Supplier credit method, device, electronic equipment and medium

Abstract

A supplier credit method, device, electronic equipment and medium are provided, which can be applied to the fields of block chain, big data technology and artificial intelligence technology. The method comprises: collecting historical order data of a supplier, performing data processing on the historical order data to obtain historical target data; performing block chain encryption processing on the historical target data to obtain order encryption data; inputting the order encryption data into a pre-trained order information prediction model to output an order risk prediction result of the supplier; obtaining predicted order information of the supplier in a first prediction period based on the order risk prediction result; inputting the predicted order information into a pre-constructed dynamic credit model to calculate credit information; and in response to the supplier passing identity information verification, providing the credit information to the supplier, so that the supplier obtains a credit result according to the credit information.

Description

Technical Field

[0001] The present invention relates to the fields of blockchain, big data technology and artificial intelligence technology, and more specifically to a supplier credit granting method, device, electronic device and medium. Background Art

[0002] In today's globalized business environment, supply chains have become a crucial component of business operations. Encompassing a range of key activities, from raw material sourcing and manufacturing to distribution and sales, supply chains play an irreplaceable role in business operations and growth. However, capital flow has become a key bottleneck in supply chain operations, particularly for small and medium-sized enterprises and emerging markets, and traditional supply chain financing models are clearly insufficient in meeting these funding needs.

[0003] Specifically, in traditional financing models, companies in the supply chain often face a lack of order data. In this situation, supply chain financial institutions struggle to accurately understand and analyze companies' operations, order volumes, and future funding needs. This lack of order data makes it impossible to precisely match companies' funding needs, leading to difficulties for companies in the supply chain in the financing process and restricting their production and development. Furthermore, traditional financing models often make credit decisions based on static indicators, such as a company's credit rating and historical financial performance. However, this approach ignores the dynamic and complex nature of supply chain operations. Static indicators fail to fully account for factors such as market fluctuations, supply chain relationships, and risk volatility, resulting in limitations in matching actual financing needs. Summary of the Invention

[0004] In view of the above problems, according to the first aspect of the present invention, a supplier credit method is provided, which is characterized in that the method includes: collecting the supplier's historical order data, performing data processing on the historical order data, and obtaining historical target data; performing blockchain encryption processing on the historical target data to obtain order encrypted data; inputting the order encrypted data into a pre-trained order information prediction model, and outputting the supplier's order risk prediction result; based on the order risk prediction result, obtaining the supplier's predicted order information within the first prediction period; inputting the predicted order information into a pre-built dynamic credit model to calculate credit information; and in response to the supplier passing the verification of identity information, providing the credit information to the supplier, so that the supplier obtains the credit result based on the credit information.

[0005] According to some exemplary embodiments, the historical target data is subjected to blockchain encryption processing to obtain order encrypted data, specifically including: encrypting the historical target data using a hash algorithm to generate a data fingerprint; encapsulating the fingerprint data into transaction information, broadcasting it to the blockchain network, and verifying it using a consensus mechanism; packaging the verified transaction information into blocks and adding them to the end of the blockchain; and obtaining order encrypted data based on the blockchain.

[0006] According to some exemplary embodiments, the order information prediction model is trained using a LightGBM model.

[0007] According to some exemplary embodiments, it is characterized in that before the historical target data is subjected to blockchain encryption processing, the method further includes: using a pre-constructed feature matrix to extract features from the historical target data to obtain order features, wherein the feature matrix is ​​constructed based on indicator features for measuring order prediction capabilities, indicator features for measuring order reliability, and indicator features for measuring order fulfillment capabilities.

[0008] According to some exemplary embodiments, the scale and term prediction model includes N decision trees, where N is a positive integer. Inputting the encrypted order data into the pre-trained scale and term prediction model and outputting the supplier's order risk prediction result specifically includes: inputting the encrypted order data into the N decision trees to obtain the prediction results corresponding to the N decision trees; calculating the integrated prediction results of the N decision trees based on the decision weights of the N decision trees; and obtaining the order risk prediction result based on the integrated prediction result.

[0009] According to some exemplary embodiments, the credit information includes a credit limit and a credit period, and the pre-built dynamic credit model includes a credit limit calculation model and a credit period calculation model, wherein the credit limit calculation model is expressed as the product of the predicted order size and the credit rating weighting coefficient, and the sum of the risk assessment weighting coefficient; the credit period calculation model is expressed as the product of the predicted order collection time and the credit rating weighting coefficient, and the sum of the risk assessment weighting coefficient.

[0010] According to some exemplary embodiments, in response to the supplier passing the verification of identity information, providing the credit information to the supplier specifically includes: deploying a smart contract on the blockchain and sending the credit information to the smart contract; authenticating the supplier based on the smart contract; and if the supplier's identity authentication is passed, providing the credit information to the supplier.

[0011] According to some exemplary embodiments, the method further includes: obtaining the current order data of the supplier in real time, and re-obtaining the order risk prediction result based on the current order data; updating the predicted order information when the order risk prediction result changes; and recalculating the credit information based on the updated predicted order information, so that the supplier is granted credit according to the recalculated credit information.

[0012] According to a second aspect of the present invention, a supplier credit device is proposed, which includes: a historical target data acquisition module, which is used to collect the supplier's historical order data, perform data processing on the historical order data, and obtain historical target data; an order encryption data acquisition module, which is used to perform blockchain encryption processing on the historical target data and obtain order encryption data; an order risk prediction result output module, which is used to input the order encryption data into a pre-trained scale and term prediction model, and output the supplier's order risk prediction result; a predicted order information acquisition module, which is used to obtain the supplier's predicted order information within the first prediction period based on the order risk prediction result; a credit information calculation module, which is used to input the predicted order information into a pre-built dynamic credit model and calculate credit information; and a credit information providing module, which is used to provide the supplier with the credit information in response to the supplier passing the verification of the identity information, so that the supplier obtains the credit result based on the credit information.

[0013] According to some exemplary embodiments, the order encryption data acquisition module may include a blockchain encryption processing module.

[0014] According to some exemplary embodiments, the blockchain encryption processing module includes an encryption unit, a verification unit, a packaging unit, and an order encryption data acquisition unit.

[0015] According to some exemplary embodiments, the encryption unit may be configured to encrypt the historical target data using a hash algorithm to generate a data fingerprint.

[0016] According to some exemplary embodiments, the verification unit may be configured to encapsulate the fingerprint data into transaction information, broadcast it to a blockchain network, and perform verification using a consensus mechanism.

[0017] According to some exemplary embodiments, the packaging unit may be configured to package verified transaction information into a block and add the block to the end of the blockchain.

[0018] According to some exemplary embodiments, the order encryption data acquisition unit may be configured to acquire order encryption data based on the blockchain.

[0019] According to some exemplary embodiments, the supplier credit authorization device further includes a feature extraction unit.

[0020] According to some exemplary embodiments, the feature extraction unit can be used to perform feature extraction on the historical target data using a pre-constructed feature matrix to obtain order features, wherein the feature matrix is ​​constructed based on indicator features for measuring order prediction capability, indicator features for measuring order reliability, and indicator features for measuring order fulfillment capability.

[0021] According to some exemplary embodiments, the order risk prediction result output module may include a prediction module.

[0022] According to some exemplary embodiments, the prediction module may include an N-tree decision tree result acquisition unit, a prediction result integration unit, and an order risk prediction result acquisition unit.

[0023] According to some exemplary embodiments, the N decision tree result acquisition unit may be configured to input the order encryption data into the N decision trees to obtain prediction results corresponding to the N decision trees.

[0024] According to some exemplary embodiments, the prediction result integration unit may be configured to calculate an integrated prediction result of the N decision trees based on the decision weights of the N decision trees.

[0025] According to some exemplary embodiments, the order risk prediction result acquisition unit may be configured to obtain the order risk prediction result based on the integrated prediction result.

[0026] According to some exemplary embodiments, the credit information calculation module may include a credit information calculation module.

[0027] According to some exemplary embodiments, the credit information calculation module may include a credit limit calculation unit and a credit term calculation unit.

[0028] According to some exemplary embodiments, the credit limit calculation unit can be used to calculate the credit limit based on a pre-built credit limit calculation model, wherein the credit limit calculation model is represented as the product of the predicted order size and the credit rating weighting coefficient, and the sum of the predicted order size and the risk assessment weighting coefficient.

[0029] According to some exemplary embodiments, the credit period calculation unit can be used to calculate the credit period based on a pre-built credit period calculation model, wherein the credit period calculation model is represented as the product of the predicted order collection time and the credit rating weighting coefficient, and the sum of the predicted order collection time and the risk assessment weighting coefficient.

[0030] According to some exemplary embodiments, the credit information providing module may include a credit information providing module.

[0031] According to some example embodiments, the credit information providing module can comprise a credit information uploading unit, an identity verification unit and a credit information providing unit.

[0032] According to some example embodiments, the credit information uploading unit can be configured to deploy a smart contract on a blockchain and upload the credit information into the smart contract.

[0033] According to some example embodiments, the identity verification unit can be configured to verify the identity of the supplier based on the smart contract.

[0034] According to some example embodiments, the credit information providing unit can be configured to provide the credit information to the supplier if the identity verification of the supplier is passed.

[0035] According to some example embodiments, the apparatus can further comprise a credit information updating module.

[0036] According to some example embodiments, the credit information updating module can comprise a prediction result reacquiring unit, a predicted order information updating unit and a re-credit unit.

[0037] According to some example embodiments, the prediction result reacquiring unit can be configured to acquire current order data of the supplier in real time and reacquire the order risk prediction result based on the current order data.

[0038] According to some example embodiments, the predicted order information updating unit can be configured to update the predicted order information when the order risk prediction result changes.

[0039] According to some example embodiments, the re-credit unit can be configured to recompute the credit information based on the updated predicted order information, so that the supplier is credentialed according to the recomputed credit information.

[0040] According to a third aspect of the present disclosure, there is provided an electronic device comprising: one or more processors; and a storage device storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method as described above.

[0041] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method as described above.

[0042] According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program that, when executed by a processor, implements the method as described above.

[0043] The one or more embodiments have the advantages or benefits that the supplier credit method provided by the application combines the blockchain, the prediction model and the credit model, ensures the data security, trust establishment, decision support and cooperation enhancement in the supply chain management, provides comprehensive supplier credit evaluation, and enhances the scientificity and accuracy of financing decision. The blockchain technology provides high security and non-tamperability of data, the credit information and identity verification record of the supplier are always trusted and are not controlled by any single entity, and the prediction model is integrated to more accurately evaluate the risk and reliability of the order. BRIEF DESCRIPTION OF DRAWINGS

[0044] The above content and other purposes, features and advantages of the application will be more apparent from the following description of the embodiments of the application with reference to the accompanying drawings, in which:

[0045] Figure 1 An application scenario diagram of the supplier credit method, device, equipment and medium according to the embodiments of the application is schematically shown.

[0046] Figure 2 A flowchart of the supplier credit method according to the embodiments of the application is schematically shown.

[0047] Figure 3 A flowchart of the method of the blockchain encryption processing according to the embodiments of the application is schematically shown.

[0048] Figure 4 A flowchart of the method of the feature extraction according to the embodiments of the application is schematically shown.

[0049] Figure 5 A flowchart of the method of obtaining the prediction result through the prediction model according to the embodiments of the application is schematically shown.

[0050] Figure 6 A flowchart of the method of calculating the credit information according to the embodiments of the application is schematically shown.

[0051] Figure 7 A flowchart of the method of providing the credit information to the supplier according to the embodiments of the application is schematically shown.

[0052] Figure 8 A flowchart of the method of dynamically updating the credit information according to the embodiments of the application is schematically shown.

[0053] Figure 9 A structural block diagram of the supplier credit device according to the embodiments of the application is schematically shown.

[0054] Figure 10 A block diagram of the electronic equipment suitable for the supplier credit method according to the embodiments of the application is schematically shown. DETAILED DESCRIPTION

[0055] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0056] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the presence of the features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0057] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0058] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0059] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0060] First, the technical terms described in this article are explained and described as follows.

[0061] Blockchain: A distributed, decentralized database technology that organizes and stores data in blocks. Each block contains the hash of the previous block, forming an immutable chain structure. Blockchain technology ensures data security, transparency, and immutability, enabling multiple participants to verify and record transactions on a shared network.

[0062] LightGBM (Light Gradient Boosting Machine) is a machine learning framework based on gradient boosting trees. It is a powerful ensemble learning algorithm used to process structured data such as tabular data. LightGBM builds multiple decision trees in an efficient and accurate manner and combines them into a more powerful predictive model. It has high performance and the ability to handle large-scale data.

[0063] Smart contract: A self-executing computer code stored on a blockchain. It defines a set of conditions and operations that will be automatically executed when certain conditions are met. Smart contracts can be used for automation and ensure the execution of transactions without the need for third-party intervention. They have features such as transparency, automation, and security.

[0064] Decision tree: A tree-based machine learning model used for classification and regression tasks. In classification tasks, a decision tree gradually partitions data based on input features, dividing data into different categories. In regression tasks, a decision tree predicts continuous numerical values. Each node in a decision tree represents a feature, each branch represents a feature value, and leaf nodes represent the final prediction results.

[0065] Financing credit: Refers to the financial institution providing a certain amount of financing funds to enterprises or individuals. This means that the financing credit institution agrees to provide financing to the borrower under certain conditions, so that the borrower can obtain funds when needed.

[0066] Credit limit: Refers to the maximum amount of loans that a financial institution or credit party is willing to provide to a borrower, which represents the upper limit of the borrower's loans from the financial institution within a certain period. Credit limit is usually determined based on the borrower's credit rating, debt situation and other relevant factors.

[0067] Credit period: Refers to the time period during which a borrower can extend loans agreed by a financial institution or credit party, i.e. the borrower can make multiple loans within a certain period without the need to reapply. Credit period can be a specific period of time, such as one or two years, or it can be an unlimited period. Within the credit period, the borrower can borrow multiple times as needed, but the total amount of loans cannot exceed the credit limit.

[0068] With the gradual close contact between upstream and downstream enterprises in the supply chain, the supply chain central enterprise and its suppliers have the demand for financing based on the order transactions between the two parties. Banks can provide financing credit for enterprises in the supply chain. However, in the traditional supply chain financing model, due to the lack of order data, it is impossible to accurately match the financing demand of enterprises in the supply chain, and there is a risk of financing fraud. At the same time, the traditional supply chain financing model usually makes credit decision based on static indicators, and cannot reflect the changes and dynamic demand in the supply chain in time. Therefore, there is an urgent need to make intelligent and automatic order prediction analysis based on real order historical data, so as to provide basis for bank supply chain financing credit and realize dynamic update.

[0069] Based on this, the embodiment of the present application provides a supplier credit method, characterized in that the method comprises: collecting historical order data of a supplier, processing the historical order data to obtain historical target data; performing blockchain encryption processing on the historical target data to obtain order encryption data; inputting the order encryption data into a pre-trained order information prediction model to output an order risk prediction result of the supplier; obtaining predicted order information of the supplier in a first prediction period based on the order risk prediction result; inputting the predicted order information into a pre-constructed dynamic credit model to calculate credit information; and in response to the verification of the supplier through identity information, providing the credit information to the supplier, so that the supplier obtains a credit result according to the credit information. According to the supplier credit method provided by the present application, the method combining blockchain, prediction model and credit model ensures data security, trust establishment, decision support and cooperation enhancement in supply chain management, provides comprehensive supplier credit evaluation, and enhances the scientificity and accuracy of financing decision. Among them, the blockchain technology provides high security and non-tamperability of data, and the credit information and identity verification record of the supplier will always remain credible and not be controlled by any single entity; by integrating the prediction model, the risk and reliability of the order can be more accurately evaluated.

[0070] It should be noted that the supplier credit method, device, equipment and medium determined by the present application can be used in the fields of blockchain, big data technology and artificial intelligence technology, and can also be used in the field of finance, and can also be used in various fields other than the fields of blockchain, big data technology and artificial intelligence technology and finance. The application field of the supplier credit method, device, equipment and medium provided by the embodiment of the present application is not limited.

[0071] In the technical solution of the present invention, the user information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0072] Figure 1 The application scenario diagram of the supplier credit granting method, apparatus, device, and medium according to an embodiment of the present invention is schematically shown.

[0073] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0074] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0075] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0076] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0077] It should be noted that the supplier credit granting method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the supplier credit granting device provided in the embodiment of the present invention can generally be set in the server 105. The supplier credit granting method provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the supplier credit granting device provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0078] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0079] Figure 2 The flowchart of the supplier credit granting method according to an embodiment of the present invention is schematically shown.

[0080] like Figure 2 As shown, the supplier credit granting method 200 of this embodiment may include operations S210 to S260.

[0081] In operation S210 , historical order data of a supplier is collected, and data processing is performed on the historical order data to obtain historical target data.

[0082] In an embodiment of the present invention, historical order data may be obtained from databases of supply chain center enterprises and their suppliers.

[0083] In an embodiment of the present invention, in order to further focus on key data, appropriate tools (such as SQL queries, data analysis software) can be used to extract historical target data such as historical order quantity, order completion time, order price information, product category information, etc. from the historical order data.

[0084] In operation S220, the historical target data is encrypted using blockchain technology to obtain encrypted order data.

[0085] In an embodiment of the present invention, historical target data can be encrypted using an asymmetric encryption algorithm, and a distributed storage method can be used to disperse and store the data on multiple nodes or servers of the blockchain network.

[0086] Figure 3 The figure schematically shows a flowchart of a method for blockchain encryption processing according to an embodiment of the present invention.

[0087] like Figure 3As shown, the method of blockchain encryption processing of the embodiment can include operations S310-S340, which can at least partially perform the operation S220 described above.

[0088] In operation S310, the historical target data is encrypted using a hash algorithm to generate a data fingerprint.

[0089] In an embodiment of the application, the hash algorithm is an encryption technique that converts input data into a fixed-length string. By using a hash algorithm to encrypt order prediction data, a string called "data fingerprint" can be generated. The data fingerprint is unique, and even a slight change in the input data will result in a completely different fingerprint. The process of hash encryption is irreversible, which means that the original data cannot be restored from the data fingerprint.

[0090] In operation S320, the fingerprint data is packaged into transaction information and broadcast to the blockchain network for verification using a consensus mechanism.

[0091] In an embodiment of the application, the encrypted data fingerprint is packaged into transaction information, which includes the data fingerprint itself and other possible metadata such as timestamps, transaction initiators, etc. The transaction information can be designed as a data structure of a transaction.

[0092] In an embodiment of the application, the packaged transaction information is broadcast to the blockchain network, and multiple nodes in the network verify the transaction information. The verification process can involve a consensus mechanism to ensure the legality and consistency of the transaction. Specifically, the consensus mechanism can include Proof of Work (PoW) and Proof of Stake (PoS), etc.

[0093] In operation S330, the verified transaction information is packaged into a block and added to the end of the blockchain.

[0094] In an embodiment of the application, once the transaction information is verified by the network, multiple verification nodes will package these verified transaction information into a block, which also contains the hash value of the previous block, timestamp, etc. information, to form a growing chain structure.

[0095] In an embodiment of the application, the packaged block is added to the end of the blockchain, extending the length of the blockchain. This process ensures the non-tamperability of the transaction. Once the data is added to the blockchain, it will become a permanent record and is difficult to change.

[0096] In operation S340, order encryption data is obtained based on the blockchain.

[0097] In embodiments of the present application, a decentralized identity verification and permission management method can be used to ensure that only authorized users can access the data of the above-mentioned blockchain. Users can obtain the hash value of a specific order by querying the blockchain, and further obtain encrypted data. Once the user obtains the encrypted data, the data can be decrypted using the corresponding encryption key for subsequent processing.

[0098] In addition, in order to increase the prediction accuracy of the model, before the historical target data is processed by the blockchain encryption, the method further includes a feature extraction operation.

[0099] Figure 4 A flowchart of a feature extraction method according to an embodiment of the present application is schematically shown.

[0100] As Figure 4 shown, the feature extraction method of this embodiment can include operation S410.

[0101] In operation S410, the historical target data is subjected to feature extraction using a pre-constructed feature matrix, and order features are obtained, wherein the feature matrix is constructed based on index features measuring order prediction ability, index features measuring order reliability, and index features measuring order fulfillment ability.

[0102] In embodiments of the present application, the historical target data can be pre-processed by data cleaning, data transformation, and data reduction to eliminate noise and unnecessary information in the data, and the historical target data is arranged into a feature matrix, each row representing an order sample and each column representing a feature.

[0103] In embodiments of the present application, feature extraction can be performed by index features measuring order prediction ability, index features measuring order reliability, and index features measuring order fulfillment ability to obtain order features. The index features measuring order prediction ability measure the ability of order prediction; the index features measuring order reliability reflect the reliability of order fulfillment by the supplier; and the index features measuring order fulfillment ability reflect whether the supplier can fulfill the order on time and in quantity. The index features include but are not limited to those described in Table 1.

[0104] Table 1

[0105]

[0106] In embodiments of the present application, the order features can replace the original historical target data for subsequent encryption and model input operations to improve the performance of the model.

[0107] Referring back to Figure 2In operation S230, the encrypted order data is input into a pre-trained order information prediction model, and the supplier's order risk prediction result is output.

[0108] In an embodiment of the present invention, the order information prediction model is trained using the LightGBM model.

[0109] According to an embodiment of the present invention, LightGBM is a powerful tool suitable for order information prediction. Through the efficient gradient boosting tree algorithm, an accurate prediction model can be established based on historical data to support decision making and risk management.

[0110] In an embodiment of the present invention, specifically, the historical target data can be split into a training set and a validation set, the training set is used for model training, and the validation set is used for performance evaluation. Evaluation indicators (such as root mean square error, mean absolute error, etc.) can be used to judge the performance of the model. Furthermore, if the model performance is found to be poor, the model parameters can be adjusted, such as the number of trees, the number of leaf nodes, etc., to optimize the predictive ability of the model. After the training is completed, the trained model can be used to predict order information. By inputting new feature data (such as the features of the predicted order) into the model, the corresponding prediction results can be obtained.

[0111] Figure 5 The flowchart of the method for obtaining prediction results through a prediction model according to an embodiment of the present invention is schematically shown.

[0112] like Figure 5 As shown, the method for obtaining a prediction result through a prediction model in this embodiment may include operations S510 to S530, and the operations S510 to S530 may at least partially execute the above-mentioned operation S230.

[0113] In operation S510, the encrypted order data is input into the N decision trees to obtain prediction results corresponding to the N decision trees.

[0114] In operation S520 , an integrated prediction result of the N decision trees is calculated based on the decision weights of the N decision trees.

[0115] In operation S530, the order risk prediction result is obtained based on the integrated prediction result.

[0116] In an embodiment of the present invention, LightGBM obtains the final prediction value by weighted averaging the prediction results of multiple decision trees. The prediction result of each decision tree depends on the leaf node it is located in, and the value of each leaf node is learned during the training process by the gradient boosting algorithm. Among them, the prediction value of the i-th order can be expressed by the following formula:

[0117]

[0118] Among them, ntrees represents the number of decision trees, w j Represents the weight of the j-th decision tree and predicts the leaf node value ij Represents the predicted value of order i at the leaf node of the jth decision tree.

[0119] Return to reference Figure 2 In operation S240, based on the order risk prediction result, the predicted order information of the supplier in the first prediction period is obtained.

[0120] In an embodiment of the present invention, the supplier's forecasted order information for the first forecast period indicates the supplier's forecasted order size and forecasted order payment time over the next period. The forecasted order size and forecasted order payment time can be used to calculate subsequent credit information.

[0121] In operation S250, the predicted order information is input into a pre-built dynamic credit model to calculate credit information.

[0122] Figure 6 The flowchart of the method for calculating credit information according to an embodiment of the present invention is schematically shown.

[0123] In an embodiment of the present invention, a dynamic credit model can calculate a supplier's credit information based on the supplier's predicted order size and predicted order payment time. This credit information includes the credit limit and credit period, thereby helping to determine the credit limit to be provided and the loan period to be extended given the supplier's credit risk.

[0124] like Figure 6 As shown, the method for calculating credit information in this embodiment may include operations S610 to S620, and the operations S610 to S620 may at least partially execute the above-mentioned operation S250.

[0125] In operation S610, the credit limit is calculated based on a pre-built credit limit calculation model, wherein the credit limit calculation model is represented by the product of the predicted order size and the credit rating weighting coefficient, and the sum of the predicted order size and the risk assessment weighting coefficient. The credit limit calculation model can be represented as:

[0126] Credit limit i = Forecast order size i × Credit rating weighting factor i +Risk assessment weighting factor i (2)

[0127] Among them, i represents the i-th supplier, and the predicted order size iThe order size of the supplier i in a future period, the credit rating weighted coefficient and the risk assessment weighted coefficient are determined according to the supplier credit rating and risk rating preset by the bank system.

[0128] In operation S620, the credit period is calculated based on a pre-constructed credit period calculation model, where the credit period calculation model is represented as the product of the predicted order repayment time length and the credit rating weighted coefficient, and the sum of the risk assessment weighted coefficient. The credit period calculation model can be represented as:

[0129] Credit period i = Predicted repayment time length i × Credit rating weighted coefficient i + Risk assessment weighted coefficient i (3)

[0130] Wherein, the predicted repayment time length i represents the repayment time length of the supplier i in a future period.

[0131] It should be noted that operations S610 and S620 can be executed simultaneously or sequentially, and the execution order is not required.

[0132] Referring back to Figure 2 , in operation S260, in response to the verification of the supplier's identity information, the credit information is provided to the supplier, so that the supplier obtains the credit result according to the credit information.

[0133] In an embodiment of the present application, the credit information calculated by predicting the order information reflects the decision of the supplier's credit, that is, the credit result. According to the credit result, the supplier can decide whether to accept the credit condition.

[0134] Figure 7 The flowchart of the method for providing credit information to a supplier according to an embodiment of the present application is schematically shown.

[0135] As Figure 7 shown, the method for providing credit information to a supplier of this embodiment can include operation S710-S730, which can at least partially execute the above-mentioned operation S260.

[0136] In operation S710, a smart contract is deployed on the blockchain, and the credit information is uploaded to the smart contract.

[0137] In an embodiment of the present application, the smart contract is a kind of calculation code automatically executed on the blockchain. Therefore, the smart contract containing the credit information verification logic can be deployed to the blockchain network. In this smart contract, the specific steps and conditions for verifying the supplier's identity and providing the credit information will be defined.

[0138] In an embodiment of the present invention, the calculated credit limit and term can be encrypted through an asymmetric algorithm and sent to the smart contract.

[0139] In operation S720 , the supplier is authenticated based on the smart contract.

[0140] In this embodiment of the present invention, the identity information of suppliers participating in the supply chain is encrypted and stored in the blockchain. When suppliers obtain the credit result information, they need to verify their identity through digital signatures. Based on the set authorization rules and conditions, the smart contract automatically determines whether the supplier meets the authorization conditions.

[0141] In operation S730, if the identity verification of the supplier is successful, the credit information is provided to the supplier.

[0142] In addition, in order to further utilize the real-time changes in order data to dynamically update the credit limit and term, the embodiment of the present invention also provides the following method.

[0143] Figure 8 The flowchart of the method for dynamically updating credit information according to an embodiment of the present invention is schematically shown.

[0144] like Figure 8 As shown, the method for dynamically updating credit information in this embodiment may include operations S810 to S830.

[0145] In operation S810, the current order data of the supplier is obtained in real time, and the order risk prediction result is re-obtained based on the current order data.

[0146] In operation S820, when the order risk prediction result changes, the predicted order information is updated.

[0147] In operation S830, the credit information is recalculated based on the updated forecast order information, so that the supplier is granted credit according to the recalculated credit information.

[0148] The supplier credit granting method provided by this invention combines blockchain, predictive models, credit models, and smart contracts to ensure data security, trust building, decision support, and enhanced collaboration in supply chain management. It provides comprehensive supplier credit assessments and enhances the scientific nature and accuracy of financing decisions. Blockchain technology provides high data security and immutability, ensuring that supplier credit information and identity verification records remain reliable and uncontrolled by any single entity. The integrated predictive model enables more accurate assessment of order risk and reliability. Specifically, it achieves the following beneficial effects:

[0149] 1. Blockchain technology provides a high degree of data security and immutability. Once data is added to the blockchain, it is virtually impossible to modify or delete. This means that supplier trust information and authentication records remain reliable and are not controlled by any single entity.

[0150] 2. LightGBM is a powerful tool for order information prediction. Through the efficient gradient boosting tree algorithm, you can build accurate prediction models based on historical order data to support decision making and risk management.

[0151] 3. The characteristics of blockchain enable all transactions and operations to be recorded and verified by every node on the network. This transparency and traceability ensure that suppliers and other relevant parties have access to real-time, reliable information, thereby improving trust and cooperation. Blockchain is a decentralized database that does not rely on a single central organization to maintain and manage data. This means that there is no single controller, and all participants can jointly maintain and supervise the data, thereby reducing the risk of single points of failure and manipulation.

[0152] 4. Smart contracts allow for the automatic execution of predefined actions under specific conditions. This means that supplier identity verification and trust information provision can be automated, reducing human error and time consumption.

[0153] 5. It can realize data sharing and collaboration between supply chain center enterprises and suppliers, optimize supply chain financial service processes, improve the overall efficiency of the supply chain, and enhance customer satisfaction and user experience;

[0154] 6. Through real-time updates of risk assessments and credit information, credit limits and terms can be dynamically updated, providing greater flexibility. Supply chain managers can better manage potential risks, avoid transactions with high-risk suppliers, and thus reduce the possibility of losses.

[0155] Based on the above supplier credit granting method, the present invention also provides a supplier credit granting device. Figure 9 The device is described in detail.

[0156] Figure 9 The structure block diagram of the supplier credit granting device according to an embodiment of the present invention is schematically shown.

[0157] like Figure 9 As shown, according to this embodiment, the supplier credit device 900 includes an order forecast data extraction module 910, an order encryption data acquisition module 920, an order risk prediction result output module 930, a forecast order information acquisition module 940, a credit information calculation module 950 and a credit information provision module 960.

[0158] The historical target data acquisition module 910 can be used to collect historical order data of suppliers, perform data processing on the historical order data, and obtain historical target data. In one embodiment, the historical target data acquisition module 910 can be used to perform the operation S210 described above, which will not be repeated here.

[0159] The order encrypted data acquisition module 920 can be used to perform blockchain encryption processing on the historical target data to obtain the order encrypted data. In one embodiment, the order encrypted data acquisition module 920 can be used to perform the operation S220 described above, which will not be repeated here.

[0160] The order risk prediction result output module 930 can be used to input the encrypted order data into a pre-trained scale and term prediction model to output the supplier's order risk prediction result. In one embodiment, the order risk prediction result output module 930 can be used to perform operation S230 described above, which will not be repeated here.

[0161] The predicted order information obtaining module 940 can be used to obtain the supplier's predicted order information within the first forecast period based on the order risk prediction results, where the predicted order information includes the predicted order size and the predicted order payment collection time. In one embodiment, the predicted order information obtaining module 940 can be used to perform operation S240 described above, which will not be repeated here.

[0162] The credit information calculation module 950 can be used to input the predicted order information into a pre-built dynamic credit model to calculate credit information. In one embodiment, the credit information calculation module 950 can be used to perform the operation S250 described above, which will not be repeated here.

[0163] The credit information providing module 960 can be used to provide the credit information to the supplier in response to the supplier passing the identity information verification, so that the supplier obtains the credit result based on the credit information. In one embodiment, the credit information providing module 960 can be used to perform the operation S250 described above, which will not be repeated here.

[0164] According to an embodiment of the present invention, the order encryption data acquisition module 920 may include a blockchain encryption processing module.

[0165] According to an embodiment of the present invention, the blockchain encryption processing module includes an encryption unit, a verification unit, a packaging unit and an order encryption data acquisition unit.

[0166] The encryption unit can be configured to encrypt the historical target data by using a hash algorithm to generate a data fingerprint. In an embodiment, the encryption unit can be configured to perform operation S310 described above, which will not be repeated here.

[0167] The verification unit can be configured to encapsulate the fingerprint data into transaction information, broadcast to a blockchain network, and verified by using a consensus mechanism. In an embodiment, the verification unit can be configured to perform operation S320 described above, which will not be repeated here.

[0168] The packaging unit can be configured to package the verified transaction information into a block and add the block to the end of the blockchain. In an embodiment, the packaging unit can be configured to perform operation S330 described above, which will not be repeated here.

[0169] The order encryption data obtaining unit can be configured to obtain order encryption data based on the blockchain. In an embodiment, the order encryption data obtaining unit can be configured to perform operation S340 described above, which will not be repeated here.

[0170] According to an embodiment of the present application, the supplier credit device 900 further comprises a feature extraction unit.

[0171] The feature extraction unit can be configured to extract features from the historical target data by using a pre-constructed feature matrix to obtain order features, wherein the feature matrix is constructed based on index features measuring order prediction ability, index features measuring order reliability, and index features measuring order fulfillment ability. In an embodiment, the feature extraction unit can be configured to perform operation S410 described above, which will not be repeated here.

[0172] According to an embodiment of the present application, the order risk prediction result output module 930 can comprise a prediction module.

[0173] According to an embodiment of the present application, the prediction module can comprise an N-decision tree result obtaining unit, a prediction result integration unit, and an order risk prediction result obtaining unit.

[0174] The N-decision tree result obtaining unit can be configured to input the order encryption data into the N-decision trees to obtain prediction results corresponding to the N-decision trees. In an embodiment, the N-decision tree result obtaining unit can be configured to perform operation S510 described above, which will not be repeated here.

[0175] The prediction result integration unit can be configured to calculate integrated prediction results of the N-decision trees based on decision weights of the N-decision trees. In an embodiment, the prediction result integration unit can be configured to perform operation S520 described above, which will not be repeated here.

[0176] The order risk prediction result acquisition unit may be configured to obtain the order risk prediction result based on the integrated prediction result. In one embodiment, the order risk prediction result acquisition unit may be configured to perform the operation S530 described above, which will not be described in detail here.

[0177] According to an embodiment of the present invention, the credit information calculation module 950 may include a credit information calculation module.

[0178] The credit information calculation module may include a credit limit calculation unit and a credit term calculation unit.

[0179] The credit limit calculation unit may be configured to calculate a credit limit based on a pre-established credit limit calculation model, wherein the credit limit calculation model is represented by the product of the predicted order size and the credit rating weighting coefficient, and the sum of the predicted order size and the risk assessment weighting coefficient. In one embodiment, the credit limit calculation unit may be configured to perform operation S610 described above, which will not be further described here.

[0180] The credit period calculation unit may be configured to calculate the credit period based on a pre-established credit period calculation model, wherein the credit period calculation model is represented by the product of the predicted order payment collection time and the credit rating weighting coefficient, and the sum of the predicted order payment collection time and the risk assessment weighting coefficient. In one embodiment, the credit period calculation unit may be configured to perform operation S620 described above, which will not be further described here.

[0181] According to an embodiment of the present invention, the credit information providing module 960 may include a credit information providing module.

[0182] The credit information providing module may include a credit information sending unit, an identity verification unit and a credit information providing unit.

[0183] The credit information uploading unit may be used to deploy a smart contract on a blockchain and upload the credit information to the smart contract. In one embodiment, the credit information uploading unit may be used to perform operation S710 described above, which will not be described in detail here.

[0184] The identity verification unit may be used to authenticate the supplier based on the smart contract. In one embodiment, the identity verification unit may be used to perform operation S720 described above, which will not be repeated here.

[0185] The credit information providing unit may be used to provide the credit information to the supplier if the supplier's identity verification passes. In one embodiment, the credit information providing unit may be used to perform the operation S730 described above, which will not be repeated here.

[0186] According to an embodiment of the present invention, the apparatus 900 may further include a credit information updating module.

[0187] According to an embodiment of the present invention, the credit information updating module may include a prediction result re-acquisition unit, a prediction order information updating unit and a re-crediting unit.

[0188] The prediction result re-acquisition unit can be used to obtain the current order data of the supplier in real time and re-acquire the order risk prediction result based on the current order data. In one embodiment, the prediction result re-acquisition unit can be used to perform the operation S810 described above, which will not be repeated here.

[0189] The predicted order information updating unit may be configured to update the predicted order information when the order risk prediction result changes. In one embodiment, the predicted order information updating unit may be configured to perform the operation S820 described above, which will not be described in detail here.

[0190] The re-crediting unit may be configured to recalculate the credit information based on the updated predicted order information, so that the supplier is granted credit based on the recalculated credit information. In one embodiment, the re-crediting unit may be configured to perform operation S830 described above, which will not be described in detail here.

[0191] According to an embodiment of the present invention, any multiple modules among the historical target data acquisition module 910, the encrypted order data acquisition module 920, the order risk prediction result output module 930, the predicted order information acquisition module 940, the credit information calculation module 950, and the credit information provision module 960 can be combined into a single module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module. According to an embodiment of the present disclosure, at least one of the historical target data acquisition module 910, the order encrypted data acquisition module 920, the order risk prediction result output module 930, the predicted order information acquisition module 940, the credit information calculation module 950, and the credit information provision module 960 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware through any other reasonable means of integrating or packaging circuits, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the historical target data acquisition module 910, the order encrypted data acquisition module 920, the order risk prediction result output module 930, the predicted order information acquisition module 940, the credit information calculation module 950, and the credit information provision module 960 can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is executed.

[0192] Figure 10 The block diagram schematically shows an electronic device suitable for the supplier credit authorization method according to an embodiment of the present invention.

[0193] like Figure 10 As shown, the electronic device 1000 according to an embodiment of the present invention includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 into a random access memory (RAM) 1003. The processor 1001 may, for example, include a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0194] Various programs and data required for the operation of electronic device 1000 are stored in RAM 1003. Processor 1001, ROM 1002, and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations according to the method flow of an embodiment of the present invention by executing the programs in ROM 1002 and / or RAM 1003. It should be noted that the programs may also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 may also perform various operations according to the method flow of an embodiment of the present invention by executing the programs stored in the one or more memories.

[0195] According to an embodiment of the present invention, electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to bus 1004. Electronic device 1000 may also include one or more of the following components connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 1008 including a hard disk; and a communication section 1009 including a network interface card such as a LAN card or a modem. Communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in drive 1010 as needed, so that computer programs read from the removable media can be installed into storage section 1008 as needed.

[0196] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0197] According to an embodiment of the present application, the computer readable storage medium can be a non-transitory computer readable storage medium, for example, can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present application, the computer readable storage medium can include one or more of the above-described ROM 1002 and / or RAM 1003 and / or a memory other than the ROM 1002 and the RAM 1003.

[0198] An embodiment of the present application also includes a computer program product comprising a computer program containing program code for executing the methods shown in the flowcharts. When the computer program product is run in a computer system, the program code is used to make the computer system implement the methods provided by the embodiments of the present application.

[0199] The above-described functions defined in the system / apparatus / module / unit of the embodiments of the present application are performed when the computer program is executed by the processor 1001. According to an embodiment of the present application, the above-described system, apparatus, module, unit, etc. can be implemented by the computer program modules.

[0200] In one embodiment, the computer program can rely on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program can also be transmitted, distributed, and downloaded in the form of a signal over a network medium and installed and executed by the communication part 1009 and / or installed from the detachable medium 1011. The program code contained in the computer program can be transmitted by any suitable network medium, including, but not limited to, wireless, wired, etc., or any suitable combination of the foregoing.

[0201] In such an embodiment, the computer program can be downloaded and installed from a network by the communication part 1009 and / or installed from the detachable medium 1011. When the computer program is executed by the processor 1001, the above-described functions defined in the system of the embodiments of the present application are performed. According to an embodiment of the present application, the above-described system, apparatus, device, module, unit, etc. can be implemented by the computer program modules.

[0202] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, Python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect via the Internet).

[0203] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0204] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A supplier credit granting method, characterized in that: The method comprises: Collecting historical order data of suppliers, performing data processing on the historical order data, and obtaining historical target data; The historical target data is encrypted by blockchain to obtain encrypted order data; Inputting the encrypted order data into a pre-trained order information prediction model and outputting the supplier's order risk prediction result; Based on the order risk prediction result, obtaining predicted order information of the supplier in a first prediction period; Inputting the predicted order information into a pre-built dynamic credit model to calculate credit information; and In response to the supplier passing the identity information verification, providing the supplier with the credit information, so that the supplier obtains the credit result according to the credit information, The order information prediction model includes a scale and term prediction model, and the scale and term prediction model includes N decision trees, where N is a positive integer; The method of inputting the encrypted order data into a pre-trained scale and term prediction model and outputting the supplier's order risk prediction result includes: inputting the encrypted order data into the N decision trees to obtain the prediction results corresponding to the N decision trees; calculating the integrated prediction results of the N decision trees based on the decision weights of the N decision trees; and obtaining the order risk prediction result based on the integrated prediction results.

2. The method according to claim 1, characterized in that The block chain encryption processing of the historical target data to obtain the encrypted order data specifically includes: Using a hash algorithm to encrypt the historical target data to generate a data fingerprint; Encapsulate the fingerprint data into transaction information, broadcast it to the blockchain network, and verify it using the consensus mechanism; Packing verified transaction information into blocks and adding them to the end of the blockchain; and Based on the blockchain, order encryption data is obtained.

3. The method according to claim 1, characterized in that The order information prediction model is trained using the LightGBM model.

4. The method according to any one of claims 1 to 3, characterized in that: Before performing blockchain encryption processing on the historical target data, the method further includes: A pre-constructed feature matrix is ​​used to extract features from the historical target data to obtain order features, wherein the feature matrix is ​​constructed based on indicator features for measuring order prediction capability, indicator features for measuring order reliability, and indicator features for measuring order fulfillment capability.

5. The method according to claim 1, wherein The credit information includes the credit limit and the credit period, and the pre-built dynamic credit model includes a credit limit calculation model and a credit period calculation model. Among them, the credit limit calculation model is expressed as the product of the predicted order size and the credit rating weighted coefficient, and the sum of the risk assessment weighted coefficient; the credit period calculation model is expressed as the product of the predicted order collection time and the credit rating weighted coefficient, and the sum of the risk assessment weighted coefficient.

6. The method according to claim 5, characterized in that In response to the supplier passing the identity information verification, providing the supplier with the credit information specifically includes: Deploy a smart contract on the blockchain and send the credit information to the smart contract; authenticating the supplier based on the smart contract; and If the supplier's identity verification is passed, the credit information is provided to the supplier.

7. The method according to any one of claims 1 to 3, 5 to 6, characterized in that: The method further comprises: Acquire the supplier's current order data in real time, and re-acquire the order risk prediction result based on the current order data; When the order risk prediction result changes, updating the predicted order information; and Based on the updated forecast order information, the credit information is recalculated so that the supplier is granted credit according to the recalculated credit information.

8. A supplier credit granting device, characterized in that: The device comprises: A historical target data acquisition module is used to: collect historical order data of suppliers, perform data processing on the historical order data, and obtain historical target data; An order encryption data acquisition module is used to: perform blockchain encryption processing on the historical target data to obtain order encryption data; An order risk prediction result output module is used to: input the order encrypted data into a pre-trained order information prediction model, and output the supplier's order risk prediction result, wherein the order information prediction model includes a scale and term prediction model, and the scale and term prediction model includes N decision trees, wherein N is a positive integer; the order risk prediction result output module includes a prediction module, and the prediction module includes an N decision tree result acquisition unit, a prediction result integration unit and an order risk prediction result acquisition unit, and the N decision tree result acquisition unit is used to input the order encrypted data into the N decision trees to obtain the prediction results corresponding to the N decision trees; the prediction result integration unit is used to calculate the integrated prediction results of the N decision trees based on the decision weights of the N decision trees; the order risk prediction result acquisition unit is used to obtain the order risk prediction result based on the integrated prediction result; A forecast order information obtaining module is used to obtain the supplier's forecast order information within the first forecast period based on the order risk forecast result; a credit information calculation module, configured to: input the predicted order information into a pre-built dynamic credit model to calculate credit information; and The credit information providing module is used to: in response to the supplier passing the identity information verification, provide the credit information to the supplier, so that the supplier obtains the credit result according to the credit information.

9. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to perform the method according to any one of claims 1 to 7.

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