CN104463424A - Crowdsourcing task optimal allocation method and system - Google Patents
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Abstract
The invention provides a crowdsourcing task optimal allocation method and system. The method comprises the steps that firstly, according to the bidding condition of users, the number of the users selecting each task is counted; secondly, whether the set number of the users required by each task for crowdsourcing is smaller than the number of the users selecting the corresponding tasks is judged so as to determine whether to adjust the transaction price to achieve change of the number of the users selecting each task. In the whole process, on the basis of the transaction price set for crowdsourcing and the bidding condition of the users, the requirement of each task for the number of the users completing the task is met, so the crowdsourcing tasks are completed efficiently and orderly, and allocation of the crowdsourcing tasks is optimized.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a task optimal allocation method in crowdsourcing and a system thereof.
Background
Crowdsourcing refers to the practice of a company or organization outsourcing work tasks performed by employees in the past to an unspecified (and often large) mass network in a free-voluntary manner. I.e., companies or organizations utilize the internet to distribute work tasks, discover creatives, or solve technical problems. Through internet control, the creative idea and the capability of the majors of the volunteers are utilized to complete the work tasks published by the internet, and the volunteers can obtain small rewards from the completed tasks while contributing personal spare time so as to compensate the contribution made by the volunteers.
The advantages of crowdsourcing are mainly: 1. the problem can be explored and discussed at a relatively low cost, often quickly in time; 2. payment is made with the result, sometimes even without payment; 3. the organization can rely on more extensive talents than the organization itself; 4. by listening to the voice of the crowd, the organization can firstly insights the requirements of the customers; 5. communities feel a brand-building consanguineous through crowdsourcing organizations, which is also a property gained through sharing and collaboration.
However, in the process of task completion in crowdsourcing, the problem that many or few people who are willing to do a certain task cannot determine which people complete the task often occurs. How to reasonably distribute the tasks ensures the benefits of both parties, and simultaneously can stimulate the masses to do the tasks, which becomes a problem to be solved urgently in the crowdsourcing process.
Disclosure of Invention
The invention aims to provide a task optimal allocation method in crowdsourcing and a system thereof, which are used for solving the problem that too many or too few people willing to complete tasks often appear in the process of task completion in crowdsourcing, so that the tasks in crowdsourcing cannot be allocated in the most reasonable mode.
In order to solve the technical problem, the invention provides a method for optimally distributing tasks in crowdsourcing, which comprises the following steps:
s1: crowdsourcing issues a plurality of tasks and sets a transaction price paid by each task and the number of required users;
s2: each user selects one task which is most to be completed in the tasks which are released by crowdsourcing, and bids on the task;
s3: according to the bidding conditions of the users, counting the number of the users selecting each task;
s4: judging whether the number of the required users set by crowdsourcing of each task is smaller than the number of the users selecting the corresponding task, if so, adjusting the transaction price and executing the step S3 according to the adjusted transaction price; otherwise, go to step S5;
s5: each task is respectively distributed to all users selecting the corresponding task.
Optionally, in the method for optimally allocating tasks in crowdsourcing, in step S4, when the number of required users set for each task crowdsourcing is less than the number of users selecting a corresponding task, adjusting a transaction price and executing step S3 according to the adjusted transaction price; when the number of required users set for the crowd-sourcing of each task is greater than or equal to the number of users selecting the corresponding task, step S5 is performed.
Optionally, in the method for optimally allocating a task in crowdsourcing, during the step S4, the transaction price is adjusted by a budget step size to obtain an adjusted transaction price.
Optionally, in the method for optimally allocating tasks in crowdsourcing, in step S2, each user obtains a profit as a criterion for selecting a task.
Optionally, in the method for optimally allocating tasks in crowd sourcing, after the step S5 is completed, the platform pays the transaction price of each task to all users who select the corresponding task in the step S5.
The invention also provides a task optimal distribution system in crowdsourcing, which comprises the following steps: the setting module is used for setting the transaction price paid by each task which is subjected to crowdsourcing and issued and the number of the required users;
the system comprises a user module, a bid module and a bid module, wherein the user module is used for determining that each user selects one task which is most desired to be completed in a plurality of tasks which are released by crowdsourcing and bidding;
the statistic module is used for counting the number of the users selecting each task according to the bidding conditions of the users;
the judging module is used for judging whether the number of the required users set by crowdsourcing of each task is less than the number of the users selecting the corresponding task; if so, adjusting the transaction price and re-counting the number of users completing each task according to the adjusted transaction price; otherwise, distributing each task to all users selecting the corresponding task respectively;
and the task allocation module is used for allocating each task to all the users selecting the corresponding task respectively.
Optionally, in the system for optimally allocating tasks in crowdsourcing, the determining module is configured to determine whether the number of required users set for crowdsourcing of each task is less than the number of users selecting the corresponding task, and when the number of required users set for crowdsourcing of each task is greater than or equal to the number of users selecting the corresponding task, allocate each task to all users selecting the corresponding task; and when the quantity of the required users set by crowdsourcing of each task is less than the quantity of the users for selecting the corresponding task, adjusting the transaction price in the setting module and re-counting the quantity of the users for completing each task according to the adjusted transaction price.
Optionally, in the crowd-sourced task optimal allocation system, the determining module adjusts the transaction price through a budget step length to obtain the adjusted transaction price.
Optionally, in the system for task optimal allocation in crowd sourcing, the user module takes the income obtaining condition of each user as a criterion for selecting a task when determining that each user selects one of the tasks issued by crowd sourcing which the user most wants to complete.
In the task optimal distribution method and the system thereof in crowdsourcing, provided by the invention, the number of users for selecting each task is counted according to the bidding condition of the users; and then, whether the number of the required users set by crowdsourcing of each task is smaller than the number of the users selecting the corresponding task is judged to determine whether to adjust the transaction price so as to realize the change of the number of the users selecting each task, and the requirements of each task on the number of people finishing the task are met in the whole process based on the transaction price set by crowdsourcing and the bidding conditions of the users, so that a plurality of tasks in the crowdsourcing are efficiently and orderly finished, and the distribution of the tasks in the crowdsourcing is optimized.
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FIG. 1 is a flowchart of a method for optimally allocating tasks in crowd sourcing according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a task optimal allocation system in crowdsourcing according to an embodiment of the present invention.
Detailed Description
The best task allocation method and system for crowdsourcing proposed by the invention are further described in detail below with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
Referring to fig. 1, it is a flowchart of a method for optimally allocating tasks in crowdsourcing according to the present invention, and as shown in fig. 1, the method for optimally allocating tasks in crowdsourcing includes the following steps:
first, step S1 is executed to crowd-source issue a plurality of tasks and set a transaction price paid for each task and the number of required users.
Next, step S2 is performed, and each user selects one of the tasks that the user wants to accomplish most among the plurality of tasks that are distributed, and places a bid on the task.
The user-acquired profit situation refers to that the user acquires consideration of external factors such as a transaction price of each task and a geographical location where the user is located according to step S1, the user estimates cost actually consumed for completing each task in advance, and selects one of the crowdsourcing tasks to complete with the user-acquired maximum profit as a target, where the user-acquired profit is equal to the transaction price paid by each task minus the cost estimated in advance by the user.
Next, step S3 is executed to count the number of users who select each task according to the bidding conditions of the users.
Specifically, through steps S1 to S3, each user who wants to participate in the task of crowdsourcing has completed the selection and bidding of the task of crowdsourcing, and at this time, the number of users who select each task is counted according to the bidding condition of the user.
Next, step S4 is executed, it is determined whether the number of required users set for crowdsourcing for each task is less than the number of users selecting the corresponding task, if yes, the transaction price is adjusted and step S3 is executed according to the adjusted transaction price; otherwise, step S5 is executed.
Specifically, when the number of the required users set by crowdsourcing for each task is less than the number of the users selecting the corresponding task, adjusting the transaction price and executing the step S3 according to the adjusted transaction price; when the number of required users set for the crowd-sourcing of each task is greater than or equal to the number of users selecting the corresponding task, step S5 is performed.
If using miIndicating the number of users required for i tasks, diRepresenting the number of users selecting the i task, there are two cases: m isi≥di(supply and demand), mi<di(supply shortfall); in both cases, the number of users who want to complete the task is adjusted by adjusting the transaction price of the task to change the bid condition of the users for the task, mainly considering that there are too many or too few users who want to complete the same task (the condition of unbalanced supply and demand). For example, when there are too many users who want to complete the same task (at this time, the number of required users set by the task crowdsourcing is smaller than the number of users who select the task), the transaction price of the task is lowered, thereby having different effects on the self-income of each user to promote the user to reselect whether to complete the task, so that the number of users who select the task is reduced.
Further, during the step S4, the transaction price is adjusted by the budget step size to obtain the adjusted transaction price.
The budget step is an initially set iteration step, and in order to obtain the optimal trading price of each task, the budget step is adjusted to ensure that the trading price is equal to the initial valueThe grid is continuously adjusted to meet the requirement of executing step S5, and finally the optimal step size is determined, and the determination process of this optimal step size is as follows: suppose thatThe step size of the i task in the t round of iteration is carried out. Without loss of generality, we assume that there are too many users selecting the i task, which is a case of over supply and over demand, and that user n (n ═ 1.. k) is requesting the i task. For each user, we define:
wherein, the limiting condition for the above formula is that when x is not less than 0, [ x ]]+X; when x < 0, [ x ]]+＝0；
Without loss of generality, we assume the iteration step size Λ1≤Λ2…ΛK. When the budget step length is increased during the task i, the transaction price is improved, the user income is reduced, when some users who select the task i currently select the task to be completed most in the next round, other tasks which can maintain the original user income are selected, and if the users want to select the task t +1 roundThe number of changes in the number of users who select the i task after iteration does not exceed one, then the requirement is metThus, in theory, the upper bound of the adjusted budget step size isDue to controllability and limitation of the adjusted budget step length, the budget step length can be converged quickly after limited iterations, and therefore the optimal step length is obtained.
Next, step S5 is executed to assign each task to all users who selected the corresponding task upon matching in step S4, respectively.
Further, after the step S5 is completed, the platform pays the transaction price of each task to all users who selected the corresponding task in the step S5.
In this embodiment, the social benefit maximization requirement is also satisfied in the process of determining which users perform each task through step S4. Specifically, we demonstrate in two steps using the dual theory of linear programming: 1. the distribution matrix R is proved to meet the complementary relaxation condition in the original dual method, and the distribution scheme determined by the distribution matrix R is distributed optimally in time; 2. it is proved that the allocation matrix determined by the user corresponding to the completion of each task determined in step S4 satisfies the complementary relaxation condition.
Proof step 1:
let Sni＝vi-cni；
θni＝αn+βi-Sni；
Wherein v isiFor the cost of i tasks, SniSocial benefit of selecting i task for n users, θniTo define a relaxation, αnIs a dual variable of n users, betaiRespectively with the dual variables of the i task.
And if the distribution scheme corresponding to the R accords with the maximum social benefit according to the dual theory, the following complementary relaxation conditions in the original dual method need to be met:
(1)θni≥0，αn≥0，βi≥0；
(2)θni0, if and only if rni＝1；(rniAn element that is R);
(3)αn0 if and only if n users are not assigned a task in R;
(4)βiat present, only when the i task is in short supply, i.e. di<mi；
Given the complementary relaxation conditions in the above-described original dual method, it is only necessary to prove that the social benefit calculated by the assignment matrix of the final assignment task is the greatest next.
R '═ R'ni}N×MFor any feasible distribution matrix, the social benefit S (R') is:
similarly, the social benefit s (R) of the allocation matrix R is:
at this time, as long as S (R) is satisfied, the allocation matrix corresponding to R at this time is the optimal allocation matrix.
And (3) a proving step 2:
let the dual variable a of the usernComprises the following steps:
dual variable beta for setting crowdsourcing release tasksiComprises the following steps:
therefore, the relaxation θniComprises the following steps:
wherein,pifor the transaction price of the i task, [ Delta n ] is greater than or equal to 0]+Δ n; when Δ n < 0, [ Δ n [)]+＝0。
Next, it is verified whether the above equation satisfies the complementary relaxation condition,
for the condition (1), it is easy to see by definition for arbitrary i and n, αn≥0,βi≥0；
For thetaniWhen Δ n is not less than 0, θni＝Δn-(pi-cni) Not less than 0; when Δ n < 0, pi-cni＜0，[Δn]+0, soni＝0-(pi-cni)>0。
For the condition (2), the distribution matrix R corresponding to the task distribution scheme determined by step S4 and which users finish each task is actually the final round of decision of the users, and is the optimal strategy in time, so pi-cniNot less than 0, any task i' belongs to M and has pi-cni≥pi’-cni’I.e. Δ n ═ pi-cniTherefore, the temperature of the molten steel is controlled,
θni＝[Δn]+-(pi-cni)＝(pi-cni)-(pi-cni)＝0。
for the condition (3), if n users do not appear in the distribution matrix R, that is, n users are not assigned tasks, it means that n users cannot obtain positive benefits for all tasks, and thus Δ n<0, and αn＝[Δn]+＝0。
For condition (4), when the i task is in short supply, p is now presenti＝viSo thati＝vi-pi＝0。
In conclusion, the task allocation scheme finally determined by the invention satisfies the relaxation condition, namely, the social benefit can be maximized.
Correspondingly, the invention also provides a task optimal distribution system in crowdsourcing. Referring to fig. 2, it is a schematic structural diagram of a system for optimally distributing tasks in crowd-sourcing according to an embodiment of the present invention, and as shown in fig. 2, the system for optimally distributing tasks in crowd-sourcing includes:
a setting module 10, configured to set a transaction price paid by each task of crowdsourcing distribution and a number of required users; a user module 11 for determining that each user selects one of the tasks which is most desired to be completed among the tasks issued by crowdsourcing and bidding for the task; the statistic module 12 is used for counting the number of users selecting each task according to the bidding conditions of the users; a judging module 13, configured to judge whether the number of required users set for crowdsourcing for each task is less than the number of users selecting the corresponding task; if so, adjusting the transaction price and re-counting the number of users completing each task according to the adjusted transaction price; otherwise, distributing each task to all users selecting the corresponding task respectively; and the task distribution module 14 is used for respectively distributing each task to all the users selecting the corresponding task.
Further, the determining module 13 is configured to determine whether the number of required users set for crowdsourcing of each task is less than the number of users selecting the corresponding task, and when the number of required users set for crowdsourcing of each task is greater than or equal to the number of users selecting the corresponding task, allocate each task to all users selecting the corresponding task; when the number of the required users set by crowdsourcing for each task is smaller than the number of the users selecting the corresponding task, the transaction price in the setting module 10 is adjusted, and the number of the users completing each task is counted again according to the adjusted transaction price.
Further, the determining module 13 adjusts the transaction price through the budget step size to obtain the adjusted transaction price.
Further, the user module 11 uses the self-income-acquiring situation of each user as a criterion for selecting a task when determining that each user selects one of the tasks that the user most wants to complete in crowd-sourcing the issued plurality of tasks.
In summary, in the task optimal allocation method in crowdsourcing provided by the invention, the number of users selecting each task is counted according to the bidding conditions of the users; and then, whether the number of the required users set by crowdsourcing of each task is smaller than the number of the users selecting the corresponding task is judged to determine whether to adjust the transaction price so as to realize the change of the number of the users selecting each task, and the requirements of each task on the number of people finishing the task are met in the whole process based on the transaction price set by crowdsourcing and the bidding conditions of the users, so that a plurality of tasks in the crowdsourcing are efficiently and orderly finished, and the distribution of the tasks in the crowdsourcing is optimized.
The above description is only for the purpose of describing the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention, and any variations and modifications made by those skilled in the art based on the above disclosure are within the scope of the appended claims.
Claims (9)
1. A task optimal allocation method in crowdsourcing is characterized by comprising the following steps:
s1: crowdsourcing issues a plurality of tasks and sets a transaction price paid by each task and the number of required users;
s2: each user selects one task which is most to be completed in the tasks which are released by crowdsourcing, and bids on the task;
s3: according to the bidding conditions of the users, counting the number of the users selecting each task;
s4: judging whether the number of the required users set by crowdsourcing of each task is smaller than the number of the users selecting the corresponding task, if so, adjusting the transaction price and executing the step S3 according to the adjusted transaction price; otherwise, go to step S5;
s5: each task is respectively distributed to all users selecting the corresponding task.
2. The method for optimally allocating a task in crowdsourcing, according to claim 1, wherein in the step S4, when the number of the required users set for each task crowdsourcing is less than the number of users selecting the corresponding task, the transaction price is adjusted and the step S3 is performed according to the adjusted transaction price; when the number of required users set for the crowd-sourcing of each task is greater than or equal to the number of users selecting the corresponding task, step S5 is performed.
3. The method for optimally allocating a crowdsourced task as claimed in claim 1, wherein during the step S4, the transaction price is adjusted by a budget step size to obtain an adjusted transaction price.
4. The method for optimally distributing a task in crowdsourcing, according to claim 1, wherein in said step S2, each user obtains a profit situation by itself as a criterion for selecting a task.
5. The method for optimally allocating tasks among crowdsourcing, according to any one of claims 1 to 4, further comprising the step of the platform paying the transaction price of each task to all users selecting the corresponding task in step S5 after performing said step S5.
6. A system for optimally allocating tasks in crowd sourcing, comprising:
the setting module is used for setting the transaction price paid by each task which is subjected to crowdsourcing and issued and the number of the required users;
the system comprises a user module, a bid module and a bid module, wherein the user module is used for determining that each user selects one task which is most desired to be completed in a plurality of tasks which are released by crowdsourcing and bidding;
the statistic module is used for counting the number of the users selecting each task according to the bidding conditions of the users;
the judging module is used for judging whether the number of the required users set by crowdsourcing of each task is less than the number of the users selecting the corresponding task; if so, adjusting the transaction price and re-counting the number of users completing each task according to the adjusted transaction price; otherwise, distributing each task to all users selecting the corresponding task respectively;
and the task allocation module is used for allocating each task to all the users selecting the corresponding task respectively.
7. The system for optimally distributing tasks in crowdsourcing, according to claim 6, wherein the judging module is configured to judge whether the number of the required users set for crowdsourcing each task is less than the number of users selecting the corresponding task, and when the number of the required users set for crowdsourcing each task is greater than or equal to the number of users selecting the corresponding task, distribute each task to all users selecting the corresponding task; and when the quantity of the required users set by crowdsourcing of each task is less than the quantity of the users for selecting the corresponding task, adjusting the transaction price in the setting module and re-counting the quantity of the users for completing each task according to the adjusted transaction price.
8. The crowd-sourced task-optimal allocation system of claim 6, wherein the determination module adjusts the transaction price by a budget step size to obtain an adjusted transaction price.
9. The system of claim 6, wherein the user module is configured to use the user's own earning profile as a criterion for selecting the task when determining that each user selects one of the plurality of tasks that is most likely to be completed by crowd-sourcing.
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