Supply chain transaction risk identification method and device

Abstract

The embodiment of the present application provides a supply chain transaction risk identification method and device, which can be used in the field of blockchain technology. The method includes: selecting one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, and the credit merchant sub-card contains multiple scores corresponding to each evaluation index group; obtaining the credit merchant sub-card calculation model corresponding to the target credit merchant sub-card, and obtaining the total credit score of the target transaction object from the multiple scores corresponding to each evaluation index group in the target credit merchant sub-card based on the credit merchant sub-card calculation model, so as to determine the risk identification result of the target transaction object according to the total credit score. The present application can effectively improve the comprehensiveness and effectiveness of the supply chain transaction risk identification process, can achieve targeted and accurate identification of supply chain transaction objects, and can improve the efficiency, automation and accuracy of the supply chain transaction risk identification process.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, in particular to the field of blockchain technology, and specifically to a method and device for identifying supply chain transaction risks. Background Art

[0002] With the rapid development of Internet technology in recent years, the traditional supply chain transaction object identification method dominated by offline manual judgment has been difficult to adapt to the supply chain service industry that has penetrated into all walks of life. Not only is it difficult to achieve multi-dimensional objective identification, but it also provides room for human subjective consciousness for the judgment operator.

[0003] In supply chain transactions, the traditional supply chain transaction object risk identification method dominated by offline manual judgment is difficult to achieve comprehensive and effective identification due to the lack of identification basis; due to the lack of a systematic credit evaluation system for supply chain transaction objects, it is difficult to achieve targeted and accurate identification of supply chain transaction objects, which in turn leads to low efficiency in supply chain transaction risk identification; and in the internal supply chain transaction object management, it is limited to human subjective consciousness, which leads to problems such as insufficient accuracy in supply chain transaction risk identification. Summary of the Invention

[0004] In response to the problems in the existing technology, the present application provides a supply chain transaction risk identification method and device, which can effectively improve the comprehensiveness and effectiveness of the supply chain transaction risk identification process, can achieve targeted and accurate identification of supply chain transaction objects, and can improve the efficiency, automation and accuracy of the supply chain transaction risk identification process, and can effectively improve the reliability and security of supply chain transactions.

[0005] To solve the above technical problems, this application provides the following technical solutions:

[0006] In a first aspect, the present application provides a supply chain transaction risk identification method, comprising:

[0007] Select one of the credit merchant score cards pre-stored in the blockchain as the target credit merchant score card corresponding to the current target transaction object of the supply chain, wherein each of the credit merchant score cards contains multiple scores corresponding to each evaluation indicator group;

[0008] Obtain a credit merchant score card calculation model corresponding to the target credit merchant score card, and based on the credit merchant score card calculation model, obtain the total credit score of the target transaction object from the multiple scores corresponding to each evaluation indicator group in the target credit merchant score card, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0009] Furthermore, it also includes:

[0010] Receive characteristic variable data of a target characteristic variable, wherein the characteristic variable data is used to store corresponding relationships between characteristic variable parameters, characteristic variable logical relationships, scores, and characteristic variable contents of the target characteristic variable;

[0011] According to the type of characteristic variable parameters in the target characteristic variable, an evaluation index group corresponding to the target characteristic variable is generated, wherein the evaluation index group includes multiple evaluation indicators with the same name, different codes, different characteristic variable parameters, different scores and characteristic variable contents;

[0012] The generated evaluation index group is stored in the blockchain.

[0013] Furthermore, it also includes:

[0014] receiving a credit merchant score card generation instruction, wherein the credit merchant score card generation instruction includes names of multiple evaluation indicators;

[0015] Extracting corresponding evaluation indicators from the blockchain according to the names of the evaluation indicators in the credit score card generation instruction, and generating corresponding credit score cards according to the extracted evaluation indicators;

[0016] The generated credit merchant sub-card and its corresponding characteristic data are stored in the blockchain.

[0017] Furthermore, the step of selecting one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain includes:

[0018] receiving a transaction risk identification request for a target transaction object in a supply chain, wherein the transaction risk identification request includes characteristic data of the target transaction object;

[0019] According to the characteristic data of the target transaction object, a credit merchant sub-card corresponding to the characteristic data of the target transaction object is searched in the blockchain, and the credit merchant sub-card is determined as the target credit merchant sub-card.

[0020] Furthermore, the step of obtaining a credit score card calculation model corresponding to the target credit score card includes:

[0021] Receiving a credit score card calculation model, wherein the credit score card calculation model includes: a decision-making judgment method, and a corresponding relationship between a judgment upper limit score and a judgment lower limit score;

[0022] The decision-making and discrimination methods include: manual discrimination, automatic pass discrimination and automatic rejection discrimination.

[0023] Furthermore, the calculation model based on the credit score card is used to obtain the total credit score of the target transaction object from the multiple scores corresponding to the evaluation indicators in the target credit score card, so as to determine the risk identification result of the target transaction object according to the total credit score, including:

[0024] Acquiring characteristic data of the target transaction object, and selecting an evaluation indicator from each evaluation indicator group in the target credit merchant score card according to the characteristic data, and determining the score of the evaluation indicator as the target score of the corresponding evaluation indicator group;

[0025] Adding up the target scores of the evaluation index groups in the target credit score card to obtain the total credit score of the target transaction object;

[0026] Determining a correspondence between the target transaction object's total credit score and the upper and lower discrimination scores based on the upper and lower discrimination scores corresponding to the credit merchant score card calculation model;

[0027] Based on the correspondence between the total reputation score of the target transaction object and the upper and lower limit scores, the decision-making and discrimination methods of the target transaction object are determined, and the risk identification result of the target transaction object is determined according to the decision-making and discrimination methods of the target transaction object.

[0028] Furthermore, the determining of a decision-making method for the target transaction object based on the correspondence between the total reputation score of the target transaction object and the upper and lower limit scores, and determining a risk identification result of the target transaction object according to the decision-making method of the target transaction object, includes:

[0029] If the total reputation score of the target transaction object is less than the lower limit score, the decision-making judgment method of the target transaction object is determined to be the automatic rejection judgment, and the risk identification result of the target transaction object is determined to be a risk identification result of rejecting the transaction;

[0030] If the total reputation score of the target transaction object is greater than the upper limit score, the decision discrimination mode of the target transaction object is determined to be the automatic pass discrimination, and the risk identification result of the target transaction object is determined to be a risk identification result of transaction pass;

[0031] If the total credit score of the target transaction object is within the numerical range formed by the lower limit score and the upper limit score, the decision-making judgment method of the target transaction object is determined to be the manual judgment, the risk identification result of the target transaction object is determined to be the manually verified risk identification result, and the manually verified risk identification result is output.

[0032] In a second aspect, the present application provides a supply chain transaction risk identification device, comprising:

[0033] A data selection module is configured to select one of the credit merchant sub-cards pre-stored in the blockchain as a target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein each credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group;

[0034] A risk identification module is used to obtain a credit score calculation model corresponding to the target credit score, and based on the credit score calculation model, obtain the total credit score of the target transaction object from multiple scores corresponding to each evaluation indicator group in the target credit score, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0035] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the supply chain transaction risk identification method when executing the program.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the supply chain transaction risk identification method when executed by a processor.

[0037] It can be seen from the above technical solution that the present application provides a supply chain transaction risk identification method and device, the method comprising: selecting one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the credit merchant sub-card contains multiple scores corresponding to each evaluation index group; obtaining the credit merchant sub-card calculation model corresponding to the target credit merchant sub-card, and obtaining the total credit score of the target transaction object from the multiple scores corresponding to each evaluation index group in the target credit merchant sub-card based on the credit merchant sub-card calculation model, so as to determine the risk identification result of the target transaction object according to the total credit score; by setting a credit merchant sub-card composed of multiple scores corresponding to multiple evaluation index groups, it is possible to identify the risk of the target transaction object for the supply chain. It provides a large amount of effective identification basis for risk identification, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, thereby enabling targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, thereby effectively improving the accuracy of supply chain transaction risk identification, and effectively improving the reliability and security of supply chain transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 It is a schematic diagram of the relationship between the supply chain transaction risk identification device and the client device in the embodiment of the present application.

[0040] Figure 2 This is a first flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0041] Figure 3 This is a second flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0042] Figure 4 This is a third flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0043] Figure 5 This is a fourth flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0044] Figure 6 This is a fifth flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0045] Figure 7 This is a sixth flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0046] Figure 8 This is the seventh flow chart of the supply chain transaction risk identification method in the embodiment of the present application.

[0047] Figure 9 It is a structural diagram of the supply chain transaction risk identification device in an embodiment of the present application.

[0048] Figure 10 It is a logical diagram of the supply chain transaction risk identification method provided by the application example of this application.

[0049] Figure 11 This is a flow chart of the credit merchant card definition provided by the application example of this application.

[0050] Figure 12 This is an example diagram of the credit merchant card configuration page provided in the application example of this application.

[0051] Figure 13 This is an example diagram of the feature variable configuration page provided by the application example of this application.

[0052] Figure 14 This is an application diagram of the credit merchant card calculation model provided in the application example of this application.

[0053] Figure 15 This is a diagram of the credit score card calculation model configuration page and score range example provided in the application example of this application.

[0054] Figure 16 This is an example diagram of the internal application calculation credit merchant card architecture provided by the application example of this application.

[0055] Figure 17 It is a structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0056] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] It should be noted that the supply chain transaction risk identification method and device disclosed in this application can be used in the field of blockchain technology, and can also be used in any field other than the field of blockchain technology. The application field of the supply chain transaction risk identification method and device disclosed in this application is not limited.

[0058] In view of the fact that the existing supply chain transaction risk identification method has few identification basis, no systematic reputation evaluation system for supply chain transaction objects, and is limited to human subjective consciousness, it has the problems of poor comprehensiveness and effectiveness of supply chain transaction risk identification, difficulty in achieving targeted and accurate identification of supply chain transaction objects, low efficiency, and insufficient accuracy of supply chain transaction risk identification, the embodiment of the present application provides a supply chain transaction risk identification method, which selects one of the various reputation merchant sub-cards pre-stored in the blockchain as the target reputation merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the reputation merchant sub-card contains multiple scores corresponding to each evaluation index group; obtains the reputation merchant sub-card calculation model corresponding to the target reputation merchant sub-card, and obtains the total reputation score of the target transaction object from the multiple scores corresponding to each evaluation index group in the target reputation merchant sub-card based on the reputation merchant sub-card calculation model, so as to determine the total reputation score according to the total reputation score. Determine the risk identification result of the target transaction object; by setting a credit merchant score card composed of multiple scores corresponding to multiple evaluation indicator groups, it can provide a large amount of effective identification basis for the risk identification of supply chain transaction objects, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, it can form a systematic credit evaluation system for supply chain transaction objects, thereby achieving targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, thereby effectively improving the accuracy of supply chain transaction risk identification, and effectively improving the reliability and security of supply chain transactions.

[0059] Based on the above content, the present application also provides a supply chain transaction risk identification device for implementing the supply chain transaction risk identification method provided in one or more embodiments of the present application. The supply chain transaction risk identification device can be a server, see Figure 1 The supply chain transaction risk identification device can communicate and connect with each client device in sequence by itself or through a third-party server, etc. The supply chain transaction risk identification device can receive the supply chain transaction risk identification instruction sent by the client device, and select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain according to the supply chain transaction risk identification instruction, wherein the credit merchant sub-cards each contain multiple scores corresponding to each evaluation indicator group; obtain the credit merchant sub-card calculation model corresponding to the target credit merchant sub-card, and obtain the total credit score of the target transaction object from the multiple scores corresponding to each evaluation indicator group in the target credit merchant sub-card based on the credit merchant sub-card calculation model, so as to determine the risk identification result of the target transaction object according to the total credit score, and then send the risk identification result to the user's client device, etc.

[0060] In another practical application scenario, the supply chain transaction risk identification portion of the aforementioned supply chain transaction risk identification device may be executed on a server as described above, or all operations may be performed on the user-end device. The specific selection may depend on the processing capabilities of the user-end device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are performed on the user-end device, the user-end device may also include a processor for the specific processing of supply chain transaction risk identification.

[0061] It is understood that the mobile terminal may include any mobile device capable of loading an application, such as a smart phone, a tablet electronic device, a network set-top box, a portable computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. The smart wearable device may include smart glasses, smart watches, smart bracelets, etc.

[0062] The mobile terminal may include a communication module (i.e., a communication unit) that can establish a communication connection with a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0063] The server and the mobile terminal may communicate using any suitable network protocol, including network protocols that have not yet been developed as of the filing date of this application. Examples of such network protocols include TCP / IP, UDP / IP, HTTP, and HTTPS. Furthermore, examples of such network protocols include RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer) protocols, which are used on top of the aforementioned protocols.

[0064] In one or more embodiments of the present application, the credit score card refers to a model or method for defining the creditworthiness and availability of a transaction object by calculating scores based on various indicator dimensions.

[0065] The details are described in detail through the following embodiments and application examples.

[0066] In order to solve the problems of existing supply chain transaction risk identification methods, such as lack of identification basis, lack of a systematic reputation evaluation system for supply chain transaction objects, and limitation to human subjective consciousness, which result in poor comprehensiveness and effectiveness of supply chain transaction risk identification, difficulty in achieving targeted and accurate identification of supply chain transaction objects, low efficiency, and insufficient accuracy of supply chain transaction risk identification, this application provides an embodiment of a supply chain transaction risk identification method, see Figure 2 The supply chain transaction risk identification method executed by the supply chain transaction risk identification device specifically includes the following contents:

[0067] Step 100: Select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group.

[0068] It is understandable that the credit score card for supply chain transaction objects based on blockchain technology fully utilizes the characteristics of blockchain technology and combines it with the development requirements of the new generation of Internet technology. It uses blockchain technology to obtain and form judgment and evaluation indicators, match calculation models of credit score cards, and internal management applications of supply chain transaction objects, and realize the implementation and recording of credit score cards for supply chain transaction objects on the blockchain.

[0069] Step 200: Obtain a credit merchant score card calculation model corresponding to the target credit merchant score card, and based on the credit merchant score card calculation model, obtain the total credit score of the target transaction object from multiple scores corresponding to each evaluation indicator group in the target credit merchant score card, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0070] It's understandable that blockchain technology can automatically identify and identify each supply chain transaction partner, match them to a credit score card model, and securely analyze and identify supply chain transaction partners, match them to a credit score card calculation model, and calculate credit score card scores. Furthermore, the identification of each indicator, the defined credit score card, the configured calculation model, and each credit score card calculated through a decision-making strategy provide business personnel with convenient methods and devices for auditing, querying, and summarizing.

[0071] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application, by setting a credit merchant score card composed of multiple scores corresponding to multiple evaluation indicator groups, can provide a large number of effective identification bases for the risk identification of supply chain transaction objects, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, thereby enabling targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, thereby effectively improving the accuracy of supply chain transaction risk identification, and effectively improving the reliability and security of supply chain transactions.

[0072] In order to further improve the comprehensiveness and effectiveness of the supply chain transaction risk identification process, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 3 The supply chain transaction risk identification method may specifically include the following content before step 100 or after any step:

[0073] Step 010: Receive characteristic variable data of a target characteristic variable, wherein the characteristic variable data is used to store the corresponding relationship between each characteristic variable parameter, characteristic variable logical relationship, score and characteristic variable content of the target characteristic variable.

[0074] Step 020: Based on the type of characteristic variable parameters in the target characteristic variable, generate an evaluation index group corresponding to the target characteristic variable, wherein the evaluation index group includes multiple evaluation indicators with the same name, different codes, different characteristic variable parameters, different scores and characteristic variable contents.

[0075] Step 030: Store the generated evaluation indicator group in the blockchain.

[0076] Specifically, the evaluation index is defined by the configured characteristic variables, which mainly include four elements, namely, characteristic variable parameters, characteristic variable logical relationships, characteristic variable content and characteristic variable scores.

[0077] Evaluation indicators are comprehensively defined based on the characteristics of the transaction industry and the company. The selection of evaluation indicators is primarily based on experience and is determined by the supply chain operator based on the industry, current policies, company characteristics, and the services provided. No screening rules will be set during initial operations, and decisions will be made based on research.

[0078] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application can further improve the comprehensiveness and effectiveness of the supply chain transaction risk identification process by pre-setting evaluation indicators based on the characteristic variable data used to store the corresponding relationship between each characteristic variable parameter, characteristic variable logical relationship, score and characteristic variable content of the target characteristic variable, and storing the evaluation indicators in the blockchain, and can further ensure the reliability and non-tamperability of the risk identification results, and can improve the efficiency and intelligence of the supply chain transaction risk identification process.

[0079] In order to improve the non-tamperability of credit merchant card applications, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 4 The supply chain transaction risk identification method may specifically include the following content before step 100 or after any step:

[0080] Step 040: Receive a credit merchant card generation instruction, wherein the credit merchant card generation instruction includes the names of multiple evaluation indicators.

[0081] Step 050: According to the names of the evaluation indicators in the credit score card generation instruction, the corresponding evaluation indicators are extracted from the blockchain, and the corresponding credit score card is generated according to the extracted evaluation indicators.

[0082] Step 060: Store the generated credit merchant sub-card and its corresponding feature data in the blockchain.

[0083] Specifically, define a credit score card A, obtain evaluation indicators from the blockchain, and add evaluation indicators 1, 2, ..., and N to credit score card A to complete the definition of credit score card A. The completed evaluation indicators can be stored on the blockchain to ensure that the indicators cannot be tampered with and that the existing credit score card definition is irreversible.

[0084] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application, by extracting the corresponding evaluation indicators in the blockchain in advance according to the names of each evaluation indicator in the credit merchant sub-card generation instruction, generating corresponding credit merchant sub-cards according to each of the extracted evaluation indicators, and storing the generated credit merchant sub-cards and their corresponding feature data in the blockchain, can effectively improve the non-tamperability of the credit merchant sub-card application, and can effectively improve the efficiency and reliability of retrieving the target credit merchant sub-card corresponding to the target transaction object, and further, by storing multiple credit merchant sub-cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, and thus targeted and accurate identification of supply chain transaction objects can be achieved, and the reliability and non-tamperability of the risk identification results can be effectively guaranteed, and the efficiency and intelligence of the supply chain transaction risk identification process can be improved.

[0085] In order to improve the efficiency and reliability of retrieving the target credit merchant sub-card corresponding to the target transaction object, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 5 Step 100 of the supply chain transaction risk identification method specifically includes the following:

[0086] Step 110: Receive a transaction risk identification request for a target transaction object in the supply chain, wherein the transaction risk identification request includes feature data of the target transaction object.

[0087] Step 120: Based on the characteristic data of the target transaction object, search the blockchain for a credit merchant sub-card corresponding to the characteristic data of the target transaction object, and determine the credit merchant sub-card as the target credit merchant sub-card.

[0088] Specifically, a credit score card formed on the blockchain is selected for supply chain transaction objects.

[0089] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application can effectively improve the efficiency and reliability of retrieving the target credit merchant sub-card corresponding to the target transaction object by searching for the credit merchant sub-card corresponding to the characteristic data of the target transaction object in the blockchain based on the characteristic data of the target transaction object, thereby further improving the efficiency of the supply chain transaction risk identification process.

[0090] In order to improve the automation level of the supply chain transaction risk identification process, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 6 Step 200 of the supply chain transaction risk identification method specifically includes the following:

[0091] Step 210: Receive a credit card scoring calculation model, wherein the credit card scoring calculation model includes: a decision-making method, and a corresponding relationship between an upper limit score and a lower limit score; the decision-making method includes: manual judgment, automatic pass judgment, and automatic rejection judgment.

[0092] Step 220: Obtain the total credit score of the target transaction object from the multiple scores corresponding to the evaluation indicators in the target credit score card based on the credit score calculation model, and determine the risk identification result of the target transaction object according to the total credit score.

[0093] It is understandable that for each supply chain transaction object, according to its characteristics, the corresponding credit merchant score card is selected, and combined with the construction of the internal management application system, the decision-making method (manual judgment, automatic pass judgment, automatic rejection judgment, etc.), the basis for decision-making judgment, the upper and lower limits of the score value judgment range, etc. are determined.

[0094] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application can effectively improve the degree of automation of the supply chain transaction risk identification process by calculating the credit card score based on the user input specifically for the target transaction object. It does not need to rely entirely on human subjective consciousness, and can therefore effectively improve the accuracy of supply chain transaction risk identification and effectively improve the reliability and security of supply chain transactions.

[0095] In order to further improve the automation level of the supply chain transaction risk identification process, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 7 Step 220 of the supply chain transaction risk identification method specifically includes the following:

[0096] Step 221: Acquire characteristic data of the target transaction object, and select an evaluation indicator from each evaluation indicator group in the target credit merchant score card according to the characteristic data, and determine the score of the evaluation indicator as the target score of the corresponding evaluation indicator group.

[0097] Step 222: summing up the target scores of the evaluation index groups in the target credit score card to obtain the total credit score of the target transaction object.

[0098] Step 223: Determine the corresponding relationship between the total credit score of the target transaction object and the upper and lower limit scores according to the upper and lower limit scores corresponding to the credit merchant score card calculation model.

[0099] Step 224: Determine a decision-making method for the target transaction object based on the correspondence between the total reputation score of the target transaction object and the upper and lower limit scores, and determine a risk identification result for the target transaction object based on the decision-making method of the target transaction object.

[0100] It's understandable that when calculating the decision, the upper limit (smax) and lower limit (smin) of the score are configured, and the score calculated by the credit score card of the supply chain transaction object is matched according to the set score range. The activation limit within the corresponding range of {smin}≤{score}<{smax} is the decision result for that object.

[0101] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application can further improve the degree of automation of the supply chain transaction risk identification process, and does not need to rely entirely on human subjective consciousness. Therefore, it can effectively improve the accuracy of supply chain transaction risk identification and can effectively improve the reliability and security of supply chain transactions.

[0102] In order to achieve the automatic processing of supply chain transaction risk identification results to the greatest extent possible, an embodiment of the supply chain transaction risk identification method provided in this application is provided. Figure 8 Step 224 of the supply chain transaction risk identification method specifically includes the following:

[0103] Step 2241: If the total reputation score of the target transaction object is less than the lower threshold score, the decision-making judgment method for the target transaction object is determined to be the automatic rejection judgment, and the risk identification result of the target transaction object is determined to be a risk identification result of rejecting the transaction;

[0104] Step 2242: If the total reputation score of the target transaction object is greater than the upper limit score, the decision discrimination method for the target transaction object is determined to be the automatic pass discrimination, and the risk identification result of the target transaction object is determined to be a transaction pass risk identification result;

[0105] Step 2243: If the total reputation score of the target transaction object is within the numerical range formed by the lower limit score and the upper limit score, then the decision-making judgment method of the target transaction object is determined to be the manual judgment, and the risk identification result of the target transaction object is determined to be a manually verified risk identification result, and the manually verified risk identification result is output.

[0106] From the above description, it can be seen that the supply chain transaction risk identification method provided in the embodiment of the present application can effectively improve the correspondence between the total reputation score of the target transaction object and the upper limit score and the lower limit score, determine the pertinence and applicability of the decision-making and judgment method of the target transaction object, and can realize the automatic processing of the supply chain transaction risk identification results to the maximum extent. For the results that need manual verification, they can be output to the client device of the verifier in a timely manner, thereby further improving the efficiency of supply chain transaction risk identification.

[0107] From the software level, in order to solve the problems of existing supply chain transaction risk identification methods, such as lack of identification basis, lack of a systematic reputation evaluation system for supply chain transaction objects, and limitation to human subjective consciousness, which result in poor comprehensiveness and effectiveness of supply chain transaction risk identification, difficulty in achieving targeted and accurate identification of supply chain transaction objects, low efficiency, and insufficient accuracy of supply chain transaction risk identification, this application provides an embodiment of a supply chain transaction risk identification device for executing all or part of the content of the supply chain transaction risk identification method, see Figure 9 The supply chain transaction risk identification device specifically includes the following contents:

[0108] The data selection module 10 is used to select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group.

[0109] The risk identification module 20 is used to obtain the credit merchant score card calculation model corresponding to the target credit merchant score card, and based on the credit merchant score card calculation model, obtain the total credit score of the target transaction object from the multiple scores corresponding to each evaluation indicator group in the target credit merchant score card, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0110] The embodiment of the supply chain transaction risk identification device provided in this application can be specifically used to execute the processing flow of the embodiment of the supply chain transaction risk identification method in the above embodiment. Its functions will not be repeated here, and reference can be made to the detailed description of the above method embodiment.

[0111] From the above description, it can be seen that the supply chain transaction risk identification device provided in the embodiment of the present application can provide a large number of effective identification bases for the risk identification of supply chain transaction objects by setting a credit merchant score card composed of multiple scores corresponding to multiple evaluation indicator groups, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, thereby enabling targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, thereby effectively improving the accuracy of supply chain transaction risk identification, and effectively improving the reliability and security of supply chain transactions.

[0112] In order to further illustrate this solution, the application example of this application provides a supply chain transaction risk identification method. The application example of this application overcomes the current problems of transaction objects participating in supply chain behavior, which are mainly based on offline manual judgment, incomplete identification indicators, easy to be manipulated by humans, and the lack of a credit card model. This leads to inaccurate identification of transaction objects, easy to be subjective, unfair calculated credit card scores, and low data security.

[0113] The application example of this application provides a method and device that can automatically identify and distinguish indicators and match credit card models when each supply chain transaction object joins through blockchain technology, and use blockchain technology to safely analyze and distinguish supply chain transaction objects, match credit card calculation models, and calculate credit card scores. The identification of each indicator, the defined credit card, the configured calculation model, and the credit card score results calculated by the decision strategy each time can provide business personnel with convenient auditing, querying, and summarizing methods and devices. The specific instructions are as follows:

[0114] See also Figure 10 The application example of this application provides a supply chain transaction risk identification method, a credit score card for supply chain transaction objects based on blockchain technology, which fully utilizes the characteristics of blockchain technology and combines the development requirements of the new generation of Internet technology. In terms of the acquisition and formation of discrimination evaluation indicators, the matching of calculation models of credit score cards, and the internal management application of supply chain transaction objects, blockchain technology is used to implement and record the credit score cards of supply chain transaction objects on the blockchain.

[0115] Configure the characteristic variables of the evaluation indicators, define the multi-dimensional evaluation indicators of the credit merchant score card, and form a credit merchant score card that realizes multi-dimensional discrimination of supply chain transaction objects.

[0116] See also Figure 11 , define a credit score card-A, obtain evaluation indicators from the blockchain, add evaluation indicators-1, evaluation indicators-2, ..., evaluation indicators-N to credit score card-A, and complete the definition of credit score card-A. For example, the credit score card configuration page Figure 12 The completed evaluation indicators can be stored in the blockchain to ensure that the indicators cannot be tampered with and the existing business score card definitions are irreversible.

[0117] The evaluation index is defined by the configured feature variables. Feature variables mainly include four elements, namely feature variable parameters, feature variable logical relationships, feature variable content and feature variable scores. For example, the feature variable configuration page Figure 13 shown.

[0118] For supply chain transaction objects, a credit score card formed on the blockchain is selected, and a credit score card calculation model for auxiliary decision-making is built for it. The configured calculation model is saved in the blockchain. The application diagram of the credit score card calculation model is as follows: Figure 14 shown.

[0119] For each supply chain transaction object, according to its characteristics, select the corresponding credit score card, and combine the construction of the internal management application system to determine the decision-making method (manual judgment, automatic approval judgment, automatic rejection judgment, etc.), the basis for decision-making judgment, and the upper and lower limits of the score value range. Figure 15 As shown. When calculating the decision, configure the upper limit (smax) and lower limit (smin) of the score. The score calculated by the credit score card of the supply chain transaction object is matched according to the set score range. The activation limit within the corresponding range of {smin}≤{score}<{smax} is the decision result for the object.

[0120] In the internal management application link of supply chain financial products, the credit card of the transaction object and the credit card calculation model are downloaded in real time, and then the credit card logic calculation interface of the blockchain is triggered to complete the calculation of the credit card score. The calculated credit card score is uploaded in real time and saved in the blockchain. Specifically, the internal application calculation credit card architecture is as follows: Figure 16 shown.

[0121] The supply chain transaction risk identification method provided by the application example of this application is a credit score card for supply chain transaction objects based on blockchain technology. It can make full use of the characteristics of blockchain technology and combine with the development requirements of the new generation of Internet technology. In terms of the acquisition and formation of discrimination and evaluation indicators, the matching of calculation models of credit score cards, and the internal management application of supply chain transaction objects, it uses blockchain technology to create a discrimination and evaluation indicator system for supply chain transaction objects on the blockchain, establish a multi-dimensional credit score card calculation model, and construct a secure, traceable, and tamper-proof credit score card, so that the acquired evaluation indicators, the formed credit score card, and the score results of the transaction objects calculated according to the credit score card are all implemented, recorded, stored, and queried on the blockchain.

[0122] 1. The traceability, immutability and security of blockchain technology provide more accurate and objective judgment rules for all parties involved in supply chain transactions.

[0123] 2. By applying blockchain technology, we can obtain traceable multi-dimensional identification indicators, form transparent and open identification indicators for supply chain transaction objects, and improve the identification indicator system.

[0124] 3. By analyzing the characteristics of transaction objects involved in supply chain transactions, we can improve the identification method, establish an unalterable credit card model, realize real-time identification of supply chain transaction objects, realize real-time calculation of the unalterable credit card scores of transaction objects, and realize real-time, indiscriminate, traceable and secure identification.

[0125] 4. This will further promote the internal management mechanism of supply chain transaction objects, reduce the risk of human subjective consciousness in judgment, and help build a risk safe haven for supply chain transactions.

[0126] From a hardware perspective, in order to address the problems of existing supply chain transaction risk identification methods, such as lack of identification basis, lack of a systematic reputation evaluation system for supply chain transaction objects, and limitation to human subjective consciousness, resulting in poor comprehensiveness and effectiveness of supply chain transaction risk identification, difficulty in achieving targeted and accurate identification of supply chain transaction objects, low efficiency, and insufficient accuracy in supply chain transaction risk identification, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the supply chain transaction risk identification method, and the electronic device specifically includes the following contents:

[0127] Figure 17 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 17 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 17is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0128] In one embodiment, the supply chain transaction risk identification function may be integrated into a central processing unit. The central processing unit may be configured to perform the following controls:

[0129] Step 100: Select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group.

[0130] Step 200: Obtain a credit merchant score card calculation model corresponding to the target credit merchant score card, and based on the credit merchant score card calculation model, obtain the total credit score of the target transaction object from multiple scores corresponding to each evaluation indicator group in the target credit merchant score card, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0131] From the above description, it can be seen that the electronic device provided in the embodiment of the present application can provide a large number of effective identification bases for the risk identification of supply chain transaction objects by setting a credit merchant score card composed of multiple scores corresponding to multiple evaluation indicator groups, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, thereby enabling targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, and therefore can effectively improve the accuracy of supply chain transaction risk identification, and can effectively improve the reliability and security of supply chain transactions.

[0132] In another embodiment, the supply chain transaction risk identification device can be configured separately from the central processor 9100. For example, the supply chain transaction risk identification device can be configured as a chip connected to the central processor 9100, and the supply chain transaction risk identification function can be realized through the control of the central processor.

[0133] like Figure 17 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 17In addition, the electronic device 9600 may also include all components shown in Figure 17 For components not shown, reference may be made to the prior art.

[0134] like Figure 17 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0135] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0136] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0137] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0138] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0139] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in a conventional mobile communication terminal.

[0140] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0141] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the supply chain transaction risk identification method in the above-mentioned embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the computer program implements all steps of the supply chain transaction risk identification method in the above-mentioned embodiments, where the execution subject is a server or a client. For example, when the processor executes the computer program, the following steps are implemented:

[0142] Step 100: Select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain, wherein the credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group.

[0143] Step 200: Obtain a credit merchant score card calculation model corresponding to the target credit merchant score card, and based on the credit merchant score card calculation model, obtain the total credit score of the target transaction object from multiple scores corresponding to each evaluation indicator group in the target credit merchant score card, so as to determine the risk identification result of the target transaction object according to the total credit score.

[0144] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application can provide a large number of effective identification bases for the risk identification of supply chain transaction objects by setting a credit merchant score card composed of multiple scores corresponding to multiple evaluation indicator groups, thereby effectively improving the comprehensiveness and effectiveness of the supply chain transaction risk identification process; by storing multiple credit merchant score cards in the blockchain, a systematic credit evaluation system for supply chain transaction objects can be formed, thereby enabling targeted and accurate identification of supply chain transaction objects, and effectively ensuring the reliability and non-tamperability of risk identification results, and improving the efficiency and intelligence of the supply chain transaction risk identification process; through the combined application of the credit merchant score card calculation model and the target credit merchant score card, the degree of automation of the supply chain transaction risk identification process can be effectively improved, without relying entirely on human subjective consciousness, thereby effectively improving the accuracy of supply chain transaction risk identification, and effectively improving the reliability and security of supply chain transactions.

[0145] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A supply chain transaction risk identification method, characterized in that: include: From among the credit score cards pre-stored in the blockchain, one is selected as the target credit score card corresponding to the current target transaction partner of the supply chain, wherein each credit score card contains multiple scores corresponding to each evaluation indicator group; the credit score card is used to define the creditworthiness and availability of the transaction partner; Obtaining a credit score calculation model corresponding to the target credit score, and obtaining a total credit score of the target transaction object from a plurality of scores corresponding to each evaluation indicator group in the target credit score based on the credit score calculation model, so as to determine a risk identification result of the target transaction object based on the total credit score; The obtaining of the credit score card calculation model corresponding to the target credit score card includes: Receiving a credit score card calculation model, wherein the credit score card calculation model includes: a decision-making judgment method, and a corresponding relationship between a judgment upper limit score and a judgment lower limit score; The decision-making and discrimination methods include: manual discrimination, automatic pass discrimination and automatic rejection discrimination; The step of obtaining the total credit score of the target transaction object from the multiple scores corresponding to the respective evaluation indicators in the target credit score card based on the credit score card calculation model, and determining the risk identification result of the target transaction object according to the total credit score, includes: Acquiring characteristic data of the target transaction object, and selecting an evaluation indicator from each evaluation indicator group in the target credit merchant score card according to the characteristic data, and determining the score of the evaluation indicator as the target score of the corresponding evaluation indicator group; Adding up the target scores of the evaluation index groups in the target credit score card to obtain the total credit score of the target transaction object; Determining a correspondence between the target transaction object's total credit score and the upper and lower discrimination scores based on the upper and lower discrimination scores corresponding to the credit merchant score card calculation model; Determining a decision-making and discrimination method for the target transaction object based on a correspondence between the target transaction object's total reputation score and the discrimination upper and lower score limits, and determining a risk identification result for the target transaction object based on the decision-making and discrimination method for the target transaction object; The determining of a decision-making and discrimination method for the target transaction object based on the correspondence between the total reputation score of the target transaction object and the discrimination upper limit score and the discrimination lower limit score, and determining a risk identification result of the target transaction object according to the decision-making and discrimination method of the target transaction object includes: If the total reputation score of the target transaction object is less than the lower limit score, the decision-making judgment method of the target transaction object is determined to be the automatic rejection judgment, and the risk identification result of the target transaction object is determined to be a risk identification result of rejecting the transaction; If the total reputation score of the target transaction object is greater than the upper limit score, the decision discrimination mode of the target transaction object is determined to be the automatic pass discrimination, and the risk identification result of the target transaction object is determined to be a risk identification result of transaction pass; If the total credit score of the target transaction object is within the numerical range formed by the lower limit score and the upper limit score, the decision-making judgment method of the target transaction object is determined to be the manual judgment, the risk identification result of the target transaction object is determined to be the manually verified risk identification result, and the manually verified risk identification result is output.

2. The supply chain transaction risk identification method according to claim 1, characterized in that: Also includes: Receive characteristic variable data of a target characteristic variable, wherein the characteristic variable data is used to store corresponding relationships between characteristic variable parameters, characteristic variable logical relationships, scores, and characteristic variable contents of the target characteristic variable; According to the type of characteristic variable parameters in the target characteristic variable, an evaluation index group corresponding to the target characteristic variable is generated, wherein the evaluation index group includes multiple evaluation indicators with the same name, different codes, different characteristic variable parameters, different scores and characteristic variable contents; The generated evaluation index group is stored in the blockchain.

3. The supply chain transaction risk identification method according to claim 2, characterized in that: Also includes: receiving a credit merchant score card generation instruction, wherein the credit merchant score card generation instruction includes names of multiple evaluation indicators; Extracting corresponding evaluation indicators from the blockchain according to the names of the evaluation indicators in the credit score card generation instruction, and generating corresponding credit score cards according to the extracted evaluation indicators; The generated credit merchant sub-card and its corresponding characteristic data are stored in the blockchain.

4. The supply chain transaction risk identification method according to claim 3 is characterized in that: The step of selecting one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction object of the supply chain includes: receiving a transaction risk identification request for a target transaction object in a supply chain, wherein the transaction risk identification request includes characteristic data of the target transaction object; According to the characteristic data of the target transaction object, a credit merchant sub-card corresponding to the characteristic data of the target transaction object is searched in the blockchain, and the credit merchant sub-card is determined as the target credit merchant sub-card.

5. A supply chain transaction risk identification device, characterized in that: include: A data selection module is configured to select one of the credit merchant sub-cards pre-stored in the blockchain as the target credit merchant sub-card corresponding to the current target transaction partner of the supply chain, wherein each credit merchant sub-card contains multiple scores corresponding to each evaluation indicator group; the credit merchant sub-card is used to define the creditworthiness and availability of the transaction partner; a risk identification module configured to obtain a credit score calculation model corresponding to the target credit score calculation model, obtain a total credit score of the target transaction object from a plurality of scores corresponding to each evaluation indicator group in the target credit score calculation model, and determine a risk identification result for the target transaction object based on the total credit score; The risk identification module is specifically used to: Receiving a credit score card calculation model, wherein the credit score card calculation model includes: a decision-making judgment method, and a corresponding relationship between a judgment upper limit score and a judgment lower limit score; The decision-making and discrimination methods include: manual discrimination, automatic pass discrimination and automatic rejection discrimination; The risk identification module is further specifically used to: Acquiring characteristic data of the target transaction object, and selecting an evaluation indicator from each evaluation indicator group in the target credit merchant score card according to the characteristic data, and determining the score of the evaluation indicator as the target score of the corresponding evaluation indicator group; Adding up the target scores of the evaluation index groups in the target credit score card to obtain the total credit score of the target transaction object; Determining a correspondence between the target transaction object's total credit score and the upper and lower discrimination scores based on the upper and lower discrimination scores corresponding to the credit merchant score card calculation model; Determining a decision-making and discrimination method for the target transaction object based on a correspondence between the target transaction object's total reputation score and the discrimination upper and lower score limits, and determining a risk identification result for the target transaction object based on the decision-making and discrimination method for the target transaction object; The determining of a decision-making and discrimination method for the target transaction object based on the correspondence between the total reputation score of the target transaction object and the discrimination upper limit score and the discrimination lower limit score, and determining a risk identification result of the target transaction object according to the decision-making and discrimination method of the target transaction object includes: If the total reputation score of the target transaction object is less than the lower limit score, the decision-making judgment method of the target transaction object is determined to be the automatic rejection judgment, and the risk identification result of the target transaction object is determined to be a risk identification result of rejecting the transaction; If the total reputation score of the target transaction object is greater than the upper limit score, the decision discrimination mode of the target transaction object is determined to be the automatic pass discrimination, and the risk identification result of the target transaction object is determined to be a risk identification result of transaction pass; If the total credit score of the target transaction object is within the numerical range formed by the lower limit score and the upper limit score, the decision-making judgment method of the target transaction object is determined to be the manual judgment, the risk identification result of the target transaction object is determined to be the manually verified risk identification result, and the manually verified risk identification result is output.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the supply chain transaction risk identification method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the supply chain transaction risk identification method according to any one of claims 1 to 4 is implemented.

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