CN113344384A - Task allocation method based on crowd sensing - Google Patents

Task allocation method based on crowd sensing Download PDF

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CN113344384A
CN113344384A CN202110627772.8A CN202110627772A CN113344384A CN 113344384 A CN113344384 A CN 113344384A CN 202110627772 A CN202110627772 A CN 202110627772A CN 113344384 A CN113344384 A CN 113344384A
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task
employee
staff
work order
crowd sensing
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姚信威
虞紫倩
邢伟伟
齐楚锋
李强
姚远
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Zhejiang University of Technology ZJUT
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group

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Abstract

The invention relates to a task allocation method based on crowd sensing, which comprises the steps of generating a work order related to a task, processing the work order based on data obtained by crowd sensing, screening out employees meeting conditions, and distributing the work order; if the work order is responded in the preset time, the task allocation corresponding to the current work order is finished, otherwise, whether secondary order dispatching is needed or not is judged, and a processing mechanism is started. The invention can efficiently and accurately distribute tasks, solves the defects of long time and inaccurate task distribution of the traditional community manual management, meets the task distribution requirement of the intelligent community, reasonably distributes the tasks, greatly reduces the labor cost and improves the efficiency and the accuracy of task distribution.

Description

Task allocation method based on crowd sensing
Technical Field
The present invention relates to resource, workflow, personnel or project management, such as organizing, planning, scheduling or allocating time, personnel or machine resources; planning an enterprise; the technical field of organizational models, in particular to a task allocation method based on crowd sensing.
Background
In the management of current wisdom community, the administrator often need not only consume a large amount of manpowers with numerous and complicated tasks of manual distribution, has moreover that the task allocation is untimely, the task allocation inefficiency, the task allocation lacks rationality, resource optimization utilizes a great deal of shortcoming such as the degree is not enough.
Specifically, situations that may arise include, but are not limited to:
1. the order is dispatched to improper staff, so that the reallocation is needed, and the efficiency of task allocation is greatly reduced;
2. the task allocation method comprises the following steps that a plurality of tasks are repeatedly allocated to employees who are executing tasks, tasks are not allocated to employees who are idle in a working state, and the task allocation is unequal;
3. the task is distributed to the staff farther away from the task position, so that the processing timeliness of the task is poor, and the staff with the more matched distance is not fully utilized.
Disclosure of Invention
The invention solves the problems that the community property company consumes a large amount of manpower and material resources due to long time and high cost of manual task allocation in the traditional community in the prior art, provides an optimized task allocation method based on crowd sensing, and enables the task allocation problem of the intelligent community to be more efficient and accurate through a staff star-level updating mechanism and a task allocation process.
The technical scheme adopted by the invention is that a task allocation method based on crowd sensing comprises the following steps:
step 1: generating a work order related to the task;
step 2: processing the work order based on data obtained by crowd sensing, and screening out the employees meeting the conditions;
and step 3: dispatching the work order;
and 4, step 4: if the work order is responded within the preset time, ending the task allocation corresponding to the current work order, otherwise, performing the next step;
and 5: and (5) judging whether secondary dispatching is needed, if so, returning to the step 2, and starting a processing mechanism.
Preferably, in step 1, the work order is generated from a user, an AI device, and an employee, and the task information corresponding to the work order includes a task type, a task position, and a task level.
Preferably, in the step 2, the data obtained based on crowd sensing includes employee status, employee location, employee star level, task type and service location; the screening conditions comprise that the staff state is idle, the staff position and the service position meet preset conditions, the staff star level meets the preset conditions, the task types are matched, and the staff judgment standard score reaches a threshold value.
Preferably, the employee judgment criterion is W, W ═ PS·PNS/DPTWherein P isSFor employee status, Idle is 1, not Idle is 0, PNSIs the star rating of the staff, DPTThe distance between the employee location and the service location; setting a threshold eta, and selecting staff with W judgment standard greater than eta, wherein eta is greater than 0.
Preferably, the step of obtaining the staff star rating comprises the following steps:
step a.1: let the current star rating of any non-new employee be PCSLet the current star level of the new employee be PIS；
Step a.2: for non-new employees, any task evaluation T is obtainedEActual execution time of task ATAnd expected completion time E of taskT(ii) a Computing employee execution efficiency PE＝ET/AT；
Step a.3: setting a default rating of a task to TDThen the star level of the employee is updated to PNS，PNS＝(1-m)·PCS+m·TE·PEWhere m is the update factor, 0<m<1; if any task does not obtain evaluation, automatically obtaining default evaluation, TE＝TD；
Step a.4: if PNSGreater than 10, then PNSIs 10, otherwise P calculated in step a.3 is retainedNSNumerical value, return data.
Preferably, said dispatchable employee's employee location is between the service location and a service locationA distance DPT，DPT＝|XT-XP|+|YT-YPL wherein (X)T,YT) As task position coordinates, (X)p,YP) Is the employee position coordinates.
Preferably, in step 3, the dispatching of the work order includes the following steps:
step 3.1: based on the number of the staff meeting the conditions screened in the step 2, if the number of the matched staff is 0, directly judging that the scheduling fails, recording the times and feeding back the times to a manager, and manually distributing the task, otherwise, carrying out the next step;
step 3.2: if the number of matched employees is equal to 1, directly distributing the tasks to the matched employees in a dispatching mode and carrying out the step 4, otherwise, carrying out the next step;
step 3.3: if the work order is assigned for the first time, the task is assigned in an order grabbing mode, otherwise, the task is assigned in an order assigning mode; and 4, after the task is distributed, performing step 4.
Preferably, in step 3.3, the task allocation includes the following steps:
step 3.3.1: obtaining information of all screened employees meeting the conditions;
step 3.3.2: adaptively selecting an employee interval;
step 3.3.3: if the order grabbing mode is adopted, directly grabbing orders or grabbing orders of the employees in the selected employee interval, otherwise dispatching orders in the selected employee interval;
step 3.3.4: and (5) completing task allocation, and quitting the staff with the list grabbing failure in the list grabbing mode.
Preferably, in the step 3.3.2, selecting the employee interval includes the following steps:
step 3.3.2.1: initializing each employee judgment standard score;
step 3.3.2.2: calculating any employee x based on employee judgment standard scoresiSelection probability of
Figure BDA0003102460910000031
Wherein, w (x)i) For employee xiThe judgment standard is divided, wherein N is the total number of the staff;
step 3.3.2.3: calculating the percentage of the selected probability of each employee by taking the sum of the selected probabilities of all employees as 1, and calculating the cumulative probability of each employee in sequence
Figure BDA0003102460910000032
Step 3.3.2.4: in the interval [0,1]Randomly generating a number r, judging which interval the number falls in, and if the number falls in the staff xiCorresponding interval Qxi-1～QxiThen employee x corresponding to the intervaliAnd (6) selecting.
Preferably, if the employee of the order snatching or the employee assigned the order does not execute the work order within the preset time, a punishment mechanism is carried out, and the management personnel are fed back.
The invention relates to an optimized task allocation method based on crowd sensing, which comprises the steps of generating a work order related to a task, processing the work order based on data obtained by crowd sensing, screening out employees meeting conditions, and distributing the work order; if the work order is responded in the preset time, the task allocation corresponding to the current work order is finished, otherwise, whether secondary order dispatching is needed or not is judged, and a processing mechanism is started.
The invention can efficiently and accurately distribute tasks, solves the defects of long time and inaccurate task distribution of the traditional community manual management, meets the task distribution requirement of the intelligent community, reasonably distributes the tasks, greatly reduces the labor cost and improves the efficiency and the accuracy of task distribution.
Drawings
FIG. 1 is a schematic view of a main process for work order platform processing according to the present invention;
FIG. 2 is a schematic diagram of a main flow of employee work order processing according to the present invention;
FIG. 3 is a schematic flow chart of the order grabbing process in the present invention;
FIG. 4 is a flow chart illustrating the process of dispatching orders in the present invention.
Detailed Description
The present invention is described in further detail with reference to the following examples, but the scope of the present invention is not limited thereto.
The invention relates to a task allocation method based on crowd sensing, which comprises the following steps.
Step 1: and generating a work order related to the task.
In the step 1, the work order is generated from the user, the AI device and the staff, and the task information corresponding to the work order includes the task type, the task position and the task grade.
Step 2: and processing the work order based on the data obtained by crowd sensing, and screening out the staff meeting the conditions.
In the step 2, the data obtained based on crowd sensing comprises staff states, staff positions, staff star levels, task types and service positions; the screening conditions comprise that the staff state is idle, the staff position and the service position meet preset conditions, the staff star level meets the preset conditions, the task types are matched, and the staff judgment standard score reaches a threshold value.
The employee judgment standard is W, and W is equal to PS·PNS/DPTWherein P isSFor employee status, Idle is 1, not Idle is 0, PNSIs the star rating of the staff, DPTThe distance between the employee location and the service location; setting a threshold eta, and selecting staff with W judgment standard greater than eta, wherein eta is greater than 0.
The step of obtaining the staff star rating comprises the following steps:
step a.1: let the current star rating of any non-new employee be PCSLet the current star level of the new employee be PIS；
Step a.2: for non-new employees, any task evaluation T is obtainedEActual execution time of task ATAnd expected completion time E of taskT(ii) a Computing employee execution efficiency PE＝ET/AT；
Step a.3: setting a default rating of a task to TDThen the star level of the employee is updated to PNS，PNS＝(1-m)·PCS+m·TE·PEWhere m is the update factor, 0<m<1; if it isIf no evaluation is obtained in any task, automatically obtaining default evaluation, TE＝TD；
Step a.4: if PNSGreater than 10, then PNSIs 10, otherwise P calculated in step a.3 is retainedNSNumerical value, return data.
The distance between the employee position and the service position of the schedulable employee is DPT，DPT＝|XT-XP|+|YT-YPL wherein (X)T,YT) As task position coordinates, (X)p,YP) Is the employee position coordinates.
In the invention, the information of the tasks and the information of the staff are concerned.
In the invention, the task type is used for the follow-up task allocation and the matching of staff, including maintenance tasks, security tasks, cleaning tasks and the like; the task position is a position coordinate (X) obtained according to the area modeled by the city rangeT,YT) For calculating the distance between the staff; in the practical application extension, the task level can be increased to indicate the urgency or priority of the task.
In the invention, the working state of the staff comprises on duty or busy, so that whether the working state of the staff is idle or whether the task is saturated or not is judged when the task is distributed; the employee type facilitates matching tasks of corresponding types, such as maintainers, security, cleaning and the like, when subsequently distributing tasks of different types; employee location is the employee's coordinates (X) obtained from a GPS high-precision map according to the area modeled by the city horizonP,YP) (ii) a The staff star level means that the current star level of the staff is set as PCS(Partner Current Star) with the Current Star rating of the New employee PIS(Partner Initial Star), then after each time the employee completes a task, iteratively updating the Star rating of the employee, with the Star rating after the employee update being PNS(Partner New Star); for example, obtain task evaluation TE7, take the total number of stars 10 as an example, m is 1/2, PE＝1，PCSWhen the value is 6, then PNSWas 6.5.
In the invention, the distance between the employee position and the service position is calculated by adopting a Manhattan distance calculation mode, and the calculation can be carried out according to the region modeled in the city range, so that the method is more practical in field operation.
In the invention, staff with W > eta is selected, the shorter the distance is, the higher the star level is, and staff in an idle state becomes an object to be selected.
And step 3: and dispatching the work order.
In the step 3, the dispatch of the work order comprises the following steps:
step 3.1: based on the number of the staff meeting the conditions screened in the step 2, if the number of the matched staff is 0, directly judging that the scheduling fails, recording the times and feeding back the times to a manager, and manually distributing the task, otherwise, carrying out the next step;
step 3.2: if the number of matched employees is equal to 1, directly distributing the tasks to the matched employees in a dispatching mode and carrying out the step 4, otherwise, carrying out the next step;
step 3.3: if the work order is assigned for the first time, the task is assigned in an order grabbing mode, otherwise, the task is assigned in an order assigning mode; and 4, after the task is distributed, performing step 4.
In step 3.3, the task allocation includes the following steps:
step 3.3.1: obtaining information of all screened employees meeting the conditions;
step 3.3.2: adaptively selecting an employee interval;
in the step 3.3.2, selecting the employee interval includes the following steps:
step 3.3.2.1: initializing each employee judgment standard score;
step 3.3.2.2: calculating any employee x based on employee judgment standard scoresiSelection probability of
Figure BDA0003102460910000071
Wherein, w (x)i) For employee xiThe judgment standard is divided, wherein N is the total number of the staff;
step 3.3.2.3: calculating each employee with the sum of the selected probabilities of all employees as 1Percentage of selected probability of worker, calculating the cumulative probability of each worker in sequence
Figure BDA0003102460910000072
Step 3.3.2.4: in the interval [0,1]Randomly generating a number r, judging which interval the number falls in, and if the number falls in the staff xiCorresponding interval Qxi-1～QxiThen employee x corresponding to the intervaliAnd (6) selecting.
Step 3.3.3: if the order grabbing mode is adopted, directly grabbing orders or grabbing orders of the employees in the selected employee interval, otherwise dispatching orders in the selected employee interval;
step 3.3.4: and (5) completing task allocation, and quitting the staff with the list grabbing failure in the list grabbing mode.
In the invention, the probability of individual selection in the group is in direct proportion to the fitness of the individual selection, the fitness can be equal to the judgment standard score in the scene, and the probability of each individual selection is obtained based on the judgment standard score.
In the invention, the sum of all probabilities is recorded as 1, and the proportion of the current individual is calculated according to the probabilities, so that the proportion of each individual in the whole with the sum of 1 can be obtained, and further, the cumulative probability can be obtained.
In the invention, the random number between 0 and 1 is obtained, and when the random number falls into the interval corresponding to a certain individual, the current individual is selected, so that the probability and the actual capacity of the staff are considered.
In the invention, in the form-robbing mode, the form-robbing can be directly opened for the staff meeting the requirements, the range can also be reduced, the form-robbing can be opened for the staff in the selected staff interval, and the effective rotation of the work form can be ensured.
And 4, step 4: and if the work order is responded within the preset time, ending the task allocation corresponding to the current work order, otherwise, carrying out the next step.
And 5: and (5) judging whether secondary dispatching is needed, if so, returning to the step 2, and starting a processing mechanism.
And if the employee of the order robber or the employee of the dispatched order does not execute the work order within the preset time, performing a punishment mechanism and feeding back the management personnel.
In the invention, after the order is robbed or dispatched, whether the work order is responded or received needs to be judged, if the response time of the task exceeds the preset time, such as 2 minutes, the secondary dispatching is carried out, and the secondary dispatching is recorded into a punishment mechanism and fed back to a manager; if the task response time does not exceed 2 minutes, the waiting is continued, and if the task is normally received, the task is executed.
In the invention, after the task is finished, the task initiator comprehensively evaluates the staff according to the task completion quality, the task completion efficiency and the like of the staff.
In the invention, after the task is completed, the state of the staff is switched to an idle state to prepare for the execution of the next task, and meanwhile, after the evaluation of the task is received, the star level of the staff is updated.
In the present invention, as shown in fig. 1, from the main flow of the work order platform processing, the principle is as follows:
after the work order is generated, if the work order is firstly dispatched, the work order grabbing mode is carried out, otherwise, the work order dispatching mode is the work order dispatching mode, the work order dispatching is carried out after the determined staff are obtained, whether the work order is dispatched again (secondary dispatching) is determined according to timeliness of work order response, and the staff who do not receive the work order in time are punished and fed back to the management staff.
In the present invention, as shown in fig. 2, from the main flow of employee work order processing, the principle is as follows:
after the work order response, the current staff is in the selected staff interval, the current staff is subjected to order grabbing based on the requirement if the current staff is in an order grabbing mode, otherwise, the current staff is subjected to order dispatching for the order dispatching mode, no matter which mode is adopted, as long as the work order is successfully obtained, the work order is required to be confirmed in the preset time, feedback is executed and submitted until updated star-level information is obtained after the task is completed, and a punishment link is entered if the work order is not confirmed in the preset time.
In the invention, the flow of the order grabbing processing and the flow of the order dispatching processing are shown in fig. 3 and fig. 4, both are carried out on the basis of screening the employees meeting the conditions, when the number of matched employees is 0 and 1, the matching employees are the same in nature, and the difference between the two is that when the number of matched employees is more than 1, the employees in the selected employee interval can be directly grabbed and the orders can be grabbed in the order grabbing mode, and the orders are directly selected in the order dispatching mode.

Claims (10)

1. A task allocation method based on crowd sensing is characterized in that: the method comprises the following steps:
step 1: generating a work order related to the task;
step 2: processing the work order based on data obtained by crowd sensing, and screening out the employees meeting the conditions;
and step 3: dispatching the work order;
and 4, step 4: if the work order is responded within the preset time, ending the task allocation corresponding to the current work order, otherwise, performing the next step;
and 5: and (5) judging whether secondary dispatching is needed, if so, returning to the step 2, and starting a processing mechanism.
2. The task allocation method based on crowd sensing as claimed in claim 1, wherein: in the step 1, the work order is generated from the user, the AI device and the staff, and the task information corresponding to the work order includes the task type, the task position and the task grade.
3. The task allocation method based on crowd sensing as claimed in claim 1, wherein: in the step 2, the data obtained based on crowd sensing comprises staff states, staff positions, staff star levels, task types and service positions; the screening conditions comprise that the staff state is idle, the staff position and the service position meet preset conditions, the staff star level meets the preset conditions, the task types are matched, and the staff judgment standard score reaches a threshold value.
4. The task allocation method based on crowd sensing as claimed in claim 3, wherein: the employee judgment standard is W, and W is equal to PS·PNS/DPTWherein P isSFor employee status, Idle is 1, not nullIdle is 0, PNSIs the star rating of the staff, DPTThe distance between the employee location and the service location; setting a threshold eta, and selecting staff with W judgment standard greater than eta, wherein eta is greater than 0.
5. The method of claim 4, wherein the task allocation method based on crowd sensing comprises: the step of obtaining the staff star rating comprises the following steps:
step a.1: let the current star rating of any non-new employee be PCSLet the current star level of the new employee be PIS；
Step a.2: for non-new employees, any task evaluation T is obtainedEActual execution time of task ATAnd expected completion time E of taskT(ii) a Computing employee execution efficiency PE＝ET/AT；
Step a.3: setting a default rating of a task to TDThen the star level of the employee is updated to PNS，PNS＝(1-m)·PCS+m·TE·PEWhere m is the update factor, 0<m<1; if any task does not obtain evaluation, automatically obtaining default evaluation, TE＝TD；
Step a.4: if PNSGreater than 10, then PNSIs 10, otherwise P calculated in step a.3 is retainedNSNumerical value, return data.
6. The method of claim 4, wherein the task allocation method based on crowd sensing comprises: the distance between the employee position and the service position of the schedulable employee is DPT，DPT＝|XT-XP|+|YT-YPL wherein (X)T,YT) As task position coordinates, (X)p,YP) Is the employee position coordinates.
7. The task allocation method based on crowd sensing as claimed in claim 1, wherein: in the step 3, the dispatch of the work order comprises the following steps:
step 3.1: based on the number of the staff meeting the conditions screened in the step 2, if the number of the matched staff is 0, directly judging that the scheduling fails, recording the times and feeding back the times to a manager, and manually distributing the task, otherwise, carrying out the next step;
step 3.2: if the number of matched employees is equal to 1, directly distributing the tasks to the matched employees in a dispatching mode and carrying out the step 4, otherwise, carrying out the next step;
step 3.3: if the work order is assigned for the first time, the task is assigned in an order grabbing mode, otherwise, the task is assigned in an order assigning mode; and 4, after the task is distributed, performing step 4.
8. The task allocation method based on crowd sensing as claimed in claim 7, wherein: in step 3.3, the task allocation includes the following steps:
step 3.3.1: obtaining information of all screened employees meeting the conditions;
step 3.3.2: adaptively selecting an employee interval;
step 3.3.3: if the order grabbing mode is adopted, directly grabbing orders or grabbing orders of the employees in the selected employee interval, otherwise dispatching orders in the selected employee interval;
step 3.3.4: and (5) completing task allocation, and quitting the staff with the list grabbing failure in the list grabbing mode.
9. The method of claim 8, wherein the task allocation method based on crowd sensing comprises: in the step 3.3.2, selecting the employee interval includes the following steps:
step 3.3.2.1: initializing each employee judgment standard score;
step 3.3.2.2: calculating any employee x based on employee judgment standard scoresiSelection probability of
Figure FDA0003102460900000031
Wherein, w (x)i) For employee xiThe judgment standard is divided, wherein N is the total number of the staff;
step (ii) of3.3.2.3: calculating the percentage of the selected probability of each employee by taking the sum of the selected probabilities of all employees as 1, and calculating the cumulative probability of each employee in sequence
Figure FDA0003102460900000032
Step 3.3.2.4: in the interval [0,1]Randomly generating a number r, judging which interval the number falls in, and if the number falls in the staff xiCorresponding interval Qxi-1～QxiThen employee x corresponding to the intervaliAnd (6) selecting.
10. The task allocation method based on crowd sensing as claimed in claim 7, wherein: and if the employee of the order robber or the employee of the dispatched order does not execute the work order within the preset time, performing a punishment mechanism and feeding back the management personnel.
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Application publication date: 20210903