CN113487400A - Financial credit consensus method based on honesty bidirectional selection - Google Patents
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Abstract
The invention discloses a financial credit granting consensus method based on honesty bidirectional selection, and belongs to the technical field of block chain consensus. Firstly, preprocessing the honesty to eliminate the influence of financial credit-granting times on the honesty, and calculating the leadership by considering the recent profit condition of an enterprise so as to determine an enterprise leader node; secondly, considering the error factors of the enterprises in combination with the leadership, and determining the packaging and chaining sequence of the enterprises; and finally, the honesty is changed according to whether the enterprise repayment condition is on time or not so as to urge the enterprise to repay on time. The invention judges the stability of the profit of the enterprise by introducing error factors, greatly respects the intention of the enterprise by adopting a mode of mutual selection of the enterprise and the financial institution, writes the credit granting process into an intelligent contract, realizes the automation of financial credit granting, reduces the artificial influence and ensures that the credit granting process is open, transparent and traceable.
Description
Technical Field
The invention belongs to an application in the field of block chains, relates to the technologies of consensus mechanisms, machine learning and the like, and particularly relates to a financial credit consensus method based on honesty bidirectional selection.
Background
The block chain technology is a current emerging technology and has the characteristics of decentralization, no tampering, openness and transparency, traceability, automation and the like.
The traditional financial credit granting process has large manual intervention, subjective consciousness exists, fairness and impartiality can not be realized, the duration of the credit granting process is long, meanwhile, the possibility of information counterfeiting by a client is high, and a financial institution can not grant credit in a targeted manner.
The invention provides a financial credit consensus method based on honesty bidirectional selection in order to solve the defects of the traditional financial credit, combines a block chain technology and the financial credit, and utilizes the characteristics of the block chain technology, so that the financial credit process is fair and transparent.
Disclosure of Invention
The invention aims to provide a reliable financial credit consensus method based on honesty bidirectional selection aiming at a financial credit block chain. The method measures whether the enterprise is timely credited or not by introducing honesty, and measures the operation stability of the enterprise by introducing error factors. In order to make the credit granting process more stable, the initial credit granting amount of the enterprise is set to be a small amount; the method has the advantages that information such as financial institution sequencing trusted by enterprises, requirements of the financial institutions on the enterprises and the like is utilized to formulate corresponding rules of bidirectional selection of the financial institutions and the enterprises, and intentions of the enterprises and the financial institutions are greatly respected; monitoring the enterprise repayment process according to a machine learning algorithm in a form of a month period, so as to ensure that the enterprise data is real and reliable; the method has the advantages that the credit granting process is automatically carried out in an intelligent contract mode, human participation is reduced, the credit granting efficiency is improved, and the credit granting reliability is increased.
In order to achieve the purpose, the invention adopts the following technical scheme: the financial credit-granting consensus method based on two-way selection of honesty degree is characterized by comprising the following steps:
step 1: the financial credit-granting enterprise provides monthly information of the prestore years of the company and leads the information into an enterprise node, and a financial institution capable of granting credit leads the information of the organization into a financial node; taking the enterprise nodes meeting the conditions according to preset rules as enterprise candidate nodes, and taking all financial nodes as financial candidate nodes;
step 2: electing an enterprise leader node by the enterprise candidate nodes in the step 1 according to a preset rule 1, packaging the enterprise candidate nodes by the enterprise leader node according to a sequence to generate a block, and writing the block into the enterprise sub-chain; the financial candidate nodes elect financial leader nodes according to a preset rule 2, pack the financial candidate nodes in sequence to generate blocks by the financial leader nodes, and write the blocks into the financial subchains;
and step 3: selecting a certain number of super nodes by the nodes written into the financial sub-chains in the step 2 according to a preset rule, determining a financial credit-granting relationship by the super nodes, and finally packaging and writing the financial credit-granting relationship into a credit-granting main chain;
and 4, step 4: predicting part of information of the next period of the financial credit enterprise according to a preset rule at the end date of each period;
and 5: on the starting date of each period, an enterprise carrying out financial credit granting elects an information leader node according to a preset rule 1, and the information leader node forms transactions of the enterprise carrying out financial credit granting in the current period and the profit condition, and packages and links; if the period is the last period of the enterprise financial credit, the information leader node increases the enterprise financial credit formation times x once for the enterprise according to a preset rule 2, namely, the transaction of x is x +1, and the transaction is packed and written into the enterprise sub-chain;
step 6: and updating the error factor of the enterprise according to the information obtained in the step 4 and the step 5 according to a preset rule 1, and updating the honesty of the enterprise according to a preset rule 2.
Further, in step 1, the enterprise information should include basic information of the enterprise, business information in an enterprise period, decision variables related to the system and the enterprise, and the like. The basic information of the enterprise comprises: mortgage value k, credit line w wanted to be obtained, credit return cycle number d, trusted financial institution sequence Y (Y is an array, the head of the array is the most trusted financial institution of the enterprise, and the tail of the array is the least trusted financial institution of the enterprise); information within the enterprise cycle includes: purchase generation cost a1, payable a2, tax payable a3, welfare payable a4, other expenses a5, repayment amount a6, total expenses a, normal sales b1, other business income b2, total income b, profit c, whether financial credit is performed q (q takes a value of 0 or 1,1 represents that the period is within the period of validity of financial credit, and 0 represents that the period is not within the period of validity of financial credit); decision variables related to the system and enterprises are information specially needed by the system, are updated in real time in the process of credit granting and comprise: the financial credit granting times x (x is the financial credit granting times of the enterprise on the system and is initially set to be 0, in the final cycle of repayment process after the enterprise obtains credit, the financial credit granting times are updated according to the preset rule 2 in the step 5), the integrity degree epsilon (epsilon measures the index of whether the enterprise repayment is carried out on time or not, the initial setting is 0, in the repayment process after the enterprise obtains credit, the repayment process is updated according to the preset rule 2 in the step 6), the error factor eta (eta is the index of measuring the enterprise operation stability degree and is initially set to be 0, in the repayment process after the enterprise obtains credit, the repayment process is updated according to the preset rule 1 in the step 6), whether the enterprise is effective enterprise Vc (Vc is the financial credit granting process of recording whether the enterprise in the enterprise sub-chain already participates in the current time or not, the initial setting is 0 to represent that the enterprise is effective, in the financial credit granting process, updating the data in real time according to preset rules in the steps 1 and 3). Wherein, the units of the amount related to the parameters are ten thousand yuan.
Further, in step 1, the financial institution information includes: whether the profit is an effective financial institution (Vb), tolerable minimum value (m) of profit of nearly four seasons, return on investment (n) and credit amount calculation formula. And the value of Vb is 0 or 1, the default value is 0, the financial institution is valid, otherwise, the financial institution is invalid, the unit m is ten thousand yuan, and the credit amount calculation formula can be provided or not provided.
Further, in step 1, the preset rule is:
(1) verifying the enterprise node wanting to obtain the credit:
(c) continuous n profits of an enterprise1The quarter is positive, or negative values exist but the profit tends to rise;
p of loan amount lower than the amount of mortgage1％；
Wherein n is1、p1Are all a constant, and p1Has a value range of(0,100]；
(2) And forming a transaction with Vc 1 which is invalid for the enterprise nodes which do not meet the conditions.
Further, in step 2, the preset rule 1 is:
(1) competitive selection of an enterprise leader node:
defining the leadership (L), and calculating the formula as follows:
wherein epsilon is the honesty of the enterprise candidate nodes, x is the financial crediting times of the enterprise candidate nodes, and alpha1Is a very small positive number, the prevention denominator is 0, C is the sum of the total profits of the enterprise candidate node in nearly four quarters, and β is 10N+1N is the maximum number of total profits of all enterprises in nearly four seasons;
calculating the leadership L of each enterprise candidate node, wherein the enterprise candidate with the largest leadership is used as an enterprise leader node;
(2) the enterprise leader node determines the packing order of the enterprise candidate nodes:
defining a priority value (P) and calculating as follows:
wherein L is the leadership of the enterprise candidate node, η is an error factor of the enterprise candidate node, x is the financial crediting times of the enterprise candidate node, and α2Is a very small positive number, the prevention denominator is 0;
the enterprise leader node calculates the priority value of each enterprise candidate node, and packages the nodes according to the priority value, wherein the larger the priority value is, the more the packaging sequence is;
(3) the enterprise leader node issues a validation to the network:
the enterprise leader node packs the enterprise candidate nodes which are not packed according to the packing sequence and sends a verification request to the network;
(4) enterprise candidate node response request:
the enterprise candidate node responds after receiving the verification invitation sent by the enterprise leader node;
(5) the enterprise leader node processes the response result:
if it exceeds p2% of the enterprise candidate nodes respond to the verification request sent by the enterprise leader node, if the consensus is passed, the enterprise leader node writes the information of the enterprise candidate nodes into the enterprise sub-chain; wherein p is2Is a constant with the value range of (0, 100);
(6) and (3) circularly executing until finishing:
if there are still enterprise candidate nodes that are not packaged, repeating (2) through (6).
Further, in step 2, the preset rule 2 is:
(1) competitively selecting a financial leader node:
obtaining the probability P that the ith financial candidate node is taken as the financial leader nodeiAnd cumulative probability Qi：
Wherein P isiThe calculation method of (2) is as follows:
wherein R isiFor the ith financial institution return on investment, QiThe calculation method of (c) is as follows:
selecting a financial leader according to roulette, particularly in [0,1 ]]Generating a random number r over the interval, e.g. r<Q1Then financial institution B1Successfully selecting as a leader; qk-1≤r<QkIf yes, the kth financial candidate node is successfully elected as the financial leader;
(2) the financial leader node issues a verification to the network:
the financial leader node selects one of the unpacked financial candidate nodes to pack, and sends a verification request to the network;
(3) the financial candidate node responds to the request:
the financial candidate node responds after receiving the verification invitation sent by the financial leader node;
(4) the financial leader node processes the response results:
if it exceeds p3% of the financial candidate nodes respond to the verification request sent by the financial leader node, if the consensus is passed, the financial leader node writes the information of the financial candidate nodes into the financial subchain; wherein p is3% is a constant with the value range of (0, 100);
(5) and (3) circularly executing until finishing:
if there are unpacked financial candidate nodes, repeating (2) through (5).
Further, in step 3, the preset rule is:
(1) competing and electing the super nodes:
determining a super node according to the sequence (Y) of the financial institutions trusted by each enterprise written into the enterprise sub-chain, and defining the Super Leadership (SL), wherein the calculation formula is as follows:
wherein,
for the currently valid financial institution BtThe super leadership, Sc is the total number of effective enterprise nodes in the sub-chain of the enterprise at the current time, namely the number of enterprise nodes with Vc ═ 0, Sb is the total number of effective financial nodes in the financial sub-chain at the current time, namely the number of financial nodes with Vb ═ 0, and G (Y)ck[i]) Whether the ith bit of the financial institution sequence representing the trust of the enterprise is BtThe calculation formula is as follows：
Wherein, Yck[i]For enterprise CkIth trusted financial institution, BtA financial institution for which the super-leadership is currently to be calculated;
therefore, a fixed number of super nodes are elected according to the super leadership, the super leadership is larger, the super nodes are easier to be selected, and if financial institutions with the same super leadership value exist, the investment return rate (n) is larger, the super nodes are easier to be selected;
(2) drawing a super-node block-out sequence:
randomly planning the sequence of blocks in the selected super nodes with fixed number;
(3) the super node determines the credit granting relationship:
determining a super node of a current block according to a randomly drawn block output sequence, taking out the earliest packed and effective enterprise nodes in the sub-chain of the enterprise by the super node, comparing the effective financial nodes in the sub-chain one by one, and recording a financial institution which can give credit to the enterprise, namely the minimum value of the profit of the enterprise in four seasons is more than or equal to the minimum value (m) of the profit of the financial institution in four seasons which can be tolerated; the super node selects a financial institution which can authorize the enterprise according to the order (Y) of the financial institutions trusted by the enterprise from the financial institutions which can authorize the enterprise; if the enterprise does not meet the conditions of all the financial nodes, the super node writes a transaction with Vc 1 formed by the current enterprise into the enterprise sub-chain, and then the operation (3) is carried out again;
(4) the super node calculates the credit amount:
the credit amount may be obtained from a calculation formula provided by the financial institution, and if the financial institution does not provide the calculation formula, the following formula may be referred to:
wherein: w is the credit amount, the min function is the minimum value of the two, W is the credit amount which the enterprise wants to obtain, k is the collateral value of the enterprise, eta is the error factor of the enterprise, x is the financial credit times of the enterprise, and alpha3、α4Is an extremely small number, the prevention denominator is 0, AVGc is the average value of profits of enterprises in nearly three quarters, epsilon is the honesty of the enterprises, n2、n3All the constants are constants, and the value ranges of all the constants are positive integers;
(5) packaging the credit information by the super node and sending verification:
the super node responsible for the current block packaging the information of the enterprise obtaining the credit, the financial institution giving the credit, the credit amount and the like, and sending verification requests to all the super nodes;
(6) the super node responds to the request:
the super node responds after receiving the verification request of the super node responsible for the current block;
(7) the super node processes the response result:
if it exceeds p4And% of the super nodes respond to the verification request sent by the super node which is currently responsible for the block, if the consensus is passed, the super node which is currently responsible for the block writes the credit information into the credit main chain, and forms a transaction with Vc 1 for the enterprise in the current credit information and writes the transaction into the enterprise sub-chain. Wherein p is4Is a constant with the value range of (0, 100);
(8) and circulating until the round of credit granting is finished:
and (3) when the effective enterprise nodes still exist in the enterprise sub-chain, if all the super nodes in the randomly drawn block-out sequence have all the blocks in the round, executing (2) to (8), and otherwise, executing (3) to (8).
Further, in step 4, the preset rule is:
(1) predicting the enterprise part information of the next period according to the uplink data of the enterprise, wherein the calculation formula is as follows:
Y＝RNN(X) (8)
wherein, X is the enterprise part information and the credit granting condition in all the periods of the enterprise in the chain, and Y is the predicted enterprise part information in the next period.
Further, in step 4, the partial information of the enterprise includes: purchase generating cost (a1), payroll (a2), welfare (a4), other expenses (a5), normal sales (b1), other business income (b 2).
Further, in step 5, the preset rule 1 is:
(1) competitive election information leader node:
the average honesty (a epsilon) is defined and calculated as follows:
wherein epsilon is the honesty of the enterprise candidate nodes, x is the financial crediting times of the enterprise candidate nodes, and alpha5Is a very small positive number, the prevention denominator is 0;
calculating the average honesty (A epsilon) of each enterprise node, wherein the enterprise with the maximum average honesty is used as an information leader node;
(2) the information leader node issues a verification to the network:
the information leader node selects an enterprise from enterprise nodes which do not form a transaction, packs the repayment situation and the profit situation of the enterprise to form the transaction, and sends a verification request to the network;
(3) the enterprise node responds to the request:
the enterprise node responds after receiving the verification invitation sent by the information leader node;
(4) the information leader node processes the response result:
if it exceeds p5% of the enterprise nodes respond to the verification request sent by the information leader node, the consensus is passed, and the information leader node writes the transaction into the credit main chain, wherein p5Is a constant with the value range of (0, 100);
(5) and circulating until finishing:
and (5) if the enterprise nodes which do not form the transaction exist in the credit main chain, repeating the steps (2) to (5).
Further, in step 5, the preset rule 2 is:
(1) the information leader node issues a verification to the network:
the information leader node forms a transaction of x +1 to the enterprise and sends a verification request to the network;
(2) the enterprise node responds to the request:
the enterprise node responds after receiving the verification invitation sent by the information leader node;
(3) the information leader node processes the response result:
if it exceeds p6% of the enterprise nodes respond to the verification request sent by the information leader node, and if the consensus is passed, the information leader node writes the transaction into the enterprise sub-chain, wherein p6Is a constant with the value range of (0, 100).
Further, in step 6, the preset rule 1 is:
the formula for updating the error factor is:
wherein η is an error factor, a1 'predicts the cost of purchasing the enterprise in the next period, a 2' predicts the wage due for the enterprise in the next period, a4 'predicts the benefit fee due for the enterprise in the next period, a 5' predicts the other expenses for the enterprise in the next period, b1 'predicts the normal sale of the enterprise in the next period, b 2' predicts the other business income for the enterprise in the next period, a1 predicts the cost of purchasing the enterprise in the next period, a2 predicts the wage due for the enterprise in the next period, a4 is the benefit fee due for the enterprise in the next period, a5 is the other expenses for the enterprise in the next period, b1 is the normal sale of the enterprise in the next period, and b2 is the income for the other businesses in the next period.
Further, in step 6, the preset rule 2 is:
if the enterprise repays on time, the honesty is as follows:
wherein epsilon is honesty, d is the cycle number of loan return;
if the enterprise is not credited on time, the honesty is not changed.
Through the design scheme, the invention can bring the following beneficial effects: the invention provides a financial credit consensus method based on honesty bidirectional selection, and firstly, the method urges enterprises to repay on time by introducing honesty; secondly, an error factor is introduced, so that the stability of the profit of the enterprise is concerned, and a reference is provided for the later financial credit granting; moreover, the enterprise selects the financial institution which gives credit to the enterprise within the allowed range of the financial institution in view of the enterprise's will; and finally, the credit granting implementation mechanism is written into the intelligent contract, so that the automation of the financial credit granting process is realized, the manual intervention is reduced, and the financial credit granting time is saved.
Drawings
FIG. 1 is a block diagram of an algorithm in accordance with the present invention; FIG. 2 is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to specific examples.
A financial credit-granting consensus method based on honesty is applied to a financial credit-granting system based on a block chain, and the credit-granting system comprises an enterprise management module, a financial institution management module, a credit-granting request module, a credit-granting module and a loan-repaying module.
The enterprise management module is used for managing account information of an enterprise;
the financial institution management module is used for managing account information of the financial institution;
the credit granting request module is used for managing account information of a financial credit granting enterprise;
the credit granting module is used for recording the credit granting relationship between the enterprise and the corresponding financial institution;
and the loan repaying module is used for recording the information of the returned loan of the enterprise.
The embodiment shows an application of the method in the operation process of a block chain-based financial credit granting system.
The financial credit-granting consensus method based on two-way selection of integrity degree comprises the following steps:
1. the financial credit-granting enterprise provides monthly information of the prestore years of the company and leads the information into an enterprise node, and a financial institution capable of granting credit leads the information of the organization into a financial node; taking the enterprise nodes meeting the conditions according to preset rules as enterprise candidate nodes, and taking all financial nodes as financial candidate nodes;
in the example of the present invention, the history is 2, and there are 5 enterprises, and the information of each enterprise is as follows:
TABLE 1 Enterprise C1Information table
TABLE 2 Enterprise C2Information table
TABLE 3 Enterprise C3Information table
TABLE 4 Enterprise C4Information table
TABLE 5 Enterprise C5Information table
There are 5 financial institutions, and the information of each financial institution is as follows:
table 6 financial institution information table
1.1 verifying enterprise nodes: in this case, take n1Is 3, p1Is 70. Obtaining enterprise node C by preset rules4Negative values exist in profits in nearly 3 seasons, but the profits do not rise, the profits cannot serve as enterprise candidate nodes, and other enterprise nodes pass verification and become enterprise candidate nodes;
1.2 verifying the financial node: all financial nodes become financial candidate nodes.
2. The enterprise candidate node elects an enterprise leader node according to a preset rule 1, and the enterprise leader node packs the enterprise candidate nodes in sequence to generate a block, and writes the block into the enterprise sub-chain; the financial candidate nodes elect financial leader nodes according to a preset rule 2, pack the financial candidate nodes in sequence to generate blocks by the financial leader nodes, and write the blocks into the financial subchains;
2.1 election of Enterprise leader: calculate the leadership of 4 enterprises according to formula 1,
2.2 determining the packing order: the priority values of the 4 enterprises are calculated according to formula 2,
2.3 the Enterprise leader node issues a validation to the network: enterprise leader node C1Enterprise candidate node C that is not packaged according to packaging order1Packaging and sending a verification request to a network;
2.4 Enterprise candidate node response request: enterprise candidate node C3、C5Receiving the verification request and responding to the verification request;
2.5 the Enterprise leader node processes the response results: in this case, take p2Is 66.67. 66.67% of the enterprise candidates respond to the request, and the consensus passes, the enterprise passesBusiness leader node C1Node C as a candidate of an enterprise1Writing the information into the enterprise sub-chain;
2.6 repeat 2.3 through 2.5 until the packed order queue is empty. At this point, Enterprise C1、C3、C2、C5All have been written into the enterprise sub-chain;
2.7 election financial leader nodes: obtaining P of 5 financial candidate nodes according to formula 3i，
Calculating Q of 5 financial candidate nodes according to equation 4i，
Q1＝0.225688073，Q2＝0.423853211，Q3＝0.590835688，Q4＝0.726605505，Q5＝1；
Random number r is 0.631, so B4Becoming a financial candidate node;
2.8 the financial leader node issues a verification to the network: finance leader node B4Selecting B from unpacked financial candidate nodes1Packaging and sending a verification request to a network;
2.9 financial candidate node responds to the request: financial candidate node B3、B4、B5Receiving the verification request and responding to the verification request;
2.10 financial leader node processes the response results: in this case, take p3Is 66.67. Having 66.67% of the financial candidate nodes responded to the request, the consensus passes, and the financial leader node B4Node B financial candidate1Writing the information into the financial subchain;
2.11 repeat 2.8 through 2.10 until all financial candidate nodes are packaged. At this time, financial institution B1、B2、B3、 B4、B5Have all been written into the finance subchain.
3. Selecting a certain number of super nodes by the nodes written in the financial sub-chain according to a preset rule, determining a financial credit-granting relationship by the super nodes, and finally packaging and writing the financial credit-granting relationship into a credit-granting main chain;
3.1 competitive election of super nodes: calculating the super leadership of the currently valid financial institution according to formula 5:
assuming that the number of the selected super nodes is 4, the selected super nodes are: b is1、B3、B2、B4；
3.2 drawing up the super-node block-out sequence: randomly drawing the block output sequence of the super nodes as follows: b is2、B3、B1、B4；
3.3, the super node determines the credit granting relationship: the super node responsible for the current block is B2Therefore, B2Take out enterprise node C that was earliest packed and is valid in enterprise sub-chain1Obtaining the minimum value of profit of 50.6 ten thousand yuan of the enterprise nodes in nearly four seasons, and connecting the minimum value with the effective financial node B in the financial sub-chain1、B2、B3、B4、B5Comparing to obtain: can be to enterprise node C1Financial node B for financial credit1、B2、B3、B4Comparing enterprise node C1Trusted financial institution sequentially (Y) available to Enterprise node C1The financial institution which carries out the credit is B1；
3.4 the super node calculates the credit amount: since the financial institution B1The credit amount calculation formula is not provided, so formula 7 is used to calculate the credit amount, in this case, n is taken2Is 3, take n3Is as follows (6):
3.5, packaging the trust information by the super node and sending verification: super node B responsible for current out-of-block2Enterprise C1Financial institution B1Packaging information such as credit amount and the like, and sending verification requests to all super nodes;
3.6 response request of super node: super node B1、B4Receiving the verification request and responding to the verification request;
3.7 super node processes response result: in this case, take p4Is 66.67. If 66.67% of the super nodes respond to the request, the consensus is passed, and the super node B responsible for the current block is responsible2Writing the packaged credit information into a credit main chain and writing the enterprise C related in the current credit information1Forming a transaction write enterprise sub-chain with Vc 1;
3.8 according to the proposed super node block-out sequence, the super node responsible for the current block-out is changed into B3And repeating the steps 3.3-3.7 until all effective enterprises in the enterprise sub-chain perform the authorization process, and if the block output sequence of the super node is drawn up and is finished, randomly determining the block sequence again.
4. Predicting part of information of the next period of the financial credit enterprise according to a preset rule at the end date of each period; forecasting Enterprise C according to equation 81The partial information results of (a):
the purchase cost a 1' is 7.88 ten thousand yuan; payroll a 2' is 5.8 ten thousand yuan; the amount of the welfare a 4' is 5.2 ten thousand yuan; the other payout a 5' is 1.64 ten thousand yuan; normal sale b 1' is 36.84 ten thousand yuan; the other business income b 2' is 4.3 ten thousand yuan.
5. On the starting date of each period, an enterprise carrying out financial credit granting elects an information leader node according to a preset rule 1, and the information leader node forms transactions of the enterprise carrying out financial credit granting in the current period and the profit condition, and packages and links; if the period is the last period of the enterprise financial credit, the information leader node forms one transaction of increasing the enterprise financial credit times to the enterprise according to a preset rule 2, namely x is x +1, and the transaction is packed and written into an enterprise sub-chain:
5.1 competing election information leader node: the average honesty is calculated according to equation 9,
Aεc1＝1.4，Aεc2＝0，Aεc3＝0，Aεc5enterprise C1 is the information leader node, so 0;
5.2 the information leader node issues a validation to the network: information leader node C1Enterprise C1The repayment condition (repayment) and the profit condition (the purchase generation cost a1 is 8 ten thousand yuan, the payable a2 bit is 5.7 ten thousand yuan, the welfare a4 is 5 ten thousand yuan, the other expenditure a5 is 1.8 ten thousand yuan, the normal sale b1 is 38 ten thousand yuan, and the other business income b2 is 4 ten thousand yuan) of the period form a transaction and send a verification request to the network;
5.3 Enterprise response request: enterprise candidate node C2、C5Receiving the verification request and responding to the verification request;
5.4 the information leader node processes the response results: in this case, take p5Is 66.67. 66.67% of the enterprise candidate nodes respond to the request, and the consensus passes, the information leader node C1Writing the enterprise transaction into a credit main chain;
5.5, repeating the steps from 5.2 to 5.4 until all enterprises which are carrying out financial credit authorization form a transaction writing credit authorization main chain; at this point, Enterprise C1、C2、C3、C5Both the loan repayment scenario and the profit scenario of (c) have formed a transaction write credit backbone.
6. Updating error factors of the enterprise according to a preset rule 1, and updating the honesty of the enterprise according to a preset rule 2;
6.1 update C according to equation 101Error factor of enterprise
6.2 updating the honesty according to equation 11
In conclusion, the application screens the enterprises needing financial credit authorization to the enterprises which carry out financial credit authorization, and clearly displays the credit authorization process. According to the invention, the honesty is introduced firstly, enterprises are supervised and urged to repay on time, and the burden of financial institutions is reduced; secondly, introducing an error factor, predicting the profit of the enterprise in the next period in a machine learning mode, and judging the stability of the profit of the enterprise by combining the data uploaded by the enterprise to make a reference for the later financial credit; moreover, the enterprise and the financial institution mutually select, so that the intention of the enterprise is greatly respected; and finally, the credit granting process is written into an intelligent contract in advance, so that the automation of financial credit granting is realized, the artificial influence is reduced, and the credit granting process is open and transparent and traceable.
Claims (11)
1. A financial credit consensus method based on honesty bidirectional selection is characterized by mainly comprising the following steps:
step 1: the financial credit-granting enterprise provides monthly information of the prestore years of the company and leads the information into an enterprise node, and a financial institution capable of granting credit leads the information of the organization into a financial node; taking the enterprise nodes meeting the conditions according to preset rules as enterprise candidate nodes, and taking all financial nodes as financial candidate nodes;
step 2: electing an enterprise leader node by the enterprise candidate nodes in the step 1 according to a preset rule 1, packaging the enterprise candidate nodes by the enterprise leader node according to a sequence to generate a block, and writing the block into the enterprise sub-chain; the financial candidate nodes elect financial leader nodes according to a preset rule 2, pack the financial candidate nodes in sequence to generate blocks by the financial leader nodes, and write the blocks into the financial subchains;
and step 3: selecting a certain number of super nodes by the nodes written into the financial sub-chains in the step 2 according to a preset rule, determining a financial credit-granting relationship by the super nodes, and finally packaging and writing the financial credit-granting relationship into a credit-granting main chain;
and 4, step 4: predicting part of information of the next period of the financial credit enterprise according to a preset rule at the end date of each period;
and 5: on the starting date of each period, an enterprise carrying out financial credit granting elects an information leader node according to a preset rule 1, and the information leader node forms transactions of the enterprise carrying out financial credit granting in the current period and the profit condition, and packages and links; if the period is the last period of the enterprise financial credit, the information leader node increases the enterprise financial credit formation times x once for the enterprise according to a preset rule 2, namely, the transaction of x is x +1, and the transaction is packed and written into the enterprise sub-chain;
step 6: and updating the error factor of the enterprise according to the information obtained in the step 4 and the step 5 according to a preset rule 1, and updating the honesty of the enterprise according to a preset rule 2.
2. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: in step 1, the enterprise information includes basic information of an enterprise, operation information in an enterprise period, decision variables related to the system and the enterprise, and the like; the basic information of the enterprise comprises: mortgage value k, credit line w wanted to be obtained, credit return cycle number d, trusted financial institution sequence Y (Y is an array, the head of the array is the most trusted financial institution of the enterprise, and the tail of the array is the least trusted financial institution of the enterprise); information within the enterprise cycle includes: purchase generation cost a1, payable a2, tax payable a3, welfare payable a4, other expenses a5, repayment amount a6, total expenses a, normal sales b1, other business income b2, total income b, profit c, whether financial credit is performed q (q takes a value of 0 or 1,1 represents that the period is within the period of validity of financial credit, and 0 represents that the period is not within the period of validity of financial credit); decision variables related to the system and enterprises are information specially needed by the system, are updated in real time in the process of credit granting and comprise: the financial credit granting times x (x is the financial credit granting times of the enterprise on the system and is initially set to be 0, in the final cycle of repayment process after the enterprise obtains credit, the financial credit granting times are updated according to the preset rule 2 in the step 5), the integrity degree epsilon (epsilon measures the index of whether the enterprise repayment is carried out on time or not, the initial setting is 0, in the repayment process after the enterprise obtains credit, the repayment process is updated according to the preset rule 2 in the step 6), the error factor eta (eta is the index of measuring the enterprise operation stability degree and is initially set to be 0, in the repayment process after the enterprise obtains credit, the repayment process is updated according to the preset rule 1 in the step 6), whether the enterprise is effective enterprise Vc (Vc is the financial credit granting process of recording whether the enterprise in the enterprise sub-chain already participates in the current time or not, the initial setting is 0 to represent that the enterprise is effective, in the financial credit granting process, updating the data in real time according to preset rules in the steps 1 and 3); wherein, the units of the amount related to the parameters are ten thousand yuan.
3. The financial credit consensus method based on honesty changes of claim 1, wherein: the preset rule in the step 1 is as follows: verifying the enterprise node wanting to obtain the credit: (1) profit of the corporation (c) n consecutive1Positive quarterly or negative quarterly, but increasing profit (2) loan amounts lower than mortgage amounts1Percent; wherein n is1、p1Are all a constant, and p1Has a value range of (0,100)]。
4. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: the rule for selecting the enterprise leader node in the step 2 is as follows: defining the leadership (L), and calculating the formula as follows:
wherein epsilon is the honesty of the enterprise candidate nodes, x is the financial crediting times of the enterprise candidate nodes, and alpha1Is a very small positive number, the prevention denominator is 0, C is the sum of the total profits of the enterprise candidate node in nearly four quarters, and β is 10N+1N is the maximum number of total profits of all enterprises in nearly four seasons; calculate eachAnd the leadership L of each enterprise candidate node, and the enterprise candidate with the highest leadership is taken as the enterprise leader node.
5. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: in step 2, the packing sequence determination rule is as follows: defining a priority value (P) and calculating as follows:
wherein L is the leadership of the enterprise candidate node, η is an error factor of the enterprise candidate node, x is the financial crediting times of the enterprise candidate node, and α2Is a very small positive number, the prevention denominator is 0; and the enterprise leader node calculates the priority value of each enterprise candidate node, and packages the nodes according to the priority value, wherein the larger the priority value is, the more the packaging sequence is.
6. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: the rule for selecting the super node in the step 3 is as follows: determining a super node according to the sequence (Y) of the financial institutions trusted by each enterprise written into the enterprise sub-chain, and defining the Super Leadership (SL), wherein the calculation formula is as follows:
wherein,
for the currently valid financial institution BtThe super leadership, Sc is the total number of effective enterprise nodes in the sub-chain of the enterprise at the current time, namely the number of enterprise nodes with Vc ═ 0, Sb is the total number of effective financial nodes in the financial sub-chain at the current time, namely the number of financial nodes with Vb ═ 0, and G (Y)ck[i]) Trusted on behalf of an enterpriseWhether the ith order of the financial institution is BtThe calculation formula is as follows:
wherein, Yck[i]For enterprise CkIth trusted financial institution, BtA financial institution for which the super-leadership is currently to be calculated;
therefore, a fixed number of super nodes are selected according to the super leadership, the larger the super leadership is, the easier the super nodes are selected, and if financial institutions with the same super leadership value exist, the larger the investment return rate (n) is, the easier the super nodes are selected.
7. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: in step 3, the rules for determining the financial credit authorization relationship by the super node are as follows: determining a super node of a current block according to a randomly drawn block output sequence, taking out the earliest packed and effective enterprise nodes in the sub-chain of the enterprise by the super node, comparing the effective financial nodes in the sub-chain one by one, and recording a financial institution which can give credit to the enterprise, namely the minimum value of the profit of the enterprise in four seasons is more than or equal to the minimum value (m) of the profit of the financial institution in four seasons which can be tolerated; the super node selects a financial institution which can authorize the enterprise according to the order (Y) of the financial institutions trusted by the enterprise from the financial institutions which can authorize the enterprise; if the enterprise does not meet the conditions of all the financial nodes, the super node forms a transaction with Vc 1 for the current enterprise and writes the transaction into the enterprise sub-chain.
8. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: in step 3, after the credit relationship is determined, the rule for calculating the credit amount is as follows: the credit amount may be obtained from a calculation formula provided by the financial institution, and if the financial institution does not provide the calculation formula, the following formula may be referred to:
wherein: w is the credit amount, the min function is the minimum value of the two, W is the credit amount which the enterprise wants to obtain, k is the collateral value of the enterprise, eta is the error factor of the enterprise, x is the financial credit times of the enterprise, and alpha3、α4Is an extremely small number, the prevention denominator is 0, AVGc is the average value of profits of enterprises in nearly three quarters, epsilon is the honesty of the enterprises, n2、n3All are constants, and the value ranges of all the constants are positive integers.
9. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: the rule of competing election information leader nodes in step 5 is: the average honesty (a epsilon) is defined and calculated as follows:
wherein epsilon is the honesty of the enterprise candidate nodes, x is the financial crediting times of the enterprise candidate nodes, and alpha5Is a very small positive number, the prevention denominator is 0;
and calculating the average honesty (Abeta) of each enterprise node, wherein the enterprise with the maximum average honesty serves as an information leader node.
10. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: the rule for updating the error factors of the enterprises in the step 6 is as follows: the formula for updating the error factor is:
wherein η is an error factor, a1 'predicts the cost of purchasing the enterprise in the next period, a 2' predicts the wage due for the enterprise in the next period, a4 'predicts the benefit fee due for the enterprise in the next period, a 5' predicts the other expenses for the enterprise in the next period, b1 'predicts the normal sale of the enterprise in the next period, b 2' predicts the other business income for the enterprise in the next period, a1 predicts the cost of purchasing the enterprise in the next period, a2 predicts the wage due for the enterprise in the next period, a4 is the benefit fee due for the enterprise in the next period, a5 is the other expenses for the enterprise in the next period, b1 is the normal sale of the enterprise in the next period, and b2 is the income for the other businesses in the next period.
11. The financial credit consensus method based on honesty two-way selection as claimed in claim 1, wherein: the rule for updating the honesty of the enterprise in the step 6 is as follows: if the enterprise repays on time, the honesty is as follows:
wherein epsilon is the honesty, d is the loan repayment cycle number, and if the enterprise does not repay on time, the honesty is not changed.
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Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107944714A (en) * | 2017-11-28 | 2018-04-20 | 常州天正工业发展股份有限公司 | One kind debt-credit methods of risk assessment and device |
| US20180285971A1 (en) * | 2017-03-31 | 2018-10-04 | International Business Machines Corporation | Management of consumer debt collection using a blockchain and machine learning |
| US20190095879A1 (en) * | 2017-09-26 | 2019-03-28 | Cornell University | Blockchain payment channels with trusted execution environments |
| CN109544334A (en) * | 2018-10-22 | 2019-03-29 | 绿州蔚来(深圳)控股有限公司 | A kind of network scalability block chain implementation method |
| CN109547530A (en) * | 2018-10-17 | 2019-03-29 | 北京瑞卓喜投科技发展有限公司 | Region common recognition method, system and equipment |
| CN110870254A (en) * | 2017-06-01 | 2020-03-06 | 斯凯维公司D/B/A阿索尼 | Distributed private subspace blockchain data structure with secure access restriction management |
| US20200104293A1 (en) * | 2018-09-28 | 2020-04-02 | Thunder Token Inc. | High throughput blockchain consensus systems and methods with low finalization time |
| WO2020082213A1 (en) * | 2018-10-22 | 2020-04-30 | 深圳市哈希树科技有限公司 | Network expandability blockchain implementation method |
| CN111145010A (en) * | 2019-12-20 | 2020-05-12 | 湖南大学 | Credit granting and financing method and device |
| CN111429267A (en) * | 2020-03-26 | 2020-07-17 | 深圳壹账通智能科技有限公司 | Face examination risk control method and device, computer equipment and storage medium |
| CN111489248A (en) * | 2020-03-10 | 2020-08-04 | 天元大数据信用管理有限公司 | Loan risk control method, system, equipment and medium based on block chain |
| US20200294165A1 (en) * | 2018-05-21 | 2020-09-17 | Alibaba Group Holding Limited | Blockchain-based allocation methods and apparatuses |
| CN111861710A (en) * | 2020-07-21 | 2020-10-30 | 安徽高山科技有限公司 | Supply chain financial service method based on block chain |
| US20200349613A1 (en) * | 2018-06-19 | 2020-11-05 | Tencent Technology (Shenzhen) Company Limited | Media resource allocation method, apparatus, and system, storage medium, and computer device |
| CN112118305A (en) * | 2020-09-11 | 2020-12-22 | 北京易安睿龙科技有限公司 | Method for reducing invalid requests in block chain consensus system |
| CN112436944A (en) * | 2020-11-06 | 2021-03-02 | 深圳前海微众银行股份有限公司 | POW-based block chain consensus method and device |
| CN112488819A (en) * | 2020-10-29 | 2021-03-12 | 中国农业银行股份有限公司福建省分行 | Fast farming loan business credit line verification method, system, equipment and medium |
| CN112632186A (en) * | 2020-12-23 | 2021-04-09 | 远光软件股份有限公司 | Block chain consensus method, computer equipment and block chain system |
| US20210118072A1 (en) * | 2019-03-29 | 2021-04-22 | Launch Tech Co., Ltd. | Method and apparatus for hotel management and blockchain node server |
-
2021
- 2021-06-04 CN CN202110623034.6A patent/CN113487400B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180285971A1 (en) * | 2017-03-31 | 2018-10-04 | International Business Machines Corporation | Management of consumer debt collection using a blockchain and machine learning |
| CN110870254A (en) * | 2017-06-01 | 2020-03-06 | 斯凯维公司D/B/A阿索尼 | Distributed private subspace blockchain data structure with secure access restriction management |
| US20190095879A1 (en) * | 2017-09-26 | 2019-03-28 | Cornell University | Blockchain payment channels with trusted execution environments |
| CN107944714A (en) * | 2017-11-28 | 2018-04-20 | 常州天正工业发展股份有限公司 | One kind debt-credit methods of risk assessment and device |
| US20200294165A1 (en) * | 2018-05-21 | 2020-09-17 | Alibaba Group Holding Limited | Blockchain-based allocation methods and apparatuses |
| US20200349613A1 (en) * | 2018-06-19 | 2020-11-05 | Tencent Technology (Shenzhen) Company Limited | Media resource allocation method, apparatus, and system, storage medium, and computer device |
| US20200104293A1 (en) * | 2018-09-28 | 2020-04-02 | Thunder Token Inc. | High throughput blockchain consensus systems and methods with low finalization time |
| CN109547530A (en) * | 2018-10-17 | 2019-03-29 | 北京瑞卓喜投科技发展有限公司 | Region common recognition method, system and equipment |
| CN109544334A (en) * | 2018-10-22 | 2019-03-29 | 绿州蔚来(深圳)控股有限公司 | A kind of network scalability block chain implementation method |
| WO2020082213A1 (en) * | 2018-10-22 | 2020-04-30 | 深圳市哈希树科技有限公司 | Network expandability blockchain implementation method |
| US20210118072A1 (en) * | 2019-03-29 | 2021-04-22 | Launch Tech Co., Ltd. | Method and apparatus for hotel management and blockchain node server |
| CN111145010A (en) * | 2019-12-20 | 2020-05-12 | 湖南大学 | Credit granting and financing method and device |
| CN111489248A (en) * | 2020-03-10 | 2020-08-04 | 天元大数据信用管理有限公司 | Loan risk control method, system, equipment and medium based on block chain |
| CN111429267A (en) * | 2020-03-26 | 2020-07-17 | 深圳壹账通智能科技有限公司 | Face examination risk control method and device, computer equipment and storage medium |
| CN111861710A (en) * | 2020-07-21 | 2020-10-30 | 安徽高山科技有限公司 | Supply chain financial service method based on block chain |
| CN112118305A (en) * | 2020-09-11 | 2020-12-22 | 北京易安睿龙科技有限公司 | Method for reducing invalid requests in block chain consensus system |
| CN112488819A (en) * | 2020-10-29 | 2021-03-12 | 中国农业银行股份有限公司福建省分行 | Fast farming loan business credit line verification method, system, equipment and medium |
| CN112436944A (en) * | 2020-11-06 | 2021-03-02 | 深圳前海微众银行股份有限公司 | POW-based block chain consensus method and device |
| CN112632186A (en) * | 2020-12-23 | 2021-04-09 | 远光软件股份有限公司 | Block chain consensus method, computer equipment and block chain system |
Non-Patent Citations (4)
| Title |
|---|
| XIAOBING HUANG ET AL: ""Enterprise credit risk evaluation based on neural network algorithm"", 《COGNITIVE SYSTEMS RESEARCH》 * |
| 张国华: ""一种基于信誉的双向选择机制"", 《计算机应用》 * |
| 洪德荣: ""H企业供应链金融项目风险管理研究"", 《中国优秀硕士学位论文全文数据库 经济与管理科学辑》 * |
| 魏明侠 等: ""网上信用风险的演化:一个探索性分析框架"", 《经济管理 》 * |
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