Financing method and device based on block chain, equipment and storage medium
By constructing a multi-level credit topology function using blockchain technology and combining bills, orders, and logistics information to assess supplier credit, the problem of discrepancies between credit lines and actual operating conditions in existing financing methods has been solved, enabling more accurate determination of financing amounts.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-03-27
AI Technical Summary
Existing financing methods rely on a single supplier credit rating system, which leads to discrepancies between credit lines and actual business conditions, makes it difficult to identify fraudulent trade, and results in inaccurate financing amounts.
By using blockchain technology, a multi-level credit topology function is constructed. Based on invoice information, order hash value, tax identification, and logistics information, the authenticity of trade, tax compliance, and logistics consistency scores are determined. The credit score is then corrected through the multi-level credit topology function to determine the financing amount.
This improved the accuracy of financing amounts, ensuring they align with the actual operating conditions of suppliers, reduced the risk of fraudulent trade, and enhanced the transparency and accuracy of the financing process.
Smart Images
Figure CN121746084A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of blockchains, and in particular to a financing method and device based on a blockchain, equipment and a storage medium. BACKGROUND
[0002] At present, in the process of enterprise transactions, suppliers need to finance the enterprise for the need of transactions. The traditional financing method needs to manually review the credit of multiple suppliers, rate the credit of the suppliers based on the review results, and then determine the financing credit amount of the suppliers.
[0003] However, the above-mentioned method of rating the credit of the suppliers is relatively single, and only manual credit review is used as the basis for credit rating; it is difficult to identify false trade, and the finally determined credit amount does not match the actual operating status of the suppliers, resulting in inaccurate financing amount. SUMMARY
[0004] The embodiments of the present application provide a financing method, device, equipment and storage medium based on a blockchain, aiming to solve the technical problem that the credit amount in the existing financing method does not match the actual operating status of the suppliers, resulting in inaccurate financing amount.
[0005] In a first aspect, the embodiments of the present application provide a financing method based on a blockchain, which comprises:
[0006] receiving a financing request sent by a first object;
[0007] In response to the financing request, obtaining a multi-level credit topology function corresponding to a second object and a first credit score corresponding to the first object, the second object being associated with the first object, and the multi-level credit topology function being used to represent the credit scores of multiple objects associated with the second object;
[0008] determining a first score, a second score and a third score based on the multi-level credit topology function, the first score being used to represent the trade authenticity corresponding to the first object, the second score being used to represent the tax compliance corresponding to the first object, and the third score being used to represent the logistics consistency corresponding to the first object;
[0009] determining a multi-level credit score corresponding to the first object according to the second credit score corresponding to the first object and the multi-level credit topology function, the second credit score being obtained by correcting the first credit score based on the first score, the second score and the third score;
[0010] determining a financing amount corresponding to the first object according to the second credit score and the multi-level credit score.
[0011] Optionally, the determining, based on the multi-level credit topology function, the first score, the second score and the third score corresponding to the first object comprises:
[0012] extracting, from the financing request, invoice information, an order hash value, a tax identification and logistics information;
[0013] constructing a first verification layer based on the invoice information and the order hash value, and obtaining the first score corresponding to the first object according to the multi-level credit topology function and the first verification layer;
[0014] constructing a second verification layer based on the tax identification, and obtaining the second score corresponding to the first object according to the multi-level credit topology function and the second verification layer;
[0015] constructing a third verification layer based on the logistics information, and obtaining the third score corresponding to the first object according to the multi-level credit topology function and the third verification layer.
[0016] Optionally, before the determining, according to the second credit score corresponding to the first object and the multi-level credit topology function, the multi-level credit score corresponding to the first object, the method further comprises:
[0017] in a case where the first score, the second score and the third score corresponding to the first object satisfy a first preset condition, determining a product of the first credit score corresponding to the first object and a first numerical value as the second credit score corresponding to the first object, the first numerical value being a sum value between a first preset numerical value and a second numerical value, the second numerical value being a ratio between a credit entropy value corresponding to the first object and a preset credit entropy threshold value;
[0018] in a case where the first score, the second score and the third score corresponding to the first object satisfy a second preset condition, determining a smaller value between the first credit score corresponding to the first object and a third numerical value as the second credit score corresponding to the first object, the third numerical value being an average value of the first score, the second score and the third score;
[0019] in a case where the first score, the second score and the third score corresponding to the first object satisfy a third preset condition, determining the second credit score corresponding to the first object as a second preset numerical value;
[0020] wherein the first preset condition comprises that the first score is greater than or equal to a third preset numerical value, the second score is greater than or equal to a fourth preset numerical value, and the third score is equal to a fifth preset numerical value, the fifth preset numerical value being greater than the third preset numerical value, and the third preset numerical value being greater than the fourth preset numerical value;
[0021] The second preset condition comprises at least two of the following: the first score is greater than or equal to a third preset value, the second score is greater than or equal to a fourth preset value, and the third score is equal to a fifth preset value.
[0022] The third preset condition comprises: the first score is less than a sixth preset value, the second score is less than the sixth preset value, or the third score is less than the sixth preset value, and the sixth preset value is less than the fourth preset value.
[0023] Optionally, the determining the financing amount corresponding to the first object according to the second credit score and the multi-level credit score comprises:
[0024] In a case where the first object corresponds to a tariff fluctuation rate less than or equal to a first preset fluctuation rate, a first product between the multi-level credit score, a base amount, and a preset first influence factor is calculated.
[0025] The first product is determined as the financing amount corresponding to the first object.
[0026] Optionally, the determining the financing amount corresponding to the first object according to the second credit score and the multi-level credit score comprises:
[0027] In a case where the first object corresponds to a tariff fluctuation rate greater than the first preset fluctuation rate and less than or equal to a second preset fluctuation rate, a second product between a base amount, a preset second influence factor, and the second credit score is calculated.
[0028] The second product is determined as the financing amount corresponding to the first object.
[0029] Optionally, the determining the financing amount corresponding to the first object according to the second credit score and the multi-level credit score comprises:
[0030] In a case where the first object corresponds to a tariff fluctuation rate greater than the second preset fluctuation rate, a third product between the multi-level credit score and a collateral amount corresponding to the first object is calculated.
[0031] A smaller value between the third product and a historical financing amount corresponding to the first object is determined as the financing amount corresponding to the first object.
[0032] Optionally, the method further comprises:
[0033] receiving a verification request sent by a second object, the verification request being used to represent a verification of a credit score of a first object associated with the second object;
[0034] In response to the verification request, the original credit credential of the second object is encrypted to obtain a first trust credential;
[0035] The first trust credential is subjected to cross-chain topology obfuscation processing to obtain a second trust credential;
[0036] According to the first trust credential and the second trust credential, a multi-level credit topology function corresponding to the second object is generated;
[0037] According to the multi-level credit topology function corresponding to the second object, a first credit score corresponding to the first object associated with the second object is determined.
[0038] In a second aspect, an embodiment of the present application provides a financing device based on a block chain, the device comprising:
[0039] A first receiving module configured to receive a financing request sent by a first object;
[0040] An obtaining module configured to, in response to the financing request, obtain a multi-level credit topology function corresponding to a second object and a first credit score corresponding to the first object, the second object being associated with the first object, and the multi-level credit topology function being used to represent credit scores of a plurality of objects associated with the second object;
[0041] A first determining module configured to determine a first score, a second score and a third score based on the multi-level credit topology function, the first score being used to represent trade authenticity corresponding to the first object, the second score being used to represent tax compliance corresponding to the first object, and the third score being used to represent logistics consistency corresponding to the first object;
[0042] A second determining module configured to determine a multi-level credit score corresponding to the first object according to the second credit score corresponding to the first object and the multi-level credit topology function, the second credit score being obtained by correcting the first credit score based on the first score, the second score and the third score;
[0043] A third determining module configured to determine a financing amount corresponding to the first object according to the second credit score and the multi-level credit score.
[0044] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a program stored in the memory and executable on the processor, the program being executed by the processor to implement the steps of the financing method based on the block chain according to the first aspect.
[0045] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the blockchain-based financing method as described in the first aspect.
[0046] In this embodiment, a first credit score representing the authenticity of trade corresponding to the first object, a second credit score representing the tax compliance of the first object, and a third credit score representing the consistency of logistics corresponding to the first object are determined. Based on the first, second, and third credit scores, the first credit score is corrected to obtain a second credit score. Then, based on the second credit score and the multi-level credit scores, the financing amount corresponding to the first object is determined. In this way, the financing amount of the first object is determined according to its trade authenticity, tax compliance, and logistics situation, ensuring that the financing amount matches the actual operating conditions of the first object and improving the accuracy of the financing amount. Attached Figure Description
[0047] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 This is a flowchart illustrating a blockchain-based financing method provided in an embodiment of this application;
[0049] Figure 2 This is an application flowchart of a blockchain-based financing method provided in an embodiment of this application;
[0050] Figure 3 This is a schematic diagram of the structure of a blockchain-based financing device provided in an embodiment of this application;
[0051] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0053] See Figure 1 , Figure 1This is a flowchart illustrating a blockchain-based financing method provided in an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:
[0054] Step 101: Receive the financing request sent by the first object.
[0055] Step 102: In response to the financing request, obtain the multi-level credit topology function corresponding to the second object and the first credit score corresponding to the first object. The second object is associated with the first object, and the multi-level credit topology function is used to characterize the credit scores of multiple objects associated with the second object.
[0056] In one optional application scenario, the second entity is a domestic enterprise, the first entity is a foreign enterprise, and the first entity is a supplier of the second entity. It is easy to understand that the domestic enterprise corresponds to multiple suppliers, and these suppliers are hierarchically structured, meaning that there is a hierarchical relationship between the multiple second entities corresponding to the first entity.
[0057] In this step, the first object sends a financing request to the blockchain. Upon receiving the financing request, the multi-level credit topology function corresponding to the second object and the first credit score corresponding to the first object are obtained.
[0058] The aforementioned multi-level credit topology function, also known as the credit manifold topology, can be represented as follows: The aforementioned first credit score, also known as credit penetration, can be expressed as... .
[0059] The aforementioned multi-level credit topology function is used to characterize the credit scores of multiple objects associated with the second object. For specific implementation methods for determining the multi-level credit topology function and the first credit score, please refer to the following embodiments.
[0060] Step 103: Based on the multi-level credit topology function, determine the first score, the second score, and the third score. The first score is used to characterize the authenticity of the trade corresponding to the first object, the second score is used to characterize the tax compliance of the first object, and the third score is used to characterize the logistics consistency of the first object.
[0061] In this step, a first score, a second score, and a third score corresponding to the first object can be determined based on a multi-level credit topology function. For specific implementation details, please refer to subsequent embodiments.
[0062] The first score is used to characterize the authenticity of the trade corresponding to the first object. The first score is also known as the trade authenticity verification score, and can be expressed as follows: The second score, also known as the tax compliance verification score, can be represented as... The third score, also known as the logistics consistency verification score, can be represented as... .
[0063] Step 104: Determine the multi-level credit score corresponding to the first object based on the second credit score corresponding to the first object and the multi-level credit topology function. The second credit score is obtained by correcting the first credit score based on the first score, the second score and the third score.
[0064] In this step, after obtaining the first score, the second score, and the third score, the first credit score can be corrected based on the first score, the second score, and the third score, and the corrected first credit score can be determined as the second credit score.
[0065] Then, based on the second credit score corresponding to the first object and the multi-level credit topology function, the multi-level credit score corresponding to the first object is determined. This multi-level credit score, also known as a multi-level penetration mapping score, can be expressed as follows: .
[0066] Specifically, multi-level credit scores can be determined using the following formula:
[0067]
[0068] in, This indicates a multi-level credit score. This indicates the credit penetration level corresponding to the first object. This indicates the credit penetration level of the object preceding the first object. This represents the cumulative path weight term. Represents the topological distance penalty term. This indicates the preset baseline credit score.
[0069] in, , Represents the topology of the credit manifold Extract the path weight of the current level supplier. The path weights of suppliers at all levels are aggregated by tracing back through the supply chain to the top level. Then all Multiplying them together yields the result. .
[0070] in, By calculating the credit manifold The Euclidean distance between adjacent nodes at different levels is obtained.
[0071] Step 105: Determine the financing amount corresponding to the first object based on the second credit score and the multi-level credit score.
[0072] In this step, after obtaining the second credit score and the multi-level credit score, the financing amount corresponding to the first target is determined based on the second credit score and the multi-level credit score. For specific implementation methods, please refer to the following embodiments.
[0073] In this embodiment, a first credit score representing the authenticity of trade corresponding to the first object, a second credit score representing the tax compliance of the first object, and a third credit score representing the consistency of logistics corresponding to the first object are determined. Based on the first, second, and third credit scores, the first credit score is corrected to obtain a second credit score. Then, based on the second credit score and the multi-level credit scores, the financing amount corresponding to the first object is determined. In this way, the financing amount of the first object is determined according to its trade authenticity, tax compliance, and logistics situation, ensuring that the financing amount matches the actual operating conditions of the first object and improving the accuracy of the financing amount.
[0074] Optionally, determining the first score, second score, and third score corresponding to the first object based on the multi-level credit topology function includes:
[0075] Extract invoice information, order hash value, tax identification number, and logistics information from the financing request;
[0076] A first verification layer is constructed based on the invoice information and the order hash value, and a first score corresponding to the first object is obtained according to the multi-level credit topology function and the first verification layer.
[0077] A second verification layer is constructed based on the tax identifier, and a second score corresponding to the first object is obtained according to the multi-level credit topology function and the second verification layer.
[0078] A third verification layer is constructed based on the logistics information, and a third score corresponding to the first object is obtained according to the multi-level credit topology function and the third verification layer.
[0079] The aforementioned first verification layer, also known as the trade authenticity verification layer, can be represented as follows: .
[0080] In an alternative embodiment, the first verification layer can be constructed in the following manner to obtain the first score corresponding to the first object:
[0081] The document information and order hash value are extracted from the financing request, and the document information is encrypted using the SimpleEncrypted Arithmetic Library (SEAL). The encrypted document information and order hash value are then processed using Identity-Based Encryption (IBE) to obtain the first verification layer.
[0082] Next, we will explain how to obtain the first score corresponding to the first object based on the multi-level credit topology function and the first verification layer.
[0083] 1. From the topology of credit manifold Extract the credit projection value corresponding to the current first object. The first target is a k-level supplier.
[0084] 2. Obtain invoice-order matching score from the first verification layer. .
[0085] 3. Multiply the two results above to obtain the base score. .
[0086] 4. Set a tiered attenuation rule and update the attenuation factor according to fluctuations in tariff policies.
[0087] When the tariff policy monitoring coefficient is normal, the first score corresponding to the first object is as follows: Attenuation. When the tariff policy monitoring coefficient indicates significant policy fluctuations, the first score corresponding to the first object is adjusted accordingly. Double the attenuation.
[0088] in, This is the preset attenuation coefficient.
[0089] The aforementioned second verification layer, also known as the tax compliance verification layer, can be represented as follows: .
[0090] In an alternative embodiment, the second verification layer can be constructed in the following manner to obtain the second score corresponding to the first object:
[0091] The tax identifier is hidden to obtain the hidden tax identifier. The hidden tax identifier is used as a parameter to construct a three-layer zero-knowledge proof tree, thereby obtaining the second verification layer.
[0092] Next, we will introduce how to obtain the second score corresponding to the first object based on the multi-level credit topology function and the second verification layer.
[0093] 1. From the topology of credit manifold Extract the credit projection value corresponding to the current first object. The first target is a k-level supplier.
[0094] 2. Obtain tax compliance strength from the second verification layer .
[0095] 3. Add the credit projection value and the tax compliance strength to form the basic tax credit value.
[0096] 4. Use the digital signature verification result from the reverse sequence (i.e., the second sequence in the following text) as the confidence coefficient and multiply it by the basic tax credit value.
[0097] 5. Introduce the policy safety coefficient as a dynamic adjustment factor, and multiply it with the multiplication result of step 4 to obtain the second score.
[0098] The aforementioned third verification layer, also known as the logistics consistency verification layer, can be represented as follows: .
[0099] In an alternative embodiment, the third verification layer can be constructed in the following manner to obtain the third score corresponding to the first object:
[0100] The logistics document number in the logistics information is used to perform a tensor product operation with the dynamic anchoring factor. The blockchain structure of all logistics document numbers is constructed based on the timestamp sequence, thus obtaining the third verification layer.
[0101] Next, we will explain how to obtain the third score corresponding to the first object based on the multi-level credit topology function and the third verification layer.
[0102] 1. From the topology of credit manifold Extract the credit projection value corresponding to the current first object. The first target is a k-level supplier.
[0103] 2. Obtain the real-time matching degree of orders and invoices from the third verification layer. By calculating the ratio This is used to measure the credit strength of logistics, and a minimum function constraint is applied to prevent excessive amplification, resulting in... .
[0104] 3. Finally, Multiply by the customs clearance consistency flag, which is assigned a value by verifying whether the logistics track hash is consistent with the customs clearance status identifier in the first trust credential. If they are consistent, the value is 1; if they are inconsistent, the value is 0.
[0105] In this embodiment, the first score is determined based on the first object's trade situation, the second score is determined based on the first object's tax situation, and the third score is determined based on the first object's logistics situation, thereby ensuring that the first score, the second score, and the third score can accurately represent the first object's actual operating situation.
[0106] The following section details the scheme for correcting the first credit score to obtain the second credit score.
[0107] Optionally, before determining the multi-level credit score corresponding to the first object based on the second credit score corresponding to the first object and the multi-level credit topology function, the method further includes:
[0108] If the first score, second score and third score corresponding to the first object meet the first preset condition, the product between the first credit score corresponding to the first object and the first value is determined as the second credit score corresponding to the first object. The first value is the sum between the first preset value and the second value, and the second value is the ratio between the credit entropy value corresponding to the first object and the preset credit entropy threshold.
[0109] If the first score, second score and third score corresponding to the first object meet the second preset condition, the smaller value between the first credit score and the third value corresponding to the first object is determined as the second credit score corresponding to the first object, and the third value is the average value of the first score, the second score and the third score.
[0110] If the first score, second score and third score corresponding to the first object meet the third preset condition, the second credit score corresponding to the first object is determined to be the second preset value;
[0111] The first preset condition includes: the first score is greater than or equal to the third preset value, the second score is greater than or equal to the fourth preset value and the third score is equal to the fifth preset value, the fifth preset value is greater than the third preset value and the third preset value is greater than the fourth preset value;
[0112] The second preset condition includes at least two of the following: the first score is greater than or equal to the third preset value, the second score is greater than or equal to the fourth preset value, and the third score is equal to the fifth preset value;
[0113] The third preset condition includes: the first score is less than the sixth preset value, the second score is less than the sixth preset value, or the third score is less than the sixth preset value, and the sixth preset value is less than the fourth preset value.
[0114] Optionally, the third preset value is 0.8, the fourth preset value is 0.7, and the fifth preset value is 1.
[0115] In one optional implementation, if the first score is greater than or equal to a third preset value, the second score is greater than or equal to a fourth preset value, and the third score is equal to a fifth preset value, indicating that the credit verification of the first object has passed, then the second credit score corresponding to the first object is determined by the following formula:
[0116]
[0117] in, This indicates the second credit score. Indicates the first credit score. This represents the credit entropy value corresponding to the first object. This represents the credit entropy threshold, which is the maximum credit entropy value within the statistical period.
[0118] In one optional implementation, if the first score, second score, and third score meet a second preset condition, the second credit score corresponding to the first object is determined by the following formula. The second preset condition includes at least two of the following: the first score is greater than or equal to a third preset value, the second score is greater than or equal to a fourth preset value, and the third score is equal to a fifth preset value.
[0119]
[0120] in, This indicates the second credit score. Indicates the first credit score. This represents the third numerical value.
[0121] In one optional implementation, if the first score, the second score, and the third score meet a third preset condition, the second credit score is determined to be a second preset value.
[0122] The third preset condition includes the first score being less than the sixth preset value, the second score being less than the sixth preset value, or the third score being less than the sixth preset value.
[0123] Optionally, the sixth preset value is 0.5 and the second preset value is 0.
[0124] Optionally, determining the financing amount corresponding to the first object based on the second credit score and the multi-level credit score includes:
[0125] If the tariff volatility corresponding to the first object is less than or equal to the first preset volatility, calculate the first product between the multi-level credit score, the base amount and the preset first influence factor.
[0126] The first product is determined as the financing amount corresponding to the first object.
[0127] In this embodiment, when the tariff volatility is less than or equal to a first preset volatility, a direct penetration path is established, and the financing amount is determined through this direct penetration path. Optionally, the aforementioned first preset volatility is 5%.
[0128] In this embodiment, the financing amount corresponding to the first object can be determined by the following formula:
[0129]
[0130] in, Indicates the amount of financing. This indicates a multi-level credit score. Indicates the base amount. This indicates the first impact factor.
[0131] Among them, when the tariff policy is detected to be stable, It equals 0.2, otherwise it is 1.
[0132] In this embodiment, the method for determining the financing amount is adjusted based on tariff volatility to ensure that the financing amount conforms to tariff fluctuations and improves the accuracy of the financing amount.
[0133] Optionally, determining the financing amount corresponding to the first object based on the second credit score and the multi-level credit score includes:
[0134] If the tariff volatility corresponding to the first object is greater than the first preset volatility and less than or equal to the second preset volatility, calculate the second product between the base amount, the preset second influence factor and the second credit score;
[0135] The second product is determined as the financing amount corresponding to the first object.
[0136] In this embodiment, when the tariff volatility is greater than a first preset volatility and less than or equal to a second preset volatility, a multi-level attenuation path is established, and the financing amount is determined through this multi-level attenuation path. Optionally, the first preset volatility is 5%, and the second preset volatility is 20%.
[0137] In this embodiment, the financing amount corresponding to the first object can be determined by the following formula:
[0138]
[0139] in, Indicates the amount of financing. Indicates the base amount. This indicates the second impact factor. This indicates the second credit score.
[0140] In this embodiment, the method for determining the financing amount is adjusted based on tariff volatility to ensure that the financing amount conforms to tariff fluctuations and improves the accuracy of the financing amount.
[0141] Optionally, determining the financing amount corresponding to the first object based on the second credit score and the multi-level credit score includes:
[0142] If the tariff volatility corresponding to the first object is greater than the second preset volatility, calculate the third product between the multi-level credit score and the amount of collateral corresponding to the first object;
[0143] The smaller value between the third product and the historical financing amount corresponding to the first object is determined as the financing amount corresponding to the first object.
[0144] In this embodiment, when the tariff volatility exceeds a second preset volatility, an emergency recovery path is established, and the financing amount is determined through this path. Optionally, the second preset volatility is 20%.
[0145] In this embodiment, the financing amount corresponding to the first object can be determined by the following formula:
[0146]
[0147] in, Indicates the amount of financing. This indicates a multi-level credit score. This indicates the amount of the collateral.
[0148] In this embodiment, the method for determining the financing amount is adjusted based on tariff volatility to ensure that the financing amount conforms to tariff fluctuations and improves the accuracy of the financing amount.
[0149] In existing technologies, if a supplier seeks financing based on credit documents, it requires manual verification of the supplier's multi-country trade documents to verify the supplier's credit documents, which is time-consuming.
[0150] To address the above problems, this embodiment provides the following solution:
[0151] Optionally, the method further includes:
[0152] Receive a verification request sent by a second object, the verification request being used to verify the credit score of a first object associated with the second object;
[0153] In response to the verification request, the original credit certificate of the second object is encrypted to obtain the first trust certificate;
[0154] The first trust credential is subjected to cross-chain topology obfuscation to obtain the second trust credential;
[0155] Based on the first trust credential and the second trust credential, generate a multi-level credit topology function corresponding to the second object;
[0156] Based on the multi-level credit topology function corresponding to the second object, determine the first credit score corresponding to the first object associated with the second object.
[0157] In this embodiment, upon receiving a verification request, a dynamic encrypted anchoring factor is generated through a pre-defined heterogeneous cross-chain swapping operation protocol between the first and second objects, using a dual-chain dynamic anchoring algorithm. This dynamic encrypted anchoring factor can be expressed as... .
[0158] Specifically, it includes the following steps:
[0159] 1. By using a pre-defined heterogeneous cross-chain swapping operation protocol, a two-way verification channel is established between the domestic consortium blockchain where the second object is located and the overseas blockchain where the first object is located, and the Enterprise Resource Planning (ERP) data of the N-level first object corresponding to the second object is collected synchronously.
[0160] 2. Based on the two-way verification channel, a quantum-safe hash operation is performed on each collected ERP data, and the data is concatenated with the corresponding chain identifier to generate the original anchoring fragment of the anchoring factor.
[0161] 3. For the original anchor fragment, XOR obfuscate the first object's tax identifier and the real-time clock offset, and generate a dynamic hidden layer using a zero-knowledge proof encryption algorithm.
[0162] 4. Implement a cross-chain signature mechanism for the dynamic hidden layer and encode the importing country's customs clearance policy as a policy-sensitive factor.
[0163] 5. By fusing the original anchoring fragment and the dynamic hidden layer through tensor product operation, a dynamic encrypted anchoring factor is obtained.
[0164] In this embodiment, after obtaining the dynamic encryption anchoring factor, the original credit certificate is encrypted using the dynamic encryption anchoring factor to obtain the first trust certificate.
[0165] Specifically, it includes the following steps:
[0166] 1. Divide the original credit certificate into multiple data blocks, and encrypt each data block using a fully homomorphic encryption algorithm to obtain the result. .
[0167] 2. Inject the i-th level component of the dynamically anchored factor calculated above into each data block to obtain... ,right Performing elliptic curve dot product operation yields the ciphertext block of the letter of credit. .
[0168] 3. Construct a three-level Merkle tree structure.
[0169] 4. Add a signature to the end of each node in the tree structure, encrypt the importing country's customs clearance policy in binary code as a policy-sensitive factor, and thus obtain the first trust credential with cross-border traceability capability.
[0170] In this embodiment, the first trust credential is subjected to cross-chain topology obfuscation to obtain the second trust credential.
[0171] Specifically, it includes the following steps:
[0172] 1. Based on the first trust credential obtained above, for odd-ordered items... The random numbers are merged and submitted to the consensus function for distributed fragmentation verification and transformation, resulting in... .
[0173] 2. Dual Ordinal Term Perform collision-resistant hashing to generate ;Will and Multiply.
[0174] 3. To Inject random numbers and perform tensor obfuscation operations to generate This disrupts the original topology.
[0175] 4. Based on the results obtained in steps 1, 2, and 3 above, the verification results and the obfuscated data are fused together.
[0176] 5. Perform the above operations on all items in the first trust credential to obtain the second trust credential.
[0177] In this embodiment, a multi-level credit topology function corresponding to the second object is generated based on the first trust credential and the second trust credential.
[0178] Specifically, it includes the following steps:
[0179] 1. Extracting the second object's document from the first credit document The clearance status is decomposed into the product of the clearance status indicator and the credit decay coefficient using the manifold projection operator, expressed as: .
[0180] 2. Performing a manifold tensor product operation with the corresponding level of dynamic encryption anchoring factor to generate a dual-track credit projection can be represented as follows: .
[0181] 3. Obtain the digital signature from the second trust credential. and homomorphic rotation parameters .
[0182] 4. Calculate the confidence level of the reverse verification. Using the confidence score of reverse verification as the denominator, and... After normalization, we get .
[0183] 5. Calculate the curvature factor of the tariff policy This factor is achieved by monitoring the frequency of HS code changes in importing countries in real time. and tariff rate gradient The calculated value is given by the curvature influence factor function expanded as follows: .
[0184] 6. The results obtained from the above steps are superimposed step by step through manifold summation operations to form a complete credit manifold topology, which is then expressed as a function.
[0185] In this embodiment, the first credit score corresponding to the first object associated with the second object is determined based on the multi-level credit topology function corresponding to the second object. Specifically, the first credit score can be determined using the following formula:
[0186]
[0187] in, This indicates the first credit score.
[0188] in, This indicates the base penetration value that the first object inherits from its parent object. ,original Equals 1, and for Applying exponential decay The attenuation coefficient Obtained through training on historical transaction data (default value 0.18).
[0189] in, To generate a reverse verification gain factor for the confidence level based on the second credit certificate. And then The result is obtained by performing a second correction operation.
[0190] in, Based on the manifold curvature of the first object Perform manifold curvature penalty judgment processing, i.e., take 0 and The maximum value between θ and θ is a predefined threshold.
[0191] In this embodiment, by obtaining the original credit documents of the supplier's associated enterprise, a multi-level credit topology function corresponding to the enterprise is constructed based on the original credit documents, thereby obtaining the first credit score of the supplier associated with the enterprise, i.e., the credit document. In this way, the supplier's credit document can be verified without manual verification of the supplier's multi-country trade documents, which greatly shortens the verification time and improves the verification efficiency.
[0192] For a better understanding of the overall technical solution, please refer to [link / reference]. Figure 2 ,like Figure 2 As shown, the system receives a verification request from the second object; through a pre-defined heterogeneous cross-chain swapping protocol, a heterogeneous bridging channel is established between the first and second objects. Based on this heterogeneous bridging channel, a dynamic encrypted anchoring factor is generated using a dual-chain dynamic anchoring algorithm; the original credit certificate of the second object is encrypted using the dynamic encrypted anchoring factor to obtain a first trust certificate; the first trust certificate is subjected to cross-chain topology obfuscation to obtain a second trust certificate; based on the first and second trust certificates, a multi-level credit topology function corresponding to the second object is generated; based on the multi-level credit topology function corresponding to the second object, a first credit score corresponding to the first object is determined; and the first credit score, the multi-level credit topology function, the first trust certificate, and the second trust certificate are uploaded to the blockchain.
[0193] The system receives a financing request from a first party; extracts invoice information, order hash value, tax identification, and logistics information from the financing request to construct a first verification layer, a second verification layer, and a third verification layer; obtains a first score based on a multi-level credit topology function and the first verification layer; obtains a second score based on a multi-level credit topology function and the second verification layer; obtains a third score based on a multi-level credit topology function and the third verification layer; corrects the first credit score based on the first, second, and third scores to obtain a second credit score; determines a multi-level credit score based on the second credit score and the multi-level credit topology function; establishes three types of smart contract execution paths: a direct penetration path, a multi-level attenuation path, and an emergency recovery path, and selects the appropriate execution path based on tariff volatility; and determines the financing amount corresponding to the first party based on the second credit score and the multi-level credit score.
[0194] See Figure 3 , Figure 3 This is a schematic diagram of the structure of a blockchain-based financing device provided in an embodiment of this application, as shown below. Figure 3 As shown, the blockchain-based financing device 300 includes:
[0195] The first receiving module 301 is used to receive the financing request sent by the first object;
[0196] The acquisition module 302 is used to, in response to the financing request, acquire the multi-level credit topology function corresponding to the second object and the first credit score corresponding to the first object, wherein the second object is associated with the first object, and the multi-level credit topology function is used to characterize the credit scores of multiple objects associated with the second object;
[0197] The first determining module 303 is used to determine a first score, a second score and a third score based on the multi-level credit topology function. The first score is used to characterize the trade authenticity corresponding to the first object, the second score is used to characterize the tax compliance corresponding to the first object, and the third score is used to characterize the logistics consistency corresponding to the first object.
[0198] The second determining module 304 is used to determine the multi-level credit score corresponding to the first object based on the second credit score corresponding to the first object and the multi-level credit topology function, wherein the second credit score is obtained by correcting the first credit score based on the first score, the second score and the third score;
[0199] The third determining module 305 is used to determine the financing amount corresponding to the first object based on the second credit score and the multi-level credit score.
[0200] Optionally, the first determining module 303 is specifically used for:
[0201] Extract invoice information, order hash value, tax identification number, and logistics information from the financing request;
[0202] A first verification layer is constructed based on the invoice information and the order hash value, and a first score corresponding to the first object is obtained according to the multi-level credit topology function and the first verification layer.
[0203] A second verification layer is constructed based on the tax identifier, and a second score corresponding to the first object is obtained according to the multi-level credit topology function and the second verification layer.
[0204] A third verification layer is constructed based on the logistics information, and a third score corresponding to the first object is obtained according to the multi-level credit topology function and the third verification layer.
[0205] Optionally, the blockchain-based financing device 300 further includes:
[0206] The fourth determining module is used to determine the product between the first credit score and the first value of the first object as the second credit score of the first object when the first score, the second score and the third score of the first object meet the first preset condition. The first value is the sum of the first preset value and the second value, and the second value is the ratio between the credit entropy value of the first object and the preset credit entropy threshold.
[0207] The fifth determining module is used to determine the smaller value between the first credit score and the third value of the first object as the second credit score of the first object when the first score, the second score and the third score of the first object meet the second preset condition, wherein the third value is the average value of the first score, the second score and the third score.
[0208] The sixth determining module is used to determine the second credit score corresponding to the first object as a second preset value when the first score, second score and third score corresponding to the first object meet the third preset condition;
[0209] The first preset condition includes: the first score is greater than or equal to the third preset value, the second score is greater than or equal to the fourth preset value and the third score is equal to the fifth preset value, the fifth preset value is greater than the third preset value and the third preset value is greater than the fourth preset value;
[0210] The second preset condition includes at least two of the following: the first score is greater than or equal to the third preset value, the second score is greater than or equal to the fourth preset value, and the third score is equal to the fifth preset value;
[0211] The third preset condition includes: the first score is less than the sixth preset value, the second score is less than the sixth preset value, or the third score is less than the sixth preset value, and the sixth preset value is less than the fourth preset value.
[0212] Optionally, the third determining module 305 is specifically used for:
[0213] If the tariff volatility corresponding to the first object is less than or equal to the first preset volatility, calculate the first product between the multi-level credit score, the base amount and the preset first influence factor.
[0214] The first product is determined as the financing amount corresponding to the first object.
[0215] Optionally, the third determining module 305 is further specifically used for:
[0216] If the tariff volatility corresponding to the first object is greater than the first preset volatility and less than or equal to the second preset volatility, calculate the second product between the base amount, the preset second influence factor and the second credit score;
[0217] The second product is determined as the financing amount corresponding to the first object.
[0218] Optionally, the third determining module 305 is further specifically used for:
[0219] If the tariff volatility corresponding to the first object is greater than the second preset volatility, calculate the third product between the multi-level credit score and the amount of collateral corresponding to the first object;
[0220] The smaller value between the third product and the historical financing amount corresponding to the first object is determined as the financing amount corresponding to the first object.
[0221] Optionally, the blockchain-based financing device 300 further includes:
[0222] The second receiving module is used to receive a verification request sent by the second object, the verification request being used to verify the credit score of the first object associated with the second object;
[0223] The first processing module is configured to, in response to the verification request, encrypt the original credit certificate of the second object to obtain the first trust certificate;
[0224] The second processing module is used to perform cross-chain topology obfuscation on the first trust credential to obtain the second trust credential.
[0225] The generation module is used to generate a multi-level credit topology function corresponding to the second object based on the first trust credential and the second trust credential.
[0226] The seventh determining module is used to determine the first credit score corresponding to the first object associated with the second object based on the multi-level credit topology function corresponding to the second object.
[0227] The blockchain-based financing device 300 is capable of implementing the various processes described above in the embodiments of the blockchain-based financing method. The technical features correspond one-to-one and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0228] This application also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the various processes of the above-described blockchain-based financing method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.
[0229] For details, see Figure 4 This application also provides an electronic device, including a bus 401, a transceiver 402, an antenna 403, a bus interface 404, a processor 405, and a memory 406.
[0230] The transceiver 402 is used to receive a financing request sent by the first object;
[0231] In response to the financing request, a multi-level credit topology function corresponding to the second object and a first credit score corresponding to the first object are obtained. The second object is associated with the first object. The multi-level credit topology function is used to characterize the credit scores of multiple objects associated with the second object.
[0232] The processor 405 is used to determine a first score, a second score, and a third score based on the multi-level credit topology function. The first score is used to characterize the authenticity of trade corresponding to the first object, the second score is used to characterize the tax compliance of the first object, and the third score is used to characterize the logistics consistency of the first object.
[0233] Based on the second credit score corresponding to the first object and the multi-level credit topology function, the multi-level credit score corresponding to the first object is determined, and the second credit score is obtained by correcting the first credit score based on the first score, the second score and the third score;
[0234] The financing amount corresponding to the first object is determined based on the second credit score and the multi-level credit score.
[0235] exist Figure 4 In this context, a bus architecture (represented by bus 401) is used. Bus 401 can include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 405 and memory represented by memory 406. Bus 401 can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 404 provides an interface between bus 401 and transceiver 402. Transceiver 402 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 405 is transmitted over a wireless medium via antenna 403, which further receives data and transmits data to processor 405.
[0236] Processor 405 is responsible for managing bus 401 and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 406 can be used to store data used by processor 405 during operation.
[0237] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described blockchain-based financing method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0238] This application also provides a computer program product, including computer instructions. When executed by a processor, these computer instructions implement the various processes of the above-described blockchain-based financing method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.
[0239] It should be noted that, in this document, 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. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0240] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0241] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A blockchain-based financing method, characterized in that, The method comprises: receiving a financing request sent by a first object; in response to the financing request, obtaining a multi-level credit topology function corresponding to a second object and a first credit score corresponding to the first object, the second object being associated with the first object, the multi-level credit topology function being used to represent credit scores of a plurality of objects associated with the second object; determining a first score, a second score and a third score based on the multi-level credit topology function, the first score being used to represent trade authenticity corresponding to the first object, the second score being used to represent tax compliance corresponding to the first object, and the third score being used to represent logistics consistency corresponding to the first object; determining a multi-level credit score corresponding to the first object according to the second credit score corresponding to the first object and the multi-level credit topology function, the second credit score being obtained by correcting the first credit score based on the first score, the second score and the third score; determining a financing amount corresponding to the first object according to the second credit score and the multi-level credit score.
2. The method of claim 1, wherein, The method further comprises: extracting invoice information, order hash value, tax identification and logistics information from the financing request; constructing a first verification layer based on the invoice information and the order hash value, and obtaining the first score corresponding to the first object according to the multi-level credit topology function and the first verification layer; constructing a second verification layer based on the tax identification, and obtaining the second score corresponding to the first object according to the multi-level credit topology function and the second verification layer; constructing a third verification layer based on the logistics information, and obtaining the third score corresponding to the first object according to the multi-level credit topology function and the third verification layer.
3. The method of claim 1, wherein, Before the multi-level credit score corresponding to the first object is determined according to the second credit score corresponding to the first object and the multi-level credit topology function, the method further comprises: in a case where the first score, the second score and the third score corresponding to the first object satisfy a first preset condition, determining a product of the first credit score corresponding to the first object and a first value as the second credit score corresponding to the first object, the first value being a sum value between a first preset value and a second value, and the second value being a ratio between a credit entropy value corresponding to the first object and a preset credit entropy threshold value; in a case where the first score, the second score and the third score corresponding to the first object satisfy a second preset condition, determining a smaller value between the first credit score corresponding to the first object and a third value as the second credit score corresponding to the first object, the third value being an average value of the first score, the second score and the third score; in a case where the first score, the second score and the third score corresponding to the first object satisfy a third preset condition, determining the second credit score corresponding to the first object as a second preset value. The first preset condition comprises that the first score is greater than or equal to a third preset value, the second score is greater than or equal to a fourth preset value, and the third score is equal to a fifth preset value, the fifth preset value is greater than the third preset value, and the third preset value is greater than the fourth preset value. The second preset condition comprises at least two of the following: the first score is greater than or equal to a third preset value, the second score is greater than or equal to a fourth preset value, and the third score is equal to a fifth preset value. The third preset condition comprises that the first score is less than a sixth preset value, the second score is less than the sixth preset value, or the third score is less than the sixth preset value, and the sixth preset value is less than the fourth preset value.
4. The method of claim 1, wherein, The method further comprises: In a case where the first object corresponds to a tariff fluctuation rate less than or equal to a first preset fluctuation rate, calculating a first product between the multi-level credit score, a base amount, and a preset first influence factor; Determining the first product as the financing amount corresponding to the first object.
5. The method of claim 1, wherein, The method further comprises: In a case where the first object corresponds to a tariff fluctuation rate greater than a first preset fluctuation rate and less than or equal to a second preset fluctuation rate, calculating a second product between a base amount, a preset second influence factor, and the second credit score; Determining the second product as the financing amount corresponding to the first object.
6. The method of claim 1, wherein, The method further comprises: In a case where the first object corresponds to a tariff fluctuation rate greater than a second preset fluctuation rate, calculating a third product between the multi-level credit score and a collateral amount corresponding to the first object; Determining a smaller value between the third product and a historical financing amount corresponding to the first object as the financing amount corresponding to the first object.
7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: receiving a verification request sent by a second object, the verification request being used to represent verification of a credit score of a first object associated with the second object; in response to the verification request, performing encryption processing on an original credit certificate of the second object to obtain a first trust certificate; performing cross-chain topology obfuscation processing on the first trust certificate to obtain a second trust certificate; generating a multi-level credit topology function corresponding to the second object according to the first trust certificate and the second trust certificate; determining a first credit score corresponding to the first object associated with the second object according to the multi-level credit topology function corresponding to the second object. 8.A blockchain-based financing apparatus, characterized by, The apparatus comprises: a first receiving module configured to receive a financing request sent by a first object; obtain, in response to the financing request, a multi-level credit topology function corresponding to a second object and a first credit score corresponding to the first object, the second object being associated with the first object, the multi-level credit topology function being used to represent credit scores of a plurality of objects associated with the second object; determine, based on the multi-level credit topology function, a first score, a second score, and a third score, the first score being used to represent trade authenticity corresponding to the first object, the second score being used to represent tax compliance corresponding to the first object, and the third score being used to represent logistics consistency corresponding to the first object; determine, according to the second credit score corresponding to the first object and the multi-level credit topology function, a multi-level credit score corresponding to the first object, the second credit score being obtained by correcting the first credit score based on the first score, the second score, and the third score; determine, according to the second credit score and the multi-level credit score, a financing amount corresponding to the first object.
9. An electronic device, comprising: comprise: a processor, a memory, and a program stored on the memory and executable on the processor, the program, when executed by the processor, implementing steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, a computer program is stored on the computer readable storage medium, and the computer program, when executed by a processor, implements steps of the method of any one of claims 1 to 7.