Task Optimization Method, Task Optimization Device, Electronic Device, and Computer-Readable Storage Medium

Abstract

This application discloses a task optimization method, a task optimization device, an electronic device, and a computer-readable storage medium. The task optimization method includes the steps of: evaluating tasks to identify a target task that needs optimization (101); adjusting a target task parameter corresponding to the target task (102); executing the target task parameter by virtual scheduling and performing a global scheduling rehearsal (103); and, if the rehearsal effect of the task rehearsal is as expected, determining a target task parameter and executing the target task based on the target task parameter (104).

Description

Technical Field

[0001] This application is filed based on a Chinese patent application with application number 202111482034.5 and filing date December 6, 2021, and claims the priority of the Chinese patent application. All the contents of the Chinese patent application are incorporated herein by reference.

[0002] Embodiments of the present application relate to the field of information technology, but are not limited thereto, and particularly relate to a task optimization method, apparatus, and computer-readable storage medium.

Background Art

[0003] The data middleware platform constructs a unified standard at the enterprise level, opens up enterprise data channels, breaks the information island effect, and reduces the cost caused by repeated construction. Thereby, not only can data service support be quickly provided to various business departments, but also the burden on the backend can be reduced, and the overall work efficiency and quality can be improved.

[0004] In the data middleware platform, a large number of data processing tasks are being executed. These tasks constitute a huge directed acyclic graph (DAG). Among them, some process relatively basic data and have many dependencies on subsequent tasks, some have requirements regarding the output time, and it is necessary to generate results by a specified time. Also, these tasks are usually developed individually by different teams, the quality of the tasks is different, and usually, each team applies for higher task resources and priorities to ensure the operation of its respective tasks, but there is no overall standard for this, which is disadvantageous to the overall efficiency.

[0005] Currently, when optimizing tasks, usually tasks with operation time or resources are selected as the optimization targets. However, just looking at time and resources alone does not necessarily mean there are problems with the tasks. Also, to determine whether the optimization of a task affects the whole, usually actual tests are required. This entire process requires a long cycle and a great deal of trial-and-error costs, which have an adverse effect on the efficiency and success rate of task optimization.

Summary of the Invention

Problems to be Solved by the Invention

[0006] The following is a summary of the subject matter described in detail in this specification. This summary is not intended to limit the scope of the claims.

[0007] Embodiments of the present application provide a task optimization method, a task optimization device, an electronic device, and a computer-readable storage medium.

Means for Solving the Problems

[0008] In a first aspect, embodiments of the present application include a step of evaluating a task to identify a target task that requires optimization, a step of adjusting target task parameters corresponding to the target task, a step of executing the target task parameters by virtual scheduling to perform a global scheduling preview, when the preview effect of the global scheduling preview is as expected, determining the target task parameters and executing the target task based on the target task parameters. Executed by an electronic device A task optimization method is provided.

[0009] In a second aspect, embodiments of the present application include A task optimization device is provided, which includes a memory, a processor, and a computer program stored in the memory and operable on the processor. By executing the computer program, the processor implements the task optimization method according to the first aspect.

[0010] In the third aspect, the embodiments of the present application An electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and operable on the processor. By executing the computer program, the processor implements the task optimization method according to the first aspect.

[0011] In the fourth aspect, the embodiments of the present application A computer-readable storage medium is provided, which stores a computer-executable program for causing a computer to execute the task optimization method according to the first aspect.

[0012] Other features and advantages of the present application will be described in the following specification, and will be partially apparent from the specification, or will be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained by the configurations particularly pointed out in the specification, claims and drawings. The drawings are used to provide a further understanding of the technical solutions of the present application, constitute a part of the specification, and are used to interpret the technical solutions of the present application together with the embodiments of the present application, but do not limit the technical solutions of the present application.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0014] To make the objectives, technical solutions, and advantages of the present application clearer, the present application will be described in more detail below with reference to the drawings and embodiments. The specific embodiments described in this specification are only used for interpreting the present application and are not used for limiting the present application.

[0015] In the description of the embodiments of the present application, the meaning of multiple (or multiple types) is two or more. It is understood that "greater than", "less than", "exceeding", etc. do not include the number, and "above", "below", "within", etc. include the number. The descriptions such as "first", "second", etc. are only for the purpose of distinguishing technical features and should not be understood as indicating or implying relative importance, or implicitly indicating the number of the indicated technical features or the sequence relationship of the indicated technical features.

[0016] The data middle platform constructs a unified standard at the enterprise level, opens up the enterprise data channel, eliminates the phenomenon of information silos, and reduces the cost caused by repeated construction. Thereby, not only can data service support be quickly provided to various business departments, but also the burden on the back end can be reduced, and the overall work efficiency and quality can be improved.

[0017] In the data middle platform, a large number of data processing tasks are being executed. These tasks constitute a huge directed acyclic graph (DAG), among which there are those that process relatively basic data and have many dependencies on subsequent tasks, and there are also those with requirements regarding output time and need to generate results by a specified time. Moreover, these tasks are usually developed individually by different teams, the quality of the tasks varies, and usually, each team applies for higher task resources and priorities to ensure the operation of their respective tasks, but there is no overall standard for this, which is disadvantageous to the overall efficiency.

[0018] Currently, when optimizing tasks, usually, tasks with operation time or resources are selected as the optimization targets, but just looking at time and resources does not necessarily mean there are problems with the tasks. Also, to determine whether the optimization of a single task affects the whole, usually, actual tests are required, and this entire process requires a long cycle and a great deal of trial-and-error costs, which have an adverse impact on the efficiency and success rate of task optimization.

[0019] In response to the current problem of the existence of blind optimization, the embodiments of the present application identify a target task that requires optimization by evaluating a task, adjust target task parameters corresponding to the target task, execute the target task parameters by virtual scheduling, perform a global scheduling preview, and when the preview effect of the task preview meets expectations, determine the target task parameters and execute the target task based on the target task parameters, and provide a task optimization method, apparatus, and computer-readable storage medium. Based on this, by evaluating a task to identify a task that requires optimization, adjusting task parameters, and performing a global scheduling preview by virtual scheduling, it is possible to quickly iterate to find the optimal task optimization method, improve the efficiency and success rate of task optimization, and avoid the situation where the verification after optimization by blind optimization does not meet the criteria. Therefore, the present application can quickly discover optimal task parameters from a global perspective, thereby enabling the data output capacity of the entire data middleware platform system to reach an ideal target state.

[0020] As shown in FIG. 1, FIG. 1 is a flowchart of a task optimization method according to an embodiment of the present application. The task optimization method includes, but is not limited to, the following steps 101 to 104.

[0021] Step 101: Evaluate a task to identify a target task that requires optimization.

[0022] Step 102: Adjust target task parameters corresponding to the target task.

[0023] Step 103: Execute the target task parameters by virtual scheduling and perform a global scheduling preview.

[0024] Step 104: When the preview effect of the global scheduling preview meets expectations, determine the target task parameters and execute the target task based on the target task parameters.

[0025] This method can be used to optimize tasks operating on a data middleware platform. Evaluate the tasks to identify target tasks that require optimization, adjust the target task parameters corresponding to the target tasks, execute the target task parameters through virtual scheduling, perform a global scheduling preview, and if the preview effect of the task preview meets expectations, determine the target task parameters and execute the target tasks based on the target task parameters. Based on this, by evaluating the tasks to identify tasks that require optimization, adjusting the task parameters, and performing a global scheduling preview through virtual scheduling, it is possible to quickly iterate to find the optimal task optimization method, improve the efficiency and success rate of task optimization, and avoid the situation where the verification after optimization does not meet the standard due to blind optimization. Therefore, this application can quickly discover the optimal task parameters from a global perspective, thereby enabling the data output capacity of the entire data middleware platform system to reach an ideal target state. Note that the identification and adjustment of tasks can be performed either manually by humans or automatically through machine learning.

[0026] A method for evaluating tasks to identify target tasks that require optimization may be to collect static and dynamic information of the tasks, create an evaluation model of the tasks from the static and dynamic information, and identify the target tasks that require optimization from the evaluation results of the evaluation model. Note that the static information is the configuration information of the tasks, for example, including input forms, output forms, resource allocation, task priorities, scheduling cycles, etc., but is not limited thereto, and the dynamic information is the historical execution information of the tasks, for example, including historical average execution time, average startup delay, average data processing volume, degree of dependence, etc., but is not limited thereto.

[0027] The task evaluation model can be expressed as S (task evaluation dimension) = Exec (task influencing factor). Here, S represents the evaluation of several dimensions of the task, and Exec is a fitted task execution model that can estimate the values of each evaluation dimension from several influencing factors. For example, the task evaluation model created from the static and dynamic information of the task is S (importance, algorithm efficiency, delay, time) = Exec (dependency, algorithm efficiency, priority, resource allocation). Here, the dependency refers to the lineage of the task, the importance is defined as the total number of subsequent tasks that directly or indirectly depend on that task, and the algorithm efficiency refers to the amount of data that can be processed per unit time per unit resource.

[0028] In this application, by collecting the static and dynamic information of the task, the task evaluation model S (task evaluation dimension) = Exec (task influencing factor) can be created from the information of each task. Here, S represents the evaluation of several dimensions of the task, and Exec is a fitted task execution model that can estimate the values of each evaluation dimension from several influencing factors. Identify the target task that requires optimization from the evaluation results. The identification method can be either manual identification by humans or automatic identification by rules. For the tasks determined to require optimization, adjust the target task parameters of the task execution model according to the optimization direction, which means the evaluation dimension to be improved. Perform an overall scheduling preview of the adjusted task execution model by virtual scheduling. If the preview effect is as expected, apply the adjusted task configuration to the production system. Otherwise, continue to adjust the parameters of the task execution model for optimization. Based on this, this application can identify which tasks require optimization from a global perspective, optimize the task parameters through global scheduling preview by virtual scheduling, and improve the operating efficiency of the system.

[0029] As shown in Figure 2, step 101 may include the following steps 1011 to 1013, but is not limited thereto.

[0030] Step 1011: Collect the static information and dynamic information of the task. The static information is the configuration information of the task, and the dynamic information is the historical execution information of the task.

[0031] Step 1012: Create an evaluation model of the task from the static information and dynamic information.

[0032] Step 1013: Identify the target tasks that require optimization from the evaluation results of the evaluation model.

[0033] The static information of the task can be collected from the configuration information of the task. The static information may include, but is not limited to, input forms, output forms, resource allocation, task priorities, scheduling cycles, etc. The historical execution information of the task can be obtained from the execution logs of the task. The dynamic information may include, but is not limited to, historical average execution time, average startup delay, average data processing volume, degree of dependence, etc.

[0034] Determine the evaluation dimensions and influencing factors of the task from the static information and dynamic information, and an evaluation model of the task can be created from the evaluation dimensions and influencing factors. The evaluation model of the task can be expressed as S (evaluation dimensions of the task) = Exec (influencing factors of the task). Here, S represents the evaluation of several dimensions of the task, and Exec is a fitted task execution model that can estimate the values of each evaluation dimension from several influencing factors. For example, the evaluation model of the task created from the static information and dynamic information of the task is S (importance, algorithm efficiency, delay, time) = Exec (dependency relationship, algorithm efficiency, priority, resource allocation). Here, the dependency relationship refers to the blood relationship of the task, the importance is defined as the total number of subsequent tasks directly or indirectly dependent on that task, and the algorithm efficiency refers to the amount of data that can be processed per unit time per unit resource. Based on this, the present application can quickly simulate the optimization effect through the creation of the task evaluation model and virtual scheduling, and improve the optimization efficiency of the entire task. Provide effective guidance for the direction of task optimization with multi-dimensional and three-dimensional task evaluation indicators.

[0035] In this application, by creating a task evaluation model, task evaluation information of interest to the user is associated with a simplified execution model. Based on the evaluation information and business goals, tasks that require optimization can be quickly identified. By adjusting task parameters and performing task pre-execution with a virtual scheduling engine, an optimal task optimization method can be quickly found through repeated iterations, thereby improving the efficiency and success rate of task optimization and avoiding the situation where the verification after optimization does not meet the criteria due to blind optimization.

[0036] As shown in FIG. 3, step 102 may include, but is not limited to, the following step 1021 and step 1022.

[0037] Step 1021: Determine the optimization direction of the target task, which is the evaluation dimension to be improved for the target task.

[0038] Step 1022: Adjust the target task parameters corresponding to the target task according to the optimization direction of the target task.

[0039] For the tasks determined to require optimization, the parameters of the task execution model are adjusted according to the optimization direction. Here, the optimization direction is the evaluation dimension to be improved, and the evaluation dimension may include, but is not limited to, importance, algorithm efficiency, delay, time, etc. For example, when the algorithm efficiency is within the normal range but the time is too long, it is necessary to consider increasing resources; when the delay is too large, it is necessary to consider optimizing the scheduling logic. The target task parameters corresponding to the target task are adjusted according to the evaluation dimension to be improved for the target task.

[0040] As shown in FIG. 4, step 103 may include, but is not limited to, the following step 1031 and step 1032.

[0041] Step 1031: Create an execution model of the target task obtained by fitting the influencing factors of the target task.

[0042] Step 1032: Use the execution model to execute the target task parameters through virtual scheduling and perform a global scheduling preview.

[0043] The influencing factors of the task may include, but are not limited to, dependencies, algorithm efficiency, priority, resource allocation, etc. Exec is a fitting task execution model that can estimate the values of each evaluation dimension from several influencing factors. The evaluation model of the task is S (importance, algorithm efficiency, delay, time) = Exec (dependencies, algorithm efficiency, priority, resource allocation). Here, the dependency refers to the lineage relationship of the task, the importance is defined as the total number of subsequent tasks directly or indirectly dependent on that task, and the algorithm efficiency refers to the amount of data that can be processed per unit time per unit resource. Use the execution model to execute the target task parameters through virtual scheduling and perform a global scheduling preview. Based on this, the present application performs an overall scheduling preview through the adjusted execution model by virtual scheduling, quickly simulates the optimization effect, and improves the optimization efficiency of the entire task.

[0044] In the present application, by creating a task evaluation model, the task evaluation information that the user is interested in is associated with a simplified execution model. The tasks that require optimization can be quickly identified based on the evaluation information and business goals. By adjusting the task parameters and executing the task preview with a virtual scheduling engine, the optimal task optimization method can be quickly found through repeated iterations, thereby improving the efficiency and success rate of task optimization and avoiding the situation where the verification after optimization does not meet the criteria due to blind optimization.

[0045] Hereinafter, with reference to the drawings and specific embodiments, the task optimization method according to the present application will be further described.

[0046] Taking one simplified task of the data middle platform as an example, if the total amount of resources of the data middle platform is 10, the steps corresponding to task optimization are as follows.

[0047] a. Mainly, collect the static information of the task, including the input form, output form, resource allocation, task priority, scheduling cycle, etc. from the configuration information, and obtain the dynamic information, which is the historical execution information including the historical average execution time, average startup delay, average data processing volume, degree of dependence, etc. from the task execution log.

[0048] b. Create an evaluation model of the task from the static information and dynamic information of each task, and the overall task DAG graph is shown in Figure 5.

[0049] c. Identify the tasks that require optimization from the evaluation results. Here, in order to define the goal of optimizing the startup delay of t31, as a result of the identification, the tasks that require optimization are t12 and t22.

[0050] d. First, optimize t12. Since the resources and priority are insufficient, t12 needs to wait for the completion of t11 to execute, and as a result, a delay occurs.

[0051] (1) The priority of t12 was adjusted to 100 for a preview. Although the delays of t12 and t22 became 0, the delays of t11 and t21 increased. As a result, the delay of t31 did not change, so this adjustment was not adopted.

[0052] (2) The t12 task resources were reduced to 5 for a preview. As shown in Figure 6, since the t31 task delay was reduced to 3, this optimization was adopted.

[0053] e. Based on the previous step, since it is difficult to optimize the next efficiency parameter of t12, instead, optimize t22.

[0054] (1) Adjust the task resource of t22 to 6, reduce the delay of t31 to 1, and adopt this optimization to proceed to the next step.

[0055] (2) Since the efficiency of the t22 algorithm is low, after raising the algorithm efficiency of t22 to 0.7, a preview is performed. As shown in Figure 7, the delay of the t31 task disappears, and this optimization is adopted.

[0056] f. Apply the algorithm optimization of reducing the task resource in t11 and increasing the task resource in t22 to the production system for the adjusted task configuration.

[0057] Note that the above steps c, d, and e can be executed manually or automatically by a program.

[0058] As shown in Figure 8, the embodiment of the present application also provides a task optimization device.

[0059] In some embodiments, this task optimization device includes one or more processors and a memory. In Figure 8, one processor and a memory are illustrated. The processor and the memory may be connected by a bus or other means. In Figure 8, an example of being connected via a bus is shown.

[0060] The memory can be used as a non - transient computer - readable storage medium to store non - transient software programs and non - transient computer - executable programs such as the task optimization method in the above embodiments of the present application. The processor realizes the task optimization method in the above embodiments of the present application by executing the non - transient software programs and programs stored in the memory.

[0061] The memory may include a program storage area capable of storing an operating system and applications necessary for at least one function, and a data storage area capable of storing data necessary for executing the task optimization method in the above embodiments of the present application. Further, the memory may include a high-speed random access memory, and may include a non-temporary memory such as at least one disk memory device, a flash memory device, or other non-temporary solid-state memory devices. In some embodiments, the memory may include a memory remotely located from the processor, and such remotely located memory may be connected to this task optimization device via a network. Examples of the above network include, but are not limited to, the Internet, a corporate intranet, a local area network, a mobile communication network, and combinations thereof.

[0062] The non-transitory software programs and programs necessary to implement the task optimization method in the above embodiments of the present application are stored in a memory and, when executed by one or more processors, execute the task optimization method in the above embodiments of the present application, for example, steps 101 to 104 of the method in FIG. 1 above, steps 1011 to 1013 of the method in FIG. 2, steps 1021 to 1022 of the method in FIG. 3, and steps 1031 to 1032 of the method in FIG. 4. Evaluate the task to identify the target task that requires optimization, adjust the target task parameters corresponding to the target task, execute the target task parameters by virtual scheduling, perform a global scheduling preview, and if the preview effect of the task preview is as expected, determine the target task parameters and execute the target task based on the target task parameters. Based on this, by evaluating the task to identify the task that requires optimization, adjusting the task parameters, and performing a global scheduling preview by virtual scheduling, it is possible to quickly iterate to find the optimal task optimization method, improve the efficiency and success rate of task optimization, and avoid the situation where the verification after optimization does not meet the standard due to blind optimization. Therefore, the present application can quickly discover the optimal task parameters from a global perspective, thereby enabling the data output capacity of the entire data middle platform system to reach an ideal target state.

[0063] As shown in FIG. 9, the embodiments of the present application also provide an electronic device.

[0064] In some embodiments, this electronic device includes one or more processors and a memory, and one processor and a memory are illustrated in FIG. 9. The processor and the memory may be connected by a bus or other means, and FIG. 9 shows an example of being connected via a bus.

[0065] The memory can be used as a non-transitory computer-readable storage medium to store non-transitory software programs such as the task optimization method in the above embodiments of the present application and non-transitory computer-executable programs. The processor realizes the task optimization method in the above embodiments of the present application by executing the non-transitory software program and program stored in the memory.

[0066] The memory may include a program storage area capable of storing an operating system and applications required for at least one function, and a data storage area capable of storing data required for executing the task optimization method in the above embodiments of the present application. Further, the memory may include a high-speed random access memory, and may include non-transitory memory such as at least one disk memory device, a flash memory device, or other non-transitory solid-state memory devices. In some embodiments, the memory may include memory remotely located from the processor, and such remotely located memory may be connected to the task optimization device via a network. Examples of the above network include, but are not limited to, the Internet, a corporate intranet, a local area network, a mobile communication network, and combinations thereof.

[0067] The non-transitory software programs and programs necessary to implement the task optimization method in the above embodiments of the present application are stored in a memory and, when executed by one or more processors, perform the task optimization method in the above embodiments of the present application, for example, steps 101 to 104 above, steps 1011 to 1013 of the method in FIG. 2, steps 1021 to 1022 of the method in FIG. 3, and steps 1031 to 1032 of the method in FIG. 4. Evaluate the task to identify the target task that requires optimization, adjust the target task parameters corresponding to the target task, execute the target task parameters by virtual scheduling, perform a global scheduling preview, and if the preview effect of the task preview is as expected, determine the target task parameters and execute the target task based on the target task parameters. Based on this, by evaluating the task to identify the task that requires optimization, adjusting the task parameters, and performing a global scheduling preview by virtual scheduling, an optimal task optimization method can be quickly iteratively found, improving the efficiency and success rate of task optimization, and avoiding the situation where the verification after optimization does not reach the standard due to blind optimization. Therefore, the present application can quickly discover optimal task parameters from a global perspective, thereby enabling the data output capacity of the entire data middle platform system to reach an ideal target state.

[0068] Furthermore, the embodiments of the present application also provide a computer-readable storage medium storing a computer-executable program, which, when executed by one or more control processors, such as one processor in FIG. 8, causes the task optimization method in the above embodiments of the present application, such as steps 101-104 above, steps 1011-1013 of the method in FIG. 2, steps 1021-1022 of the method in FIG. 3, and steps 1031-1032 of the method in FIG. 4, to be executed by the above one or more processors. Identify target tasks that need to be optimized by evaluating tasks, adjust the target task parameters corresponding to the target tasks, execute the target task parameters by virtual scheduling, perform a global scheduling preview, and if the preview effect of the task preview meets expectations, determine the target task parameters and execute the target tasks based on the target task parameters. Based on this, by evaluating tasks to identify tasks that need to be optimized, adjusting task parameters, and performing a global scheduling preview by virtual scheduling, an optimal task optimization method can be quickly iteratively found, the efficiency and success rate of task optimization can be increased, and it can be avoided that the verification after optimization by blind optimization does not meet the criteria. Therefore, the present application can quickly discover optimal task parameters from a global perspective, thereby enabling the data output capacity of the entire data middle platform system to reach an ideal target state.

[0069] The embodiments of the present application include the steps of evaluating a task to identify a target task that requires optimization, adjusting target task parameters corresponding to the target task, executing the target task parameters by virtual scheduling to perform a global scheduling preview, and when the preview effect of the task preview meets expectations, determining the target task parameters and executing the target task based on the target task parameters. Based on this, by evaluating the task to identify the task that requires optimization, adjusting the task parameters, and performing the global scheduling preview by virtual scheduling, it is possible to quickly iterate to find the optimal task optimization method, improve the efficiency and success rate of task optimization, and avoid the situation where the verification after optimization does not meet the standard due to blind optimization. Therefore, the present application can quickly discover the optimal task parameters from a global perspective, thereby enabling the data output ability of the entire data middle platform system to reach an ideal target state.

[0070] All or part of the steps in the method disclosed above, the system may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor such as a central processor, a digital signal processor, a microprocessor, etc., or as hardware, or as an integrated circuit such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium that may include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage devices, magnetic cartridges, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Further, it is well known to those skilled in the art that communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information distribution medium.

[0071] As described above, some embodiments of the present application have been specifically described. However, the present application is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without departing from the gist of the present application, and all of these equivalent modifications or substitutions shall be included within the scope defined by the claims of the present application.

Claims

1. A method for task optimization executed by an electronic device, comprising: identifying a target task that requires optimization by evaluating tasks; adjusting target task parameters corresponding to the target task; executing the target task parameters by virtual scheduling to perform a global scheduling preview; if the preview effect of the global scheduling preview is as expected, determining the target task parameters and executing the target task based on the target task parameters.

2. The step of identifying the target task that requires optimization by evaluating the tasks includes: collecting static information and dynamic information of the task, where the static information is the configuration information of the task and the dynamic information is the historical execution information of the task; creating an evaluation model of the task from the static information and the dynamic information; identifying the target task that requires optimization from the evaluation results of the evaluation model. The method according to claim 1.

3. The static information includes at least one of: a task input form, or a task output form, or task resource allocation, or the priority of the task, or a task schedule cycle. The method according to claim 2.

4. The dynamic information includes at least one of: the historical average execution time of the task, or the average startup delay of the task, or the average data processing volume of the task, or the degree of dependence of the task. The method according to claim 2.

5. The step of creating the evaluation model of the task from the static information and the dynamic information includes: determining an evaluation dimension and an influencing factor of the task from the static information and the dynamic information; creating the evaluation model from the evaluation dimension and the influencing factor. The method according to claim 2.

6. The step of adjusting the target task parameters corresponding to the target task includes: determining an optimization direction of the target task, which is the evaluation dimension to be improved for the target task; adjusting the target task parameters corresponding to the target task according to the optimization direction of the target task. The method according to claim 5.

7. The step of performing virtual scheduling on the target task parameters and performing a global scheduling preview is as follows: creating an execution model of the target task obtained by fitting the influencing factor of the target task; performing virtual scheduling on the target task parameters using the execution model and performing a global scheduling preview, the method according to claim 6.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein the processor realizes the task optimization method according to any one of claims 1 to 7 by executing the computer program.

9. A computer-readable storage medium storing a computer-executable program for causing a computer to execute the task optimization method according to any one of claims 1 to 7.

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