CN115640958A

CN115640958A - Task scheduling method and device, readable storage medium and electronic equipment

Info

Publication number: CN115640958A
Application number: CN202211200920.9A
Authority: CN
Inventors: 王微; 解斐
Original assignee: Iflytek Information Technology Co Ltd
Current assignee: Iflytek Information Technology Co Ltd
Priority date: 2022-09-29
Filing date: 2022-09-29
Publication date: 2023-01-24

Abstract

The application provides a task scheduling method and device, a storage medium and electronic equipment, and relates to the field of data processing. The task scheduling method can respond to a task scheduling request instruction and acquire node data of a plurality of task nodes and a plurality of task nodes of a target task; determining task scheduling process information of a target task based on respective node data of a plurality of task nodes; the task scheduling method based on the target task achieves the purpose of executing task scheduling aiming at the target task, and can improve scheduling efficiency of a business flow, so that effects of quick task processing response and timely processing are achieved.

Description

Task scheduling method and device, readable storage medium and electronic equipment

Technical Field

The application relates to the technical field of data processing, in particular to a task scheduling method and device, a readable storage medium and electronic equipment.

Background

At present, the court trial business and the business execution business have more affairs processed by a single case of a case handling personnel, for example, 55 backlogs exist in the business execution link, and 65 backlogs exist in the trial link.

Although the court trial business system has preliminary flow guidance, the decision making of the relationship and the path among tasks still needs to be controlled manually, and accurate guidance cannot be given, so that the situations of step omission or repetition may exist. In addition, due to the fact that backlogs are manually controlled, cases are more, and case handling personnel are required to carry out batch processing, the problem that the task processing is not timely, and the case handling period is too long exists.

Disclosure of Invention

The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides a task scheduling method and device, a readable storage medium and electronic equipment.

In a first aspect, an embodiment of the present application provides a task scheduling method, which, in response to a task scheduling request instruction, obtains node data of a plurality of task nodes of a target task and node data of each of the plurality of task nodes; determining task scheduling process information of a target task based on respective node data of a plurality of task nodes; and executing task scheduling aiming at the target task based on the task scheduling flow information of the target task.

With reference to the first aspect, in some implementation manners of the first aspect, the determining task scheduling flow information of a target task based on respective node data of a plurality of task nodes includes: determining a key path of the target task based on respective node data of the plurality of task nodes, wherein the key path is a path which consumes the longest time for the task in the plurality of task nodes; and determining task scheduling flow information of the target task based on the key path of the target task.

With reference to the first aspect, in some implementations of the first aspect, determining a critical path of the target task based on respective node data of a plurality of task nodes includes: determining respective dependency relationship data of the plurality of task nodes based on respective node data of the plurality of task nodes; determining task handling duration data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes; and determining a key path of the target task based on the respective dependency relationship data and task handling duration data of the plurality of task nodes.

With reference to the first aspect, in some implementation manners of the first aspect, the determining task transaction duration data of each of the plurality of task nodes based on node data of each of the plurality of task nodes includes: determining a task duration prediction model corresponding to each of the plurality of task nodes, wherein the task duration prediction model is used for predicting the handling duration of the task nodes according to the execution scene of the task nodes; and determining task handling time length data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes and the task time length prediction model corresponding to each of the plurality of task nodes.

With reference to the first aspect, in some implementations of the first aspect, the performing task scheduling for the target task based on task scheduling flow information of the target task includes: circularly traversing the plurality of task nodes until the plurality of task nodes are scheduled; wherein, the plurality of task nodes are circularly traversed until the plurality of task nodes are scheduled, and the method comprises the following steps: aiming at each task node in the plurality of task nodes, if all the preposed task nodes corresponding to the task nodes are successfully executed based on the task scheduling flow information, the task nodes are executed; and if the fact that all the pre-task nodes corresponding to the task nodes are not successfully executed is determined based on the task scheduling process information, checking whether a next reachable task node corresponding to the task node exists or not after the last pre-task node in the pre-task nodes corresponding to the task nodes is completely executed, and if the next reachable task node exists, executing the task node.

With reference to the first aspect, in some implementations of the first aspect, circularly traversing the plurality of task nodes until the plurality of task nodes are scheduled to be completed includes: and aiming at each task node in the plurality of task nodes, under the condition that the task node is determined to be an artificial task node based on the task scheduling process information, distributing backlogs corresponding to the task node to task processing users so as to continue scheduling the process based on the node processing completion information fed back by the task processing users.

With reference to the first aspect, in some implementations of the first aspect, before performing task scheduling for the target task based on the task scheduling flow information of the target task, the method includes: and acquiring node form information corresponding to the task node aiming at each task node in the plurality of task nodes so as to judge whether the task node which has a dependency relationship with the task node in the plurality of task nodes needs to be executed or not based on a feedback result of the node form information.

With reference to the first aspect, in some implementations of the first aspect, before performing task scheduling for the target task based on the task scheduling flow information of the target task, the method further includes: and presenting a task node arrangement window so that a user can carry out flow configuration on the task nodes in a graph dragging mode, and accordingly acquiring connection information among the task nodes and the task nodes with the dependency relationship with the task nodes and execution condition information corresponding to the connection information for each task node in the task nodes.

With reference to the first aspect, in some implementations of the first aspect, the target task is a court trial service, and the task node includes at least one of an information initial reception node, a network investigation and control node, a complex and simple distribution node, an intensive investigation and control node, an identity information check node, and a network point-to-point investigation and control node.

In a second aspect, an embodiment of the present application provides a task scheduling apparatus, including: the acquisition module is used for responding to the task scheduling request instruction and acquiring node data of a plurality of task nodes and a plurality of task nodes of a target task; the determining module is used for determining task scheduling flow information of the target task based on respective node data of the plurality of task nodes; and the execution module is used for executing task scheduling aiming at the target task based on the task scheduling flow information of the target task.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and the computer program is configured to execute the method mentioned in the first aspect.

In a fourth aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory for storing processor-executable instructions; the processor is adapted to perform the method of the first aspect.

According to the task scheduling method provided by the embodiment of the application, node data of a plurality of task nodes and respective node data of the plurality of task nodes of a target task are obtained by responding to a task scheduling request instruction; and then, according to respective node data of the plurality of task nodes, determining task scheduling flow information of the target task, and executing scheduling of the target task according to the task scheduling flow information, wherein the task scheduling flow information comprises optimization information for scheduling of the target task, so that when scheduling is performed through the task scheduling flow information, scheduling efficiency of a service flow can be improved, effects of quick task processing response and timely processing are achieved, and an application range is further expanded. Particularly in the task scheduling process of court business, the task scheduling method provided by the embodiment of the application can help the case handling personnel to handle cases quickly, enables human tracking to become data and flow driving, helps the case handling personnel to make decisions on pending matters, and improves the case handling efficiency and accuracy.

Drawings

Fig. 1a is a schematic diagram of a system architecture of a task scheduling method in a scenario example provided in an embodiment.

Fig. 1b is a schematic flowchart illustrating a task scheduling method according to an embodiment of the present application.

Fig. 2 is a schematic flowchart of a task scheduling method according to another embodiment of the present application.

Fig. 3a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 3b is a node relationship diagram of a task scheduling method according to an embodiment of the present application.

Fig. 3c is a flowchart illustrating a bucket calculation of a task scheduling method according to an embodiment of the present application.

Fig. 3d is a schematic diagram illustrating bucket calculation of a task scheduling method according to an embodiment of the present application.

Fig. 3e is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 4 is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 5 is a schematic flowchart of a task scheduling method according to another embodiment of the present application.

Fig. 6a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 6b is a flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 6c is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 7a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 7b is a flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 7c is a flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 7d is a flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 8 is a flowchart illustrating a task scheduling method according to another embodiment of the present application.

Fig. 9 is a schematic structural diagram illustrating a task scheduling method according to yet another embodiment of the present application.

Fig. 10 is a schematic structural diagram of a task scheduling device according to an embodiment of the present application.

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Exemplary System

An application scenario is illustrated below with reference to fig. 1 a.

Fig. 1a is a schematic diagram of a system architecture of a task scheduling method in a scenario example provided in an embodiment. As shown in fig. 1a, the application scenario mentioned in this embodiment includes a client 110 and a server 120 communicatively connected to the client. The server 120 may serve as a container bearer node of the task scheduling method. The server 120 is configured to schedule a task and send the task to be executed to the client 110 of the corresponding party, and the client 110 is configured to execute the received task. It is understood that the number of the servers 120 and the clients 110 may be set according to actual situations, and this embodiment is not limited in this respect.

Illustratively, in an actual application process, the server 120, in response to the task scheduling request instruction, obtains node data of a plurality of task nodes of the target task and node data of each of the plurality of task nodes; determining task scheduling process information of a target task based on respective node data of a plurality of task nodes; and executing task scheduling aiming at the target task based on the task scheduling flow information of the target task, and under the condition that the task node is an artificial task node, allocating backlogs corresponding to the task node to the client 110 corresponding to the task processing user so that the task processing user can process the backlogs and feed back results.

Further detailed, the application scenario shown in fig. 1a may be an application scenario of court trial business, or the like. Specifically, the method can be applied to the scenes of executing a case collecting, mediating, transferring and foretuning, civil first trial and the like, and more specifically to the scenes of specific tasks such as initial information reception, network investigation and control, complicated and simplified distribution, intensive investigation and control, identity information check, network point-to-point investigation and control and the like.

Taking the server 120 shown in fig. 1a as an example, the server 120 may include a server providing various services, such as a server providing communication services for a plurality of clients 110, a server providing support for a model used on the client 110 for background training, a server processing data sent by the client 110, and the like.

It should be noted that the server 120 may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. The server may also be a server of a distributed system, or a server incorporating a blockchain. The server may also be a cloud server of basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology.

The client 110 may be a browser, an APP (Application), or a web Application such as H5 (HyperText Markup Language version 5) Application, or a light Application (also referred to as an applet, a light Application), or a cloud Application, and the client 110 may be based on an SDK (Software Development Kit) of a corresponding service provided by the server 120, such as an RTC developed and obtained based on the SDK. The client 110 may be deployed in an electronic device, need to run depending on the device running or some app in the device, etc. The electronic device may have a display screen and support information browsing, etc., for example, and may be a personal mobile terminal such as a mobile phone, a tablet computer, a personal computer, etc. Various other types of applications may also be typically deployed in an electronic device, such as human-machine conversation-type applications, model training-type applications, text processing-type applications, web browser applications, shopping-type applications, search-type applications, instant messaging tools, mailbox clients, social platform software, and so forth.

The task scheduling method of the present specification is described in detail below with reference to fig. 1b to 9.

Fig. 1b is a schematic flowchart illustrating a task scheduling method according to an embodiment of the present application. As shown in fig. 1b, a task scheduling method provided in this embodiment of the present application includes the following steps.

Step S110, in response to the task scheduling request instruction, obtaining node data of a plurality of task nodes and a plurality of task nodes of the target task.

Illustratively, responding to a task scheduling request instruction may be understood as a request to trigger task scheduling by operating a touch button. The touch button may be an execution button on the user operation interface corresponding to the task scheduling method. It should be appreciated that the task scheduling request may be based on any task trigger, and the application is not limited in this respect.

Illustratively, the target task may be a task in a task library, i.e., a task uploaded in advance; it may also be a task that is uploaded on-the-fly, i.e. a task that needs to be processed is uploaded while the task scheduling method is executed. The target tasks may be various types of tasks, such as, for example, court trial tasks, hospital physical examination tasks, loan acceptance tasks, insurance promotion tasks, information entry tasks, and the like. The target task can also correspond to different application scenes, such as a case receiving execution scene, a pre-adjustment scene for adjusting and turning to a complaint, a first trial scene for people and the like. It should be understood that the processed target tasks may be the same type of task or different types of tasks, and are not limited in this application.

Illustratively, the node data includes at least one of a front node, a back-drive node, a node data source, closed-loop logic, a start-end time, and an expiration time. The node data can be acquired by docking with a third-party system or automatically performing automatic trace marking, and the node data is collected and analyzed.

It should be noted that the target tasks of each scene may be decomposed into a plurality of independent atomic services through task scheduling, and each atomic task corresponds to one task node. It should be understood that a work task or operational activity that cannot be further decomposed is referred to as an atomic task. An atomic task is a basic unit of execution in a workflow.

Step S120, determining task scheduling flow information of the target task based on respective node data of the plurality of task nodes.

Illustratively, the task scheduling process information may be understood as process optimization information determined by dynamically searching for a critical path according to a pre-and-post dependency relationship and a time history experience value of each task node.

Step S130, based on the task scheduling flow information of the target task, performs task scheduling for the target task.

Specifically, the plurality of task nodes are sequentially scheduled according to the respective corresponding dependency relationships and scheduling sequences of the plurality of task nodes, and the task state of each task node is monitored in real time until the plurality of task nodes are scheduled, so that task scheduling of the target task is realized.

According to the task scheduling method provided by the embodiment of the application, node data of a plurality of task nodes and a plurality of task nodes of a target task are obtained by responding to a task scheduling request instruction; and then, according to respective node data of the plurality of task nodes, determining task scheduling flow information of the target task, and executing scheduling of the target task according to the task scheduling flow information, wherein the task scheduling flow information comprises optimization information for scheduling of the target task, so that when scheduling is performed through the task scheduling flow information, scheduling efficiency of a service flow can be improved, effects of quick task processing response and timely processing are achieved, and an application range is further expanded. Particularly in the task scheduling process of court business, the task scheduling method provided by the embodiment of the application can help the case handling personnel to quickly handle a case, enables artificial tracking to be data and flow driving, assists the case handling personnel to make decisions on backlogs, and improves case handling efficiency and accuracy.

Fig. 2 is a schematic flowchart of a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 2 is extended from the embodiment shown in fig. 1, and the differences between the embodiment shown in fig. 2 and the embodiment shown in fig. 1 will be mainly described below, and the same parts will not be described again.

As shown in fig. 2, the task scheduling method provided in this embodiment of the present application determines task scheduling flow information of a target task based on respective node data of a plurality of task nodes (step S120), and includes the following steps.

Step S210, determining a critical path of the target task based on the respective node data of the plurality of task nodes.

Illustratively, the critical path is a path that takes the longest task time among the plurality of task nodes.

Step S220, determining task scheduling flow information of the target task based on the critical path of the target task.

In particular, the length of time consumed by the entire target task is determined by the critical path in the task, since the critical path has the longest execution time, and any activity deferral on the critical path will defer the entire target task, since the critical path is the most delayed path through which the target task is completed, which determines the shortest time for completion of the target task.

According to the task scheduling method provided by the embodiment of the application, the critical path of the big data task is determined based on the respective node data of the plurality of task nodes, so that the flow can be optimized, and the execution efficiency of the target task is improved. The target task scheduling is executed on the basis of determining the key path, so that the scheduling and processing efficiency of the target task can be improved, the time consumption of the whole processing flow is reduced, the case handling process is saved, the scheduling is simple, and the complex business scene requirements are further met.

Fig. 3a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 3 is extended based on the embodiment shown in fig. 2, and the differences between the embodiment shown in fig. 3 and the embodiment shown in fig. 2 will be emphasized below, and the descriptions of the same parts will not be repeated.

As shown in fig. 3a, the task scheduling method provided in this embodiment of the present application determines a critical path of a target task based on respective node data of a plurality of task nodes (step S210), including the following steps.

Step S310 determines dependency relationship data of each of the plurality of task nodes based on node data of each of the plurality of task nodes.

It should be understood that the premise of determining the critical path of the target task is to mark the front-back dependency relationship between task nodes, that is, determine the respective dependency relationship data of a plurality of task nodes.

Illustratively, the front-back dependency relationship between the task nodes is actually a sequential relationship between the task nodes, i.e., which task node performs scheduling first and which task node performs scheduling later.

Step S320, determining task transaction duration data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes.

Illustratively, the task processing duration data refers to the time from generation to completion of the picked-up processing of the atomic task corresponding to each task node. For example, in the court case handling process, a plurality of documents need to be delivered to the parties, and the delivery time duration prediction is different according to different delivery speeds of addresses of the parties and different delivery scenes.

And step S330, determining a key path of the target task based on the respective dependency relationship data and task handling duration data of the plurality of task nodes.

Illustratively, a bucket computing method can be utilized to determine a critical path of a target task based on respective dependency relationship data and task handling duration data of a plurality of task nodes.

It should be understood that the process optimization needs to be comprehensively optimized according to the predicted time length of each task node and the front-back dependency relationship of the task node, and the purpose is to complete the whole process task in the shortest time.

According to the task scheduling method provided by the embodiment of the application, the dependency relationship data and the task handling duration data of the task nodes are determined based on the node data of the task nodes; and then, determining a key path of the target task based on the respective dependency relationship data and task handling duration data of the plurality of task nodes, so that the aim of optimizing the whole task flow is fulfilled, and the overall execution efficiency of all tasks can be effectively improved.

In one embodiment, the dependency relationship of the task node includes an immediate relationship, a causal relationship, a parallel relationship, and an unrelated relationship. Wherein the close proximity relationship is x- > y if and only if there is a track such that activity x is immediately followed by y; the causal relationship is x- > > y if and only if x- > y and not y- > x; the parallel relationship is x | | y if and only if x- > y and y- > x; the irrelevant relations are x # y if and only if x- > > y and not y- > > x. In nodes such as system task scheduling flow L = { < a, B, C, D >, < a, C, B, D >, < E, F >, < G >, < H >, < I > }, if a > B, B- > C, C- > D, a- > C, C- > B, B- > D, E- > F, the dependency matrix is as shown in table 1.

A

B

C

D

E

F

G

H

I

A

#

->>

#

B

<<-

#

||

->>

#

C

<<-

||

#

->>

#

D

#

<<-

#

E

#

->>

#

F

#

<<-

#

G

#

H

#

I

#

TABLE 1

Fig. 3b is a node relationship diagram of a task scheduling method according to an embodiment of the present application. From the above data, a node relationship graph as shown in fig. 3b can be derived.

In order to complete task scheduling under the most reasonable resource allocation rule in the shortest time, the system adopts a bucket-dividing calculation mode, and 1 person is adopted for each optimal path of resources, so that the task can be completed most quickly on the premise of using the minimum resources. The relationship between each node and the average completion time of the node is shown in table 2.

Node point	Time (sky)
		A	2
B	2
		C	4
D	1
		E	2
F	4
		G	2
H	3
		I	4

TABLE 2

Wherein, the time in table 2 is the average completion time of the node. Average completion time the average time of the last 30 days of the history data calculation process is currently selected.

And combining the data, and realizing flow optimization by adopting barrel calculation. The barrel-dividing calculation adopts a dividing and treating idea to improve the calculation efficiency, and combines the key paths to combine the small barrels into a big barrel with the path smaller than the key path, thereby realizing the path optimization.

Fig. 3c is a flowchart illustrating a bucket-based calculation of a task scheduling method according to an embodiment of the present disclosure. As shown in fig. 3c, the bucket calculation flow includes the following steps.

And step S340, setting the front and back dependency relationship of each node.

In step S341, nodes having dependency relationships are connected.

Step S342, traverse all nodes, and put the connected nodes into the same bucket.

In step S343, when a node exists in multiple buckets, multiple buckets need to be merged.

In step S344, the time consumption of all paths from the node with the in-degree of 0 to the node with the out-degree of 0 in each bucket is determined.

In step S345, the longest time-consuming path in the bucket is taken out as the critical path.

Step S346, calculating the longest path in all buckets as the critical path of the whole task scheduling flow.

Step S347 ranks the critical paths of the remaining buckets.

Step S348, the sum of the maximum and minimum critical paths combined into the barrel combination barrel is taken to be less than the maximum value each time, and then the next largest of the remaining nodes is included in the path comparison.

It will be appreciated that each time a keg is combined into a vat, the maximum path of the new keg cannot exceed the critical path, requiring comparison with the critical path.

Step S349, combining the critical paths in different buckets, and combining them together to form a path in series.

It will be appreciated that nodes in a new bucket formed of kegs may be linked to form a new path.

In step S350, the path formed by the current bucket cannot be added any more, which is a new path.

It should be appreciated that when the total length of time in a bucket approaches the critical path and no other keg can be added to the bucket and the total length of time is guaranteed not to exceed the critical path, indicating that the bucket cannot grow and nodes in the bucket are linked to form a new path.

In step S351, all buckets are traversed to guide all buckets to form new paths.

In step S352, the buckets and the coalesce bucket form a task scheduling flowchart in parallel.

And step S353, adding a starting node and an ending node, and finishing the construction of the flow chart.

Fig. 3d is a schematic diagram illustrating a bucket-based calculation of a task scheduling method according to an embodiment of the present application. The bucket 1 to bucket 5 shown in fig. 3d can be obtained from the bucket calculation flowchart of fig. 3 c. With reference to fig. 3b, 3c and 3d, the calculation of the critical path for each bucket is as follows.

Bucket 1 contains 2 paths: ABD takes about 5 days; ACD takes 7 days; the critical path is 7 days;

bucket 2 contains 1 path, EF takes 6 days; the critical path is 6 days;

bucket 3 critical path is 2 days;

bucket 4 critical path is 3 days;

bucket 5 critical path is 4 days.

According to the sorting result of the bucket critical paths, bucket 3 (2), bucket 4 (3), bucket 5 (4), bucket 2 (6) and bucket 1 (7) are arranged, and the ACD in the bucket 1 as the longest path of the whole process is 7 days;

critical path-bucket 2 time =1 day, bucket 1 day cannot be found, so bucket 2 is parallel to bucket 1;

critical path-bucket 5 time =3 days, finding bucket 4 for 3 days, so bucket 5 is in series with bucket 4 and then in parallel with bucket 1;

the remaining buckets 3 are directly independent in parallel with bucket 1.

Fig. 3e is a flowchart illustrating a task scheduling method according to another embodiment of the present application, where a final path is calculated according to a bucket, and an optimized path is obtained for 7 days, so as to obtain a task scheduling flowchart illustrated in fig. 3 e.

Fig. 4 is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 4 is extended based on the embodiment shown in fig. 3, and the differences between the embodiment shown in fig. 4 and the embodiment shown in fig. 3 will be emphasized below, and the descriptions of the same parts will not be repeated.

As shown in fig. 4, the task scheduling method provided in the embodiment of the present application determines task processing duration data of each of a plurality of task nodes based on node data of each of the plurality of task nodes (step S320), and includes the following steps.

Step S410, determining task duration prediction models corresponding to the plurality of task nodes respectively.

Illustratively, the task duration prediction model is used for predicting the transaction duration of the task node according to the execution scene of the task node.

Step S420, based on the respective node data of the plurality of task nodes and the respective corresponding task duration prediction models of the plurality of task nodes, determining the respective task handling duration data of the plurality of task nodes.

In an embodiment, the task node may be a common case task, the processing time of the common case task is strongly related to two factors of processing manpower and task amount, and the processing time and the related factors are in a linear relationship, so that a binary linear regression method is adopted to predict the time. The task handling time is a dependent variable, and a linear regression model for predicting the task handling time is shown in formula (1).

y＝b ₀ +b ₁ *x ₁ +b ₂ *x ₂ (1)

Wherein x is ₁ Representing the manpower of task processing, x ₂ Indicating the amount of system inventory tasks, b ₀ Is a constant coefficient, b ₁ b ₂ Are regression coefficients.

In another embodiment, for the delivery scene task, a plurality of documents in the court transaction process need to be delivered to the parties, the delivery speed is different according to the addresses of the parties, and the time length of the delivery scene task needs to be predicted according to the positions and attributes of the parties. In the scene, only the 2 data are taken as independent variables, the task handling time is a dependent variable, and a linear regression model for predicting the task time is shown as a formula (2).

y＝b ₀ +b ₁ *x ₁ +b ₂ *x ₂ (2)

Wherein, represents x ₁ Party location information, x ₂ Representing the nature of the party (e.g., person, business, individual merchant, property, etc.), b ₀ Is a constant coefficient, b ₁ b ₂ Are regression coefficients.

The task prediction mainly adopts a linear regression method, the duration prediction is carried out on the atomic tasks by utilizing 2 task duration prediction models, the model is likely to have deviation due to factors such as manpower proficiency, and the like, the model needs to be frequently recalculated and updated, and the model is generally updated once a month in the court case handling process at present. According to the task scheduling method, the path calculation basis is provided for task scheduling of subsequent flow self-optimization through task duration prediction. And the optimal scheduling of the tasks is realized by calculating the key path through time length prediction during each process of flow optimization, so that the resource waste is reduced, and the task scheduling efficiency is improved.

Fig. 5 is a flowchart illustrating a task scheduling method according to another embodiment of the present application. Fig. 6a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 5 is extended from the embodiment shown in fig. 1, and the differences between the embodiment shown in fig. 5 and the embodiment shown in fig. 1 will be mainly described below, and the same parts will not be described again.

As shown in fig. 5, the task scheduling method provided in the embodiment of the present application executes task scheduling for a target task based on task scheduling flow information of the target task (step S130), and includes the following steps.

Step S510, a plurality of task nodes are traversed in a loop until the plurality of task nodes are scheduled.

As shown in fig. 6, the plurality of task nodes are traversed in a loop until the plurality of task nodes are scheduled to be completed (step S510), and the following steps are included for each task node in the plurality of task nodes.

Step S610, if it is determined that all pre-task nodes corresponding to the task node are successfully executed based on the task scheduling flow information, step S620 is executed.

And step S620, executing the task node.

Step S630, if it is determined that all pre-task nodes corresponding to the task node are not successfully executed based on the task scheduling flow information, step S640 is executed.

Step S640, after the last pre-task node in the pre-task nodes corresponding to the task node is executed, checking whether there is a reachable next task node corresponding to the task node, and if there is a reachable next task node, executing step S620.

Fig. 6b is a flowchart illustrating a task scheduling method according to another embodiment of the present application. Fig. 6c is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. As shown in fig. 6b and fig. 6c, the task scheduling method adopts a directed cyclic graph calculation manner to drive task scheduling. There are dependency nodes before and after the directed cyclic graph, such as node a, node B, node C, node D, node E, and node F in fig. 6B. In the practical application process, the current node can be started only after all the front nodes are completed, and the current node is in waiting after one of the front nodes is completed and can be started only after the other nodes are completed. If a normal preposed node is completed first, and other preposed nodes which do not meet the conditions are completed later, whether a correct reachable node exists or not needs to be checked after the last preposed node is completed, and the current node can be started only if the correct reachable node exists. After the front node B finishes analyzing and the abnormal front node E finishes analyzing, analyzing the next node F, judging whether the front nodes are completely finished or not, checking whether a correct marking path exists or not, starting the node F if the front nodes are completely finished, and otherwise, not processing the node.

According to the task scheduling method provided by the embodiment of the application, the plurality of task nodes are sequentially and circularly traversed, the state of each task node is monitored and inquired in real time, omission or repeated scheduling is avoided, a large amount of repeated calculation can be reduced, and the scheduling efficiency is improved.

Fig. 7a is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 7a is extended based on the embodiment shown in fig. 5, and the differences between the embodiment shown in fig. 7a and the embodiment shown in fig. 5 are emphasized below, and the descriptions of the same parts are omitted.

As shown in fig. 7a, in the embodiment of the present application, the plurality of task nodes are traversed in a loop until the plurality of task nodes are scheduled to be completed (step S510), and the following steps are included for each task node in the plurality of task nodes.

Step S710, in the case that the task node is determined to be an artificial task node based on the task scheduling process information, allocating the backlog corresponding to the task node to the task processing user, so as to continue the scheduling process based on the node processing completion information fed back by the task processing user.

Fig. 7b is a flowchart illustrating a task scheduling method according to another embodiment of the present application. As shown in fig. 7b, the payment inquiry is an automatic node, and the node for scheduling, charging and waiting for payment is a manual task node. And if the automatic completion of the payment inquiry node is confirmed, distributing the manual task node for scheduling to the task processing user. And if the payment inquiry node is not automatically completed, distributing the manual task node for prompting payment to the task processing user, and ending the task flow after the payment is completed.

Fig. 7c is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. As shown in fig. 7c, when scheduling a target task based on task scheduling flow information, analyzing whether a task node is an artificial node, if so, generating a to-do item to be distributed to a handler, if not, using a service interface of the interface to obtain return information, checking whether a correct marked path exists, if so, ending the current node, and if not, circularly calling a result query interface.

The task scheduling method provided by the embodiment of the application supports manual and automatic nodes, wherein part of the nodes need to be completed manually in the whole task scheduling process, but part of the nodes can be realized through a system without manual completion. Part of nodes are completed through the system, so that the labor cost can be saved, the whole task execution period is shortened through automatic completion of the system, and the aim of man-machine coupling is fulfilled.

Fig. 7d is a flowchart illustrating a task scheduling method according to another embodiment of the present application. As shown in fig. 7d, the task scheduling method provided in the embodiment of the present application supports loop processing, and realizes flow loop based on condition judgment, that is, it is necessary to trace back to a previous node and re-walk other nodes after processing of a current node fails, where the core design is to return to node a again after node B fails, and node a needs to be re-executed when node B re-processes node a, and node a needs to be set to node B, node B to node a, and node B to end node B are all set to be not processed. For example, after the electronic delivery fails, the phone call needs to be made again to confirm the addressee of the party, and the delivery is mailed again. Since the task node does not need to copy multiple copies, the flow configuration work can be simplified.

Fig. 8 is a schematic flowchart illustrating a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 8 is extended based on the embodiment shown in fig. 1, and the differences between the embodiment shown in fig. 8 and the embodiment shown in fig. 1 will be emphasized below, and the descriptions of the same parts will not be repeated.

As shown in fig. 8, the method includes the following steps for each of the plurality of task nodes before performing task scheduling for the target task based on the task scheduling flow information of the target task (step S130).

Step S810, obtaining node form information corresponding to the task node, so as to determine whether a task node having a dependency relationship with the task node in the plurality of task nodes needs to be executed based on a feedback result of the node form information.

Illustratively, the node form information contains at least one of input box information, radio box information, check box information.

It should be understood that the self-defined node form information is designed for flexibly realizing the data of the node processing result, that is, different nodes can be set to fill different data after the task is completed.

In one embodiment, the node form information corresponding to the task node is uniformly represented by form codes in the process, and the value of the form codes can be referred to as a condition judgment basis in the task scheduling condition, so that the flexibility of process judgment is improved. For example: according to the electronic delivery result, two conditions are set, the success of delivery is directly carried out in a court, the failure of delivery is carried out by the announcement and the delivery, namely, the success of delivery is carried out in the court or the announcement is carried out after the delivery is finished, and the system automatically judges according to the node of the delivery result.

It should be noted that there is no data condition judgment for the task connection in the existing court trial business system, and some case handling personnel are needed to judge which tasks are to be executed in the case handling process. In the task scheduling method provided by the embodiment of the application, each task node supports a user-defined form, and supports the application of the form with completed task nodes to the whole task scheduling process, so that the form is used as a judgment basis for judging whether other task nodes need to execute, and the task scheduling system software judges to replace manual judgment logic, thereby further improving the task handling efficiency.

Fig. 9 is a schematic flowchart of a task scheduling method according to another embodiment of the present application. The embodiment shown in fig. 9 is extended based on the embodiment shown in fig. 1, and the differences between the embodiment shown in fig. 9 and the embodiment shown in fig. 1 will be emphasized below, and the descriptions of the same parts will not be repeated.

As shown in fig. 9, before performing task scheduling for a target task based on task scheduling flow information of the target task (step S130), the method includes the following steps.

Step S1101, presenting a task node arrangement window, so that a user performs flow configuration on a plurality of task nodes by using a graph dragging manner, thereby acquiring, for each task node in the plurality of task nodes, connection information between the task node and a task node having a dependency relationship with the task node in the plurality of task nodes, and execution condition information corresponding to the connection information.

The task scheduling method provided by the embodiment of the application realizes flow configuration, condition configuration, feedback result configuration and the like in a dragging mode. A line is determined through two vertexes (task nodes), an execution condition is set on each line, each vertex sets the content fed back by the current task node to provide basis for judging task scheduling conditions, and meanwhile, the content fed back by the previous task node is displayed for follow current, task completion is assisted, and convenience and running speed of the task scheduling method are greatly improved.

The method embodiment of the present application is described in detail above with reference to fig. 1b to 9, and the device embodiment of the present application is described in detail below with reference to fig. 10 and 11. It is to be understood that the description of the method embodiments corresponds to the description of the apparatus embodiments, and therefore reference may be made to the preceding method embodiments for parts not described in detail.

Fig. 10 is a schematic structural diagram of a task scheduling device according to an embodiment of the present application. As shown in fig. 10, the task scheduling apparatus provided in the embodiment of the present application includes an obtaining module 1000, a determining module 1001, and an executing module 1002. The obtaining module 1000 is configured to obtain node data of each of a plurality of task nodes and a plurality of task nodes of a target task in response to a task scheduling request instruction. The determining module 1001 is configured to determine task scheduling process information of a target task based on respective node data of a plurality of task nodes. The execution module 1002 is configured to execute task scheduling for the target task based on the task scheduling flow information of the target task.

In an embodiment, the determining module 1001 is further configured to determine a critical path of the target task based on node data of each of the plurality of task nodes, where the critical path is a path that consumes the longest time for a task in the plurality of task nodes; and determining task scheduling flow information of the target task based on the key path of the target task.

In an embodiment, the determining module 1001 is further configured to determine dependency data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes; determining task handling duration data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes; and determining a key path of the target task based on the respective dependency relationship data and task handling duration data of the plurality of task nodes.

In an embodiment, the determining module 1001 is further configured to determine a task duration prediction model corresponding to each of the plurality of task nodes, where the task duration prediction model is configured to predict a transaction duration of the task node according to an execution scenario of the task node; and determining task handling time length data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes and the task time length prediction model corresponding to each of the plurality of task nodes.

In one embodiment, the executing module 1002 is further configured to cycle through a plurality of task nodes until the plurality of task nodes are scheduled; wherein, the plurality of task nodes are circularly traversed until the plurality of task nodes are scheduled, and the method comprises the following steps: aiming at each task node in the plurality of task nodes, if all the preposed task nodes corresponding to the task nodes are successfully executed based on the task scheduling flow information, the task nodes are executed; if the task scheduling process information is used for determining that all the pre-task nodes corresponding to the task nodes are not successfully executed, after the execution of the last pre-task node in the pre-task nodes corresponding to the task nodes is completed, whether a next reachable task node corresponding to the task node exists is checked, and if the next reachable task node exists, the task node is executed.

In an embodiment, the executing module 1002 is further configured to, for each task node in the plurality of task nodes, allocate a to-do item corresponding to the task node to the task processing user if the task node is determined to be an artificial task node based on the task scheduling process information, so as to continue the scheduling process based on the node processing completion information fed back by the task processing user.

In an embodiment, the task scheduling apparatus further includes an obtaining module. The obtaining module is used for obtaining node form information corresponding to the task node aiming at each task node in the plurality of task nodes so as to judge whether the task node which has a dependency relationship with the task node in the plurality of task nodes needs to be executed or not based on a feedback result of the node form information.

In an embodiment, the task scheduling device further comprises a presentation module. The presentation module is used for presenting a task node arrangement window so that a user can perform flow configuration on a plurality of task nodes in a graph dragging mode, and therefore, for each task node in the plurality of task nodes, connection information between the task node and the task node in the plurality of task nodes and having a dependency relationship with the task node and execution condition information corresponding to the connection information are obtained.

Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 11. Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the application.

As shown in fig. 11, the electronic device 1100 includes one or more processors 1101 and memory 1102.

The processor 1102 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 1100 to perform desired functions.

Memory 1102 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), a hard disk, flash memory, and the like. One or more computer program instructions may be stored on a computer-readable storage medium and executed by the processor 1102 to implement the task scheduling methods of the various embodiments of the present application mentioned above and/or other desired functions. Various contents such as node data and the like can also be stored in the computer-readable storage medium.

In one example, the electronic device 1100 may further include: an input device 1103 and an output device 1004, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

The input device 1103 may include, for example, a keyboard, a mouse, and the like.

The output unit 1104 can output various information including a task scheduling result to the outside. The output devices 1104 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.

Of course, for simplicity, only some of the components of the electronic device 1100 relevant to the present application are shown in fig. 11, and components such as buses, input/output interfaces, and the like are omitted. In addition, electronic device 1100 may include any other suitable components depending on the particular application.

In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps in the task scheduling methods according to the various embodiments of the present application described above in this specification.

The computer program product may include program code for carrying out operations for embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the task scheduling method according to various embodiments of the present application described above in this specification.

A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is provided for purposes of illustration and understanding only, and is not intended to limit the application to the details which are set forth in order to provide a thorough understanding of the present application.

The block diagrams of devices, apparatuses, devices, systems referred to in this application are only used as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

It should also be noted that in the devices, apparatuses, and methods of the present application, each component or step can be decomposed and/or re-combined. These decompositions and/or recombinations should be considered as equivalents of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims

1. A method for task scheduling, comprising:

responding to a task scheduling request instruction, and acquiring a plurality of task nodes of a target task and node data of the task nodes;

determining task scheduling flow information of the target task based on respective node data of the plurality of task nodes;

and executing task scheduling aiming at the target task based on the task scheduling flow information of the target task.

2. The task scheduling method according to claim 1, wherein the determining task scheduling flow information of the target task based on the node data of each of the plurality of task nodes comprises:

determining a critical path of the target task based on respective node data of the plurality of task nodes, wherein the critical path is a path which consumes the longest time for a task in the plurality of task nodes;

and determining task scheduling process information of the target task based on the key path of the target task.

3. The task scheduling method according to claim 2, wherein the determining a critical path of the target task based on the node data of each of the plurality of task nodes comprises:

determining respective dependency relationship data for the plurality of task nodes based on the respective node data for the plurality of task nodes;

determining task handling duration data of each of the plurality of task nodes based on the node data of each of the plurality of task nodes;

and determining a key path of the target task based on the respective dependency relationship data and task handling duration data of the plurality of task nodes.

4. The task scheduling method according to claim 3, wherein the determining task transaction duration data for each of the plurality of task nodes based on the node data for each of the plurality of task nodes comprises:

determining a task duration prediction model corresponding to each of the plurality of task nodes, wherein the task duration prediction model is used for predicting the handling duration of the task nodes according to the execution scene of the task nodes;

and determining task handling time length data of the task nodes based on the node data of the task nodes and the task time length prediction models corresponding to the task nodes.

5. The task scheduling method according to any one of claims 1 to 4, wherein the performing task scheduling for the target task based on the task scheduling flow information of the target task includes:

circularly traversing the plurality of task nodes until the plurality of task nodes are scheduled;

wherein, the circularly traversing the plurality of task nodes until the plurality of task nodes are scheduled, comprises:

for each task node of the plurality of task nodes,

if all the preposed task nodes corresponding to the task nodes are successfully executed based on the task scheduling flow information, executing the task nodes;

if it is determined that all the pre-task nodes corresponding to the task nodes are not successfully executed based on the task scheduling process information, after the execution of the last pre-task node in the pre-task nodes corresponding to the task nodes is completed, whether a next reachable task node corresponding to the task node exists is checked, and if the next reachable task node exists, the task node is executed.

6. The task scheduling method of claim 5, wherein said looping through the plurality of task nodes until the plurality of task nodes are scheduled to be completed comprises:

and aiming at each task node in the plurality of task nodes, under the condition that the task node is determined to be an artificial task node based on the task scheduling process information, allocating backlogs corresponding to the task node to task processing users so as to continue scheduling processes based on node processing completion information fed back by the task processing users.

7. The task scheduling method according to any one of claims 1 to 4, wherein before the task scheduling process information based on the target task performs task scheduling for the target task, the method includes:

and acquiring node form information corresponding to the task node aiming at each task node in the plurality of task nodes so as to judge whether the task node which has a dependency relationship with the task node in the plurality of task nodes needs to be executed or not based on a feedback result of the node form information.

8. The task scheduling method according to any one of claims 1 to 4, further comprising, before the performing task scheduling for the target task based on the task scheduling flow information of the target task,:

and presenting a task node arrangement window so that a user can carry out flow configuration on the task nodes in a graph dragging mode, and accordingly acquiring connection information between the task nodes and task nodes which have a dependency relationship with the task nodes in the task nodes and execution condition information corresponding to the connection information for each task node in the task nodes.

9. The task scheduling method according to any one of claims 1 to 4, wherein the target task is a court trial business, and the task nodes include at least one of an information initial reception node, a network check and control node, a simplified and unsimplified distribution node, an intensive check and control node, an identity information check node, and a network point-to-point check and control node.

10. A task scheduling apparatus, comprising:

the system comprises an acquisition module, a task scheduling module and a task scheduling module, wherein the acquisition module is used for responding to a task scheduling request instruction and acquiring a plurality of task nodes of a target task and respective node data of the task nodes;

the determining module is used for determining task scheduling flow information of the target task based on respective node data of the plurality of task nodes;

and the execution module is used for executing task scheduling aiming at the target task based on the task scheduling flow information of the target task.

11. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1 to 9.

12. An electronic device, characterized in that the electronic device comprises:

a processor;

a memory for storing the processor-executable instructions;

the processor configured to perform the method of any of the preceding claims 1 to 9.