CN108768700A - Reward distribution method, apparatus and system in a kind of block chain - Google Patents

Reward distribution method, apparatus and system in a kind of block chain Download PDF

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CN108768700A
CN108768700A CN201810450842.5A CN201810450842A CN108768700A CN 108768700 A CN108768700 A CN 108768700A CN 201810450842 A CN201810450842 A CN 201810450842A CN 108768700 A CN108768700 A CN 108768700A
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node
active user
block chain
user
contribution margin
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CN108768700B (en
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赵化强
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Beijing Qihoo Technology Co Ltd
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    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/32—Specific management aspects for broadband networks
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    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/14—Charging, metering or billing arrangements for data wireline or wireless communications
    • H04L12/1485—Tariff-related aspects
    • H04L12/1496—Tariff-related aspects involving discounts
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H04L43/0882—Utilisation of link capacity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H04L43/0894—Packet rate
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/50—Testing arrangements
    • H04L43/55—Testing of service level quality, e.g. simulating service usage

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Abstract

The invention discloses reward distribution method, apparatus and systems in a kind of block chain, including：Obtain the contribution margin of total contribution margin and active user's node within a preset period of time of all user nodes within a preset period of time in block chain, wherein the contribution margin of each user node is related to the performance of the user node；Obtain the reward total amount to be allocated of the block chain within a preset period of time；According to the contribution margin of total contribution margin, reward total amount and active user's node to be allocated in the preset time period, the reward amount of active user's node within a preset period of time is determined.It is inaccurate to solve the problems, such as that the contribution ability to user node is weighed.

Description

Reward distribution method, apparatus and system in a kind of block chain
Technical field
The present invention relates to distribution method, apparatus and system are rewarded in block chain field more particularly to a kind of block chain.
Background technology
Shared calculating, block chain are the trend of hot spot and the Fashion of Future instantly, and block chain+shared calculating double-core pattern Then let us sees the future possibility broken cloud computing homogeneity hedge and solve the uncontrollable pain spot of block chain.Block chain point public affairs There are chain, alliance's chain, privately owned chain.And shared calculate is the computing resource that magnanimity is collected with shared model and block chain technology, will be saved Point resource consolidation, it is open to vast Internet enterprises, it realizes higher price-performance ratio, is more easy to extend, more environmentally friendly calculation.Altogether It enjoys in calculating, user node goes out the idle resource sharing such as calculating, storage, bandwidth resources, for creating value, you can obtains Corresponding reward.And the contribution for verifying user node is wherein very important link.
In the prior art, it is that the contribution of user node is determined in a manner of forming to the contribution of user node, what user did Task quantity determines that the contribution of user node, the task that user completes are more, then it is assumed that the contribution of the user is higher, it is clear that lacks Few performance to user node itself considers, causes the contribution ability to user node to be weighed inaccurate.
Invention content
The embodiment of the present invention is solved by reward distribution method, apparatus and system in a kind of block chain of offer to user The contribution ability of node weighs inaccurate problem.
In a first aspect, distribution method is rewarded in a kind of offer block chain of the embodiment of the present invention, including：
All user nodes total contribution margin within a preset period of time and active user's node are obtained in block chain in institute State the contribution margin in preset time period, wherein the contribution margin of each user node is related to the performance of the user node；
Obtain to be allocated reward total amount of the block chain in the preset time period；
According to total contribution margin, the reward total amount to be allocated and active user's node in the preset time Contribution margin in section, determines reward amount of the active user's node in the preset time period.
Optionally, the total contribution margin of all user nodes within a preset period of time in the acquisition block chain, including：
Obtain each contribution margin of the user node in the preset time period in the block chain；
Read group total is carried out to contribution margin of all user nodes in the block chain in the preset time period, is obtained Total contribution margin.
Optionally, described to obtain each contribution margin of the user node in the preset time period, packet in the block chain It includes：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user's node in the preset time period in chain time ratio；
According to the hardware coefficient, described in chain time ratio and the N kinds equipment score, the active user is determined jointly The contribution margin of node.
Optionally, the N kind equipment scores for obtaining active user's node, including：
One or more of the calculation power score, bandwidth score and storage score for obtaining active user's node are set Back-up number.
Optionally, the calculation power score for obtaining active user's node, including：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of the CPU effective performances value, it is determined as the calculation power of active user's node Score.
Optionally, the cpu performance parameter value of detection active user's node, including：
The cpu performance of active user's node described in repeated detection, obtains more than two CPU in the preset time period Performance parameter value；
According to described two above cpu performance parameter values, the CPU effective performance values of active user's node are determined.
Optionally, the storage score for obtaining active user's node, including：
Detect effective memory space of active user's node；
According to effective memory space, the storage score of active user's node is determined.
Optionally, the bandwidth score for obtaining active user's node, including：
Obtain the bandwidth ability value of active user's node；
According to pre-set bandwidths weight and the bandwidth ability value, the bandwidth score of active user's node is determined.
Optionally, the bandwidth ability value for obtaining active user's node, including：
Obtain the ability value that tests the speed of active user's node；
Obtain the real fast ability value of active user's node；
According to test the speed ability value and the real fast ability value, the bandwidth ability value of active user's node is determined.
Optionally, the real fast ability value for obtaining active user's node, including：
Active user's node is obtained in the preset time from the block chain or on the reward node of chain Resources contribution amount in section；
The real fast ability value of active user's node is determined according to the resources contribution amount.
Optionally, the real fast ability value that active user's node is determined according to the resources contribution amount, including：
The real fast flow of active user's node is determined according to the resources contribution amount；
Real speed processing is carried out to the real fast flow, obtains the real fast ability value of active user's node.
Optionally, the ability value that tests the speed for obtaining active user's node, including：
Detect the sharing bandwidth of active user's node；
Speed measurement treatment is carried out to the sharing bandwidth, obtains the ability value that tests the speed of active user's node.
Optionally, described that speed measurement treatment is carried out to the sharing bandwidth, obtain the energy that tests the speed of active user's node Force value, including：
The number of devices of network egress where detecting active user's node；
Speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtains testing the speed for active user's node Ability value.
Optionally, described that speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtain the current use The ability value that tests the speed of family node, including：
It is the discount factor value for active user's node with the number of devices, the sharing bandwidth is carried out Discount processing, obtains bandwidth after the first folding；
Logarithmic function processing is carried out to bandwidth after first folding, obtains the ability value that tests the speed of active user's node.
Optionally, described that speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtain the current use The ability value that tests the speed of family node, including：
The number of devices is smoothed, the optimization discount factor value for active user's node is obtained；
Discount processing is carried out to the sharing bandwidth with the optimization discount factor value, obtains bandwidth after the second folding；
Logarithmic function processing is carried out to bandwidth after second folding, obtains the ability value that tests the speed of active user's node.
Optionally, described that discount processing is carried out to the sharing bandwidth with the optimization discount factor value, obtain second Bandwidth after folding carries out logarithmic function processing to bandwidth after second folding, obtains the ability value that tests the speed of active user's node, Specially following formula：
Wherein, ptestFor the ability value that tests the speed, PMbps2For the sharing bandwidth,For the optimization discount system Numerical value.
Optionally, described that speed measurement treatment is carried out to the sharing bandwidth, obtain the energy that tests the speed of active user's node Force value, including：
Discount processing is carried out to the sharing bandwidth with default fixed discounting value, obtains bandwidth after third folding；
Bandwidth carries out logarithmic function processing after being rolled over to the third, obtains the ability value that tests the speed of active user's node.
Optionally, it after the reward amount in determination active user's node in the preset time period, also wraps It includes：
The corresponding reward of the reward amount is issued to active user's node.
Second aspect, an embodiment of the present invention provides rewarding distributor in a kind of block chain, including：
Contribution margin acquiring unit, for obtaining the total contribution margin of all user nodes within a preset period of time in block chain, And contribution margin of active user's node in the preset time period, wherein the contribution margin of each user node with should The performance of user node is related；
Total amount acquiring unit is rewarded, it is total for obtaining to be allocated reward of the block chain in the preset time period Amount；
Reward amount determination unit, for according to total contribution margin, the reward total amount to be allocated and the current use Contribution margin of the family node in the preset time period determines reward of the active user's node in the preset time period Amount.
Optionally, the reward total amount acquiring unit, including：
Contribution margin obtains subelement, for obtaining in the block chain each user node in the preset time period Contribution margin；
Sum subelement, for contribution margin of all user nodes in the preset time period in the block chain into Row read group total obtains total contribution margin.
Optionally, the contribution margin obtains subelement, is specifically used for：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user's node in the preset time period in chain time ratio；
According to the hardware coefficient, described in chain time ratio and the N kinds equipment score, the active user is determined jointly The contribution margin of node.
Optionally, the contribution margin obtains subelement, is specifically used for：
One or more of the calculation power score, bandwidth score and storage score for obtaining active user's node are set Back-up number.
Optionally, the contribution margin obtains subelement, is specifically used for：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of the CPU effective performances value, it is determined as the calculation power of active user's node Score.
Optionally, the contribution margin obtains subelement, is specifically used for：
The cpu performance of active user's node described in repeated detection, obtains more than two CPU in the preset time period Performance parameter value；
According to described two above cpu performance parameter values, the CPU effective performance values of active user's node are determined.
Optionally, the contribution margin obtains subelement, is specifically used for：
Detect effective memory space of active user's node；
According to effective memory space, the storage score of active user's node is determined.
Optionally, the contribution margin obtains subelement, is specifically used for：
Obtain the bandwidth ability value of active user's node；
According to pre-set bandwidths weight and the bandwidth ability value, the bandwidth score of active user's node is determined.
Optionally, the contribution margin obtains subelement, is specifically used for：
Obtain the ability value that tests the speed of active user's node；
Obtain the real fast ability value of active user's node；
According to test the speed ability value and the real fast ability value, the bandwidth ability value of active user's node is determined.
Optionally, the contribution margin obtains subelement, is specifically used for：
Active user's node is obtained in the preset time from the block chain or on the reward node of chain Resources contribution amount in section；
The real fast ability value of active user's node is determined according to the resources contribution amount.
Optionally, the contribution margin obtains subelement, is specifically used for：
The real fast flow of active user's node is determined according to the resources contribution amount；
Real speed processing is carried out to the real fast flow, obtains the real fast ability value of active user's node.
Optionally, the contribution margin obtains subelement, is specifically used for：
Detect the sharing bandwidth of active user's node；
Speed measurement treatment is carried out to the sharing bandwidth, obtains the ability value that tests the speed of active user's node.
Optionally, the contribution margin obtains subelement, is specifically used for：
The number of devices of network egress where detecting active user's node；
Speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtains testing the speed for active user's node Ability value.
Optionally, the contribution margin obtains subelement, is specifically used for：
It is the discount factor value for active user's node with the number of devices, the sharing bandwidth is carried out Discount processing, obtains bandwidth after the first folding；
Logarithmic function processing is carried out to bandwidth after first folding, obtains the ability value that tests the speed of active user's node.
Optionally, the contribution margin obtains subelement, is specifically used for：
The number of devices is smoothed, the optimization discount factor value for active user's node is obtained；
Discount processing is carried out to the sharing bandwidth with the optimization discount factor value, obtains bandwidth after the second folding；
Logarithmic function processing is carried out to bandwidth after second folding, obtains the ability value that tests the speed of active user's node.
Optionally, the contribution margin obtains subelement, is specifically used for executing following formula：
Wherein, ptestFor the ability value that tests the speed, PMbps2For the sharing bandwidth,For the optimization discount system Numerical value.
Optionally, the contribution margin obtains subelement, is specifically used for：
Discount processing is carried out to the sharing bandwidth with default fixed discounting value, obtains bandwidth after third folding；
Bandwidth carries out logarithmic function processing after being rolled over to the third, obtains the ability value that tests the speed of active user's node.
Optionally, described device further includes：
Issuance unit is rewarded, for issuing the corresponding reward of the reward amount to active user's node.
The third aspect, an embodiment of the present invention provides a kind of computer readable storage mediums, are stored thereon with computer journey Sequence realizes the step described in any embodiment of first aspect when the program is executed by processor.
Fourth aspect an embodiment of the present invention provides a kind of Centroid, including memory, processor and is stored in storage On device and the computer program that can run on a processor, the processor realize any of first aspect when executing described program Kind embodiment any one of them step.
5th aspect, an embodiment of the present invention provides a kind of block catenary systems, including any embodiment in second aspect Distributor is rewarded in the block chain.
The one or more technical solutions provided in the embodiment of the present invention, have at least the following technical effects or advantages：
By obtaining all user nodes total contribution margin within a preset period of time and active user's node in block chain Contribution margin within a preset period of time, wherein the contribution margin of each user node is related to the performance of the user node；Obtain area The reward total amount to be allocated of block chain within a preset period of time；It is saved according to total contribution margin, reward total amount to be allocated and active user The contribution margin of point within a preset period of time, determines the reward amount of active user's node within a preset period of time, due to being saved to user The reward amount of point is not only weighed with the done task amount of user node, but with the performance phase of the user node itself It closes, because the performance of user node is higher, does the ability of task with regard to big, therefore, it is possible to more reasonably to being contributed in block chain User node carry out contribution ability measurement, and then improve reward score match accuracy.
Description of the drawings
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment Attached drawing is briefly described, it should be apparent that, drawings in the following description are some embodiments of the invention, for this For the those of ordinary skill of field, without creative efforts, others are can also be obtained according to these attached drawings Attached drawing.
Fig. 1 is the Organization Chart of block chain provided in an embodiment of the present invention；
Fig. 2 is the flow chart that distribution method is rewarded in block chain provided in an embodiment of the present invention；
Fig. 3 is the Program modual graph that distributor is rewarded in block chain provided in an embodiment of the present invention；
Fig. 4 is the structure chart of Centroid provided in an embodiment of the present invention；
Fig. 5 is the structure chart of computer storage media provided in an embodiment of the present invention.
Specific implementation mode
The embodiment of the present invention is solved by reward distribution method, apparatus and system in a kind of block chain of offer to user The contribution ability of node weighs inaccurate technical problem, and general thought is as follows：
By obtaining all user nodes total contribution margin within a preset period of time and active user's node in block chain Contribution margin within a preset period of time, wherein the contribution margin of each user node is related to the performance of the user node；Obtain area The reward total amount to be allocated of block chain within a preset period of time；It is saved according to total contribution margin, reward total amount to be allocated and active user The contribution margin of point within a preset period of time, determines the reward amount of active user's node within a preset period of time, due to being saved to user The reward amount of point is not only weighed with the done task amount of user node, but with the performance phase of the user node itself It closes, because the performance of user node is higher, does the ability of task with regard to big, therefore, it is possible to more reasonably to being contributed in block chain User node carry out contribution ability measurement, and then improve reward score match accuracy.
In order to better understand the above technical scheme, in conjunction with appended figures and specific embodiments to upper Technical solution is stated to be described in detail.
Distribution method is rewarded in a kind of block chain provided in an embodiment of the present invention, can be used for the center in block catenary system Node 104 or each user node 103.Refering to what is shown in Fig. 1, the block chain includes Centroid 104, reward node 102, hair Cloth node 101 and multiple user nodes 103, publisher node 101 are used for release tasks, and each user node 103 utilizes itself Resource complete being issued of the task of publisher node 101, and Centroid 104 is for contributing each user node 103 It is counted, and is the distribution reward of each user node, and reward node 102 for issuing reward to user node 103.
Refering to what is shown in Fig. 2, rewarding distribution method in block chain provided in an embodiment of the present invention, include the following steps：
Step S201：Obtain the total contribution margin of all user nodes within a preset period of time in block chain, and current use The contribution margin of family node within a preset period of time, wherein the contribution margin of each user node is related to the performance of the user node.
Specifically, preset time period is arranged according to actual demand.For example, could be provided as one day, one week or one month Etc..
Wherein, the total contribution margin of all user nodes within a preset period of time in block chain is obtained, specially：Obtain block The each contribution margin of user node within a preset period of time in chain, within a preset period of time to all user nodes in block chain Contribution margin carries out read group total, obtains total contribution margin.
It should be noted that the contribution margin of active user's node is included among total contribution margin, and each used in block chain The acquisition modes all same or similar of the contribution margin of family node within a preset period of time, is existed with obtaining active user's node below For contribution margin in preset time period, the process to obtaining the contribution margin of each user node within a preset period of time is retouched It states：
Specifically, obtain the contribution margin of active user node within a preset period of time, include the following steps S2011~ S2013：
S2011：The N kind equipment scores of active user's node are obtained, N is the integer more than 1.Each equipment score and user The performance parameter of node is related.
In specific implementation process, the N kind equipment scores of active user's node are obtained, detailed process is：Obtain current use One or more of calculation power score, bandwidth score and storage score of family node.It is of course also possible to according to actual demand Increase the type of acquired equipment score.
In the following, the acquisition process for calculating power score, bandwidth score and storage score is described in detail respectively：
One, the acquisition process of power score is calculated：
Step a1：Detect the CPU effective performance values of active user's node.
In step a1, the cpu performance of access active user's node is detected according to prefixed time interval.It can be spaced rather Clock detection is primary, for example, interval detection in 1~10 minute is primary, once on active user's node access block chain, then Centroid It is able to detect that the cpu performance parameter value of active user's node.
In specific implementation process, cpu performance includes one or more in dominant frequency, outer frequency, frequency multiplication, memory etc..
More specifically, Ke Yishi：The cpu performance of repeated detection active user node within a preset period of time obtains two A above cpu performance parameter value；According to more than two cpu performance parameter values, the CPU validity of active user's node is determined It can value.
For example, more than two cpu performance parameter values that detection is obtained carry out average computation, mean calculations conduct The CPU effective performance values of active user's node.If there is a plurality of types of cpu performance parameter values, then by the cpu performance of same type Parameter value carries out average computation, obtains the CPU effective performance values of multiple types.
It is of course also possible to be only will be when the cpu performance parameter value of previous detection is as CPU effective performance values.
After step a1, step a2 is executed：By the corresponding default CPU conversions value of CPU effective performance values, it is determined as working as The calculation power score of preceding user node.
Specifically, be pre-established with CPU effective performances value and default CPU conversion value between correspondence list or Respective function determines corresponding with the CPU effective performance values of active user's node according to the correspondence list or respective function One default CPU conversions value, and in this, as the calculation power score of active user's node.CPU effective performance values are bigger, then calculate power Score is bigger.
Two, the acquisition process of score is stored：
Step b1：Detect effective memory space of active user's node.
Specifically, the memory space of repeated detection active user node within a preset period of time, can be spaced a few minutes inspection It surveys primary.For example, interval detection in 1~10 minute is primary, once on active user's node access block chain, then Centroid can Detect the memory space of active user's node.The memory space of repeated detection is subjected to average computation, obtains active user's section Effective memory space of point.It should be noted that the storage location of active user's node include built-in hard disk, it is external USB flash disk, external Hard disk etc..
Specifically, can also be using the memory space that previous ought be detected as effective memory space of active user's node.
Step b2：According to effective memory space, the storage score of active user's node is determined.
Specifically, using default storage numerical value corresponding with effective memory space as the storage of active user's node point Number.
In specific implementation process, it is pre-established in Centroid between default storage numerical value and memory space one by one Either respective function saves active user according to the correspondence list or the respective function for corresponding correspondence list Storage score of the default storage numerical value as active user's node corresponding to effective memory space of point.
For example, effective memory space is 0~100G, and corresponding default storage numerical value is 0；Effective memory space is 100~600G, corresponding default storage numerical value are 5；Effective memory space is 600G~2T, and corresponding default storage numerical value is 10.Certainly, in specific implementation process, the range of effective memory space, default storage numerical value can be according to actual demand tune It is whole, however it is not limited to the example above.
Three, the acquisition process of bandwidth score：
Step c1：Obtain the bandwidth ability value of active user's node.
Specifically, bandwidth ability value can be obtained by following process：Obtain active user's node test the speed ability value and The real fast ability value of active user's node；The bandwidth ability of active user's node is determined according to the fast ability value of ability value and reality that tests the speed Value.
Specifically, can be：Bandwidth ability value=ability value the * that tests the speed tests the speed the real fast weights of the fast ability value * of weight+reality.Its In, the weight that tests the speed, real fast weight can be fixed values, can also be arranged one according to actual conditions can be according to adjustable strategies The variable value being automatically adjusted, then for different user nodes, the weight that tests the speed, real fast weight can be different, to more accord with Close actual demand.
Step c2：According to the bandwidth ability value of pre-set bandwidths weight and active user's node, active user's node is determined Bandwidth score.
In the following, the acquisition process of real speed ability value in step c1 is described in detail：
Step c11：From on block chain reward node or block chain in obtain active user's node in preset time period Interior resources contribution amount.
It should be noted that the resources contribution amount of active user's node within a preset period of time is by rewarding node default Reconciliation result in period determines.Since the resources contribution amount in preset time period can be written in block chain for reward node, The resources contribution amount of active user's node within a preset period of time can also be obtained from block chain.Can also be reward node to Centroid reports the resources contribution amount in preset time period.In another optional embodiment, active user's node is pre- If the resources contribution amount in the period node itself reconciliation result can determine centered on.
Specifically, the resources contribution amount in preset time period, Ke Yiwei：Active user's node is complete within a preset period of time The floating resources spent by whole flow type tasks issued at publisher node.
Step c12：The real fast ability value of active user's node is determined according to the resources contribution amount in preset time period.
In step c12, the real fast ability value of active user's node is determined according to the resources contribution amount in preset time period, A kind of embodiment can be：The real fast flow of active user's node is determined according to resources contribution amount；Real fast flow is carried out again Real speed processing obtains the real fast ability value of active user's node.
Can be following process specifically, determining the real fast flow of active user's node according to resources contribution amount：
First, the real fast flow of active user's node is determined according to the resources contribution amount and preset duration.Specifically, directly Connecing is：The resources contribution amount divided by preset duration of active user's node within a preset period of time, obtain the reality of active user's node Fast flow.
In specific implementation process, preset duration is arranged according to the size of preset time period.For example, preset time period is One day, then preset duration can be the duration value less than or equal to one day, can be 8 hours~12 hours.For example, when default Length could be provided as 8 hours.For example, preset time period is two days, then preset duration can be less than or equal to two days Duration value, for example, 16 hours~24 hours.
For example, active user's node is 3GB in one day resources contribution amount, and preset duration is 8 hours, then current to use The real fast flow of family node is 1.5Mbps.
Then, real speed processing is carried out to real fast flow, obtains the real fast ability value of active user's node.Specifically, right The mode that real speed flow carries out real speed processing can use logarithmic function, in specific implementation process, be carried out to real fast flow The formula of following logarithmic function may be used in real speed processing：pReal=ln (1+pMbps1)；Wherein, pRealFor real fast ability value, pMbps1For real fast flow, to obtain real fast ability value.
The real fast ability value of active user's node can be effectively obtained by the above embodiment.
In the following, the acquisition process for the ability value that tests the speed in step c1 is described in detail：
Step c11 '：Detect the sharing bandwidth of active user's node.
Specifically, can be repeatedly detected to the bandwidth of active user's node according to prefixed time interval, can be spaced A few minutes detect the bandwidth of active user's node, for example, interval detection in 1~10 minute is primary, once active user's node It accesses on block chain, then Centroid is able to detect that the bandwidth of active user's node.The bandwidth of repeated detection is averaged It calculates, obtains the sharing bandwidth of active user's node.It should be noted that detecting obtained bandwidth every time can be：Currently User's maximum bandwidth of user node.
Specifically, sharing bandwidth can also be using the user's maximum bandwidth that previous ought be detected as sharing bandwidth.
Step c12 '：Speed measurement treatment is carried out to sharing bandwidth, obtains the ability value that tests the speed of active user's node.
In specific implementation process, the mode of speed measurement treatment is carried out to sharing bandwidth to be：It is saved based on active user The number of devices of network egress where point can also be based on default fixed discounting value.In the following, illustrating respectively：
In the embodiment based on number of devices, specific implementation process is：Network where detection active user's node goes out The number of devices of mouth；Speed measurement treatment is carried out to sharing bandwidth with number of devices, obtains the ability value that tests the speed of active user's node.
It should be noted that the number of devices of network egress where active user's node, refers to and active user's node category In same IP outlet number of devices (including active user's node), and same IP outlet refer to each user node it is public this IP.For example, shared with the user equipment one of the public same IP of active user's node 10 equipment (including active user section Point), then the number of devices of network egress where active user's node is 10.
More specifically, speed measurement treatment is carried out to sharing bandwidth with number of devices, but can there are many realization method, into Row is explained respectively：
Wherein, a kind of realization method is：Using number of devices as the discount factor value for active user's node, pair can be total to It enjoys bandwidth and carries out discount processing, obtain bandwidth after the first folding；Logarithmic function processing is carried out to bandwidth after the first folding, is currently used The ability value that tests the speed of family node.
For example, the sharing bandwidth of active user's node is 100,000,000, and number of devices is 5, then to sharing bandwidth Discount processing is carried out, bandwidth is 20,000,000 after the first obtained folding.Then, logarithmic function processing is carried out to bandwidth after the first folding： ptest=ln (1+20), the ability value that tests the speed for obtaining active user's node is ln21.For another example：Active user's node is shared Bandwidth is 70,000,000, and number of devices is 10, and bandwidth is 7,000,000 after the first obtained folding, then carries out logarithm letter to bandwidth after the first folding Number processing：ptest=ln (1+7), the ability value that tests the speed for obtaining active user's node is ln8.
It in this way being capable of the ability value that tests the speed that is simple, being quickly obtained active user's node.But in order into one Step improves the reasonability for the ability value that tests the speed, and the mode of being implemented as follows may be used：
Another embodiment：Number of devices is smoothed, the optimization discount system for active user's node is obtained Numerical value；Discount processing is carried out to sharing bandwidth to optimize discount factor value, obtains bandwidth after the second folding；To bandwidth after the second folding Logarithmic function processing is carried out, the ability value that tests the speed of active user's node is obtained.
In specific implementation process, present embodiment may be used following formula and realize： Wherein, ptestFor the ability value that tests the speed, PMbps2For sharing bandwidth,To optimize discount factor value.It should be noted thatIn x be adjustment factor, DEV is number of devices.
In specific implementation process, the numerical value of x can be adjusted according to actual demand, sharing bandwidth is beaten to adjust Folding degree.Wherein, it is to the mode that number of devices is smoothed:For example, adjustment factor x is 2, equipment QuantityDEVIt it is 8, then the optimization discount factor value after being smoothed based on number of devices is 3.
In the following, carrying out discount processing to sharing bandwidth to optimize discount factor value, bandwidth is specially after obtaining the second folding：For example, adjustment factor x is 2, number of devicesDEVIt it is 8, then sharing bandwidth is 100,000,000, then the second folding Bandwidth is 25,000,000 afterwards, and the obtained ability value that tests the speed is ptest=ln (1+24)=ln25.
By this realization method, the ability value that tests the speed calculated is enabled to more to meet reality, to more accurate.
Speed measurement treatment is carried out to sharing bandwidth based on default fixed discounting value, specific implementation process can be：With default Fixed discounting value carries out discount processing to sharing bandwidth, obtains bandwidth after third folding；Bandwidth carries out logarithm letter after being rolled over to third Number processing, obtains the ability value that tests the speed of active user's node.Specifically, presetting fixed discounting value can be referred to by Centroid It is fixed, can also by Centroid based on the difference between the obtained user's maximum bandwidth of big data system statistics and actual bandwidth not It is disconnected that default fixed discounting value is modified, to adapt to the continuous promotion of network.
Step S2012：Obtain the corresponding hardware coefficient of active user's node.
Specifically, in step S2012, hardware coefficient can be adjusted according to actual conditions, to each user The contribution margin of node is adjusted.It should be noted that hardware coefficient is related to the equipment of active user's node itself, with current For user node is the hardware of certain style number, hardware coefficient can be 1.0.And the hardware that active user's node is other moneys When, then hardware coefficient can be adjusted based on 1.0.
Step S2013：Obtain active user node within a preset period of time in chain time ratio.
It should be noted that by Centroid detection active user's node in the chain time, specially：Centroid detects Whether heartbeat is kept between active user's node and Centroid, if it exceeds default heart beat cycle, it is determined that active user saves Point is not online, to obtain each in the chain time of active user's node；Then, statistics active user's node is in preset time period Interior is each in the sum of chain time, be determined as in preset time period always in chain duration.It will be always in chain duration divided by preset time The result of section is used as in chain time ratio.By taking preset time period is one day (24 hours) as an example, if always in chain a length of 8 hours, Then in chain time ratio=8 hour/24 hour=1/3.By taking preset time period is two days (48 hours) as an example, if always in chain duration It it is 24 hours, then in chain time ratio=24 hour/48 hour=0.5.
It should be noted that step S2011, step S2012, step S2013 can be in no particular order sequence while hold Row, or executed according to arbitrary sequencing.
After completing step S2011~S2013, then, step S2014 is executed：According to hardware coefficient, in chain time ratio With N kind equipment scores, the common contribution margin for determining active user's node.
Specifically, the calculating process of step S2014 can be：First, the sum of N kind equipment scores are calculated, then, by N kinds The sum of equipment score is multiplied with hardware coefficient, in the chain time than carrying out three, obtains the contribution margin of active user's node.With equipment Score includes for calculating power score, bandwidth score and storage score, then the contribution margin of active user's node=hardware coefficient Х (calculating power score+bandwidth score+storage score) Х is in chain time ratio.
Can be that weighted value is arranged in each equipment score, to adjust each equipment score to calculating in specific implementation process The influence of the contribution margin of active user's node.For example, in the application scenarios for completing store tasks, the power of storage score can be turned up Weight.For example, in the application scenarios for completing calculating task, the weight for calculating power score can be turned up.In the application for completing search mission The weight of bandwidth score can be turned up in scene.
Execute step S202：Obtain the reward total amount to be allocated of block chain within a preset period of time.
It should be noted that step S201 may be performed simultaneously with step S202, successively can also sequentially execute.
Specifically, reward total amount to be allocated is determined according to fixed prize total amount set in block chain.For example, it waits for Distribution reward total amount decays according to another time interval.For example, it is to be allocated reward total amount can be decay year by year, Can also be to decay month by month, then according to where fixed prize total amount and preset time period year or months and decaying Coefficient determines reward total amount to be allocated jointly.
For decaying year by year, if fixed prize total amount is 1,000,000,000, attenuation coefficient 1/3.Then First Year is daily waits for Distribution reward total amount is 920,000, and the next year of decaying 1/3, then the daily reward total amount to be allocated of second year is 92,/30,000, and so on.
After step S201 and S202, step S203 is then executed：According to total contribution margin, reward total amount to be allocated and The contribution margin of active user's node within a preset period of time determines the reward amount of active user's node within a preset period of time.
Specifically, the following calculation amount of receiving awards specifically may be used in step S203：Active user's node is pre- If the contribution margin in the period divided by total contribution margin, the contribution ratio of active user's node is obtained, by the contribution of active user's node Than being multiplied by reward total amount to be allocated, the reward amount of active user's node within a preset period of time is obtained.Specifically, calculation formula Can be：The reward amount of active user's node=reward to be allocated total amount/total contribution margin Х active user's nodes are in preset time Contribution margin in section.
Certainly, in specific implementation process, it is also contemplated that other factors adjust the reward amount of active user's node.Than Such as, the reward amount in a upper preset time period for active user's node can be combined to be adjusted in this preset time period Contribution margin.For example, the reward amount in last preset time period is 0, then not to the reward amount in this preset time period Be adjusted, otherwise, according to the reward amount in last preset time period to the reward in this preset time period measure into Row adjustment, for example, can be the raising of preset ratio or the reduction of preset ratio.
Further include following steps after executing step S203：The corresponding reward of reward amount is issued to active user's node. Or block chain being written into the reward amount of active user's node so that reward node obtains reward amount from block chain, and according to Reward amount issues reward to active user's node.Specifically, the reward issued can be ideal money, virtual objects, empirical value Etc..
Based on same inventive concept, an embodiment of the present invention provides distributor is rewarded in a kind of block chain, with reference to 3 institute of figure Show, including such as lower structure：
Contribution margin acquiring unit 301, for obtaining the total contribution of all user nodes within a preset period of time in block chain The contribution margin of value and active user's node within a preset period of time, wherein the contribution margin of each user node is saved with the user The performance of point is related；
Total amount acquiring unit 302 is rewarded, for obtaining the reward total amount to be allocated of block chain within a preset period of time；
Reward amount determination unit 303 is used for according to total contribution margin, reward total amount to be allocated and active user's node pre- If the contribution margin in the period, the reward amount of active user's node within a preset period of time is determined.
Optionally, reward total amount acquiring unit 302, including：
Contribution margin obtains subelement, for obtaining each contribution margin of user node within a preset period of time in block chain；
Summation subelement, used in block chain, the contribution margin of all user nodes within a preset period of time carries out based on summation It calculates, obtains total contribution margin.
Optionally, contribution margin obtains subelement, is specifically used for：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user node within a preset period of time in chain time ratio；
According to hardware coefficient, in chain time ratio and N kind equipment scores, the common contribution margin for determining active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Obtain one or more of the calculation power score, bandwidth score and storage score of active user's node equipment point Number.
Optionally, contribution margin obtains subelement, is specifically used for：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of CPU effective performance values, it is determined as the calculation power score of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
The cpu performance of repeated detection active user node within a preset period of time obtains more than two cpu performance parameters Value；
According to more than two cpu performance parameter values, the CPU effective performance values of active user's node are determined.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Detect effective memory space of active user's node；
According to effective memory space, the storage score of active user's node is determined.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Obtain the bandwidth ability value of active user's node；
According to pre-set bandwidths weight and bandwidth ability value, the bandwidth score of active user's node is determined.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Obtain the ability value that tests the speed of active user's node；
Obtain the real fast ability value of active user's node；
According to ability value and the real fast ability value of testing the speed, the bandwidth ability value of active user's node is determined.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
The resource tribute of active user's node within a preset period of time is obtained from block chain or on the reward node of chain The amount of offering；
The real fast ability value of active user's node is determined according to resources contribution amount.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
The real fast flow of active user's node is determined according to resources contribution amount；
Real speed processing is carried out to real fast flow, obtains the real fast ability value of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Detect the sharing bandwidth of active user's node；
Speed measurement treatment is carried out to sharing bandwidth, obtains the ability value that tests the speed of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
The number of devices of network egress where detecting active user's node；
Speed measurement treatment is carried out to sharing bandwidth with number of devices, obtains the ability value that tests the speed of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
It is the discount factor value for active user's node with number of devices, discount processing is carried out to sharing bandwidth, is obtained Bandwidth after to the first folding；
Logarithmic function processing is carried out to bandwidth after the first folding, obtains the ability value that tests the speed of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Number of devices is smoothed, the optimization discount factor value for active user's node is obtained；
Discount processing is carried out to sharing bandwidth to optimize discount factor value, obtains bandwidth after the second folding；
Logarithmic function processing is carried out to bandwidth after the second folding, obtains the ability value that tests the speed of active user's node.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for executing following formula：
Wherein, ptestFor the ability value that tests the speed, PMbps2For sharing bandwidth,To optimize discount factor value.
In a preferred embodiment, contribution margin obtains subelement, is specifically used for：
Discount processing is carried out to sharing bandwidth with default fixed discounting value, obtains bandwidth after third folding；
Bandwidth carries out logarithmic function processing after being rolled over to third, obtains the ability value that tests the speed of active user's node.
In a preferred embodiment, described device further includes：
Issuance unit is rewarded, for issuing the corresponding reward of the reward amount to active user's node.
Based on same inventive concept, present embodiments provide a kind of Centroid, as shown in figure 4, including memory 410, Processor 420 and it is stored in the computer program 411 that can be run on memory 410 and on processor 420, processor 420 is held Following steps are realized when line program 410：
All user nodes total contribution margin within a preset period of time and active user's node are obtained in block chain pre- If the contribution margin in the period, wherein the contribution margin of each user node is related to the performance of the user node；Obtain block chain Reward total amount to be allocated within a preset period of time；Existed according to total contribution margin, reward total amount to be allocated and active user's node Contribution margin in preset time period determines the reward amount of active user's node within a preset period of time.
In specific implementation process, when processor 420 executes computer program 411, reward score in block chain may be implemented Any embodiment in method of completing the square embodiment.
Based on same inventive concept, a kind of computer readable storage medium is present embodiments provided, as shown in figure 5, calculating Computer program 511 is stored on machine readable storage medium storing program for executing, which realizes following steps when being executed by processor 511：
All user nodes total contribution margin within a preset period of time and active user's node are obtained in block chain pre- If the contribution margin in the period, wherein the contribution margin of each user node is related to the performance of the user node；Obtain block chain Reward total amount to be allocated within a preset period of time；Existed according to total contribution margin, reward total amount to be allocated and active user's node Contribution margin in preset time period determines the reward amount of active user's node within a preset period of time.
In specific implementation process, when computer program 511 is executed by processor, it may be implemented to reward distribution in block chain Any embodiment in embodiment of the method.
The one or more technical solutions provided in the embodiment of the present invention, have at least the following technical effects or advantages：
By obtaining all user nodes total contribution margin within a preset period of time and active user's node in block chain Contribution margin within a preset period of time, wherein the contribution margin of each user node is related to the performance of the user node；Obtain area The reward total amount to be allocated of block chain within a preset period of time；It is saved according to total contribution margin, reward total amount to be allocated and active user The contribution margin of point within a preset period of time, determines the reward amount of active user's node within a preset period of time, due to being saved to user The reward amount of point is not only weighed with the done task amount of user node, but with the performance phase of the user node itself It closes, because the performance of user node is higher, does the ability of task with regard to big, therefore, it is possible to more reasonably to being contributed in block chain User node carry out reward measurement.
It should be understood by those skilled in the art that, the embodiment of the present invention can be provided as method, system or computer program Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention Apply the form of example.Moreover, the present invention can be used in one or more wherein include computer usable program code computer The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) The form of product.
The present invention be with reference to according to the method for the embodiment of the present invention, the flow of equipment (system) and computer program product Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one The step of function of being specified in a box or multiple boxes.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art God and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies Within, then the present invention is also intended to include these modifications and variations.
The invention discloses rewarding distribution method in A1, a kind of block chain, including：
All user nodes total contribution margin within a preset period of time and active user's node are obtained in block chain in institute State the contribution margin in preset time period, wherein the contribution margin of each user node is related to the performance of the user node；
Obtain to be allocated reward total amount of the block chain in the preset time period；
According to total contribution margin, the reward total amount to be allocated and active user's node in the preset time Contribution margin in section, determines reward amount of the active user's node in the preset time period.
Distribution method is rewarded in A2, the block chain as described in A1, all user nodes are default in the acquisition block chain Total contribution margin in period, including：
Obtain each contribution margin of the user node in the preset time period in the block chain；
Read group total is carried out to contribution margin of all user nodes in the block chain in the preset time period, is obtained Total contribution margin.
Distribution method is rewarded in A3, the block chain as described in A1 or A2, it is described to obtain each user's section in the block chain Contribution margin of the point in the preset time period, including：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user's node in the preset time period in chain time ratio；
According to the hardware coefficient, described in chain time ratio and the N kinds equipment score, the active user is determined jointly The contribution margin of node.
Distribution method, the N kind equipment for obtaining active user's node point are rewarded in A4, the block chain as described in A1 Number, including：
One or more of the calculation power score, bandwidth score and storage score for obtaining active user's node are set Back-up number.
Reward distribution method in A5, the block chain as described in A4, the calculation power score for obtaining active user's node, Including：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of the CPU effective performances value, it is determined as the calculation power of active user's node Score.
Distribution method, the cpu performance ginseng of detection active user's node are rewarded in A6, the block chain as described in A5 Numerical value, including：
The cpu performance of active user's node described in repeated detection, obtains more than two CPU in the preset time period Performance parameter value；
According to described two above cpu performance parameter values, the CPU effective performance values of active user's node are determined.
Reward distribution method in A7, the block chain as described in A4, the storage score for obtaining active user's node, Including：
Detect effective memory space of active user's node；
According to effective memory space, the storage score of active user's node is determined.
Reward distribution method in A8, the block chain as described in A4, the bandwidth score for obtaining active user's node, Including：
Obtain the bandwidth ability value of active user's node；
According to pre-set bandwidths weight and the bandwidth ability value, the bandwidth score of active user's node is determined.
Distribution method, the bandwidth ability for obtaining active user's node are rewarded in A9, the block chain as described in A8 Value, including：
Obtain the ability value that tests the speed of active user's node；
Obtain the real fast ability value of active user's node；
According to test the speed ability value and the real fast ability value, the bandwidth ability value of active user's node is determined.
Distribution method, the real fast ability for obtaining active user's node are rewarded in A10, the block chain as described in A9 Value, including：
Active user's node is obtained in the preset time from the block chain or on the reward node of chain Resources contribution amount in section；
The real fast ability value of active user's node is determined according to the resources contribution amount.
Distribution method is rewarded in A11, the block chain as described in A10, it is described to work as according to described in resources contribution amount determination The real fast ability value of preceding user node, including：
The real fast flow of active user's node is determined according to the resources contribution amount；
Real speed processing is carried out to the real fast flow, obtains the real fast ability value of active user's node.
Distribution method, the ability that tests the speed for obtaining active user's node are rewarded in A12, the block chain as described in A9 Value, including：
Detect the sharing bandwidth of active user's node；
Speed measurement treatment is carried out to the sharing bandwidth, obtains the ability value that tests the speed of active user's node.
Distribution method is rewarded in A13, the block chain as described in A12, it is described that speed measurement treatment is carried out to the sharing bandwidth, The ability value that tests the speed of active user's node is obtained, including：
The number of devices of network egress where detecting active user's node；
Speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtains testing the speed for active user's node Ability value.
Distribution method is rewarded in A14, the block chain as described in A13, it is described that band is shared to described with the number of devices Width carries out speed measurement treatment, obtains the ability value that tests the speed of active user's node, including：
It is the discount factor value for active user's node with the number of devices, the sharing bandwidth is carried out Discount processing, obtains bandwidth after the first folding；
Logarithmic function processing is carried out to bandwidth after first folding, obtains the ability value that tests the speed of active user's node.
Distribution method is rewarded in A15, the block chain as described in A13, it is described that band is shared to described with the number of devices Width carries out speed measurement treatment, obtains the ability value that tests the speed of active user's node, including：
The number of devices is smoothed, the optimization discount factor value for active user's node is obtained；
Discount processing is carried out to the sharing bandwidth with the optimization discount factor value, obtains bandwidth after the second folding；
Logarithmic function processing is carried out to bandwidth after second folding, obtains the ability value that tests the speed of active user's node.
Reward distribution method in A16, the block chain as described in A15, it is described with the optimization discount factor value to it is described can Shared bandwidth carries out discount processing, obtains bandwidth after the second folding, carries out logarithmic function processing to bandwidth after second folding, obtains The ability value that tests the speed of active user's node, specially following formula：
Wherein, ptestFor the ability value that tests the speed, PMbps2For the sharing bandwidth,For the optimization discount system Numerical value.
Distribution method is rewarded in A17, the block chain as described in A12, it is described that speed measurement treatment is carried out to the sharing bandwidth, The ability value that tests the speed of active user's node is obtained, including：
Discount processing is carried out to the sharing bandwidth with default fixed discounting value, obtains bandwidth after third folding；
Bandwidth carries out logarithmic function processing after being rolled over to the third, obtains the ability value that tests the speed of active user's node.
Distribution method is rewarded in A18, the block chain as described in any one of A1-A17, in the determination active user After reward amount of the node in the preset time period, further include：
The corresponding reward of the reward amount is issued to active user's node.
The present invention discloses in a kind of B19, a kind of block chain and rewards distributor, including：
Contribution margin acquiring unit, for obtaining the total contribution margin of all user nodes within a preset period of time in block chain, And contribution margin of active user's node in the preset time period, wherein the contribution margin of each user node with should The performance of user node is related；
Total amount acquiring unit is rewarded, it is total for obtaining to be allocated reward of the block chain in the preset time period Amount；
Reward amount determination unit, for according to total contribution margin, the reward total amount to be allocated and the current use Contribution margin of the family node in the preset time period determines reward of the active user's node in the preset time period Amount.
Reward distributor in B20, the block chain as described in B19, the reward total amount acquiring unit, including：
Contribution margin obtains subelement, for obtaining in the block chain each user node in the preset time period Contribution margin；
Sum subelement, for contribution margin of all user nodes in the preset time period in the block chain into Row read group total obtains total contribution margin.
Distributor is rewarded in B21, the block chain as described in B19 or B20, the contribution margin obtains subelement, specific to use In：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user's node in the preset time period in chain time ratio；
According to the hardware coefficient, described in chain time ratio and the N kinds equipment score, the active user is determined jointly The contribution margin of node.
Distributor is rewarded in B22, the block chain as described in B21, the contribution margin obtains subelement, is specifically used for：
One or more of the calculation power score, bandwidth score and storage score for obtaining active user's node are set Back-up number.
Distributor is rewarded in B23, the block chain as described in B22, the contribution margin obtains subelement, is specifically used for：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of the CPU effective performances value, it is determined as the calculation power of active user's node Score.
Distributor is rewarded in B24, the block chain as described in B23, the contribution margin obtains subelement, is specifically used for：
The cpu performance of active user's node described in repeated detection, obtains more than two CPU in the preset time period Performance parameter value；
According to described two above cpu performance parameter values, the CPU effective performance values of active user's node are determined.
Distributor is rewarded in B25, the block chain as described in B22, the contribution margin obtains subelement, is specifically used for：
Detect effective memory space of active user's node；
According to effective memory space, the storage score of active user's node is determined.
Distributor is rewarded in B26, the block chain as described in B22, the contribution margin obtains subelement, is specifically used for：
Obtain the bandwidth ability value of active user's node；
According to pre-set bandwidths weight and the bandwidth ability value, the bandwidth score of active user's node is determined.
Distributor is rewarded in B27, the block chain as described in B26, the contribution margin obtains subelement, is specifically used for：
Obtain the ability value that tests the speed of active user's node；
Obtain the real fast ability value of active user's node；
According to test the speed ability value and the real fast ability value, the bandwidth ability value of active user's node is determined.
Distributor is rewarded in B28, the block chain as described in B27, the contribution margin obtains subelement, is specifically used for：
Active user's node is obtained in the preset time from the block chain or on the reward node of chain Resources contribution amount in section；
The real fast ability value of active user's node is determined according to the resources contribution amount.
Distributor is rewarded in B29, the block chain as described in B28, the contribution margin obtains subelement, is specifically used for：
The real fast flow of active user's node is determined according to the resources contribution amount；
Real speed processing is carried out to the real fast flow, obtains the real fast ability value of active user's node.
Distributor is rewarded in B30, the block chain as described in B27, the contribution margin obtains subelement, is specifically used for：
Detect the sharing bandwidth of active user's node；
Speed measurement treatment is carried out to the sharing bandwidth, obtains the ability value that tests the speed of active user's node.
Distributor is rewarded in B31, the block chain as described in B30, the contribution margin obtains subelement, is specifically used for：
The number of devices of network egress where detecting active user's node；
Speed measurement treatment is carried out to the sharing bandwidth with the number of devices, obtains testing the speed for active user's node Ability value.
Distributor is rewarded in B32, the block chain as described in B31, the contribution margin obtains subelement, is specifically used for：
It is the discount factor value for active user's node with the number of devices, the sharing bandwidth is carried out Discount processing, obtains bandwidth after the first folding；
Logarithmic function processing is carried out to bandwidth after first folding, obtains the ability value that tests the speed of active user's node.
Distributor is rewarded in B33, the block chain as described in B31, the contribution margin obtains subelement, is specifically used for：
The number of devices is smoothed, the optimization discount factor value for active user's node is obtained；
Discount processing is carried out to the sharing bandwidth with the optimization discount factor value, obtains bandwidth after the second folding；
Logarithmic function processing is carried out to bandwidth after second folding, obtains the ability value that tests the speed of active user's node.
Distributor is rewarded in B34, the block chain as described in B33, the contribution margin obtains subelement, is specifically used for executing Following formula：
Wherein, ptestFor the ability value that tests the speed, PMbps2For the sharing bandwidth,For the optimization discount system Numerical value.
Distributor is rewarded in B35, the block chain as described in B31, the contribution margin obtains subelement, is specifically used for：
Discount processing is carried out to the sharing bandwidth with default fixed discounting value, obtains bandwidth after third folding；
Bandwidth carries out logarithmic function processing after being rolled over to the third, obtains the ability value that tests the speed of active user's node.
Distributor is rewarded in B36, the block chain as described in any one of B19-B25, further includes：
Issuance unit is rewarded, for issuing the corresponding reward of the reward amount to active user's node.
The invention also discloses C37, a kind of computer readable storage mediums, are stored thereon with computer program, the program The step described in any one of A1-A18 is realized when being executed by processor.
The invention also discloses D38, a kind of Centroid, including memory, processor and storage are on a memory and can The computer program run on a processor, the processor realize the step described in any one of A1-A18 when executing described program Suddenly.
E39, a kind of block catenary system, including：In B19~B36 distributor is rewarded in any block chain.

Claims (10)

1. rewarding distribution method in a kind of block chain, which is characterized in that including：
All user nodes total contribution margin within a preset period of time and active user's node are obtained in block chain described pre- If the contribution margin in the period, wherein the contribution margin of each user node is related to the performance of the user node；
Obtain to be allocated reward total amount of the block chain in the preset time period；
According to total contribution margin, the reward total amount to be allocated and active user's node in the preset time period Contribution margin, determine reward amount of the active user's node in the preset time period.
2. rewarding distribution method in block chain as described in claim 1, which is characterized in that institute is useful in the acquisition block chain The total contribution margin of family node within a preset period of time, including：
Obtain each contribution margin of the user node in the preset time period in the block chain；
Read group total is carried out to contribution margin of all user nodes in the block chain in the preset time period, is obtained described Total contribution margin.
3. rewarding distribution method in block chain as claimed in claim 1 or 2, which is characterized in that described to obtain the block chain In each contribution margin of the user node in the preset time period, including：
The N kind equipment scores of active user's node are obtained, N is the integer more than 1；
Obtain the corresponding hardware coefficient of active user's node；
Obtain active user's node in the preset time period in chain time ratio；
According to the hardware coefficient, described in chain time ratio and the N kinds equipment score, active user's node is determined jointly Contribution margin.
4. rewarding distribution method in block chain as described in claim 1, which is characterized in that described to obtain active user's section The N kind equipment scores of point, including：
Obtain one or more of the calculation power score, bandwidth score and storage score of active user's node equipment point Number.
5. rewarding distribution method in block chain as claimed in claim 4, which is characterized in that described to obtain active user's section The calculation power score of point, including：
Detect the CPU effective performance values of active user's node；
By the corresponding default CPU conversions value of the CPU effective performances value, it is determined as the calculation power point of active user's node Number.
6. rewarding distribution method in block chain as claimed in claim 5, which is characterized in that detection active user's section The cpu performance parameter value of point, including：
The cpu performance of active user's node described in repeated detection, obtains more than two cpu performances in the preset time period Parameter value；
According to described two above cpu performance parameter values, the CPU effective performance values of active user's node are determined.
7. rewarding distributor in a kind of block chain, which is characterized in that including：
Contribution margin acquiring unit, for obtaining the total contribution margin of all user nodes within a preset period of time in block chain, and Contribution margin of active user's node in the preset time period, wherein the contribution margin of each user node and the user The performance of node is related；
Total amount acquiring unit is rewarded, for obtaining to be allocated reward total amount of the block chain in the preset time period；
Reward amount determination unit, for being saved according to total contribution margin, the reward total amount to be allocated and the active user Contribution margin of the point in the preset time period, determines reward amount of the active user's node in the preset time period.
8. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor The step described in any one of claim 1-6 is realized when row.
9. a kind of Centroid, including memory, processor and storage are on a memory and the calculating that can run on a processor Machine program, which is characterized in that the processor realizes the step described in any one of claim 1-6 when executing described program.
10. a kind of block catenary system, which is characterized in that including：Distributor is rewarded in block chain described in claim 7.
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