Task scheduling optimization method and system based on collaborative analysis

Abstract

The invention relates to the technical field of electrical digital data processing, and provides a task scheduling optimization method and system based on collaborative analysis. Initializing task information of the target task, and disassembling the target task into a task set composed of a plurality of sub-tasks; performing priority ranking on the sub-tasks according to the emergency degrees, the important parameters and the resource requirements of the sub-tasks; obtaining current execution resources through a resource manager to generate an execution unit set, and generating a collaborative strategy in combination with the priority ranking of the subtasks; and according to the priority ranking and the collaborative strategy, calculating a target value of each task scheduling mode in the intelligent scheduling algorithm to select the scheduling mode, and finally arranging the sub-tasks to the corresponding execution units. Through fine task disassembly, priority ranking, real-time resource monitoring and an intelligent scheduling algorithm, the accuracy and efficiency of task execution are improved, and the flexibility and expandability of the system are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of electronic digital data processing, and in particular to a task scheduling optimization method and system based on collaborative analysis. Background Art

[0002] The task scheduling optimization method based on collaborative analysis is an advanced scheduling strategy developed in modern computing environments and distributed systems to cope with complex and changing task requirements and resource constraints. With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, task scheduling systems are facing unprecedented challenges, including large task scales, high resource heterogeneity, and strict real-time requirements.

[0003] Collaborative analysis requires comprehensive consideration of multiple objectives and constraints, such as task completion time, resource utilization, energy consumption, etc. This makes algorithm design very complicated, requiring multiple factors to be weighed to find the optimal solution, and it is impossible to comprehensively judge and evaluate the task situation, which in turn causes the problem of low efficiency of collaborative scheduling. Summary of the invention

[0004] The embodiments of the present application provide a task scheduling optimization method and system based on collaborative analysis, which can at least to a certain extent solve the problem of being unable to comprehensively judge and evaluate task situations, thereby causing low efficiency of collaborative scheduling.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of the present application, a task scheduling optimization method based on collaborative analysis is provided, including: initializing task information of a target task, and based on the task information, decomposing the target task into a task set consisting of multiple subtasks; generating a weight vector based on the urgency, important parameters and resource requirements of the subtasks; generating a relationship matrix based on the execution relationship between the subtasks; generating a priority parameter based on the relationship matrix and the weight vector, and prioritizing the subtasks according to the priority parameter; obtaining the current execution resources through a resource manager, generating an execution unit set, and generating a collaborative strategy based on the priority ranking of the subtasks; the collaborative strategy includes the dependency relationship and execution conditions between the subtasks; calculating the target value of each task scheduling method in the intelligent scheduling algorithm based on the priority ranking and the collaborative strategy, and selecting a scheduling method based on the target value; arranging the subtasks to the corresponding execution units according to the scheduling method.

[0007] In the present application, based on the aforementioned scheme, the target task is decomposed into a task set consisting of multiple subtasks based on the task information, including: based on the functional information and process information in the task information, the target task is decomposed based on multiple dimensions to generate multiple subtasks; a task set is constructed based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons and time nodes.

[0008] In the present application, based on the aforementioned scheme, the current execution resources are obtained through the resource manager to generate a set of execution units, including: obtaining the current execution resources through the resource manager to determine the current occupancy and free amount of the execution resources; based on the current occupancy and free amount of the execution resources, generating a set of execution units.

[0009] In the present application, based on the aforementioned scheme, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated according to the priority sorting and the collaborative strategy, and the scheduling method is selected according to the target value, including: calculating the time continuity constraint parameters and processor load parameters corresponding to each task scheduling method in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy; calculating the target value according to the time continuity constraint parameters and the processor load parameters; and selecting the scheduling method according to the target value.

[0010] In the present application, based on the aforementioned scheme, it also includes: when the execution unit is working, monitoring the execution status and resource utilization of the execution unit, and obtaining the execution result of the subtask after the task is completed; based on the execution status, the execution result and resource utilization, feedback and optimization of the task scheduling method.

[0011] In the present application, based on the above-mentioned solution, it also includes: after all the subtasks are executed, the results are summarized and an execution report is generated; and the execution report is sent to the management terminal.

[0012] According to one aspect of the present application, a task scheduling optimization system based on collaborative analysis is provided, comprising: A task unit, used to initialize task information of a target task, and based on the task information, decompose the target task into a task set consisting of a plurality of subtasks; A priority unit, for generating a weight vector based on the urgency, important parameters and resource requirements of the subtasks; generating a relationship matrix based on the execution relationship between the subtasks; generating a priority parameter based on the relationship matrix and the weight vector, and prioritizing the subtasks according to the priority parameter; A collaboration unit, used to obtain current execution resources through a resource manager, generate an execution unit set, and generate a collaboration strategy in combination with the priority ranking of the subtasks; the collaboration strategy includes dependencies and execution conditions between the subtasks; A scheduling unit, configured to calculate a target value of each task scheduling method in an intelligent scheduling algorithm according to the priority ranking and the coordination strategy, and select a scheduling method according to the target value; The execution unit is used to arrange the subtask to the corresponding execution unit according to the scheduling method.

[0013] In the present application, based on the aforementioned scheme, the target task is decomposed into a task set consisting of multiple subtasks based on the task information, including: based on the functional information and process information in the task information, the target task is decomposed based on multiple dimensions to generate multiple subtasks; a task set is constructed based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons and time nodes.

[0014] In the present application, based on the aforementioned scheme, the current execution resources are obtained through the resource manager to generate a set of execution units, including: obtaining the current execution resources through the resource manager to determine the current occupancy and free amount of the execution resources; based on the current occupancy and free amount of the execution resources, generating a set of execution units.

[0015] In the present application, based on the aforementioned scheme, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated according to the priority sorting and the collaborative strategy, and the scheduling method is selected according to the target value, including: calculating the time continuity constraint parameters and processor load parameters corresponding to each task scheduling method in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy; calculating the target value according to the time continuity constraint parameters and the processor load parameters; and selecting the scheduling method according to the target value.

[0016] In the present application, based on the aforementioned scheme, it also includes: when the execution unit is working, monitoring the execution status and resource utilization of the execution unit, and obtaining the execution result of the subtask after the task is completed; based on the execution status, the execution result and resource utilization, feedback and optimization of the task scheduling method.

[0017] In the present application, based on the above-mentioned solution, it also includes: after all the subtasks are executed, the results are summarized and an execution report is generated; and the execution report is sent to the management terminal.

[0018] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the task scheduling optimization method based on collaborative analysis as described in the above embodiment is implemented.

[0019] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the task scheduling optimization method based on collaborative analysis as described in the above embodiments.

[0020] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the task scheduling optimization method based on collaborative analysis provided in the above-mentioned various optional implementations.

[0021] In the technical solution of the present application, the task information of the target task is initialized, and based on the task information, the target task is disassembled into a task set consisting of multiple subtasks; based on the urgency, important parameters and resource requirements of the subtasks, a weight vector is generated; based on the execution relationship between the subtasks, a relationship matrix is ​​generated; according to the relationship matrix and the weight vector, a priority parameter is generated, and the subtasks are prioritized according to the priority parameter; the current execution resources are obtained through the resource manager, an execution unit set is generated, and a collaborative strategy is generated in combination with the priority ranking of the subtasks; the collaborative strategy includes the dependency relationship and execution conditions between the subtasks; according to the priority ranking and the collaborative strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value; according to the scheduling method, the subtask is arranged to the corresponding execution unit. Through the mechanisms of fine task disassembly, reasonable priority ranking, real-time resource monitoring, intelligent scheduling algorithm and comprehensive execution report generation, the optimization and efficiency improvement of task scheduling are achieved. This method not only improves the accuracy and efficiency of task execution, but also enhances the flexibility and scalability of the system.

[0022] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flowchart of a task scheduling optimization method based on collaborative analysis in an embodiment of the present application is schematically shown.

[0025] Figure 2 The flowchart of generating a task set in one embodiment of the present application is schematically shown.

[0026] Figure 3 A schematic diagram of a task scheduling optimization system based on collaborative analysis in an embodiment of the present application is schematically shown.

[0027] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0029] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0032] The implementation details of the technical solution of this application are described in detail below: Figure 1 FIG. 1 is a flowchart of a task scheduling optimization method based on collaborative analysis according to an embodiment of the present application. Figure 1 As shown, the task scheduling optimization method based on collaborative analysis includes at least steps S110 to S150, which are described in detail as follows: In step S110 , task information of a target task is initialized, and based on the task information, the target task is decomposed into a task set consisting of a plurality of subtasks.

[0033] In one embodiment of the present application, initializing the task information of the target task is the starting point of the entire scheduling process, which covers the core elements such as the basic description of the task, expected goals, key milestones and necessary constraints. Subsequently, based on these detailed task information, a systematic and structured method is adopted to carefully decompose the target task into a series of interrelated and relatively independent subtasks, which together constitute a well-organized and hierarchical task set. Each subtask inherits some of the attributes and goals of the target task, and at the same time clarifies more specific execution steps, expected results, responsibility attribution and completion time, laying a solid foundation for subsequent task scheduling and execution.

[0034] like Figure 2 As shown, in one embodiment of the present application, based on the task information, the target task is decomposed into a task set consisting of multiple subtasks, including: S210, based on the function information and process information in the task information, decompose the target task based on multiple dimensions to generate multiple subtasks; S220, constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons, and time nodes.

[0035] In an embodiment of the present application, a work breakdown structure, functional decomposition, process decomposition and other methods are used to decompose the target task into subtasks at multiple levels and dimensions. Each subtask has a clear goal, achievement standard, responsible person and time node.

[0036] Conduct a detailed resource requirement analysis for each subtask, including human resources (such as professional skills, experience level, working hours, etc.), material resources (such as equipment, materials, venues, etc.), financial resources (such as budget, cost, etc.) and information resources (such as data, knowledge, technology, etc.).

[0037] According to the results of resource demand analysis, formulate resource allocation plan corresponding to the information of subtasks to ensure effective use of resources and avoid waste. At the same time, analyze the resource demand conflicts between different subtasks and formulate conflict resolution strategies.

[0038] Based on the functional information and process information in the task information, multi-level and multi-dimensional decomposition of the target task can ensure that the task is moderately detailed, neither too trivial nor too general. This decomposition method helps to more accurately evaluate the needs and resources of each subtask, thereby providing a solid foundation for subsequent task scheduling. At the same time, building a task collection containing detailed information on subtasks makes task management clearer and more orderly.

[0039] In step S120, a weight vector is generated based on the urgency, important parameters and resource requirements of the subtasks; a relationship matrix is ​​generated based on the execution relationship between the subtasks; a priority parameter is generated according to the relationship matrix and the weight vector, and the subtasks are prioritized according to the priority parameter.

[0040] In one embodiment of the present application, during the task scheduling process, a comprehensive and detailed evaluation is performed on each subtask based on multiple dimensions, such as the urgency of the subtask (i.e., the time urgency of task completion), important parameters (such as the contribution or scope of influence of the task to the overall goal), and resource requirements (including the type and quantity of required resources and the priority of resource use). On this basis, a scientific priority sorting algorithm is adopted to comprehensively consider the interdependence between tasks and resource competition conditions, and assign a clear priority order to all subtasks. This step is intended to ensure that the most urgent, most important and most resource-matched subtasks can be given priority processing and resource allocation, thereby optimizing the overall task execution process, improving resource utilization efficiency, and ensuring the smooth achievement of project goals.

[0041] In one embodiment of the present application, there is a subtask set Each subtask has corresponding attribute values ​​in three dimensions: urgency Important parameters and resource requirements .in, i Indicates the subtask ID. n Indicates the total number of subtasks.

[0042] Generate a judgment matrix based on the scores of subtasks in terms of urgency, important parameters, and resource requirements ,in, j Represents the dimension identifier. Represents a subtask i In Dimension j The score situation on.

[0043] After that, the judgment matrix is ​​normalized to generate the elements in the normalized matrix for:

[0044] Based on the elements in the normalized matrix , determine the elements in the weight vector for:

[0045] Then, based on the elements in the weight vector , the weight vector is: .

[0046] At the same time, based on the execution relationship between each subtask, a relationship matrix is ​​generated. In this embodiment, the relationship matrix Each element in Represents a subtask i With subtasks j The degree of fuzzy relationship between them, that is, the degree of association between the two tasks, can be represented by a value between 0 and 1. The larger the value, the closer the relationship.

[0047] Finally, the priority parameter is generated according to the relationship matrix and the weight vector for:

[0048] Afterwards, the subtasks are prioritized according to the priority parameters.

[0049] In one embodiment of the present application, by comprehensively considering the urgency, important parameters and resource requirements of the subtasks, a weight vector and a relationship matrix are generated, and then the subtasks are prioritized. This method can ensure that key tasks are given priority, while taking into account the dependencies between tasks to avoid conflicts and delays in task execution. Priority sorting provides clear guidance for the intelligent scheduling algorithm, making task scheduling more reasonable and efficient.

[0050] In step S130, the current execution resources are acquired through the resource manager, an execution unit set is generated, and a coordination strategy is generated in combination with the priority ranking of the subtasks; the coordination strategy includes the dependency relationship and execution conditions between the subtasks.

[0051] In one embodiment of the present application, by integrating an efficient resource manager, currently available execution resource information is captured and summarized in real time, and this information covers key attributes such as resource type, quantity, status and available time period. Based on these real-time data, a set of execution units containing multiple execution units is dynamically generated, and each execution unit represents the execution capability of a specific resource combination. Afterwards, a set of collaborative strategies is formulated using algorithms and models in combination with the previously determined subtask priority ranking. This set of strategies not only describes in detail the logical dependencies and sequence between subtasks, but also clarifies the specific execution conditions of each subtask, including the required resources, execution time window, and any preceding or succeeding tasks. This step aims to maximize the use of existing resources and ensure that subtasks can be executed efficiently and without conflict in the optimal order, thereby achieving a significant improvement in the overall task scheduling performance.

[0052] In one embodiment of the present application, the current execution resources are obtained through the resource manager to generate an execution unit set, including: Acquire the current execution resources through the resource manager, and determine the current occupied amount and idle amount of the execution resources; Based on the current occupancy and idleness of the execution resources, an execution unit set is generated.

[0053] In one embodiment of the present application, the current execution resources are obtained through the resource manager, and the current occupancy refers to the amount of execution resources currently occupied or in use. This includes the resources occupied by the running tasks; the current idle amount refers to the amount of execution resources that are not currently occupied or available for new tasks. This is the remaining resources that the system can use to start new tasks or execution units. Once the current occupancy and idle amount of the execution resources are obtained, the execution unit set can be generated based on this information.

[0054] After generating the execution unit set, the coordination strategy is generated in combination with the priority ranking of the subtasks. Specifically, the currently executable subtasks are selected from the priority ranking of the subtasks. According to the system resource situation, the executable subtasks are assigned to the appropriate execution units, and so on, until all subtasks are assigned, and the final coordination strategy is generated, including the execution order of the subtasks, dependencies, and execution unit allocation and execution conditions.

[0055] In one embodiment of the present application, the current execution resource information, including the occupied amount and the idle amount, is obtained in real time through the resource manager, thereby generating an execution unit set. This method can ensure that the availability of resources is fully considered when scheduling tasks, avoiding resource conflicts and waste. At the same time, the generation of the execution unit set facilitates the subsequent task allocation, so that tasks can be quickly and accurately allocated to appropriate execution units.

[0056] In step S140, according to the priority ranking and the coordination strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value.

[0057] In one embodiment of the present application, an intelligent scheduling algorithm is used to conduct an in-depth analysis of various possible task scheduling schemes based on the priority sorting and coordination strategy of the subtasks. In this process, multiple key indicators such as the time efficiency, resource utilization, satisfaction of task dependencies, and execution cost of each scheme are evaluated, and these indicators together constitute the target value of the task scheduling method. By comprehensively comparing the target values ​​of different schemes, it is possible to accurately identify those scheduling methods that can not only meet the requirements of task urgency and importance, but also efficiently utilize resources and minimize execution conflicts and costs. Finally, based on these detailed analysis results, the optimal task scheduling scheme is selected to ensure the efficient operation of the entire task scheduling process and the smooth achievement of project goals.

[0058] In one embodiment of the present application, according to the priority ranking and the coordination strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value, including: According to the priority sorting and the coordination strategy, the time continuity constraint parameters and processor load parameters corresponding to each task scheduling mode in the intelligent scheduling algorithm are calculated; Calculating a target value according to the time continuity constraint parameter and the processor load parameter; A scheduling method is selected according to the target value.

[0059] In one embodiment of the present application, according to the priority sorting and the coordination strategy, the time continuity constraint parameters corresponding to each task scheduling method in the intelligent scheduling algorithm are calculated. for:

[0060] in, Represents a subtask The start time of Subtasks The execution time, It is a subtask and subtasks Minimum interval between; time continuity constraint parameters Used to measure the time required for a subtask to execute.

[0061] After that, calculate the processor load parameter for:

[0062] in, Indicates the task Assigned to the processor The probability of processor load parameter To measure the processor Current load conditions.

[0063] Then, the target value is calculated according to the time continuity constraint parameter and the processor load parameter. for:

[0064] in, is a weight factor used to balance the numerical relationship between completion time and processor load.

[0065] After calculating the target value, select the scheduling method that corresponds to the minimum target value and arrange the subtasks. According to the priority sorting and coordination strategy, calculate the time continuity constraint parameters and processor load parameters corresponding to each task scheduling method in the intelligent scheduling algorithm, and then calculate the target value. This method can comprehensively consider multiple aspects of task execution, such as time efficiency, resource utilization, etc., so as to select the optimal scheduling method. The application of intelligent scheduling algorithms makes task scheduling more intelligent and automated, and improves the efficiency and accuracy of task execution.

[0066] In step S150, the subtasks are arranged to corresponding execution units according to the scheduling method.

[0067] In one embodiment of the present application, after determining the optimal task scheduling scheme, the disassembled subtasks are accurately assigned to the execution units that match them according to the specific requirements of the scheme. This step requires not only a deep understanding of the characteristics and requirements of each subtask, but also full consideration of the resource allocation, execution capability and current workload of the execution unit. Through careful matching and arrangement, it is ensured that each subtask can be assigned to the execution unit that is most suitable for its execution, thereby maximizing the efficiency and quality of task execution. At the same time, this step also lays a solid foundation for subsequent task monitoring and management, so as to more effectively track the progress of tasks and promptly discover and solve potential problems.

[0068] According to the scheduling method, the subtasks are arranged to the corresponding execution units. When the execution units are working, their execution status and resource utilization are monitored in real time, and the execution results of the subtasks are obtained after the tasks are completed. This monitoring mechanism can timely discover problems in task execution, such as insufficient resources, execution delays, etc., so as to take corresponding measures to correct them. At the same time, feedback and optimization of the task scheduling method based on the execution status, execution results and resource utilization can continuously improve the performance and efficiency of task scheduling.

[0069] In one embodiment of the present application, it also includes: When the execution unit is working, monitoring the execution status of the execution unit and the utilization status of resources, and obtaining the execution result of the subtask after the task is completed; Feedback and optimization of the task scheduling method are performed based on the execution status, the execution results and the utilization of resources.

[0070] During the operation of the execution unit, the execution status of the execution unit and the utilization of resources need to be continuously monitored. The execution status includes the execution progress of the task, whether errors or exceptions are encountered, and whether the dependencies between tasks are correctly maintained. Resource utilization involves resource consumption such as resource utilization rate. The system needs to monitor these resources to identify potential bottlenecks or overloads.

[0071] The completion status of the task is detected through the status code or signal returned by the task execution unit. Once the task is completed, the system needs to collect and store the output or result data of the task. This data may include calculation results, log information, status updates, etc. Based on the monitored execution and resource utilization, as well as the collected subtask execution results, the system can provide feedback and optimize the task scheduling method.

[0072] In one embodiment of the present application, it also includes: After all the subtasks are executed, the results are summarized and an execution report is generated; The execution report is sent to the management terminal.

[0073] In one embodiment of the present application, after all subtasks are executed, the system will first collect the result data of these tasks. These data may include calculation results, log files, status information, etc. The collected data is integrated into a unified format or data structure to facilitate subsequent analysis and report generation. The integrated subtask result data is filled into a report template to generate a final execution report.

[0074] Identify and determine the specific location or identity of the management terminal. Based on the system configuration and user preferences, the system will select an appropriate delivery method, such as email, SMS notification, instant message push, or direct display on the management interface.

[0075] After all subtasks are executed, the results are summarized and an execution report is generated, which is then sent to the management terminal. This reporting mechanism can provide managers with a comprehensive overview of task execution, including task completion status, resource usage, etc. Managers can further evaluate and optimize task scheduling based on the information in the report, thereby continuously improving the overall performance of the system.

[0076] In the technical solution of the present application, the task information of the target task is initialized, and based on the task information, the target task is disassembled into a task set consisting of multiple subtasks; based on the urgency, important parameters and resource requirements of the subtasks, a weight vector is generated; based on the execution relationship between the subtasks, a relationship matrix is ​​generated; according to the relationship matrix and the weight vector, a priority parameter is generated, and the subtasks are prioritized according to the priority parameter; the current execution resources are obtained through the resource manager, an execution unit set is generated, and a collaborative strategy is generated in combination with the priority ranking of the subtasks; the collaborative strategy includes the dependency relationship and execution conditions between the subtasks; according to the priority ranking and the collaborative strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value; according to the scheduling method, the subtask is arranged to the corresponding execution unit. Through the mechanisms of fine task disassembly, reasonable priority ranking, real-time resource monitoring, intelligent scheduling algorithm and comprehensive execution report generation, the optimization and efficiency improvement of task scheduling are achieved. This method not only improves the accuracy and efficiency of task execution, but also enhances the flexibility and scalability of the system, and provides a new solution for task scheduling in modern computing environments and distributed systems.

[0077] The following describes an embodiment of the device of the present application, which can be used to execute the task scheduling optimization method based on collaborative analysis in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the task scheduling optimization method based on collaborative analysis in the above-mentioned embodiment of the present application.

[0078] Figure 3 A block diagram of a task scheduling optimization system based on collaborative analysis according to an embodiment of the present application is shown.

[0079] Reference Figure 3As shown, according to an embodiment of the present application, a task scheduling optimization system based on collaborative analysis includes: A task unit 310 is used to initialize task information of a target task and, based on the task information, decompose the target task into a task set consisting of a plurality of subtasks; The priority unit 320 is used to generate a weight vector based on the urgency, important parameters and resource requirements of the subtasks; generate a relationship matrix based on the execution relationship between the subtasks; generate a priority parameter according to the relationship matrix and the weight vector, and prioritize the subtasks according to the priority parameter; The coordination unit 330 is used to obtain the current execution resources through the resource manager, generate an execution unit set, and generate a coordination strategy in combination with the priority ranking of the subtasks; the coordination strategy includes the dependency relationship and execution conditions between the subtasks; A scheduling unit 340 is used to calculate the target value of each task scheduling method in the intelligent scheduling algorithm according to the priority ranking and the coordination strategy, and select a scheduling method according to the target value; The execution unit 350 is used to arrange the subtask to the corresponding execution unit according to the scheduling method.

[0080] In the present application, based on the aforementioned scheme, the target task is decomposed into a task set consisting of multiple subtasks based on the task information, including: based on the functional information and process information in the task information, the target task is decomposed based on multiple dimensions to generate multiple subtasks; a task set is constructed based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons and time nodes.

[0081] In the present application, based on the aforementioned scheme, the current execution resources are obtained through the resource manager to generate a set of execution units, including: obtaining the current execution resources through the resource manager to determine the current occupancy and free amount of the execution resources; based on the current occupancy and free amount of the execution resources, generating a set of execution units.

[0082] In the present application, based on the aforementioned scheme, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated according to the priority sorting and the collaborative strategy, and the scheduling method is selected according to the target value, including: calculating the time continuity constraint parameters and processor load parameters corresponding to each task scheduling method in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy; calculating the target value according to the time continuity constraint parameters and the processor load parameters; and selecting the scheduling method according to the target value.

[0083] In the present application, based on the aforementioned scheme, it also includes: when the execution unit is working, monitoring the execution status and resource utilization of the execution unit, and obtaining the execution result of the subtask after the task is completed; based on the execution status, the execution result and resource utilization, feedback and optimization of the task scheduling method.

[0084] In the present application, based on the above-mentioned solution, it also includes: after all the subtasks are executed, the results are summarized and an execution report is generated; and the execution report is sent to the management terminal.

[0085] In the technical solution of the present application, the task information of the target task is initialized, and based on the task information, the target task is disassembled into a task set consisting of multiple subtasks; based on the urgency, important parameters and resource requirements of the subtasks, a weight vector is generated; based on the execution relationship between the subtasks, a relationship matrix is ​​generated; according to the relationship matrix and the weight vector, a priority parameter is generated, and the subtasks are prioritized according to the priority parameter; the current execution resources are obtained through the resource manager, an execution unit set is generated, and a collaborative strategy is generated in combination with the priority ranking of the subtasks; the collaborative strategy includes the dependency relationship and execution conditions between the subtasks; according to the priority ranking and the collaborative strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value; according to the scheduling method, the subtask is arranged to the corresponding execution unit. Through the mechanisms of fine task disassembly, reasonable priority ranking, real-time resource monitoring, intelligent scheduling algorithm and comprehensive execution report generation, the optimization and efficiency improvement of task scheduling are achieved. This method not only improves the accuracy and efficiency of task execution, but also enhances the flexibility and scalability of the system, and provides a new solution for task scheduling in modern computing environments and distributed systems.

[0086] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0087] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0088] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory 402 or the program loaded from the storage part 408 to the random access memory 403, such as executing the task scheduling optimization method based on collaborative analysis described in the above embodiment. Various programs and data required for system operation are also stored in the random access memory 403. The central processing unit 401, the read-only memory 402 and the random access memory 403 are connected to each other through a bus 404. The input / output interface 405 is also connected to the bus 404.

[0089] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0090] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit 401, various functions defined in the system of the present application are executed.

[0091] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0092] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0094] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0095] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the task scheduling optimization method based on collaborative analysis described in the above embodiment.

[0096] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0097] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0098] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0099] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A task scheduling optimization method based on collaborative analysis, characterized in that: include: Initialize task information of a target task, and based on the task information, decompose the target task into a task set consisting of a plurality of subtasks; Based on the urgency, important parameters and resource requirements of the subtasks, a weight vector is generated; based on the execution relationship between the subtasks, a relationship matrix is ​​generated; Generate a priority parameter according to the relationship matrix and the weight vector, and prioritize the subtasks according to the priority parameter; Obtain the current execution resources through the resource manager, generate an execution unit set, and generate a coordination strategy in combination with the priority ranking of the subtasks; The collaboration strategy includes dependencies and execution conditions between the subtasks; Calculate the target value of each task scheduling method in the intelligent scheduling algorithm according to the priority ranking and the coordination strategy, and select the scheduling method according to the target value; The subtasks are arranged to corresponding execution units according to the scheduling method.

2. The task scheduling optimization method based on collaborative analysis according to claim 1 is characterized in that: Based on the task information, the target task is decomposed into a task set consisting of multiple subtasks, including: Based on the function information and process information in the task information, the target task is decomposed based on multiple dimensions to generate multiple subtasks; A task set is constructed based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons and time nodes.

3. The task scheduling optimization method based on collaborative analysis according to claim 1 is characterized in that: Get the current execution resources through the resource manager and generate a set of execution units, including: Acquire the current execution resources through the resource manager, and determine the current occupied amount and idle amount of the execution resources; Based on the current occupancy and idleness of the execution resources, an execution unit set is generated.

4. The task scheduling optimization method based on collaborative analysis according to claim 1 is characterized in that: According to the priority ranking and the coordination strategy, the target value of each task scheduling method in the intelligent scheduling algorithm is calculated, and the scheduling method is selected according to the target value, including: According to the priority sorting and the coordination strategy, the time continuity constraint parameters and processor load parameters corresponding to each task scheduling mode in the intelligent scheduling algorithm are calculated; Calculating a target value according to the time continuity constraint parameter and the processor load parameter; A scheduling method is selected according to the target value.

5. The task scheduling optimization method based on collaborative analysis according to claim 1 is characterized in that: Also includes: When the execution unit is working, monitoring the execution status of the execution unit and the utilization status of resources, and obtaining the execution result of the subtask after the task is completed; Feedback and optimization of the task scheduling method are performed based on the execution status, the execution results and the utilization of resources.

6. The task scheduling optimization method based on collaborative analysis according to claim 1 is characterized in that: Also includes: After all the subtasks are executed, the results are summarized and an execution report is generated; The execution report is sent to the management terminal.

7. A task scheduling optimization system based on collaborative analysis, characterized in that: include: A task unit, used to initialize task information of a target task, and based on the task information, decompose the target task into a task set consisting of a plurality of subtasks; A priority unit, used to generate a weight vector based on the urgency, important parameters and resource requirements of the subtasks; and generate a relationship matrix based on the execution relationship between the subtasks; Generate a priority parameter according to the relationship matrix and the weight vector, and prioritize the subtasks according to the priority parameter; A coordination unit, used to obtain current execution resources through a resource manager, generate an execution unit set, and generate a coordination strategy in combination with the priority ranking of the subtasks; The collaboration strategy includes dependencies and execution conditions between the subtasks; A scheduling unit, configured to calculate a target value of each task scheduling method in an intelligent scheduling algorithm according to the priority ranking and the coordination strategy, and select a scheduling method according to the target value; The execution unit is used to arrange the subtask to the corresponding execution unit according to the scheduling method.

8. The task scheduling optimization system based on collaborative analysis according to claim 7 is characterized in that: Based on the task information, the target task is decomposed into a task set consisting of multiple subtasks, including: Based on the function information and process information in the task information, the target task is decomposed based on multiple dimensions to generate multiple subtasks; A task set is constructed based on the information of the subtasks; the information of the subtasks includes target information, achievement standards, responsible persons and time nodes.

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