CN113627765A - User satisfaction-based distributed space crowdsourcing task allocation method and system - Google Patents

User satisfaction-based distributed space crowdsourcing task allocation method and system Download PDF

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CN113627765A
CN113627765A CN202110877549.9A CN202110877549A CN113627765A CN 113627765 A CN113627765 A CN 113627765A CN 202110877549 A CN202110877549 A CN 202110877549A CN 113627765 A CN113627765 A CN 113627765A
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李肯立
谢缘
段明星
周旭
刘楚波
唐卓
阳王东
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Abstract

The invention discloses a distributed space crowdsourcing task allocation method based on user satisfaction, which comprises the steps of dividing a preset geographical range related to all crowdsourcing workers and tasks into a plurality of sub-areas with equal size after receiving a plurality of task allocation requests in a fixed time interval, obtaining position information, historical service task information, reachable service range, skill information, unit cost, position information, skill requirement and task budget of each task of each sub-area, obtaining the satisfaction of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the area for each sub-area, obtaining an initial allocation result of each task according to the satisfaction, and constructing a game model according to the obtained initial allocation result in the sub-area for each sub-area, so that all crowdsourcing workers in the sub-region reach a nash equilibrium state and further obtain the final allocation result of the sub-region.

Description

User satisfaction-based distributed space crowdsourcing task allocation method and system
Technical Field
The invention belongs to the technical field of space-time big data and distributed computing, and particularly relates to a distributed space crowdsourcing task allocation method and system based on user satisfaction.
Background
With ubiquitous mobile sensing devices, spatial crowdsourcing techniques have been widely applied to the daily life of the present invention. As a novel computing paradigm, it employs mobile users as staff, requiring crowd-sourced workers to physically move to the location of the task and perform it. Task allocation is a basic and important problem in spatial crowdsourcing, however, since tasks involved in spatial crowdsourcing include not only simple and easy-to-complete card-type tasks, but also some complex crowdsourcing tasks which require multiple crowdsourcing workers to participate together to complete, the crowdsourcing task allocation has become a research hotspot in spatial crowdsourcing technology.
The existing space crowdsourcing task allocation method mainly comprises a greedy allocation method, a random allocation method, a minimum cost maximum flow allocation method and the like. The greedy allocation method circularly selects the crowd-sourced worker and task matching pair with the maximum benefit value until allocation is completed; randomly selecting crowdsourcing workers by adopting a random distribution method, wherein the crowdsourcing workers select task matching with the maximum benefit value; the minimum-cost maximum-flow distribution method is to construct a bipartite graph of crowdsourcing workers and tasks, and find an optimal matching result by searching for an augmentation road in the bipartite graph.
However, the above spatial crowdsourcing task allocation methods all have some non-negligible technical problems: firstly, the distribution methods cannot solve the problem of large-scale crowdsourcing task distribution scenes, and a large number of orders need to be processed in a short time when the crowdsourcing task orders are in a peak time, so that the above-mentioned existing methods cannot realize optimal task distribution quickly and efficiently; secondly, with increasingly complex user requirements, it is required to consider that crowdsourcing tasks require multiple crowdsourcing workers to participate in cooperation and complete together so as to realize task allocation under a complex crowdsourcing task scene, but the allocation methods are only applied to a scene of simple crowdsourcing task allocation, so that the methods are not suitable for the scene of complex crowdsourcing task allocation; thirdly, the distribution method is based on task distribution of a crowdsourcing platform, the situation that crowdsourcing workers actively select tasks is ignored, the participation enthusiasm of the crowdsourcing workers is reduced, the benefit of the crowdsourcing platform is reduced, and the method is not suitable for the actual crowdsourcing task distribution scene.
Disclosure of Invention
Aiming at the defects or improvement requirements of the prior art, the invention provides a distributed space crowdsourcing task allocation method and system based on user satisfaction, and aims to solve the technical problems that the conventional space crowdsourcing task allocation method cannot realize optimal task allocation quickly and efficiently, is only applied to a scene of simple crowdsourcing task allocation, and is neglected to actively select tasks by crowdsourcing workers, so that the participation enthusiasm of the crowdsourcing workers is reduced, the income of a crowdsourcing platform is reduced, and the method and system are not in line with the technical problems of the actual crowdsourcing task allocation scene.
To achieve the above object, according to an aspect of the present invention, there is provided a distributed spatial crowd-sourced task allocation method based on user satisfaction, including the steps of:
(1) after receiving a plurality of task allocation requests within a fixed time interval, dividing a predetermined geographic area involved by all crowdsourcing workers and tasks into a plurality of equally sized sub-areas.
(2) Acquiring the position information, the historical service task information, the reachable service range, the skill information and the unit cost of each crowdsourcing worker in each sub-area range obtained by dividing in the step (1), and the position information, the skill requirement and the task budget of each task;
(3) for each sub-area in the step (1), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the sub-area obtained in the step (2), and obtaining the initial distribution result of each task according to the satisfaction degree;
(4) and (4) for each sub-area, constructing a game model according to the initial distribution result in the sub-area obtained in the step (3), so that all crowdsourcing workers in the sub-area reach a Nash equilibrium state, and further obtaining a final distribution result of the sub-area.
Preferably, step (3) comprises the sub-steps of:
(3-1) initializing a set of tasks to be allocated to be equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated to be the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether task allocation results are generated for all tasks in the sub-area, if so, turning to the step (3-15), otherwise, turning to the step (3-3);
(3-3) setting a counter i equal to 1;
(3-4) setting the initial maximum satisfaction value U of the ith task to the crowdsourcing worker to be 0, and setting the sequence number jm of the crowdsourcing worker corresponding to the maximum satisfaction value to be 0;
(3-5) setting a counter j equal to 1;
(3-6) judging whether the crowdsourcing worker can reach the position of the ith task before the deadline of the ith task according to the reachable service range corresponding to the jth crowdsourcing worker in the sub-area, if so, turning to the step (3-7), and otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the sub-area can become a candidate worker of the ith task, if so, entering the step (3-8), and otherwise, entering the step (3-11);
(3-8) calculating the satisfaction degree S (t) of the jth crowdsourcing worker to the ith taski,j)；
(3-9) judging whether the satisfaction value of the jth crowdsourcing worker to the ith task is larger than the maximum satisfaction value U, if so, turning to the step (3-10), otherwise, turning to the step (3-11);
(3-10) setting the maximum satisfaction value U as S (t)i,j) Setting the crowdsourcing worker serial number jm corresponding to the maximum satisfaction value U as j, and turning to the step (3-11);
(3-11) judging whether one round of traversal has been completed on all crowdsourcing workers in the sub-area, if so, turning to the step (3-12), otherwise, setting j to j +1, and returning to the step (3-6);
(3-12) allocating the ith task to the jm crowdsourcing worker, deleting the jm crowdsourcing worker from the crowdsourcing worker set to be allocated, and turning to the step (3-13);
(3-13) judging whether the skill information union of all crowdsourcing workers allocated with the ith task in the sub-area covers the skill requirement of the ith task, if so, deleting the ith task from the task set to be allocated, and turning to the step (3-14), otherwise, turning to the step (3-14);
(3-14) judging whether the task distribution is finished for all the tasks in the sub-area, if so, returning to the step (3-2), otherwise, setting i to i +1, and returning to the step (3-4);
(3-15) outputting an initial task allocation result;
preferably, the step (3-7) is to judge whether the crowdsourcing worker can become a candidate worker for the ith task according to the skill matching, if so
Figure BDA0003190869430000041
It means that the crowdsourcing worker assigned the ith task can become a candidate worker for the ith task. Wherein
Figure BDA0003190869430000042
For the skill information of the jth crowd-sourced worker in the sub-region,
Figure BDA0003190869430000043
for the skill requirement of the ith task in this sub-area,
Figure BDA0003190869430000044
is the union of the skill information of all workers assigned the ith task.
Preferably, the step (3-8) is specifically:
S(ti，j)＝θ·P(Wi，j)+(1-θ)·C(Wi，j)
where θ is a weighting factor, P (W)i，j) To representPrice satisfaction, C (W) for jth crowd-sourced workers by users who issued ith taski，j) Indicating the degree of satisfaction of the user who issued the ith task on the cooperative quality of the jth crowdsourcing worker with other crowdsourcing workers who have been assigned the ith task.
Preferably, the mathematical formalization of price satisfaction is defined as:
Figure BDA0003190869430000045
Figure BDA0003190869430000046
wherein DijDistance from jth worker to ith task, v is unit distance cost,
Figure BDA0003190869430000047
the task budget for the ith task.
The mathematical formalization of the quality of collaboration satisfaction is defined as:
Figure BDA0003190869430000048
Wirepresenting a set of crowdsourced workers assigned the ith task, qj(wk) W in the representation setiHistorical collaboration values of crowdsourcing worker j and crowdsourcing worker k, j, k ∈ [1 ], total number of crowdsourcing workers in the sub-region]And k ≠ j:
Figure BDA0003190869430000049
Figure BDA00031908694300000410
service information for historical tasks of jth crowd-sourced workers, and accordingly,
Figure BDA00031908694300000411
service information for the kth crowd-sourced worker's historical tasks.
Preferably, step (4) comprises in particular the following sub-steps:
(4-1) setting a counter k to 1;
(4-2) setting the kth crowdsourcing worker in the sub-area as the kth gambling party in the gambling model, setting the crowdsourcing task allocated to the kth crowdsourcing worker in the sub-area as a task strategy in a strategy space corresponding to the kth gambling party in the gambling model, judging whether the kth crowdsourcing worker selects the initial allocation result obtained in the step (3) as an initial task strategy, if so, entering the step (4-4), and if not, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial distribution result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the sub-area use the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k to k +1, and returning to the step (4-2);
(4-5) setting a counter m-1;
(4-6) judging whether all crowdsourcing workers in the sub-area have corresponding strategy spaces, if so, turning to the step (4-11), and otherwise, turning to the step (4-7);
(4-7) setting a counter n-1;
(4-8) judging whether the mth crowdsourcing worker can replace any crowdsourcing worker in the set formed by the crowdsourcing workers assigned with the nth task according to the skill information of the mth crowdsourcing worker in the sub-area, and if so, putting the nth task into the strategy space S corresponding to the mth crowdsourcing workermOtherwise, turning to the step (4-9);
(4-9) judging whether one round of traversal is performed on all tasks in the sub-area, if so, turning to the step (4-10), otherwise, setting n to n +1, and returning to the step (4-8);
(4-10) setting m ═ m +1, and returning to the step (4-6);
(4-11) returning the strategy spaces corresponding to all crowdsourced workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach the Nash equilibrium state, if not, switching to the step (4-13), and if so, switching to the step (4-21);
(4-13) judging whether the optimal task strategy is selected for all crowdsourcing workers in the subarea, if so, returning to the step (4-12), and otherwise, turning to the step (4-14);
(4-14) setting a counter p ═ 1;
(4-15) setting a counter q to 1;
(4-16) judging whether the benefit value of the qth task strategy selected by the p crowdsourcing worker in the corresponding strategy space is greater than the benefit value of the current task strategy, if so, turning to the step (4-17), otherwise, turning to the step (4-18);
(4-17) changing the task strategy selection of the p crowdsourcing worker from the current task strategy to the selection of the q task strategy, and turning to the step (4-19);
(4-18) setting q ═ q +1, and returning to step (4-16);
(4-19) setting p ═ p +1, and returning to step (4-15);
and (4-20) outputting a Nash equilibrium solution and returning a final task allocation result.
Preferably, in step (4-12), for each crowdsourcing worker in a sub-region, all crowdsourcing workers in the sub-region are considered to have achieved nash equilibrium if the following mathematical form expression is satisfied:
Figure BDA0003190869430000061
where u represents a currently selected crowdsourcing worker in the sub-region, u represents all crowdsourcing workers in the sub-region except the u-th crowdsourcing worker, and u e [1, the total number of crowdsourcing workers in the sub-region]，
Figure BDA0003190869430000062
Representing the optimal task strategy selected by the u-th crowdsourcing worker in the sub-area; s-uRepresenting the task policy construct selected by all crowdsourcing workers in the sub-region except the u-th crowdsourcing workerA set of; suIndicating other task strategies than the optimal task strategy selected by the u-th crowd-sourced worker in the sub-area,
Figure BDA0003190869430000063
represents the benefit increment generated by the selection of the optimal task strategy by the U (th) crowdsourcing worker in the subarea, U(s)u，s-u) Indicating the gain in benefit that the u-th crowdsourcing worker in the sub-area does not select the optimal task strategy but selects other task strategies,
Figure BDA0003190869430000064
representing the total value of the global satisfaction degree, Q(s), of the u-th crowdsourcing worker in the sub-area after selecting the optimal task strategyu，s-u) The overall global satisfaction value after the u-th crowdsourcing worker does not select the optimal task strategy is shown.
Preferably, the first and second electrodes are formed of a metal,
Figure BDA0003190869430000065
the calculation formula of (2) is as follows:
Figure BDA0003190869430000071
wherein
Figure BDA0003190869430000072
Representing the set of all crowdsourced workers in the sub-area that have selected the best task strategy,
Figure BDA0003190869430000073
representing the u-th crowdsourcing worker in the sub-region joining the set of crowdsourcing workers
Figure BDA0003190869430000074
In (1),
Figure BDA0003190869430000075
Figure BDA00031908694300000711
indicating that the u-th crowdsourcing worker joins the set of crowdsourcing workers in the sub-region
Figure BDA0003190869430000076
The degree of satisfaction in the time of day,
Figure BDA0003190869430000077
representing the satisfaction of the set of all crowdsourced workers in the sub-area who selected the best task strategy.
U(su，s-u) The calculation formula of (2) is as follows:
U(su，s-u)＝S(Wv∪u)-S(Wv)
Figure BDA0003190869430000078
the calculation formula of (2) is as follows:
Figure BDA0003190869430000079
where T represents all tasks in the sub-region, WiRepresenting the set of crowdsourced workers in the sub-region assigned the ith task.
Q(su，s-u) The calculation formula of (2) is as follows:
Figure BDA00031908694300000710
according to another aspect of the present invention, there is provided a distributed spatial crowd-sourced task allocation system based on user satisfaction, comprising:
the system comprises a first module and a second module, wherein the first module is used for dividing a predetermined geographical range related to all crowdsourcing workers and tasks into a plurality of sub-areas with equal sizes after receiving a plurality of task allocation requests within a fixed time interval.
The second module is used for acquiring the position information, the historical service task information, the reachable service range, the skill information and the unit cost of each crowdsourcing worker in each sub-area range obtained by the division of the first module, and the position information, the skill requirement and the task budget of each task;
the third module is used for acquiring the satisfaction degree of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the sub-area, which are acquired by the second module, of each sub-area in the first module, and acquiring the initial distribution result of each task according to the satisfaction degree;
and the fourth module is used for constructing a game model according to the initial distribution result in the sub-area obtained by the third module for each sub-area, so that all crowdsourcing workers in the sub-area reach a Nash equilibrium state, and further obtaining a final distribution result of the sub-area.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) according to the invention, as the steps (1) to (2) are adopted, a distributed task allocation framework is applied to divide the overall sub-area into a plurality of sub-areas, crowdsourcing workers and tasks of different sub-areas are matched at the same time, and the technical problem that the optimal task allocation cannot be realized quickly and efficiently by the conventional method can be solved;
(2) in the invention, as the step (3) is adopted, a new user satisfaction degree measurement standard is designed, and the price satisfaction degree and the crowdsourcing worker cooperation quality satisfaction degree are comprehensively considered, so that the problem that the existing method cannot be suitable for the distribution of complex crowdsourcing tasks can be solved;
(3) the invention adopts the steps (4-12) to (4-20) and designs the game model, so that crowdsourcing workers can independently select the optimal task, and the problems that the conventional method ignores the enthusiasm of the crowdsourcing workers and is not in line with the actual crowdsourcing distribution scene can be solved
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FIG. 1 is a flow chart of a distributed spatial crowd-sourced task allocation method based on user satisfaction in accordance with the present invention;
FIG. 2 is a schematic diagram of a framework of a distributed spatial crowd-sourced task allocation method based on user satisfaction according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The basic idea of the invention is that by enabling crowdsourcing workers to autonomously select the task with the maximum benefit, the task is more in line with the actual situation compared with the mode distributed by a crowdsourcing platform. The initial task allocation result is obtained firstly, and secondly, the game model is applied to enable crowdsourcing workers to select the best task result for the crowdsourcing workers. In order to accelerate the efficiency of task allocation and reduce redundant computation, a sub-region is divided in a distributed allocation mode, each sub-region independently performs task allocation, and finally a global task allocation result is obtained. The steps not only improve the task allocation efficiency of space crowdsourcing, but also further improve the allocation rate.
As shown in fig. 1 and fig. 2, the present invention provides a distributed spatial crowd-sourced task allocation method based on user satisfaction, including the following steps:
(1) after receiving a plurality of Task allocation requests within a fixed time interval, dividing a predetermined geographical range involved by all crowdsourcing workers (Worker) and tasks (Task) into a plurality of sub-areas of equal size.
(2) Acquiring position information of each crowdsourcing worker in each sub-area range obtained by division in the step (1), historical service task information (namely a task served by the crowdsourcing worker), reachable service range (circular reachable sub-area with a specific size and expanded by taking the position of the worker as a circle center, and the maximum reachable distance of the crowdsourcing worker from the current position to a circle boundary), skill information (skill information possessed by the crowdsourcing worker), unit cost (cost expense of unit moving distance), position information of each task, skill requirement (skill information of the crowdsourcing worker required by the task), and task budget (payment to be paid by a user when the task is completed);
specifically, tasks and crowdsourcing workers in different sub-areas are strictly independent of each other, and when a task and crowdsourcing worker belong to one sub-area, they do not belong to another sub-area at the same time. Thus, a crowdsourcing worker can only choose to complete the crowdsourcing task for the subregion in which it is currently located, and even if the tasks for other subregions are within reach of the crowdsourcing worker, the crowdsourcing worker cannot complete the tasks for other subregions across the subregions. In this way, the tasks in all the task processing servers and the crowd-sourced worker information do not conflict with each other. The task allocation part performs initial task allocation on crowdsourcing tasks and crowdsourcing workers in the sub-region, and then establishes a game model to enable the crowdsourcing workers to autonomously select the best task optimization task allocation effect.
(3) For each sub-area in the step (1), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the sub-area obtained in the step (2), and obtaining the initial distribution result of each task according to the satisfaction degree;
specifically, step (3) includes the following substeps:
(3-1) initializing a set of tasks to be allocated to be equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated to be the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether task allocation results are generated for all tasks in the sub-area, if so, turning to the step (3-15), otherwise, turning to the step (3-3);
(3-3) setting a counter i equal to 1;
(3-4) setting the initial maximum satisfaction value U of the ith task to the crowdsourcing worker to be 0, and setting the sequence number jm of the crowdsourcing worker corresponding to the maximum satisfaction value to be 0;
(3-5) setting a counter j equal to 1;
(3-6) judging whether the crowdsourcing worker can reach the position of the ith task before the deadline of the ith task according to the reachable service range corresponding to the jth crowdsourcing worker in the sub-area, if so, turning to the step (3-7), and otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the sub-area can become a candidate worker of the ith task, if so, entering the step (3-8), and otherwise, entering the step (3-11);
the invention judges whether crowdsourcing workers can become candidate workers of the ith task or not according to skill matching, if so, the crowdsourcing workers can be candidate workers of the ith task
Figure BDA0003190869430000101
It means that the crowdsourcing worker assigned the ith task is qualified to serve the ith task, i.e., become a candidate worker for the ith task. Wherein
Figure BDA0003190869430000102
For the skill information of the jth crowd-sourced worker in the sub-region,
Figure BDA0003190869430000103
for the skill requirement of the ith task in this sub-area,
Figure BDA0003190869430000104
is the union of the skill information of all workers assigned the ith task. When the jth crowdsourcing worker has skill information that is not available to all crowdsourcing workers assigned to the ith task, the crowdsourcing worker is considered to be qualified as a candidate worker for the ith task.
(3-8) calculating the satisfaction degree S (t) of the jth crowdsourcing worker to the ith taski，j)；
S(ti，j)＝θ·P(Wi，j)+(1-θ)·C(Wi，j)
Where θ is a weighting factor, ranging from 0 to 1, preferably 0.5, P (W)i，j) Represents the price satisfaction of the user who issued the ith task to the jth crowd-sourced worker, C (W)i，j) Representing the degree of satisfaction of the users who issue the ith task on the cooperative quality of the jth crowdsourcing worker and other crowdsourcing workers who have been assigned the ith task;
wherein, the mathematical formalization of the price satisfaction is defined as:
Figure BDA0003190869430000111
wherein DijDistance from jth worker to ith task, v is unit distance cost,
Figure BDA0003190869430000112
the task budget for the ith task.
Second, the mathematical formalization of the collaboration quality satisfaction is defined as:
Figure BDA0003190869430000113
Figure BDA0003190869430000114
Wirepresenting a set of crowdsourced workers assigned the ith task, qj(wk) W in the representation setiHistorical collaboration values of crowdsourcing worker j and crowdsourcing worker k, j, k ∈ [1 ], total number of crowdsourcing workers in the sub-region]And k ≠ j:
Figure BDA0003190869430000115
Figure BDA0003190869430000116
service information for historical tasks of jth crowd-sourced workers, and accordingly,
Figure BDA0003190869430000117
serving information for the kth crowd-sourced worker's historical tasks;
(3-9) judging whether the satisfaction value of the jth crowdsourcing worker to the ith task is larger than the maximum satisfaction value U, if so, turning to the step (3-10), otherwise, turning to the step (3-11);
(3-10) setting the maximum satisfaction value U as S (t)i，j) Setting the crowdsourcing worker serial number jm corresponding to the maximum satisfaction value U as j, and turning to the step (3-11);
(3-11) judging whether one round of traversal has been completed on all crowdsourcing workers in the sub-area, if so, turning to the step (3-12), otherwise, setting j to j +1, and returning to the step (3-6);
(3-12) allocating the ith task to the jm crowdsourcing worker, deleting the jm crowdsourcing worker from the crowdsourcing worker set to be allocated, and turning to the step (3-13);
(3-13) judging whether the skill information union of all crowdsourcing workers allocated with the ith task in the sub-area covers the skill requirement of the ith task, if so, deleting the ith task from the task set to be allocated, and turning to the step (3-14), otherwise, turning to the step (3-14);
(3-14) judging whether the task distribution is finished for all the tasks in the sub-area, if so, returning to the step (3-2), otherwise, setting i to i +1, and returning to the step (3-4);
(3-15) outputting an initial task allocation result;
the game model has been widely used in economics, politics, computer science, and even in the fields of law, biology, and sports. In policy gaming, there are three basic components: gambling parties, strategy space and revenues. For each gambling party u in the pool of gambling parties N, he selects his dominance policy S in the policy space SuThis will correspond to the utility of the revenue set U. In the model of the invention, each gaming party is allowed to select a policy at a single time. This is also a pure policy game where nash equilibrium exists and only if all gambling parties choose the best response policy in their policy space. In other words, when the gambling party u selects the best strategy of the gambling party u
Figure BDA0003190869430000121
The strategy of the other game parties is selected as s-uThen, the utility of the gambling party u satisfies the following condition:
Figure BDA0003190869430000122
wherein s isuIs selected for a non-optimal strategy. Nash equilibrium can be reached throughout the process if each betting party chooses its own best strategy or best reaction.
(4) And (4) for each sub-area, constructing a game model according to the initial distribution result in the sub-area obtained in the step (3), so that all crowdsourcing workers in the sub-area reach a Nash equilibrium state, and further obtaining a final distribution result of the sub-area.
The step (4) specifically comprises the following substeps:
(4-1) setting a counter k to 1;
(4-2) setting the kth crowdsourcing worker in the sub-area as the kth gambling party in the gambling model, setting the crowdsourcing task allocated to the kth crowdsourcing worker in the sub-area as a task strategy in a strategy space corresponding to the kth gambling party in the gambling model, judging whether the kth crowdsourcing worker selects the initial allocation result obtained in the step (3) as an initial task strategy, if so, entering the step (4-4), and if not, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial distribution result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the sub-area use the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k to k +1, and returning to the step (4-2);
(4-5) setting a counter m-1;
(4-6) judging whether all crowdsourcing workers in the sub-area have corresponding strategy spaces, if so, turning to the step (4-11), and otherwise, turning to the step (4-7);
(4-7) setting a counter n-1;
(4-8) judging whether the mth crowdsourcing worker can replace any crowdsourcing worker in the set formed by the crowdsourcing workers assigned with the nth task according to the skill information of the mth crowdsourcing worker in the sub-area, and if so, putting the nth task into the strategy space S corresponding to the mth crowdsourcing workermIn, otherwise, turn toEntering the step (4-9);
(4-9) judging whether one round of traversal is performed on all tasks in the sub-area, if so, turning to the step (4-10), otherwise, setting n to n +1, and returning to the step (4-8);
(4-10) setting m ═ m +1, and returning to the step (4-6);
(4-11) returning the strategy spaces corresponding to all crowdsourced workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach the Nash equilibrium state, if not, switching to the step (4-13), and if so, switching to the step (4-21);
in this step, for each crowdsourcing worker in a sub-region, all crowdsourcing workers in that sub-region are considered to have achieved nash equilibrium if the following mathematical form expression is satisfied:
Figure BDA0003190869430000131
where u represents a currently selected crowdsourcing worker in the sub-region, u represents all crowdsourcing workers in the sub-region except the u-th crowdsourcing worker, and u e [1, the total number of crowdsourcing workers in the sub-region]，
Figure BDA0003190869430000141
Representing the optimal task strategy selected by the u-th crowdsourcing worker in the sub-area; s-uRepresenting a set of task strategies selected by all crowdsourcing workers except the u-th crowdsourcing worker in the sub-region; suRepresenting the task strategies selected by the u-th crowd-sourced worker in the sub-area other than the optimal task strategy.
Figure BDA0003190869430000142
The benefit increment generated by the u-th crowdsourcing worker in the sub-area selecting the optimal task strategy is represented by the following calculation formula:
Figure BDA0003190869430000143
wherein
Figure BDA0003190869430000144
Representing the set of all crowdsourced workers in the sub-area that have selected the best task strategy,
Figure BDA0003190869430000145
representing the u-th crowdsourcing worker in the sub-region joining the set of crowdsourcing workers
Figure BDA0003190869430000146
In (1),
Figure BDA0003190869430000147
Figure BDA0003190869430000148
indicating that the u-th crowdsourcing worker joins the set of crowdsourcing workers in the sub-region
Figure BDA00031908694300001412
The degree of satisfaction in the time of day,
Figure BDA0003190869430000149
representing the satisfaction of the set of all crowdsourced workers in the sub-area who selected the best task strategy.
U(su，s-u) The benefit increment generated by selecting other task strategies instead of the optimal task strategy by the u-th crowdsourcing worker in the sub-area is shown, and the calculation formula is as follows:
U(su，s-u)＝S(Wv∪u)-S(Wv)
Figure BDA00031908694300001410
representing the total value of the global satisfaction degree of the u-th crowdsourcing worker in the sub-area after selecting the optimal task strategyThe calculation formula is as follows:
Figure BDA00031908694300001411
where T represents all tasks in the sub-region, WiRepresenting the set of crowdsourced workers in the sub-region assigned the ith task.
Q(su，s-u) The overall satisfaction total value after the u-th crowdsourcing worker does not select the optimal task strategy is represented, and the calculation formula is as follows:
Figure BDA0003190869430000151
(4-13) judging whether the optimal task strategy is selected for all crowdsourcing workers in the subarea, if so, returning to the step (4-12), and otherwise, turning to the step (4-14);
(4-14) setting a counter p ═ 1;
(4-15) setting a counter q to 1;
(4-16) judging whether the benefit value of the qth task strategy selected by the p crowdsourcing worker in the corresponding strategy space is greater than the benefit value of the current task strategy, if so, turning to the step (4-17), otherwise, turning to the step (4-18);
(4-17) changing the task strategy selection of the p crowdsourcing worker from the current task strategy to the selection of the q task strategy, and turning to the step (4-19);
(4-18) setting q ═ q +1, and returning to step (4-16);
(4-19) setting p ═ p +1, and returning to step (4-15);
(4-20) outputting a Nash equilibrium solution and returning a final task distribution result;
the game model adopted in the step (4) is a process of continuously iteratively selecting the best task for the game model, and finally the stability, namely the Nash equilibrium is achieved. The step (4) can realize the result of Nash equilibrium. The game model constructed by the method can be proved to be a potential game model which is proved to have certain Nash equilibrium.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. A distributed space crowdsourcing task allocation method based on user satisfaction is characterized by comprising the following steps:
(1) after receiving a plurality of task allocation requests within a fixed time interval, dividing a predetermined geographic area involved by all crowdsourcing workers and tasks into a plurality of equally sized sub-areas.
(2) Acquiring the position information, the historical service task information, the reachable service range, the skill information and the unit cost of each crowdsourcing worker in each sub-area range obtained by dividing in the step (1), and the position information, the skill requirement and the task budget of each task;
(3) for each sub-area in the step (1), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the sub-area obtained in the step (2), and obtaining the initial distribution result of each task according to the satisfaction degree;
(4) and (4) for each sub-area, constructing a game model according to the initial distribution result in the sub-area obtained in the step (3), so that all crowdsourcing workers in the sub-area reach a Nash equilibrium state, and further obtaining a final distribution result of the sub-area.
2. The user satisfaction-based distributed spatial crowd-sourced task allocation method of claim 1, wherein step (3) comprises the sub-steps of:
(3-1) initializing a set of tasks to be allocated to be equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated to be the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether task allocation results are generated for all tasks in the sub-area, if so, turning to the step (3-15), otherwise, turning to the step (3-3);
(3-3) setting a counter i equal to 1;
(3-4) setting the initial maximum satisfaction value U of the ith task to the crowdsourcing worker to be 0, and setting the sequence number jm of the crowdsourcing worker corresponding to the maximum satisfaction value to be 0;
(3-5) setting a counter j equal to 1;
(3-6) judging whether the crowdsourcing worker can reach the position of the ith task before the deadline of the ith task according to the reachable service range corresponding to the jth crowdsourcing worker in the sub-area, if so, turning to the step (3-7), and otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the sub-area can become a candidate worker of the ith task, if so, entering the step (3-8), and otherwise, entering the step (3-11);
(3-8) calculating the satisfaction degree S (t) of the jth crowdsourcing worker to the ith taski,j)；
(3-9) judging whether the satisfaction value of the jth crowdsourcing worker to the ith task is larger than the maximum satisfaction value U, if so, turning to the step (3-10), otherwise, turning to the step (3-11);
(3-10) setting the maximum satisfaction value U as S (t)i,j) Setting the crowdsourcing worker serial number jm corresponding to the maximum satisfaction value U as j, and turning to the step (3-11);
(3-11) judging whether one round of traversal has been completed on all crowdsourcing workers in the sub-area, if so, turning to the step (3-12), otherwise, setting j to j +1, and returning to the step (3-6);
(3-12) allocating the ith task to the jm crowdsourcing worker, deleting the jm crowdsourcing worker from the crowdsourcing worker set to be allocated, and turning to the step (3-13);
(3-13) judging whether the skill information union of all crowdsourcing workers allocated with the ith task in the sub-area covers the skill requirement of the ith task, if so, deleting the ith task from the task set to be allocated, and turning to the step (3-14), otherwise, turning to the step (3-14);
(3-14) judging whether the task distribution is finished for all the tasks in the sub-area, if so, returning to the step (3-2), otherwise, setting i to i +1, and returning to the step (3-4);
and (3-15) outputting the initial task allocation result.
3. The distributed space crowd-sourced task allocation method based on user satisfaction as claimed in claim 1 or 2, wherein the step (3-7) is to judge whether the crowd-sourced worker can become a candidate worker for the ith task according to skill matching, if any
Figure FDA0003190869420000021
It means that the crowdsourcing worker assigned the ith task can become a candidate worker for the ith task. Wherein
Figure FDA0003190869420000022
For the skill information of the jth crowd-sourced worker in the sub-region,
Figure FDA0003190869420000023
for the skill requirement of the ith task in this sub-area,
Figure FDA0003190869420000024
is the union of the skill information of all workers assigned the ith task.
4. The distributed spatial crowd-sourcing task allocation method based on user satisfaction according to any of claims 1 to 3, wherein the step (3-8) is specifically:
Figure FDA0003190869420000031
wherein
Figure FDA0003190869420000032
Is a weight factor, P (W)i,j) Represents the price satisfaction of the user who issued the ith task to the jth crowd-sourced worker, C (W)i,j) Indicating the degree of satisfaction of the user who issued the ith task on the cooperative quality of the jth crowdsourcing worker with other crowdsourcing workers who have been assigned the ith task.
5. The distributed spatial crowd-sourced task allocation method based on user satisfaction according to claim 4, wherein,
the mathematical formalization of price satisfaction is defined as:
Figure FDA0003190869420000033
wherein DijDistance from jth worker to ith task, v is unit distance cost,
Figure FDA0003190869420000034
the task budget for the ith task.
The mathematical formalization of the quality of collaboration satisfaction is defined as:
Figure FDA0003190869420000035
Wirepresenting a set of crowdsourced workers assigned the ith task, qj(wk) W in the representation setiHistorical collaboration values of crowdsourcing worker j and crowdsourcing worker k, j, k ∈ [1 ], total number of crowdsourcing workers in the sub-region]And k ≠ j:
Figure FDA0003190869420000036
Figure FDA0003190869420000037
service information for historical tasks of jth crowd-sourced workers, and accordingly,
Figure FDA0003190869420000038
service information for the kth crowd-sourced worker's historical tasks.
6. The distributed spatial crowd-sourced task allocation method based on user satisfaction according to claim 1, wherein step (4) specifically comprises the following sub-steps:
(4-1) setting a counter k to 1;
(4-2) setting the kth crowdsourcing worker in the sub-area as the kth gambling party in the gambling model, setting the crowdsourcing task allocated to the kth crowdsourcing worker in the sub-area as a task strategy in a strategy space corresponding to the kth gambling party in the gambling model, judging whether the kth crowdsourcing worker selects the initial allocation result obtained in the step (3) as an initial task strategy, if so, entering the step (4-4), and if not, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial distribution result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the sub-area use the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k to k +1, and returning to the step (4-2);
(4-5) setting a counter m-1;
(4-6) judging whether all crowdsourcing workers in the sub-area have corresponding strategy spaces, if so, turning to the step (4-11), and otherwise, turning to the step (4-7);
(4-7) setting a counter n-1;
(4-8) judging whether the mth crowdsourcing worker can replace any crowdsourcing worker in the set formed by the crowdsourcing workers assigned with the nth task according to the skill information of the mth crowdsourcing worker in the sub-area, and if so, putting the nth task into the strategy space S corresponding to the mth crowdsourcing workermOtherwise, turning to the step (4-9);
(4-9) judging whether one round of traversal is performed on all tasks in the sub-area, if so, turning to the step (4-10), otherwise, setting n to n +1, and returning to the step (4-8);
(4-10) setting m ═ m +1, and returning to the step (4-6);
(4-11) returning the strategy spaces corresponding to all crowdsourced workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach the Nash equilibrium state, if not, switching to the step (4-13), and if so, switching to the step (4-21);
(4-13) judging whether the optimal task strategy is selected for all crowdsourcing workers in the subarea, if so, returning to the step (4-12), and otherwise, turning to the step (4-14);
(4-14) setting a counter p ═ 1;
(4-15) setting a counter q to 1;
(4-16) judging whether the benefit value of the qth task strategy selected by the p crowdsourcing worker in the corresponding strategy space is greater than the benefit value of the current task strategy, if so, turning to the step (4-17), otherwise, turning to the step (4-18);
(4-17) changing the task strategy selection of the p crowdsourcing worker from the current task strategy to the selection of the q task strategy, and turning to the step (4-19);
(4-18) setting q ═ q +1, and returning to step (4-16);
(4-19) setting p ═ p +1, and returning to step (4-15);
and (4-20) outputting a Nash equilibrium solution and returning a final task allocation result.
7. The user satisfaction-based distributed spatial crowd-sourcing task allocation method of claim 6, wherein in steps (4-12), for each crowd-sourcing worker in a sub-region, all crowd-sourcing workers in the sub-region are considered to have achieved nash equilibrium if the following mathematical formal expression is satisfied:
Figure FDA0003190869420000051
wherein u denotes the currently selected sub-regionA certain crowdsourcing worker in the domain, -u represents all crowdsourcing workers in the sub-domain except the u-th crowdsourcing worker, and u e [1 ], the total number of crowdsourcing workers in the sub-domain]，
Figure FDA0003190869420000052
Representing the optimal task strategy selected by the u-th crowdsourcing worker in the sub-area; s-uRepresenting a set of task strategies selected by all crowdsourcing workers except the u-th crowdsourcing worker in the sub-region; suIndicating other task strategies than the optimal task strategy selected by the u-th crowd-sourced worker in the sub-area,
Figure FDA0003190869420000053
represents the benefit increment generated by the selection of the optimal task strategy by the U (th) crowdsourcing worker in the subarea, U(s)u,s-u) Indicating the gain in benefit that the u-th crowdsourcing worker in the sub-area does not select the optimal task strategy but selects other task strategies,
Figure FDA0003190869420000054
representing the total value of the global satisfaction degree, Q(s), of the u-th crowdsourcing worker in the sub-area after selecting the optimal task strategyu,s-u) The overall global satisfaction value after the u-th crowdsourcing worker does not select the optimal task strategy is shown.
8. The distributed spatial crowd-sourced task allocation method based on user satisfaction according to claim 7, wherein,
Figure FDA0003190869420000055
the calculation formula of (2) is as follows:
Figure FDA0003190869420000061
wherein
Figure FDA0003190869420000062
Representing the set of all crowdsourced workers in the sub-area that have selected the best task strategy,
Figure FDA0003190869420000063
representing the u-th crowdsourcing worker in the sub-region joining the set of crowdsourcing workers
Figure FDA0003190869420000064
In (1),
Figure FDA0003190869420000065
Figure FDA0003190869420000066
indicating that the u-th crowdsourcing worker joins the set of crowdsourcing workers in the sub-region
Figure FDA0003190869420000067
The degree of satisfaction in the time of day,
Figure FDA0003190869420000068
representing the satisfaction of the set of all crowdsourced workers in the sub-area who selected the best task strategy.
U(su,s-u) The calculation formula of (2) is as follows:
U(su,s-u)＝S(Wv∪u)-S(Wv)
Figure FDA0003190869420000069
the calculation formula of (2) is as follows:
Figure FDA00031908694200000610
where T represents all tasks in the sub-region, WiRepresents the quilt in the sub-areaA set of crowdsourced workers assigned the ith task.
Q(su,s-u) The calculation formula of (2) is as follows:
Figure FDA00031908694200000611
9. a distributed spatial crowd-sourced task allocation system based on user satisfaction, comprising:
the system comprises a first module and a second module, wherein the first module is used for dividing a predetermined geographical range related to all crowdsourcing workers and tasks into a plurality of sub-areas with equal sizes after receiving a plurality of task allocation requests within a fixed time interval.
The second module is used for acquiring the position information, the historical service task information, the reachable service range, the skill information and the unit cost of each crowdsourcing worker in each sub-area range obtained by the division of the first module, and the position information, the skill requirement and the task budget of each task;
the third module is used for acquiring the satisfaction degree of each crowdsourcing worker in the sub-area to each task according to the crowdsourcing worker and the task information in the sub-area, which are acquired by the second module, of each sub-area in the first module, and acquiring the initial distribution result of each task according to the satisfaction degree;
and the fourth module is used for constructing a game model according to the initial distribution result in the sub-area obtained by the third module for each sub-area, so that all crowdsourcing workers in the sub-area reach a Nash equilibrium state, and further obtaining a final distribution result of the sub-area.
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