CN112258320A - Payment center placement method, system, medium and equipment based on block chain - Google Patents

Payment center placement method, system, medium and equipment based on block chain Download PDF

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CN112258320A
CN112258320A CN202010997564.2A CN202010997564A CN112258320A CN 112258320 A CN112258320 A CN 112258320A CN 202010997564 A CN202010997564 A CN 202010997564A CN 112258320 A CN112258320 A CN 112258320A
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沈玉龙
杨凌霄
董学文
佟威
田文生
乔慧
马诗洋
谷鑫雨
冶英杰
郭校杰
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Abstract

The invention belongs to the technical field of under-chain payment channel centers of block chains, and discloses a payment center placement method, a system, a medium and equipment based on the block chains, which are used for analyzing and balancing the payment center placement cost of the block chains; modeling the problem of the payment center placement by using a formalization method; solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology; and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology. Aiming at the problems of low transaction performance, high channel management cost and the like of the current block chain payment channel network, the invention researches the method for placing the payment center of the block chain and realizes the purpose of minimizing the cost for operating the payment center. The payment center placement method based on the blockchain is beneficial to the landing of blockchain application, and particularly has profound practical significance for high-frequency low-volume transaction in an intelligent service transaction scene. The overhead of running payment centers is adjusted by increasing or decreasing the number of payment centers as appropriate based on the simulation results.

Description

Payment center placement method, system, medium and equipment based on block chain
Technical Field
The invention belongs to the technical field of under-chain payment channel centers of block chains, and particularly relates to a payment center placement method, a system, a medium and equipment based on the block chains.
Background
At present: as the technology of developing the blockchain rises to the national strategic altitude, the blockchain application gradually falls to the ground in a large scale, and the demand for high-performance blockchain application is increasingly urgent. Cryptocurrency, such as bitcoin and ethernet, is becoming increasingly popular and used in financial ecosystems. However, the scalability problem of these blockchain-based cryptocurrencies has also proliferated as the demand for transactions grows. For example, bitcoin can only process 7 transactions per second, requiring 10 minutes on average to confirm a new transaction. Ethernet is promoted to about 15TPS, while other payment networks, such as Visa, support peaks of up to 47,000 TPS. The root of the blockchain scalability challenge is the underlying consensus mechanism, i.e., each transaction needs to be confirmed by the consistent consensus of all nodes in the network, which can take minutes to hours.
Currently, the prior art relating to sub-chain payment centers: the schemes of TumbleBit, Commit-chains, Perun and the like propose a payment channel center to maintain multiple channels to reduce routing complexity. The drawbacks of these methods are: the existing work inherits the challenge of the payment channel network, and does not consider the position of the payment center, which causes the problems of low transaction performance, high channel management cost and the like of the block chain payment channel network.
Through the above analysis, the problems and defects of the prior art are as follows: the existing work inherits the challenge of the payment channel network, and does not consider the position of the payment center, which causes lower transaction performance and higher channel management cost of the block chain payment channel network.
The difficulty in solving the above problems and defects is: since the nodes in the payment tunnel network are decentralized, it is difficult to decide how many payment hub nodes are needed and where in the topology they should be placed. Due to the different distances between the nodes, operating only one payment center node may result in a large management overhead, which may be reduced by placing multiple payment center nodes based on the distribution of the nodes. But the synchronization overhead arises because of multiple payment hub nodes, so the present invention needs to balance these two overheads. The invention provides the placement problem of the payment center node and provides two solutions for placement optimization under different network scales.
The significance of solving the problems and the defects is as follows: the method for placing the payment center based on the block chain has profound practical significance.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a payment center placement method, a system, a medium and equipment based on a block chain.
The invention is realized in such a way that a payment center placement method based on a block chain comprises the following steps:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
Further, the overhead analysis and weighing placed on the payment center of the blockchain specifically includes:
firstly, a target is placed in a payment center node for analysis;
then, the payment center is placed for overhead analysis;
and finally, running the overhead balance problem analysis of the payment center.
Further, the analysis of the target placed by the payment center node specifically includes: the state information of the network node needs to be reported to the central node in time, and the payment center makes effective routing management decision; uniformly placing the payment center nodes in the vicinity of the network nodes;
the analysis of the payment center placement overhead specifically comprises: the payment central nodes are physically distributed but logically centralized, and the central nodes cooperate to manage the routing of payments;
the operation of the payment center overhead balance problem analysis specifically comprises the following steps:
1) the payment centre nodes should be close to the network nodes they serve;
2) the payment hub nodes should be close to each other.
Further, the modeling of the payment center placement problem by using the formalization method specifically includes: using a binary variable xnE {0, 1} represents a node in the network
Figure BDA0002693071710000039
Whether it is a payment hub node or not,
Figure BDA00026930717100000310
for a set of network-wide nodes, the placement strategy is represented by the vector x:
Figure BDA0002693071710000031
if the computing power of one network node is not strong enough, the network node cannot be used as a payment center node:
Figure BDA0002693071710000032
wherein ,
Figure BDA00026930717100000311
a set of payment center nodes in the network;
using a binary variable ymnE {0, 1} represents a node
Figure BDA00026930717100000312
Whether or not to be assigned to a payment centre node
Figure BDA00026930717100000313
The allocation policy is represented by the vector y:
Figure BDA0002693071710000033
each network node needs to be controlled by a central node, which needs to:
Figure BDA0002693071710000034
node n in the network must act as a central node to which node m can be assigned:
Figure BDA0002693071710000035
using ζmnRepresenting the assignment of node m to central node n, the total administrative cost in the network is represented as:
Figure BDA0002693071710000036
by deltanlRepresenting the synchronization cost between the two payment hub nodes n, l, the total synchronization cost in the network is then represented as:
Figure BDA0002693071710000037
wherein ,∈nlRepresents the constant cost of synchronization between n, l;
thus, the overhead tradeoff problem is converted into a formula
Figure BDA00026930717100000314
And
Figure BDA00026930717100000315
the balance between the two costs represented, with ω ≧ 0 representing the weight between the two costs, then the balance cost is represented as:
Figure BDA0002693071710000038
the placement problem for the payment center is expressed as:
Figure BDA0002693071710000041
wherein the constraint condition is a formula
Figure BDA0002693071710000042
Figure BDA0002693071710000043
Figure BDA0002693071710000044
Further, the solving of the optimal solution to the small-scale center placement problem by using the linearization technique specifically includes: minigauge for converting placement problem into mixed integer linear programming MILP problemThe optimal solution of the model network, using standard linearization technique to realize the conversion process, introduces two vectors theta and
Figure BDA0002693071710000046
as additional optimization variables:
Figure BDA0002693071710000047
Figure BDA0002693071710000048
where the linear constraint of θ is:
Figure BDA00026930717100000410
Figure BDA00026930717100000411
Figure BDA00026930717100000412
Figure BDA00026930717100000413
the linear constraint of (c) is:
Figure BDA00026930717100000414
Figure BDA00026930717100000415
Figure BDA00026930717100000416
thus, the placement cost function translates to:
Figure BDA00026930717100000417
finally, the MILP problem is expressed as:
Figure BDA00026930717100000418
wherein the constraint condition is
Figure BDA00026930717100000419
Figure BDA00026930717100000420
Figure BDA00026930717100000421
And
Figure BDA00026930717100000422
Figure BDA00026930717100000423
Figure BDA00026930717100000424
Figure BDA0002693071710000051
further, the solving of the approximate solution of the large-scale center placement problem by using the supermode function technology specifically comprises: introducing a lemma, disclosing the relationship between the placement policy x and the allocation policy y: y ismnWhen 1 is equal to
Figure BDA0002693071710000052
Otherwise, y mn0. Thus, for a given placement strategy X, it is easy to find an allocation strategy y, with XnRepresenting the placement of the payment hub node, the set of all possible locations of the payment hub node is represented as:
Figure BDA0002693071710000053
if and only if
Figure BDA0002693071710000054
Subsets
Figure BDA0002693071710000055
Representing a placement strategy x, i.e. xn1 is ═ 1; by xXTo represent
Figure BDA0002693071710000056
In binary form, then the cost objective function
Figure BDA0002693071710000057
Expressed as a set function
Figure BDA0002693071710000058
Figure BDA0002693071710000059
Then, a set function of the supermode function is used. Given a finite set
Figure BDA00026930717100000510
Aggregation function
Figure BDA00026930717100000511
Called supermode function, when all subsets
Figure BDA00026930717100000513
And is
Figure BDA00026930717100000514
For any element
Figure BDA00026930717100000515
Comprises the following steps:
Figure BDA00026930717100000512
when standard cost deltanl＝δn′l′＝δ，
Figure BDA00026930717100000516
Time, set function
Figure BDA00026930717100000517
Is a supermode function;
the placement problem is converted into a minimized supermode function f, and solving the minimum value of the supermode function is equivalent to solving the maximum value of the submode function; by fubTo represent
Figure BDA00026930717100000518
Upper bound of possible maximum, the submodular function is expressed as
Figure BDA00026930717100000519
Maximization using approximation algorithm
Figure BDA00026930717100000520
The approximate boundary ψ represents the ratio of the value of the approximate solution to the value of the optimum solution, which is always at least ψ, and the approximate solution is found using a random greedy algorithm of ψ 1/2.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
Another object of the present invention is to provide a system for placing a blockchain-based payment center, which implements the method for placing a blockchain-based payment center, the system comprising:
the overhead balancing module is used for analyzing and balancing the overhead of the payment center of the block chain;
the placement modeling module is used for modeling the placement problem of the payment center by using a formalization method;
the linear solving module is used for solving the optimal solution of the small-scale center placement problem by utilizing a linear technology;
and the supermode function solving module is used for solving an approximate solution of the large-scale center placement problem by utilizing the supermode function technology.
The invention also aims to provide a terminal, wherein the terminal is provided with the payment center placement system based on the block chain, and the terminal is a transaction information processing terminal and an intelligent service transaction terminal.
By combining all the technical schemes, the invention has the advantages and positive effects that: firstly, overhead analysis and balance are placed on a payment center of a block chain; secondly, modeling the problem of the payment center placement by using a formalization method; secondly, solving an optimal solution to the small-scale center placement problem by utilizing a linearization technology; and finally, solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology. The experimental result shows that the model successfully simulates the relationship between two communication overheads of the payment center node, and the number of the payment centers can be properly increased or decreased to adjust the overheads of operating the payment centers based on the simulation result.
The invention analyzes and balances the overhead of the payment center of the block chain; modeling the problem of the payment center placement by using a formalization method; solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology; and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology. The experimental result shows that the model successfully simulates the relationship between two communication overheads of the payment center node, and the number of the payment centers can be properly increased or decreased to adjust the overheads of operating the payment centers based on the simulation result. Partial experimental results of the present invention are shown in fig. 7, fig. 7(a) is a graph of the balance cost varying with the weight value, the results show that the performance of the model of the present method is close to the optimal value of almost all weights, indicating that the model successfully simulates the trade-off between two communication costs of the payment center node, fig. 7(b) is a graph of the trade-off between two communication costs, which can be adjusted by appropriately increasing or decreasing the number of the payment center nodes based on the simulation results. The payment center placement method based on the blockchain is beneficial to the landing of blockchain application, and particularly has profound practical significance for high-frequency low-volume transaction in an intelligent service transaction scene.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a payment center placement method based on a blockchain according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a payment center placement system based on a blockchain according to an embodiment of the present invention;
in fig. 2: 1. an overhead trade-off module; 2. placing a modeling module; 3. a linearization solution module; 4. and a supermode function solving module.
Fig. 3 is a schematic diagram of an overhead balancing module according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a placement modeling module provided by an embodiment of the present invention.
FIG. 5 is a block diagram of a linearization solution module according to an embodiment of the invention.
Fig. 6 is a schematic diagram of a supermode function solving module according to an embodiment of the present invention.
Fig. 7(a) is a graph of the balance cost as a function of weight provided by an embodiment of the present invention.
Fig. 7(b) is a graph of a trade-off between two communication overheads provided by an embodiment 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 the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method, a system, a medium, and a device for placing a payment center based on a blockchain, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for placing a payment center based on a blockchain provided by the present invention includes the following steps:
s101: overhead analysis and balancing are placed on a payment center of the block chain;
s102: modeling the problem of the payment center placement by using a formalization method;
s103: solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
s104: and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
Those skilled in the art can also implement the method of placing a payment center based on a block chain, and the method of placing a payment center based on a block chain provided by the present invention in fig. 1 is only a specific example.
As shown in fig. 2, the system for placing a payment center based on a blockchain provided by the present invention includes:
the overhead balancing module 1 is used for analyzing and balancing the overhead of the payment center of the block chain;
the placement modeling module 2 is used for modeling the placement problem of the payment center by using a formalization method;
the linearization solving module 3 is used for solving the optimal solution of the small-scale center placement problem by utilizing the linearization technology;
and the supermode function solving module 4 is used for solving an approximate solution of the large-scale center placement problem by utilizing the supermode function technology.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
As shown in fig. 3-6, the present invention includes four modules, a cost tradeoff module 1, a placement modeling module 2, a linearization solution module 3, and a supermode function solution 4.
(1) Overhead trade-off module 1:
as shown in fig. 3, in the overhead balancing module 1, first, the payment center node places a target analysis; then, the payment center is placed for overhead analysis; and finally, running the overhead balance problem analysis of the payment center.
The target placement analysis of the payment center node specifically comprises the following steps:
in practice, nodes of the payment channel network are relatively dispersed, and the payment center may be far away from some nodes, so that the connection between the nodes is unstable or the communication delay is large. However, in order for the routing mechanism in the payment center to work normally, the state information of the network node needs to be reported to the central node in time, so that the payment center makes an effective routing management decision. It is therefore an object of the invention to place the payment hub nodes evenly in the vicinity of the network nodes.
The payment center placement overhead analysis specifically comprises the following steps:
in the method the payment central nodes are physically distributed but logically centralized and the central nodes cooperate to manage the routing of payments. This centering strategy can shorten the distance between the network node and the center node, but it will generate two overheads: 1) the payment center collects and counts the management overhead of the network node state; 2) and paying the information synchronization overhead between the central nodes.
The operation of the payment center overhead balance problem analysis specifically comprises the following steps:
1) the payment hub nodes should be close to the network nodes they serve to reduce the delay (administrative overhead) of collecting statistics and route management.
2) The payment hub nodes should be close to each other to reduce the delay of the synchronization state (synchronization overhead).
(2) The placement modeling module 2:
as shown in FIG. 4, a placement modeling module 2 of the present invention is depicted. The modeling of the payment center placement problem by using the formalization method specifically comprises the following steps:
the invention uses a binary variable xnE {0, 1} represents a node in the network
Figure BDA0002693071710000094
Whether it is a payment hub node or not,
Figure BDA0002693071710000095
for the set of full network nodes, the present invention uses vector x to represent the placement strategy:
Figure BDA0002693071710000091
if the computing power of one network node is not strong enough, the network node cannot be used as a payment center node:
Figure BDA0002693071710000092
wherein ,
Figure BDA0002693071710000096
is a collection of payment hub nodes in the network.
The invention uses a binary variable ymnE {0, 1} represents a node
Figure BDA0002693071710000097
Whether or not to be assigned to a payment centre node
Figure BDA0002693071710000098
The invention then represents the allocation policy by a vector y:
Figure BDA0002693071710000093
each network node needs to be controlled by one central node, thus requiring:
Figure BDA0002693071710000101
node n in the network must act as a central node to which node m can be assigned:
Figure BDA0002693071710000102
zeta for the present inventionmnRepresenting the assignment of node m to central node n, the total administrative cost in the network can be expressed as:
Figure BDA0002693071710000103
delta for the inventionnlRepresenting the synchronization cost between two payment hub nodes n, l, the total synchronization cost in the network can be expressed as:
Figure BDA0002693071710000104
wherein ,∈nlRepresenting the constant cost of synchronization between n, l.
Therefore, the overhead tradeoff problem translates into a balance between the two costs represented by equations (6) and (7), where ω ≧ 0 represents the weight value between the two costs, the balance cost can be expressed as:
Figure BDA0002693071710000105
the placement problem for the payment center is expressed as:
Figure BDA0002693071710000106
wherein the constraint conditions are equations (1) - (5).
(3) The linearization solution module 3:
as shown in fig. 5, the linearization solution module 3 is depicted. The method for solving the optimal solution of the small-scale center placement problem by utilizing the linearization technology specifically comprises the following steps:
the invention converts the placement problem into a Mixed Integer Linear Programming (MILP) problem to solve the optimal solution of the small-scale network. The present invention uses a standard linearization technique to achieve this conversion process, introducing two vectors, θ and
Figure BDA0002693071710000108
as additional optimization variables:
Figure BDA0002693071710000109
Figure BDA00026930717100001010
where the linear constraint of θ is:
Figure BDA00026930717100001011
Figure BDA00026930717100001012
Figure BDA0002693071710000111
Figure BDA0002693071710000112
the linear constraint of (c) is:
Figure BDA0002693071710000113
Figure BDA0002693071710000114
Figure BDA0002693071710000115
thus, the placement cost function can be converted to:
Figure BDA0002693071710000116
finally, the MILP problem can be expressed as:
Figure BDA0002693071710000117
wherein the constraint conditions are (1) - (5) and (12) - (17).
(4) And a supermode function solving module 4:
as shown in fig. 6, the supermode function solving module 4 is described. Solving an approximate solution to the large-scale center placement problem by using a supermode function technology specifically comprises the following steps:
the present invention introduces a lemma that reveals the relationship between placement policy x and allocation policy y: y ismnWhen 1 is equal to
Figure BDA0002693071710000118
Otherwise, y mn0. Thus, for a given placement strategy x, an allocation strategy y is easily found. By X in the inventionnRepresenting the placement of the payment hub node, the set of all possible locations of the payment hub node is represented as:
Figure BDA0002693071710000119
this means if and only if
Figure BDA00026930717100001113
Subsets
Figure BDA00026930717100001114
Representing a placement strategy x, i.e. xn1. By x in the inventionXTo represent
Figure BDA00026930717100001115
In binary form, then the cost objective function
Figure BDA00026930717100001116
Can be expressed as a set function
Figure BDA00026930717100001110
Figure BDA00026930717100001111
The invention then utilizes a set function, the supermode function. Given a finite set
Figure BDA00026930717100001117
Aggregation function
Figure BDA00026930717100001118
Called supermode function, when all subsets
Figure BDA00026930717100001119
And is
Figure BDA00026930717100001120
For any element
Figure BDA00026930717100001121
Comprises the following steps:
Figure BDA00026930717100001112
when standard cost deltanl＝δn′l′＝δ，
Figure BDA0002693071710000121
Time, set function
Figure BDA0002693071710000122
Is a supermode function.
Thus, the placement problem can be translated into minimizing the supermode function f, solving the minimum of the supermode function is equivalent to solving the maximum of its submode function. The value is obtained. For the invention fubTo represent
Figure BDA0002693071710000123
Upper bound of possible maximum, the submodular function is expressed as
Figure BDA0002693071710000124
Some approximation algorithm may be usedTo maximize
Figure BDA0002693071710000125
The approximation boundary ψ means that the ratio of the value of the approximation solution and the value of the optimum solution is always at least ψ. The invention uses a random greedy algorithm of psi-1/2 to find its approximate solution.
The technical effects of the present invention will be described in detail with reference to experiments.
The invention analyzes and balances the overhead of the payment center of the block chain; modeling the problem of the payment center placement by using a formalization method; solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology; and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology. The experimental result shows that the model successfully simulates the relationship between two communication overheads of the payment center node, and the number of the payment centers can be properly increased or decreased to adjust the overheads of operating the payment centers based on the simulation result. The payment center placement method based on the blockchain is beneficial to the landing of blockchain application, and particularly has profound practical significance for high-frequency low-volume transaction in an intelligent service transaction scene.
Fig. 7(a) is a graph of balancing the cost against the weight, showing that the performance of the model of the method is close to the optimum of almost all weights, indicating that the model successfully models the trade-off between the two communication costs of the payment hub nodes, as shown in fig. 7(b), which is a graph of the trade-off between the two communication costs, which can be adjusted by increasing or decreasing the number of payment hub nodes appropriately based on the simulation results.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A payment center placement method based on a block chain is characterized by comprising the following steps:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
2. The blockchain-based payment center placement method of claim 1, wherein the analyzing and weighing the blockchain payment center placement overhead specifically comprises:
firstly, a target is placed in a payment center node for analysis;
then, the payment center is placed for overhead analysis;
and finally, running the overhead balance problem analysis of the payment center.
3. The blockchain-based payment center placement method of claim 2, wherein the payment center node placement target analysis specifically comprises: the state information of the network node needs to be reported to the central node in time, and the payment center makes effective routing management decision; uniformly placing the payment center nodes in the vicinity of the network nodes;
the analysis of the payment center placement overhead specifically comprises: the payment central nodes are physically distributed but logically centralized, and the central nodes cooperate to manage the routing of payments;
the operation of the payment center overhead balance problem analysis specifically comprises the following steps:
1) the payment centre nodes should be close to the network nodes they serve;
2) the payment hub nodes should be close to each other.
4. The blockchain-based payment center placement method of claim 1, wherein the modeling of the payment center placement problem using a formalization method specifically comprises: using a binary variable xnE {0, 1} represents a node in the network
Figure FDA0002693071700000011
Whether it is a payment hub node or not,
Figure FDA0002693071700000012
for a set of network-wide nodes, the placement strategy is represented by the vector x:
Figure FDA0002693071700000013
if the computing power of one network node is not strong enough, the network node cannot be used as a payment center node:
Figure FDA0002693071700000021
wherein ,
Figure FDA0002693071700000022
a set of payment center nodes in the network;
using a binary variable ymnE {0, 1} represents a node
Figure FDA0002693071700000023
Whether or not to be assigned to a payment centre node
Figure FDA0002693071700000024
The allocation policy is represented by the vector y:
Figure FDA0002693071700000025
each network node needs to be controlled by a central node, which needs to:
Figure FDA0002693071700000026
node n in the network must act as a central node to which node m can be assigned:
Figure FDA0002693071700000027
using ζmnRepresenting the assignment of node m to central node n, the total administrative cost in the network is represented as:
Figure FDA0002693071700000028
by deltanlRepresenting the synchronization cost between the two payment hub nodes n, l, the total synchronization cost in the network is then represented as:
Figure FDA0002693071700000029
wherein ,∈nlRepresents the constant cost of synchronization between n, l;
thus, the overhead tradeoff problem is converted into a formula
Figure FDA00026930717000000210
And
Figure FDA00026930717000000211
the balance between the two costs represented, with ω ≧ 0 representing the weight between the two costs, then the balance cost is represented as:
Figure FDA00026930717000000212
the placement problem for the payment center is expressed as:
Figure FDA00026930717000000213
wherein the constraint condition is a formula
Figure FDA00026930717000000214
Figure FDA00026930717000000215
Figure FDA00026930717000000216
5. The blockchain-based payment center placement method of claim 1, wherein the optimizing a small-scale center placement problem using a linearization technique specifically comprises: will put questionsConverting the problem into a mixed integer linear programming MILP problem to solve the optimal solution of a small-scale network, realizing the conversion process by using a standard linearization technique, and introducing two vectors theta and theta
Figure FDA0002693071700000031
As additional optimization variables:
Figure FDA0002693071700000032
Figure FDA0002693071700000033
where the linear constraint of θ is:
Figure FDA0002693071700000034
Figure FDA0002693071700000035
Figure FDA0002693071700000036
Figure FDA0002693071700000037
the linear constraint of (c) is:
Figure FDA0002693071700000038
Figure FDA0002693071700000039
Figure FDA00026930717000000310
thus, the placement cost function translates to:
Figure FDA00026930717000000311
finally, the MILP problem is expressed as:
Figure FDA00026930717000000312
wherein the constraint condition is
Figure FDA00026930717000000313
Figure FDA00026930717000000314
Figure FDA00026930717000000315
And
Figure FDA00026930717000000316
Figure FDA00026930717000000317
Figure FDA00026930717000000318
Figure FDA00026930717000000319
6. the blockchain-based payment center placement method of claim 1, wherein the using of the hyper-modulus function technique is for a large scaleSolving the approximate solution of the problem of the center placement of the mold specifically comprises the following steps: introducing a lemma, disclosing the relationship between the placement policy x and the allocation policy y: y ismnWhen 1 is equal to
Figure FDA00026930717000000320
Otherwise, ymn0, therefore, for a given placement strategy X, it is easy to find an allocation strategy y, with XnRepresenting the placement of the payment hub node, the set of all possible locations of the payment hub node is represented as:
Figure FDA0002693071700000041
if and only if
Figure FDA0002693071700000042
Subsets
Figure FDA0002693071700000043
Representing a placement strategy x, i.e. xn1 is ═ 1; by xXTo represent
Figure FDA0002693071700000044
In binary form, then the cost objective function
Figure FDA0002693071700000045
Expressed as a set function
Figure FDA0002693071700000046
Figure FDA0002693071700000047
Then, a finite set is given by using a set function of the supermode function
Figure FDA0002693071700000048
Aggregation function
Figure FDA0002693071700000049
Called supermode function, when all subsets
Figure FDA00026930717000000410
And is
Figure FDA00026930717000000411
For any element
Figure FDA00026930717000000412
Comprises the following steps:
Figure FDA00026930717000000413
when standard cost
Figure FDA00026930717000000414
Time, set function
Figure FDA00026930717000000415
Is a supermode function;
the placement problem is converted into a minimized supermode function f, and solving the minimum value of the supermode function is equivalent to solving the maximum value of the submode function; by fubTo represent
Figure FDA00026930717000000416
Upper bound of possible maximum, the submodular function is expressed as
Figure FDA00026930717000000417
Maximization using approximation algorithm
Figure FDA00026930717000000418
The approximation boundary psi indicates that the ratio of the value of the approximation solution and the value of the optimum solution is always at least psi, usingAn approximate solution is found by a random greedy algorithm of psi 1/2.
7. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
8. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
overhead analysis and balancing are placed on a payment center of the block chain;
modeling the problem of the payment center placement by using a formalization method;
solving the optimal solution of the small-scale center placement problem by utilizing a linearization technology;
and (4) solving an approximate solution of the large-scale center placement problem by utilizing a supermode function technology.
9. A blockchain-based payment center placement system implementing the blockchain-based payment center placement method according to any one of claims 1 to 6, wherein the blockchain-based payment center placement system comprises:
the overhead balancing module is used for analyzing and balancing the overhead of the payment center of the block chain;
the placement modeling module is used for modeling the placement problem of the payment center by using a formalization method;
the linear solving module is used for solving the optimal solution of the small-scale center placement problem by utilizing a linear technology;
and the supermode function solving module is used for solving an approximate solution of the large-scale center placement problem by utilizing the supermode function technology.
10. A terminal, characterized in that the terminal is equipped with the block chain-based payment center placement system of claim 9, and the terminal is a transaction information processing terminal or an intelligent service transaction terminal.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2013134441A (en) * 2013-07-24 2015-01-27 Федеральное государственное бюджетное учреждение науки Институт катализа им. Г.К. Борескова Сибирского отделения Российской академии наук (ИК СО РАН) CATALYTIC SYSTEM AND METHOD FOR PRODUCING REACTOR POWDER OF SUPER HIGH-MOLECULAR POLYETHYLENE
KR20180075450A (en) * 2018-06-15 2018-07-04 정기영 Peer to peer transmission based data marketplace with cryptocurrency payment, building method of the same
CN109173243A (en) * 2018-07-04 2019-01-11 清华大学 The online game of center community is gone completely based on block chain technology
CN109727017A (en) * 2018-12-26 2019-05-07 成都爱灵格教育科技有限公司 A kind of shopping at network method, apparatus, system, server and storage medium
CN110751469A (en) * 2019-10-25 2020-02-04 浙江工商大学 Encrypted currency multichannel payment method based on intelligent contract
CN110839056A (en) * 2018-08-17 2020-02-25 搜游网络科技(北京)有限公司 Data processing method and device based on block chain and node network
CN110928678A (en) * 2020-01-20 2020-03-27 西北工业大学 Block chain system resource allocation method based on mobile edge calculation

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2013134441A (en) * 2013-07-24 2015-01-27 Федеральное государственное бюджетное учреждение науки Институт катализа им. Г.К. Борескова Сибирского отделения Российской академии наук (ИК СО РАН) CATALYTIC SYSTEM AND METHOD FOR PRODUCING REACTOR POWDER OF SUPER HIGH-MOLECULAR POLYETHYLENE
KR20180075450A (en) * 2018-06-15 2018-07-04 정기영 Peer to peer transmission based data marketplace with cryptocurrency payment, building method of the same
CN109173243A (en) * 2018-07-04 2019-01-11 清华大学 The online game of center community is gone completely based on block chain technology
CN110839056A (en) * 2018-08-17 2020-02-25 搜游网络科技(北京)有限公司 Data processing method and device based on block chain and node network
CN109727017A (en) * 2018-12-26 2019-05-07 成都爱灵格教育科技有限公司 A kind of shopping at network method, apparatus, system, server and storage medium
CN110751469A (en) * 2019-10-25 2020-02-04 浙江工商大学 Encrypted currency multichannel payment method based on intelligent contract
CN110928678A (en) * 2020-01-20 2020-03-27 西北工业大学 Block chain system resource allocation method based on mobile edge calculation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JIAWEI ZHENG; XUEWEN DONG; QIHANG LIU; XINGHUI ZHU; WEI TONG: "Blockchain-based secure digital asset exchange scheme with QoS-aware incentive mechanism", IEEE/2019 IEEE 20TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE SWITCHING AND ROUTING (HPSR) *
熊辉;张?;雷蕾;: "区块链与远程医疗大数据安全研究", 科技智囊, no. 09 *
罗鑫: "基于区块链的可信存储系统设计与实现", 万方 *

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