A loan limit authorization method, device and equipment

Abstract

The application relates to a loan quota credit method, device and equipment, which comprises the following steps: obtaining credit application information of a loan party, and obtaining financing credit evaluation information and prior business verification data of the loan party from a pre-constructed credit evaluation alliance chain; generating current business verification data according to the obtained current evaluation business data of the loan party, an investment party and a guarantee party, and verifying the prior business verification data according to the current business verification data; after verification, verifying the credit application information based on the financing credit evaluation information; if the credit application information meets preset credit conditions, obtaining credit quotas given by each financial institution in a pre-constructed credit alliance chain; determining a loan quota according to the credit quotas given by each financial institution, and if the loan quota meets preset consensus conditions, storing the loan quota in the credit alliance chain. The fairness, independence and verifiability of the credit quotas given by the financial institutions are ensured, and the individual evaluation risk of the financial institutions is effectively reduced.

Description

Technical Field

[0001] This invention relates to a method, apparatus, and equipment for granting loan limits. Background Technology

[0002] Small and medium-sized enterprises (SMEs) are relatively small in scale, have more variables in their operations, and face greater risks, which determines their higher level of credit risk and makes it difficult for SMEs to obtain loans.

[0003] Blockchain is a novel application model that integrates computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Applying blockchain technology in the supply chain sector can break down centralized management models, reduce the risk of data falsification in traditional supply chains, and play a significant role in SME lending. Summary of the Invention

[0004] The inventors of this invention have discovered that while existing small business loans utilize blockchain technology, the lack of a sound social credit system and insufficient corporate creditworthiness forces financial institutions to conduct loan risk assessments based solely on information provided by businesses. Furthermore, the on-chain storage of the data provided by businesses is limited to data that has not been verified for its authenticity, thus failing to adequately assess loan risk. In view of these problems, this invention proposes a loan limit granting method, apparatus, and device to solve or partially solve these issues. The technical solution proposed by this invention is as follows:

[0005] As a first aspect of the present invention, the present invention provides a loan limit credit granting method, comprising:

[0006] Obtain the credit application information of the lender, and obtain the financing credit rating information and prior business verification data of the lender from the pre-built credit rating alliance chain; wherein, the financing credit rating information and the prior business verification data are obtained based on the prior evaluation business data of the lender, the funder and the guarantor;

[0007] Based on the current evaluation business data of the borrower, funder and guarantor, the current business verification data is generated, and the prior business verification data is verified based on the current business verification data.

[0008] After successful verification, the credit application information is verified based on the financing credit rating information.

[0009] After verification, if the credit application information meets the preset credit conditions, the credit limit provided by each financial institution in the pre-built credit alliance chain is obtained.

[0010] The loan amount is determined based on the credit lines granted by the financial institutions. If the loan amount meets the preset consensus conditions, the loan amount is stored on the credit alliance blockchain.

[0011] In one or more embodiments, the financing credit rating information is obtained in the following manner:

[0012] Construct a supply chain alliance that includes the lender and the guarantor;

[0013] Construct a credit rating consortium blockchain that includes the lender, the guarantor, and the funding party;

[0014] Obtain the credit rating application information submitted by the lender, and obtain the supply chain credit rating information of the lender from the supply alliance chain;

[0015] The credit rating application information is used to verify the lender's identity information, and after the identity information verification is passed, the credit rating application information is verified based on the supply chain credit rating information.

[0016] After verification, the system determines whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract.

[0017] If so, financing credit rating information is generated based on the prior evaluation business data through a preset credit rating contract, and the financing credit rating information is stored in the credit rating consortium blockchain.

[0018] In one or more embodiments, the supply chain credit rating information is obtained in the following manner:

[0019] Construct a supply chain credit management contract, and deploy the supply chain credit management contract, which is verified through consensus between the lender and the guarantor, on the supply alliance chain;

[0020] Obtain supply chain business data from each entity in the supply alliance chain, and generate a first Merkle tree based on the supply chain business data using the first Merkle tree construction method;

[0021] Based on the supply chain business data, supply chain credit evaluation information is generated through the supply chain credit management contract, and the supply chain credit evaluation information, the first Merkle tree, and the first Merkle tree construction method are stored in the supply chain consortium.

[0022] In one or more embodiments, before determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract after successful verification, the method further includes:

[0023] Obtain the current business data of the lender, as well as the first Merkle tree and the method for constructing the first Merkle tree stored in the supply chain alliance;

[0024] A second Merkle tree is generated based on the current business data according to the first Merkle tree construction method;

[0025] Determine whether the first Merkle tree and the second Merkle tree are consistent;

[0026] If they match, proceed with the step of determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract.

[0027] In one or more embodiments, the prior service verification data is generated in the following manner:

[0028] A third Merkle tree is generated based on prior evaluation business data provided by the lender, funder, and guarantor according to the third Merkle tree construction method, and the third Merkle tree and the third Merkle tree construction method are stored in the credit rating consortium blockchain.

[0029] In one or more embodiments, the step of generating current business verification data based on the acquired current evaluation business data of the lender, funder, and guarantor, and verifying the prior business verification data based on the current business verification data, includes:

[0030] Based on the current evaluation business data provided by the lender, funder and guarantor, and the third Merkle tree stored in the credit rating consortium chain and the third Merkle tree construction method;

[0031] Based on the current evaluation business data, a fourth Merkle tree is generated according to the third Merkle tree construction method;

[0032] If the third Merkle tree and the fourth Merkle tree are consistent, then the step of verifying the credit application information based on the financing credit evaluation information is performed.

[0033] In one or more embodiments, the step of determining the loan amount based on the credit lines granted by various financial institutions, and storing the loan amount on the credit consortium blockchain if the loan amount meets preset consensus conditions, includes:

[0034] Calculate the average credit limit offered by each financial institution. If the average value meets the preset credit limit standard, obtain the consensus result of the average value.

[0035] If the consensus result meets the preset percentage threshold, the average value will be stored in the trusted consortium blockchain.

[0036] The preset limit standard is determined by the following formula 1:

[0037] Q y =Q′*(1±k%), formula 1;

[0038] Among them, Q y Q′ is the preset loan limit, and k is the floating coefficient.

[0039] The floating coefficient k is determined by the following formula 2:

[0040]

[0041] Where S is the number of financial institutions and α is the influence coefficient.

[0042] As a second aspect of the present invention, the present invention provides a loan limit granting device, comprising:

[0043] The first execution module is used to obtain the credit application information of the lender and obtain the financing credit evaluation information and prior business verification data of the lender from the pre-built credit evaluation alliance chain; wherein, the financing credit evaluation information and the prior business verification data are obtained based on the prior evaluation business data of the lender, the funder and the guarantor.

[0044] The first verification module is used to generate current business verification data based on the current evaluation business data of the borrower, funder and guarantor, and to verify the prior business verification data based on the current business verification data.

[0045] The second verification module is used to verify the credit application information based on the financing credit evaluation information after the verification is passed.

[0046] The second execution module is used to obtain the credit limit given by each financial institution in the pre-built credit alliance chain if the credit application information meets the preset credit conditions after the verification is passed.

[0047] The loan amount determination module is used to determine the loan amount based on the credit limits provided by various financial institutions. If the loan amount meets the preset consensus conditions, the loan amount is stored in the credit alliance chain.

[0048] As a third aspect of the present invention, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0049] Memory, used to store computer programs;

[0050] When a processor executes a program stored in memory, it implements the steps of the loan limit credit method described in the first aspect.

[0051] As a fourth aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the loan limit credit method as described in the first aspect.

[0052] The loan limit granting method provided in this invention uses financing credit rating information and prior business verification data obtained from prior rating business data of the lender, funder, and guarantor. It generates current business verification data based on the acquired current rating business data of the lender, funder, and guarantor, and verifies the prior business verification data based on the current business verification data. This verifies the prior rating business data provided by the lender, ensuring its authenticity and thus confirming the authenticity of the financing credit rating information obtained from the credit rating consortium blockchain. Furthermore, it verifies the credit application information provided by the lender based on the financing credit rating information, further ensuring the authenticity of the credit application information provided by the lender. The authenticity of the credit application information provided by the lender significantly increases the difficulty of falsifying such information. By verifying whether the credit application information meets the preset credit conditions, and when such conditions are met, the credit limits offered by each financial institution in the pre-built credit consortium blockchain are obtained. The loan amount is then determined based on these limits, ensuring the fairness, independence, and verifiability of the credit limits offered by financial institutions and effectively reducing the individual assessment risk of these institutions. Furthermore, by determining whether the loan amount meets the preset consensus conditions, and after the loan amount passes consensus, it is recorded on the credit consortium blockchain for evidence storage, ensuring the rationality of the loan amount and reducing the systemic risk for financial institutions in loan amount granting.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating the loan limit credit granting method provided in an embodiment of the present invention;

[0057] Figure 2 This is a flowchart illustrating the financing credit rating information generation method provided in an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the loan limit granting device provided in an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0060] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0062] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0063] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0064] Example 1

[0065] Small and medium-sized enterprises (SMEs) are relatively small in scale, their operations are subject to more variables and greater risks, which inherently results in a higher level of credit risk. The difficulty SMEs face in obtaining loans is a global problem. Specifically, SME lending suffers from three main pain points: first, a lack of corporate credit and an imperfect social credit system; second, an inadequate loan guarantee system; and third, inflexible operating mechanisms of commercial banks.

[0066] Blockchain is a novel application model integrating distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Applying blockchain technology in the supply chain sector can break centralized management models, reduce the risk of data falsification in traditional supply chains, and play a crucial role in SME lending. In a P2P structure, each node (peer) typically functions as an information consumer, information provider, and information communicator. From a computational perspective, P2P breaks the traditional Client / Server (C / S) model, ensuring equal status for every node in the network. Each node acts as a server, providing services to other nodes while also enjoying the services provided by other nodes. This distributes the server burden of traditional methods across every node in the network, with each node undertaking limited storage and computational tasks. The more nodes join the network, the more resources each node contributes, and the higher the quality of service.

[0067] While existing SME loans utilize blockchain technology, they only store the data provided by the enterprise on the blockchain without verifying the authenticity of the data itself. Financial institutions cannot adequately assess the risk of granting loans, and guarantors cannot verify the enterprise's true creditworthiness when providing guarantees. If data falsification occurs, there are dual risks to data security and social credit security. Therefore, this invention provides a loan limit granting method, such as... Figure 1 As shown, it includes:

[0068] S101. Obtain the credit application information of the lender, and obtain the financing credit rating information and prior business verification data of the lender from the pre-built credit rating alliance chain; wherein, the financing credit rating information and the prior business verification data are obtained based on the prior rating business data of the lender, the funder and the guarantor.

[0069] In step S101 above, the financing credit rating information is obtained through credit rating based on prior rating business data of the lender, funder, and guarantor through a preset credit rating contract. The preset credit rating contract is deployed on the credit rating consortium blockchain in the following manner:

[0070] Based on the credit rating parameter set, the credit rating parameters corresponding to the constructed credit rating contract are selected. The deployment server generates the credit rating contract. The credit rating parameter set is generated through negotiation among members of the credit rating consortium blockchain. All members of the credit rating consortium blockchain can conduct credit rating through the credit rating parameters in this set. The credit rating parameter set may include: business information, social credit information, such as administrative penalty records on credit websites, time of joining the supply chain consortium, number of supply chain activities completed, historical performance, credit rating level, number of risky behaviors, corporate litigation information, corporate administrative penalty information, and more credit rating parameters. Depending on the members of the credit rating consortium blockchain, the credit rating contract can be divided into the following smart contracts: credit loan contract, credit guarantee contract, and credit supervision contract. This division considers that the proposer of the smart contract has a clearer and more accurate understanding of the parameters corresponding to the proposed smart contract, can determine the rights and responsibilities of the proposer, and ensures the effective operation of the smart contract and the accuracy of the parameters. Lenders, guarantors, and regulators can specify the corresponding credit loan contract, credit guarantee contract, and credit supervision contract according to their requirements for credit rating parameters. When a funding party generates a credit loan contract, it may impose restrictions on business registration information, social credit information, or other parameters. When a guarantor generates a credit guarantee contract, it may impose restrictions on the time of joining the supply chain alliance, the number of supply chain activities completed, historical performance, credit rating, number of risky behaviors, or other parameters. When a regulator generates a credit supervision contract, it may impose restrictions on the company's litigation information, administrative penalty information, or other parameters. During the processes of generating credit loan contracts, credit guarantee contracts, and credit supervision contracts, each party may set restrictions on its credit evaluation parameters according to its own business needs. The specific process is not specifically limited in this invention.

[0071] After receiving the credit rating contract, other participating nodes verify the credit rating contract and sign it after successful verification.

[0072] Once the credit rating contract is verified and signed, it will be deployed on the credit rating consortium blockchain. After deployment, the credit rating contract will run on the credit rating consortium blockchain to conduct credit rating on the prior rating business data of lenders, funders, and guarantors.

[0073] S102. Based on the current evaluation business data of the borrower, funder and guarantor obtained, generate current business verification data, and verify the prior business verification data based on the current business verification data.

[0074] The current evaluation business data and prior evaluation business data mentioned in step S102 above include business registration information, social credit information, etc. The current evaluation business data is the latest real-time data obtained by calling the interface of an authoritative institution, such as business registration information obtained by calling the interface of the Administration for Industry and Commerce. The prior business verification data is compared with the current business verification data. If they match, it means that the prior business verification data provided by the lender is authentic. The current evaluation business data obtained by calling the interface of an authoritative institution is also the latest information. Verifying the prior business verification data based on the current business verification data realizes the verification of the prior evaluation business data provided by the lender itself, ensuring the authenticity of the prior evaluation business data provided by the lender. Since the financing credit rating information is obtained based on the prior evaluation business data of the lender, the funder, and the guarantor, the obtained financing credit rating information is also authentic if the prior business verification data is authentic.

[0075] S103. After verification, the credit application information is verified based on the financing credit evaluation information;

[0076] After verifying the authenticity of the prior business verification data in step S102 above, and confirming the authenticity of the financing credit rating information, the credit application information provided by the lender is then verified based on the authentic financing credit rating information. If the verification passes, it proves that the credit application information provided by the lender is authentic. This invention ensures the authenticity of the prior business verification data and credit application information provided by the lender through layer-by-layer verification. Simultaneously, by constructing a credit rating consortium blockchain, i.e., utilizing blockchain technology, it significantly increases the difficulty of falsifying financing credit rating information and reduces the systemic risk for financial institutions in loan amount granting. The credit application information includes financing credit rating information. By comparing the financing credit rating information provided by the lender with the financing credit rating information stored on the credit rating consortium blockchain, if they match, the financing credit rating information verification passes.

[0077] S104. After verification, if the credit application information meets the preset credit conditions, obtain the credit limit given by each financial institution in the pre-built credit alliance chain.

[0078] After the credit application information is verified, a preset smart contract determines that the credit application information meets preset credit conditions. This preset smart contract can be set according to actual business needs and is not specifically limited here. For example, it could be that the lender's risk behavior count is 0, or the number of specific supply chain actions completed is not less than a certain number, such as 10 or 100 times. The credit alliance chain includes multiple financial institutions (such as state-owned banks and commercial banks) that jointly approve and grant credit to lenders in a certain region. The credit limit of each financial institution is generated based on the financing credit evaluation information according to its preset credit limit calculation method. Each financial institution gives a credit limit according to its specific and unique preset credit limit calculation method, ensuring the fairness, independence, and verifiability of the credit limits given by financial institutions, and effectively reducing the individual assessment risk of financial institutions.

[0079] S105. Determine the loan amount based on the credit lines granted by each financial institution. If the loan amount meets the preset consensus conditions, store the loan amount on the credit alliance chain.

[0080] Each financial institution calculates its credit limit based on its unique, pre-defined credit line calculation method, using blockchain-based credit assessment information to determine the credit limit. This ensures the authenticity of the credit application information provided by the lender. Furthermore, a consensus is reached on the loan amount based on the credit lines provided by different financial institutions, guaranteeing the fairness, independence, and verifiability of the credit lines provided by financial institutions. This effectively reduces the individual assessment risk of financial institutions, ensuring the rationality of the final loan amount and mitigating systemic risk in credit line allocation.

[0081] The loan limit granting method provided in this invention uses financing credit rating information and prior business verification data obtained from prior rating business data of the lender, funder, and guarantor. It generates current business verification data based on the acquired current rating business data of the lender, funder, and guarantor, and verifies the prior business verification data based on this current business verification data. This verifies the prior rating business data provided by the lender, ensuring its authenticity and thus confirming the authenticity of the financing credit rating information obtained from the credit rating consortium blockchain. Furthermore, it verifies the credit application information provided by the lender based on the financing credit rating information, thereby verifying the credit application information provided by the lender. This system ensures the authenticity of credit application information provided by lenders, significantly increasing the difficulty of falsifying such information. By verifying whether the credit application information meets preset credit conditions, and when such conditions are met, it obtains the credit limits offered by each financial institution in the pre-built credit consortium blockchain. The loan amount is then determined based on these limits, guaranteeing the fairness, independence, and verifiability of the credit limits offered by financial institutions and effectively reducing individual assessment risks. Furthermore, by determining whether the loan amount meets preset consensus conditions, and after consensus is reached, the loan amount is recorded on the credit consortium blockchain for evidence storage, ensuring the rationality of the loan amount and reducing systemic risks for financial institutions in loan granting.

[0082] In one embodiment, the financing credit rating information is obtained through the following methods, such as... Figure 2 As shown:

[0083] S201. Construct a supply chain alliance that includes the lender and the guarantor;

[0084] The supply chain alliance includes several entities participating in various links of the supply chain, such as enterprises corresponding to roles like producers, warehousing providers, logistics providers, and sellers. Any entity joining the supply chain alliance can act as a financing party, i.e., a lender, and all participants in the supply chain alliance can act as guarantors.

[0085] S202. Construct a credit rating alliance chain that includes the lender, the guarantor, and the funder;

[0086] The lenders are individuals requiring financing, such as businesses, who have joined the aforementioned supply chain consortium. The guarantors are all participants in the supply chain consortium, and their guarantees are based on supply chain credit rating information stored on the consortium. This credit rating information is a consensus result of all consortium members and has been confirmed by all participants. This approach ties the interests of consortium members to the entire supply chain system, increasing the difficulty of falsifying credit rating information and supplementing the credit rating system of supply chain members with business attributes—a supplement difficult to achieve in traditional lending systems. The business attribute information includes the funding party's (i.e., the aforementioned financial institution) business registration information, social credit information, time of joining the consortium, number of supply chain activities completed, historical performance records, credit rating, number of risky behaviors, litigation information, and administrative penalty information.

[0087] In existing technologies, credit evaluation of borrowers requires collecting various business attribute information. This information is scattered across different management agencies; for example, the business registration information of borrowing companies comes from the Administration for Industry and Commerce, and the number of times a borrowing company completes supply chain activities requires verification from relevant supply chain management units or all units to obtain supporting documentation. Without the supply chain-based credit evaluation management method of this invention, companies need to communicate offline with multiple departments and units to obtain relevant supporting documents when applying for loans. In some cases, such as when supply chain-related companies refuse to provide the necessary supporting documents to reduce their own liability, companies may be unable to provide the required loan materials and thus fail to obtain a loan. Alternatively, by not requiring companies to provide supporting documents from supply chain-related companies and directly granting credit lines and conducting loan reviews, it becomes impossible to fully assess loan risks. This invention improves the overall efficiency of loan approval and credit rating processes by constructing a supply chain alliance chain and a credit rating alliance chain. It pre-screens aspects such as credit rating that may require human intervention and forms corresponding smart contracts. Furthermore, it handles loan-related credit rating and credit rating based on smart contracts, thereby improving the automation of the loan approval process. By eliminating human intervention, it reduces execution, calculation, and implementation costs.

[0088] S203. Obtain the credit rating application information submitted by the lender, and obtain the supply chain credit rating information of the lender from the supply alliance chain;

[0089] S204. Verify the lender's identity information based on the credit rating application information, and after the identity information verification is passed, verify the credit rating application information based on the supply chain credit rating information;

[0090] The steps for verifying the lender's identity information include:

[0091] The lender enters the credit rating consortium blockchain client, generates a public key and private key for registering their identity in the credit rating consortium blockchain, signs the public key to obtain a signing public key, and then sends the public key and signing public key to the verifier.

[0092] The credit rating consortium blockchain client uses its private key to sign the signing public key and the credit rating application information to form signature information, and stores the signature information in the credit rating consortium blockchain;

[0093] The verifier receives the public key and the signing public key, and obtains the signature information from the credit rating consortium blockchain;

[0094] The verifier uses the public key to verify the signature information. If the verification passes, the verification is successful; otherwise, the verification fails. If the verification is successful, the credit rating application information is used to verify the signature public key. If the verification passes, the identity information is successfully verified, confirming the credit rating consortium chain identity as the borrower.

[0095] The process of verifying the credit rating application information based on the supply chain credit rating information includes: comparing the credit rating application information with the supply chain credit rating information; if they match, the credit rating application information is successfully verified. The credit rating application information is relevant information provided by the lender for the credit rating application, originating from the authoritative institution that generated this information; the credit rating application information includes: business registration information, social credit information, time of joining the consortium blockchain, number of completed supply chain activities, historical performance records, credit rating level, number of risky behaviors, corporate litigation information, corporate administrative penalty information, bank information, etc.

[0096] S205. After verification, determine whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract.

[0097] The first preset smart contract can be configured according to the actual requirements for credit rating application conditions; no specific limitations are made here. Credit rating conditions are only determined after confirming the authenticity of the credit rating application information, thus avoiding credit ratings based on false data and subsequent loan approval operations.

[0098] S206. If so, based on the prior evaluation business data, generate financing credit evaluation information through a preset credit evaluation contract, and store the financing credit evaluation information on the credit evaluation consortium blockchain.

[0099] The pre-set credit rating contract here is described in detail in step S101 above, and will not be repeated here.

[0100] In one embodiment, the supply chain credit rating information is obtained in the following manner:

[0101] S301. Construct a supply chain credit management contract and deploy the supply chain credit management contract, which is verified through consensus between the lender and the guarantor, on the supply alliance chain.

[0102] The supply chain credit management contract is deployed on the supply chain after being verified, agreed upon, and signed by the main entities of the supply chain.

[0103] S302. Obtain the supply chain business data of each entity in the supply alliance chain, and generate a first Merkle Tree D1 based on the supply chain business data according to the first Merkle tree construction method.

[0104] S303. Based on the supply chain business data, generate supply chain credit evaluation information through the supply chain credit management contract, and store the supply chain credit evaluation information, the first Merkle Tree D1, and the first Merkle Tree construction method in the supply chain consortium. The supply chain credit evaluation information is generated based on the supply chain business data of the funder, guarantor, lender, and regulator, and can be pre-classified according to the funder, guarantor, lender, and regulator.

[0105] The supply chain consortium described in this invention is a type of blockchain. Based on supply chain credit management, this invention binds the interests of each individual member in the supply chain to the entire supply chain system. Through profit sharing and risk-sharing among supply chain members, it achieves the guarantee function of the supply chain system. Utilizing blockchain technology significantly increases the difficulty of credit rating fraud, supplementing the credit rating system of supply chain members in terms of business attributes—a supplement not achieved in traditional lending systems. By confirming the accounting nodes through smart contracts, human intervention in the selection of accounting nodes can be avoided, further increasing the difficulty of data fraud.

[0106] In one embodiment, after the verification described in step S205 above is passed, before determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract, the method further includes:

[0107] S401. Obtain the current business data of the lender and the first Merkle Tree D1 and the first Merkle Tree construction method stored in the supply chain.

[0108] S402. Based on the current business data, generate a second Merkle Tree D2 according to the first Merkle tree construction method;

[0109] S403. Determine whether the first Merkle Tree D1 and the second Merkle Tree D2 are consistent;

[0110] S404. If they match, execute the step described in step S205 above, which involves determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract.

[0111] If there is a discrepancy, an error message is sent. Based on the error message, the lender resubmits the credit rating application information, re-acquires the current business data, and reconstructs the Merkle Tree D2 based on the re-acquired current business data. That is, the above steps S402 and S403 are repeated until the Merkle Tree D1 is consistent with the Merkle Tree D2.

[0112] Furthermore, if an error message is reported n times or more within a preset time period (n ≥ 1), the submission of credit rating application information will be restricted to another preset time period. By limiting the number of times credit rating application information can be submitted, the lender is encouraged to provide authentic data, increasing the difficulty of falsifying credit rating application information. The restriction method could be: after receiving n or more error messages within a preset time period and verifying the lender's identity information, the aforementioned supply chain business data will no longer be obtained.

[0113] In one embodiment, the prior business verification data is generated in the following manner:

[0114] The prior business verification data provided in this embodiment is in the form of a Merkle tree. The process of generating prior business verification data is as follows: based on the prior evaluation business data provided by the lender, funder, and guarantor, a third Merkle tree D3 is generated according to the third Merkle tree construction method, and the third Merkle tree D3 and the third Merkle tree construction method are stored in the credit rating consortium blockchain.

[0115] In one embodiment, step S102 above, which involves generating current business verification data based on the acquired current evaluation business data of the lender, funder, and guarantor, and verifying the prior business verification data based on the current business verification data, includes:

[0116] S1021. Based on the current evaluation business data provided by the lender, funder and guarantor and the third Merkle Tree D3 stored in the credit rating consortium chain and the third Merkle Tree construction method.

[0117] S1022. Based on the current evaluation business data, generate a fourth Merkle Tree D4 according to the third Merkle tree construction method;

[0118] S1023. If the third Merkle Tree D3 and the fourth Merkle Tree D4 are consistent, then the step of verifying the credit application information based on the financing credit evaluation information described in step S103 above is executed.

[0119] In one embodiment, step S105 above, which involves determining the loan amount based on the credit lines granted by various financial institutions, and storing the loan amount on the credit consortium blockchain if the loan amount meets preset consensus conditions, includes:

[0120] S1051. Calculate the credit limit W offered by each financial institution. j The average value is calculated based on the credit limits provided by various financial institutions. If the average value meets the preset credit limit standard, a consensus result for the average value is obtained. For example, the average value of the credit limits provided by a certain financial institution is calculated as follows:

[0121]

[0122] Where M is the total number of financial institutions, W j Let Q be the credit limit granted by the j-th financial institution; Q is the calculated average of the credit limits.

[0123] After obtaining the credit limits offered by various financial institutions, the average of these credit limits is calculated and verified to determine if it meets the preset credit limit standard. If the verification result is satisfactory, the credit alliance chain members reach a consensus on this average value. If the consensus is successful, the average value is uploaded to the credit alliance chain for storage. Using the average of the credit limits offered by various financial institutions is relatively objective, as it comprehensively considers the credit limits offered by all financial institutions, and is therefore relatively reasonable.

[0124] The preset credit limit standard can be configured with conditions for successful verification. The preset credit limit standard is obtained in the following manner:

[0125] Based on a pre-established mapping relationship between credit ratings and loan amounts, a corresponding loan amount is obtained according to the lender's credit rating as a preset loan amount. Then, a preset loan amount standard is established based on this preset loan amount. Specifically, a floating coefficient for the preset loan amount is determined based on the number of financial institutions, and then the preset loan amount and the floating coefficient are used to establish the preset loan amount standard. The preset loan amount standard is determined using the following formula 1:

[0126] Q y =Q′*(1±k%), formula 1;

[0127] Among them, Q y Q′ is the preset loan limit, and k is the floating coefficient.

[0128] For example, when Q′=100 (ten thousand yuan) and k=30, it means that the preset quota standard Q y (That is, the floating range of the preset loan amount) is 70-130 (ten thousand yuan); the verification condition is met only when the average value of the credit limit to be verified is within the floating range of the preset loan amount Q′; when the average value of the credit limit does not meet the preset amount standard Q. y If the conditions are not met, then the conditions for verification are not met.

[0129] The floating coefficient k can be determined based on the number of financial institutions that provide credit lines. The number S of financial institutions that provide credit lines is obtained, and the floating coefficient k is determined by the following formula 2:

[0130]

[0131] Where S is the number of financial institutions and α is the influence coefficient.

[0132] Specifically, α is the influence coefficient of the number of financial institutions providing credit limits on the credit limit. Here, α is a random value, specifically taking the last two digits of the current nanosecond time. For example, the following time represents four consecutive seconds of the current nanosecond value, and its corresponding α value:

[0133] time:617539559, corresponding to α=59;

[0134] time:618429050, corresponding to α=50;

[0135] time:618784257, corresponding to α=57;

[0136] time:619326890, corresponding to α=90.

[0137] For example, when S = 100 and α = 59, then k = 100 * (59 + 1)% = 60%, and 60% < 1, so we take k = 60%; when S = 120 and α = 89, then k = 120 * (89 + 1)% = 108%, and 108% > 1, so we take k = 100.

[0138] S1052. If the consensus result meets the preset percentage threshold, then the average value is stored in the trusted consortium blockchain.

[0139] If the consensus result meets the preset percentage threshold mentioned in step S1052 above, it means that the percentage of the number of people whose average value is confirmed to pass meets the preset percentage threshold. The preset percentage threshold can be set according to actual needs, such as 66%, 75% or 100%.

[0140] If the consensus result does not meet the preset percentage threshold, the highest and lowest values ​​among all credit limits provided by financial institutions are removed, and the second average value is used for consensus until consensus is successful. Alternatively, based on the redefined α, the credit limit verification and consensus are repeated (steps S1051 and S1052 above) until consensus is successful; the maximum number of times α is redefined can be limited to 1-5 times to ensure consensus efficiency. In this way, the consensus is ensured to be automatically implemented based on smart contracts, the consensus process and results are traceable, and system security and the validity of credit limits are guaranteed.

[0141] Example 2

[0142] The loan application process includes the following steps:

[0143] S501. Obtain loan information provided by the lender, construct a fifth Merkle tree (Merkle TreeD5) based on the loan information, and record the method for constructing the fifth Merkle tree; wherein, the loan information includes credit rating application information, financing credit rating information, and credit limit;

[0144] S502. Based on the unique identifier on the loan information, obtain the evidence data corresponding to the loan information stored on the supply chain alliance chain, credit rating alliance chain, and credit granting alliance chain, namely the supply chain credit rating information in the supply chain alliance chain, the financing credit rating information in the credit rating alliance chain, and the credit limit stored on the credit granting alliance chain; based on the evidence data corresponding to the loan information, construct Merkle Tree D6 according to the fifth Merkle tree construction method.

[0145] S503. Compare whether Merkle Tree D5 and Merkle Tree D6 are consistent. If they are consistent, proceed with the loan application.

[0146] The method for constructing Merkle trees involved in the embodiments of the present invention can refer to the description of Merkle tree generation methods in the prior art, and the present invention does not make specific limitations.

[0147] Based on the same inventive concept, this invention provides a loan limit granting device, such as... Figure 3 As shown, it includes:

[0148] The first execution module 601 is used to obtain the credit application information of the lender and obtain the financing credit evaluation information and prior business verification data of the lender from the pre-built credit evaluation alliance chain; wherein, the financing credit evaluation information and the prior business verification data are obtained based on the prior evaluation business data of the lender, the funder and the guarantor.

[0149] The first verification module 602 is used to generate current business verification data based on the current evaluation business data of the lender, funder and guarantor, and to verify the prior business verification data based on the current business verification data.

[0150] The second verification module 603 is used to verify the credit application information based on the financing credit evaluation information after the verification is passed.

[0151] The second execution module 604 is used to obtain the credit limit given by each financial institution in the pre-built credit alliance chain if the credit application information meets the preset credit conditions after the verification is passed.

[0152] The loan amount determination module 605 is used to determine the loan amount based on the credit limits provided by various financial institutions. If the loan amount meets the preset consensus conditions, the loan amount is stored in the credit alliance chain.

[0153] The specific implementation of the loan limit granting device can be referred to the detailed description of the loan limit granting method in Embodiment 1 above, and will not be repeated here.

[0154] The present invention also provides an electronic device, such as... Figure 4 As shown, it includes a processor 701, a communication interface 702, a memory 703, and a communication bus 704, wherein the processor 701, the communication interface 702, and the memory 703 communicate with each other through the communication bus 704.

[0155] Memory 703 is used to store computer programs;

[0156] When the processor 701 executes the program stored in the memory 703, it implements the steps of the loan limit credit granting method described in Embodiment 1.

[0157] The electronic device provided in this embodiment of the invention has a similar implementation principle and technical effect to the above embodiments, and will not be described again here.

[0158] The aforementioned memory 703 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 703 has storage space for program code used to perform any of the method steps described above. For example, the storage space for program code may include individual program codes for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, optical discs (CDs), memory cards, or floppy disks. Such computer program products are typically portable or fixed storage units. The storage unit may have storage segments or storage spaces arranged similarly to the memory 703 in the aforementioned electronic device. The program code may, for example, be compressed in a suitable form. Typically, the storage unit includes programs for performing the method steps according to embodiments of the invention, i.e., code that can be read by a processor, which, when run by the electronic device, causes the electronic device to perform the various steps in the methods described above.

[0159] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the loan limit credit method as described above.

[0160] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0161] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other. This invention is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of these aspects and / or embodiments. Each aspect and / or embodiment of this invention can be used alone, or in combination with one or more other aspects and / or other embodiments.

[0162] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of granting a loan limit, characterized by, include: Obtain the credit application information of the lender, and obtain the financing credit rating information and prior business verification data of the lender from the pre-built credit rating alliance chain; wherein, the financing credit rating information and the prior business verification data are obtained based on the prior evaluation business data of the lender, the funder and the guarantor; The financing credit rating information was obtained through the following methods: Build a supply chain alliance that includes lenders and guarantors; Construct a credit rating alliance chain that includes lenders, guarantors, and funders; Obtain the credit rating application information submitted by the lender, and obtain the supply chain credit rating information of the lender from the supply alliance chain; The credit rating application information is used to verify the lender's identity information, and after the identity information verification is passed, the credit rating application information is verified based on the supply chain credit rating information. After verification, the system determines whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract. If so, financing credit rating information is generated through a preset credit rating contract based on prior evaluation business data of the lender, funder, and guarantor, and the financing credit rating information is stored in the credit rating consortium blockchain; Based on the current evaluation business data of the borrower, funder and guarantor, the current business verification data is generated, and the prior business verification data is verified based on the current business verification data. After successful verification, the credit application information is verified based on the financing credit rating information. After verification, if the credit application information meets the preset credit conditions, the credit limit provided by each financial institution in the pre-built credit alliance chain is obtained. Calculate the average credit limit offered by each financial institution. If the average value meets the preset credit limit standard, obtain the consensus result of the average value. If the consensus result meets the preset percentage threshold, the average value will be stored in the trusted consortium blockchain. The preset limit standard is determined by the following formula 1: , Equation 1 ; wherein, is a preset quota standard, is a preset loan quota, is a floating coefficient; the floating coefficient is determined by the following equation 2: , Equation 2; wherein, is the number of financial institutions, is the influence coefficient.

2. The loan line of credit method of claim 1, wherein, The supply chain credit rating information was obtained through the following methods: Construct a supply chain credit management contract, and deploy the supply chain credit management contract, which is verified through consensus between the lender and the guarantor, on the supply alliance chain; Obtain supply chain business data from each entity in the supply alliance chain, and generate a first Merkle tree based on the supply chain business data using the first Merkle tree construction method; Based on the supply chain business data, supply chain credit evaluation information is generated through the supply chain credit management contract, and the supply chain credit evaluation information, the first Merkle tree, and the first Merkle tree construction method are stored in the supply chain consortium.

3. The method of claim 2, wherein, After verification, before determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract, the process further includes: Obtain the current business data of the lender, as well as the first Merkle tree and the method for constructing the first Merkle tree stored in the supply chain alliance; A second Merkle tree is generated based on the current business data according to the first Merkle tree construction method; Determine whether the first Merkle tree and the second Merkle tree are consistent; If they match, proceed with the step of determining whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract.

4. The method of claim 1, wherein, The prior business verification data is generated in the following manner: A third Merkle tree is generated based on prior evaluation business data provided by the lender, funder, and guarantor according to the third Merkle tree construction method, and the third Merkle tree and the third Merkle tree construction method are stored in the credit rating consortium blockchain.

5. The method of claim 4, wherein, The step of generating current business verification data based on the acquired current evaluation business data of the lender, funder, and guarantor, and verifying the prior business verification data based on the current business verification data, includes: Based on the current evaluation business data provided by the lender, funder and guarantor, and the third Merkle tree stored in the credit rating consortium chain and the third Merkle tree construction method; Based on the current evaluation business data, a fourth Merkle tree is generated according to the third Merkle tree construction method; If the third Merkle tree and the fourth Merkle tree are consistent, then the step of verifying the credit application information based on the financing credit evaluation information is performed.

6. A loan line of credit device, comprising: include: The first execution module is used to obtain the credit application information of the lender and obtain the financing credit evaluation information and prior business verification data of the lender from the pre-built credit evaluation alliance chain. The financing credit rating information is obtained through the following methods: Build a supply chain alliance that includes lenders and guarantors; Construct a credit rating alliance chain that includes lenders, guarantors, and funders; Obtain the credit rating application information submitted by the lender, and obtain the supply chain credit rating information of the lender from the supply alliance chain; The credit rating application information is used to verify the lender's identity information, and after the identity information verification is passed, the credit rating application information is verified based on the supply chain credit rating information. After verification, the system determines whether the credit rating application information meets the preset credit rating application conditions based on the first preset smart contract. If so, financing credit rating information is generated through a pre-set credit rating contract based on prior evaluation business data from the lender, funder, and guarantor, and the financing credit rating information is stored on the credit rating consortium blockchain; The first verification module is used to generate current business verification data based on the current evaluation business data of the borrower, funder and guarantor, and to verify the prior business verification data based on the current business verification data. The second verification module is used to verify the credit application information based on the financing credit evaluation information after the verification is passed. The second execution module is used to obtain the credit limit given by each financial institution in the pre-built credit alliance chain if the credit application information meets the preset credit conditions after the verification is passed. The loan limit determination module is used to calculate the average credit limit given by various financial institutions. If the average value meets the preset limit standard, the consensus result of the average value is obtained. If the consensus result meets a preset proportion threshold, the average value is stored in the credit alliance chain; The preset quota criterion is determined by Formula 1: , Equation 1 ; wherein, is a preset quota standard, is a preset loan quota, is a floating coefficient; the floating coefficient is determined by the following equation 2: , Equation 2; wherein, is the number of financial institutions, is the influence coefficient.

7. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus. The memory is used for storing a computer program. The processor is used for executing the program stored on the memory, and realizes the steps of the loan quota credit method in any one of claims 1-5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the loan quota credit method in any one of claims 1-5.

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