CN114119174A - Credit score generation method and device based on block chain - Google Patents
Credit score generation method and device based on block chain Download PDFInfo
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Abstract
The application provides a credit score generation method and device based on a block chain, which comprises the steps of obtaining and storing target data, and determining the data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type; determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model; determining credit scores of the target data according to a scoring formula corresponding to each data type contained in the target data; and determining a comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data. And processing target data related to the supplier through an intelligent contract model in the block chain to obtain a comprehensive credit value of the supplier, and simultaneously saving the target data and the comprehensive credit value of the supplier so as to realize the non-tampering and traceability of the credit information of the supplier.
Description
Technical Field
The application relates to the technical field of electronic commerce, in particular to a credit score generation method and device based on a block chain.
Background
With the rapid development of economic and electronic information technologies, the management of supply chains plays an important role in the development and competition of enterprises. As a source of supply chain management and a terminal point of supply chain circulation feedback, a supplier plays an important role in the supply chain management process, influences the quality and the technology of products and restricts the development of the whole supply chain.
Existing database storage technologies can see the stored data in the background or database, and workers can intentionally or unintentionally tamper or reveal the data; in addition, in order to make the data traceable, a log record table is additionally added to record the process of modifying the key data, but the log record table itself may be tampered.
The prior art therefore has the following drawbacks:
1. data is easy to be illegally tampered or leaked;
2. data backup management of centralized storage is troublesome, and once a machine room suffers from natural disasters and personal accidents, data can be lost;
3. data tracing is difficult and opaque.
Disclosure of Invention
In view of the above, the present application is proposed to provide a block chain based credit score generation method and apparatus that overcomes or at least partially solves the above problems, comprising:
a credit score generation method based on a block chain is applied to the block chain to evaluate credit of a supplier to obtain a comprehensive credit value; wherein the method involves a server comprising an intelligent contract model; the method comprises the following steps:
acquiring and storing target data, and determining a data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model;
determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data;
and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
Optionally, the data types further comprise an auxiliary evaluation type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data; after the step of determining the credit score of the target data according to the scoring formula corresponding to each data type contained in the target data, the method further includes:
calling auxiliary evaluation type data in the target data;
and correcting the credit score of the target data according to the auxiliary evaluation type data.
Optionally, the step of determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type included in the target data includes:
according to the formula:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Credit score of contract type for said supplier, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
Optionally, the step of acquiring and storing the target data and determining the data type included in the target data includes:
acquiring the target data;
verifying the target data based on a preset consensus mechanism;
and if the verification is successful, encrypting and storing the target data, and determining the data type contained in the target data.
Optionally, after the step of determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type included in the target data, the method further includes:
verifying the comprehensive credit value based on a preset consensus mechanism;
and if the verification is successful, encrypting and storing the comprehensive credit value.
A credit score generation device based on a block chain is applied to the block chain to evaluate credit of a supplier to obtain a comprehensive credit value; wherein the apparatus relates to a server, the server comprising an intelligent contract model; the device comprises:
the acquisition module is used for acquiring and storing target data and determining the data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
the first determining module is used for determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model;
the second determining module is used for determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data;
and the third determining module is used for determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
Optionally, the data types further comprise an auxiliary evaluation type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data; the second determining module further comprises:
the calling module is used for calling auxiliary evaluation type data in the target data;
and the fourth determination module is used for correcting the credit score of the target data according to the auxiliary evaluation type data.
Optionally, the third determining module includes:
a calculation module for, according to a formula:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Credit score of contract type for said supplier, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
An electronic device comprising a processor, a memory and a computer program stored on the memory and being executable on the processor, the computer program, when executed by the processor, implementing the steps of the credit score generation method as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the credit score generation method as described above.
The application has the following advantages:
in the embodiment of the application, target data is obtained and stored, and the data type contained in the target data is determined; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type; determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model; determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data; and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data. And processing target data related to the supplier through an intelligent contract model in the block chain to obtain a comprehensive credit value of the supplier, and simultaneously saving the target data and the comprehensive credit value of the supplier so as to realize the non-tampering and traceability of the credit information of the supplier.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the present application will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a flowchart illustrating steps of a block chain-based credit score generation method according to an embodiment of the present application;
fig. 2 is a block diagram of a block chain-based credit score generation apparatus according to an embodiment of the present application;
fig. 3 is another block diagram illustrating a block chain-based credit score generation apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The concept of blockchain proposed in 2008 has received attention due to its characteristics of decentralization, data non-falsification and forgery, anonymity, traceability, and the like. A blockchain is a distributed database that records a number of transactions performed and shared among multiple nodes, where each transaction is verified by consensus among a large number of nodes in the system. The block chain of the intelligent contract is introduced, and the era of the block chain 2.0 is entered, and essentially, the intelligent contract is a new protocol or system running on the block chain and aims to expand the functions of the block chain. The smart contracts running on blockchain 2.0 contain business logic to interact with other contracts, make decisions (depending on the program without ambiguity), store data, send corresponding digital cryptocurrency, etc. The contract is defined by the creator but its execution and other services provided by the contract are dependent only on blockchain 2.0 itself, which can be executed as long as the chain maintained by blockchain 2.0 exists, and the contract disappears only if self-destruct code exists when the contract is programmed. The contracts themselves contain code and data stored at a particular address on the chain, and the corresponding contract is also an account presence that can pass messages between contract accounts, and can actually do a well-defined calculation. The execution of the contract code relies on the virtual machine designed in the blockchain 2.0, and each node in the blockchain network runs the virtual machine so as to run the contract code. Therefore, by using the blockchain 2.0 technology, the method has many advantages, such as that transactions are not interfered and do not depend on trust of a third party, the blockchain can ensure the integrity and the accuracy of data, the distributed peer-to-peer network has robustness, transaction execution has transactional performance, transaction is transparent and unchangeable, and accounts are unified.
Referring to fig. 1, a block chain-based credit score generation method provided in an embodiment of the present application is illustrated; the method is applied to block chains to carry out credit evaluation on suppliers to obtain a comprehensive credit value; wherein the method involves a server comprising an intelligent contract model;
the method comprises the following steps:
s110, acquiring and storing target data, and determining a data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
s120, determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model;
s130, determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data;
s140, determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
In the embodiment of the application, target data is obtained and stored, and the data type contained in the target data is determined; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type; determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model; determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data; and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data. And processing target data related to the supplier through an intelligent contract model in the block chain to obtain a comprehensive credit value of the supplier, and simultaneously saving the target data and the comprehensive credit value of the supplier so as to realize the non-tampering and traceability of the credit information of the supplier.
Next, a credit score generation method based on a block chain in the present exemplary embodiment will be further described.
As stated in step S110, obtaining and storing target data, and determining a data type included in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type.
In an embodiment of the present application, a specific process of "acquiring and saving the target data, and determining a data type included in the target data" in step S110 may be further described with reference to the following description.
It should be noted that the target data is originated from a multi-party system or a plurality of nodes, and is mainly divided into two dimensions: commodity marketing data and user satisfaction data. The commodity marketing data comprises order commodity circulation data, commodity purchasing data, order commodity logistics data, commodity warehouse occupation data and the like; the user satisfaction data comprises commodity after-sale service data, user complaint data, user evaluation data and the like.
It should be noted that the target data is usually key transaction data, such as after-sales data, order data, evaluation data, purchase data and logistics data; the method mainly comprises the steps of supplier contract type, supplier competitiveness type and commodity user experience type; of course, other types can be added according to actual needs. Wherein the supplier contract type comprises a supplier qualification violation condition, a supplier contract execution condition, a supplier cooperative support condition and a complaint condition; the supplier competitiveness type comprises an order completion rate, a logistics circulation rate, an order return rate, an after-sale processing efficiency, an inventory turnover rate, a commodity price advantage, a marketing activity participation rate and a commodity yield; the commodity user experience type comprises a user commodity quality good evaluation rate, a user commodity logistics good evaluation rate, a user commodity customer service good evaluation rate, a user return visit good evaluation rate and a user collection commodity rate.
Acquiring the target data as described in the following steps;
as an example, the server is a federation link server, and the federation link server obtains the target data from a certain underlying associated system by means of an asynchronous message or a reset interface.
Verifying the target data based on a preset consensus mechanism as described in the following steps;
it should be noted that trust of the blockchain is mainly embodied in that users distributed in the blockchain do not need to trust another party of the transaction, and do not need to trust a centralized mechanism, and only need to trust a software system under the blockchain protocol to realize the transaction. The prerequisite of this kind of trust is the Consensus mechanism (Consensus) of the blockchain, that is, in a mutually untrusted market, the necessary condition for each node to agree is that each node, considering its own interest maximization, will spontaneously and honestly obey the rules preset in the protocol, determine the authenticity of each record, and finally record the record determined to be authentic into the blockchain. In other words, if the nodes have independent interests and compete with each other, the nodes are almost impossible to collude to cheat you, which is especially evident when the nodes have a common reputation in the network. The blockchain technology just applies a set of consensus-based mathematical algorithm to establish a 'trust' network between machines, so that brand-new credit creation is performed through technical endorsements rather than centralized credit organizations.
Specifically, the so-called "consensus mechanism" is to complete the verification and confirmation of the transaction in a short time through the voting of a special node; for a transaction, if several nodes with irrelevant benefits can achieve consensus, we can consider that the whole network can achieve consensus for the node. Further colloquially, if a microblog in China, a virtual coin player in the United states, an African student, and a European traveler are not acquainted with each other, but they all agree that you are a good person, then basically you can conclude that you are not bad.
And as described in the following steps, if the verification is successful, encrypting and storing the target data, and determining the data type contained in the target data.
As an example, if the verification is successful, the target data is encrypted and saved. Meanwhile, when the target data is acquired, the data type of the target data is judged so as to determine a corresponding weight value and a corresponding scoring formula in the following.
In step S120, determining, according to the intelligent contract model, a weight value and a scoring formula corresponding to each data type included in the target data.
It should be noted that the concept of Smart contracts (English: Smart Contract), which was first proposed in 1995, is a computer protocol intended to propagate, verify or execute contracts in an informational manner. Smart contracts allow trusted transactions to be conducted without third parties, which transactions are traceable and irreversible.
Among other things, the purpose of smart contracts is to provide a secure method over traditional contracts and to reduce other transaction costs associated with contracts.
It should be noted that the intelligent contract model includes a weight value and a scoring formula of the supplier contract type data, a weight value and a scoring formula of the supplier competitiveness data, and a weight value and a scoring formula of the commodity user experience data.
As an example, the scoring formulas of different item data in the supplier contract type are different, the scoring formulas of different item data in the supplier competitiveness type are different, and the scoring formulas of different item data in the commodity user experience type are different.
In a specific implementation, the weight value and the scoring formula of the supplier contract type data, the weight value and the scoring formula of the supplier competitiveness data, and the weight value and the scoring formula of the commodity user experience data in the intelligent contract model are as follows:
in step S130, the credit score of the target data is determined according to the scoring formula corresponding to each data type included in the target data.
In an embodiment of the present application, the specific process of "determining the credit score of the target data according to the scoring formula corresponding to each data type included in the target data" in step S130 may be further described with reference to the following description.
It should be noted that the supplier's integrated credit value is divided into the following 5 levels:
| Range | rank of |
| 0~49 | Difference (D) |
| 50-59 | Is poor |
| 60~75 | Medium and high grade |
| 76~89 | Good effect |
| 90~100 | Is excellent in |
As an example, each data scoring item in the data types is scored in a percentage or percentage mode, and is scored in a scoring formula or an expert scoring mode to obtain a credit score of each data scoring item in each data type; and calculating the comprehensive credit value of the supplier according to the credit score of each data scoring item in the data type and the corresponding weight value of the data type.
In an embodiment of the present application, a specific process after "determining the credit score of the target data according to the scoring formula corresponding to each data type included in the target data" in step S130 may be further described with reference to the following description.
It should be noted that the data types further include an auxiliary evaluation type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data.
Calling auxiliary evaluation type data in the target data as described in the following steps;
and modifying the credit score of the target data according to the auxiliary evaluation type data as described in the following steps.
As an example, in order to improve the accuracy of credit evaluation, it is determined whether to pull down or pull up the credit score of the target data on the original basis according to the data of the auxiliary evaluation type.
In a specific implementation, in the absence of the auxiliary evaluation type data, the supplier competitiveness type data including data that a supplier sells 3C merchandise is compliant; if the auxiliary evaluation type data is added, it is likely that compliance is not established. That is, if the added qualification data of the auxiliary evaluation type supplier includes data that the supplier only sells the office supply qualification, the supplier is not compliant (the supplier qualification violation condition is yes), and then the credit score of the data scoring item is correspondingly reduced.
In another implementation, if the customer selects "7 days unpruned returns" for returns at the time of return, even a strong complaint is that the order return rate in the supplier competitiveness data increases. If the auxiliary evaluation type data is added, the possible order return rate is not increased; that is, if the auxiliary evaluation type data includes no reason for returning the goods for 7 days, it will be determined whether the return or complaint is within the calculation range.
In step S140, the comprehensive credit value is determined according to the weight value and the credit score corresponding to each data type included in the target data.
In an embodiment of the present application, the specific process of "determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type included in the target data" in step S140 may be further described with reference to the following description.
According to the formula, as described in the following steps:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Is the supply ofCredit score of merchant contract type, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
In an embodiment of the present application, a specific process after "determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type included in the target data" in step S140 may be further described with reference to the following description.
Verifying the composite credit value based on a preset consensus mechanism, as described in the following steps;
if the verification is successful, the composite credit value is encrypted and saved, as described in the following steps.
It should be noted that, by storing both the target data and the comprehensive credit value of the supplier in the federation chain server, both the target data and the comprehensive credit value cannot be deleted or modified, and data is kept untrustworthy and traceable.
In one embodiment of the application, different modes of data verification and data consensus schemes are determined according to different business purposes (such as business purposes of storage and calculation) of data in a blockchain platform. Wherein the data types include: the first is data that needs to be changed as the business develops, such as: data rating data (data rating + weight); the second is evidence data, such as basic data, commodity marketing data, user satisfaction data, supplier account data and the like which need to be stored, wherein the supplier account data comprises data related to credit calculated through the intelligent contract model.
Aiming at data scoring item data, adding, deleting and modifying the data scoring items by providing a proposal and voting mechanism, ensuring the continuous development of services and the self evolution of a block chain platform, and providing a convenient tool for the credit scoring management of suppliers in the block chain platform.
The method comprises the following implementation steps:
1. the proposal party initiates a transaction statement by calling a mode of changing an intelligent contract by using the data scoring item, appoints the conditions of the proposal such as voting cut-off height, effective height, cut-off time, effective voting rate and the like, and sends out voting invitations to all suppliers in the block chain network;
2. after receiving the voting invitation, the voting party votes for the proposal by initiating a transaction, and when the voting result reaches the agreed achievement condition of the intelligent contract, the intelligent contract is automatically called to regenerate the intelligent contract of the data scoring item;
3. to prevent misuse of the proposal voting mechanism, the voted transaction needs to freeze the participant's credit value until the smart contract becomes effective or the smart contract fails and then the freeze is released.
Basic data, commodity marketing data and user satisfaction data are input parameters for evaluating credit of a supplier, a block chain platform collects the data (an acquisition submodule) by externally packaging a unified Application Programming Interface (API) and a Software Development Kit (SDK), and when a service system generates the data, the API Interface is called to upload the data, and the execution of an intelligent contract is triggered.
The method comprises the following implementation steps:
1. the acquisition sub-module corresponds to an intelligent contract, and the block chain platform encapsulates the intelligent contract into an API/SDK aggregate, so that the interaction between a service system and the block chain platform is facilitated;
2. when the business system generates transaction data, the time stamp and the random number are jointly encrypted and signed through encryption algorithms such as SM3 and SHA-512, and the transaction data are uploaded by calling an API/SDK aggregate;
3. and after receiving the data uploading request or acquiring the data, the block chain platform executes the intelligent contract, initiates a transaction statement, and executes data evidence storage logic and credit scoring logic. The encryption storage module is internally provided with data evidence storage logic, the intelligent contract model is internally provided with credit scoring logic, and the data evidence storage logic and the credit scoring logic are respectively an independent intelligent contract; logic related to transaction data, such as uploading and inquiring of transaction data, is processed by the intelligent contract in the encryption storage module, and logic related to inquiring of credit score, comprehensive credit value and the like is processed by the intelligent contract in the intelligent contract model;
4. the intelligent contract model is called back after the intelligent contract in the encryption storage module is executed, the intelligent contract in the intelligent contract model judges whether the transaction data type belongs to data for calculating credit score, if so, a score calculation method of the intelligent contract model is called, and commodity data, supplier account number data and supplier basic information data related to the score calculation method are directly inquired from a chain through the intelligent contract;
5. the data evidence storage logic and the credit scoring logic are a complete transaction, and only if the data evidence storage logic and the credit scoring logic are successful at the same time, the data evidence storage logic and the credit scoring logic are stored into the block chain platform;
6. all the data are stored in a block chain platform, and a PBFT (physical Byzantine Fault Tolerance) consensus algorithm is adopted to ensure the consistency of the data in a distributed environment;
if the other service systems need to use the comprehensive credit value of the supplier, the other service systems can inquire and pay corresponding punishment through the API/SDK aggregate. If the supplier disagrees with the comprehensive credit value of the supplier, historical transaction data and credit scores can be inquired for playback calculation.
In an embodiment of the present application, the alliance-link server provides data query service interfaces to the outside, and the two most important data query service interfaces are a comprehensive credit value query interface and a target data query interface. Through the comprehensive credit value query interface, the upstream or downstream system can query the comprehensive credit value of the supplier so as to treat the reward and penalty of the supplier according to the comprehensive credit value; through the target data query interface, the supplier or the platform staff can trace the relevant data of the supplier.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
Referring to fig. 2 to fig. 3, a block chain-based credit score generation apparatus according to an embodiment of the present application is shown;
the method specifically comprises the following steps:
an obtaining module 210, configured to obtain and store target data, and determine a data type included in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
a first determining module 220, configured to determine, according to the intelligent contract model, a weight value and a scoring formula corresponding to each data type included in the target data;
a second determining module 230, configured to determine a credit score of the target data according to a scoring formula corresponding to each data type included in the target data;
a third determining module 240, configured to determine the comprehensive credit value according to the weight value and the credit score corresponding to each data type included in the target data.
In an embodiment of the present application, the data types further include an auxiliary evaluation type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data; the second determining module 230 further comprises:
the calling module is used for calling auxiliary evaluation type data in the target data;
and the fourth determination module is used for correcting the credit score of the target data according to the auxiliary evaluation type data.
The third determining module 240 includes:
a calculation module for, according to a formula:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Credit score of contract type for said supplier, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
The obtaining module 210 includes:
the acquisition submodule is used for acquiring the target data;
the verification module is used for verifying the target data based on a preset consensus mechanism;
and the encryption storage module is used for encrypting and storing the target data and determining the data type contained in the target data if the verification is successful.
Referring to fig. 4, a computer device of a block chain-based credit score generation method according to the present application is shown, which may specifically include the following:
the computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 that couples various system components including the memory 28 and the processing unit 16.
The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (commonly referred to as "hard drives"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, with the program modules 42 configured to carry out the functions of embodiments of the application.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The processing unit 16 executes programs stored in the memory 28 to execute various functional applications and data processing, for example, to implement a block chain-based credit score generation method provided in the embodiment of the present application.
That is, the processing unit 16 implements, when executing the program,: acquiring and storing target data, and determining a data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type; determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model; determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data; and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
In an embodiment of the present application, a computer-readable storage medium is further provided, on which a computer program is stored, which when executed by a processor, implements a block chain based credit score generation method as provided in all embodiments of the present application.
That is, the program when executed by the processor implements: acquiring and storing target data, and determining a data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type; determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model; determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data; and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, 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), an optical fiber, 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 context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the operator's computer, partly on the operator's computer, as a stand-alone software package, partly on the operator's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the operator's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The above detailed description is given to a block chain-based credit score generation method and apparatus, and a specific example is applied in the detailed description to explain the principle and implementation of the present application, and the description of the above embodiment is only used to help understand the method and core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A credit score generation method based on a block chain is characterized in that the method is applied to the block chain to evaluate credit of a supplier to obtain a comprehensive credit value; wherein the method involves a server comprising an intelligent contract model; the method comprises the following steps:
acquiring and storing target data, and determining a data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model;
determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data;
and determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
2. The method of claim 1, wherein the data types further comprise a secondary evaluative type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data; after the step of determining the credit score of the target data according to the scoring formula corresponding to each data type contained in the target data, the method further includes:
calling auxiliary evaluation type data in the target data;
and correcting the credit score of the target data according to the auxiliary evaluation type data.
3. The method of claim 1, wherein the step of determining the composite credit value according to the weight value and the credit score corresponding to each data type included in the target data comprises:
according to the formula:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Credit score of contract type for said supplier, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
4. The method of claim 3, wherein the steps of obtaining and saving target data and determining the type of data contained in the target data comprises:
acquiring the target data;
verifying the target data based on a preset consensus mechanism;
and if the verification is successful, encrypting and storing the target data, and determining the data type contained in the target data.
5. The method of claim 4, wherein after the step of determining the composite credit value according to the weight value and the credit score corresponding to each data type included in the target data, the method further comprises:
verifying the comprehensive credit value based on a preset consensus mechanism;
and if the verification is successful, encrypting and storing the comprehensive credit value.
6. A block chain-based credit score generation device is characterized in that the device is applied to a block chain to evaluate credit of a supplier to obtain a comprehensive credit value; wherein the apparatus relates to a server, the server comprising an intelligent contract model; the device comprises:
the acquisition module is used for acquiring and storing target data and determining the data type contained in the target data; the target data at least comprises a data type, wherein the data type comprises a supplier contract type, a supplier competitiveness type and a commodity user experience type;
the first determining module is used for determining a weight value and a scoring formula corresponding to each data type contained in the target data according to the intelligent contract model;
the second determining module is used for determining credit scores of the target data according to scoring formulas corresponding to the data types contained in the target data;
and the third determining module is used for determining the comprehensive credit value according to the weight value and the credit score corresponding to each data type contained in the target data.
7. The apparatus of claim 6, wherein the data types further comprise a secondary evaluation type; wherein the auxiliary evaluation type comprises commodity data, supplier qualification data and business contract data; the second determining module further comprises:
the calling module is used for calling auxiliary evaluation type data in the target data;
and the fourth determination module is used for correcting the credit score of the target data according to the auxiliary evaluation type data.
8. The apparatus of claim 6, wherein the third determining module comprises:
a calculation module for, according to a formula:
calculating the composite credit value;
wherein n = f1+f2+f3；X1Credit score of contract type for said supplier, f1A weight value, X, of said supplier contract type2Credit score, f, of said supplier competitiveness type2A weight value, X, of said supplier competitiveness type3Credit score, f, for the commodity user experience type3And X is the weight value of the commodity user experience type, and is the comprehensive credit value.
9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and capable of running on the processor, the computer program, when executed by the processor, implementing the method of any of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 5.
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