Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a credit investigation data security management system based on cloud service, which comprises a cloud server, wherein the cloud server is in communication connection with a data encryption module, a data decryption module, a cross verification module, a data chaining module and a secondary identity verification module;
the cloud server is used for constructing a credit data alliance sharing platform, and collecting credit data uploaded by the alliance credit terminal by taking the alliance credit terminal as a blockchain node;
the data encryption module is used for encrypting the credit investigation data before the alliance credit investigation terminal transmits the credit investigation data of the user to the credit investigation data alliance sharing platform to generate an encrypted data packet;
the data decryption module is used for decrypting the encrypted data packet transmitted to the block chain node and judging whether to perform data cross verification or not according to a decryption result;
the cross verification module is used for carrying out data feature extraction on the decrypted data by utilizing a pre-constructed credit sign feature model, constructing a credit sign feature vector matrix, and carrying out data cross verification on the credit sign feature vector matrix and the credit sign feature vector matrix stored by each block chain node in the block chain network;
the data uplink module is used for carrying out data uplink operation on the credit investigation data processed by the cross verification module;
The secondary identity verification module is used for constructing a temporary data verification library according to the stored biological identification data of each allied credit terminal, generating a corresponding biological identification data verification scheme according to the reliability level of the credit data, and performing secondary identity verification on the credit data.
Further, the cloud server builds a credit data alliance sharing platform, and the process of collecting credit data uploaded by the alliance credit terminal by taking the alliance credit terminal as a blockchain node comprises the following steps:
A credit investigation data alliance sharing platform is constructed by utilizing a block chain technology, a plurality of block chain nodes are arranged in the credit investigation data alliance sharing platform, each block chain node is mutually linked to form a block chain network, each block chain node is in communication link with a corresponding alliance credit investigation terminal, and the alliance credit investigation terminal is used for uploading credit investigation data by a user;
each alliance credit terminal is provided with a local database, the local database is used for storing basic identity data of a user and biological identification data of the user, generating an anonymous identification sequence of the user according to the basic identity data of the user, associating the anonymous identification sequence of the user with the biological identification data of the user, and publishing the anonymous identification sequence of the user to the blockchain copies of all the blockchain nodes.
Further, the data encryption module encrypts the credit data before the credit terminal transmits the credit data of the user to the credit data alliance sharing platform, and the process of generating the encrypted data packet includes:
Presetting a first key pair and a second key pair between each alliance credit terminal and a credit data alliance sharing platform through an asymmetric encryption algorithm, wherein the key pair comprises a public key and a private key, when the alliance credit terminal transmits credit data of a user to the credit data alliance sharing platform, acquiring an anonymous identification sequence associated with the user in a local database, carrying out data format preprocessing on the anonymous identification sequence and the credit data, converting the anonymous identification sequence into an anonymous binary character string, converting the credit data into binary data with a fixed length, adding the anonymous binary character string into the first segment of the binary data, encrypting the binary data added with the anonymous binary character string by using the first private key to generate encrypted data, applying an SHA-256 hash function to the encrypted data to generate a hash value of the encrypted data, encrypting the hash value of the encrypted data by using the second private key to generate a digital signature of the encrypted data, packaging the encrypted data and the digital signature to generate an encrypted data packet, and sending the encrypted data packet to a node of a hash chain linked with the credit terminal.
Further, the process of decrypting the encrypted data packet transmitted to the blockchain node by the data decryption module and judging whether to perform data cross-validation according to the decryption result includes:
The block chain node decrypts the encrypted data in the encrypted data packet through the first public key to generate decrypted data, decrypts the digital signature through the second public key to obtain a hash value, performs hash operation on the decrypted data by applying an SHA-256 hash function to obtain a hash value of the decrypted data, and compares the hash value of the decrypted data with the hash value in consistency;
if the hash value of the decrypted data is consistent with the hash value, sending the decrypted data to a cross verification module for data cross verification;
if the hash value of the decrypted data is inconsistent with the hash value, the decrypted data is removed, and a data tampering early warning signal is sent to the alliance credit investigation terminal.
Further, the process of performing data feature extraction on the decrypted data by the cross verification module through a pre-constructed credit feature model, constructing a credit feature vector matrix, and performing data cross verification on the credit feature vector matrix and the credit feature vector matrix stored by each blockchain node in the blockchain network comprises the following steps:
Pre-constructing a credit feature model, extracting anonymous binary character strings corresponding to the first segment in binary data corresponding to decrypted data, processing and converting the anonymous binary character serial data format into an anonymous identification sequence, processing and converting the decrypted data into credit data, inputting the credit data into the credit feature model, and carrying out data feature extraction on the credit data to obtain feature vector data, wherein the feature vector data comprises a plurality of feature vector types and feature values corresponding to the feature vector types, acquiring the feature vector types and the feature values corresponding to the feature vector types from the feature vector data, constructing a credit feature vector matrix, and associating the anonymous identification sequence with the credit feature vector matrix;
Each block link point in the block chain network is stored with a plurality of anonymous identification sequences and credit investigation feature vector matrixes associated with the anonymous identification sequences, the anonymous identification sequences are searched and matched with the anonymous identification sequences stored in each block chain node in the block chain network, the anonymous identification sequences consistent with the anonymous identification sequences in each block chain node and the credit investigation feature vector matrixes associated with the anonymous identification sequences are screened out, the credit investigation feature vector matrixes associated with the anonymous identification sequences in each block chain node are marked as credit investigation feature vector matrixes to be verified, and the credit investigation feature vector matrixes to be verified in each block chain node are subjected to matrix fusion to generate credit investigation feature vector dense matrixes to be verified;
Acquiring the reliability grade of the credit information data according to the credit information feature vector matrix and the credit information feature vector dense matrix to be verified, presetting a reliability grade threshold, comparing the reliability grade of the credit information data with the reliability grade threshold, and if the reliability grade of the credit information data is greater than or equal to the reliability grade threshold, transmitting the credit information data to a data uplink module;
and if the reliability level of the credit data is smaller than the reliability level threshold, transmitting the credit data and the reliability level of the credit data to the secondary identity verification module.
Further, the process of obtaining the reliability level of the credit-feature data according to the credit-feature vector matrix and the credit-feature vector dense matrix to be verified comprises the following steps:
And comparing feature values corresponding to the feature vector types in the feature vector matrix with feature values corresponding to the feature vector types of the same type in the feature vector dense matrix to be verified one by one, obtaining the similarity of the feature vector types in the feature vector matrix, taking the similarity of the feature vector types as an evaluation index, presetting an index weight matrix and a reliability grade of the evaluation index, wherein the weight vector is determined according to the experience of an expert, so that uncertainty in the fuzzy comprehensive evaluation process is reduced, judging a membership matrix of the feature data to the reliability grade through the fuzzy comprehensive evaluation, and obtaining the reliability grade of the feature data according to the membership matrix and the index weight matrix.
Further, the process of the data uplink module for performing the data uplink operation on the credit information data processed by the cross verification module includes:
Presetting an excavating node, a verification rule and a consensus mechanism of a blockchain network, wherein the excavating node is used for creating a new block, packaging credit investigation data to be subjected to data uplink operation into the new block, broadcasting the new block to the blockchain network, and verifying the new block by other blockchain nodes in the blockchain network based on the verification rule and the consensus mechanism;
After verification of the new block is passed, the new block is added to the end of the blockchain, and the credit information of the new block is updated into the blockchain copies of all blockchain nodes.
Further, the secondary identity verification module constructs a temporary data verification library according to the stored biometric data of each allied credit terminal, generates a corresponding biometric data verification scheme according to the reliability level of the credit data, and performs secondary identity verification on the credit data, wherein the process comprises the following steps:
Presetting a biological identification data verification scheme corresponding to different reliability levels, acquiring anonymous identification sequences associated with credit data when the reliability levels of the credit data are received, constructing a temporary data verification library of the anonymous identification sequences, acquiring biological identification data associated with the anonymous identification sequences stored in a local database of each allied credit terminal, and storing the biological identification data in the temporary data verification library;
Generating a corresponding biological identification data verification scheme according to the reliability level of the credit investigation data, acquiring biological identification data to be verified of the credit investigation data according to the biological identification data verification scheme, matching the biological identification data to be verified with the biological identification data in the temporary data verification library, and if the biological identification data to be verified is successfully matched, transmitting the credit investigation data to the data uplink module;
if the matching of the to-be-verified biological identification data is unsuccessful, eliminating the credit investigation data.
The method has the advantages that a credit data alliance sharing platform is built, the alliance credit terminals are used as blockchain nodes, credit data uploaded by the alliance credit terminals are collected, data characteristic extraction is carried out on decrypted data through a cross verification module by using a pre-built credit characteristic model, a credit characteristic vector matrix is built, data cross verification is carried out on the credit characteristic vector matrix and the credit characteristic vector matrix stored by each blockchain node in a blockchain network, the situation that when login account passwords of credit data users are stolen or leaked, false credit data are generated and uploaded to the credit platform, misleading information is caused in credit records of the users is avoided, and due to the fact that the credit data of different alliance credit terminals in the blockchain network comprise but are not limited to multidimensional data such as transaction behaviors, social behaviors, payment behaviors, consumption characteristics and the like, habit, characters, behavior and preference of individuals are objectively reflected, the credit data are relatively stable, and the reliability and reliability of the credit data are more comprehensively evaluated through mining the credit data, and the reliability of the credit data are more obviously and comprehensively evaluated through mining the conventional credit account passwords of the credit, the credit data and the credit data, the identity, the reliability and the reliability of the credit data are more obviously evaluated.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As shown in fig. 1, a credit investigation data security management system based on cloud service comprises a cloud server, wherein the cloud server is in communication connection with a data encryption module, a data decryption module, a cross verification module, a data uplink module and a secondary identity verification module;
the cloud server is used for constructing a credit data alliance sharing platform, and collecting credit data uploaded by the alliance credit terminal by taking the alliance credit terminal as a blockchain node;
The data encryption module encrypts the credit data before the alliance credit terminal transmits the credit data of the user to the credit data alliance sharing platform to generate an encrypted data packet;
the data decryption module decrypts the encrypted data packet transmitted to the block chain node, and judges whether to perform data cross verification according to a decryption result;
The cross verification module performs data feature extraction on the decrypted data by utilizing a pre-constructed credit feature model, constructs a credit feature vector matrix, and performs data cross verification on the credit feature vector matrix and credit feature vector matrices stored by each block chain node in the block chain network;
The data uplink module carries out data uplink operation on the credit investigation data processed by the cross verification module;
The secondary identity verification module constructs a temporary data verification library according to the stored biological identification data of each allied credit terminal, generates a corresponding biological identification data verification scheme according to the reliability level of the credit data, and performs secondary identity verification on the credit data.
It should be further described that, in the implementation process, the cloud server builds a credit data alliance sharing platform, uses the alliance credit terminal as a blockchain node, and the process of collecting credit data uploaded by the alliance credit terminal includes:
A credit investigation data alliance sharing platform is constructed by utilizing a block chain technology, a plurality of block chain nodes are arranged in the credit investigation data alliance sharing platform, each block chain node is mutually linked to form a block chain network, each block chain node is in communication link with a corresponding alliance credit investigation terminal, and the alliance credit investigation terminal is used for uploading credit investigation data by a user;
each alliance credit terminal is provided with a local database, the local database is used for storing basic identity data of a user and biological identification data of the user, generating an anonymous identification sequence of the user according to the basic identity data of the user, associating the anonymous identification sequence of the user with the biological identification data of the user, and publishing the anonymous identification sequence of the user to the blockchain copies of all the blockchain nodes.
It should be further described that, the allied credit terminal is provided with a registration port, a login port and a data input port besides the local database, and the data input port is used for inputting credit data by a user, and before the user inputs the credit data to be input, the method further comprises the following steps:
the user inputs basic identity data and biological identification data of the user through the registration port, and a corresponding login account and a login password are generated;
the user inputs the login account number and the login password through the login port to enter an alliance credit investigation terminal;
It should be further noted that, the basic identity data of the user includes personal identity information such as name, gender, age, identity card number, etc., and the allied credit terminal includes various banks, credit bureau, loan institution, e-commerce platform, etc.
It should be further noted that, in the implementation process, the data encryption module encrypts the credit data before the credit terminal transmits the credit data of the user to the credit data alliance sharing platform, and the process of generating the encrypted data packet includes:
Presetting a first key pair and a second key pair between each alliance credit terminal and a credit data alliance sharing platform through an asymmetric encryption algorithm, wherein the key pair comprises a public key and a private key, when the alliance credit terminal transmits credit data of a user to the credit data alliance sharing platform, acquiring an anonymous identification sequence associated with the user in a local database, preprocessing a data format of the anonymous identification sequence and the credit data, converting the anonymous identification sequence into an anonymous binary character string, converting the credit data into binary data with a fixed length, adding the anonymous binary character string to a first section of the binary data, encrypting the binary data added with the anonymous binary character string by using the first private key to generate encrypted data, for example, if the asymmetric encryption algorithm used in the invention is an RSA algorithm, the credit data alliance sharing platform presets a first key pair for anonymous encryption and a second key pair for digital signature of each alliance terminal through the RSA algorithm, dividing the credit data to be transmitted into proper data blocks and converting the anonymous identification sequence into hash data into binary data, encrypting the hash data according to the hash data, applying the hash data to the hash data with the hash value, performing encryption algorithm to which is not applied to the hash data, and performing encryption operation on the hash data with the hash data by using the hash value, generating a binary data with the hash data, encrypting the hash data with the hash value, and encrypting the hash data with the hash value, generating a binary data after the hash data with the hash data has been encrypted data, and the hash value has been used to be used for a full encryption value, and a encryption function is generated, and a encryption data is encrypted, for a encryption data is generated, and generating an encrypted data packet and transmitting the encrypted data packet to a blockchain node in communication link with the alliance communication terminal.
It should be further noted that, in the implementation process, the data decryption module decrypts the encrypted data packet transmitted to the blockchain node, and determines whether to perform data cross-validation according to the decryption result, where the process includes:
The block chain node decrypts the encrypted data in the encrypted data packet through the first public key to generate decrypted data, decrypts the digital signature through the second public key to obtain a hash value, performs hash operation on the decrypted data by applying an SHA-256 hash function to obtain a hash value of the decrypted data, and compares the hash value of the decrypted data with the hash value in consistency;
if the hash value of the decrypted data is consistent with the hash value, sending the decrypted data to a cross verification module for data cross verification;
if the hash value of the decrypted data is inconsistent with the hash value, the decrypted data is removed, and a data tampering early warning signal is sent to the alliance credit investigation terminal.
It should be further noted that, in the implementation process, the process of performing data feature extraction on the decrypted data by using the pre-constructed credit sign feature model by the cross verification module, constructing a credit sign feature vector matrix, and performing data cross verification on the credit sign feature vector matrix and the credit sign feature vector matrix stored by each blockchain node in the blockchain network includes:
Pre-constructing a credit feature model, extracting anonymous binary character strings corresponding to the first segment in binary data corresponding to decrypted data, processing and converting the anonymous binary character serial data format into an anonymous identification sequence, processing and converting the decrypted data into credit data, inputting the credit data into the credit feature model, and carrying out data feature extraction on the credit data to obtain feature vector data, wherein the feature vector data comprises a plurality of feature vector types and feature values corresponding to the feature vector types, acquiring the feature vector types and the feature values corresponding to the feature vector types from the feature vector data, constructing a credit feature vector matrix, and associating the anonymous identification sequence with the credit feature vector matrix;
Each block link point in the block chain network is stored with a plurality of anonymous identification sequences and credit investigation feature vector matrixes associated with the anonymous identification sequences, the anonymous identification sequences are searched and matched with the anonymous identification sequences stored in each block chain node in the block chain network, the anonymous identification sequences consistent with the anonymous identification sequences in each block chain node and the credit investigation feature vector matrixes associated with the anonymous identification sequences are screened out, the credit investigation feature vector matrixes associated with the anonymous identification sequences in each block chain node are marked as credit investigation feature vector matrixes to be verified, and the credit investigation feature vector matrixes to be verified in each block chain node are subjected to matrix fusion to generate credit investigation feature vector dense matrixes to be verified;
Acquiring the reliability grade of the credit information data according to the credit information feature vector matrix and the credit information feature vector dense matrix to be verified, presetting a reliability grade threshold, comparing the reliability grade of the credit information data with the reliability grade threshold, and if the reliability grade of the credit information data is greater than or equal to the reliability grade threshold, transmitting the credit information data to a data uplink module;
and if the reliability level of the credit data is smaller than the reliability level threshold, transmitting the credit data and the reliability level of the credit data to the secondary identity verification module.
It should be further described that, in the specific implementation process, matrix fusion is performed on the feature vector matrix to be verified in each blockchain node, and in the process of generating the feature vector dense matrix to be verified, since the feature vector types included in the feature vector matrix to be verified in each blockchain node of the blockchain network may be different, the feature vector types included in the feature vector matrix to be verified in one blockchain node may not have the corresponding feature vector types in the feature vector matrix to be verified in other blockchain nodes, so that matrix fusion is performed on the feature vector matrix to be verified in each blockchain node, and the blank of the feature vector types in the feature vector matrix to be verified in the original blockchain node is filled;
The specific process of carrying out matrix fusion on the feature vector matrix of the credit to be verified in each block chain node is that each feature vector type contained in the feature vector matrix of the credit to be verified in each block chain node is obtained, each feature vector type contained in the feature vector matrix of the credit to be verified in each block chain node is combined to be used as a plurality of feature vector types contained in the feature vector dense matrix of the credit to be verified, then feature values corresponding to each feature vector type contained in the feature vector matrix of the credit to be verified in each block chain node are obtained, data mean value operation is carried out on the feature values corresponding to each feature vector type contained in the feature vector matrix of the credit to be verified in all block chain nodes, feature value mean values corresponding to each feature vector type are obtained, and the feature value mean values corresponding to each feature vector type are used as the feature values of each feature vector type corresponding to the feature vector dense matrix of the credit to be verified, so that the construction of the feature vector dense matrix of the credit to be verified is completed.
In the present invention, feature vector data of credit investigation data includes various feature values reflecting credit conditions, personal information, behavior data and financial data of users, and feature values corresponding to these different feature vector types are used for describing credit states and credit risks of users, and feature vector types and feature values in feature vectors of credit investigation data of the present invention include, but are not limited to:
Financial information features including income level, liability amount, loan amount, credit card use amount, etc., property status, property liability status of house property vehicle information, etc.;
Credit history information such as repayment records, overdue conditions, credit card bill repayment records and the like;
credit card related information such as credit line, credit line usage, etc.;
the inquiry record features are inquiry information related to credit examination, such as credit inquiry records, credit inquiry times and the like;
The behavior data is characterized by personal behavior data such as consumption amount records, loan amount records, investment financial records and the like.
It should be further noted that, in the implementation process, the process of obtaining the reliability level of the credit data according to the credit feature vector matrix and the credit feature vector dense matrix to be verified includes:
And comparing feature values corresponding to the feature vector types in the feature vector matrix with feature values corresponding to the feature vector types of the same type in the feature vector dense matrix to be verified one by one, obtaining the similarity of the feature vector types in the feature vector matrix, taking the similarity of the feature vector types as an evaluation index, presetting an index weight matrix and a reliability grade of the evaluation index, wherein the weight vector is determined according to the experience of an expert, so that uncertainty in the fuzzy comprehensive evaluation process is reduced, judging a membership matrix of the feature data to the reliability grade through the fuzzy comprehensive evaluation, and obtaining the reliability grade of the feature data according to the membership matrix and the index weight matrix.
It should be further noted that, in the implementation process, the formula for obtaining the similarity of each feature vector type is as follows:
Wherein Dai represents the characteristic value of the i-th type of characteristic vector in the credit-feature vector matrix, Dbi represents the characteristic value of the i-th type of characteristic vector in the credit-feature vector dense matrix to be verified, and Fi represents the similarity of the i-th type of characteristic vector in the credit-feature vector matrix;
It should be further noted that, in the implementation process, the process of obtaining the reliability level of the credit investigation data according to the membership matrix and the index weight matrix includes:
The method comprises the steps of fusing index weights of evaluation indexes and membership matrixes through the following formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indexes, and obtaining membership grades of different reliability grades corresponding to credit investigation data according to the fuzzy comprehensive evaluation matrix;
wherein, the formula is:
M=αM1×βM2;
Wherein M is a fuzzy comprehensive evaluation matrix of the evaluation index, M1 is a weight matrix of index weight of the evaluation index, M2 is the membership matrix, and "×" represents addition of elements at corresponding positions of the weight matrix of the evaluation index and the membership matrix, and α and β are weighting parameters for controlling balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.
It should be further noted that, in the implementation process, the process of the data uplink module for performing the data uplink operation on the credit information processed by the cross validation module includes:
Presetting an excavating node, a verification rule and a consensus mechanism of a blockchain network, wherein the excavating node is used for creating a new block, packaging credit investigation data to be subjected to data uplink operation into the new block, broadcasting the new block to the blockchain network, and verifying the new block by other blockchain nodes in the blockchain network based on the verification rule and the consensus mechanism;
After verification of the new block is passed, the new block is added to the end of the blockchain, and the credit information of the new block is updated into the blockchain copies of all blockchain nodes.
It should be further noted that, in the specific implementation process, the secondary identity verification module constructs a temporary data verification library according to the stored biometric data of each allied credit terminal, and generates a corresponding biometric data verification scheme according to the reliability level of the credit data, and the process of performing secondary identity verification on the credit data includes:
Presetting a biological identification data verification scheme corresponding to different reliability levels, acquiring anonymous identification sequences associated with credit data when the reliability levels of the credit data are received, constructing a temporary data verification library of the anonymous identification sequences, acquiring biological identification data associated with the anonymous identification sequences stored in a local database of each allied credit terminal, and storing the biological identification data in the temporary data verification library;
Generating a corresponding biological identification data verification scheme according to the reliability level of the credit investigation data, acquiring biological identification data to be verified of the credit investigation data according to the biological identification data verification scheme, matching the biological identification data to be verified with the biological identification data in the temporary data verification library, and if the biological identification data to be verified is successfully matched, transmitting the credit investigation data to the data uplink module;
if the matching of the to-be-verified biological identification data is unsuccessful, eliminating the credit investigation data.
It should be further described that, in the specific implementation process, the biometric data includes but is not limited to fingerprint data, face data, iris data, DNA data, etc., the biometric data verification scheme is to set different types of biometric data according to the reliability level to verify, specifically, for example, the lower the reliability level is, the worse the reliability is represented, the range of the characteristic value in the biometric data does not conform to the range of the characteristic value in the existing historical credit data in the blockchain network, the reliability level 1 is set, the type of the biometric data to be verified in the biometric data verification scheme corresponding to the reliability level 1 includes fingerprint data, face data, iris data, DNA data, etc., the reliability level 2 is set, the type of the biometric data to be verified in the biometric data verification scheme corresponding to the reliability level 2 includes fingerprint data, face data, iris data, the reliability level 3 is set, the type of the biometric data to be verified in the biometric data verification scheme corresponding to the reliability level 3 includes fingerprint data, face data, the face data is set, the reliability level 4 is set, the type of the biometric data to be verified in the biometric data verification scheme corresponding to the user terminal is to be verified according to the authentication data, the type of the biometric data to be verified is temporarily applied to the user data, the type of the biometric data to be verified is verified by the user data, the authentication is temporarily-verified by the authentication terminal, the type of the biometric data to be verified is verified by the authentication data is required to be verified by the user data, if the biometric data to be verified is successfully matched, the credit investigation data is uploaded by the user, the credit investigation data is sent to the data uplink module, and if the biometric data to be verified is not successfully matched, the credit investigation data is false data, and the credit investigation data is removed.
The rapid development of the Internet enables all network data to become a data source of credit evaluation, greatly enriches the information source channel of traditional credit, and the data used by Internet credit messengers not only comprises traditional banking credit records, consumption records, payment pipelining, but also can comprise identity, social and business, daily activity and behavior preference data, behavior characteristic data and the like, and the large data technology is utilized to integrate more Internet credit information of each allied credit terminal into a credit data alliance sharing platform, so that risk identification can be effectively carried out and trend can be predicted, therefore, in the present information explosion age, the invention plays an important role in enhancing the accuracy, timeliness, predictability and the like of credit data by cleaning, effectively classifying, merging and deeply mining the data of the original massive and messy information under the condition of credit evaluation requirements which are hardly met by the data of a personnel credit system.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.