CN111461793B - Integration chain consensus method based on liveness probability selection - Google Patents
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Abstract
The invention discloses an integral chain consensus method based on liveness probability selection, which belongs to the technical field of block chain consensus, and firstly, the invention normalizes related information of user liveness acquired in an integral chain system, and introduces a normalization mechanism based on maximum and minimum digits into dynamic change characteristics, thereby eliminating dimension influence among different characteristics and solving the problem of poor comparability among data; secondly, a weighted probability mechanism is introduced into an integral chain system to select a leader, so that the reliability and fairness of the integral chain are enhanced; finally, the user's viscosity and loyalty are improved through a reasonable beneficiary reward distribution mechanism. The invention can realize that the integral cannot be tampered, ensure the integral storage safety and improve the activity of users.
Description
Technical Field
The invention relates to the technical field of block chain consensus, in particular to an integral chain consensus method based on liveness probability selection.
Background
The blockchain is used as an integrated application of technologies such as distributed data storage, point-to-point transmission, a consensus algorithm, an encryption algorithm and the like, and is a distributed account book which is formed by combining data blocks in a sequential connection mode according to a time sequence in a narrow sense and is not tamperable and not counterfeitable in a cryptographic mode. In broad terms, blockchain technology is a completely new distributed infrastructure and computing paradigm that uses a blockchain data structure to verify and store data, a distributed node consensus algorithm to generate and update data, a cryptographic way to secure data transmission and access, and an intelligent contract consisting of automated script code to program and manipulate data. The consensus algorithm is an algorithm protocol for coordinating all data consistency in the whole network, and is a core problem in the block chain technology.
Points are rewards that merchants offer while they consume in order to hold customers, typically redeem some form of privileges. The point is widely used by merchants as a digital asset with a certain value, and aims to lock old customers, attract new customers, and improve the consumption frequency, the quota and the loyalty of users. The point chain system integrates the points of different cooperators based on the principle of point integration equivalent exchange and the blockchain technology, and enables users to be more active and sticky through functions of communication, transfer and the like.
Disclosure of Invention
The invention aims to provide an integral chain consensus method based on liveness probability selection, which is applied to an integral chain system, can reduce the calculated amount, improve the stability, does not need to do a large amount of invalid workload proof calculation, improves the efficiency in time, ensures the safety and fairness, improves the user loyalty and constructs a more active integral chain.
In order to achieve the above purpose, the invention adopts the following technical scheme: an integral chain consensus method based on liveness probability selection is characterized by comprising the following steps:
step 1: acquiring user liveness characteristic data, and carrying out normalization processing on the user liveness characteristic data according to a preset normalization rule to obtain normalized user liveness characteristic data;
step 2: obtaining a comprehensive weighting value of the user, namely the weighted liveness of the user, according to the normalized user liveness characteristic data and the weighting coefficient of the user liveness characteristic data;
step 3: according to the comprehensive weighted value of the user, obtaining the user meeting the preset rule according to the preset rule, wherein the user is used as a leader of the current point transaction in the point chain, and the leader is responsible for packaging the transaction to form a block;
step 4: the leader in the step 3 determines the point beneficiary of the current point transaction in the point chain according to the set rule;
step 5: users who are the point beneficiaries sign in the point chain, and users who are the point beneficiaries and leaders are rewarded according to preset rules.
Further, the user liveness characteristic data is obtained from a score chain system database.
Further, the user liveness characteristic data comprises a member grade, a transaction amount, a conversion amount and an online time length.
Further, the user liveness characteristic is divided into two kinds of user liveness fixed characteristic and user liveness dynamic characteristic,
the preset normalization rule adopted by the user liveness fixing feature is as follows:
X′＝(X-X_min)/(X_max-X_min)
wherein X' represents the normalized value of the current user activity characteristic data, X represents the actual value of the current user activity characteristic data, X_min is the minimum value of all the user activity characteristic data, X_max is the maximum value of all the user activity characteristic data, and the maximum value is a preset value;
the preset normalization rule adopted by the dynamic characteristics of the user liveness is as follows:
X′＝(X-X_min)/(X_max-X_min)
wherein X' represents the normalized value of the current user activity characteristic data, X represents the actual value of the current user activity characteristic data, X_min is the minimum integer value corresponding to the bit number corresponding to the minimum value of all the user activity characteristic data, and X_max is the maximum integer value corresponding to the bit number corresponding to the maximum value of all the user activity characteristic data.
Further, the preset rule in step 3 is:
(1) acquiring weighted liveness of a user:
the weighted liveness calculation formula of the user is as follows:
wherein A is j Representing the weighted liveness of the jth user; m represents the number of user liveness features; omega i Representing the weight assigned to the ith user activity feature according to its importance level; x is X ij Ith user activity characteristic data representing a jth user; n is the total number of users;
(2) acquiring a leader probability P that the jth user is selected as the current credit transaction in the credit chain j And cumulative probability Q j ：
Defining a tabu set T, and initially being empty;
wherein A is k Weighted liveness for the kth user;
wherein P is j Representing the probability that the jth user is selected as the leader of the current credit transaction in the credit chain, Q j Representing the cumulative probability that the jth user is selected as the leader of the current credit transaction in the credit chain, A j Weighted liveness for the jth user, n is the total number of users, P t Representing a probability that the t-th user is selected as a leader of a current points transaction in the points chain;
(3) selecting a leader by probability: selecting leaders based on roulette rules, particularly in the bonus chain at [0,1 ]]Generating a random number r over the interval, e.g. r < Q 1 User 1 successfully electing to be the leader; q (Q) k-1 ＜r≤Q k User k successfully electing to be the leader;
(4) if user j continuously selects the leader twiceWhen the second election leader ends, add it to the tabu set T and let P j Let t=Φ at the end of the third competing leader=0; Φ represents the empty set.
Further, the process of determining the credit beneficiary of the current credit transaction in the credit chain according to the set rule in the step 4 is as follows: and if the total amount of all the points generated in the current point transaction is y, sorting according to the sequence in which the points are dug, generating a certain amount of random numbers which are uniformly distributed between [1, y ] by a leader as the numbers of the points, and respectively tracing through a point chain according to the sequence of the numbers of the points to obtain the current last user of the holder as a point beneficiary, and finding out l different point beneficiaries to finish searching.
Further, in step 5, the rewarding process for the users as the point beneficiaries and leaders according to the preset rules is as follows: after successful signing, one half of the transaction rewards are owned by the leader, and the other half is halved by the beneficiary who completes the signing; if the signature fails, then one half of the transaction rewards are owned by the leader and one half are owned by the credit chain system.
Further, the procedure of sorting in the order in which the integrals are dug out is as follows: the leader generates a random number sequence, the pseudo-random number value in the random number sequence corresponds to the point value, the pseudo-random number value is uniformly selected from all generated points before the current point transaction, the pseudo-random index between zero and the total number of points before the current point transaction is completed, after the selection, the block for creating the point is checked, each transaction for transferring the point to a subsequent address is tracked until the last holder is found, and the point beneficiary is obtained.
Through the design scheme, the invention has the following beneficial effects: the invention provides an integral chain consensus method based on liveness probability selection, which comprises the following steps of firstly, normalizing related information of user liveness acquired in an integral chain system, and introducing a normalization mechanism based on the maximum and minimum digits into dynamic change characteristics, so that dimension influence among different characteristics is eliminated, and the problem of poor comparability among data is solved; secondly, a weighted probability mechanism is introduced into an integral chain system to select a leader, so that the reliability and fairness of the integral chain are enhanced; finally, the user's viscosity and loyalty are enhanced through a reasonable point beneficiary reward distribution mechanism. The invention can realize that the integral cannot be tampered, ensure the integral storage safety and improve the activity of users. The method is applied to the point chain system, can reduce the calculated amount, improve the stability, does not need to do a large amount of invalid workload proof calculation, improves the efficiency in time, ensures the safety and fairness, improves the loyalty of users, and constructs a more active point chain.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
A point chain consensus method based on liveness probability selection is applied to a point chain system based on a blockchain, wherein the point chain system comprises a blockchain network, an account management module, a point integration module, a point exchange function module, a point generation module and a point transfer module.
The account management module is used for managing account information of a user;
the integration module is used for integrating the integration of each business system;
the point exchange function module is used for realizing the exchange of user points;
the integral generation module is used for generating an integral through tasks;
the point transfer module is used for transferring points to others.
This example demonstrates one application of the present method in an integrated chain system run.
The integral chain consensus method based on the liveness probability selection comprises the following steps:
1. acquiring user liveness characteristic data, and carrying out normalization processing on the user liveness characteristic data according to a preset normalization rule to obtain normalized user liveness characteristic data;
in the competition leader process, user liveness feature data are required to calculate the liveness of the user, and as different feature data often have different dimensions and dimension units and influence the result of data analysis, in order to eliminate the dimension influence among the feature data, normalization is required to be carried out on the data so as to increase the comparability among the feature data. The invention divides the user liveness characteristic into two kinds of user liveness fixed characteristic and user liveness dynamic characteristic, for the user liveness fixed characteristic, adopt the maximum value and minimum value normalization of the characteristic data, can calculate its normalized value in advance, only need the data in the look-up table can while using; and for the dynamic characteristics of the user liveness, adopting a digit dynamic normalization mode based on the characteristic data. By introducing the normalization mechanism, the invention saves a large amount of time for normalizing and searching the maximum value and the minimum value when the data volume is large, greatly accelerates the normalization process, improves the stability and reduces the calculated amount.
Extracting user liveness characteristic data through an account management module, and normalizing the user liveness characteristic data; the user liveness characteristic data comprises member grades (grade 1-20), transaction amount (coin), exchange amount (coin) and online time length (seconds), and the liveness characteristic data of all users are normalized, wherein the formula (1) is as follows:
X′＝(X-X_min)/(X_max-X_min) (1)
in the present example, the total number of users n=5; taking the number of the activity characteristic data of each user as m=4, wherein the number of the activity characteristic data of each user is respectively a member grade (grade 1-20), a transaction amount (coin), a conversion amount (coin) and an online time length (second), and the weight given by the activity characteristic data of each user is omega 1 、ω 2 、ω 3 、ω 4 Wherein the membership grade pertains to a user liveness fixed feature; the other three are dynamic characteristics of the user activity; the relevant raw data, normalized values, and relevant calculation results for each user are shown in table 1. The data will be described by taking user 2 as an example.
Table 1 weighted probability calculation table
The calculation process according to the normalization formula (1) is as follows:
user liveness fixed feature:
membership grade: x=2, and the membership grade normalized value X' =0.05 of the user 2 can be obtained through the lookup table data;
user liveness dynamic feature:
transaction amount: because the maximum value bit number is 3 and the minimum value bit number is 3 in all the user transaction amount data, X_max=999, X_min=100 and X=200, calculating the transaction amount normalization value of the available user 2, and X' =0.11;
exchange amount: because the maximum value bit number is 3 and the minimum value bit number is 2 in the exchange amount data of all users, calculating the normalized value X' =0.04 of the exchange amount of the available user 2 according to the conditions that X_max=999, X_min=10 and X=50;
on-line duration: because the maximum value bit number is 4 and the minimum value bit number is 3 in the exchange amount data of all users, X_max=9999, X_min=100 and X=500, and calculating an online time length normalization value X' =0.04 of the available user 2;
2. obtaining a comprehensive weighting value of the user, namely the weighted liveness of the user, according to the normalized user liveness characteristic data and the weighting coefficient of the user liveness characteristic data; according to the comprehensive weighted value of the user, obtaining the user meeting the preset rule according to the preset rule, wherein the user is used as a leader of the current point transaction in the point chain, and the leader is responsible for packaging the transaction to form a block;
in a point chain system, a user initiates point exchange, namely, the user is used as a transaction in the system, a leader is required to be selected from competitors (namely, users), the activity of the competitors is calculated according to the characteristics of the users, namely, a corresponding weight is given to the competitors according to the importance degree of the characteristics of the activity of different users, and the weighted sum of the normalized actual value and the weight of the different characteristics of each competitor is calculated; secondly, calculating the selected probability and the accumulated probability; and finally, selecting a leader according to the probability, wherein the leader is responsible for packaging the transaction, and generating a block. In order to prevent the probability that a certain user selects the leader from becoming larger and larger, if the leader is selected twice in succession, the qualification of the third competing leader is canceled;
the specific user initiates the point exchange to generate a transaction, and the transaction generated in a period of time is recorded in a block. Firstly, according to different user liveness characteristics, liveness of each user is calculated according to weights, secondly, selected probability and accumulated probability are calculated, and finally, a leader is elected in a competitive mode according to a roulette rule, and the leader is taken out.
2.1 calculating the comprehensive weight of the user, namely the weighted liveness A of the user 2 ：
An example of the weighted liveness formula (2) for calculating the user is as follows:
the leader elects the data description:
weighted liveness specification: calculated according to weighted liveness formula (2), wherein ω 1 ＝30，ω 2 ＝20，ω 3 ＝25，ω 4 ＝25，X 12 ＝0.05，X 22 ＝0.11，X 32 ＝0.04，X 32 The weighted sum a of user 2 =0.04 2 =5.7, the remaining user liveness is shown in table 1.
2.2 calculating the probability P that the user is selected as the leader 2 And cumulative probability Q 2 ：
The selected probability calculation formula (3) example is as follows:
selected probability description: calculated according to formula (3): wherein A is 2 =5.7, all user liveness sums 67.4, then probability P of user 2 being selected 2 = 0.0846, the probabilities of the remaining users being selected and the cumulative probabilities are shown in table 1;
2.3 probability selection of leaders: in the roulette manner, if the generated random number is in the corresponding cumulative probability interval, the user in the interval selects the leader, r=0.89 is taken in the embodiment of the invention, and as 0.7685 < r < 1, the user 5 successfully competes as the leader.
3. The leader determines the point beneficiary of the current point transaction in the point chain according to the set rule; users who are the point beneficiaries sign in the point chain, and users who are the point beneficiaries and leaders are rewarded according to preset rules.
3.1 generating an integral beneficiary: if the total amount of all the currently generated integrals is 100000, the integrals are sorted according to the dug sequence, 10 uniformly distributed random numbers are generated between [1,100000] by a leader to serve as the numbers of the integrals, the last current holder is obtained through an integral chain in a tracing mode according to the integral number sequence, and the current holder stops after 3 different holders are found. In this example, 10 random numbers generated by uniform distribution are in turn: 267 3845,2, 8256, 6666, 721, 86, 3163, 7324, 12; the holders numbered 3845 and 2 are the same person by tracing back, so the holder of points 267, 3845, 8256 is selected as three point beneficiaries.
3.2 signature and prize distribution: the point beneficiaries are responsible for signing the blocks, three point beneficiaries complete the signing of the blocks, one half of the transaction rewards are owned by the leader, and the other half of the transaction rewards are halved by the point beneficiaries completing the signing; the reward is sent to the benefit of the points after signing.
To sum up, points are rewards that merchants give to customers in order to maintain them, which can typically be redeemed for some form of privileges. The point chain system is a blockchain system for redeeming points of different partners. In order to improve the activity of a user and accelerate the integral circulation, the invention discloses an integral chain consensus method based on activity probability selection. Firstly, normalizing the related information of the user liveness acquired in an integral chain system, and introducing a normalization mechanism based on the maximum and minimum digits into dynamic change characteristics, so that the dimensional influence among different characteristics is eliminated, and the problem of poor comparability among data is solved; secondly, a weighted probability mechanism is introduced into an integral chain system to select a leader, so that the reliability and fairness of the integral chain are enhanced; finally, the user's viscosity and loyalty are improved through a reasonable beneficiary reward distribution mechanism. The invention can realize that the integral cannot be tampered, ensure the integral storage safety and improve the activity of users.
Claims (5)
1. An integral chain consensus method based on liveness probability selection is characterized by comprising the following steps:
step 1: acquiring user liveness characteristic data, and carrying out normalization processing on the user liveness characteristic data according to a preset normalization rule to obtain normalized user liveness characteristic data;
step 2: obtaining a comprehensive weighting value of the user, namely the weighted liveness of the user, according to the normalized user liveness characteristic data and the weighting coefficient of the user liveness characteristic data;
step 3: according to the comprehensive weighted value of the user, obtaining the user meeting the preset rule according to the preset rule, wherein the user is used as a leader of the current point transaction in the point chain, and the leader is responsible for packaging the transaction to form a block;
the preset rule is as follows:
(1) acquiring weighted liveness of a user:
the weighted liveness calculation formula of the user is as follows:
wherein A is j Representing the weighted liveness of the jth user; m represents the number of user liveness features; omega i Representing the weight assigned to the ith user activity feature according to its importance level; x is X ij Ith user activity characteristic data representing a jth user; n is the total number of users;
(2) acquiring that the jth user is selected as the leader of the current credit transaction in the credit chainProbability P j And cumulative probability Q j ：
Defining a tabu set T, and initially being empty;
wherein A is k Weighted liveness for the kth user;
wherein P is j Representing the probability that the jth user is selected as the leader of the current credit transaction in the credit chain, Q j Representing the cumulative probability that the jth user is selected as the leader of the current credit transaction in the credit chain, A j Weighted liveness for the jth user, n is the total number of users, P t Representing a probability that the t-th user is selected as a leader of a current points transaction in the points chain;
(3) selecting a leader by probability: selecting leaders based on roulette rules, particularly in the bonus chain at [0,1 ]]Generating a random number r over the interval, e.g. r < Q 1 User 1 successfully electing to be the leader; q (Q) k-1 ＜r≤Q k User k successfully electing to be the leader;
(4) if user j selects the leader twice in succession, the second leader is added to the tabu set T and P is caused to j Let t=Φ at the end of the third competing leader=0; Φ represents an empty set;
step 4: the leader in the step 3 determines the point beneficiary of the current point transaction in the point chain according to the set rule;
the process of determining the point beneficiary of the current point transaction in the point chain according to the set rule is as follows: if the total amount is y, sorting according to the sequence in which the points are dug, generating a certain amount of random numbers which are uniformly distributed between [1, y ] by a leader as the numbers of the points, and tracing through an integral chain according to the sequence of the numbers of the points to obtain the current last user of the holder as an integral beneficiary, and finding out l different integral beneficiaries to finish searching;
the sequence of the dug integration is as follows: the leader generates a random number sequence, the pseudo-random number value in the random number sequence corresponds to the point value, the pseudo-random number value is uniformly selected from all generated points before the current point transaction, the pseudo-random number index between zero and the total number of points before the current point transaction is completed, after the pseudo-random number index is selected, the block for creating the point is checked, each transaction for transferring the point to a subsequent address is tracked until the last holder is found, and the point beneficiary is obtained;
step 5: users who are the point beneficiaries sign in the point chain, and users who are the point beneficiaries and leaders are rewarded according to preset rules.
2. The method of integrating chain consensus based on liveness probability selection as claimed in claim 1, wherein: the user liveness characteristic data is obtained from a score chain system database.
3. The method for integrating chain consensus based on liveness probability selection according to claim 1 or 2, wherein: the user liveness characteristic data comprises a member grade, a transaction amount, a conversion amount and an online time length.
4. The method of integrating chain consensus based on liveness probability selection as claimed in claim 3 wherein: the user liveness features are divided into two kinds of user liveness fixed features and user liveness dynamic features,
the preset normalization rule adopted by the user liveness fixing feature is as follows:
X′＝(X-X-min)/(X-max-X-min)
wherein X' represents the normalized value of the current user activity characteristic data, X represents the actual value of the current user activity characteristic data, X-min is the minimum value of all the user activity characteristic data, X-max is the maximum value of all the user activity characteristic data, and the maximum value is a preset value;
the preset normalization rule adopted by the dynamic characteristics of the user liveness is as follows:
X′＝(X-X-min)/(X-max-X-min)
wherein X' represents the normalized value of the current user activity characteristic data, X represents the actual value of the current user activity characteristic data, X-min is the minimum integer value corresponding to the bit number corresponding to the minimum value of all the user activity characteristic data, and X-max is the maximum integer value corresponding to the bit number corresponding to the maximum value of all the user activity characteristic data.
5. The method of integrating chain consensus based on liveness probability selection as claimed in claim 1, wherein: in the step 5, the rewarding process of the users as the point beneficiaries and leaders according to the preset rules is as follows: after successful signing, one half of the transaction rewards are owned by the leader, and the other half is halved by the beneficiary who completes the signing; if the signature fails, then one half of the transaction rewards are owned by the leader and one half are owned by the credit chain system.
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