CN113627765B - Distributed space crowdsourcing task distribution method and system based on user satisfaction - Google Patents
Distributed space crowdsourcing task distribution method and system based on user satisfaction Download PDFInfo
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Abstract
The invention discloses a distributed space crowdsourcing task distribution 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 equal-size subareas after receiving a plurality of task distribution requests within a fixed time interval, acquiring the position information, historical service task information, reachable service range, skill information and unit cost of each crowdsourcing worker in each subarea range, acquiring the satisfaction degree of each crowdsourcing worker in each subarea to each task according to the crowdsourcing workers and task information in each subarea, acquiring the initial distribution result of each task according to the satisfaction degree, constructing a game model according to the acquired initial distribution result in each subarea so as to enable all crowdsourcing workers in each subarea to reach a Nash equilibrium state, and further acquiring the final distribution result of each subarea.
Description
Technical Field
The invention belongs to the technical field of space-time big data and distributed computation, and particularly relates to a distributed space crowdsourcing task distribution method and system based on user satisfaction.
Background
With ubiquitous mobile awareness devices, spatial crowdsourcing technology has been widely used in our daily lives. As a novel computing paradigm, it employs mobile users as staff, requiring crowdsourcing staff to physically move to the location of the task and perform. Task allocation is a fundamental and important problem in space crowdsourcing, however, since the tasks involved in space crowdsourcing include not only simple and easy-to-complete card punching tasks, but also some crowdsourcing tasks that are complex and require multiple crowdsourcing workers to participate together to complete, crowdsourcing task allocation has become a research hotspot in space crowdsourcing technology.
The existing space crowdsourcing task allocation method mainly comprises a greedy allocation method, a random allocation method, a minimum cost maximum stream allocation method and the like. The greedy distribution method circularly selects matching pairs of crowdsourcing workers and tasks with the largest benefit value until distribution is completed; the random distribution method adopts a random selection crowdsourcing worker, and the crowdsourcing worker selects task matching with the largest benefit value; the minimum-cost maximum flow distribution method constructs a bipartite graph of crowdsourcing workers and tasks, and finds the optimal matching result by searching an amplification way in the bipartite graph.
However, the above space crowdsourcing task allocation methods have some non-negligible technical problems: firstly, the allocation methods cannot solve the large-scale crowdsourcing task allocation scene, face the peak time of crowdsourcing task orders, and process a large number of orders in a short time, so that the above-mentioned existing methods cannot achieve the purpose of rapidly and efficiently realizing optimal task allocation; secondly, as the demands of users are increasingly complex, the fact that the crowdsourcing task requires a plurality of crowdsourcing workers to participate in cooperation to complete is considered, so that task distribution under a complex crowdsourcing task scene is realized, but the distribution methods are only applied to a simple crowdsourcing task distribution scene, so that the methods are not suitable for the complex crowdsourcing task distribution scene; thirdly, the allocation method is based on the allocation of the crowdsourcing platform, and the situation that the crowdsourcing workers actively select tasks is ignored, so that the crowdsourcing workers and the enthusiasm are reduced, the income of the crowdsourcing platform is reduced, and the situation of real crowdsourcing task allocation is not met.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a distributed space crowdsourcing task allocation method and system based on user satisfaction, which aim to solve the technical problems that the conventional space crowdsourcing task allocation method cannot achieve the purpose of realizing the optimal task allocation quickly and efficiently, and is only applied to a scene of simple crowdsourcing task allocation, and the problems that crowdsourcing workers and enthusiasm are reduced due to the fact that the situation of actively selecting tasks by the crowdsourcing workers is ignored, so that the income of a crowdsourcing platform is reduced, and the technical problems that the scene of real crowdsourcing task allocation is not met are solved.
To achieve the above object, according to one aspect of the present invention, there is provided a distributed space crowdsourcing 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 range related to all crowdsourcing workers and tasks into a plurality of sub-areas with equal size.
(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 subarea 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), according to the crowdsourcing workers and task information in the sub-area obtained in the step (2), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area for each task, and obtaining an initial allocation result of each task according to the satisfaction degree;
(4) And (3) constructing a game model according to the initial allocation result in the subarea obtained in the step (3) aiming at each subarea, so that all crowdsourcing workers in the subarea reach Nash equilibrium state, and further obtaining the final allocation result of the subarea.
Preferably, step (3) comprises the sub-steps of:
(3-1) initializing a set of tasks to be allocated equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated as the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether a task allocation result is generated for all the tasks in the subarea, if so, turning to the step (3-15), otherwise turning to the step (3-3);
(3-3) setting a counter i=1;
(3-4) setting an initial maximum satisfaction value U=0 of the ith task for the crowdsourcing workers, and setting a crowdsourcing worker serial number jm=0 corresponding to the maximum satisfaction value;
(3-5) setting a counter j=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 subarea, if so, turning to the step (3-7), otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the subarea can become a candidate worker of the ith task, if so, entering a step (3-8), otherwise, entering a step (3-11);
(3-8) calculating the satisfaction S (t) of the jth crowdsourcing worker with the ith task i,j )；
(3-9) judging whether the satisfaction degree value of the jth crowdsourcing worker on the ith task is larger than a maximum satisfaction degree 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 to S (t) i,j ) Setting a crowdsourcing worker sequence number jm corresponding to the maximum satisfaction value U as j, and transferring to the step (3-11);
(3-11) judging whether a round of traversal is completed for all crowdsourcing workers in the subarea, if so, turning to step (3-12), otherwise setting j=j+1, and returning to step (3-6);
(3-12) distributing the ith task to the jm crowdsourcing workers, deleting the jm crowdsourcing workers from the crowdsourcing workers to be distributed, and transferring to the step (3-13);
(3-13) judging whether the union of the skill information of all crowdsourcing workers which are already allocated with the ith task in the subarea 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 task allocation has been completed for all tasks in the subarea, if so, returning to the step (3-2), otherwise setting i=i+1, and returning to the step (3-4);
(3-15) outputting an initial task allocation result;
preferably, step (3-7) is determining whether the crowdsourcing worker can become a candidate for the ith task based on skill matching, if anyIt means that the crowdsourcing worker assigned the ith task can become a candidate for the ith task. Wherein->Skill information for the jth crowd-sourced worker in the subregion>Skill requirement for the ith task in the subregion, < ->A union of skill information for all workers assigned the ith task.
Preferably, the step (3-8) is specifically:
S(t i，j )＝θ·P(W i，j )+(1-θ)·C(W i，j )
where θ is a weight factor, P (W i，j ) Representing price satisfaction of a user who issues an ith task with a jth crowdsourcing worker, C (W i，j ) Indicating the quality satisfaction of the user who issued the ith task with the j-th crowdsourcing worker with other crowdsourcing workers that have been assigned the ith task.
Preferably, the mathematical formalization of price satisfaction is defined as: wherein D is ij For the distance of the jth worker to the ith task, v is the unit distance cost, +.>Budgeting for the task of the ith task.
Mathematical formalization of the satisfaction of the quality of collaboration is defined as:W i representing a set of crowdsourcing workers assigned an ith task, q j (w k ) Representing W in a collection i Historical collaboration value of crowdsourcing worker j and crowdsourcing worker k, j, k e [1 ], total number of crowdsourcing workers in the sub-area]And there is k+.j:
service information for historical tasks of the jth crowd-sourced worker, correspondingly,/for>The historical task service information for the kth crowd-sourced worker.
Preferably, step (4) specifically comprises the following sub-steps:
(4-1) setting a counter k=1;
(4-2) setting a kth crowdsourcing worker in the subarea as a kth gaming party in a gaming model, setting a crowdsourcing task allocated to the kth crowdsourcing worker in the subarea as a task strategy in a strategy space corresponding to the kth gaming party in the gaming 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), otherwise, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial allocation result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the subarea take the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k=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 subarea have corresponding strategy spaces, if so, turning to the step (4-11), 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 one crowdsourcing worker in the set formed by the crowdsourcing workers allocated with the nth task according to the skill information of the mth crowdsourcing worker in the subarea, if so, placing the nth task into a strategy space S corresponding to the mth crowdsourcing worker m If not, the step (4-9) is carried out;
(4-9) judging whether all tasks in the subarea are traversed for one time, if so, turning to the step (4-10), otherwise, setting n=n+1, and returning to the step (4-8);
(4-10) setting m=m+1, and returning to step (4-6);
(4-11) returning to the policy space corresponding to all crowdsourcing workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach a Nash equilibrium state, if not, turning to the step (4-13), and if so, turning 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), otherwise, turning to the step (4-14);
(4-14) setting a counter p=1;
(4-15) setting a counter q=1;
(4-16) judging whether the benefit value of the q-th task strategy selected by the p-th crowdsourcing worker in the corresponding strategy space is larger 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-th crowdsourcing worker from the current task strategy to the q-th task strategy selection, and transferring 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 Nash equilibrium solution and returning final task allocation result.
Preferably, in step (4-12), for each crowdsourcing worker in a subregion, all crowdsourcing workers in that subregion are considered to have achieved Nash equilibrium if the following mathematical form expression is satisfied:
where u represents a currently selected crowdsourcing worker in the subregion, -u represents all crowdsourcing workers in the subregion except the u' th crowdsourcing worker, and u e [1 ], the total number of crowdsourcing workers in the subregion]，Representing an optimal task strategy selected by a u-th crowdsourcing worker in the subregion; s is(s) -u Representing a set of task strategies selected by all crowdsourcing workers except the u-th crowdsourcing worker in the subarea; s is(s) u Representing the other task strategies than the best one selected by the u-th crowdsourcing worker in the subregion,/->Represents the benefit increment, U(s) u ，s -u ) Indicating benefit increment generated by selecting other task strategies without selecting the optimal task strategy by the u-th crowdsourcing worker in the subarea, and adding->Representing global satisfaction of the u-th crowdsourcing worker in the sub-region after selecting the optimal task strategyTotal value, Q(s) u ，s -u ) And the global satisfaction total value after the u-th crowdsourcing worker does not select the optimal task strategy is represented.
Preferably, the method comprises the steps of,the calculation formula of (2) is as follows:
wherein the method comprises the steps ofA set of all crowdsourcing workers representing the best task strategy selected in the sub-area, +.>Representing that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>In (I)> Indicating that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>Satisfaction in time, ->Representing satisfaction of the set of all crowdsourcing workers in the sub-region that selected the best task strategy.
U(s u ，s -u ) The calculation formula of (2) is as follows:
U(s u ，s -u )＝S(W v ∪u)-S(W v )
the calculation formula of (2) is as follows:
wherein T represents all tasks in the sub-region, W i Representing the set of crowdsourcing workers in the sub-region that are assigned the ith task.
Q(s u ，s -u ) The calculation formula of (2) is as follows:
。
according to another aspect of the present invention, there is provided a distributed space crowdsourcing task allocation system based on user satisfaction, comprising:
and the first module is used for dividing the 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.
The second module is used for acquiring the position information, the history 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 the first module, and the position information, the skill requirement and the task budget of each task;
the third module is used for obtaining the satisfaction degree of each crowdsourcing person in the subarea to each task according to the crowdsourcing person and the task information in the subarea obtained by the second module and obtaining the initial allocation result of each task according to the satisfaction degree;
and a fourth module, configured to construct a game model according to the initial allocation result in the subregion obtained by the third module for each subregion, so that all crowdsourcing workers in the subregion reach a nash equilibrium state, and further obtain a final allocation result of the subregion.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
(1) According to the invention, as the steps (1) to (2) are adopted, the global subarea is divided into a plurality of subareas by using the distributed task allocation framework, so that crowdsourcing workers and tasks in different subareas can be matched at the same time, and the technical problem that the conventional method cannot realize the optimal task allocation quickly and efficiently can be solved;
(2) The invention adopts the step (3) which designs a new user satisfaction degree measurement standard and comprehensively considers the price satisfaction degree and the crowdsourcing worker cooperation quality satisfaction degree, thereby solving the problem that the prior method cannot be suitable for complex crowdsourcing task allocation;
(3) The invention adopts the steps (4-12) to (4-20), designs the game model, and can enable the crowdsourcing workers to autonomously select the optimal task, thereby solving the problems that the prior method ignores the enthusiasm of the crowdsourcing workers and does not accord with the actual crowdsourcing distribution scene
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FIG. 1 is a flow chart of a distributed spatial crowdsourcing task allocation method based on user satisfaction of the present invention;
FIG. 2 is a schematic diagram of a framework of the distributed spatial crowdsourcing task allocation method based on user satisfaction of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The basic idea of the invention is that by enabling crowdsourcing workers to autonomously select tasks with the greatest benefit, compared with a mode distributed by a crowdsourcing platform, the mode distributed by the crowdsourcing platform is more in line with the actual situation. Firstly, obtaining an initial task allocation result, and secondly, applying a game model to enable crowdsourcing workers to select the best task result for themselves. In order to accelerate the task allocation efficiency, redundant calculation is reduced, sub-areas are divided in a distributed allocation mode, the task allocation is independently unfolded by each sub-area, and finally a global task allocation result is obtained. The above steps not only improve the task allocation efficiency of space crowdsourcing, but also further improve the allocation rate.
As shown in fig. 1 and 2, the present invention provides a distributed space crowdsourcing task allocation method based on user satisfaction, which includes the following steps:
(1) After receiving a plurality of Task allocation requests within a fixed time interval, dividing a predetermined geographic range related to all crowdsourcing workers (workers) and tasks (tasks) into a plurality of sub-areas with equal size.
(2) Acquiring the position information of each crowdsourcing worker, the history service task information (i.e. the task served by the crowdsourcing worker), the reachable service range (the round reachable subarea with a specific size which is unfolded by taking the position of the worker as the circle center), the skill information (the skill information possessed by the crowdsourcing worker) the unit cost (the cost of the unit moving distance), the position information of each task, the skill requirement (the skill information of the crowdsourcing worker required by the task), the task budget (the payment paid by a user when the task is completed), and the maximum reachable distance of the crowdsourcing worker in the range of each subarea obtained by dividing in the step (1);
in particular, tasks and crowdsourcing workers in different sub-areas are strictly independent of each other, and when the tasks and crowdsourcing workers belong to one sub-area, they do not belong to another sub-area at the same time. Therefore, the crowdsourcing worker can only choose to complete the crowdsourcing task of the subarea where the crowdsourcing worker is currently located, and even if the task of other subareas is within the reach of the crowdsourcing worker, the crowdsourcing worker cannot complete the task of other subareas across subareas. In this way, the tasks and crowdsourcing worker information in all task processing servers do not conflict with each other. The task allocation part in the invention performs initialization task allocation on crowdsourcing tasks and crowdsourcing workers in the subareas, 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), according to the crowdsourcing workers and task information in the sub-area obtained in the step (2), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area for each task, and obtaining an initial allocation result of each task according to the satisfaction degree;
specifically, step (3) includes the sub-steps of:
(3-1) initializing a set of tasks to be allocated equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated as the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether a task allocation result is generated for all the tasks in the subarea, if so, turning to the step (3-15), otherwise turning to the step (3-3);
(3-3) setting a counter i=1;
(3-4) setting an initial maximum satisfaction value U=0 of the ith task for the crowdsourcing workers, and setting a crowdsourcing worker serial number jm=0 corresponding to the maximum satisfaction value;
(3-5) setting a counter j=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 subarea, if so, turning to the step (3-7), otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the subarea can become a candidate worker of the ith task, if so, entering a step (3-8), otherwise, entering a step (3-11);
the invention judges whether crowdsourcing workers can become candidates of the ith task according to skill matching, if soIt means that the crowdsourcing worker assigned the ith task is eligible to serve the ith task, i.e., becomes a candidate for the ith task. Wherein->Skill information for the jth crowd-sourced worker in the subregion>Skill requirement for the ith task in the subregion, < ->A union of skill information for all workers assigned the ith task. When the jth crowdsourcing worker has skill information that is not available to all crowdsourcing workers assigned the ith task, then the crowdsourcing worker is considered eligible as a candidate for the ith task.
(3-8) calculating the satisfaction S (t) of the jth crowdsourcing worker with the ith task i，j )；
S(t i，j )＝θ·P(W i，j )+(1-θ)·C(W i，j )
Wherein θ is a weight factor having a value in the range of 0 to 1, preferably 0.5, P (W i，j ) Representing price satisfaction of a user who issues an ith task with a jth crowdsourcing worker, C (W i，j ) Representing the satisfaction of the user who issues the ith task for the cooperative quality of the jth crowdsourcing worker and other crowdsourcing workers assigned with the ith task;
wherein the mathematical formalization of price satisfaction is defined as:wherein D is ij For the distance of the jth worker to the ith task, v is the unit distance cost, +.>Budgeting for the task of the ith task.
Second, mathematical formalization of the satisfaction of the cooperative qualities is defined as: W i representing a set of crowdsourcing workers assigned an ith task, q j (w k ) Representing W in a collection i Historical collaboration value of crowdsourcing worker j and crowdsourcing worker k, j, k e [1 ], total number of crowdsourcing workers in the sub-area]And there is k+.j:
service information for historical tasks of the jth crowd-sourced worker, correspondingly,/for>Service information for historical tasks of kth crowd-sourced workers;
(3-9) judging whether the satisfaction degree value of the jth crowdsourcing worker on the ith task is larger than a maximum satisfaction degree 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 to S (t) i，j ) Setting a crowdsourcing worker sequence number jm corresponding to the maximum satisfaction value U as j, and transferring to the step (3-11);
(3-11) judging whether a round of traversal is completed for all crowdsourcing workers in the subarea, if so, turning to step (3-12), otherwise setting j=j+1, and returning to step (3-6);
(3-12) distributing the ith task to the jm crowdsourcing workers, deleting the jm crowdsourcing workers from the crowdsourcing workers to be distributed, and transferring to the step (3-13);
(3-13) judging whether the union of the skill information of all crowdsourcing workers which are already allocated with the ith task in the subarea 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 task allocation has been completed for all tasks in the subarea, if so, returning to the step (3-2), otherwise setting i=i+1, and returning to the step (3-4);
(3-15) outputting an initial task allocation result;
gaming models have been widely used in the fields of economics, politics, computer science, even law, biology and sports. In policy gaming, there are three basic components: gaming parties, policy space, and revenues. For each gaming party u in the set of gaming parties N he selects his dominant strategy S in the strategy space S u This will correspond to the utility of the revenue set U. In the model of the present invention, each gaming party is allowed to select a strategy a single time. This is also a pure strategy game, where the pure strategy game has Nash equalization if and only if all the players choose the best response strategy in their strategy space. In other words, when the betting party u selects its own best strategyThe strategy selection of other betting parties is s -u When the utility of the game party u meets the following conditions:
wherein s is u Is selected for non-optimal strategies. Nash equilibrium can be achieved throughout the process if each gaming party selects its own best strategy or best response.
(4) And (3) constructing a game model according to the initial allocation result in the subarea obtained in the step (3) aiming at each subarea, so that all crowdsourcing workers in the subarea reach Nash equilibrium state, and further obtaining the final allocation result of the subarea.
The step (4) specifically comprises the following substeps:
(4-1) setting a counter k=1;
(4-2) setting a kth crowdsourcing worker in the subarea as a kth gaming party in a gaming model, setting a crowdsourcing task allocated to the kth crowdsourcing worker in the subarea as a task strategy in a strategy space corresponding to the kth gaming party in the gaming 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), otherwise, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial allocation result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the subarea take the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k=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 subarea have corresponding strategy spaces, if so, turning to the step (4-11), 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 one crowdsourcing worker in the set formed by the crowdsourcing workers allocated with the nth task according to the skill information of the mth crowdsourcing worker in the subarea, if so, placing the nth task into a strategy space S corresponding to the mth crowdsourcing worker m If not, the step (4-9) is carried out;
(4-9) judging whether all tasks in the subarea are traversed for one time, if so, turning to the step (4-10), otherwise, setting n=n+1, and returning to the step (4-8);
(4-10) setting m=m+1, and returning to step (4-6);
(4-11) returning to the policy space corresponding to all crowdsourcing workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach a Nash equilibrium state, if not, turning to the step (4-13), and if so, turning to the step (4-21);
in this step, for each crowdsourcing worker in a subregion, all crowdsourcing workers in that subregion are considered to have achieved Nash equilibrium if the following mathematical form expression is satisfied:
where u represents a currently selected crowdsourcing worker in the subregion, -u represents all crowdsourcing workers in the subregion except the u' th crowdsourcing worker, and u e [1 ], the total number of crowdsourcing workers in the subregion]，Representing an optimal task strategy selected by a u-th crowdsourcing worker in the subregion; s is(s) -u Representing a set of task strategies selected by all crowdsourcing workers except the u-th crowdsourcing worker in the subarea; s is(s) u Representing other task strategies than the best one selected by the u-th crowdsourcing worker in the sub-region.
The benefit increment generated by selecting the optimal task strategy by the u-th crowdsourcing worker in the subarea is represented, and the calculation formula is as follows:
wherein the method comprises the steps ofA set of all crowdsourcing workers representing the best task strategy selected in the sub-area, +.>Representing that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>In (I)> Indicating that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>Satisfaction in time, ->Representing satisfaction of the set of all crowdsourcing workers in the sub-region that selected the best task strategy.
U(s u ，s -u ) The value added benefit generated by the u-th crowdsourcing worker in the subarea without selecting the optimal task strategy and selecting other task strategies is represented by the following calculation formula:
U(s u ，s -u )＝S(W v ∪u)-S(W v )
the global satisfaction total value after the u-th crowdsourcing worker in the subarea selects the optimal task strategy is represented, and the calculation formula is as follows:
wherein T represents all tasks in the sub-region, W i Representing the set of crowdsourcing workers in the sub-region that are assigned the ith task.
Q(s u ，s -u ) The global 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:
(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), otherwise, turning to the step (4-14);
(4-14) setting a counter p=1;
(4-15) setting a counter q=1;
(4-16) judging whether the benefit value of the q-th task strategy selected by the p-th crowdsourcing worker in the corresponding strategy space is larger 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-th crowdsourcing worker from the current task strategy to the q-th task strategy selection, and transferring 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 Nash equilibrium solution and returning a final task allocation result;
the game model adopted in the step (4) is a process of selecting the optimal task for the game model through continuous iteration, and finally, stability, namely Nash equilibrium, is achieved. The step (4) can certainly realize the Nash equilibrium result. The game model constructed by the method can be reduced to a potential game model, and the potential game model has proved that Nash equilibrium exists.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The distributed space crowdsourcing task allocation method based on the user satisfaction is characterized by comprising the following steps of:
(1) After receiving a plurality of task allocation requests within a fixed time interval, dividing a preset geographic range related to all crowdsourcing workers and tasks into a plurality of sub-areas with equal size;
(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 subarea 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), according to the crowdsourcing workers and task information in the sub-area obtained in the step (2), obtaining the satisfaction degree of each crowdsourcing worker in the sub-area for each task, and obtaining an initial allocation result of each task according to the satisfaction degree; step (3) comprises the following sub-steps:
(3-1) initializing a set of tasks to be allocated equal to the total number of tasks in the sub-area, and initializing a set of crowdsourcing workers to be allocated as the total number of crowdsourcing workers in the sub-area;
(3-2) judging whether a task allocation result is generated for all the tasks in the subarea, if so, turning to the step (3-15), otherwise turning to the step (3-3);
(3-3) setting a counter i=1;
(3-4) setting an initial maximum satisfaction value U=0 of the ith task for the crowdsourcing workers, and setting a crowdsourcing worker serial number jm=0 corresponding to the maximum satisfaction value;
(3-5) setting a counter j=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 subarea, if so, turning to the step (3-7), otherwise, turning to the step (3-11);
(3-7) judging whether the jth crowdsourcing worker in the subarea can become a candidate worker of the ith task, if so, entering a step (3-8), otherwise, entering a step (3-11);
(3-8) calculating the satisfaction S (t) of the jth crowdsourcing worker with the ith task i,j )；
(3-9) judging whether the satisfaction degree value of the jth crowdsourcing worker on the ith task is larger than a maximum satisfaction degree 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 to S (t) i,j ) Setting a crowdsourcing worker sequence number jm corresponding to the maximum satisfaction value U as j, and transferring to the step (3-11);
(3-11) judging whether a round of traversal is completed for all crowdsourcing workers in the subarea, if so, turning to step (3-12), otherwise setting j=j+1, and returning to step (3-6);
(3-12) distributing the ith task to the jm crowdsourcing workers, deleting the jm crowdsourcing workers from the crowdsourcing workers to be distributed, and transferring to the step (3-13);
(3-13) judging whether the union of the skill information of all crowdsourcing workers which are already allocated with the ith task in the subarea 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 task allocation has been completed for all tasks in the subarea, if so, returning to the step (3-2), otherwise setting i=i+1, and returning to the step (3-4);
(3-15) outputting an initial task allocation result;
(4) For each sub-area, constructing a game model according to the initial allocation result in the sub-area obtained in the step (3), so that all crowdsourcing workers in the sub-area reach Nash equilibrium state, and further obtaining the final allocation result of the sub-area; the step (4) specifically comprises the following substeps:
(4-1) setting a counter k=1;
(4-2) setting a kth crowdsourcing worker in the subarea as a kth gaming party in a gaming model, setting a crowdsourcing task allocated to the kth crowdsourcing worker in the subarea as a task strategy in a strategy space corresponding to the kth gaming party in the gaming 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), otherwise, entering the step (4-3);
(4-3) setting the initial strategy of the kth crowdsourcing worker as the initial allocation result obtained in the step (3), and then entering the step (4-4);
(4-4) judging whether all crowdsourcing workers in the subarea take the initial allocation result of the step (3) as an initial task strategy, if so, turning to the step (4-5), otherwise, setting k=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 subarea have corresponding strategy spaces, if so, turning to the step (4-11), 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 one crowdsourcing worker in the set formed by the crowdsourcing workers allocated with the nth task according to the skill information of the mth crowdsourcing worker in the subarea, if so, placing the nth task into a strategy space S corresponding to the mth crowdsourcing worker m If not, the step (4-9) is carried out;
(4-9) judging whether all tasks in the subarea are traversed for one time, if so, turning to the step (4-10), otherwise, setting n=n+1, and returning to the step (4-8);
(4-10) setting m=m+1, and returning to step (4-6);
(4-11) returning to the policy space corresponding to all crowdsourcing workers in the subarea, and then entering the step (4-12);
(4-12) judging whether all crowdsourcing workers reach a Nash equilibrium state, if not, turning to the step (4-13), and if so, turning 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), otherwise, turning to the step (4-14);
(4-14) setting a counter p=1;
(4-15) setting a counter q=1;
(4-16) judging whether the benefit value of the q-th task strategy selected by the p-th crowdsourcing worker in the corresponding strategy space is larger 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-th crowdsourcing worker from the current task strategy to the q-th task strategy selection, and transferring 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 Nash equilibrium solution and returning final task allocation result.
2. The method of claim 1, wherein the step (3-7) is to determine whether the crowdsourcing worker can become a candidate for the ith task based on skill matching, if anyIt means that the crowdsourcing worker assigned the ith task can become a candidate worker for the ith task; wherein->Skill information for the jth crowd-sourced worker in the subregion>Skill requirement for the ith task in the subregion, < ->A union of skill information for all workers assigned the ith task.
3. The method for distributing distributed space crowdsourcing tasks based on user satisfaction as recited in claim 2, wherein the step (3-8) is specifically:
wherein the method comprises the steps ofIs a weight factor, P (W) i,j ) Representing price satisfaction of a user who issues an ith task with a jth crowdsourcing worker, C (W i,j ) Indicating the quality satisfaction of the user who issued the ith task with the j-th crowdsourcing worker with other crowdsourcing workers that have been assigned the ith task.
4. The method for distributed spatial crowdsourcing task allocation based on user satisfaction of claim 3, wherein,
mathematical formalization of price satisfaction is defined as:wherein D is ij For the distance of the jth worker to the ith task, v is the unit distance cost, +.>Budgeting for the task of the ith task;
mathematical formalization of the satisfaction of the quality of collaboration is defined as:W i representing a set of crowdsourcing workers assigned an ith task, q j (w k ) Representing W in a collection i Historical collaboration value of crowdsourcing worker j and crowdsourcing worker k, j, k e [1 ], total number of crowdsourcing workers in the sub-area]And there is k+.j:
service information for historical tasks of the jth crowd-sourced worker, correspondingly,/for>The historical task service information for the kth crowd-sourced worker.
5. The method of claim 4, wherein in steps (4-12), for each crowdsourcing worker in a sub-area, all crowdsourcing workers in the sub-area are considered to achieve nash equalization if the following mathematical formal expression is satisfied:
where u represents a currently selected crowdsourcing worker in the subregion, -u represents all crowdsourcing workers in the subregion except the u' th crowdsourcing worker, and u e [1 ], the total number of crowdsourcing workers in the subregion]，Representing an optimal task strategy selected by a u-th crowdsourcing worker in the subregion; s is(s) -u Representing a set of task strategies selected by all crowdsourcing workers except the u-th crowdsourcing worker in the subarea; s is(s) u Representing the other task strategies than the best one selected by the u-th crowdsourcing worker in the subregion,/->Represents the benefit increment, U(s) u ,s -u ) Indicating benefit increment generated by selecting other task strategies without selecting the optimal task strategy by the u-th crowdsourcing worker in the subarea, and adding->Representing the global satisfaction total value, Q(s) of the u-th crowdsourcing worker in the subarea after selecting the optimal task strategy u ,s -u ) And the global satisfaction total value after the u-th crowdsourcing worker does not select the optimal task strategy is represented.
6. The method for user satisfaction-based distributed spatial crowdsourcing task allocation of claim 5, wherein,
the calculation formula of (2) is as follows:
wherein the method comprises the steps ofA set of all crowdsourcing workers representing the best task strategy selected in the sub-area, +.>Representing that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>In (I)> Indicating that the u th crowdsourcing person in the subregion joins the set of crowdsourcing workers +.>Satisfaction in time, ->Representing satisfaction of the set of all crowdsourcing workers in the sub-region that selected the best task strategy;
U(s u ，s -u ) The calculation formula of (2) is as follows:
U(s u ，s -u )＝S(W v ∪u)-S(W v )
the calculation formula of (2) is as follows:
wherein T represents all tasks in the sub-region, W i Representing a set of crowdsourcing workers in the sub-region assigned the ith task;
Q(s u ，s -u ) The calculation formula of (2) is as follows:
。
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