CN116843121A - Mobile crowd sensing task allocation method and management system based on level matching degree - Google Patents

Mobile crowd sensing task allocation method and management system based on level matching degree Download PDF

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CN116843121A
CN116843121A CN202310631297.0A CN202310631297A CN116843121A CN 116843121 A CN116843121 A CN 116843121A CN 202310631297 A CN202310631297 A CN 202310631297A CN 116843121 A CN116843121 A CN 116843121A
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worker
tasks
workers
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于彦涛
何小雪
冯祉睿
凌俊峰
陈世勇
吴玉成
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Chongqing University
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Abstract

The invention relates to the technical field of mobile crowd sensing, and particularly discloses a mobile crowd sensing task allocation method and a management system based on level matching, wherein the method comprises the following steps: grading all tasks to be distributed according to the difficulty, and grading all schedulable workers according to the capacity; setting rewards for different tasks to be distributed according to the difficulty level; calculating the grade matching degree of each grade of the schedulable workers and all tasks to be distributed; calculating profits of workers for completing tasks based on the level matching degree and corresponding time; calculating the utility by taking the ratio of profit to time as the utility; constructing a task allocation problem; and solving the task allocation problem to generate a task sequence, wherein the task sequence is the result that the task allocation is the path planning. The invention provides a grading matching degree based on worker and task grading, wherein the higher the grading matching degree is, the more suitable the task is allocated to the worker to finish, and the higher the reward proportion obtained by the worker to finish the task is, the more equal the reward method is.

Description

Mobile crowd sensing task allocation method and management system based on level matching degree
Technical Field
The invention relates to the technical field of mobile crowd sensing, in particular to a mobile crowd sensing task allocation method and a management system based on level matching degree.
Background
Mobile Crowd Sensing (MCS) is an innovative data collection model that utilizes "wisdom" to collect geospatial data from urban environments, typically three roles in the MCS system: the task requester issues a perception task on a cloud platform, and the platform is responsible for distributing the perception task to a proper mobile worker. The workers perform the sensory tasks assigned to them, and then upload and submit the sensory data to the task requester, the platform feeds back the results to the mobile workers and calculates the remuneration of the workers. MCS has many practical and valuable uses; such as checking traffic conditions, detecting air quality, constructing smart cities, etc.
Task allocation refers to allocating a set of tasks to a set of staff and then moving the workers to a task location to complete the tasks, which is a fundamental problem in MCS systems and applications. Task allocation may ensure that each worker gets tasks appropriate for their skills and resources, thereby maximizing their abilities and expertise. Reasonable path planning can reduce the travel distance and time waste of workers when executing tasks, and improve the efficiency of the whole workflow.
However, due to the rapid development of the mobile crowd sensing system, the number of tasks and the number of mobile workers are rapidly increasing, which aggravates the heterogeneity of tasks. Because of the variability in the ability of mobile workers, the uncertainty in the quality of completing tasks, and the propensity of mobile workers to become more aggressive, some high-ability workers may also tend to choose simple tasks because they are easier and faster to complete, rather than being willing to devote more time and effort to complete difficult tasks, resulting in a low rate of difficult task completion and wasted worker resources. It is therefore of great importance to match the mobile worker with the appropriate perception task. While more and more mobile workers are joining mobile crowd sensing systems. Most mobile workers are typically part-time employees who perform sensing tasks in excess time to earn additional rewards with limited time budget. In consideration of the time availability of the mobile workers, proper task matching and path planning methods are required to be designed, so that the time cost of the mobile workers is reduced, the task execution efficiency is improved, and the benefit of the mobile workers in the task execution process is improved.
To address situations where workers tend to accomplish simple tasks, existing studies take motivational measures, such as providing additional rewards or rewards to workers to accomplish difficult tasks, or setting up points or ranking regimes for task completion, thereby encouraging workers to actively participate in and accomplish more complex tasks. While providing rewards or rewards may motivate workers to participate in and complete difficult tasks, their motivational effects may be limited. Workers may still be inclined to select simple tasks because they are easier and faster to accomplish and are reluctant to devote more time and effort to accomplish difficult tasks, especially if the worker's own interests are maximized. Setting a task completion score or ranking regimen may result in some workers being unfavorably located in the score or ranking, thereby causing uneven and unequal situations. For example, new workers or low-capacity workers may be at a disadvantage in ranking, making it difficult to obtain more tasks or rewards, thereby exacerbating the phenomenon of inequality between workers. Providing rewards or rewards at the same time, setting up a task to complete a point or ranking regimen, etc. may increase the cost and resource investment of the system, for example, requiring higher fees to be paid to encourage workers to complete difficult tasks, or requiring additional manpower and technical support to manage the point or ranking regimen.
Disclosure of Invention
The invention provides a mobile crowd sensing task allocation method and a management system based on level matching degree, which solve the technical problems that: how to fairly and simply avoid high-capacity workers from executing tasks with low difficulty so as to improve the completion rate of difficult tasks.
In order to solve the technical problems, the invention provides a mobile crowd sensing task allocation method based on level matching degree, which comprises the following steps:
s1, grading all tasks to be distributed according to difficulty, and grading all schedulable workers according to capability;
s2, setting rewards for different tasks to be distributed according to the difficulty level;
s3, calculating the grade matching degree of each grade of the schedulable workers and all tasks to be distributed;
s4, calculating profit of each schedulable worker for completing each task to be allocated based on the level matching degree, and calculating time of each schedulable worker for completing each task to be allocated;
s5, calculating the utility of each schedulable worker for completing each task to be distributed by taking the ratio of profit to time as the utility;
s6, constructing task allocation problems as follows:
where U represents the total utility of assigning n tasks, max represents maximizing the total utility U, m represents the total number of schedulable workers,is the ith worker w i Completion of the jth task t j Utility of s i Is assigned to worker w i Is the number of tasks Tf i Is worker w i Is available for a period of time;
and S7, solving the task allocation problem, generating a task sequence, allocating corresponding tasks to corresponding workers according to the generated task sequence, and executing the tasks by the workers according to the path planning result in the allocated task sequence.
Further, in the step S3, the degree of matching of the grade of each schedulable worker with the grade of all tasks to be assigned is calculated by the following formula:
wherein match (i, j) is task t j And worker w i Rank matching degree, gw i Is worker w i Grade of gt j Is task t j Is a class of (c).
Further, in the step S4, the profit of each worker to be scheduled to complete each task to be allocated is calculated by the following formula:
wherein ,is worker w i Completion of task t j Profit of->Is worker w i Completion of task t i The platform pays the payment to the worker, < +.>Is worker w i Completion of task t j Task cost of->Is worker w i Completion of task t j Is a cost of the journey of (a).
Further, in the step S2, the rewards set for the tasks with different difficulty levels are:
R j ＝R*dif(t j )
wherein ,Rj Is task t j The consideration set, R is the maximum consideration set for the task, dif (t j ) Is task t j Normalized value after difficulty quantization；
In the step S4, worker w i Completion of task t i Consideration R paid to workers by platform i j is calculated by:
further, in said step S4, the worker w i Completion of task t j Task cost of (2)Calculated by the following formula:
wherein B is the maximum cost corresponding to the maximum consideration.
Further, in said step S4, the worker w i Completion of task t j Distance cost of (2)Calculated by the following formula:
where lambda is a road toll linear factor, lambda is related to the traffic equipment of the worker, typically set to 3 yuan/km,is worker w i Execute task t j Is a path of (c). When t j Is worker w i Is>Is worker w i From the initial position to task t j Distance of position, t j Not the first task, +.>Is worker w i Last execution task t k To task t j Distance of the location.
Further, in the step S4, the time for each worker to complete each task to be allocated is calculated by the following formula:
wherein ,is worker w i Completion of task t j Time of (2)>Is worker w i Completion of task t j Execution time of->Is worker w i Completion of task t j Is a function of the trip time of the vehicle.
Further, worker w i Completion of task t j Execution time of (a)Calculated by the following formula:
where η is the execution time factor, ab (w i ) Is worker w i Normalized values after the capability level quantization.
Further, worker w i Completion of task t j Is a journey time of (2)Calculated by the following formula:
wherein ,is worker w i Execute task t j V of (v) i Is worker w i Is provided.
The invention also provides a mobile crowd sensing task allocation method and a management system based on the level matching degree, wherein the key points are as follows: the method comprises a mobile crowd sensing platform, wherein the mobile crowd sensing platform is used for steps S1-S7 in a mobile crowd sensing task distribution method based on the level matching degree, and is also used for calculating rewards for workers according to the situation that the workers finish tasks.
According to the mobile crowd sensing task distribution method and the management system based on the level matching degree, provided by the invention, the capability difference of different workers is considered, simple tasks can be distributed to workers with lower capability, complex tasks can be distributed to workers with higher capability, the level matching degree based on classification of the workers and the tasks is provided, the tasks are classified according to the self difficulty difference and the workers are classified according to the self capability difference, the level matching degree of the tasks and the workers is calculated, the higher the level matching degree is, the more suitable the tasks are distributed to the workers to finish, the higher the compensation proportion obtained by the workers is, and the compensation method is equal. The worker is encouraged to finish the task of the corresponding grade through the grade matching degree, so that the task with low difficulty can be prevented from being executed by the high-capacity worker, the resource waste is caused, and the completion rate of the difficult task can be improved. And the measures such as providing high rewards or rewards, setting task completion points or ranking system and the like are not needed, and the addition of new cost and resource investment is avoided.
Drawings
FIG. 1 is a diagram of a mobile crowd sensing architecture in an embodiment of the invention;
FIG. 2 is a flow chart of a mobile crowd sensing task allocation method based on rank matching according to an embodiment of the present invention;
FIG. 3 is a graph comparing task completion rates of all completed tasks matched with worker grades under the two conditions of the mobile crowd sensing task allocation method based on the grade matching degree and the mobile crowd sensing task allocation method without the grade matching degree;
FIG. 4 is a graph comparing the completion rates of difficult tasks in all completed tasks in the case of the mobile crowd sensing task allocation method based on the level matching degree and the mobile crowd sensing task allocation method without the level matching degree provided by the embodiment of the invention;
FIG. 5 is a comparison chart of profits of all workers in completing tasks under two conditions of the mobile crowd-sourced task allocation method based on the level matching degree and the mobile crowd-sourced task allocation method without the level matching degree provided by the embodiment of the invention;
fig. 6 is a comparison chart of the number of tasks completed by all workers in the case of the mobile crowd sensing task allocation method based on the level matching degree and the mobile crowd sensing task allocation method without the level matching degree provided by the embodiment of the invention.
Detailed Description
The following examples are given for the purpose of illustration only and are not to be construed as limiting the invention, including the drawings for reference and description only, and are not to be construed as limiting the scope of the invention as many variations thereof are possible without departing from the spirit and scope of the invention.
A diagram of a mobile crowd sensing architecture is shown in fig. 1, including objects including task requesters, a mobile crowd sensing platform, and mobile workers. The task requester issues a perception task on a cloud platform, which is responsible for distributing the perception task to the appropriate mobile workers. The mobile workers perform the sensory tasks assigned to them, and then upload and submit the sensory data to the task requester, and the platform feeds back the results to the mobile workers and calculates the remuneration of the workers. In the whole process, the most important link of the mobile crowd sensing platform is task allocation, and aiming at the problems presented in the current background technology part, the embodiment of the invention firstly provides a mobile crowd sensing task allocation method based on the level matching degree, as shown in fig. 2, which comprises the following steps:
s1, grading all tasks to be distributed according to difficulty, and grading all schedulable workers according to capability;
s2, setting rewards for different tasks to be distributed according to the difficulty level;
s3, calculating the grade matching degree of each grade of the schedulable workers and all tasks to be distributed;
s4, calculating profit of each schedulable worker for completing each task to be allocated based on the level matching degree, and calculating time of each schedulable worker for completing each task to be allocated;
s5, calculating the utility of each schedulable worker for completing each task to be distributed by taking the ratio of profit to time as the utility;
s6, constructing task allocation problems;
and S7, solving the task allocation problem, generating a task sequence, allocating corresponding tasks to corresponding workers according to the generated task sequence, and executing the tasks by the workers according to the path planning result in the allocated task sequence.
The steps are described one by one.
And S1, grading all tasks to be distributed according to the difficulty, and grading all schedulable workers according to the capability.
The task difficulty grading and the worker capacity grading can be carried out by adopting a chi-square box grading method. In this embodiment, the optimal box number is determined to be 3, that is, the task difficulty and the worker capacity are equally divided into 3 levels, where the total level of workers and the total level of tasks are required to be equal, and the workers and the tasks of the same level are optimally adapted. Here, the 3 stages are only for illustration, and do not represent an embodiment in which this is optimal, and may be specifically set according to practical circumstances.
And S2, setting rewards for different tasks to be distributed according to the difficulty level.
The calculation formula of the task rewards set in the mobile crowd sensing platform is as follows:
R j ＝R*dif(t j )
wherein ,Rj Is the platform as task t j The set reward, R is the maximum reward for the platform, dif (t j ) Is task t j Normalized values after difficulty quantization.
S3, calculating the grade matching degree of each grade of the schedulable workers and all tasks to be distributed;
the calculation formula for the level matching degree between the worker and the task is as follows:
wherein match (i, j) is worker w i And task t j Rank matching degree, gw i Is worker w i Capability class gt j Is task t j Difficulty rating of (c). Taking the division of worker grade and task into three stages as an example, the grade matching degree is calculatedTo encourage the worker to complete the task at the corresponding level, the level matching value determines the proportion of rewards that the worker may obtain from the platform to perform the task. That is, when the worker completes the task of the same level, the level matching degree is 1, when the worker completes the task differing from the own level by one level, the level matching degree +.>When the worker completes the task of which the level is different from the own level by two levels, the level matching degree +.>
Based on the level matching degree, a reward calculation formula for the worker to finish the task platform payment is as follows:
wherein ,worker w i Completion of task t i Remuneration paid to workers by platform R j Is the platform as task t j And setting a reward. Worker w i And execute task t j The value of the rank match (i, j) determines worker w i The larger the available reward proportion and the grade matching degree value is, the worker w i Execute task t j The higher the rate of return obtained. When worker w i Completing task t of corresponding grade i At this time, the level matching degree match (i, j) =1, and the worker can take the platform as a task t j All remuneration R of the setting j The method comprises the steps of carrying out a first treatment on the surface of the When worker w i Completing task t with grade difference of one grade j At this time, the degree of rank matching +.>The worker can only get the reward R j Is->When worker w i Task t of completing grade difference two-stage j At this time, the degree of rank matching +.>The worker can only get +.>
S4, calculating profit of each schedulable worker for completing each task to be allocated based on the level matching degree, and calculating time of each schedulable worker for completing each task to be allocated;
in the MCS, profits obtained by the worker completing the task are rewards obtained by the worker completing the task minus the cost of the task and travel expenses during the execution of the task.
The task cost calculation formula for the worker to complete the task is as follows:
in the formula ,is worker w i Completion of task t i B is the maximum cost corresponding to the maximum consideration.
The travel cost calculation formula for the workers to complete the tasks is as follows:
in the formula ,is worker w i Execute task t j Lambda is a linear factor of road toll, lambda is related to the traffic equipment of the worker, and is usually set to 3 yuan/km +.>Is worker w i Execute task t j During the task allocation process to workers, when t j Is assigned to worker w i Is>Is worker w i From the initial position to task t j Distance of position, t j Assigned to a worker not being the first task, < ->Is worker w i Last execution task t k To task t j Distance of the location.
The profit calculation formula for the worker to perform the task is as follows:
in the formula ,is worker w i Completion of task t j Is a return of->Worker w i Completion of task t i The platform pays to the worker w i Is a return of->Is worker w i Completion of task t i Task cost of->Is worker w i Execute task t j Is a cost of the journey of (a).
In the MCS, the time for a worker to complete a task may include two parts: the execution time and the journey time of the worker.
The calculation formula of the execution time of the worker to complete the task is as follows:
in the formula ,is worker w i Completion of task t j Is an execution time factor, typically set to 10, ab (w i ) Is worker w i Normalized values after the capability level quantization.
The calculation formula of the journey time for the worker to complete the task is as follows:
in the formula ,is worker w i Completion of task t j Is>Is worker w i Execute task t j V of (v) i Is worker w i Is provided.
The calculation formula of the time for the worker to complete the task is as follows:
in the formula ,is worker w i Completion of task t j Time of (2)>Is worker w i Completion of task t j Execution time of->Is worker w i Completion of task t j Is a function of the trip time of the vehicle.
S5, calculating the utility of each schedulable worker for completing each task to be distributed by taking the ratio of profit to time as the utility.
The utility calculation formula for the worker to complete the task is as follows:
in the formula ,is worker w i Completion of task t j Utility of->Is worker w i Completion of task t j Is added to the payment of (a) to (b) the payment,is worker w i Completion of task t j Is a time of (a) to be used.
S6, constructing task allocation problems.
The problem of the mobile crowd sensing task allocation method based on the matching degree of the task levels of workers can be expressed as follows:
where U represents the total utility of assigning n tasks, max represents maximizing the total utility U, m represents the total number of schedulable workers,is the ith worker w i Completion of the jth task t j Utility of s i Is assigned to worker w i Is the number of tasks Tf i Is worker w i Is used for the time of availability. The resulting task allocation result is rl= { RL 1 ,RL 2 ,…,RL m}, wherein Is assigned to worker w i As a result, the worker performs the tasks in that order, and a path plan with the shortest total distance and the least time consumption can be generated according to the task execution order. Task number assigned to all workers->The total number of tasks n that cannot be exceeded. Worker w i Execution of the assigned task Total time->Cannot exceed worker w i Is available for time Tf of (a) i 。
And S7, solving the task allocation problem, generating a task sequence, and allocating the corresponding task to the corresponding worker according to the generated task sequence.
The workers sequentially complete tasks according to the path planning, the perception data is uploaded to a mobile crowd sensing platform, the platform submits the perception data to a task requester, and the consideration of the workers is calculated.
The mobile crowd sensing platform calculates rewards for workers according to task allocation results and level matching degree of the workers, and the formula is as follows:
where Reward is the consideration paid by the platform to all workers, m is the number of workers in the mobile crowd sensing system, s i Is assigned to worker w i Task number R of (2) j Is the platform as task t j And setting a reward. match (i, j) is worker w i And execute task t j Is a rank match of (3).
Fig. 3 is a graph showing the comparison of the task completion rates of all completed tasks matched with the worker's level in the case of the mobile crowd sensing task allocation method based on the level matching degree and the mobile crowd sensing task allocation method without the level matching degree in the implementation of the invention. As can be seen from fig. 3, in the method based on the level matching degree, the task completion rate corresponding to the worker capacity level in the task completed by the worker is higher, and reaches more than 90%; in the method without the grade matching degree, the task completion rate corresponding to the worker capacity grade in the task completion of the worker is low and is only about 50%. Fig. 3 shows that in the mobile crowd sensing task allocation method based on the matching degree of the task levels of the workers, the user can be effectively stimulated to finish the task of the corresponding level, so that the problem that the high-capacity user executes the task with low difficulty to cause resource waste is avoided.
Fig. 4 is a graph comparing the completion rates of difficult tasks in all completed tasks under the two conditions of the mobile crowd sensing task allocation method based on the level matching degree and the mobile crowd sensing task allocation method without the level matching degree in the implementation of the invention. As can be seen from fig. 4, the completion rate of the difficult task corresponding to the two curves increases as the user increases. In the method based on the level matching degree, the corresponding level tasks and difficulty tasks completed by the user are more than those in the method without the level matching degree. The mobile crowd sensing task allocation method based on the task level matching degree of workers can effectively improve the difficult task completion rate.
FIG. 5 is a graph comparing profits of all workers in the case of the mobile crowd-sourced task allocation method based on rank matching and the mobile crowd-sourced task allocation method without rank matching in the implementation of the present invention. As can be seen from fig. 5, the profits obtained by the users of all the curves increase as the number of users increases. Users in the rank matching degree-based method may obtain more profits than users without rank matching degree. Experimental results show that under the method with the grade matching degree, a user can obtain more profits at the same time.
Fig. 6 is a comparison chart of the number of tasks completed by all workers in the case of the mobile crowd sensing task allocation method based on the level matching degree and the mobile crowd sensing task allocation method without the level matching degree in the implementation of the present invention. As can be seen from fig. 6, the number of tasks performed by the users of all curves increases as the number of users increases. Users in the rank-matching-degree-based method can perform more tasks than users without rank matching degree. Experimental results show that under the method with the grade matching degree, a user can complete more tasks at the same time.
Based on the above method, the embodiment of the invention also provides a mobile crowd sensing task management system based on the level matching degree, which comprises a mobile crowd sensing platform, wherein the mobile crowd sensing platform is used for executing steps S1-S7 in the mobile crowd sensing task distribution method based on the level matching degree and is also used for calculating rewards for workers according to the condition that the workers finish tasks.
In summary, according to the mobile crowd sensing task allocation method and the management system based on the level matching degree provided by the embodiment of the invention, in consideration of the capability difference of different workers, simple tasks can be allocated to workers with lower capability, complex tasks can be allocated to workers with higher capability, the level matching degree based on the classification of the workers and the tasks is provided, the tasks are classified according to the self difficulty difference, the level matching degree of the tasks and the workers is calculated according to the self capability difference of the workers, the higher the level matching degree is, the more suitable the tasks are allocated to the workers for completion, the higher the proportion of rewards obtained by the workers is, and the rewarding method is equal. The worker is encouraged to finish the task of the corresponding grade through the grade matching degree, so that the task with low difficulty can be prevented from being executed by the high-capacity worker, the resource waste is caused, and the completion rate of the difficult task can be improved. And the measures such as providing high rewards or rewards, setting task completion points or ranking system and the like are not needed, and the addition of new cost and resource investment is avoided.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.

Claims (10)

1. The mobile crowd sensing task allocation method based on the level matching degree is characterized by comprising the following steps of:
s1, grading all tasks to be distributed according to difficulty, and grading all schedulable workers according to capability;
s2, setting rewards for different tasks to be distributed according to the difficulty level;
s3, calculating the grade matching degree of each grade of the schedulable workers and all tasks to be distributed;
s4, calculating profit of each schedulable worker for completing each task to be allocated based on the level matching degree, and calculating time of each schedulable worker for completing each task to be allocated;
s5, calculating the utility of each schedulable worker for completing each task to be distributed by taking the ratio of profit to time as the utility;
s6, constructing task allocation problems as follows:
P：max
where U represents the total utility of assigning n tasks, max represents maximizing the total utility U, m represents the total number of schedulable workers,is the ith worker w i Completion of the jth task t j Utility of s i Is assigned to worker w i Is the number of tasks Tf i Is worker w i Is available for a period of time;
and S7, solving the task allocation problem, generating a task sequence, allocating corresponding tasks to corresponding workers according to the generated task sequence, and executing the tasks by the workers according to the path planning result in the allocated task sequence.
2. The mobile crowd sensing task allocation method based on level matching according to claim 1, wherein in said step S3, the level matching of each worker' S level to all tasks to be allocated is calculated by:
wherein match (i, j) is task t j And worker w i Rank matching degree, gw i Is worker w i Grade of gt j Is task t j Is a class of (c).
3. The mobile crowd sensing task allocation method based on level matching according to claim 2, wherein in said step S4, profit for each worker to finish each task to be allocated is calculated by:
wherein ,is worker w i Completion of task t j Profit of->Is worker w i Completion of task t i The platform pays the payment to the worker, < +.>Is worker w i Completion of task t j Task cost of->Is worker w i Completion of task t j Is a cost of the journey of (a).
4. The mobile crowd sensing task allocation method based on level matching according to claim 3, wherein,
in the step S2, the rewards set for the tasks with different difficulty levels are:
R j ＝R*dif(t j )
wherein ,Rj Is task t j The consideration set, R is the maximum consideration set for the task, dif (t j ) Is task t j Normalized values after difficulty quantization;
in the step S4, worker w i Completion of task t i Consideration paid to workers by the platformCalculated by the following formula:
5. the mobile crowd sensing task allocation method based on level matching according to claim 4, wherein in said step S4, a worker w i Completion of task t j Task cost of (2)Calculated by the following formula:
wherein B is the maximum cost corresponding to the maximum consideration.
6. The mobile crowd sensing task allocation method based on level matching according to claim 5, wherein in said step S4, a worker w i Completion of task t j Distance cost of (2)Calculated by the following formula:
where lambda is a road toll linear factor, related to the worker's transportation means,is worker w i Execute task t j When the distance of the task is distributed to workers, when t j Is worker w i Is>Is worker w i From the initial position to task t j Distance of position, t j Not the first task, +.>Is worker w i Last execution task t k To task t j Distance of the location.
7. The mobile crowd sensing task allocation method based on level matching according to claim 4, wherein in said step S4, each time at which a worker can be scheduled to complete each task to be allocated is calculated by:
wherein ,is worker w i Completion of task t j Time of (2)>Is worker w i Completion of task t j Execution time of->Is worker w i Completion of task t j Is a function of the trip time of the vehicle.
8. The mobile crowd sensing task allocation method based on the level matching degree according to claim 7, wherein a worker w i Completion of task t j Execution time of (a)Calculated by the following formula:
where η is the execution time factor, related to worker capacity and task difficulty, ab (w i ) Is worker w i Normalized values after the capability level quantization.
9. The mobile crowd sensing task allocation method based on the level matching degree according to claim 7, wherein a worker w i Completion of task t j Is calculated by the following formula:
wherein ,is worker w i Execute task t j V of (v) i Is worker w i Is provided.
10. The mobile crowd sensing task management system based on the level matching degree is characterized in that: the method comprises a mobile crowd sensing platform, wherein the mobile crowd sensing platform is used for executing steps S1-S7 in the mobile crowd sensing task allocation method based on the level matching degree according to any one of claims 1-9, and is also used for calculating rewards for workers according to the situation that the workers finish tasks.
CN202310631297.0A 2023-05-30 2023-05-30 Mobile crowd sensing task allocation method and management system based on level matching degree Pending CN116843121A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117132171A (en) * 2023-10-26 2023-11-28 江苏工小兔信息科技有限公司 Automatic office information management system
CN117132171B (en) * 2023-10-26 2024-01-02 江苏工小兔信息科技有限公司 Automatic office information management system

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