning indicator; a warning generation module, which is communicated with the risk analysis module and is used to verify the risk warning indicator generated by the risk analysis module, and generate a warning signal according to the comparison between the verification result and the risk threshold; a warning interaction module, which is communicated with the warning generation module and is used to send the warning signal generated by the warning generation module to the target user and related parties.

[0047] Specifically, the risk analysis module includes: judging whether the difference range between the behavior data and the historical data is within the normal range, and generating a risk warning indicator if the difference range does not fall within the normal range. The difference range includes a first difference range and a second difference range. The first difference range includes the difference between the repayment amount in the behavior data and the amount to be repaid in the historical data; if the first difference is greater than or equal to zero, the difference range is within the normal range. The second difference range includes the difference between the number of interest-bearing days in the behavior data and the number of interest-bearing days in the historical data; if the second difference is less than or equal to zero, the difference range is within the normal range.

[0048] refer to Figure 5 The warning generation module includes: an event monitoring unit, which is used to monitor the changes of risk warning indicators. A smart contract verification unit, which is used to verify the risk warning indicators. If the risk warning indicators pass the verification, the smart contract generates a verification pass result; if the risk warning indicators fail the verification, the smart contract generates a verification failure result. A threshold judgment unit receives the risk warning indicators that pass the verification, and judges whether to generate a warning signal according to the risk threshold.

[0049] Furthermore, the risk warning indicator is compared with the risk threshold. If the risk warning indicator is smaller than the risk threshold, no operation instruction is given and no warning signal is generated; if the risk warning indicator is greater than or equal to the risk threshold, a warning signal is automatically generated.

[0050] Compared with the prior art, the solution provided by the above embodiment of the present application can realize the communication connection between the modules of the system, ensure the implementation of the above supply chain finance post-loan risk warning method, and solve the technical problems caused by low information processing efficiency, delayed warning response and insufficient data accuracy in the existing supply chain finance post-loan risk warning. The present application significantly improves the real-time and efficiency of information processing, and effectively improves the timeliness and data reliability of post-loan risk warning.

[0051] It should be noted that, for the above embodiments, for the sake of simplicity, they are all described as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps can be performed in other sequences or simultaneously.

[0052] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware devices, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a computer) to execute the methods described in each embodiment of the present application.

[0053] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A supply chain finance post-loan risk early warning method, characterized in that: The following steps are involved: A data collection step, collecting historical data and behavioral data of each supply chain finance loan event of the target user, wherein the supply chain finance loan event is an integer greater than or equal to one; The blockchain storage step is to upload the historical data and behavior data of each supply chain finance loan event to the blockchain network, and to distribute and store the data in multiple nodes of the blockchain through smart contracts; A data acquisition step, acquiring the historical data and behavior data stored in the blockchain storage step; The risk analysis step is to determine whether the difference between the behavior data and the historical data is within a normal range. If the difference is not within the normal range, a risk warning indicator is generated; The early warning generation step verifies the risk early warning indicator through a smart contract, and generates an early warning signal if the risk early warning indicator is greater than or equal to the risk threshold; The early warning interaction step pushes the early warning signal to the target user in real time through the event monitoring mechanism of the blockchain network.

2. The method according to claim 1, characterized in that The historical data and behavioral data include: The historical data includes the target user's basic information, interest calculation stage, interest-bearing principal, daily interest rate, interest accrual date, repayment date, interest calculation days, interest payable and amount payable; The behavior data includes target user transaction details data, fund flow data and repayment records.

3. The method according to claim 1, characterized in that The difference range between the behavior data and the historical data includes a first difference range and a second difference range; The first difference calculation formula is: The first difference = the amount of repayment in the behavioral data - the amount of repayment to be made in the historical data; If the first difference is greater than or equal to zero, the difference range is within a normal range; if the first difference is less than zero, the difference range is within an abnormal range; The second difference calculation formula is: The second difference = the number of days that have accrued interest in the behavioral data - the number of days that should have accrued interest in the historical data; If the second difference is less than or equal to zero, the difference range is within the normal range; If the second difference is greater than zero, the difference range is in an abnormal range.

4. The method according to claim 1, characterized in that: The smart contract verifies the risk warning indicator by the following steps: When a risk warning indicator is received, a verification instruction is triggered to execute the verification of the risk warning indicator; If the risk warning indicator meets the verification rules, a verification pass result is generated, and a comparison between the risk warning indicator and the risk threshold is triggered; If the risk warning indicator does not meet the preset verification rules, a verification failure result is generated and the verification failure result is sent to the target user as an alarm.

5. The method according to claim 1, characterized in that The warning generation and warning interaction include the following steps: When the risk warning indicator is greater than or equal to the risk threshold, a warning signal is generated; The warning signal is pushed to the target user in real time through a message push interface, and the message push interface is implemented based on the event monitoring mechanism of the blockchain network.

6. A supply chain finance post-loan risk early warning system, characterized in that: include: The data collection module is used to collect the historical data and behavior data of each supply chain financial event of the target user and upload the data to the blockchain network; A blockchain storage module, which is in communication with the data acquisition module and is used to store the data collected by the data acquisition module; A data acquisition module, which is in communication with the blockchain storage module and is used to acquire the historical data and behavior data stored in the blockchain storage module; A risk analysis module, which is in communication with the data acquisition module and is used to calculate the data acquired by the data acquisition module and generate risk warning indicators; An early warning generation module, which is in communication with the risk analysis module and is used to verify the risk early warning indicator generated by the risk analysis module and generate an early warning signal based on the comparison between the verification result and the risk threshold; The early warning interaction module is in communication with the early warning generation module and is used to send the early warning signal generated by the early warning generation module to the target user.

7. The system according to claim 6, characterized in that The risk analysis module includes: Determine whether the difference between the behavior data and the historical data is within a normal range, and if the difference is not within a normal range, generate a risk warning indicator; The difference range includes a first difference range and a second difference range; The first difference range includes the difference between the repayment amount in the behavior data and the amount to be repaid in the historical data; if the first difference is greater than or equal to zero, the difference range is within the normal range; The second difference range includes the difference between the number of interest-bearing days in the behavioral data and the number of interest-bearing days in the historical data; if the second difference is less than or equal to zero, the difference range is within a normal range.

8. The system according to claim 6, characterized in that The warning generation module comprises: Event monitoring unit, used to monitor changes in risk warning indicators; A smart contract verification unit, used to verify the risk warning indicators; If the risk warning indicator passes the verification, the smart contract generates a verification pass result; if the risk warning indicator fails the verification, the smart contract generates a verification fail result; A threshold judgment unit receives the risk warning indicator that has passed the verification, and judges whether to generate a warning signal according to the risk threshold; The risk warning indicator is compared with the risk threshold, and a warning signal is generated if the risk warning indicator is greater than or equal to the risk threshold.

9. A supply chain finance post-loan risk early warning device, characterized in that: include: processor; A memory storing a supply chain finance post-loan risk early warning program; When the processor executes the supply chain finance post-loan risk warning program, the steps of the supply chain finance post-loan risk warning method as described in any one of claims 1 to 5 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a supply chain finance post-loan risk warning program, and when the supply chain finance post-loan risk warning program is executed by the processor, the steps of the supply chain finance post-loan risk warning method as described in any one of claims 1 to 5 are implemented.

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