CN108304266A - A kind of mobile multiple target intelligent perception method for allocating tasks - Google Patents
A kind of mobile multiple target intelligent perception method for allocating tasks Download PDFInfo
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Abstract
The invention discloses a kind of mobile multiple target intelligent perception method for allocating tasks, include the following steps：S1, employer issue location-based query task to task distribution system；S2, task distribution system consider maximum awareness coverage and task completion rate to establish mobile multiple target perception task distribution model under the requirement of employer, solve best worker, and query task is distributed to worker；S3, worker execute query task, and the automatic sensing in the way for going to query task position, query task result and automatic sensing data are finally returned to task distribution system after receiving query task.The method is by considering two targets of automatic sensing task and location-based query task come algorithm for design, sensing capability and passive sensing capability so that worker takes the initiative simultaneously in task, the efficiency of perception task completion is improved, and further reduces the budget needed for employer.
Description
Technical Field
The invention relates to the field of crowd sensing, in particular to a mobile multi-target crowd sensing task allocation method.
Background
Crowd sensing is an emerging problem solution for completing sensing tasks by collecting data (such as sound, position, noise and GPS) by a large number of common mobile phone users. By utilizing the acquired sensing data, researchers can realize various large-scale sensing applications, such as noise detection, parking space detection, environment detection and the like.
There are two main types of tasks that are receiving wide attention for crowd sensing. The first category emphasizes the automatic perception of a common mobile phone user when collecting data, for example, in a road traffic detection application, a mobile device automatically perceives data and records the data for subsequent processing. Another type of task is a location-based query task that requires a positive response by the worker.
Previous techniques tend to consider these two tasks separately, however they do have some relationship between them. When the user goes to complete some query tasks, the user can acquire sensing data to complete the automatic sensing task in the process of going forward. Therefore, if a dual-target task distribution system for task combination is available, the active perception capability and the passive perception capability of each worker are fully utilized, and the completion efficiency of perception tasks is improved.
Disclosure of Invention
The invention aims to provide a mobile multi-target crowd sensing task allocation method aiming at the defects of the prior art, wherein the method improves the completion efficiency of sensing tasks and further reduces the budget required by hirers by modeling a novel multi-target crowd sensing task allocation system and designing a solving method based on a greedy algorithm aiming at the model.
The purpose of the invention can be realized by the following technical scheme:
a mobile multi-target crowd sensing task allocation method comprises the following steps:
step S1, the employer issues a location-based query task to a task distribution system;
step S2, under the requirement of an employer, the task allocation system simultaneously considers the maximum perception coverage and the task completion rate to establish a mobile multi-objective perception task allocation model, solves and selects the best worker, and allocates the query task to the worker;
and step S3, after receiving the query task, the worker assigned with the query task executes the query task, automatically senses the query task in the way of the position of the query task, and finally returns the query task result and the automatic sensing data to the task assignment system.
Further, the mobile multi-objective perception task allocation model in step S2 includes a location-based query task object allocation model and an automatic perception task object allocation model.
Further, the objective of the location-based query task objective distribution model is to maximize the success rate of completing the query task, and the objective function max F (S) is expressed as follows:
wherein, tiRepresents the ith query task, T represents all the query task sets, m represents the size of the query task set, WiRepresenting the accepting of a query task tiAll worker sets of wjRepresenting a set of workers WiOne of the workers in the group of workers,indicates the completion success rate, p, of the ith query taskjIndicating the historical task completion success rate of the jth worker;
the goal of the automatic perception task target distribution model is to maximize the perception coverage, and the objective function max G (S) is as follows:
wherein,representing the set of workers employed,represents each worker wj∈WiA predicted path;
considering the two task objective allocation models described above, and requiring that the total cost of the constraint workers is not greater than the budget given by the employer, a mobile multi-objective aware task allocation model is established as follows:
where W represents the set of all workers, S represents the set of workers hired, cjIndicating the cost of the hired worker to complete the task and B indicating the budget given by the hirer.
According to the established moving multi-target perception task allocation Model (MBC), the model can be proved to be an NP difficult problem, and the model is proved as follows:
the submodel maximization problem under the cardinality constraint (SMCC) is an NP-hard problem that is described as follows: given a set U ═ U1，u2，...，u|U|A monotonic sub-mold function f defined on U, and a base value K, the objective of the problem is to maximize f (U'), whereAnd | U' | ≦ K, we demonstrate the rationale by stating that the MBC problem is an example of the SMCC problem. Assuming there is only one location-based awareness task, all workers that can accept the task pass through the target location of the task. Assuming that the reputation value and the quote are the same for each worker, the problem amounts to maximizing the union of the perceived coverage of the selected workers, subject to the constraints of the task costs. Namely, it isWe can prove that G (S) is a monotonic sub-model function for arbitraryAnd W ∈ W \ S2Is provided withThus:
this inequality is derived from the nature of the set operation, we can find S (C)2∪{a})-S(C2) Is not less than 0, which proves that S (C) is a monotone sub-modulus function, so that the MBC problem can be explained as the SMCC problemAs an example of the problem, the MBC problem is an NP-hard problem, which is proved to be complete. Further, the design process of the model solving method MBC-Greedy algorithm is as follows:
first, the following properties are provided with respect to the query task completion probability:
for each query task tiTask completion probability functionNon-subtractive, the following are demonstrated:
based on the above property, it is derived that the average task completion probability function F (S) is non-decreasing for the set of workers S, and it can then be derived that the perceived coverage function G (S) is non-decreasing for the set of workers S.
Further, a solving method based on a greedy algorithm, namely, an MBC-greedy algorithm, is adopted for solving the mobile multi-target perception task allocation model, and the specific process is as follows:
step 110, initialize the set of workers employed S, accept the query task tiAll worker set W ofiFor an empty set, the cost C required for the hired worker to complete the task is 0;
step 120, combine all valid task-worker pairs (t)i，wj) Assigning a value to M;
step 130, if M is not empty, executing step 140, otherwise ending the algorithm;
at step 140, eliminating workers whose cost is too high to complete the task can result in exceeding the given budget of the employer;
step 150, for each task-worker pairing that remains valid (t)i，wj) Respectively calculate the completion of the query taskWeighted increments of probabilistic and perceptual coverage
Wherein
Step 160, eliminating the task-worker pairs at the disadvantage, and pairing (t)i，wj) Is superior to (t'i，w′j) Or pair (t'i，w′j) Is inferior to pairing (t)i，wj) Means thatAt the same time haveOr isAt the same time have
Step 170, sorting the remaining task-worker pairs in a descending order according to the quantity superior to other task-worker pairs;
step 180, select the top ranked pair (t)i，wj) As a result of one iteration, and all worker-containing w are removed before the next iterationjPair of { (t)i，wj)|tiE.t, then returning to step 130, and looping the iteration until the optimal number of workers selected to solve the employer's requirement is reached.
Compared with the prior art, the invention has the following advantages and beneficial effects:
compared with the traditional single-target task matching algorithm designed aiming at a single type of perception task, the moving multi-target crowd sensing task allocation method comprehensively considers two targets of an automatic perception task and a location-based query task to design the algorithm, so that a worker can simultaneously exert active perception capability and passive perception capability in the task, the efficiency of perception task completion is improved, and the budget required by a hirer is further reduced.
Drawings
Fig. 1 is a flowchart of a mobile multi-target crowd sensing task allocation method according to an embodiment of the present invention.
FIG. 2 is a schematic diagram illustrating a solution of the MBC-greedy algorithm and the C-greedy and Q-greedy comparison algorithms under the condition that the total budget and the total number of query tasks are fixed according to the embodiment of the present invention.
FIG. 3 is a comparison graph of the MBC-greedy algorithm and the C-greedy and Q-greedy comparison algorithms for perceiving coverage changes as the number of query tasks increases according to the embodiment of the present invention.
FIG. 4 is a comparison graph of the MBC-greedy algorithm and the C-greedy and Q-greedy comparison algorithms according to the embodiment of the invention, wherein the success rate of the completion of the query task changes as the number of the query tasks increases.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example (b):
the embodiment provides a mobile multi-target crowd sensing task allocation method, and a flow chart of the method is shown in fig. 1, and the method comprises the following steps:
step S1, the employer issues a location-based query task to a task distribution system;
step S2, under the requirement of an employer, the task allocation system simultaneously considers the maximum perception coverage and the task completion rate to establish a mobile multi-objective perception task allocation model, solves and selects the best worker, and allocates the query task to the worker;
and step S3, after receiving the query task, the worker assigned with the query task executes the query task, automatically senses the query task in the way of the position of the query task, and finally returns the query task result and the automatic sensing data to the task assignment system.
Taking noise monitoring tasks and completion of query tasks of specified positions in various areas of a city as an example, hiring personnel issue the query tasks to a task distribution system, and the system queries the current position of the worker and the moving path in a period of time in the future; and the task distribution system judges whether the target position of the query task is on the advancing path of the worker, so that the worker capable of receiving the task is screened out. Then, based on the idea of a greedy algorithm, a certain number of workers are selected in an iterative manner to complete the query task, the workers with excessive working cost and beyond task budget are eliminated in each iteration, and then the best workers are selected according to the increment of the task completion probability and the increment of the area of the noise perception range after the user is added. The probability of the task completion of the worker is calculated according to the historical task completion condition of the worker, and the increment of the task completion probability refers to a part which improves the task completion probability after the user is added. The noise perception range is a union set of the areas of all circles with the radius r and each point on the moving track of the user as the circle center. The noise perception range increment refers to the area size which enlarges the noise perception range of the whole system after the user is added.
The task allocation method aims to optimize a position-based query task objective and a noise perception task objective. This can be reflected by an objective function. The task based on the position aims to ensure the high-quality completion of the task as much as possible, and the task based on the position needs positive response of a worker, and the active perception capability of the worker is required; the goal of the noise perception task is to cover as much of the entire urban area as possible, requiring passive perception capabilities of the worker.
The modeling method for the whole multi-target perception task allocation model in the embodiment comprises the following steps: firstly, establishing a position-based query task target distribution model, wherein the target is to maximize the success rate of the completion of a query task, and an objective function max F (S) is as follows:
wherein, tiRepresents the ith query task, T represents all the query task sets, m represents the size of the query task set, WiRepresenting the accepting of a query task tiAll worker sets of wjRepresenting a set of workers WiOne of the workers in the group of workers,indicates the completion success rate, p, of the ith query taskjIndicating the historical task completion success rate of the jth worker;
then, a noise automatic perception task target distribution model is established, the goal is to maximize the perception coverage, and the target function max G (S) is as follows:
wherein,Wirepresenting the set of workers employed,represents each worker wj∈WiA predicted path;
considering the two task objective allocation models described above, and requiring that the total cost of the constraint workers is not greater than the budget given by the employer, a mobile multi-objective aware task allocation model is established as follows:
where W represents the set of all workers, S represents the set of workers hired, cjIndicating the cost of the hired worker to complete the task and B indicating the budget given by the hirer.
The established model solution is an NP-hard problem, no polynomial time complexity algorithm can be used for solving to obtain an optimal solution at present, and a solving method based on a greedy algorithm, namely an MBC-greedy algorithm, is adopted for solving the mobile multi-target perception task allocation model in the embodiment, so that an approximate solution can be effectively solved. The specific process is as follows:
step 110, initialize the set of workers employed S, accept the query task tiAll worker set W ofiFor an empty set, the cost C required for the hired worker to complete the task is 0;
step 120, combine all valid task-worker pairs (t)i，wj) Assigning a value to M;
step 130, if M is not empty, executing step 140, otherwise ending the algorithm;
at step 140, eliminating workers whose cost is too high to complete the task can result in exceeding the given budget of the employer;
step 150, for the restEach valid task-worker pair (t)i，wj) Respectively calculating the weighted increment of the completion probability and the perception coverage range of the query task
Wherein
Step 160, eliminating the task-worker pairs at the disadvantage, and pairing (t)i，wj) Is superior to (t'i，w′j) Or pair (t'i，w′j) Is inferior to pairing (t)i，wj) Means thatAt the same time haveOr isAt the same time have
Step 170, sorting the remaining task-worker pairs in a descending order according to the quantity superior to other task-worker pairs;
step 180, select the top ranked pair (t)i，wj) As a result of one iteration, and all worker-containing w are removed before the next iterationjPair of { (t)i，wj)|tiE.t, then returning to step 130, and looping the iteration until the optimal number of workers selected to solve the employer's requirement is reached.
As shown in fig. 2, the MBC-Greedy algorithm is an algorithm representing the design of the present embodiment, and in order to verify the performance of the present algorithm, the present embodiment is described by comparing with the C-Greedy algorithm and the Q-Greedy algorithm. The C-Greedy algorithm, which also uses a weighted Greedy strategy, attempts to maximize the noise perception coverage size under the budget constraint, each time selecting the worker that maximizes the noise perception coverage weighted increment; the Q-Greedy algorithm is to maximize the probability of completion of a location-based query task under budget constraints, and after a worker is selected, calculate the increment of new worker coverage to update the total noise-aware coverage.
Fig. 2 shows the solutions solved by the three algorithms in the simulation task allocation system, and from the perspective of multi-objective optimization, the solutions obtained by the three algorithms are all located at the non-dominant front edge, but it can be seen that the algorithm designed in the embodiment performs well at both objectives, which is more suitable for the practical application. The total number m of the query tasks adopted in the simulation task allocation system is 200, and the total budget B is 400.
Fig. 3 shows that as the number of query tasks increases, the noise perception coverage of all algorithms decreases, which is unavoidable under certain budget conditions, but it can be seen that MBC-Greedy has slightly less coverage (less than 5%) than C-Greedy and is larger (more than 15%) than Q-Greedy.
FIG. 4 illustrates that as the number of query tasks increases, the query task completion probability also decreases, but it can be seen that the task completion probability MBC-Greedy is slightly less (less than 4%) than Q-Greedy and is greater (from 15% to 38%) than C-Greedy.
The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution of the present invention and the inventive concept within the scope of the present invention, which is disclosed by the present invention, and the equivalent or change thereof belongs to the protection scope of the present invention.
Claims (4)
1. A mobile multi-target crowd sensing task allocation method is characterized by comprising the following steps:
step S1, the employer issues a location-based query task to a task distribution system;
step S2, under the requirement of an employer, the task allocation system simultaneously considers the maximum perception coverage and the task completion rate to establish a mobile multi-objective perception task allocation model, solves and selects the best worker, and allocates the query task to the worker;
and step S3, after receiving the query task, the worker assigned with the query task executes the query task, automatically senses the query task in the way of the position of the query task, and finally returns the query task result and the automatic sensing data to the task assignment system.
2. The method as claimed in claim 1, wherein the mobile multi-objective crowd sensing task assignment model in step S2 includes a location-based query task object assignment model and an auto-sensing task object assignment model.
3. The method as claimed in claim 2, wherein the objective of the location-based query task objective assignment model is to maximize the success rate of the query task, and the objective function max F (S) is as follows:
wherein, tiRepresents the ith query task, T represents all the query task sets, m represents the size of the query task set, WiRepresenting the accepting of a query task tiAll worker sets of wjRepresenting a set of workers WiOne of the workers in the group of workers,indicating the completion success rate of the ith query task,jindicating the historical task completion success rate of the jth worker;
the goal of the automatic perception task target distribution model is to maximize the perception coverage, and the objective function max G (S) is as follows:
wherein,representing the set of workers employed,represents each worker wj∈WiA predicted path;
considering the two task objective allocation models described above, and requiring that the total cost of the constraint workers is not greater than the budget given by the employer, a mobile multi-objective aware task allocation model is established as follows:
where W represents the set of all workers, S represents the set of workers hired, cjIndicating the cost of the hired worker to complete the task and B indicating the budget given by the hirer.
4. The method for distributing the mobile multi-target crowd sensing task according to claim 3, wherein solving the mobile multi-target sensing task distribution model adopts a solving method based on a greedy algorithm, and the concrete process is as follows:
step 110, initialize the set of workers employed S, accept the query task tiAll worker set W ofiFor an empty set, the cost C required for the hired worker to complete the task is 0;
step 120, combine all valid task-worker pairs (t)i,wj) Assigning a value to M;
step 130, if M is not empty, executing step 140, otherwise ending the algorithm;
at step 140, eliminating workers whose cost is too high to complete the task can result in exceeding the given budget of the employer;
step 150, for each task-worker pairing that remains valid (t)i,wj) Respectively calculating the weighted increment of the completion probability and the perception coverage range of the query task
Wherein
Step 160, eliminating the task-worker pairs at the disadvantage, and pairing (t)i,wj) Is superior to (t)i′,wj') or pairings (t)i′,wj') is inferior to the pairing (t)i,wj) Means thatAt the same time haveOr isAt the same time have
Step 170, sorting the remaining task-worker pairs in a descending order according to the quantity superior to other task-worker pairs;
step 180, select the top ranked pair (t)i,wj) As a result of one iteration, and all worker-containing w are removed before the next iterationjPair of { (t)i,wj)|tiE.t, then returning to step 130, and looping the iteration until the optimal number of workers selected to solve the employer's requirement is reached.
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| CN110110962A (en) * | 2019-04-02 | 2019-08-09 | 华南理工大学 | A kind of task gunz executes the preferred method of team |
| CN111797331A (en) * | 2020-06-09 | 2020-10-20 | 安徽师范大学 | Multi-target multi-constraint route recommendation method based on crowd sensing |
| CN112306654A (en) * | 2020-10-24 | 2021-02-02 | 西北工业大学 | Man-machine cooperation task allocation method facing mobile crowd sensing |
| CN113341905A (en) * | 2021-08-09 | 2021-09-03 | 山东华力机电有限公司 | Multi-AGV (automatic guided vehicle) collaborative planning method and system based on artificial intelligence |
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| CN109347905A (en) * | 2018-08-30 | 2019-02-15 | 天津工业大学 | A kind of space tasks distribution mechanism in mobile intelligent perception |
| CN109634725A (en) * | 2018-12-11 | 2019-04-16 | 苏州大学 | A kind of distributing method and device of intelligent perception task |
| CN109634725B (en) * | 2018-12-11 | 2023-08-15 | 苏州大学 | Method and device for dispatching crowd sensing task |
| CN110110962A (en) * | 2019-04-02 | 2019-08-09 | 华南理工大学 | A kind of task gunz executes the preferred method of team |
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| CN111797331B (en) * | 2020-06-09 | 2023-08-04 | 安徽师范大学 | Multi-target multi-constraint route recommendation method based on crowd sensing |
| CN112306654A (en) * | 2020-10-24 | 2021-02-02 | 西北工业大学 | Man-machine cooperation task allocation method facing mobile crowd sensing |
| CN112306654B (en) * | 2020-10-24 | 2022-09-13 | 西北工业大学 | Man-machine cooperation task allocation method facing mobile crowd sensing |
| CN113341905A (en) * | 2021-08-09 | 2021-09-03 | 山东华力机电有限公司 | Multi-AGV (automatic guided vehicle) collaborative planning method and system based on artificial intelligence |
| CN116541148A (en) * | 2023-05-08 | 2023-08-04 | 中国矿业大学 | Multi-task dynamic multi-target evolutionary allocation method for crowd sensing |
| CN116541148B (en) * | 2023-05-08 | 2024-10-18 | 中国矿业大学 | Multi-task dynamic multi-target evolutionary allocation method for crowd sensing |
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