CN107784561A - The implementation method of online incentive mechanism in a kind of mobile mass-rent system - Google Patents
The implementation method of online incentive mechanism in a kind of mobile mass-rent system Download PDFInfo
- Publication number
- CN107784561A CN107784561A CN201711104645.XA CN201711104645A CN107784561A CN 107784561 A CN107784561 A CN 107784561A CN 201711104645 A CN201711104645 A CN 201711104645A CN 107784561 A CN107784561 A CN 107784561A
- Authority
- CN
- China
- Prior art keywords
- mrow
- user
- mtd
- task
- msub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/08—Auctions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0611—Request for offers or quotes
Abstract
The invention discloses a kind of implementation method of online incentive mechanism in mobile mass-rent system.First, it is proposed improved multistage reverse auction algorithm, consider that location-based mobile mass-rent task covering and detecting period are modeled simultaneously, user's prestige assessment parameters are introduced in the definition of task requester benefit function, with reference to the maximum return ratio matching of " user task ", most suitable user is distributed into most suitable priority of task；Secondly, density threshold is adaptively adjusted in process of exchange by way of on-line study, optimizes user's selection result；Finally, the possibility broken a contract by evolutionary Game Analysis interests both sides, and the reputation updating algorithm with penalty period is proposed, encourage both sides to select credible behavior.Theory analysis and simulation result show that the present invention meets to calculate validity, the positive rentability of interests both sides and authenticity, more preferable system gain can be obtained under certain time and budgetary restraints, and encourage both sides to select credible behavior.
Description
Technical field
The invention belongs to mobile social networking and data mining technology field, more particularly in a kind of mobile mass-rent system
The implementation method of online incentive mechanism.
Background technology
Mass-rent is that a kind of distributed problem that internet is brought solves pattern.In recent years, with mobile device popularization and
The fast development of wireless communication technology, the concept and related application of mobile mass-rent (expanding to intelligent perception, colony's calculating etc. again)
Arise at the historic moment.User can be joined whenever and wherever possible using the abundant sensor built in smart machine and powerful storage, computing capability
With mass-rent task, large-scale, machine and personal perception or the computational problem for being difficult to complete are completed.Task requester and user it
Between the intervention of base station and mass-rent platform can also be weakened by Wifi, bluetooth, D2D direct communications, preferably carry out resource be total to
Enjoy and task cooperative.Different from traditional mass-rent pattern based on Web, mobile mass-rent system has stronger real-time and movement
Property, therefore can not solve relevant issues with existing off-line algorithm.
The resource and electricity of mobile device will necessarily be consumed because user participates in mass-rent task, or even geographical position etc. be present
The threat of privacy leakage.Therefore, the design of incentive mechanism is particularly important.Traditional recruits and swashs on the user in mobile mass-rent
The mechanism of encouraging is mostly based on offline synchronization scene, by Stackelberg games or the algorithms selection optimal user collection of greedy search,
But it requires owner while submission is bidded or information discloses each other, be not suitable for the asynchronous real field that user arrives and departs from random
Scape.Nearly 2 years, the asynchronous online auction algorithm of fixed threshold is set to be suggested first, M.bateni et al. proposes the two-stage afterwards
Auction algorithm, refuse and the user profile bidded is submitted as referring to using the first stage, but this is unfair to the user early arrived
Property；Zhao D et al. propose the thought of multistage auction algorithm, but do not take into full account the situation of interests both sides promise breaking.
The content of the invention
In order to solve the technical problem that above-mentioned background technology proposes, the present invention is intended to provide in a kind of mobile mass-rent system
The implementation method of line incentive mechanism, consider the asynchronous real scene that arrives and departs from of user, by multistage reverse auction, automatically
Threshold value is adjusted, realizes the optimal selection of both sides, and introduces Feedback Evaluation parameter and carries out reputation updating, is ensureing the same of Task Quality
When maximize system benefit.
In order to realize above-mentioned technical purpose, the technical scheme is that：
The implementation method of online incentive mechanism, comprises the following steps in a kind of mobile mass-rent system：
Step 1：Task is issued and information exchange；
Step 1-1：Task requester release task message Represent j-th of subtask information, j=
1,2 ..., m, including subtask description, task location, master budget B and mission duration T；
Step 1-2：User reaches particular task region, if interested in some subtasks, can submit personal bid information,
It is included in the subtask numbering, completeness and desired corresponding remuneration of the completion of deadline interior energy, and calculates user's completion
The expectation contribution weight of certain task；
Step 2：User is completed using multistage reverse auction algorithm to recruit；
Step 2-1：Mass-rent task is temporally divided into multiple stages, calculates the deadline in each stage and each stage
The budget of distribution；
Step 2-2：Density threshold ρ=the ε, ε for initializing the first stage are random positive nonzero number, the user's collection chosenCurrent total revenue U (S)=0, always spends P (S)=0；
Step 2-3：For submitting the user of bid information to carry out a task, it is specified that merchandising every time, preferential distribution
Benefit ratio maximum and not being done for task, are marked to the task, avoid in the subtask list that can be completed to the user
Duplicate allocation；
Step 2-4：If active user's bids less than current threshold value of bidding, and current Remaining Stages budget is enough to prop up
The remuneration of the user is paid, then receives the user, task requester advance payment of remuneration, feedback result after user's completion task；Conversely, refuse
Exhausted user, without transaction；
Step 2-5：Each stage terminates, and according to the trading situation for having selected user, updates density threshold, is recycled into down
In the individual stage, continue to recruit；
Step 2-6：Reach mission duration T or master budget B to be exhausted, recruitment terminates；
Step 3：Quality Feedback and reputation updating；
Step 3-1：Quality is completed to the task of user to judge, be divided into two levels of good and bad by task requester
Not；
Step 3-2：Credit rating is completed with reference to the prestige record before user and task, the current credit value of user is carried out
Renewal, to implement rewards and punishments excitation, and the measurement index recruited as next time.
Further, in step 1-2, if vjFor the contribution weight of j-th of subtask, representing the task please for task
The significance level and acquisition difficulty for the person of asking, xijThe proportion of the task is completed for user i, then user i completes the phase of j-th of subtask
Hope contribution weight vij=xij*vj。
Further, in step 2-2, the total revenue U (S) for defining task requester is each subtask income Uj(S)
Sum：
In above formula,λ is systematic parameter, riFor the current credit values of user i, pij
J-th of remuneration that subtask obtains is completed for user i.
Further, in step 2-3, selection makes uij/bijPriority of task distribution that is maximum and not being done, uijRepresent
User i completes edge benefit corresponding to j-th of subtask, bijThe remuneration for completing it is expected to obtain in j-th of subtask for user i,
Further, step 2-4 detailed process is as follows：
Step 2-4-1：Calculate the current threshold value u that bidsij/ ρ and stage budget B'-P (S) remaining at present, and and bijCarry out
Compare, if meeting bij≤uij/ ρ and bij≤B'-∑Pj(S), then receive the user and be traded, if not satisfied, refusal should
User, without transaction；
Step 2-4-2：For trade user, task requester pays remuneration p in advanceij=bij, feedback letter after user's completion task
Breath；Family collection S ← S ∪ { i }, total revenue U (S) ← U (S)+u are selected in renewalij, always spend P (S) ← P (S)+pij。
Further, in step 2-1, the mission duration, T was divided intoThe individual stage, i.e.,：The deadline in i-th of stage isIt is corresponding
Stage budget
Further, in step 2-5, if current time t=T', this stage terminate；Update density threshold ρ=U
(S)/(δ * P (S)), δ are the positive nonzero number of setting, update budget and the time in next stage：B' ← 2B', T' ← 2T'.
Further, in step 3-2, reputation updatings of the user i after k-th of task is completed is as follows：
In above formula,For the current credit values of user i,For the credit value after its transaction, rmaxFor system user prestige
Maximum, the prestige value set of each mission requirements is θ={ θ0,θ1,...,θm, wherein θ0It is the minimum prestige threshold value of system, θ1,
θ2,...,θm≥θ0Task is corresponded to respectivelyIt is required that minimum credit value, q represents what task requester was completed to user
The evaluation of Task Quality.
The beneficial effect brought using above-mentioned technical proposal：
The present invention is broadly divided into multistage reverse auction and two processes of reputation updating with penalty period, pre- to save respectively
It is target to calculate and control quality.In multistage reverse auction, at the same consider location-based mobile mass-rent task covering and
Detecting period is modeled, and prestige parameter is introduced to the definition of benefit function, and adaptive adjustment is each in user's selection course
The density threshold in stage, realizes the maximizing the benefits under certain budget and time-constrain, and guaranteed benefit both sides select it is credible
Behavior, mass-rent task can be preferably instructed to be smoothed out in reality.
Brief description of the drawings
Fig. 1 is the general frame figure of the present invention；
Fig. 2 is the multistage reverse auction algorithm flow chart of the present invention；
Fig. 3 is the reputation updating algorithm flow chart with penalty period of the present invention；
Fig. 4 is the present embodiment schematic diagram stage by stage；
Fig. 5 is multistage reverse auction algorithm income performance comparison diagram；
Fig. 6 is multistage reverse auction algorithm time performance comparison diagram；
Fig. 7 is the reputation updating algorithm effect comparison diagram with penalty period.
Embodiment
Below with reference to accompanying drawing, technical scheme is described in detail.
The implementation method of online incentive mechanism in a kind of mobile mass-rent system, its general frame is as shown in figure 1, including following
Step.
Step 1：Task is issued and information exchange.
Task requester release task messageIncluding subtask description, task location, master budget B and
Mission duration T.
User can combine oneself user trajectory and preference in forthcoming generations, submit what can be completed at the appointed time
(mission number, performance level, expected return) collects.Each subtask possesses different contribution weight vj, represent the task for
The significance level and acquisition difficulty of task requester, it is multiplied by the completion proportion x of userij, then obtain the user and complete the part times
The contribution weight v of businessij=xij*vj.The bid information that so user i is submitted is represented by { (vi1,bi1),(vi2,bi2),...,
(vic,bic)}。
Step 2：User is completed using multistage reverse auction algorithm to recruit；
Mass-rent task is temporally divided into multiple stages, calculate each stage deadline and each stage distribution it is pre-
Calculate.Specifically, mission duration T is divided intoThe individual stage, i.e.,：The
The deadline in i stage isCorresponding stage budget
Density threshold ρ=the ε, ε for initializing the first stage are random positive nonzero number, the user's collection chosenIt is current total
Income U (S)=0, always spend P (S)=0.Assuming that credit value current user i is ri, user i, which completes task, needs generation for spending
Valency is ci, the remuneration of acquisition is pi.Then the revenue function of user is：
Wherein S is represented and is selected family collection.The total revenue function for defining task requester is the income sum of each subtask：
WhereinFor User reliability and the product of task weight, the task is completed for weighing user
It is expected to the income brought to task requester；For total cost of the task requester to this task；λ is system
Parameter.The dull Submodular function reflects the trend of diminishing marginal benefits in economics, suitable for many reality scenes.
For submitting the user of bid information to carry out a task, it is specified that merchandising every time, the user is preferentially distributed to
Benefit ratio maximum and not being done for task, are marked to the task, avoid duplicate allocation in the subtask list that can be completed.
In bid information the set { (v that user submitsi1,bi1),(vi2,bi2),...,(vic,bic) in selection income than highest task
User preferentially is distributed to, that is, calculates user i and completes edge benefit corresponding to each subtask jSelection makes uij/bijTimes that be maximum and not being done
The preferential transaction of business.
Calculate the current threshold value u that bidsij/ ρ and stage budget B'-P (S) remaining at present, and and bijIt is compared, if
Meet bij≤uij/ ρ and bij≤B'-∑Pj(S), then receive the user and be traded, if not satisfied, refusing the user, do not enter
Row transaction.For trade user, task requester pays remuneration p in advanceij=bij, feedback information after user's completion task；Renewal has been selected
User collects S ← S ∪ { i }, total revenue U (S) ← U (S)+uij, always spend P (S) ← P (S)+pij。
If current time t=T', this stage terminate；Density threshold ρ=U (S)/(δ * P (S)) is updated, δ is the non-of setting
Zero positive number, update budget and the time in next stage：B' ← 2B', T' ← 2T'.
Reach the mission duration or master budget is exhausted, recruitment terminates.Fig. 2 is the flow chart of step 2.
Step 3：Quality Feedback and the reputation updating with penalty period.
Quality is completed to the task of user to judge, be divided into two ranks of good and bad, respectively table by task requester
Show that user takes credible behavior and insincere behavior.
Credit rating is completed with reference to the prestige record before user and task, the current credit value of user is updated, with
Implement rewards and punishments excitation, and the measurement index recruited as next time.IfFor the current credit values of user i,For its task k that merchandises
Credit value afterwards.Define rmaxFor the maximum of system user prestige, the prestige value set of each mission requirements is θ={ θ0,
θ1,...,θm, wherein θ0It is the minimum prestige threshold value of system, θ1,θ2,...,θm≥θ0Task is corresponded to respectivelyIt is required that
Minimum credit value, 0≤θ≤10.Definition represents the evaluation for the Task Quality that task requester is completed to user.Then user i is complete
It is as follows into the reputation updating algorithm after task k：
Cause is method assumes that complete task, therefore mainly user side is carried out anti-after task requester elder generation payt, user
Feedback evaluation and rewards and punishments excitation.If user first completes payt after task requester, it is also possible to which same method is to task requester
Remuneration payment behavior carry out Feedback Evaluation, to avoid the selfish violations of any one party.In improved reputation updating algorithm,
Define prestige threshold set θ={ θ0,θ1,...,θm, with the excessive punishment problem for avoiding single threshold value from bringing.
If user wins the favorable judgment in the task, credit value is+1 on the basis of original, upper limit rmax；
Commented if user obtains difference in the task, butThen credit value is reduced to θk- 1, do not allow again
Participate in shelves task；
Commented if user obtains difference in the task, andThen credit value is reduced to θ0, still have an opportunity
Credit value is set to go up by completing the relatively low task of credit value；
Commented if user obtains difference in the task, andThen credit value is reduced to 0, into penalty period,
User can only by gratuitously participate in task and obtain high praise credit value is gradually gone up to normal；
Credit value disqualification participates in the task less than the user of the lowest threshold of current task requirement, i.e.,When, hand over
Easily without, user's credit value is constant, still for
During punishment, if user wins the favorable judgment in the task, credit value+1, until credit value goes back up to system thresholds
θ0, continue normal participation task；If user continues to obtain difference in the task and commented, directly expelled, had no chance again by system
Participate in other any tasks.
Fig. 3 is the flow chart of step 3.
In the present embodiment, master budget B=80, mission duration T=8 are taken, then mass-rent process can be divided intoIndividual stage, time and budget corresponding to each stage are respectively：1,10；2,20；4,40；8,80, such as Fig. 4
It is shown.Density threshold ρ=1/2 is initialized, density threshold is the normative reference of current generation recruitment of users, is tied according to each stage
The performance information for the sample of users that Shu Shiyi chooses is calculated, and represents task requester and pays that unit remuneration is minimum to be received
Border benefit.A relatively low density threshold is initialized, had both ensured both sides' income, encourages user to reach as early as possible again.Fig. 5 is this
Invent the multistage reverse auction algorithm proposed and constant density thresholding algorithm, fixed bid thresholding algorithm and random algorithm income
Performance comparison figure.Fig. 6 is that multistage reverse auction algorithm proposed by the present invention contrasts with constant density thresholding algorithm time performance
Figure.Fig. 7 is reputation updating algorithm proposed by the present invention and single threshold value reputation updating algorithm effect comparison diagram, proposed by the present invention
The accounting of trusted users can be maintained at a higher state under reputation updating algorithm, and avoid to the excessive of potential high quality user
Punishment problem.
The technological thought of above example only to illustrate the invention, it is impossible to protection scope of the present invention is limited with this, it is every
According to technological thought proposed by the present invention, any change done on the basis of technical scheme, the scope of the present invention is each fallen within
Within.
Claims (8)
1. the implementation method of online incentive mechanism in a kind of mobile mass-rent system, it is characterised in that comprise the following steps：
Step 1：Task is issued and information exchange；
Step 1-1：Task requester release task message Represent j-th of subtask information, j=1,
2 ..., m, including subtask description, task location, master budget B and mission duration T；
Step 1-2：User reaches particular task region, if interested in some subtasks, can submit personal bid information, including
Subtask numbering, completeness and the desired corresponding remuneration that can be completed within deadline, and calculate user and complete certain
The expectation contribution weight of business；
Step 2：User is completed using multistage reverse auction algorithm to recruit；
Step 2-1：Mass-rent task is temporally divided into multiple stages, calculates deadline and the distribution of each stage in each stage
Budget；
Step 2-2：Density threshold ρ=the ε, ε for initializing the first stage are random positive nonzero number, the user's collection chosenWhen
Preceding total revenue U (S)=0, always spend P (S)=0；
Step 2-3：For submitting the user of bid information to carry out a task, it is specified that merchandising every time, this is preferentially distributed to
Benefit ratio maximum and not being done for task, are marked to the task, avoid repeating in the subtask list that user can complete
Distribution；
Step 2-4：If active user's bids less than current threshold value of bidding, and current Remaining Stages budget is enough to pay this
The remuneration of user, then receive the user, task requester advance payment of remuneration, feedback result after user's completion task；Conversely, refusal should
User, without transaction；
Step 2-5：Each stage terminates, and according to the trading situation for having selected user, updates density threshold, is recycled into next rank
Section, continues to recruit；
Step 2-6：Reach mission duration T or master budget B to be exhausted, recruitment terminates；
Step 3：Quality Feedback and reputation updating；
Step 3-1：Quality is completed to the task of user to judge, be divided into two ranks of good and bad by task requester；
Step 3-2：Credit rating is completed with reference to the prestige record before user and task, the current credit value of user is carried out more
Newly, to implement rewards and punishments excitation, and as the measurement index of recruitment next time.
2. according to claim 1 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that
In step 1-2, if vjFor the contribution weight of j-th of subtask, the task is represented for the significance level of task requester and is obtained
Take difficulty, xijThe proportion of the task is completed for user i, then user i completes the expectation contribution weight v of j-th of subtaskij=xij*
vj。
3. according to claim 2 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that
In step 2-2, the total revenue U (S) for defining task requester is each subtask income Uj(S) sum：
In above formula,λ is systematic parameter, riFor the current credit values of user i, pijFor with
Family i completes j-th of remuneration that subtask obtains.
4. according to claim 3 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that
In step 2-3, selection makes uij/bijPriority of task distribution that is maximum and not being done, uijRepresent user i and complete j-th of subtask
Corresponding edge benefit, bijThe remuneration for completing it is expected to obtain in j-th of subtask for user i,
5. according to claim 4 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that step
Rapid 2-4 detailed process is as follows：
Step 2-4-1：Calculate the current threshold value u that bidsij/ ρ and stage budget B'-P (S) remaining at present, and and bijIt is compared,
If meet bij≤uij/ ρ and bij≤B'-∑Pj(S), then receive the user and be traded, if not satisfied, refuse the user,
Without transaction；
Step 2-4-2：For trade user, task requester pays remuneration p in advanceij=bij, feedback information after user's completion task；More
Newly family collection S ← S ∪ { i }, total revenue U (S) ← U (S)+u are selectedij, always spend P (S) ← P (S)+pij。
6. according to claim 1 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that
In step 2-1, the mission duration, T was divided intoThe individual stage, i.e.,：The
The deadline in i stage isCorresponding stage budget
7. according to claim 6 in a kind of mobile mass-rent system online incentive mechanism implementation method, it is characterised in that
In step 2-5, if current time t=T', this stage terminate；Density threshold ρ=U (S)/(δ * P (S)) is updated, δ is setting
Positive nonzero number, update budget and the time in next stage：B' ← 2B', T' ← 2T'.
8. the implementation method of online incentive mechanism in a kind of mobile mass-rent system according to claim 1, it is characterised in that
In step 3-2, reputation updatings of the user i after k-th of task is completed is as follows：
<mrow>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>min</mi>
<mrow>
<mo>(</mo>
<mrow>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<msub>
<mi>r</mi>
<mi>max</mi>
</msub>
</mrow>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>q</mi>
<mo>=</mo>
<mi>g</mi>
<mi>o</mi>
<mi>o</mi>
<mi>d</mi>
<mo>,</mo>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>q</mi>
<mo>=</mo>
<mi>b</mi>
<mi>a</mi>
<mi>d</mi>
<mo>,</mo>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
<msub>
<mi>and&theta;</mi>
<mi>k</mi>
</msub>
<mo>></mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>q</mi>
<mo>=</mo>
<mi>b</mi>
<mi>a</mi>
<mi>d</mi>
<mo>,</mo>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>></mo>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
<msub>
<mi>and&theta;</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>0</mn>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>q</mi>
<mo>=</mo>
<mi>b</mi>
<mi>a</mi>
<mi>d</mi>
<mo>,</mo>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>=</mo>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
<msub>
<mi>and&theta;</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo><</mo>
<msub>
<mi>&theta;</mi>
<mi>k</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>min</mi>
<mrow>
<mo>(</mo>
<mrow>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
</mrow>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>q</mi>
<mo>=</mo>
<mi>g</mi>
<mi>o</mi>
<mi>o</mi>
<mi>d</mi>
<mo>,</mo>
<msubsup>
<mi>r</mi>
<mi>i</mi>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mo><</mo>
<msub>
<mi>&theta;</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
In above formula,For the current credit values of user i,For the credit value after its transaction, rmaxFor the maximum of system user prestige
Value, the prestige value set of each mission requirements is θ={ θ0,θ1,...,θm, wherein θ0It is the minimum prestige threshold value of system, θ1,
θ2,...,θm≥θ0Task is corresponded to respectivelyIt is required that minimum credit value, q represents what task requester was completed to user
The evaluation of Task Quality.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711104645.XA CN107784561A (en) | 2017-11-10 | 2017-11-10 | The implementation method of online incentive mechanism in a kind of mobile mass-rent system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711104645.XA CN107784561A (en) | 2017-11-10 | 2017-11-10 | The implementation method of online incentive mechanism in a kind of mobile mass-rent system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107784561A true CN107784561A (en) | 2018-03-09 |
Family
ID=61432822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711104645.XA Pending CN107784561A (en) | 2017-11-10 | 2017-11-10 | The implementation method of online incentive mechanism in a kind of mobile mass-rent system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107784561A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681921A (en) * | 2018-05-14 | 2018-10-19 | 北京信息科技大学 | A kind of method and device obtaining intelligent perception incentives strategy based on Stochastic Game |
| CN109068288A (en) * | 2018-09-06 | 2018-12-21 | 福建师范大学 | A kind of method and system selecting mobile intelligent perception incentive mechanism based on more properties users |
| CN109086976A (en) * | 2018-07-11 | 2018-12-25 | 陕西师范大学 | A kind of method for allocating tasks towards intelligent perception |
| CN109242533A (en) * | 2018-08-04 | 2019-01-18 | 福州大学 | The online motivational techniques of car networking intelligent perception user based on Game Theory |
| CN110061862A (en) * | 2019-03-25 | 2019-07-26 | 浙江理工大学 | A kind of distributed multi-task intelligent perception method based on fairness in dense network |
| CN110061863A (en) * | 2019-03-25 | 2019-07-26 | 浙江理工大学 | A kind of distributed multi-task intelligent perception method based on fairness in sparse network |
| CN110689430A (en) * | 2019-09-17 | 2020-01-14 | 中国人民大学 | Block chain cooperation method and system based on contribution excitation |
| CN111429104A (en) * | 2020-04-03 | 2020-07-17 | 青岛大学 | Crowdsourcing item execution device, method, equipment and readable storage medium |
| CN111915185A (en) * | 2020-07-31 | 2020-11-10 | 湖北大学 | Space-time crowdsourcing task allocation method and device based on path planning strategy |
| CN112766766A (en) * | 2021-01-26 | 2021-05-07 | 华南理工大学 | High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof |
| CN113037876A (en) * | 2021-05-25 | 2021-06-25 | 中国人民解放军国防科技大学 | Cooperative game-based cloud downlink task edge node resource allocation method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310349A (en) * | 2013-06-14 | 2013-09-18 | 清华大学 | On-line incentive mechanism based perceptual data acquisition method |
| CN104657893A (en) * | 2014-11-25 | 2015-05-27 | 无锡清华信息科学与技术国家实验室物联网技术中心 | Excitation method of crowd-sensing for meeting matching constraint |
-
2017
- 2017-11-10 CN CN201711104645.XA patent/CN107784561A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103310349A (en) * | 2013-06-14 | 2013-09-18 | 清华大学 | On-line incentive mechanism based perceptual data acquisition method |
| CN104657893A (en) * | 2014-11-25 | 2015-05-27 | 无锡清华信息科学与技术国家实验室物联网技术中心 | Excitation method of crowd-sensing for meeting matching constraint |
Non-Patent Citations (1)
| Title |
|---|
| 李卓青: "第31届南京地区研究生通信年会录用论文集", 《第31届南京地区研究生通信年会录用论文集》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681921A (en) * | 2018-05-14 | 2018-10-19 | 北京信息科技大学 | A kind of method and device obtaining intelligent perception incentives strategy based on Stochastic Game |
| CN109086976A (en) * | 2018-07-11 | 2018-12-25 | 陕西师范大学 | A kind of method for allocating tasks towards intelligent perception |
| CN109086976B (en) * | 2018-07-11 | 2021-11-05 | 陕西师范大学 | Task allocation method for crowd sensing |
| CN109242533A (en) * | 2018-08-04 | 2019-01-18 | 福州大学 | The online motivational techniques of car networking intelligent perception user based on Game Theory |
| CN109068288B (en) * | 2018-09-06 | 2021-04-27 | 福建师范大学 | Method and system for selecting mobile crowd sensing incentive mechanism based on multi-attribute user |
| CN109068288A (en) * | 2018-09-06 | 2018-12-21 | 福建师范大学 | A kind of method and system selecting mobile intelligent perception incentive mechanism based on more properties users |
| CN110061863B (en) * | 2019-03-25 | 2021-10-19 | 浙江理工大学 | Distributed multi-task crowd-sourcing sensing method based on fairness in sparse network |
| CN110061863A (en) * | 2019-03-25 | 2019-07-26 | 浙江理工大学 | A kind of distributed multi-task intelligent perception method based on fairness in sparse network |
| CN110061862A (en) * | 2019-03-25 | 2019-07-26 | 浙江理工大学 | A kind of distributed multi-task intelligent perception method based on fairness in dense network |
| CN110689430A (en) * | 2019-09-17 | 2020-01-14 | 中国人民大学 | Block chain cooperation method and system based on contribution excitation |
| CN110689430B (en) * | 2019-09-17 | 2022-05-17 | 中国人民大学 | Block chain cooperation method and system based on contribution excitation |
| CN111429104A (en) * | 2020-04-03 | 2020-07-17 | 青岛大学 | Crowdsourcing item execution device, method, equipment and readable storage medium |
| CN111915185A (en) * | 2020-07-31 | 2020-11-10 | 湖北大学 | Space-time crowdsourcing task allocation method and device based on path planning strategy |
| CN112766766A (en) * | 2021-01-26 | 2021-05-07 | 华南理工大学 | High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof |
| CN112766766B (en) * | 2021-01-26 | 2024-04-09 | 华南理工大学 | High-precision map crowdsourcing system based on optimal time-stop rule and data collection method thereof |
| CN113037876A (en) * | 2021-05-25 | 2021-06-25 | 中国人民解放军国防科技大学 | Cooperative game-based cloud downlink task edge node resource allocation method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107784561A (en) | The implementation method of online incentive mechanism in a kind of mobile mass-rent system | |
| Jiao et al. | Toward an automated auction framework for wireless federated learning services market | |
| Sun et al. | Joint resource allocation and incentive design for blockchain-based mobile edge computing | |
| CN108364190B (en) | Mobile crowd sensing online excitation method combined with reputation updating | |
| CN109347149A (en) | Micro-capacitance sensor energy storage dispatching method and device based on depth Q value network intensified learning | |
| CN108876567B (en) | Crowd sensing excitation method based on sensing utility maximization | |
| Zhang et al. | EV charging bidding by multi-DQN reinforcement learning in electricity auction market | |
| CN108734396A (en) | A kind of virtual plant method for optimizing scheduling bidded and provided multiple forms of energy to complement each other based on Demand-side | |
| Mai et al. | Automatic double-auction mechanism for federated learning service market in Internet of Things | |
| CN114971819A (en) | User bidding method and device based on multi-agent reinforcement learning algorithm under federal learning | |
| CN109242533A (en) | The online motivational techniques of car networking intelligent perception user based on Game Theory | |
| Chi et al. | Multistrategy repeated game-based mobile crowdsourcing incentive mechanism for mobile edge computing in Internet of Things | |
| CN112688335A (en) | Real-time demand response modeling method based on game balance | |
| Vakili et al. | Interconnected microgrids: Optimal energy scheduling based on a game‐theoretic approach | |
| Cai et al. | Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value | |
| Leong et al. | A stochastic differential game of capitalism | |
| Huang et al. | Hybrid market-based resources allocation in mobile edge computing systems under stochastic information | |
| Chuang et al. | Deep reinforcement learning based pricing strategy of aggregators considering renewable energy | |
| Ren et al. | Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation | |
| CN115796952A (en) | Distributed energy transaction incentive method based on block chain intelligent contract | |
| CN112862175B (en) | Local optimization control method and device based on P2P power transaction | |
| Sun et al. | Dpoda: Differential privacy-based online double auction for pervasive edge computing resource allocation | |
| Xu et al. | IDADET: Iterative double-sided auction-based data-energy transaction ecosystem in Internet of vehicles | |
| CN110163585B (en) | Method and device for issuing electronic red packet | |
| Yue et al. | AI-enhanced incentive design for crowdsourcing in internet of vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180309 |
|
| RJ01 | Rejection of invention patent application after publication |