CN116258580A - Mortgage loan data storage method and system based on block chain technology of Internet of things - Google Patents
Mortgage loan data storage method and system based on block chain technology of Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of data storage, in particular to a mortgage loan data storage method and system based on the block chain technology of the Internet of things, comprising the following steps: the system comprises a user data acquisition module, a database, a storage management module, a data uploading management module and a target information management module, wherein all information provided by a user who proposes a mortgage loan application is acquired through the user data acquisition module, all information provided by the user is stored in the database, the mortgage target information of the user is selected to be respectively stored in a main block chain and a sub-block chain through the storage management module, a proper storage mode is planned, the best data uploading object is selected in the sub-block chain through the data uploading management module, the mortgage target information of the user is uploaded to the main block chain, and the mortgage target of the user is monitored and managed in real time through the target information management module, so that the speed of uploading target supervision data for mortgage is accelerated, and smooth progress of a mortgage process is helped.
Description
Technical Field
The invention relates to the technical field of data storage, in particular to a mortgage loan data storage method and system based on the block chain technology of the Internet of things.
Background
Mortgage loan service of institutions such as finance and banks is heavy, a large amount of mortgage target supervision data is required to be received when living target mortgage service is handled, and the data is stored by using a blockchain technology, so that a plurality of parties can be helped to acquire the mortgage target supervision data in time, and abnormal conditions of the mortgage target can be found in time;
however, existing mortgage data storage approaches still have some problems: firstly, a plurality of objects needing mortgage target supervision data are involved, and all nodes on a blockchain are guaranteed to participate when the blockchain technology is used for storing data, so that the speed of uploading the supervision data is reduced, and a proper data storage mode cannot be selected in the prior art to accelerate the speed of uploading the supervision data; secondly, when the mortgage target state is abnormal, abnormal data needs to be reported in time, but the reasons of the abnormal data cannot be analyzed roughly to help solve the problem of abnormal mortgage target in time, so that smooth mortgage flow is not guaranteed.
Therefore, a mortgage loan data storage method and system based on the block chain technology of the internet of things are needed to solve the above problems.
Disclosure of Invention
The invention aims to provide a mortgage loan data storage method and system based on the block chain technology of the Internet of things, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: mortgage loan data storage system based on the block chain technology of the internet of things, the system comprising: the system comprises a user data acquisition module, a database, a storage management module, a data uploading management module and a target information management module;
the output end of the user data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the storage management module, the output end of the storage management module is connected with the input end of the data uploading management module, and the output end of the data uploading management module is connected with the input end of the target information management module;
collecting all information provided by a user who submits a mortgage loan application through the user data collecting module, and transmitting all information to the database;
storing all information provided by a user through the database;
selecting and storing mortgage target information of a user into a main block chain and a sub-block chain respectively through the storage management module, and planning a proper storage mode;
selecting an optimal data uploading object from the subarea block chain through the data uploading management module, and uploading mortgage target information of a user to the main subarea block chain through the optimal data uploading object;
and carrying out real-time monitoring and management on the mortgage target of the user through the target information management module.
Further, the user data acquisition module comprises an application information acquisition unit and a credit rating acquisition unit;
the output ends of the application information acquisition unit and the credit rating acquisition unit are connected with the input end of the database;
the application information acquisition unit is used for acquiring mortgage targets, the number of the mortgage targets and mortgage period information provided by different users simultaneously providing application after the user submits the borrowing application;
the credit rating collecting unit is used for collecting credit rating information of the users who simultaneously give out borrowing applications, and the credit rating is assessed by related departments participating in mortgage loan business.
Further, the storage management module comprises a user information analysis unit, a user classification unit and a data storage planning unit;
the input end of the user information analysis unit is connected with the output end of the database, the output end of the user information analysis unit is connected with the input end of the user classification unit, and the output end of the user classification unit is connected with the input end of the data storage planning unit;
the user information analysis unit is used for retrieving user information from the database and analyzing the association degree between different users who simultaneously apply for mortgage loans;
the user classifying unit is used for classifying the users according to the association degree to obtain a classifying result;
the data storage planning unit is used for storing mortgage target supervision data of users which are classified into the same class into the same sub-blockchain, taking all the objects which participate in the mortgage loan service except the users as nodes of the main blockchain, uploading the mortgage target supervision data of the users of the same class to the main blockchain by one node in the same sub-blockchain, and receiving the mortgage target supervision data by all the objects which participate in the mortgage loan service except the users.
Further, the data uploading management module comprises a user information comparison unit and an uploading object selection unit;
the input end of the user information comparison unit is connected with the output end of the user classification unit, and the output end of the user information comparison unit is connected with the input end of the uploading object selection unit;
the user information comparison unit is used for comparing the application information and the credit rating information of the same type of users and analyzing mortgage information evaluation coefficients of different users;
the uploading object selecting unit is used for comparing the mortgage information evaluation coefficients, selecting the user with the biggest mortgage information evaluation coefficient as the object for uploading the mortgage target supervision data of all users in the corresponding category optimally, and adding the blockchain node where the user with the biggest mortgage information evaluation coefficient is located into the main blockchain.
Further, the target information management module comprises a mortgage target supervision unit, an abnormal alarm unit and a supervision data uploading unit;
the output end of the mortgage target supervision unit is connected with the input end of the abnormality alarm unit, and the output ends of the abnormality alarm unit and the uploading object selection unit are connected with the input end of the supervision data uploading unit;
the mortgage target supervision unit is used for uploading supervision information of each mortgage target by users in the same sub-block chain;
the abnormal alarming unit is used for alarming when abnormal mortgage target states of random users are monitored;
and the supervision data uploading unit is used for uploading the police information and the supervision information of the user mortgage target corresponding to the abnormality to the main block chain by the user with the biggest mortgage information evaluation coefficient.
A mortgage loan data storage method based on the block chain technology of the Internet of things comprises the following steps:
s1: collecting all information provided by a user who puts forward a mortgage loan application;
s2: all information provided by the user is called, the association degree among different users who simultaneously apply for mortgage loans is analyzed, and the users are classified according to the association degree;
s3: storing mortgage target information of the user into a main block chain and a sub-block chain respectively according to the classification result, and planning a proper storage mode;
s4: selecting an optimal data uploading object in the subarea block chain, and uploading mortgage target information of a user to the main subarea block chain by the optimal data uploading object;
s5: and carrying out real-time monitoring and management on the mortgage target of the user.
Further, in step S1: after a user submits a borrowing application, mortgage targets, the number of the mortgage targets and mortgage period information provided by different users simultaneously submitting the application are collected, the users with the same mortgage targets are initially screened, and the mortgage target number set of the users with the same mortgage targets is C= { C 1 ，C 2 ，…，C m The mortgage period set of the acquired user is T= { T 1 ，T 2 ，…，T m Acquiring a credit level set of a corresponding user as L= { L } 1 ，L 2 ，…，L m Where m represents the same number of users as the mortgage target.
Further, in step S2: calculating mortgage information evaluation coefficient W of random user according to the following formula i ：
W i =(C i -C min )/(C max -C min )+(T i -T min )/(T max -T min )+(L i -L min )/(L max -L min )；
Wherein C is i Representing the number of mortgage targets, T, for a random user i Representing mortgage periods for a random user, L i Representing the credit rating of a random user, C max And C min Representing the maximum and minimum in set C, respectivelyValue, T max And T min Respectively represent the maximum value and the minimum value in the set T, L max And L min Respectively representing the maximum value and the minimum value in the set L, and obtaining the mortgage information evaluation coefficient set of the users with the same mortgage targets as W= { W by the same calculation mode 1 ，W 2 ，…，W i ，…，W m Selecting mortgage information evaluation coefficient W of random user i According to formula Z j =1/|W i -W j The correlation degree between the residual random user and the corresponding user obtained by I calculation is Z j Wherein W is j The mortgage information evaluation coefficient representing the remaining random user is obtained, and the association degree set of the remaining user and the selected user is Z= { Z 1 ，Z 2 ，…，Z j ，…，Z m-1 }；
After a user applies for mortgage loans, users with the same mortgage targets are preferentially screened out, mortgage information of the users is acquired through big data, and the association degree among the users is analyzed, so that the users with the larger association degree are used as nodes on the same sub-blockchain, the mortgage targets are supervised through various modes such as cameras, supervision data are uploaded, abnormal data can be reported in time when the states of the mortgage targets are abnormal, the users with the large association degree can mutually analyze the approximate reason of the abnormal mortgage targets according to the abnormal data, and abnormal alarm information and the approximate reason are uploaded to institutions such as banks together, so that smooth progress of the mortgage flow is guaranteed.
Further, in step S3: arranging m-1 users in order of smaller association degree with the selected users, classifying the users into k classes after arranging, wherein the association degree of each user in the k-1 classes with the selected users is smaller than that of the k classes, and acquiring a classification result with maximum X value, wherein X= [ (Σ) k i=1 (Z i ’ -(∑ k i=1 (Z i ’ ))/k) 2 )/k] 1/2 Wherein Z is i ’ Representing a random class of users and selected users in a random class of classification resultsThe average value of the association degree is used for screening a kth class user in a classification result with the maximum X value, storing the kth class user and mortgage target supervision data of the selected user into the same sub-blockchain, taking all objects participating in the mortgage loan service except the user as nodes of a main blockchain, uploading the mortgage target supervision data of the kth class user to the main blockchain by one node in the same sub-blockchain, and receiving the mortgage target supervision data by all objects participating in the mortgage loan service except the user;
and the stored data are separated, and the data are uploaded to the main blockchain by one node, so that the speed of uploading the mortgage target supervision data is increased.
Further, in step S4: counting f k-th users, calling the mortgage target number, the mortgage period and the credit level of the f users, analyzing and comparing the mortgage information evaluation coefficients of the f users, and selecting the user with the biggest mortgage information evaluation coefficient as the best data uploading object in the sub-block chain;
in step S5: the mortgage targets of the kth class of users are monitored in real time, mortgage target monitoring data of the users are uploaded to the sub-block chain, alarming is carried out when abnormal conditions of the mortgage targets of random users are monitored, and alarming information and supervision information of the mortgage targets of the corresponding users are uploaded to the main block chain through optimal data uploading objects in the sub-block chain;
and selecting one user as the optimal data uploading object to upload the supervision data of the mortgage target by combining the mortgage information and the user credit level, thereby being beneficial to reducing the probability of information leakage.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, after a user applies for a mortgage loan, users with the same mortgage target are preferentially screened out, mortgage information of the users is acquired through big data, and the association degree among the users is analyzed, so that the users with the larger association degree are used as nodes on the same sub-block chain, the mortgage target is supervised through a plurality of modes such as a camera, supervision data are uploaded, abnormal data can be reported in time when the state of the mortgage target is abnormal, and the users with the large association degree can mutually analyze the general cause of the abnormality of the mortgage target according to the abnormal data, and abnormal alarm information and the general cause are uploaded to institutions such as banks together, so that smooth progress of a mortgage process can be guaranteed;
the stored data are separated, and the data are uploaded to the main block chain by one node, so that the speed of uploading the mortgage target supervision data is increased;
and selecting one user as the optimal data uploading object to upload the supervision data of the mortgage target by combining the mortgage information and the user credit level, thereby being beneficial to reducing the probability of information leakage.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a mortgage loan data storage system, based on the blockchain technology of the Internet of things, of the present invention;
fig. 2 is a flow chart of a mortgage loan data storage method based on the blockchain technology of the internet of things of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention is further described below with reference to fig. 1-2 and the specific embodiments.
Example 1: as shown in fig. 1, the present embodiment provides a mortgage loan data storage system based on the internet of things blockchain technology, the system comprising: the system comprises a user data acquisition module, a database, a storage management module, a data uploading management module and a target information management module;
the output end of the user data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the storage management module, the output end of the storage management module is connected with the input end of the data uploading management module, and the output end of the data uploading management module is connected with the input end of the target information management module;
collecting all information provided by a user who submits a mortgage loan application through a user data collecting module, and transmitting all information to a database;
storing all information provided by the user through a database;
selecting and storing mortgage target information of a user into a main block chain and a sub-block chain respectively through a storage management module, and planning a proper storage mode;
selecting an optimal data uploading object from the subarea block chain through a data uploading management module, and uploading mortgage target information of a user to the main subarea block chain through the optimal data uploading object;
the mortgage targets of the users are monitored and managed in real time through the target information management module, and the mortgage targets refer to living targets such as living cows and the like, so that the living targets need to be monitored.
The user data acquisition module comprises an application information acquisition unit and a credit rating acquisition unit;
the output ends of the application information acquisition unit and the credit rating acquisition unit are connected with the input end of the database;
the application information acquisition unit is used for acquiring mortgage targets, the number of the mortgage targets and mortgage period information provided by different users simultaneously providing application after the user submits the borrowing application;
the credit rating collecting unit is used for collecting credit rating information of the users who simultaneously give out borrowing applications, and the credit rating is assessed by related departments participating in mortgage loan business.
The storage management module comprises a user information analysis unit, a user classification unit and a data storage planning unit;
the input end of the user information analysis unit is connected with the output end of the database, the output end of the user information analysis unit is connected with the input end of the user classification unit, and the output end of the user classification unit is connected with the input end of the data storage planning unit;
the user information analysis unit is used for retrieving user information from the database and analyzing the association degree between different users who simultaneously apply for mortgage loans;
the user classifying unit is used for classifying the users according to the association degree to obtain a classifying result;
the data storage planning unit is used for storing mortgage target supervision data of users which are classified into the same class into the same sub-blockchain, taking all objects which participate in mortgage loan service except the users as nodes of the main blockchain, uploading the mortgage target supervision data of the users of the same class to the main blockchain by one node in the same sub-blockchain, and receiving the mortgage target supervision data by all the objects which participate in the mortgage loan service except the users.
The data uploading management module comprises a user information comparison unit and an uploading object selection unit;
the input end of the user information comparison unit is connected with the output end of the user classification unit, and the output end of the user information comparison unit is connected with the input end of the uploading object selection unit;
the user information comparison unit is used for comparing the application information and the credit rating information of the same type of users and analyzing mortgage information evaluation coefficients of different users;
the uploading object selecting unit is used for comparing the mortgage information evaluation coefficients, selecting the user with the biggest mortgage information evaluation coefficient as the object for uploading the mortgage target supervision data of all users in the corresponding category optimally, and adding the blockchain node where the user with the biggest mortgage information evaluation coefficient is located into the main blockchain.
The target information management module comprises a mortgage target supervision unit, an abnormal alarm unit and a supervision data uploading unit;
the output end of the mortgage target monitoring unit is connected with the input end of the abnormal alarm unit, and the output ends of the abnormal alarm unit and the uploading object selecting unit are connected with the input end of the monitoring data uploading unit;
the mortgage target supervision unit is used for uploading supervision information of each mortgage target by users in the same sub-blockchain;
the abnormal alarming unit is used for alarming when abnormal mortgage target states of random users are monitored;
and the supervision data uploading unit is used for uploading the police information and the supervision information of the user mortgage target corresponding to the abnormality occurrence to the main blockchain by the user with the biggest mortgage information evaluation coefficient.
Example 2: as shown in fig. 2, the present embodiment provides a mortgage loan data storage method based on the block chain technology of the internet of things, which is implemented based on the data storage system in the embodiment, and specifically includes the following steps:
s1: collecting all information provided by users who propose mortgage loan applications, collecting mortgage targets, the number of the mortgage targets and mortgage period information provided by different users who propose applications simultaneously after the users propose borrowing applications, primarily screening out users with the same provided mortgage targets, wherein the number set of the mortgage targets of the users with the same mortgage targets is C= { C 1 ，C 2 ，…，C m The mortgage period set of the acquired user is T= { T 1 ，T 2 ，…，T m Acquiring a credit level set of a corresponding user as L= { L } 1 ，L 2 ，…，L m -wherein m represents the same number of users as the mortgage target;
for example: the mortgage target number set of the users with the same collected mortgage targets is C= { C 1 ，C 2 ，C 3 ，C 4 ，C 5 ，C 6 ，C 7 The set of mortgage cycles for the user is t= { T = {20, 10, 15, 16, 30, 50, 18} 1 ，T 2 ，T 3 ，T 4 ，T 5 ，T 6 ，T 7 = {1,0.5,0.8,1.2,1.6,2.5,3}, in units of: the corresponding user's credit rating set is l= { L for years 1 ，L 2 ，L 3 ，L 4 ，L 5 ，L 6 ，L 7 }={0.6，0.8，0.99，0.82，0.55，0.75，0.83}；
S2: retrieving all information provided by users, analyzing the association degree between different users simultaneously applying for mortgage loans, classifying the users according to the association degree, and obtaining a formula W i =(C i -C min )/(C max -C min )+(T i -T min )/(T max -T min )+(L i -L min )/(L max -L min ) Calculating mortgage information evaluation coefficient W of random user i Wherein C i Representing the number of mortgage targets, T, for a random user i Representing mortgage periods for a random user, L i Representing the credit rating of a random user, C max And C min Respectively represent the maximum value and the minimum value in the set C, T max And T min Respectively represent the maximum value and the minimum value in the set T, L max And L min Respectively representing the maximum value and the minimum value in the set L, and obtaining the mortgage information evaluation coefficient set of the users with the same mortgage targets as W= { W by the same calculation mode 1 ，W 2 ，W 3 ，W 4 ，W 5 ，W 6 ，W 7 Selecting a mortgage information evaluation coefficient W of random one user = {0.56,0.57,1.245,1.04,0.94,2.26,1.84} 3 =1.04, according to formula Z j =1/|W i -W j The correlation degree between the residual random user and the corresponding user obtained by I calculation is Z j Wherein W is j The mortgage information evaluation coefficient representing the remaining random user is obtained, and the association degree set of the remaining user and the selected user is Z= { Z 1 ，Z 2 ，Z 3 ，Z 4 ，Z 5 ，Z 6 }={2.08，2.13，4.88，10，0.82，1.25}；
S3: storing mortgage target information of users into a main blockchain and a subarea block chain respectively according to classification results, planning a proper storage mode, arranging m-1 users in the order of smaller association degree with the selected users, and classifying the users into k classes after arrangement, wherein the association degree of each user in the k-1 classes with the selected users is smaller than that of the k classes, and obtaining a classification result with the maximum X value, wherein X= [ (Σ) k i=1 (Z i ’ -(∑ k i=1 (Z i ’ ))/k) 2 )/k] 1/2 Wherein Z is i ’ The average value of the association degree of the random type of users and the selected users in the random classification result is represented, the kth type of users in the classification result with the maximum X value is screened, the mortgage target supervision data of the kth type of users and the selected users are stored in the same sub-blockchain, all the objects participating in the mortgage loan service except the users are taken as nodes of a main blockchain, the mortgage target supervision data of the kth type of users are uploaded to the main blockchain by one node in the same sub-blockchain, and all the objects participating in the mortgage loan service except the users receive the mortgage target supervision data;
s4: selecting the optimal data uploading object in the sub-block chain, uploading the mortgage target information of the user to the main block chain by the optimal data uploading object, counting f users in the k class, calling the mortgage target number, the mortgage period and the credit level of the f users, analyzing and comparing the mortgage information evaluation coefficients of the f users, and selecting the user with the biggest mortgage information evaluation coefficient as the optimal data uploading object in the sub-block chain;
for example: classifying users into 2 classes, and obtaining a classification result with the maximum X value as follows: z is Z 1 、Z 5 And Z 6 Of the first kind, Z 2 、Z 3 And Z 4 Storing mortgage target supervision data of the second class users and selected users into the same sub-blockchain, uploading the mortgage target supervision data of the second class users to the main blockchain by one node in the same sub-blockchain by taking all objects participating in the mortgage loan service except the users as nodes of the main blockchain, receiving the mortgage target supervision data by all the objects participating in the mortgage loan service except the users, counting the total number of the second class users, calling the mortgage target number, the mortgage period and the credit level of the 3 users, and obtaining the mortgage information evaluation coefficients of the 3 users as follows: 0.57,1.245 and 0.94, selecting the user with the biggest mortgage information evaluation coefficient: the second user is used as the best data uploading object in the sub-block chain;
s5: and carrying out real-time monitoring and management on the mortgage targets of the users, carrying out real-time monitoring on the mortgage targets of the second type of users and the selected users, uploading the monitoring data of the mortgage targets of the users to the sub-blockchain, alarming when the abnormal state of the mortgage target of one random user is monitored, and uploading alarming information and supervision information of the mortgage targets of the corresponding users to the main blockchain by the optimal data uploading object in the sub-blockchain.
Finally, it should be noted that: the foregoing is merely a preferred example of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. Mortgage loan data storage system based on thing networking blockchain technique, its characterized in that: the system comprises: the system comprises a user data acquisition module, a database, a storage management module, a data uploading management module and a target information management module;
the output end of the user data acquisition module is connected with the input end of the database, the output end of the database is connected with the input end of the storage management module, the output end of the storage management module is connected with the input end of the data uploading management module, and the output end of the data uploading management module is connected with the input end of the target information management module;
collecting all information provided by a user who submits a mortgage loan application through the user data collecting module, and transmitting all information to the database;
storing all information provided by a user through the database;
selecting and storing mortgage target information of a user into a main block chain and a sub-block chain respectively through the storage management module, and planning a proper storage mode;
selecting an optimal data uploading object from the subarea block chain through the data uploading management module, and uploading mortgage target information of a user to the main subarea block chain through the optimal data uploading object;
and carrying out real-time monitoring and management on the mortgage target of the user through the target information management module.
2. The mortgage data storage system based on the blockchain technology of the internet of things as in claim 1, wherein: the user data acquisition module comprises an application information acquisition unit and a credit rating acquisition unit;
the output ends of the application information acquisition unit and the credit rating acquisition unit are connected with the input end of the database;
the application information acquisition unit is used for acquiring mortgage targets, the number of the mortgage targets and mortgage period information provided by different users simultaneously providing application after the user submits the borrowing application;
the credit rating collection unit is used for collecting credit rating information of the user who simultaneously submits the borrowing application.
3. The mortgage data storage system based on the blockchain technology of the internet of things as in claim 1, wherein: the storage management module comprises a user information analysis unit, a user classification unit and a data storage planning unit;
the input end of the user information analysis unit is connected with the output end of the database, the output end of the user information analysis unit is connected with the input end of the user classification unit, and the output end of the user classification unit is connected with the input end of the data storage planning unit;
the user information analysis unit is used for retrieving user information from the database and analyzing the association degree between different users who simultaneously apply for mortgage loans;
the user classifying unit is used for classifying the users according to the association degree to obtain a classifying result;
the data storage planning unit is used for storing mortgage target supervision data of users which are classified into the same class into the same sub-blockchain, taking all the objects which participate in the mortgage loan service except the users as nodes of the main blockchain, uploading the mortgage target supervision data of the users of the same class to the main blockchain by one node in the same sub-blockchain, and receiving the mortgage target supervision data by all the objects which participate in the mortgage loan service except the users.
4. A mortgage data storage system based on the blockchain technology of the internet of things as in claim 3, wherein: the data uploading management module comprises a user information comparison unit and an uploading object selection unit;
the input end of the user information comparison unit is connected with the output end of the user classification unit, and the output end of the user information comparison unit is connected with the input end of the uploading object selection unit;
the user information comparison unit is used for comparing the application information and the credit rating information of the same type of users and analyzing mortgage information evaluation coefficients of different users;
the uploading object selecting unit is used for comparing the mortgage information evaluation coefficients and selecting the user with the biggest mortgage information evaluation coefficient as the object for uploading the mortgage target supervision data of all users in the corresponding category optimally.
5. The mortgage data storage system based on the blockchain technology of the internet of things as in claim 4, wherein: the target information management module comprises a mortgage target supervision unit, an abnormal alarm unit and a supervision data uploading unit;
the output end of the mortgage target supervision unit is connected with the input end of the abnormality alarm unit, and the output ends of the abnormality alarm unit and the uploading object selection unit are connected with the input end of the supervision data uploading unit;
the mortgage target supervision unit is used for uploading supervision information of each mortgage target by users in the same sub-block chain;
the abnormal alarming unit is used for alarming when abnormal mortgage target states of random users are monitored;
and the supervision data uploading unit is used for uploading the police information and the supervision information of the user mortgage target corresponding to the abnormality to the main block chain by the user with the biggest mortgage information evaluation coefficient.
6. The mortgage loan data storage method based on the block chain technology of the Internet of things is characterized by comprising the following steps of: the method comprises the following steps:
s1: collecting all information provided by a user who puts forward a mortgage loan application;
s2: all information provided by the user is called, the association degree among different users who simultaneously apply for mortgage loans is analyzed, and the users are classified according to the association degree;
s3: storing mortgage target information of the user into a main block chain and a sub-block chain respectively according to the classification result, and planning a proper storage mode;
s4: selecting an optimal data uploading object in the subarea block chain, and uploading mortgage target information of a user to the main subarea block chain by the optimal data uploading object;
s5: and carrying out real-time monitoring and management on the mortgage target of the user.
7. The mortgage data storage method based on the blockchain technology of the internet of things as in claim 6, wherein: in step S1: after a user submits a borrowing application, mortgage targets, the number of the mortgage targets and mortgage period information provided by different users simultaneously submitting the application are collected, the users with the same mortgage targets are initially screened, and the mortgage target number set of the users with the same mortgage targets is C= { C 1 ，C 2 ，…，C m The mortgage period set of the acquired user is T= { T 1 ，T 2 ，…，T m Acquiring a credit level set of a corresponding user as L= { L } 1 ，L 2 ，…，L m Where m represents the same number of users as the mortgage target.
8. The internet of things-based zone of claim 7The mortgage loan data storage method of the block chain technology is characterized in that: in step S2: calculating mortgage information evaluation coefficient W of random user according to the following formula i ：
W i =(C i -C min )/(C max -C min )+(T i -T min )/(T max -T min )+(L i -L min )/(L max -L min )；
Wherein C is i Representing the number of mortgage targets, T, for a random user i Representing mortgage periods for a random user, L i Representing the credit rating of a random user, C max And C min Respectively represent the maximum value and the minimum value in the set C, T max And T min Respectively represent the maximum value and the minimum value in the set T, L max And L min Respectively representing the maximum value and the minimum value in the set L, and obtaining the mortgage information evaluation coefficient set of the users with the same mortgage targets as W= { W by the same calculation mode 1 ，W 2 ，…，W i ，…，W m Selecting mortgage information evaluation coefficient W of random user i According to formula Z j =1/|W i -W j The correlation degree between the residual random user and the corresponding user obtained by I calculation is Z j Wherein W is j The mortgage information evaluation coefficient representing the remaining random user is obtained, and the association degree set of the remaining user and the selected user is Z= { Z 1 ，Z 2 ，…，Z j ，…，Z m-1 }。
9. The mortgage data storage method based on the blockchain technology of the internet of things as in claim 8, wherein: in step S3: arranging m-1 users in order of from small to large degree of association with the selected users, classifying the users into k classes after arranging, and obtaining classification results with maximum X value, wherein X= [ (Σ) k i=1 (Z i ’ -(∑ k i=1 (Z i ’ ))/k) 2 )/k] 1/2 Wherein，Z i ’ And (3) representing the average value of the association degree of the random type of users and the selected users in the random classification result, screening the kth type of users in the classification result with the maximum X value, storing the mortgage target supervision data of the kth type of users and the selected users in the same sub-blockchain, taking all the objects participating in the mortgage loan service except the users as nodes of a main blockchain, uploading the mortgage target supervision data of the kth type of users to the main blockchain by one node in the same sub-blockchain, and receiving the mortgage target supervision data by all the objects participating in the mortgage loan service except the users.
10. The mortgage data storage method based on the blockchain technology of the internet of things as in claim 6, wherein: in step S4: counting f k-th users, calling the mortgage target number, the mortgage period and the credit level of the f users, analyzing and comparing the mortgage information evaluation coefficients of the f users, and selecting the user with the biggest mortgage information evaluation coefficient as the best data uploading object in the sub-block chain;
in step S5: and (3) monitoring the mortgage targets of the kth class of users in real time, uploading the mortgage target monitoring data of the users to the sub-block chain, alarming when the abnormal state of the mortgage target of one random user is monitored, and uploading alarm information and supervision information of the mortgage targets of the corresponding users to the main block chain by the optimal data uploading object in the sub-block chain.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107491915A (en) * | 2017-08-03 | 2017-12-19 | 迅鳐成都科技有限公司 | Goods right management system based on block chain in a kind of steel industry WMS |
| CN108648073A (en) * | 2018-05-18 | 2018-10-12 | 深圳壹账通智能科技有限公司 | Credit methods, device, equipment and computer storage media |
| CN111415251A (en) * | 2020-05-09 | 2020-07-14 | 杭州时戳信息科技有限公司 | Block chain-based mortgage lending method and system, storage medium and computing device |
| CN112330437A (en) * | 2020-10-26 | 2021-02-05 | 成都商通时代数字科技有限公司 | Block chain-based digital liquor evidence mortgage loan system and method |
| CN112651733A (en) * | 2020-12-23 | 2021-04-13 | 平安普惠企业管理有限公司 | Channel route selection method, device, equipment and storage medium |
| US20210256451A1 (en) * | 2017-05-10 | 2021-08-19 | State Farm Mutual Automobile Insurance Company | Incentivizing Agents Based Upon Updated Mortgage Ready Data |
| CN114219607A (en) * | 2021-11-12 | 2022-03-22 | 中钞信用卡产业发展有限公司杭州区块链技术研究院 | Data processing method and device, electronic equipment and storage medium |
| US20220156861A1 (en) * | 2020-11-16 | 2022-05-19 | Jpmorgan Chase Bank, N.A. | Systems and methods for blockchain-based data-driven property management |
| CN115293884A (en) * | 2022-08-09 | 2022-11-04 | 中国银行股份有限公司 | Block chain-based mortgage loan method and device |
| CN116029825A (en) * | 2022-12-29 | 2023-04-28 | 胖迪科技(深圳)有限公司 | Block chain transaction method, device and system, electronic equipment and storage medium |
-
2023
- 2023-05-16 CN CN202310544535.4A patent/CN116258580B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210256451A1 (en) * | 2017-05-10 | 2021-08-19 | State Farm Mutual Automobile Insurance Company | Incentivizing Agents Based Upon Updated Mortgage Ready Data |
| CN107491915A (en) * | 2017-08-03 | 2017-12-19 | 迅鳐成都科技有限公司 | Goods right management system based on block chain in a kind of steel industry WMS |
| CN108648073A (en) * | 2018-05-18 | 2018-10-12 | 深圳壹账通智能科技有限公司 | Credit methods, device, equipment and computer storage media |
| CN111415251A (en) * | 2020-05-09 | 2020-07-14 | 杭州时戳信息科技有限公司 | Block chain-based mortgage lending method and system, storage medium and computing device |
| CN112330437A (en) * | 2020-10-26 | 2021-02-05 | 成都商通时代数字科技有限公司 | Block chain-based digital liquor evidence mortgage loan system and method |
| US20220156861A1 (en) * | 2020-11-16 | 2022-05-19 | Jpmorgan Chase Bank, N.A. | Systems and methods for blockchain-based data-driven property management |
| CN112651733A (en) * | 2020-12-23 | 2021-04-13 | 平安普惠企业管理有限公司 | Channel route selection method, device, equipment and storage medium |
| CN114219607A (en) * | 2021-11-12 | 2022-03-22 | 中钞信用卡产业发展有限公司杭州区块链技术研究院 | Data processing method and device, electronic equipment and storage medium |
| CN115293884A (en) * | 2022-08-09 | 2022-11-04 | 中国银行股份有限公司 | Block chain-based mortgage loan method and device |
| CN116029825A (en) * | 2022-12-29 | 2023-04-28 | 胖迪科技(深圳)有限公司 | Block chain transaction method, device and system, electronic equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 周达勇等: ""区块链技术下供应链金融与科技型中小企业融资"", 《新金融》, no. 10, pages 49 - 54 * |
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