A matching method that considers both task and participant preferences

Abstract

The present invention belongs to the field of mobile crowd intelligence perception technology, and in particular, is a matching method that takes into account the preferences of both tasks and participants, including: formulating a task and participant matching willingness rule; designing a matching willingness calculation method based on the mutual preferences of tasks and participants; proposing a task reward calculation method related to matching willingness; and constructing a task allocation model with matching willingness rule and time as constraints and platform profit as the optimization goal. The present invention designs a task allocation mechanism based on the bilateral preferences of tasks and participants to regulate the task allocation process of mobile crowd intelligence perception. Specifically, a matching willingness rule is designed based on the preferences of tasks and participants, so that the mobile crowd intelligence perception platform allocates tasks to participants with higher matching suitability, improves the platform's emphasis on task requirements and participant preferences, and thus ensures the stability of task allocation and the profits obtained by the platform.

Description

Technical Field

[0001] The present invention relates to the field of mobile crowd intelligence sensing technology, and in particular to a matching method that takes into account the preferences of both tasks and participants. Background Art

[0002] At present, some technical inventions in the field of mobile crowd sensing task allocation research mainly focus on multi-task allocation methods under budget constraints and time constraints to solve the problem of platform resource constraints under single task allocation and improve the efficiency of participants in executing tasks. However, these technical inventions ignore the situation that both the mobile crowd sensing platform and the participants are rational. The platform will not actively recruit participants who do not meet the task requirements, and the participants can refuse to accept and execute sensing tasks that they are not interested in. Therefore, when generating task allocation strategies, the platform only focuses on the spatiotemporal attributes of tasks and participants, ignoring the preferences of both parties. As a result, participants will not accept tasks, resulting in waste of platform resources and reduced profits. How to design a task allocation mechanism based on task requirements and participant preferences, and fully improve the platform's attention to task requirements and participant preferences, so as to improve the stability of task allocation strategies is a challenge. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the deficiencies of the prior art, the present invention provides a matching method that takes into account the preferences of both the task and the participants, thereby improving the problem that the existing task allocation mechanism ignores the matching willingness of both the task and the participants.

[0005] (II) Technical solution

[0006] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0007] A matching method that considers the preferences of both the task and the participants is as follows:

[0008] (1) Establish a task and a rule to match the willingness of both parties:

[0009] The mobile crowd-sensing system consists of a central platform built by a server cluster, several task requesters and participants;

[0010] Before assigning tasks, the central platform continuously waits to receive the task requirement profile sent by the task requester and the personal information profile uploaded by the participants;

[0011] During the task assignment process, the platform analyzes and calculates the matching willingness of both parties based on the information profiles of the task to be assigned and the candidate participants, and compares the matching willingness with the matching willingness threshold pre-set by the platform to judge the suitability of the task and the participant;

[0012] If the matching willingness reaches or exceeds the threshold and the participant can complete the task within the expected working time and the validity period of the task, the platform determines that the task and the participant are compatible with each other and can choose to assign the task to the participant;

[0013] After the task is completed, the platform determines the completion of the task based on the sensing data uploaded by the participants, collects the service fee of the sensing task from the requester, and pays the labor fee to the participants, thereby earning intermediate profits;

[0014] The set of tasks published by the task requester is represented as ,in, It is expressed as the number of sensing tasks;

[0015] Middle Tasks The information profile is represented as ,in, Indicates the task Location, Indicates the task During the effective time of the platform, Indicates the task Required sensing duration, Indicates the task importance;

[0016] The set of participants is represented as ,in It is expressed as the number of participants;

[0017] Middle Participants The information profile is represented as ,in Indicates participants The initial position of Indicates participants Expected working hours, Indicates participants By Location is the ideal activity radius of the center of the circle, Indicates participants Reputation value, Indicates participants social skills, Indicates participants For each task proficiency set, Indicates participants To the task proficiency, , and Respectively represent participants The remaining power of the device, computing resources, and data transmission efficiency;

[0018] Task and participants The matching willingness of both parties is expressed as , the matching willingness threshold preset by the platform is expressed as ;

[0019] when When and participants Both parties meet the matching willingness rule;

[0020] Furthermore, assuming that the platform assigns participants The ordered set of tasks is represented as ,in, express The number of tasks in

[0021] Participants Complete the collection Medium Task The cumulative consumption time after , which includes participants From the initial position Move to task location Movement duration and tasks All predecessor tasks (including tasks )’s sensing duration;

[0022] The function is shown in the following formula:

[0023] (1)

[0024] in, Indicates participants From Location Move to location Length of time;

[0025] when and When Expected working hours and task validity period Complete the task within

[0026] When the task and participants If both parties meet the matching willingness rules and time constraints, it means that both parties meet the platform matching requirements and can complete the task. Assign to participants ;

[0027] Task and participants The matching status of both parties is expressed as ,like , indicating that the platform will task Assign to participants ,like , indicating that the platform did not assign the task Assign to participants ;

[0028] When the task Successfully participated in After the task is completed and the task requester receives the sensor data, the requester will pay the platform for the service , participants Will receive labor remuneration paid by the platform , the platform can finally make a profit ;

[0029] (2) Design a method to calculate the matching willingness based on the preferences of both the task and the participants:

[0030] Task and participants The willingness of both parties to match Divided into tasks For participants Preference and participants To the task Preference Two parts, as shown in the following formula:

[0031] (2)

[0032] in, , Represent task preference , Participant Preference Willingness to match The influence weight of ;

[0033] Task Preference By Task Reputation of participants , social skills and task proficiency Preference related;

[0034] Task Reputation of participants Preference The calculation method is shown in the following formula:

[0035] (3)

[0036] in, Represents a set of participants The maximum value of the participant's reputation, participant reputation Quantified as ;

[0037] Task Social skills of participants Preference The calculation method is shown in the following formula:

[0038] (4)

[0039] in, Represents a set of participants The maximum value of the social ability of the participants, the social ability of the participants Quantified as ;

[0040] Task Proficiency of participants Preference The calculation method is shown in the following formula:

[0041] (5)

[0042] in, Represents a set of participants Middle task Highest task proficiency, participant proficiency Quantified as ;

[0043] Task Preference The calculation method is shown in the following formula:

[0044] (6)

[0045] Participant Preferences By participants To its task The distance between , Remaining battery power of the device , Task Importance Preference related;

[0046] Participants To its task The distance between Preference The calculation method is shown in the following formula:

[0047] (7)

[0048] Formula (7) can fully show that the participants The activity intention is located at the center of the circle. , the radius is within the sensing area;

[0049] Participants Remaining battery power of the device Preference The calculation method is shown in the following formula:

[0050] (8)

[0051] in, Indicates participants Execute the task The amount of power consumed by the device when collecting data. Indicates participants Execute the task The amount of power consumed by the device due to other activities. Indicates participants Execute the task The platform then estimates the remaining battery power of the device. ;

[0052] Remaining battery power of the device Quantified as , Quantified as , Quantified as ;

[0053] Participants Importance of the task Preference The calculation method is shown in the following formula:

[0054] (9)

[0055] in, It is represented as the maximum importance in the task set, task importance Quantified as ;

[0056] Participant Preferences The calculation method is shown in the following formula:

[0057] (10);

[0058] (3) Design a task reward calculation method related to matching willingness:

[0059] Using the Nash bargaining strategy, calculate the participants Complete the task The labor remuneration that can be obtained later , to ensure fairness among participants, as shown in the following formula:

[0060] (11)

[0061] in, Represents a set of participants In and Task The number of participants that meet the matching willingness rule, Indicates participants Execute the task The true cost is shown in the following formula:

[0062] (12)

[0063] in, represents the basic cost, Indicates participants Execute the task the cost level, express The correlation coefficient with the actual cost, and , which matches the willingness It forms a negative logarithmic relationship, as shown in the following formula:

[0064] (13)

[0065] Among them, matching willingness , The larger the value, The smaller the value, the more willing the participants are to accept the cost; The smaller the value, The larger the value, the less willing the participant is to accept the cost;

[0066] Mission cost level Cost of resource consumption and manual execution costs The composition is shown in the following formula:

[0067] (14)

[0068] in, , Represents resource consumption cost , Manual execution cost Task cost level The influence weight of ;

[0069] Resource consumption cost Task proficiency , computing resources and data transmission efficiency The three factors are as shown in the following formula:

[0070] (15)

[0071] in, , Represents the set of participants The maximum value of the device computing resources and transmission efficiency in , and Respectively represent the weights of measuring the relative importance of the above factors, ;

[0072] Manual execution cost By participants Execute the task The influence of the time spent and the moving distance is shown in the following formula:

[0073] (16)

[0074] in, , Respectively indicate that tasks can be executed The maximum time spent and the maximum moving distance among the participants of , Respectively represent the weights of measuring the relative importance of the above factors, ;

[0075] (4) Construct a task allocation model with matching intention rules and time as constraints and platform profit as the optimization goal:

[0076] In practical applications, the specific task allocation problem can be constructed into the following mathematical model as shown in Equations (17) to (22):

[0077] (17)

[0078] (18)

[0079] (19)

[0080] (20)

[0081] (twenty one)

[0082] (twenty two)

[0083] Among them, formula (17) is the target optimization function of the platform, represents the task allocation profit function of the platform, and equations (18) and (19) are the time constraints of both the task and the participant. Equation (18) represents the participant Expected working hours required Completed task collection , that is, participants Complete the collection Medium Task Cumulative duration after Cannot exceed the working hours , Formula (19) represents the task set Tasks in Need to be valid during Participants Completed, that is, participants Completed the task collection Medium Task Cumulative duration after Cannot exceed the mission validity period , Formula (20) is the rule constraint for matching willingness between tasks and participants, that is, task and participants The willingness to match Need to reach or exceed the threshold preset by the platform , formula (21) represents the task can only be completed by one participant. Formula (22) represents the task Can only be assigned to participants once.

[0084] (III) Beneficial effects

[0085] Compared with the prior art, the present invention provides a matching method that takes into account the preferences of both the task and the participants, and has the following beneficial effects:

[0086] The present invention applies a task allocation mechanism based on the bilateral preferences of tasks and participants to the mobile crowd intelligence perception task allocation process. Specifically, a matching willingness rule is designed based on the preferences of both tasks and participants, so that the mobile crowd intelligence perception platform can allocate tasks to participants with higher matching suitability, thereby improving the platform's emphasis on task requirements and participant preferences, and thus ensuring the stability of task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1Schematic diagram of how the average preference value of tasks / participants changes with the matching willingness threshold under different models in the present invention. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Example

[0090] An embodiment of the present invention provides a matching method that takes into account the preferences of both the task and the participants, which is as follows:

[0091] (1) Establish a task and a rule to match the willingness of both parties:

[0092] The mobile crowd-sensing system consists of a central platform built by a server cluster, several task requesters and participants;

[0093] Before assigning tasks, the central platform continuously waits to receive the task requirement profile sent by the task requester and the personal information profile uploaded by the participants;

[0094] During the task assignment process, the platform analyzes and calculates the matching willingness of both parties based on the information profiles of the task to be assigned and the candidate participants, and compares the matching willingness with the matching willingness threshold pre-set by the platform to judge the suitability of the task and the participant;

[0095] If the matching willingness reaches or exceeds the threshold and the participant can complete the task within the expected working time and the validity period of the task, the platform determines that the task and the participant are compatible with each other and can choose to assign the task to the participant;

[0096] After the task is completed, the platform determines the completion of the task based on the sensing data uploaded by the participants, collects the service fee of the sensing task from the requester, and pays the labor fee to the participants, thereby earning intermediate profits;

[0097] The set of tasks published by the task requester is represented as ,in, It is expressed as the number of sensing tasks;

[0098] Middle Tasks The information profile is represented as ,in, Indicates the task Location, Indicates the task During the effective time of the platform, Indicates the task Required sensing duration, Indicates the task importance;

[0099] The set of participants is represented as ,in It is expressed as the number of participants;

[0100] Middle Participants The information profile is represented as ,in Indicates participants The initial position of Indicates participants Expected working hours, Indicates participants By Location is the ideal activity radius of the center of the circle, Indicates participants Reputation value, Indicates participants social skills, Indicates participants For each task proficiency set, Indicates participants To the task proficiency, , and Respectively represent participants The remaining power of the device, computing resources, and data transmission efficiency;

[0101] Task and participants The matching willingness of both parties is expressed as , the matching willingness threshold preset by the platform is expressed as ;

[0102] when When and participants Both parties meet the matching willingness rule;

[0103] Furthermore, assuming that the platform assigns participants The ordered set of tasks is represented as ,in, express The number of tasks in

[0104] Participants Complete the collection Medium Task The cumulative consumption time after , which includes participants From the initial position Move to task location Movement time and tasks All predecessor tasks (including tasks )’s sensing duration;

[0105] The function is shown in the following formula:

[0106] (1)

[0107] in, Indicates participants From Location Move to location Length of time;

[0108] when and When Expected working hours and task validity period Complete the task within

[0109] When the task and participants If both parties meet the matching willingness rules and time constraints, it means that both parties meet the platform matching requirements and can complete the task. Assign to participants ;

[0110] Task and participants The matching status of both parties is expressed as ,like , indicating that the platform will task Assign to participants ,like , indicating that the platform did not assign the task Assign to participants ;

[0111] When the task Successfully participated in After the task is completed and the task requester receives the sensor data, the requester will pay the platform for the service , participants The platform will receive labor remuneration paid to them, and the platform will eventually make a profit ;

[0112] (2) Design a method to calculate the matching willingness based on the preferences of both the task and the participants:

[0113] Task and participants The willingness of both parties to match Divided into tasks For participants Preference and participants To the task Preference Two parts, as shown in the following formula:

[0114] (2)

[0115] in, , Represent task preference , Participant Preference Willingness to match The influence weight of ;

[0116] Task Preference By Task Reputation of participants , social skills and task proficiency Preference related;

[0117] Task Reputation of participants Preference The calculation method is shown in the following formula:

[0118] (3)

[0119] in, Represents a set of participants The maximum value of the participant's reputation, participant reputation Quantified as ;

[0120] Task Social skills of participants Preference The calculation method is shown in the following formula:

[0121] (4)

[0122] in, Represents a set of participants The maximum value of the social ability of the participants, the social ability of the participants Quantified as ;

[0123] Task Proficiency of participants Preference The calculation method is shown in the following formula:

[0124] (5)

[0125] in, Represents a set of participants Middle task Highest task proficiency, participant proficiency Quantified as ;

[0126] Task Preference The calculation method is shown in the following formula:

[0127] (6)

[0128] Participant Preferences By participants To its task The distance between , Remaining battery power of the device , Task Importance Preference related;

[0129] Participants To its task The distance between Preference The calculation method is shown in the following formula:

[0130] (7)

[0131] Formula (7) can fully show that the participants The activity intention is located at the center of the circle. , the radius is within the sensing area;

[0132] Participants Remaining battery power of the device Preference The calculation method is shown in the following formula:

[0133] (8)

[0134] in, Indicates participants Execute the task The amount of power consumed by the device when collecting data. Indicates participants Execute the task The amount of power consumed by the device due to other activities. Indicates participants Execute the task The platform then estimates the remaining battery power of the device. ;

[0135] Remaining battery power of the device Quantified as , Quantified as , Quantified as ;

[0136] Participants Importance of the task Preference The calculation method is shown in the following formula:

[0137] (9)

[0138] in, It is represented as the maximum importance in the task set, task importance Quantified as ;

[0139] Participant Preferences The calculation method is shown in the following formula:

[0140] (10);

[0141] (3) Design a task reward calculation method related to matching willingness:

[0142] Using the Nash bargaining strategy, calculate the participants Complete the task The labor remuneration that can be obtained later , to ensure fairness among participants, as shown in the following formula:

[0143] (11)

[0144] in, Represents a set of participants In and Task The number of participants that meet the matching willingness rule, Indicates participants Execute the task The true cost is shown in the following formula:

[0145] (12)

[0146] in, represents the basic cost, Indicates participants Execute the task the cost level, express The correlation coefficient with the actual cost, and , which matches the willingness It forms a negative logarithmic relationship, as shown in the following formula:

[0147] (13)

[0148] Among them, matching willingness , The larger the value, The smaller the value, the more willing the participants are to accept the cost; The smaller the value, The larger the value, the less willing the participant is to accept the cost;

[0149] Mission cost level Cost of resource consumption and manual execution costs The composition is shown in the following formula:

[0150] (14)

[0151] in, , Represents resource consumption cost , Manual execution cost Task cost level The influence weight of ;

[0152] Resource consumption cost Task proficiency , computing resources and data transmission efficiency The three factors are as shown in the following formula:

[0153] (15)

[0154] in, , Represents the set of participants The maximum value of the device computing resources and transmission efficiency in , and Respectively represent the weights of measuring the relative importance of the above factors, ;

[0155] Manual execution cost By participants Execute the task The influence of the time spent and the moving distance is shown in the following formula:

[0156] (16)

[0157] in, , Respectively indicate that tasks can be executed The maximum time spent and the maximum moving distance among the participants of , Respectively represent the weights of measuring the relative importance of the above factors, ;

[0158] (4) Construct a task allocation model with matching intention rules and time as constraints and platform profit as the optimization goal:

[0159] In practical applications, the specific task allocation problem can be constructed into the following mathematical model as shown in Equations (17) to (22):

[0160] (17)

[0161] (18)

[0162] (19)

[0163] (20)

[0164] (twenty one)

[0165] (twenty two)

[0166] Among them, formula (17) is the target optimization function of the platform, represents the task allocation profit function of the platform, and equations (18) and (19) are the time constraints of both the task and the participant. Equation (18) represents the participant Expected working hours required Completed task collection , that is, participants Complete the collection Medium Task Cumulative duration after Cannot exceed the working hours , Formula (19) represents the task set Tasks in Need to be valid during Participants Completed, that is, participants Completed the task collection Medium Task Cumulative duration after Cannot exceed the mission validity period , Formula (20) is the rule constraint for matching willingness between tasks and participants, that is, task and participants The willingness to match Need to reach or exceed the threshold preset by the platform , formula (21) represents the task can only be completed by one participant. Formula (22) represents the task Can only be assigned to participants once.

[0167] In terms of task allocation mechanism design, a matching willingness rule is designed based on the preferences of both tasks and participants, so that the mobile crowd intelligence perception platform can allocate tasks to participants with higher matching suitability, increase the platform's attention to task requirements and participant preferences, and thus ensure the stability of task allocation and the profit obtained by the platform. Under this rule, the factors affecting the preferences of both tasks and participants are analyzed. At the same time, through the task allocation algorithm based on the bilateral preferences of tasks and participants, the platform can generate a task allocation strategy that can maximize profits at a faster speed, thereby improving the task allocation rate and participant activity.

[0168] Compare the attention paid to task requirements and participant preferences by a mobile crowd intelligence perception platform that only considers the time constraints of both tasks and participants and the time constraints of both tasks and participants and the matching willingness rule constraints;

[0169] It can be found that the average preference values ​​of tasks and participants in the platform that considers both time constraints and matching willingness rules increase with the increase of matching willingness threshold, and the platform's attention to task requirements and participant preferences is also increasing, and is better than the mobile crowd intelligence perception platform that only considers time constraints; this phenomenon shows that the mobile crowd intelligence perception platform that introduces matching willingness rules can control the platform's attention to the preferences of both tasks and participants by adjusting the specific value of the matching willingness threshold; in the actual task allocation scenario, the platform can use the present invention to dynamically adjust the matching willingness threshold according to the specific task allocation requirements to achieve a balanced regulation of the platform's profit and the stability of the task allocation strategy.

[0170] On the basis of the task allocation mechanism that takes into account the time constraints of tasks and participants, a matching willingness rule that takes into account the preferences of both tasks and participants is introduced, and then a model for maximizing platform profits is constructed, aiming to improve the platform's emphasis on task requirements and participant preferences and the stability of task allocation. At the same time, based on the quantitative weighted analysis method, the calculation method of matching willingness and participants' task rewards under the matching willingness rule is clarified, and the platform's matching suitability calculation for tasks and participants is realized to evaluate the adaptability of tasks and participants and ensure fairness among participants.

[0171] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A matching method that takes into account the preferences of both the task and the participants, characterized in that: include: 1) Formulate a matching willingness rule for tasks and participants. During the task assignment process, the platform analyzes and calculates the matching willingness of both parties based on the information profiles of the tasks to be assigned and the candidate participants, and compares the matching willingness with the matching willingness threshold preset by the platform to judge the suitability of the tasks and participants; Among them, the mobile crowd intelligence perception system includes a central platform built by a server cluster, several task requesters and participants; Before assigning tasks, the central platform continuously waits to receive the task requirement profile sent by the task requester and the personal information profile uploaded by the participants; During the task assignment process, the platform analyzes and calculates the matching willingness of both parties based on the information profiles of the task to be assigned and the candidate participants, and compares the matching willingness with the matching willingness threshold pre-set by the platform to judge the suitability of the task and the participant; If the matching willingness reaches or exceeds the threshold and the participant can complete the task within the expected working time and the validity period of the task, the platform determines that the task and the participant are compatible with each other and chooses to assign the task to the participant; After the task is completed, the platform determines the completion of the task based on the sensing data uploaded by the participants, collects the service fee of the sensing task from the requester, and pays the labor fee to the participants, thereby earning intermediate profits; When the task Successfully participated in After the task is completed and the task requester receives the sensor data, the requester will pay the platform for the service , participants Will receive labor remuneration paid by the platform , the platform can finally make a profit ; 2) Design a matching willingness calculation method based on the preferences of both the task and the participants, which is used to divide the matching willingness of the task and the participants into two parts: the preference of the task for the participant and the preference of the participant for the task, and calculate the specific value; Among them, the participant preference is related to the participant's preference for the distance between them and the task, the remaining battery of the device, and the importance of the task; 3) Propose a task reward calculation method related to matching willingness to calculate the labor remuneration that participants can obtain after completing the task to ensure fairness among participants; 4) Construct a task allocation model with matching willingness rules and time as constraints and platform profit as the optimization goal; In practical applications, the specific task allocation problem is constructed into the following mathematical model as shown in equations (17) to (22): (17)； (18)； (19)； (20)； (21)； (22)； Among them, formula (17) is the target optimization function of the platform, represents the task allocation profit function of the platform, and equations (18) and (19) are the time constraints of both the task and the participant. Equation (18) represents the participant Expected working hours Completed task collection , that is, participants Complete the collection Medium Task Cumulative duration after Cannot exceed the working hours , Formula (19) represents the task set Tasks in Need to be valid during Participants Completed, that is, participants Completed the task collection Medium Task Cumulative duration after Cannot exceed the mission validity period , Formula (20) is the rule constraint for matching willingness between tasks and participants, that is, task and participants The willingness to match Need to reach or exceed the threshold preset by the platform , formula (21) represents the task can only be completed by one participant. Formula (22) represents the task Can only be assigned to participants once.

2. A matching method according to claim 1 that takes into account the preferences of both the task and the participants, characterized in that: Formulate a task and a matching rule between the participants, as follows: The set of tasks published by the task requester is represented as ,in It is expressed as the number of sensing tasks; Middle Tasks The information profile is represented as ,in, Indicates the task Location, Indicates the task During the effective time of the platform, Indicates the task Required sensing duration, Indicates the task importance; The set of participants is represented as ,in It is expressed as the number of participants; Middle Participants The information profile is represented as ,in, Indicates participants The initial position of Indicates participants Expected working hours, Indicates participants By Location is the ideal activity radius of the center of the circle, Indicates participants Reputation value, Indicates participants social skills, Indicates participants For each task proficiency set, Indicates participants To the task proficiency, , and Respectively represent participants The remaining power of the device, computing resources, and data transmission efficiency; Task and participants The matching willingness of both parties is expressed as , the matching willingness threshold preset by the platform is expressed as ; when When and participants Both parties meet the matching willingness rule; Furthermore, assuming that the platform assigns participants The ordered set of tasks is represented as ,in, express The number of tasks in Participants Complete the collection Medium Task The cumulative consumption time after , which includes participants From the initial position Move to task location Movement duration and tasks The sensing duration of all predecessor tasks; The function is shown in the following formula: (1)； in, Indicates participants From Location Move to location Length of time; when and When Expected working hours and task validity period Complete the task within When the task and participants Both parties meet the matching intention rules and time constraints, indicating that both parties meet the platform matching requirements and the task Assign to participants ; Task and participants The matching status of both parties is expressed as ,like , indicating that the platform will task Assign to participants ,like , indicating that the platform did not assign the task Assign to participants .

3. A matching method according to claim 1 that takes into account the preferences of both the task and the participants, characterized in that: A matching willingness calculation method based on the preferences of both the task and the participants is as follows: Task and participants The willingness of both parties to match Divided into tasks For participants Preference and participants To the task Preference Two parts, as shown in the following formula: (2)； in, , Represent task preference , Participant Preference Willingness to match The influence weight of ; Task Preference By Task Reputation of participants , social skills and task proficiency Preference related; Task Reputation of participants Preference The calculation method is shown in the following formula: (3)； in, Represents a set of participants The maximum value of the participant's reputation, participant reputation Quantified as ; Task Social skills of participants Preference The calculation method is shown in the following formula: (4)； in, Represents a set of participants The maximum value of the social ability of the participants, the social ability of the participants Quantified as ; Task Proficiency of participants Preference The calculation method is shown in the following formula: (5)； in, Represents a set of participants Middle task The highest task proficiency, the task proficiency of the participants Quantified as ; Task Preference The calculation method is shown in the following formula: (6)； Participant Preferences By participants To its task The distance between , Remaining battery power of the device , Task Importance Preference related; Participants To its task The distance between Preference The calculation method is shown in the following formula: (7)； Formula (7) can fully show that the participants The activity intention is located at the center of the circle. , the radius is within the sensing area; Participants Remaining battery power of the device Preference The calculation method is shown in the following formula: (8)； in, Indicates participants Execute the task The amount of power consumed by the device when collecting data. Indicates participants Execute the task The amount of power consumed by the device due to other activities. Indicates participants Execute the task The platform then estimates the remaining battery power of the device. ; Remaining battery power of the device Quantified as , Quantified as , Quantified as ; Participants Importance of the task Preference The calculation method is shown in the following formula: (9)； in, It is expressed as the maximum importance in the task set, task importance Quantified as ; Participant Preferences The calculation method is shown in the following formula: (10)。 4. A matching method according to claim 1 that takes into account the preferences of both the task and the participants, characterized in that: Design a task reward calculation method related to matching willingness, as follows: Using the Nash bargaining strategy, we calculate the participants Complete the task The labor remuneration that can be obtained later , to ensure fairness among participants, as shown in the following formula: (11)； in, Represents a set of participants In and Task The number of participants that meet the matching willingness rule, Indicates participants Execute the task The true cost is shown in the following formula: (12)； in, represents the basic cost, Indicates participants Execute the task the cost level, express The correlation coefficient with the actual cost, and , which matches the willingness It forms a negative logarithmic relationship, as shown in the following formula: (13)； Among them, matching willingness , The larger the value, The smaller the value, the more willing the participants are to accept the cost; The smaller the value, The larger the value, the less willing the participant is to accept the cost; Mission cost level Cost of resource consumption and manual execution costs The composition is shown in the following formula: (14)； in, , Represents resource consumption cost , Manual execution cost Task cost level The influence weight of ; Resource consumption cost Task proficiency , computing resources and data transmission efficiency The three factors are as shown in the following formula: (15)； in, , Represents the set of participants The maximum value of the device computing resources and transmission efficiency in , and Respectively represent the weights of measuring the relative importance of the above factors, ; Manual execution cost By participants Execute the task The influence of the time spent and the moving distance is shown in the following formula: (16)； in, , Respectively indicate that tasks can be executed The maximum time spent and the maximum moving distance among the participants of , Respectively represent the weights of measuring the relative importance of the above factors, .

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