CN110838056A - Debt resolving method and system based on big data - Google Patents
Debt resolving method and system based on big data Download PDFInfo
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- CN110838056A CN110838056A CN201911120908.5A CN201911120908A CN110838056A CN 110838056 A CN110838056 A CN 110838056A CN 201911120908 A CN201911120908 A CN 201911120908A CN 110838056 A CN110838056 A CN 110838056A
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Abstract
The invention discloses a debt resolving method and a system based on big data, belonging to the technical field of big data, comprising the following steps: acquiring subject information with debt rights and debts, and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a subject and a debt relation corresponding to the subject; canceling and resolving all the main bodies in the debt ring according to the minimum debt data; acquiring the debt and debt information and the number of debt subjects of each subject in the debt chain, paying the debt amount to the debtor at the tail end of the debt chain by a third-party platform, and compensating the debt to the debtor at the starting point of the debt chain to realize a cancellation solution. The invention utilizes big data technology to find out the debt ring and the debt chain for each subject with debt relationship, and carry out cancellation and solution on the debt of each subject in the debt ring and the debt chain, thereby reducing the debt cost to the maximum extent.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a debt resolving method and system based on big data.
Background
At present, the traditional direct means for clearing the triangular debt is to narrow the credit and spread goods, and the means often needs to inject a large amount of funds, so that the cost is high and the implementation is difficult.
Disclosure of Invention
The invention aims to solve the problems in the prior art so as to reduce the cost of debt cancellation and settlement.
In order to achieve the above purpose, the invention adopts a debt resolving method based on big data, comprising the following steps:
acquiring subject information with debt rights and debts, and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a subject and a debt relation corresponding to the subject;
canceling and resolving all the main bodies in the debt ring according to the minimum debt data;
acquiring the debt and debt information and the number of debt subjects of each subject in the debt chain, paying the debt amount to the debtor at the tail end of the debt chain by a third-party platform, and compensating the debt to the debtor at the starting point of the debt chain to realize a cancellation solution.
Further, the acquiring subject information with the right and the debt and obtaining the debt ring and/or the debt chain according to the subject information includes:
analyzing the subject information of the creditability and the debts based on a big data analysis method to obtain a directed graph for representing the debt relationship;
searching a directed ring graph in the directed graph, and taking the directed ring graph as the debt ring;
and searching a directed acyclic graph in a directed graph except the directed conversion graph to serve as the debt chain.
Further, the searching a directed ring graph in the directed graph and using the directed ring graph as the debt ring includes the following steps:
a1) storing the directed graph in an adjacent matrix form;
a2) traversing nodes with the out-degree larger than 0 in the directed graph, stacking the nodes with the out-degree larger than 0, and storing all connection points of the nodes into an array vect;
a3) the node is popped up, and the connection point of the node is subjected to depth-first search;
a4) in the depth-first searching process, if the out-degree of the searched node is 0, the node with the out-degree of 0 is popped;
a5) judging whether the popped node exists in the array vent, if so, executing the step a 6); if not, executing the step a 7);
a6) determining nodes with the out-degree greater than 0 and nodes with the out-degree of 0 in all the connection points of the nodes to form a directed ring graph, and storing the nodes in the ring into an array ring;
a7) and repeating the steps a2) to a6) until all the directed ring graphs are found.
Further, the method for canceling and resolving the subjects in the debt ring according to the minimum debt data comprises the following steps:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently;
b2) and canceling and resolving each main body in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve.
Further, the debt amount of the debt ring is accumulated according to the minimum amount of each main body in the debt ring.
Further, the acquiring the creditor and debt information and the number of the debt subjects of each subject in the debt chain, paying the debt amount to the creditor at the end of the debt chain and compensating the debt to the creditor at the beginning of the debt chain by the third-party platform, and implementing a cancellation solution includes:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
Further, the finding process of the starting point and the end of the debt chain comprises the following steps:
calculating the distance between any two nodes in the debt chain by adopting the following formula:
maxpath[i]＝max(maxpath[i],maxpath[k]+e[k][j])
wherein maxpath [ i ] represents the longest path from the starting point to the point i, and e [ k ] [ j ] represents the distance from the point j to the point i;
and taking the point with the largest distance from the starting point as the end of the debt chain.
On the other hand, the debt chemical solution system based on big data comprises an acquisition module, a first chemical solution module and a second chemical solution module;
the acquiring module is used for acquiring subject information with debt rights and debts and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a subject and a debt relation corresponding to the subject;
the first solution module is used for carrying out offset solution on each main body in the debt ring according to the minimum debt data;
the second resolution module is used for acquiring the credit and debt information and the number of debt subjects of each subject in the debt chain, paying the amount of the debt to a debtor at the tail end of the debt chain and compensating the debt to the debtor at the starting point of the debt chain by the third-party platform, and realizing offsetting resolution.
Further, the acquisition module comprises an analysis unit, a debt loop acquisition unit and a debt chain acquisition unit;
the analysis unit is used for analyzing the subject information with the debt right and the debt based on a big data analysis method to obtain a directed graph for representing the debt relationship;
the debt ring acquisition unit is used for searching a directed ring graph in the directed graph and taking the directed ring graph as the debt ring;
the debt chain acquisition unit is used for searching a directed acyclic graph in a directed graph except the directed transformational graph as the debt chain.
Further, the first solution module is configured to perform the following steps:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently;
b2) canceling and resolving all main bodies in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve;
the second decomposition module is used for executing the following steps:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
Compared with the prior art, the invention has the following technical effects: the invention utilizes big data technology to find out the debt ring and the debt chain for each subject with debt relationship, thereby carrying out cancellation and solution on the debt of each subject in the debt ring and the debt chain, realizing optimization of the debt relationship, reducing unnecessary fund injection and reducing debt cost to the maximum extent.
Drawings
The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a flow chart diagram of a big data based debt resolution method;
FIG. 2 is a representation of a debt relationship directed graph;
FIG. 3 is a diagram of an original debt relationship;
FIG. 4 is a maximum liability ring in the original liability relationship diagram of FIG. 3;
figure 5 is a diagram of the debt relationship after the maximum debt ring offset shown in figure 4;
FIG. 6 is a diagram of the debt relationship after the debt ring shown in FIG. 5 is cancelled;
fig. 7 is a diagram of the debt relationship after the debt ring shown in fig. 6 is cancelled;
figure 8 is a schematic diagram of a chain of debts with the most nodes;
FIG. 9 is a debt relationship diagram after the debt chain shown in FIG. 8 is resolved;
FIG. 10 is a relationship diagram of the debt relationship diagram of FIG. 9 after re-optimization;
fig. 11 is a schematic structural diagram of a debt resolving system based on big data.
Detailed Description
To further illustrate the features of the present invention, refer to the following detailed description of the invention and the accompanying drawings. The drawings are for reference and illustration purposes only and are not intended to limit the scope of the present disclosure.
As shown in fig. 1, the present embodiment discloses a debt change method based on big data, which includes the following steps S1 to S3:
s1, acquiring subject information with the debt right and the debt, and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a debt relationship corresponding to a subject and a subject;
s2, canceling and resolving all the subjects in the debt ring according to the minimum debt data;
and S3, acquiring the creditor and debt information and the number of debt subjects of each subject in the debt chain, paying the amount of the debt to the creditor at the tail end of the debt chain and compensating the debt to the creditor at the starting point of the debt chain by the third-party platform, and realizing offsetting and resolving.
In the embodiment, the linkage debt relationship is optimized in real time by means of a big data analysis technology, and the quick cleaning of various forms of 'triangle debt' and 'multi-angle debt' is realized, and the debt risk is prevented and solved on the premise of not injecting funds or only injecting a small amount of funds.
Specifically, in the step S1, acquiring the subject information of the right to debt and the debt, and obtaining the debt ring and/or the debt chain according to the subject information, includes the following steps S11 to S13:
s11, analyzing the subject information with the debt right and the debt based on a big data analysis method to obtain a directed graph for representing the debt relationship;
s12, searching a directed ring graph in the directed graph, and taking the directed ring graph as the debt ring;
and S13, searching directed acyclic graphs in directed graphs except for the directed conversion graph to serve as the debt chain.
In this embodiment, the acquired subject and the debt relationship corresponding to the subject are stored in a specific data structure form, and then a big data analysis technology is used to perform an operation on the data structure, so as to construct a directed graph for representing the debt relationship of each subject, specifically, as shown in fig. 2, each debtor in fig. 2 is a node in the graph, such as A, B, C, D, E, and the debt relationship is an edge in the graph.
It should be noted that the procedure of finding the debt ring is a procedure of finding a ring in the directed graph, and since a ring with the largest debt weight amount needs to be found, a ring with the largest weight needs to be found in the directed graph with the weight. At the above step S12: searching a directed ring graph in the directed graph, and taking the directed ring graph as the debt ring, wherein the method comprises the following steps:
a1) and storing the directed graph in an adjacent matrix form, wherein the specific form is as follows:
a2) traversing nodes with the out-degree larger than 0 in the directed graph, stacking the nodes with the out-degree larger than 0, and storing all connection points of the nodes into an array vect; for example, when the node a with the out degree greater than 0 is traversed, the node a is stacked from the node a, and all the connection points of the node are stored in the array vect.
a3) And popping the node (such as the node A) and carrying out depth-first search on the connection point of the node.
a4) And in the depth-first searching process, if the out degree of the connection points of the nodes is 0, popping the nodes with the out degree of 0.
a5) Judging whether the popped node exists in the array vent, if so, executing the step a6), and if not, executing the step a 7);
a6) determining nodes with the out-degree greater than 0 and nodes with the out-degree of 0 in all the connection points of the nodes to form a directed ring graph, and storing the nodes in the ring into an array ring;
a7) and repeating the steps a2) to a6) until all the directed ring graphs are found.
Further, the above step S2: the method specifically comprises the following steps of carrying out cancellation and solution on each main body in the debt ring according to the minimum debt data:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently; and the debt amount of the debt ring is accumulated according to the minimum amount of each main body in the debt ring to obtain:
wherein n represents the number of subjects, AmtminRepresenting a minimum amount for each principal, total representing a cumulative value of the minimum cancelable amounts for the participants.
b2) And canceling and resolving all the main bodies in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve, namely, until the debt rings cannot be found.
Further, after all the debt rings are offset, performing offset of the debt chain, including the following steps:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
It should be noted that the equal ratio is averaged according to the number of the counteracting parties. For example, three persons participate in the cancellation, cancel 100W, compensate to 100W, deduct the platform cost 10W, and leave 90W to participate in the three average scores of the cancellation. The equal proportion of the cost is returned to each main body, so that the capital investment of the participator in resolving the debt can be reduced.
Further, the finding process of the starting point and the end of the debt chain comprises the following steps:
calculating the distance between any two nodes in the debt chain by adopting the following formula:
maxpath[i]＝max(maxpath[i],maxpath[k]+e[k][j])
wherein maxpath [ i ] represents the longest path from the starting point to the point i, and e [ k ] [ j ] represents the distance from the point j to the point i;
and taking the point with the largest distance from the starting point as the end of the debt chain.
For example, the following steps are carried out: if there are A, B, C nodes, find maxpath [ C ], or directly get maxpath [ C ], or get maxpath [ A ] + e [ A ] [ C ], or get maxpath [ B ] + e [ B ] [ C ]. Specifically, a two-dimensional matrix (N x N) is created, and the longest path from each point to each point is stored; sorting the Nth column, taking out the largest number a in the Nth column, and recording a row number R and a column number C (in v 1- > v2, the row corresponds to v1, and the column corresponds to v 2); then, the column number C is pushed into the stack (the direct output is the longest route when outputting), and then the column number R 'of the same number is found according to the row number R of the number a (for example, the position of the a number is [4] [5], the row number is 4, the 4 th column is found), and let N be R'.
The solution for the offset of the liability rings is illustrated as follows:
fig. 3 shows a directed graph representation form of an original debt relationship graph, and after a debt ring cancellation process, the largest debt ring is found out, which is the ring formed by nodes 0,1,3,4,2, 5, and 6 as shown in fig. 4. The minimum amount of 150 ten thousand in the largest debt ring is cancelled, the debts between the nodes 5 and 6 are completely cancelled, and the cancellation result is shown in fig. 5. The debt rings shown in fig. 5 are searched to find out the ring formed by the largest debt ring, i.e. the nodes 2, 5, 4, as shown in fig. 6. The minimum sum of 60 million in the ring shown in fig. 6 is cancelled, the debt between the nodes 5 and 2 is completely cancelled, and the cancellation result is shown in fig. 7. And then carrying out the operation of the debt ring again, and determining all the cancellation solutions of all the debt rings if the debt rings are not found.
The cancellation solution for the chain of debts is illustrated as follows:
after all the debt rings are completely offset and contacted, the processing of the debt chains is started, and the debt chain with the most nodes is firstly found out, as shown in fig. 8, wherein the longest debt chain is the ring formed by the nodes 0,1,3,4 and 2. Wherein, the node 0 is a debtor, the node 2 is a creditor, and the middle nodes are both the creditor and the debtor. The minimum amount in the chain of debts is 30 ten thousand. 1,3,4 three nodes, each funding 30/3 is 10 ten thousand yuan to a third party platform (a third party with public trust), the platform pays to 2 nodes as creditors, in this link, the 1,3,4 nodes each offset 30 ten thousand yuan of creditors, 30 ten thousand of creditors owed by the 0 node are converted into creditors of the platform party, the platform is responsible for compensation to the creditor 0 in a unified way, and the platform can deduct a small amount of service fee after obtaining compensation and returns the service fee to the nodes 1,3,4 in equal proportion. After the settlement, the debt relationship is shown in fig. 9. The debt chain is repeatedly searched and operated, and finally, the optimized debt relationship graph is cancelled as shown in fig. 10.
It should be noted that, through the processing of the debt ring and the debt chain, the debt relationship diagram with complex original relationship is finally optimized into the debt relationship with single relationship and relatively simple relationship, and the debt chain is untied to the maximum extent, so as to avoid chain debt crises. The optimized debt relationship graph reduces the total amount of the debts and the number of subjects. After the node 3 in the above example performs multiple operations of the debt ring and the debt chain, the creditor and the debt are completely released, and the node will be issued in the debt relationship diagram.
For the optimized simple debt-debt relationship, a debt right circulation mode can be further recommended for each debt classification to perform debt right inventory and debt settlement, such as: for local national enterprise engineering money payable, the creditor certificate opened by the receivable and billable creditor management company is circulated and resolved, and for the case of ' execution ' of the people's court, the creditor certificate opened by the mediation organization is circulated and resolved.
As shown in fig. 11, the embodiment further discloses a debt chemical solution system based on big data, which includes an obtaining module 10, a first chemical solution module 20, and a second chemical solution module 30;
the acquiring module 10 is configured to acquire subject information with a right of debt and a debt, and acquire a debt ring and/or a debt chain according to the subject information, where the subject information includes a debt relationship corresponding to a subject and the subject;
the first solution module 20 is used for carrying out offset solution on each subject in the debt ring according to the minimum debt data;
the second resolution module 30 is configured to obtain the debt and debt information and the number of the debt subject of each subject in the debt chain, and the third party platform pays the debt amount to the debtor at the end of the debt chain and pays the debt to the debtor at the start of the debt chain, so as to implement cancellation and resolution.
It should be noted that, in the scheme, the offset schemes of the debt ring and the debt chain can be systematically and automatically calculated and pushed to the client corresponding to the debt and debt subject in the ring or chain, and each subject can real-timely confirm that the debt and debt are offset by equal amount, so that the income and the income can be reduced without investing funds.
Further, the acquiring module 10 includes an analyzing unit 11, a debt loop acquiring unit 12 and a debt chain acquiring unit 13;
the analysis unit 11 is configured to analyze the subject information of the creditability and the debt based on a big data analysis method to obtain a directed graph representing a debt relationship;
the debt ring acquiring unit 12 is configured to search a directed ring graph in the directed graph, and use the directed ring graph as the debt ring;
the debt chain acquiring unit 13 is configured to search a directed acyclic graph in a directed graph other than the directed transformational graph as the debt chain.
Further, the first solution module 20 is configured to perform the following steps:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently;
b2) canceling and resolving all main bodies in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve;
the second hydrolysis module 30 is configured to perform the following steps:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
It should be noted that the terms used in the present embodiment are explained as follows:
offsetting: the method refers to the cancellation specified by the contractual law, specifically refers to the mutual debt payment of the debts by the debt rights when the two parties mutually bear the debts, so that the debts and the debts of the other party mutually disappear in the equal amount or the amount agreed by the two parties. The creditor still has the right to be paid for the portion that has not been cancelled. Cancellation is also known as cancellation in daily life.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A debt resolving method based on big data is characterized by comprising the following steps:
acquiring subject information with debt rights and debts, and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a subject and a debt relation corresponding to the subject;
canceling and resolving all the main bodies in the debt ring according to the minimum debt data;
acquiring the debt and debt information and the number of debt subjects of each subject in the debt chain, paying the debt amount to the debtor at the tail end of the debt chain by a third-party platform, and compensating the debt to the debtor at the starting point of the debt chain to realize a cancellation solution.
2. The big data-based debt solution method according to claim 1, wherein the obtaining subject information with debt rights and debt and obtaining the debt ring and/or the debt chain according to the subject information comprises:
analyzing the subject information of the creditability and the debts based on a big data analysis method to obtain a directed graph for representing the debt relationship;
searching a directed ring graph in the directed graph, and taking the directed ring graph as the debt ring;
and searching a directed acyclic graph in a directed graph except the directed conversion graph to serve as the debt chain.
3. The big data-based debt solution method according to claim 2, wherein the step of finding a directed ring graph in the directed graph and using the directed ring graph as the debt ring comprises the steps of:
a1) storing the directed graph in an adjacent matrix form;
a2) traversing nodes with the out-degree larger than 0 in the directed graph, stacking the nodes with the out-degree larger than 0, and storing all connection points of the nodes into an array vect;
a3) the node is popped up, and the connection point of the node is subjected to depth-first search;
a4) in the depth-first searching process, if the out-degree of the searched node is 0, the node with the out-degree of 0 is popped;
a5) judging whether the popped node exists in the array vent, if so, executing the step a6), and if not, executing the step a 7);
a6) determining nodes with the out-degree greater than 0 and nodes with the out-degree of 0 in all the connection points of the nodes to form a directed ring graph, and storing the nodes in the ring into an array ring;
a7) and repeating the steps a2) to a6) until all the directed ring graphs are found.
4. The big data based debt solution method according to claim 1, wherein the step of offsetting each subject in the debt ring according to the minimum debt data comprises the following steps:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently;
b2) and canceling and resolving each main body in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve.
5. The big data based debt resolution method of claim 4, wherein the debt amount of the debt ring is accumulated according to the minimum amount of each subject in the debt ring.
6. The big data based debt solution method of claim 1, wherein the obtaining of the debt and debt information and the number of debt subjects of each subject in the debt chain, and the paying of the debt amount to the debtor at the end of the debt chain and the compensation of the debt to the debtor at the beginning of the debt chain by the third party platform to realize the offsetting solution comprises:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
7. The big data based debt solution method of claim 6, wherein the finding process of the starting point and the end point of the debt chain comprises:
calculating the distance between any two nodes in the debt chain by adopting the following formula:
maxpath[i]＝max(maxpath[i],maxpath[k]+e[k][j])
wherein maxpath [ i ] represents the longest path from the starting point to the point i, and e [ k ] [ j ] represents the distance from the point j to the point i;
and taking the point with the largest distance from the starting point as the end of the debt chain.
8. A debt resolving system based on big data is characterized by comprising an acquisition module, a first resolving module and a second resolving module;
the acquiring module is used for acquiring subject information with debt rights and debts and acquiring a debt ring and/or a debt chain according to the subject information, wherein the subject information comprises a subject and a debt relation corresponding to the subject;
the first solution module is used for carrying out offset solution on each main body in the debt ring according to the minimum debt data;
the second resolution module is used for acquiring the credit and debt information and the number of debt subjects of each subject in the debt chain, paying the amount of the debt to a debtor at the tail end of the debt chain and compensating the debt to the debtor at the starting point of the debt chain by the third-party platform, and realizing offsetting resolution.
9. The big-data-based debt resolution system of claim 8, wherein the obtaining module comprises an analyzing unit, a debt loop obtaining unit and a debt chain obtaining unit;
the analysis unit is used for analyzing the subject information with the debt right and the debt based on a big data analysis method to obtain a directed graph for representing the debt relationship;
the debt ring acquisition unit is used for searching a directed ring graph in the directed graph and taking the directed ring graph as the debt ring;
the debt chain acquisition unit is used for searching a directed acyclic graph in a directed graph except the directed transformational graph as the debt chain.
10. The big-data based debt resolution system of claim 9, wherein the first resolution module is configured to perform the steps of:
b1) finding out the debt ring with the maximum debt amount from all the debt rings as the debt ring needing to be solved currently;
b2) canceling and resolving all main bodies in the debt rings needing resolving currently according to the minimum debt amount, and then repeatedly executing the steps b1) -b 2) until all the debt rings cancel and resolve;
the second decomposition module is used for executing the following steps:
c1) finding the debt chain with the longest node in all the debt chains as the debt chain needing to be solved currently;
c2) the minimum sum and the number of debt main bodies of each main body in the debt chain needing to be solved currently are obtained and sent to a third party platform, and the third party platform pays the debt amount to a debtor at the tail end of the debt chain and pays the debt to the debtor at the starting point of the debt chain;
c3) and after the debtors compensate the debts, deducting the set fees and returning the debts to the subjects in equal proportion, and then repeatedly executing the steps c1) -c 3) until the number of the subjects in the debt chain needing to be solved is less than or equal to 2.
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