CN113139795B - Business process task scheduling system, equipment and method based on personal schedule assistant - Google Patents
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Abstract
The invention provides a business process task scheduling system based on a personal schedule assistant, which comprises a personal schedule assistant subsystem and a business process task scheduling engine subsystem, wherein the personal schedule assistant subsystem is configured to execute task completion time, user interaction and task scheduling; wherein the business process task scheduling engine subsystem is configured to perform task pre-allocation, task allocation, and task completion time prediction.
Description
Technical Field
The invention relates to the technical field of business process task allocation, in particular to a business process task scheduling system, equipment and method based on a personal schedule assistant.
Background
In business processes, how to select suitable personnel for processing different tasks is an important problem. The status of personnel changes at any time, and some emergency situations may occur, so that tasks cannot be completed on time.
The current way of distributing personnel in the business process mainly comprises the following steps: (1) And (3) pushing, judging personnel and task conditions by the system, and directly distributing tasks to the proper personnel. (2) And (5) pulling, wherein the system sends the task to be received to all candidate persons, and waits for the persons to accept the task and submit the task. The push-pull modes have respective advantages and disadvantages, the push-pull distribution mode is distributed by the system, the relative stability of the system can be ensured, all tasks are ensured to be in charge of personnel, the situation of the personnel is not considered, the flexibility is not realized, and the complex industrial production environment is difficult to adapt. The distribution of the pull is relatively flexible, but may result in no personnel handling of the task. And both cases cannot deal with the situation that when the assigned personnel has an emergency situation and cannot complete the task, the assigned personnel cannot timely transfer other people.
Disclosure of Invention
The invention aims to provide a business process task scheduling system, equipment and a method based on a personal schedule assistant, which can realize a distribution mode of combining push-pull and automatic feedback coordination, wherein a user interacts with the personal schedule assistant, the personal schedule assistant automatically feeds back to a business process task scheduling engine, and the task scheduling engine selects proper personnel according to feedback and a predicted result and performs real-time replacement when the distributed personnel cannot complete tasks in time.
To achieve at least one of the objects of the invention, there is provided a personal calendar assistant-based business process task scheduling system including a personal calendar assistant subsystem and a business process task scheduling engine subsystem,
The personal schedule assistant subsystem is configured to execute task completion time, user interaction and task scheduling, when a task arrives in the personal schedule assistant, the personal schedule assistant subsystem interacts with a user to acquire information of a user feedback task, performs scheduling on the task according to task priority, predicts the task completion time according to the user feedback, performs scheduling on the task, and performs interaction with the business process task scheduling engine subsystem;
The business process task scheduling engine subsystem is configured to execute task pre-allocation, task allocation and task completion time prediction, when the task arrives, the task enters a pre-allocation stage, the task is pre-allocated to all candidates, and feedback information of a user is acquired within a set time range; and after the business process task scheduling engine subsystem acquires all feedback information, the task enters an allocation stage, and optimal personnel allocation task is selected.
In some embodiments, the personal schedule assistant subsystem includes a user interaction unit, a task prediction unit and a task scheduling unit, wherein the user interaction unit is configured to interact with a user to obtain current task information, task information adjustment and personal assistant adjustment, the task prediction unit is configured to predict task completion time, and the task scheduling unit is configured to sequence task execution, feed back task information with the business process task scheduling engine subsystem in real time.
In some embodiments, the user interaction unit of the personal calendar assistant subsystem includes a task real-time acquisition module, a task real-time adjustment module and a personal assistant configuration module, where the task real-time acquisition module is configured to acquire a user pre-allocation task and an allocated task situation in real time; the task real-time adjustment module is used for executing task adjustment and task refusing, wherein executing task adjustment comprises executing task priority and executing adjustment of predicted completion time, the priority is the priority degree of the task, the predicted completion time is adjusted according to the predicted completion time of the system, the dynamic adjustment is executed in response to the actual condition requirement of a user, the task refusing is in response to the emergency task condition requirement of the user, the user is allowed to refuse to accept the task, and the task is fed back to the business process task scheduling engine subsystem through the personal schedule assistant subsystem; the personal schedule assistant configuration module is used for setting a maximum workload and a default priority, wherein the workload is a workload measurement of a task, and is evaluated by a system, the maximum workload is the maximum task workload sum allowed to be accepted by the user, and the default priority is the default priority when the task arrives.
In some embodiments, wherein for a current pre-assigned task and an assigned task, the task prediction unit of the personal calendar assistant subsystem builds a task completion time prediction algorithm model, predicts the time that the task needs to consume when it arrives or when it adjusts.
In some embodiments, the task prediction unit includes a task completion time prediction algorithm model data acquisition module, where the task completion time prediction algorithm model data acquisition module acquires all instances under the task type, and acquires task workload under each instance and task completion time, where the workload is measured by a task according to workload, the task workload of an instance is a sum of workloads of people when receiving the task, and the task completion time is time required for task completion.
In some embodiments, wherein the task prediction unit further comprises a task completion time prediction algorithm model training module that trains the regression model according to the workload and the completion time
T～aL+∈
Wherein T is the completion time, L is the workload, a is the coefficient of L, E is the residual error of the regression model, and the training model obtains the regression coefficient of the workload.
In some embodiments, wherein the task prediction unit further comprises a task completion time prediction module that predicts a completion time of a person under a task of the task type with a current person workload as inputWherein/>Regression coefficients for the workload are obtained for the training model.
In some embodiments, the task scheduling unit of the personal calendar assistant subsystem includes a task ordering module and an automatic feedback module, wherein the task ordering module orders tasks according to priorities set by a user, and determines a priority order of performing tasks when a pre-allocation task arrives or in response to a task modification operation of the user; the automatic feedback module performs pre-allocation task feedback interaction and allocated task feedback interaction with the business process task scheduling engine subsystem.
In some embodiments, the automatic feedback module includes a pre-allocation task feedback module and an allocated task feedback module, the pre-allocation task feedback module performs feedback in a pre-allocation task stage of the business process task scheduling engine subsystem, and automatically performs feedback when a user is detected to determine a task or waiting time is over, the allocated task feedback module performs feedback to the business process task scheduling engine subsystem when the business process task scheduling engine subsystem allocates a task to the user, and instructs the business process task scheduling engine subsystem to reschedule the task when the user is detected to be unable to complete the task in time or the user refuses to accept the task.
In some embodiments, wherein the task ordering module of the task scheduling unit of the personal calendar assistant subsystem performs task ordering, wherein the task ordering module is configured to: receiving a task k distributed by the business process task scheduling engine subsystem; if the current person does not execute the task, the task is taken as the task to be executed currently; if the current person is executing n tasks, wherein the workload of the n tasks is L 1,L2,…,Ln, the priority is K 1,K2,…,Kn, the predicted completion time is T 1,T2,…,Tn, the workload of the task K is L k, the priority is K k, the predicted completion time is T k, whether the person reaches the maximum workload is judged, if soThe task is judged to enter the next round; if it isJudging whether the task priority meets the condition or not; if the task priority/>The task is carried into the next round of judgment, if the task priority/>The task is taken as the currently executed task; when the task k enters the next round of judgment, the time is advanced to one task to finish, and the task prediction finishing time is increased/>I.e.At this time, the information of the personnel completing a task is received, and whether the task k is used as the task executed by the round is judged again; and the like, until the j-th loop task k is allowed to be added into the task; the final task completion time T k is calculated.
In some embodiments, the business process task scheduling engine subsystem includes a current task completion time prediction unit, a task pre-allocation scheduling unit, and a task allocation scheduling unit, wherein the current task completion time prediction unit is configured to predict a current task completion time; wherein the task pre-allocation scheduling unit is configured to perform task pre-allocation, requiring candidate feedback on the predicted completion time; the task allocation scheduling unit is configured to execute task allocation, and corresponding personnel execution allocation is selected according to the predicted time and the feedback result.
In some embodiments, the current task completion time prediction unit of the business process task scheduling engine subsystem includes a historical data acquisition module, wherein the historical data acquisition module acquires historical data of a specific person p and a specific task k, including n business process contexts C 1,C2,…,Cn, a person workload L, and a completion time T of a corresponding task.
In some embodiments, the current task completion time prediction unit of the business process task scheduling engine subsystem further includes a current task completion time prediction model training module, where the current task completion time prediction model training module trains a regression model according to the obtained n business process contexts C 1,C2,…,Cn, the personnel load L, the completion time T, and a regression model formula as follows:
Where a i represents the coefficient of the ith business process context C i, b represents the coefficient of the workload L, and ε is represented as the regression equation residuals, respectively. Training to obtain regression coefficients for business process context C 1,C2,…,Cn Regression coefficient of workload/>
In some embodiments, the current task completion time prediction unit of the business process task scheduling engine subsystem further includes a current task completion time prediction module that uses a business process context c 1,c2, …, a person load l as input, and uses a machine learning model to predict completion times of different personsThe task pre-allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate pre-allocation module and a task pushing module, wherein the candidate pre-allocation module traverses personnel in sequence and adds all candidates into a pre-allocation queue; assuming that the pre-allocation queue is not empty, the task pushing module sets waiting time, pushes the task to the personal schedule assistant subsystem of all personnel of the pre-allocation queue, and indicates the personal schedule assistant subsystem to feed back the result of completing the task within the waiting time.
In some embodiments, wherein the task allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate predicted time calculation module, wherein the feedback time validity determination module is configured to:
Acquiring feedback time T R of a corresponding person, and judging whether the feedback of the person is effective; the effective judgment formula of the feedback time is as follows:
wherein n is the historical data quantity, k is the threshold value of the historical quantity, epsilon T is the proportion of the maximum effective feedback time under the condition that n is less than or equal to k, and T c is the prediction time for predicting different personnel; r is the historical performance rate, θ is the performance rate threshold, Is a history predictive value,/>For historical feedback values, α is the amplification factor.
In some embodiments, the task allocation scheduling unit of the business process task scheduling engine subsystem further includes a feedback time valid determination module configured to:
When n is less than or equal to k, if the value of the promised time T R is greater than the specific proportion of the predicted time T c, user feedback is effective;
When n is greater than k, the personnel feedback is invalid when the historical performance rate r is less than or equal to theta;
when n > k, the historical performance rate r > θ, based on the historical performance rate r and the historical predicted value Historical feedback value/>Judging whether the personnel side is effective.
In some embodiments, the task allocation scheduling unit of the business process task scheduling engine subsystem further includes a weighted integration module, and if the personnel feedback is valid, the weighted integration module performs weighted integration on the feedback time and the predicted time according to the historical performance rate and the performance rate threshold to obtain an integration time.
In some embodiments, the task allocation scheduling unit of the business process task scheduling engine subsystem further includes a sorting task allocation module, where the sorting task allocation module sorts the people in a descending order according to the obtained integration time, and selects a person allocation task with a minimum integration time value.
According to another aspect of the present invention, there is also provided a personal calendar assistant-based business process task scheduling method, including the task scheduling steps of:
When a task arrives, a task scheduling engine acquires all candidates of the task and sends a pre-allocation task to personal schedule assistants of the candidates;
the personal schedule assistant of the candidate receives a pre-allocation task, performs interaction with a user, coordinates task priority, task monopolization and task prediction completion time;
The personal schedule assistant determines the task execution sequence according to the priority, calculates the final predicted completion time of the task, and feeds back the final predicted completion time to the business process task scheduling engine;
The business process task scheduling engine calculates the integration completion time according to the predicted completion time, the historical performance rate and the feedback time, sorts the candidates, selects a first optimal candidate, and pushes the allocated tasks and the integration completion time of the tasks to the personal schedule assistant of the first optimal candidate;
the personal schedule assistant of the distributor receives the distributed tasks, interacts with the user and indicates the user to complete the tasks in the integration completion time; if the user cannot complete the task within the integrated completion time, the personal schedule assistant sends a new task which cannot be completed on time to the business process task scheduling engine and sends feedback completion time;
the business process task scheduling engine takes the feedback completion time as the integration completion time of the first optimal candidate, sorts all candidates, selects a second optimal candidate, and pushes the allocated tasks and the integration completion time of the tasks to the personal schedule assistant of the second optimal candidate; if the re-optimal candidate is not the first optimal candidate, notifying the first optimal candidate that the task has been completed by the other people;
And
And acquiring feedback information of the personnel completing the corresponding task, and informing the personal schedule assistant by the business process task scheduling engine, wherein the task is ended.
In some embodiments, the business process task scheduling method based on a personal calendar assistant further comprises a personal calendar assistant task prediction step, wherein the personal calendar assistant task prediction step comprises the following steps:
All m instances under the task type k are acquired, and the task workload l=l 1,l2,…,lm under the m instances and the task completion time t=t 1,t2,…,tm are acquired. The workload is measured by a task according to workload, the task workload l of an instance is the sum of the workload of personnel when the task is accepted, and the task completion time is the time required by the task completion;
Training regression model based on workload L and completion time T
T～aL+∈
Wherein a is a coefficient of L, E is a residual error of a regression model, and the training model obtains the regression coefficient of the workload L
And
With the current personnel workload l as input, predict the personnel completion time under the k-type task
In some embodiments, the business process task scheduling method based on the personal calendar assistant further comprises a task ordering step, wherein the task ordering step comprises the following steps:
the business process task scheduling engine distributes tasks k to the personal schedule assistant;
If the current person does not execute the task, the task k is used as the task to be executed currently;
If the current person is executing n tasks, wherein the workload of the n tasks is L 1,L2,…,Ln, the priority is K 1,K2,…,Kn, the predicted completion time is T 1,T2,…,Tn, the workload of the task K is L k, the priority is K k, the predicted completion time is T k, and whether the person reaches the maximum workload W is judged; if it is The task is judged to enter the next round; if/>Judging whether the task priority meets the condition or not; if the task priorityThe task is carried into the next round of judgment, if the task priority/>The task is taken as the currently executed task;
And
When the task k enters the next round of judgment, the time is advanced to one task for completion, and the task prediction completion time is increasedI.e./>At this time, the personnel completes a task and judges whether the task k is used as the task executed by the round again; and so on until task k is allowed to join the task in the j-th loop. The final task completion time T k is calculated.
In some embodiments, the business process task scheduling method based on the personal calendar assistant further comprises a business process task scheduling engine prediction step, wherein the business process task scheduling engine prediction step comprises the following steps:
The method comprises the steps of obtaining historical data of a specific person p and a specific task k, wherein the historical data comprise n business process contexts C 1,C2,…,Cn, a person workload L and a completion time T of a corresponding task;
According to the acquired n business process contexts C 1,C2,…,Cn, the personnel load L, the completion time T, training a regression model, wherein the formula of the regression model is as follows:
wherein a i represents the coefficient of the ith business process context C i, b represents the coefficient of the workload L, and E represents the regression equation residuals respectively; training to obtain regression coefficients for business process context C 1,C2,…,Cn Regression coefficient of workload/>And
Taking business process contexts c 1,c2 and … and personnel load l as input, and predicting completion time of different personnel by adopting machine learning model
In some embodiments, the business process task scheduling method based on the personal calendar assistant further comprises a task pre-allocation scheduling step, wherein the task pre-allocation scheduling step comprises the following steps:
traversing the personnel in sequence, and adding all candidates into a pre-allocation queue;
And
Assuming that the pre-allocation queue is not empty, a wait time T w is set, pushing the task to the personal assistants of all people in the pre-allocation queue, requiring the personal assistants to feed back the results of completing the task within the wait time T w.
In some embodiments, the business process task scheduling method based on the personal calendar assistant further comprises a task allocation scheduling step, wherein the task allocation scheduling step comprises the following steps:
Calculating a predicted time T c of the corresponding candidate;
Acquiring feedback time T R of a corresponding person, and judging whether the feedback of the person is effective; the effective judgment formula of the feedback time is as follows:
Wherein n is the historical data quantity, k is the threshold value of the historical quantity, epsilon T is the proportion of the maximum effective feedback time under the condition that n is less than or equal to k, and T c is the prediction time for predicting different personnel; r is the historical performance rate, θ is the performance rate threshold, T pre_c is the historical predicted value, T pre_R is the historical feedback value, and α is the amplification factor;
When n is less than or equal to k, if the value of the promised time T R is greater than the specific proportion of the predicted time T c, user feedback is effective;
When n is greater than k, the personnel feedback is invalid when the historical performance rate r is less than or equal to theta;
When n > k, the historical performance rate r > theta, and according to the historical performance rate r and the historical predicted value T pre_c, the historical feedback value T pre_R judges whether the personnel side is effective or not;
If the personnel feedback is effective, the feedback time T R and the predicted time T c are integrated into integrated time in a weighting mode according to the historical performance rate r and the performance rate threshold value theta;
And
And sorting the personnel in descending order according to the integration time, and selecting the personnel with the minimum integration time value to distribute tasks.
According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the personal calendar assistant based business process task scheduling method.
According to another aspect of the present invention, there is also provided a personal calendar assistant-based business process task scheduling apparatus, including:
A memory for storing a software application,
A processor for executing the software application,
Wherein, each program of the software application program correspondingly executes the steps in the business process task scheduling method based on the personal schedule assistant.
Drawings
Fig. 1 is an architectural diagram of a personal calendar assistant-based business process task scheduling system according to one embodiment of the present invention.
Fig. 2 is an overall flowchart of a business process task scheduling method based on a personal calendar assistant according to one embodiment of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It will be understood that the terms "a" and "an" should be interpreted as referring to "at least one" or "one or more," i.e., in one embodiment, the number of elements may be one, while in another embodiment, the number of elements may be plural, and the term "a" should not be interpreted as limiting the number.
The present invention relates to computer programs. Fig. 1 is a schematic diagram of a business process task scheduling system based on a personal calendar assistant according to a preferred embodiment of the present invention. The business process task scheduling system based on the personal schedule assistant can realize a distribution mode of combining push-pull and automatic feedback coordination, a user interacts with the personal schedule assistant, the personal schedule assistant automatically feeds back to a business process task scheduling engine (for short, a task scheduling engine), the task scheduling engine selects proper personnel according to feedback and a predicted result, and the personnel can not timely complete tasks after being distributed, and the personnel can be replaced in real time.
Specifically, the business process task scheduling system based on the personal schedule assistant comprises a personal schedule assistant subsystem and a business process task scheduling engine subsystem. The personal calendar assistant subsystem is configured to perform task completion times, user interactions, and task scheduling. When a task arrives in the personal schedule assistant, the personal schedule assistant subsystem performs interaction with a user, acquires information of a task fed back by the user, performs scheduling on the task according to the task priority, predicts the completion time of the task according to the user feedback, performs scheduling on the task, and performs interaction with the business process task scheduling engine subsystem.
The business process task scheduling engine subsystem is configured to perform task pre-allocation, task allocation, and task completion time prediction. When the task arrives, the task enters a pre-allocation stage, the task is pre-allocated to all candidates, and feedback information of a user is acquired within a set time range; and after the business process task scheduling engine subsystem acquires all feedback information, the task enters an allocation stage, and optimal personnel allocation task is selected.
Specifically, the personal schedule assistant subsystem comprises a user interaction unit, a task prediction unit and a task scheduling unit. The user interaction unit is configured to perform interaction with a user, obtain current task information, task information adjustment, and personal assistant adjustment. The task prediction unit is configured to predict a task completion time. The task scheduling unit is configured to sequence the execution of the task execution sequence, and feed back task information in real time with the feedback of the business process task scheduling engine subsystem.
More specifically, the user interaction unit of the personal calendar assistant subsystem includes a task real-time acquisition module, a task real-time adjustment module, and a personal assistant configuration module. The task real-time acquisition module is used for acquiring the pre-allocation task of the user and the situation of the allocated task in real time. The task real-time adjustment module is used for executing task adjustment and task refusal. Preferably, in a particular embodiment, performing task adjustment includes performing adjustment of task priority and performing predicted completion time. The priority is the priority of the task, and can be classified into four classes A/B/C/D (A > B > C > D), for example. The predicted completion time is adjusted to perform dynamic adjustment in response to actual condition demands of the user based on the predicted completion time of the system. Task refusal is to allow the user to refuse to accept the task in response to the urgent task situation requirement of the user, and the task is fed back to the business process task scheduling engine subsystem through the personal schedule assistant subsystem. The personal schedule assistant configuration module is used for setting the maximum workload W and the default priority K: wherein the workload is a workload measurement of the task, and the maximum workload W is a maximum task workload sum allowed to be accepted by the user, which is evaluated by the system; the default priority K is the default priority at which the task arrives.
More specifically, for the current pre-assigned task and the assigned task, the task prediction unit of the personal calendar assistant subsystem establishes a task completion time prediction algorithm model, and predicts the time that the task needs to consume when the task arrives or when the task is adjusted. The task prediction unit comprises a task completion time prediction algorithm model data acquisition module, a task completion time prediction algorithm model training module and a task completion time prediction module.
In a specific embodiment, the task completion time prediction algorithm model data obtaining module obtains all m instances under the task type k, and obtains task workload l=l 1,l2,…,lm under the m instances and task completion time t=t 1,t2,…,tm. The workload is measured by the task according to workload, the task workload l of the instance is the sum of the workload of the personnel when the task is received, and the task completion time is the time required by the task completion.
Further, the task completion time prediction algorithm model training module trains a regression model according to the workload L and the completion time T
T～aL+∈
Wherein a is a coefficient of L, E is a residual error of a regression model, and the training model obtains the regression coefficient of the workload L
Further, the task completion time prediction module takes the current personnel workload l as an input to predict the completion time of the personnel under the k-type task
More specifically, the task scheduling unit of the personal calendar assistant subsystem includes a task ordering module and an automatic feedback module. The task ordering module orders the tasks according to the priorities set by the user, and when the pre-allocation task arrives or in response to the task modification operation of the user, the task ordering module determines the priority order of the tasks. The automatic feedback module performs pre-allocation task feedback interaction and allocated task feedback interaction with the business process task scheduling engine subsystem.
More specifically, the automatic feedback module comprises a pre-allocation task feedback module and an allocated task feedback module, wherein the pre-allocation task feedback module performs feedback in a pre-allocation task stage of the business process task scheduling engine subsystem, and automatically performs feedback when a user is detected to determine a task or waiting time T w is finished. When the distributed task feedback module detects that the user cannot complete tasks in time or the user refuses to accept tasks due to factors such as urgent tasks when the task scheduling engine subsystem distributes tasks to the user, the distributed task feedback module feeds back the tasks to the task scheduling engine subsystem, and instructs the task scheduling engine subsystem to reschedule the tasks.
More specifically, the task ordering module of the task scheduling unit of the personal calendar assistant subsystem performs task ordering, in a specific embodiment, the task ordering module receives a task k distributed by the business process task scheduling engine subsystem; if the current person does not execute the task, the task k is used as the task to be executed currently; if the current person is executing n tasks, wherein the workload of the n tasks is L 1,L2,…,Ln, the priority is K 1,K2,…,Kn, the predicted completion time is T 1,T2,…,Tn, the workload of the task K is L k, the priority is K k, the predicted completion time is T k, and whether the person reaches the maximum workload W is judged. If it isThe task is entered into the next round of judgment. If/>And judging whether the task priority meets the condition. If the task priority/>The task is carried into the next round of judgment, if the task priority/>The task is taken as the currently executed task; when the task k enters the next round of judgment, the time is advanced to one task to finish, and the task prediction finishing time is increased/>I.e./>At this time, the person completes one task, and again performs a judgment as to whether the task k is the task to be executed by the round. And so on until task k is allowed to join the task in the j-th loop. The final task completion time T k is calculated.
Further, the business process task scheduling engine subsystem comprises a current task completion time prediction unit, a task pre-allocation scheduling unit and a task allocation scheduling unit. The current task completion time prediction unit is configured to predict a current task completion time. The task pre-allocation scheduling unit is configured to perform task pre-allocation, requiring candidate feedback on the predicted completion time. The task allocation scheduling unit is configured to execute task allocation, and corresponding personnel execution allocation is selected according to the predicted time and the feedback result.
Specifically, the current task completion time prediction unit of the business process task scheduling engine subsystem comprises a historical data acquisition module, a current task completion time prediction model training module and a current task completion time prediction module.
In a specific embodiment, the historical data acquisition module acquires the historical data of the specific person p and the specific task k, including n business process contexts C 1,C2,…,Cn, a person workload L and a completion time T of the corresponding task.
Further, the current task completion time prediction model training module trains a regression model according to the acquired n business process contexts C 1,C2,…,Cn, the personnel load L and the completion time T, and the regression model formula is as follows:
Where a i represents the coefficient of the ith business process context C i, b represents the coefficient of the workload L, and ε is represented as the regression equation residuals, respectively. Training to obtain regression coefficients for business process context C 1,C2,…,Cn Regression coefficient of workload/>
Further, the current task completion time prediction module takes business process contexts c 1,c2, … and personnel load l as input, and adopts a machine learning model to predict completion time of different personnel
The task pre-allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate pre-allocation module and a task pushing module, wherein the candidate pre-allocation module traverses people in sequence and adds all candidates into a pre-allocation queue. Assuming that the pre-allocation queue is not empty, the task pushing module sets a waiting time Tw, pushes the task to the personal schedule assistant subsystem of all people in the pre-allocation queue, and instructs the personal schedule assistant subsystem to feed back a result of completing the task within the waiting time T w.
Specifically, the task allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate prediction time calculation module, a feedback time effective judgment module, a weighted integration module and a sequencing task allocation module.
In a specific embodiment, the candidate predicted time calculation module calculates the predicted time T c for the corresponding candidate.
Further, the feedback time effective judging module acquires feedback time T R of the corresponding person and judges whether the person feedback is effective. The effective judgment formula of the feedback time is as follows:
wherein n is the historical data quantity, k is the threshold value of the historical quantity, epsilon T is the proportion of the maximum effective feedback time under the condition that n is less than or equal to k, and T c is the prediction time for predicting different personnel; r is the historical performance rate, θ is the performance rate threshold, Is a history predictive value,/>The historical feedback value is the historical feedback value, and alpha is the amplification factor;
The feedback time validity judgment module is configured to:
When n is less than or equal to k, if the value of the promised time T R is greater than the specific proportion of the predicted time T c, user feedback is effective;
When n is greater than k, the personnel feedback is invalid when the historical performance rate r is less than or equal to theta;
when n > k, the historical performance rate r > θ, based on the historical performance rate r and the historical predicted value Historical feedback value/>Judging whether the personnel side is effective.
Further, if the person feedback is valid, the weighted integration module performs weighted integration on the feedback time T R and the predicted time T c according to the historical performance rate r and the threshold θ to obtain an integrated time T I.
Further, the sorting task allocation module sorts the personnel in a descending order according to T I, and selects the personnel with minimum T I to allocate the task.
According to another aspect of the invention, the invention further provides a business process task scheduling method based on the personal schedule assistant. Fig. 2 is a flowchart of the business process task scheduling method based on the personal schedule assistant according to the present invention, and illustrates a solution for controlling or processing a computer external object or an internal object by executing a computer program programmed according to the above process on the basis of a computer program processing flow. It will be appreciated that the term "computer" in the present invention refers not only to desktop computers, notebook computers, tablet computers, etc., but also includes other intelligent electronic devices that can run according to a program to process data.
Specifically, as shown in fig. 2, the business process task scheduling method based on the personal schedule assistant includes the following steps:
s100: when a task arrives, a task scheduling engine acquires all candidates of the task and sends a pre-allocation task to personal schedule assistants of the candidates;
S200: the personal schedule assistant of the candidate receives a pre-allocation task, performs interaction with a user, coordinates task priority, task monopolization and task prediction completion time;
s300: the personal schedule assistant determines the task execution sequence according to the priority, calculates the final predicted completion time of the task, and feeds back the final predicted completion time to the business process task scheduling engine;
s400: the business process task scheduling engine calculates an integration completion time T I according to the predicted completion time, the historical performance rate and the feedback time, sorts the candidates, selects an optimal candidate o, and pushes the allocated tasks and the integration completion time T I of the tasks to the personal schedule assistant of the optimal candidate;
S500: the personal schedule assistant of the distributor receives the distributed tasks, interacts with the user, and indicates the user to complete the tasks in the time of T I; if the user cannot complete the task in the time T I under the conditions of emergency task occurrence and the like, the personal schedule assistant sends a new task which cannot be completed in time to the business process task scheduling engine and sends feedback completion time;
S600: the business process task scheduling engine takes the feedback completion time as the integration completion time of the candidate o, sorts all candidates, selects the optimal candidate, and pushes the assigned task and the integration completion time T I of the task to the personal schedule assistant of the optimal candidate; if the optimal candidate is not candidate o, notifying the candidate o that the task is completed by other people;
S700: and acquiring feedback information of the personnel completing the corresponding task, and informing the personal schedule assistant by the business process task scheduling engine, wherein the task is ended.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided in the form of a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
It will be appreciated by those skilled in the art that the business process task scheduling method based on the personal calendar assistant of the present invention may be implemented by hardware, software, or a combination of hardware and software. The invention may be implemented in a centralized fashion in at least one computer system or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods is suited. The combination of hardware and software may be a general-purpose computer system with a computer program installed thereon, and the computer system may be controlled to operate according to the method by installing and executing the program.
A typical combination of hardware and software could be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the personal calendar assistant-based business process task scheduling method of the present invention.
The present invention can be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein. The computer program product is embodied in one or more computer-readable storage media having computer-readable program code embodied therein. According to another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, is capable of performing the steps of the personal calendar assistant-based business process task scheduling method of the present invention. Computer storage media is the medium in computer memory that stores some discrete physical quantity. Computer storage media includes, but is not limited to, semiconductors, disk storage, magnetic cores, drums, tapes, laser disks, and the like. It will be appreciated by those skilled in the art that the computer storage media is not limited to the foregoing examples, which are provided by way of example only and are not limiting of the invention.
According to another aspect of the present invention, there is also provided a personal calendar assistant-based business process task scheduling apparatus including: the system comprises a software application, a memory for storing the software application, and a processor for executing the software application. Each program of the software application program can correspondingly execute the steps in the business flow task scheduling method based on the personal schedule assistant.
It will be appreciated by persons skilled in the art that the present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products according to the invention. Each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and/or block diagram block or blocks.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are by way of example only and are not limiting. The objects of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been shown and described in the examples and embodiments of the invention may be modified or practiced without departing from such principles.
Claims (22)
1. A business process task scheduling system based on personal schedule assistant is characterized in that the business process task scheduling system based on personal schedule assistant comprises a personal schedule assistant subsystem and a business process task scheduling engine subsystem,
The personal schedule assistant subsystem is configured to execute task completion time, user interaction and task scheduling, when a task arrives in the personal schedule assistant, the personal schedule assistant subsystem interacts with a user to acquire information of a user feedback task, performs scheduling on the task according to task priority, predicts the task completion time according to the user feedback, performs scheduling on the task, and performs interaction with the business process task scheduling engine subsystem;
The business process task scheduling engine subsystem is configured to execute task pre-allocation, task allocation and task completion time prediction, when the task arrives, the task enters a pre-allocation stage, the task is pre-allocated to all candidates, and feedback information of a user is acquired within a set time range; after the business process task scheduling engine subsystem acquires all feedback information, the task enters an allocation stage, and optimal personnel allocation task is selected;
The business process task scheduling engine subsystem comprises a current task completion time prediction unit, a task pre-allocation scheduling unit and a task allocation scheduling unit, wherein the current task completion time prediction unit is configured to predict current task completion time; wherein the task pre-allocation scheduling unit is configured to perform task pre-allocation, requiring candidate feedback on the predicted completion time; the task allocation scheduling unit is configured to execute task allocation, and corresponding personnel execution allocation is selected according to the prediction time and the feedback result;
The task allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate prediction time calculation module and a feedback time effective judgment module, wherein the candidate prediction time calculation module calculates the prediction time of a corresponding candidate, and the feedback time effective judgment module is configured to:
Acquiring feedback time T R of a corresponding person, and judging whether the feedback of the person is effective; the effective judgment formula of the feedback time is as follows:
wherein n is the historical data quantity, k is the threshold value of the historical quantity, epsilon T is the proportion of the maximum effective feedback time under the condition that n is less than or equal to k, and T c is the prediction time for predicting different personnel; r is the historical performance rate, θ is the performance rate threshold, As a result of the historical prediction value,For historical feedback values, α is the amplification factor.
2. The personal calendar assistant-based business process task scheduling system according to claim 1, wherein the personal calendar assistant subsystem comprises a user interaction unit configured to interact with a user, obtain current task information, task information adjustment, and personal assistant adjustment, a task prediction unit configured to predict task completion time, and a task scheduling unit configured to order task execution order, feedback with the business process task scheduling engine subsystem, and feed back task information in real time.
3. The personal calendar assistant-based business process task scheduling system according to claim 2, wherein the user interaction unit of the personal calendar assistant subsystem comprises a task real-time acquisition module, a task real-time adjustment module and a personal calendar assistant configuration module, wherein the task real-time acquisition module is used for acquiring a user pre-allocation task and an allocated task situation in real time; the task real-time adjustment module is used for executing task adjustment and task refusing, wherein executing task adjustment comprises executing task priority and executing adjustment of predicted completion time, the priority is the priority degree of the task, the predicted completion time is adjusted according to the predicted completion time of the system, the dynamic adjustment is executed in response to the actual condition requirement of a user, the task refusing is in response to the emergency task condition requirement of the user, the user is allowed to refuse to accept the task, and the task is fed back to the business process task scheduling engine subsystem through the personal schedule assistant subsystem; the personal schedule assistant configuration module is used for setting a maximum workload and a default priority, wherein the workload is a workload measurement of a task, and is evaluated by a system, the maximum workload is the maximum task workload sum allowed to be accepted by the user, and the default priority is the default priority when the task arrives.
4. The personal calendar assistant-based business process task scheduling system as set forth in claim 2, wherein the task prediction unit of the personal calendar assistant subsystem builds a task completion time prediction algorithm model for a current pre-assigned task and an assigned task, predicting a time that the task needs to consume when the task arrives or when the task is adjusted.
5. The personal calendar assistant-based business process task scheduling system according to claim 4, wherein the task prediction unit comprises a task completion time prediction algorithm model data acquisition module, wherein the task completion time prediction algorithm model data acquisition module acquires all instances under the task type, acquires task workloads under each instance and task completion times, wherein the workload is measured by the task according to the workload, the task workload of an instance is the sum of the workload of the personnel when the task is accepted, and the task completion times are the times required for the task to complete.
6. The personal calendar assistant-based business process task scheduling system of claim 5, wherein the task prediction unit further comprises a task completion time prediction algorithm model training module that trains a regression model according to workload and completion time
T～aL+∈
Wherein T is the completion time, L is the workload, a is the coefficient of L, E is the residual error of the regression model, and the training model obtains the regression coefficient of the workload.
7. The personal calendar assistant-based business process task scheduling system as set forth in claim 6, wherein the task prediction unit further comprises a task completion time prediction module that predicts a completion time of a person under a task of the task type with a current person workload as an inputWherein the method comprises the steps ofRegression coefficients for the workload are obtained for the training model.
8. The personal calendar assistant-based business process task scheduling system according to claim 2, wherein the task scheduling unit of the personal calendar assistant subsystem comprises a task ordering module and an automatic feedback module, wherein the task ordering module orders tasks according to priorities set by a user, determines a priority of performing tasks when a pre-allocation task arrives or in response to a task modification operation of a user; the automatic feedback module performs pre-allocation task feedback interaction and allocated task feedback interaction with the business process task scheduling engine subsystem.
9. The personal calendar assistant-based business process task scheduling system of claim 8, wherein the automatic feedback module comprises a pre-allocation task feedback module that performs feedback at a pre-allocation task stage of the business process task scheduling engine subsystem, automatically performs feedback when a user determination task is detected or a waiting time is ended, and an allocated task feedback module that, when the business process task scheduling engine subsystem allocates a task to a user, instructs the business process task scheduling engine subsystem to reschedule the task when it is detected that the user cannot complete the task in time or the user refuses to accept the task.
10. The personal calendar assistant-based business process task scheduling system according to claim 8, wherein the task ordering module of the task scheduling unit of the personal calendar assistant subsystem performs task ordering, wherein the task ordering module is configured to: receiving a task k distributed by the business process task scheduling engine subsystem; if the current person does not execute the task, the task is taken as the task to be executed currently; if the current person is executing n tasks, wherein the workload of the n tasks is L 1,L2,...,Ln, the priority is K 1,K2,...,Kn, the predicted completion time is T 1,T2,...,Tn, the workload of the task K is L k, the priority is K k, the predicted completion time is T k, whether the person reaches the maximum workload is judged, if soThe task is judged to enter the next round; if/>Judging whether the task priority meets the condition or not; if the task priority/>The task is carried into the next round of judgment, if the task priority/>The task is taken as the currently executed task; when the task k enters the next round of judgment, the time is advanced to one task to finish, and the task prediction finishing time is increased/>I.e./> At this time, the information of the personnel completing a task is received, and whether the task k is used as the task executed by the round is judged again; and the like, until the j-th loop task k is allowed to be added into the task; the final task completion time T k is calculated.
11. The personal calendar assistant-based business process task scheduling system of claim 1, wherein the current task completion time prediction unit of the business process task scheduling engine subsystem comprises a historical data acquisition module, wherein the historical data acquisition module acquires historical data of a specific person p and a specific task k, including n business process contexts C 1,C2,...,Cn, a person workload L, and a completion time T of a corresponding task.
12. The personal calendar assistant-based business process task scheduling system of claim 11, wherein the current task completion time prediction unit of the business process task scheduling engine subsystem further comprises a current task completion time prediction model training module, wherein the current task completion time prediction model training module trains a regression model according to the acquired n business process contexts C 1,C2,...,Cn, the personnel workload L, the completion time T, the regression model formula as follows:
Wherein a i represents the coefficient of the ith business process context C i, b represents the coefficient of the workload L, E represents the residual of the regression equation respectively, and the regression coefficient of the business process context C 1,C2,...,Cn is obtained through training Regression coefficient of workload/>
13. The personal calendar assistant-based business process task scheduling system of claim 12, wherein the current task completion time prediction unit of the business process task scheduling engine subsystem further comprises a current task completion time prediction module that predicts completion times of different people using a machine learning model with business process context c 1,c2, personal workload L as inputThe task pre-allocation scheduling unit of the business process task scheduling engine subsystem comprises a candidate pre-allocation module and a task pushing module, wherein the candidate pre-allocation module traverses personnel in sequence and adds all candidates into a pre-allocation queue; assuming that the pre-allocation queue is not empty, the task pushing module sets waiting time, pushes the task to the personal schedule assistant subsystem of all personnel of the pre-allocation queue, and indicates the personal schedule assistant subsystem to feed back the result of completing the task within the waiting time.
14. The personal calendar assistant-based business process task scheduling system according to claim 1, wherein the feedback time validity judgment module is further configured to:
When n is less than or equal to k, if the value of the promised time T R is greater than the specific proportion of the predicted time T c, user feedback is effective;
when n is more than k, the historical performance rate r is less than or equal to theta, and the personnel feedback is invalid;
When n is larger than k, the history performance rate r is larger than theta, and according to the history performance rate r and the history predicted value Historical feedback valueJudging whether the personnel side is effective.
15. The personal calendar assistant-based business process task scheduling system of claim 14, wherein the task allocation scheduling unit of the business process task scheduling engine subsystem further comprises a weighted integration module that weight integrates feedback time and predicted time to obtain integration time based on historical performance rate and performance rate threshold if personnel feedback is available.
16. The personal calendar assistant-based business process task scheduling system according to claim 15, wherein the task allocation scheduling unit of the business process task scheduling engine subsystem further comprises a sort task allocation module that sorts persons in descending order according to the obtained integration time, and selects a person allocation task having a minimum integration time value.
17. The business process task scheduling method based on the personal schedule assistant is characterized by comprising the following task scheduling steps:
When a task arrives, a task scheduling engine acquires all candidates of the task and sends a pre-allocation task to personal schedule assistants of the candidates;
the personal schedule assistant of the candidate receives a pre-allocation task, performs interaction with a user, coordinates task priority, task monopolization and task prediction completion time;
The personal schedule assistant determines the task execution sequence according to the priority, calculates the final predicted completion time of the task, and feeds back the final predicted completion time to the business process task scheduling engine;
The business process task scheduling engine calculates the integration completion time according to the predicted completion time, the historical performance rate and the feedback time, sorts the candidates, selects a first optimal candidate, and pushes the allocated tasks and the integration completion time of the tasks to the personal schedule assistant of the first optimal candidate;
the personal schedule assistant of the distributor receives the distributed tasks, interacts with the user and indicates the user to complete the tasks in the integration completion time; if the user cannot complete the task within the integrated completion time, the personal schedule assistant sends a new task which cannot be completed on time to the business process task scheduling engine and sends feedback completion time;
the business process task scheduling engine takes the feedback completion time as the integration completion time of the first optimal candidate, sorts all candidates, selects a second optimal candidate, and pushes the allocated tasks and the integration completion time of the tasks to the personal schedule assistant of the second optimal candidate; if the re-optimal candidate is not the first optimal candidate, notifying the first optimal candidate that the task has been completed by the other people;
And
Acquiring feedback information of the completion of the corresponding task by personnel, and informing a personal schedule assistant by a business process task scheduling engine, wherein the task is ended;
The business process task scheduling method based on the personal schedule assistant further comprises a task pre-allocation scheduling step, wherein the task pre-allocation scheduling step comprises the following steps of:
traversing the personnel in sequence, and adding all candidates into a pre-allocation queue;
And
Assuming that the pre-allocation queue is not empty, setting a waiting time T w, pushing the task to personal assistants of all people in the pre-allocation queue, and requiring the personal assistants to feed back the result of completing the task within the waiting time T w;
the business process task scheduling method based on the personal schedule assistant further comprises a task allocation scheduling step, wherein the task allocation scheduling step comprises the following steps of:
Calculating a predicted time T c of the corresponding candidate;
Acquiring feedback time T R of a corresponding person, and judging whether the feedback of the person is effective; the effective judgment formula of the feedback time is as follows:
Wherein n is the historical data quantity, k is the threshold value of the historical quantity, epsilon T is the proportion of the maximum effective feedback time under the condition that n is less than or equal to k, and T c is the prediction time for predicting different personnel; r is the historical performance rate, θ is the performance rate threshold, T pre_c is the historical predicted value, T pre_R is the historical feedback value, and α is the amplification factor;
When n is less than or equal to k, if the value of the promised time T R is greater than the specific proportion of the predicted time T c, user feedback is effective;
when n is more than k, the historical performance rate r is less than or equal to theta, and the personnel feedback is invalid;
When n is larger than k, the historical performance rate r is larger than theta, and according to the historical performance rate r and the historical predicted value T pre_c, the historical feedback value T pre_R judges whether the personnel side is effective or not;
If the personnel feedback is effective, the feedback time T R and the predicted time T c are integrated into integrated time in a weighting mode according to the historical performance rate r and the performance rate threshold value theta;
And
And sorting the personnel in descending order according to the integration time, and selecting the personnel with the minimum integration time value to distribute tasks.
18. The personal calendar assistant-based business process task scheduling method of claim 17, wherein the personal calendar assistant-based business process task scheduling method further comprises a personal calendar assistant task prediction step comprising the steps of:
Acquiring all m instances under the task type k, and acquiring task workload L=l 1,l2,...,lm and task completion time T=t 1,t2,...,tm under the m instances; the workload is measured by a task according to workload, the task workload l of an instance is the sum of the workload of personnel when the task is accepted, and the task completion time is the time required by the task completion;
Training regression model based on workload L and completion time T
T～aL+∈
Wherein a is a coefficient of L, E is a residual error of a regression model, and the training model obtains the regression coefficient of the workload L
And
With the current personnel workload l as input, predict the personnel completion time under the k-type task
19. The personal calendar assistant-based business process task scheduling method of claim 17, wherein the personal calendar assistant-based business process task scheduling method further comprises a task ordering step comprising the steps of:
the business process task scheduling engine distributes tasks k to the personal schedule assistant;
If the current person does not execute the task, the task k is used as the task to be executed currently;
If the current person is executing n tasks, wherein the workload of the n tasks is L 1,L2,...,Ln, the priority is K 1,K2,...,Kn, the predicted completion time is T 1,T2,...,Tn, the workload of the task K is L k, the priority is K k, the predicted completion time is T k, and whether the person reaches the maximum workload W is judged; if it is The task is judged to enter the next round; if/>Judging whether the task priority meets the condition or not; if the task priorityThe task is carried into the next round of judgment, if the task priority/>The task is taken as the currently executed task;
And
When the task k enters the next round of judgment, the time is advanced to one task for completion, and the task prediction completion time is increasedI.e./>At this time, the personnel completes a task and judges whether the task k is used as the task executed by the round again; and the like, until the j-th loop task k is allowed to be added into the task; the final task completion time T k is calculated.
20. The personal calendar assistant-based business process task scheduling method of claim 17, wherein the personal calendar assistant-based business process task scheduling method further comprises a business process task scheduling engine prediction step comprising the steps of:
The method comprises the steps of obtaining historical data of a specific person p and a specific task k, wherein the historical data comprise n business process contexts C 1,C2,...,Cn, a person workload L and a completion time T of a corresponding task;
according to the acquired n business process contexts C 1,C2,...,Cn, the personnel workload L and the completion time T, training a regression model, wherein the formula of the regression model is as follows:
wherein a i represents the coefficient of the ith business process context C i, b represents the coefficient of the workload L, and E represents the regression equation residuals respectively; training to obtain regression coefficients for business process context C 1,C2,...,Cn Regression coefficient of workload/>And
With business process context c 1,c2, personal load L as input, machine learning models are used to predict completion times of different people
21. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs the steps of the personal calendar assistant based business process task scheduling method of any of claims 17 to 20.
22. A personal calendar assistant-based business process task scheduling device, comprising:
A memory for storing a software application,
A processor for executing the software application,
Wherein each program of the software application correspondingly performs the steps in the personal calendar assistant based business process task scheduling method of any one of claims 17 to 20.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101320329A (en) * | 2008-07-08 | 2008-12-10 | 中国科学院软件研究所 | Preemptive manpower resource collocation method and system based on task priority |
| CN101916404A (en) * | 2010-08-06 | 2010-12-15 | 沈阳工业大学 | Multi-factory cooperative scheduling optimization method during equipment manufacturing |
| WO2015099581A2 (en) * | 2013-12-27 | 2015-07-02 | Zakrytoe Aktsionernoe Obshchestvo "Ekopsi" | Method and system of employee competency assessment field of the invention |
| CN105956821A (en) * | 2016-04-22 | 2016-09-21 | 阔地教育科技有限公司 | Information management method, information management platform, information processing terminal, and information management system |
| CN106663246A (en) * | 2014-08-28 | 2017-05-10 | 谷歌公司 | Systems and methods for biasing task assistance auto-complete suggestions |
| KR20170110921A (en) * | 2016-03-24 | 2017-10-12 | 한국전자통신연구원 | Apparatus and method for recommending schedule to user using asscociation pattern learning |
| CN107392425A (en) * | 2017-06-15 | 2017-11-24 | 中国烟草总公司 | A kind of the tobacco business project implementation SOP management method and system |
| WO2017210213A1 (en) * | 2016-05-28 | 2017-12-07 | Dighe Kaustubh V | Systems and methods for monitoring and managing marine riser assets |
| CN109784646A (en) * | 2018-12-14 | 2019-05-21 | 深圳壹账通智能科技有限公司 | Method for allocating tasks, device, storage medium and server |
| CN110648047A (en) * | 2019-08-16 | 2020-01-03 | 深圳市轱辘汽车维修技术有限公司 | Task scheduling method, device, system and storage medium |
| CN110736478A (en) * | 2018-07-20 | 2020-01-31 | 华北电力大学 | unmanned aerial vehicle assisted mobile cloud-aware path planning and task allocation scheme |
| CN110781452A (en) * | 2019-09-18 | 2020-02-11 | 平安科技（深圳）有限公司 | Statistical task processing method and device, computer equipment and storage medium |
| CN111401845A (en) * | 2020-03-17 | 2020-07-10 | 支付宝(杭州)信息技术有限公司 | Service processing method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110112877A1 (en) * | 2009-11-09 | 2011-05-12 | Nirmal Govind | Method and Apparatus for Constraint-based Staff Scheduling |
| US20130246116A1 (en) * | 2012-03-16 | 2013-09-19 | International Business Machines Corporation | Assisting user to schedule a meeting with the best candidate from a list of individuals based on past communication history, calendar information and user's rules |
| US10082942B2 (en) * | 2014-03-26 | 2018-09-25 | Schlumberger Technology Corporation | Telemetry diagnostics |
-
2021
- 2021-05-08 CN CN202110501155.3A patent/CN113139795B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101320329A (en) * | 2008-07-08 | 2008-12-10 | 中国科学院软件研究所 | Preemptive manpower resource collocation method and system based on task priority |
| CN101916404A (en) * | 2010-08-06 | 2010-12-15 | 沈阳工业大学 | Multi-factory cooperative scheduling optimization method during equipment manufacturing |
| WO2015099581A2 (en) * | 2013-12-27 | 2015-07-02 | Zakrytoe Aktsionernoe Obshchestvo "Ekopsi" | Method and system of employee competency assessment field of the invention |
| CN106663246A (en) * | 2014-08-28 | 2017-05-10 | 谷歌公司 | Systems and methods for biasing task assistance auto-complete suggestions |
| KR20170110921A (en) * | 2016-03-24 | 2017-10-12 | 한국전자통신연구원 | Apparatus and method for recommending schedule to user using asscociation pattern learning |
| CN105956821A (en) * | 2016-04-22 | 2016-09-21 | 阔地教育科技有限公司 | Information management method, information management platform, information processing terminal, and information management system |
| WO2017210213A1 (en) * | 2016-05-28 | 2017-12-07 | Dighe Kaustubh V | Systems and methods for monitoring and managing marine riser assets |
| CN107392425A (en) * | 2017-06-15 | 2017-11-24 | 中国烟草总公司 | A kind of the tobacco business project implementation SOP management method and system |
| CN110736478A (en) * | 2018-07-20 | 2020-01-31 | 华北电力大学 | unmanned aerial vehicle assisted mobile cloud-aware path planning and task allocation scheme |
| CN109784646A (en) * | 2018-12-14 | 2019-05-21 | 深圳壹账通智能科技有限公司 | Method for allocating tasks, device, storage medium and server |
| CN110648047A (en) * | 2019-08-16 | 2020-01-03 | 深圳市轱辘汽车维修技术有限公司 | Task scheduling method, device, system and storage medium |
| CN110781452A (en) * | 2019-09-18 | 2020-02-11 | 平安科技（深圳）有限公司 | Statistical task processing method and device, computer equipment and storage medium |
| CN111401845A (en) * | 2020-03-17 | 2020-07-10 | 支付宝(杭州)信息技术有限公司 | Service processing method and device |
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