CN108876012B - Space crowdsourcing task allocation method - Google Patents
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Abstract
The invention discloses a space crowdsourcing task allocation method, which belongs to the technical field of internet and respectively designs a TPC (transmit power control) method for calculating task processing priority, a WFC (task force control) method for selecting workers and an MLS (task location selection) method for selecting task places.
Description
Technical Field
The invention relates to a space crowdsourcing task allocation method, and belongs to the technical field of internet.
Background
With the rapid development of mobile devices and crowdsourcing platforms, spatial crowdsourcing has attracted a great deal of attention in the database world. Space crowdsourcing is widely applied to daily life, such as drop-drop cars currently used by many people and takeaway delivery in the aspect of logistics delivery. Users using the Baidu map and the Gaode map share real-time road condition information and are potential crowdsourcing task participants. In the aspect of disaster information monitoring, people can share disaster information in real time through social media software, and each user sharing the disaster is a crowdsourcing worker. The crowdsourcing is divided into traditional crowdsourcing and spatial crowdsourcing, and the spatial crowdsourcing and the traditional crowdsourcing are different in crowdsourcing tasks, task initiators and crowdsourcing platforms. In general, the traditional crowd-sourced task is completed online, so the number of participants and the historical task completion need only be taken into consideration. The space crowdsourcing task is influenced not only by the above traditional crowdsourcing task factors, but also by the space attributes of the task, such as the density of workers near the task, the traffic conditions in the city, and the like.
Disclosure of Invention
The invention aims to solve the problem that task allocation cannot be successful due to improper processing sequence in the prior art, and provides a multi-skill space crowdsourcing task allocation method based on a multi-skill space crowdsourcing task allocation model.
The purpose of the invention is realized as follows:
a method for distributing space crowdsourcing tasks is characterized by comprising the following steps:
step one, judging whether a task to be distributed exists in a task queue to be distributed, if so, processing the task in the task queue to be distributed, and if not, executing the step three, otherwise, executing the next step;
step two, calling a TPC method to calculate the priority of the task: firstly, calculating the level of a task initiator, then calculating the number of available workers and the number of available task sites of the task according to constraint conditions, calculating the priority of the task, and executing the next step;
step three, calling a WFC method to select workers for the task: firstly, selecting a worker set meeting constraint conditions according to constraints, calculating scores of workers, sequencing the workers according to the scores, and selecting the workers according to task skill requirements in the worker sequence until the task skill requirements are met; if not, adding the task into a task queue to be distributed, and executing the next step;
step four, calling an MLS method to select a task site: if the task is a task in the task queue to be distributed, firstly, executing the second step to select available workers for the task, selecting task places where the workers can reach and the task initiator cannot reach, calculating the gravity center coordinates of the alternative workers and the task initiator, and then calculating the task place closest to the gravity center; and if the tasks which cannot be successfully distributed exist, adding the tasks into a task queue to be distributed, and waiting for the next time stamp processing.
The TPC method comprises the following steps:
step one, judging whether a task initiator set is empty, if so, ending the method, otherwise, executing the next step;
step two, calculating the grade of a task initiator, calculating a task available worker set and a worker reachable task place set according to the constraint conditions of the task, and executing the next step;
removing task sites which are inaccessible to the task initiator in the task site set, calculating task priority, and executing the next step;
step four, judging whether all tasks issued by the task initiator are processed, if so, executing the next step, otherwise, executing the step one;
and fifthly, sequencing the tasks according to the priority, outputting a priority queue and finishing the method.
The WFC method comprises the following steps:
step one, judging whether a worker set available for a task is empty, if so, executing the next step, otherwise, executing the step three;
selecting workers meeting the task constraint conditions for the task, and executing the next step;
calculating the scores of workers in the task available worker set, and executing the next step;
step four, judging whether the scores of all the workers are calculated, if so, executing the next step, otherwise, executing the step three;
fifthly, sorting workers according to scores, and executing the next step;
and step six, selecting workers in sequence, executing the next step if the task skill coverage is met, otherwise, continuing to execute the step if the workers are still available, adding the task into a task queue to be distributed if no selectable worker exists, and ending the method.
The MLS method comprises the following steps:
step one, judging whether the task place set is empty, if so, executing the next step, otherwise, executing the step three;
step two, calculating a set of task places where the workers can reach in the set of available workers for the task, and removing the task places where the task initiator cannot reach;
step three, calculating and calculating barycentric coordinates of alternative workers and a task initiator;
and fourthly, calculating a task place closest to the gravity center, outputting the task place and finishing the method.
The invention has the beneficial effects that:
the invention designs a task allocation model according to the scene according to the task type of the spatial crowdsourcing, and aims at the problems of low task allocation number of a GREEDY method, a g-D & C method and an ADAPTIVE method in the existing multi-skill spatial crowdsourcing task allocation method and no consideration of platform benefits on the basis of the multi-skill spatial crowdsourcing task allocation model.
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FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a flow chart of a TPC method;
FIG. 3 is a flow chart of a WFC method;
FIG. 4 is a flow chart of an MLS method;
FIG. 5 is a representation of the total number of assignments, assignment number, platform revenue, and runtime of the method of the present invention versus three methods when varying the budget [ B-, B + ];
FIG. 6 is a representation of the total number of assignments, assignment, platform revenue, and runtime of the method of the present invention versus three methods when varying the unit price [ C-, C + ];
FIG. 7 is a representation of the total score of assignment, number of assignment, platform revenue and runtime of the inventive method versus the three methods when varying the range of travel distance [ d-, d + ].
Detailed Description
The invention will be explained in more detail below with reference to the drawings and the movement examples.
The invention designs a task allocation model according to the scene according to the task type of the spatial crowdsourcing, and aims at the problems of low task allocation number of a GREEDY method, a g-D & C method and an ADAPTIVE method in the existing multi-skill spatial crowdsourcing task allocation method and no consideration of platform benefits on the basis of the multi-skill spatial crowdsourcing task allocation model. The main points and contents are as follows:
(1) the flow chart of the TPC method for calculating task processing priority is shown in FIG. 2. Given a set of tasks T, the order of processing may affect the final task assignment total score as they are processed, as improper processing order may result in certain tasks not finding a sufficient number of matching workers to complete, or not finding available task locations, and the task assignment will not succeed.
The number of workers available for the task and the number of task sites need to be considered when solving the task processing priority. After available workers are selected according to the level constraint of a task initiator on the workers, the skill constraint of the task, the starting time and the ending time constraint, task places which can be reached by all the workers are selected according to the distance constraint of the workers, and finally the task places which do not accord with the distance constraint of the task initiator are removed according to the distance constraint of the task initiator. Since the objective of the assignment of the mission according to the present invention is to maximize the total fraction of the mission, the higher the level of the sponsor of the mission, the more funds are invested this time or historically. Therefore, the budget of the task and the level of the task initiator also influence the processing priority of the task, and the level of the task initiator and the budget of the task are positively correlated with the processing priority of the task. The task initiator is a user crowdsourcing the task platform, and different users correspondingly have different user grades. The level of the task initiator has an impact on the calculation of the processing priority of subsequent tasks. Before the task is distributed, the grade g p' of the task distributor in the distribution is calculated, and then the grade of the task initiator is used as one of the calculation factors of the task processing priority. In the present invention, the level calculation of the task initiator is as shown in formula (1).
Gp is the grade of the task initiator before calculation, values of gp are all between 0 and 10, bt is the task budget issued by the task initiator at this time, btmax is a maximum budget value, and btmin is a minimum budget value. The division in this equation is to make the correlation factors in the calculation in the same order of magnitude.
The processing priority of a task is determined by three parts: and calculating the task initiator level gp' for issuing the task according to the formula (1), wherein the number Nw of workers and the number Nd of task sites can be selected according to the constraint. The task processing priority Lt is defined as shown in equation (2).
And (3) calculating the priority of the tasks to be distributed according to the formula (2) to select a qualified worker set and a qualified task site.
(2) The WFC method for selecting a worker is shown in FIG. 3. Given a set of multi-skill space crowd-sourced tasks T, a certain worker selection order is also required in order to more economically select workers that meet the task requirements when selecting workers that match the task. This subsection designs a worker priority calculation mode, which can decide to select the worker priority according to the worker score, and the higher the score is, the task is assigned preferentially.
According to the matching constraint conditions, the intersection of the skill Xi owned by the worker and the skill Yj required by the task is not empty, namely, the skill coverage rate is one of the factors to be considered for calculating the score of the worker. Since the location of the task site is ultimately determined by the location of the task originator and the worker, the distance of the worker from the task originator is also one of the considerations for worker score calculation. Each worker has a rating, the lower the rating of the worker, the lower the reward required after meeting the rating requirements for the task. On the premise that the platform yield is guaranteed, workers of lower levels meeting the task level requirements are preferably selected. The calculation formula of the worker score is shown in formula (3).
Wherein gw is the grade of the worker, and K is the skill coverage of the worker, that is, the proportional solving mode of the number of the intersection of the skill owned by the worker and the skill required by the task to the number of the skill required by the task is shown in formula (4).
Where xi ∈ Ψ and Yj ∈ Ψ, Ψ is the set of skills required for the task. The distance between the position (xw, yw) of the worker and the position (xp, yp) of the task initiator is as shown in formula (5), and for simplicity, the distance between the objects is represented by the euclidean distance.
(3) The MLS method for task site selection is shown in FIG. 4 as a flowchart. When a crowdsourcing task site is selected, firstly, crowdsourcing task sites which do not meet the distance constraint are screened according to the distance constraint of crowdsourcing workers and crowdsourcing task initiators. Second, the rank of the task site is also a consideration. After the distance and level constraints are met, and platform revenue is considered, selecting a task place with a lower level reduces the cost of the task place after the constraints of the task initiator on the crowd-sourced task places are met. If a task in the set of tasks that the task is ongoing with the task location satisfies the time constraint, the task location can be taken as an alternative task location. Next, the center of gravity point (x0, y0) of all the selected worker position coordinates is calculated using the principle of the center of gravity method. The higher the worker grade, the higher the journey subsidy, so the task location must be determined in consideration of the worker grade. The formula for solving the barycentric coordinates is shown in formula (6).
After the coordinates of the gravity point are obtained, the distance between the alternative task place and the gravity point is calculated, the product of the distance and the grade of the task place is calculated, as shown in formula (7), and the results are sorted from small to large. And selecting the task place with the minimum value, namely the place where the task is to be executed. The task place selection mode can ensure that all task participants have the minimum distance to the task place, and when the distance difference is small, the task place with a smaller grade is selected, so that the task place cost is smaller, and the benefit of the platform is improved.
Wherein xdi is the x-coordinate of the task location, the latitude corresponding to the position on the map, ydi is the y-coordinate of the task location, the longitude corresponding to the position on the map, gd is the rank of the task location, and when R takes the minimum value, (xd, yd) is the coordinate of the task location.
In the whole process of the multi-skill space crowdsourcing task allocation method, whether tasks which are not allocated exist in a task queue to be allocated or not is judged at first, if yes, an MS-SCTA method is called to process the tasks at first, and the tasks which are successfully processed are deleted from the task queue to be allocated. And after the tasks in the task queue to be distributed are processed, judging whether the task initiator set is empty, if the task initiator set is not empty, further calculating the task priority issued by the task initiator, and if not, ending the method. And circularly processing each task, if enough workers can be found, selecting a next task place, and otherwise, adding the task into a task queue to be distributed. When the task location is selected, if the available task location can be found, the allocation is successfully completed, and the method is ended. Otherwise, adding the task into the task queue to be distributed, and ending the method.
The invention has the technical effects that:
in order to evaluate the MS-SCTA method provided by the invention, three parameters of movable distance, task budget and route subsidy unit price of crowdsourcing participants are respectively changed in four aspects of task allocation total score, platform income, task allocation total number and running time, and are compared with a GREEDY method, a g-D & C method and an ADAPTIVE method. Wherein the total score of the task allocation is the total income of the task allocation, the platform income is obtained by subtracting the distance cost of the worker and the salary of the worker from the total income of the task allocation, and the invention represents the salary of the worker by the grade of the worker. Since the invention has more level constraints and capacity constraints than the GREEDY method, the g-D & C method and the ADAPTIVE method in terms of constraint conditions, two tests are required when the three methods are compared. Firstly, the two constraints are removed, the site of a task initiator is taken as the site of a task, the method under the condition is marked as MS-SCTA', under the scene that the related factors and the constraint conditions are completely the same, an experiment is carried out by using the same data set, then an experiment is carried out again completely according to the constraints and the method steps of the invention, and the results are compared and shown in a graphical mode to compare the performances of the methods.
Influence of the scope of the task budget [ B-, B + ]. The movable distance value is set between [0.2 and 0.3] and the unit price is set between [2 and 3], the three methods of the invention and the comparison are obtained by changing the budget [ B-, B + ] range of the tasks, and the total score of the task allocation, the platform profit, the number of the task allocation and the method running time in different budget ranges. FIG. 5 shows experimental results for different ranges [ B-, B + ] of the task budget Bj from [1, 5] to [20, 25 ]. In fig. 5(a), when the value range of the task budget becomes large, the number of task allocations becomes large because the unit price does not change, and the allocation scores of all methods increase. The MS-SCTA method and the MS-SCTA' method without some constraints of the invention both obtain higher scores than the three methods compared, wherein the total score of the task distribution obtained by the MS-SCTA method is slightly lower than that obtained by the MS-SCTA method in the initial stage due to the addition of some constraints of the MS-SCTA method, and the total score of the task distribution obtained by the MS-SCTA method is the largest when the budget range is more than 10 and 15. In fig. 5(b), as the budget increases, the number of optional workers per task increases, so the task allocation number increases, and because the priorities of the tasks and the workers are considered by the invention, the task allocation number obtained by the method of the invention is also more than that obtained by the three methods for comparison under the same condition. In fig. 5(c), the more the number of tasks, the more the total profit obtained, so the higher the total score of the task assignment, the more the invention considers the minimized distance spending in the task location selection, and the more the platform profit. In FIG. 5(d), the run time also increases as the budget increases, as the number of workers available for the task becomes greater.
Influence of the monovalent [ C-, C + ] range. In the invention, unit price is the subsidy of the distance of a worker, and the higher the grade of the worker is, the higher the subsidy is. FIG. 6 shows the effect of unit price range [ C-, C + ] on total fraction of assignments, number of assignments, platform revenue, and runtime. The task budget is set to be in the range of [5, 10], the movable distance is set to be in the range of [0.2, 0.3], and the unit price is increased from [1, 2] to [4, 5 ]. In fig. 6(a), as the price per unit trend increases, the scores of all methods decrease. The reason is that, as the unit price range C-, C + ] increases, the budget and the movable distance do not change, requiring more wages for the workers, including the workers' transportation expenses, to be paid, thereby reducing the flexible budget per task. As the range of the path cost unit price C, C + increases, the number of effective workers and task pairs decreases as the flexible budget decreases, and the number of task assignments also decreases in fig. 6 (b). Since the budget is not changed and the unit price is increased, the corresponding platform profit in fig. 6(c) is gradually decreased as the trend of the total score of the task allocation is the same. As the number of task assignments decreases, the runtime of all of the methods in FIG. 6(d) also decreases.
Influence of the movable distance range [ d-, d + ]. FIG. 4 shows the effect of the worker's maximum travel distance range [ d-, d + ] on the total fraction of assignments, the number of assignments, platform revenue, and runtime. The task budget is set to be in the range of [5, 10], the unit price is set to be in the range of [2, 3], and the movable distance is increased from the range of [0.1, 0.2] to [0.4, 0.5 ]. In fig. 7(a), as the movable distance increases, the available workers increase, and the total score of the assignment of tasks increases for all methods. However, when the constraint on distance is relaxed, constraints from other parameters prevent the fractional growth rate. For run time. In fig. 7(b), although the movable distance becomes large, the budget does not change, and the number of task allocations increases relatively little after [0.2, 0.3 ]. In fig. 7(c), since the movable distance becomes large, the distance spent by the worker is also much more than before, so that the platform profit is somewhat reduced. In fig. 7(d), an increase in the movable distance increases the number of available workers, so the method operation time increases.
According to the experimental result, by changing three parameter ranges of budget, unit price and movable distance, the method provided by the invention has better performance than the three comparative methods in the aspects of total task allocation fraction, task allocation fraction and platform profit on the whole, and the comprehensive performance is stable. In the aspect of running time, although the method is slower than Greedy, but faster than ADAPTIVE and g-D & C, the method can meet the requirement in the aspect of running time because the method is a static off-line scene and has low requirement on the time complexity of the method.
The MS-SCTA method is implemented by the following steps and is visually represented by the flow chart of fig. 1, wherein the flow of the TPC method is shown in fig. 2, the flow of the WFC method is shown in fig. 3, and the flow of the MLS method is shown in fig. 4:
(1) and (4) judging whether the task to be distributed is in the task queue to be distributed or not, if so, processing the task in the task queue to be distributed, and executing the step (3), otherwise, executing the next step.
(2) Calling a TPC method to calculate the priority of the task: firstly, calculating the level of a task initiator by adopting an equation (1), then calculating the number of available workers and the number of available task places of the task according to constraint conditions, calculating the priority of the task by adopting an equation (2), and executing the next step.
(3) Calling the WFC method to select workers for the task: firstly, selecting a worker set meeting constraint conditions according to constraints, calculating scores of workers by adopting a formula (3), sequencing the workers according to the scores, and selecting the workers according to task skill requirements in the worker sequence until the task skill requirements are met. And if not, adding the task into a task queue to be distributed, and executing the next step.
(4) Calling an MLS method to select a task site: if the task is the task in the task queue to be allocated, the step (2) is executed to select available workers for the task, the task places which can be reached by the workers and can not be reached by the task initiator are selected, the formula (6) is adopted to calculate the gravity center coordinates of the alternative workers and the task initiator, and then the task place closest to the gravity center is calculated according to the formula (7). And if the tasks which cannot be successfully distributed exist, adding the tasks into a task queue to be distributed, and waiting for the next time stamp processing.
The TPC method is implemented by the following steps and is visually represented by the flowchart of fig. 2:
(1) and judging whether the task initiator set is empty or not, if so, ending the method, and otherwise, executing the next step.
(2) And (2) calculating the grade of a task initiator by adopting an equation (1), calculating a set of workers available for the task and a set of places where the workers can reach the task according to the constraint conditions of the task, and executing the next step.
(3) And removing the task sites which are inaccessible to the task initiator in the task site set, calculating the task priority by adopting an equation (2), and executing the next step.
(4) And (3) judging whether all tasks issued by the task initiator are processed, if so, executing the next step, and if not, executing the step (1).
(5) And sequencing the tasks according to the priority, outputting a priority queue and finishing the method.
The WFC method is implemented by the following steps and is intuitively represented by the flowchart of fig. 3:
(1) and (4) judging whether the set of workers available for the task is empty, if so, executing the next step, and otherwise, executing the step (3).
(2) And selecting workers meeting the task constraint condition for the task, and executing the next step.
(3) And (4) calculating the scores of the workers in the task available worker set by adopting the formula (3) and executing the next step.
(4) And (4) judging whether the scores of all the workers are calculated, if so, executing the next step, and if not, executing the step (3).
(5) And sequencing the workers according to the scores, and executing the next step.
(6) And selecting workers in sequence, executing the next step if the task skill coverage is met, otherwise, continuing to execute the step if the workers are still available, adding the task into a task queue to be distributed if no optional worker exists, and ending the method.
The MLS method is implemented by the following steps and is visually represented by the flowchart of fig. 4:
(1) and (4) judging whether the task place set is empty, if so, executing the next step, and otherwise, executing the step (3).
(2) And calculating a set of task places which can be reached by the workers in the set of available workers for the task, and removing the task places which can not be reached by the task initiator.
(3) And (5) calculating and calculating barycentric coordinates of the alternative workers and the task initiator by adopting the formula (6).
(4) The task point closest to the center of gravity is calculated according to equation (7), the task point is output, and the method ends.
Claims (1)
1. A method for distributing space crowdsourcing tasks is characterized by comprising the following steps:
step one, judging whether a task to be distributed exists in a task queue to be distributed, if so, processing the task in the task queue to be distributed, and if not, executing the step three, otherwise, executing the next step;
step two, calling a TPC method to calculate the priority of the task: firstly, calculating the level of a task initiator, then calculating the number of available workers and the number of available task sites of the task according to constraint conditions, calculating the priority of the task, and executing the next step;
step three, calling a WFC method to select workers for the task: firstly, selecting a worker set meeting constraint conditions according to constraints, calculating scores of workers, sequencing the workers according to the scores, and selecting the workers according to task skill requirements in the worker sequence until the task skill requirements are met; if not, adding the task into a task queue to be distributed, and executing the next step;
step four, calling an MLS method to select a task site: if the task is a task in the task queue to be distributed, firstly, executing the second step to select available workers for the task, selecting task places where the workers can reach and the task initiator cannot reach, calculating the gravity center coordinates of the alternative workers and the task initiator, and then calculating the task place closest to the gravity center; if the tasks which can not be successfully distributed exist, adding the tasks into a task queue to be distributed, and waiting for the next time stamp processing;
the TPC method described in the second step includes the steps of:
step one, judging whether a task initiator set is empty, if so, ending the method, otherwise, executing the next step;
step two, calculating the grade of a task initiator, calculating a task available worker set and a worker reachable task place set according to the constraint conditions of the task, and executing the next step;
removing task sites which are inaccessible to the task initiator in the task site set, calculating task priority, and executing the next step;
step four, judging whether all tasks issued by the task initiator are processed, if so, executing the next step, otherwise, executing the step one;
fifthly, sorting the tasks according to the priority, outputting a priority queue and finishing the method;
the WFC method described in step three comprises the steps of:
step one, judging whether a worker set available for a task is empty, if so, executing the next step, otherwise, executing the step three;
selecting workers meeting the task constraint conditions for the task, and executing the next step;
calculating the scores of workers in the task available worker set, and executing the next step;
step four, judging whether the scores of all the workers are calculated, if so, executing the next step, otherwise, executing the step three;
fifthly, sorting workers according to scores, and executing the next step;
step six, selecting workers in sequence, if the task skill coverage is met, executing the next step, otherwise, if available workers still exist, continuing to execute the step, if no optional workers exist, adding the task into a task queue to be distributed, and ending the method;
the MLS method described in step four comprises the steps of:
step one, judging whether the task place set is empty, if so, executing the next step, otherwise, executing the step three;
step two, calculating a set of task places where the workers can reach in the set of available workers for the task, and removing the task places where the task initiator cannot reach;
step three, calculating barycentric coordinates of alternative workers and a task initiator;
and fourthly, calculating a task place closest to the gravity center, outputting the task place and finishing the method.
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| CN109544015B (en) * | 2018-11-26 | 2023-07-25 | 平安科技（深圳）有限公司 | Task allocation method based on data processing and related equipment |
| CN109600709B (en) * | 2018-11-27 | 2021-01-26 | 南方科技大学 | Space crowdsourcing task allocation method and system |
| CN110196763A (en) * | 2019-05-09 | 2019-09-03 | 中国科学技术大学苏州研究院 | A kind of efficient multi-task planning method of time domain continuous type space crowdsourcing |
| CN112016725B (en) * | 2019-05-28 | 2023-11-07 | 北京京东振世信息技术有限公司 | Task processing method, device, equipment and storage medium |
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| CN113627765B (en) * | 2021-08-01 | 2024-01-05 | 湖南大学 | Distributed space crowdsourcing task distribution method and system based on user satisfaction |
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