Task Scheduling Optimization Method and System Based on Collaborative Analysis

Abstract

This application relates to the technical field of electrical digital data processing, and provides a task scheduling optimization method and system based on collaborative analysis. Initialize the task information of the target task, and disassemble the target task into a task set composed of multiple subtasks; sort the subtasks according to their urgency, important parameters, and resource requirements; obtain the current execution resources through the resource manager to generate a set of execution units, and generate a collaborative strategy in combination with the priority sorting of the subtasks; according to the priority sorting and the collaborative strategy, calculate the target values of each task scheduling method in the intelligent scheduling algorithm to select the scheduling method, and finally arrange the subtasks to the corresponding execution units. Through fine-grained task disassembly, priority sorting, real-time resource monitoring, and intelligent scheduling algorithms, not only the accuracy and efficiency of task execution are improved, but also the flexibility and scalability of the system are enhanced.

Description

Technical Field

[0001] This application relates to the technical field of electronic digital data processing. Specifically, it relates 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 needs to comprehensively consider multiple objectives and constraint conditions, such as task completion time, resource utilization rate, energy consumption, etc. This makes the algorithm design very complex, and it is necessary to balance multiple factors to find the optimal solution, unable to comprehensively judge and evaluate task situations, thus resulting in the problem of low efficiency of collaborative scheduling. Summary of the Invention

[0004] Embodiments of this application provide a task scheduling optimization method and system based on collaborative analysis, which can at least to some extent solve the problem that task situations cannot be comprehensively judged and evaluated, thus resulting in low efficiency of collaborative scheduling.

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

[0006] According to one aspect of this application, there is provided a task scheduling optimization method based on collaborative analysis, including: initializing the task information of the target task, and based on the task information, decomposing the target task into a task set composed 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 relationships between the subtasks; generating a priority parameter according to the relationship matrix and the weight vector, and sorting the subtasks according to the priority parameter; obtaining the current execution resources through a resource manager, generating a set of execution units, and combining the priority sorting of the subtasks to generate a collaborative strategy; the collaborative strategy includes the dependency relationships and execution conditions between the subtasks; calculating the objective values of various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy, and selecting a scheduling method according to the objective values; arranging the subtasks to the corresponding execution units according to the scheduling method.

[0007] In this application, based on the foregoing solution, decomposing the target task into a task set composed of multiple subtasks based on the task information includes: decomposing the target task in multiple dimensions based on the function information and process information in the task information to generate multiple subtasks; constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, result criteria, responsible persons, and time nodes.

[0008] In this application, based on the foregoing solution, obtaining the current execution resources through a resource manager and generating an execution unit set includes: obtaining the current execution resources through the resource manager, and determining the current occupancy and free amount of the execution resources; generating an execution unit set based on the current occupancy and free amount of the execution resources.

[0009] In this application, based on the foregoing solution, calculating the target values of various task scheduling methods in an intelligent scheduling algorithm according to the priority ranking and the cooperation strategy, and selecting a scheduling method according to the target values includes: calculating the time continuity constraint parameters and processor load parameters corresponding to various task scheduling methods in the intelligent scheduling algorithm according to the priority ranking and the cooperation strategy; calculating the target values according to the time continuity constraint parameters and the processor load parameters; selecting a scheduling method according to the target values.

[0010] In this application, based on the foregoing solution, it further includes: when the execution unit is working, monitoring the execution situation of the execution unit and the utilization situation of resources, and after the task is completed, obtaining the execution results of the subtasks; providing feedback and optimization to the task scheduling method according to the execution situation, the execution results, and the utilization situation of resources.

[0011] In this application, based on the foregoing solution, it further includes: after all the subtasks are executed, summarizing the results and generating an execution report; sending the execution report to a management terminal.

[0012] According to one aspect of this application, a task scheduling optimization system based on collaborative analysis is provided, including:

[0013] A task unit, configured to initialize the task information of a target task, and based on the task information, decompose the target task into a task set composed of multiple subtasks;

[0014] A priority unit, configured 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 relationships between the subtasks; generate a priority parameter according to the relationship matrix and the weight vector, and perform priority ranking on the subtasks according to the priority parameter;

[0015] A collaboration unit, which is used to obtain the current execution resources through a resource manager, generate a set of execution units, and generate a collaboration policy in combination with the priority sorting of the subtasks; the collaboration policy includes the dependency relationships and execution conditions between the subtasks;

[0016] A scheduling unit, which is used to calculate the objective values of various task scheduling methods in an intelligent scheduling algorithm according to the priority sorting and the collaboration policy, and select a scheduling method according to the objective values;

[0017] An execution unit, which is used to arrange the subtasks to the corresponding execution units according to the scheduling method.

[0018] In this application, based on the foregoing solution, decomposing the target task into a task set composed of multiple subtasks based on the task information includes: decomposing the target task in a multi-dimensional manner based on the function information and process information in the task information to generate multiple subtasks; constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, result criteria, responsible persons, and time nodes.

[0019] In this application, based on the foregoing solution, obtaining the current execution resources through a resource manager and generating a set of execution units includes: obtaining the current execution resources through a resource manager, determining the current occupancy and idle amount of the execution resources; generating a set of execution units based on the current occupancy and idle amount of the execution resources.

[0020] In this application, based on the foregoing solution, calculating the objective values of various task scheduling methods in an intelligent scheduling algorithm according to the priority sorting and the collaboration policy, and selecting a scheduling method according to the objective values includes: calculating the time continuity constraint parameters and processor load parameters corresponding to various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the collaboration policy; calculating the objective values according to the time continuity constraint parameters and the processor load parameters; selecting a scheduling method according to the objective values.

[0021] In this application, based on the foregoing solution, it further includes: when the execution unit is working, monitoring the execution situation of the execution unit and the utilization situation of the resources, and after the task is completed, obtaining the execution results of the subtasks; providing feedback and optimization to the task scheduling method according to the execution situation, the execution results, and the utilization situation of the resources.

[0022] In this application, based on the foregoing solution, it further includes: after all the subtasks are executed, summarizing the results and generating an execution report; sending the execution report to a management terminal.

[0023] According to one aspect of the present application, there is provided a computer-readable medium storing a computer program, which when executed by a processor, implements the task scheduling optimization method based on collaborative analysis as described in the above embodiments.

[0024] According to one aspect of the present application, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the task scheduling optimization method based on collaborative analysis as described in the above embodiments.

[0025] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to execute the task scheduling optimization method provided in the above various optional implementation manners based on collaborative analysis.

[0026] 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 composed 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 sorted according to the priority parameter; the current execution resources are obtained through a resource manager to generate a set of execution units, and combined with the priority sorting of the subtasks, a collaborative strategy is generated; the collaborative strategy includes the dependency relationship and execution conditions between the subtasks; according to the priority sorting and the collaborative strategy, the target values of various task scheduling methods in the intelligent scheduling algorithm are calculated, and the scheduling method is selected according to the target values; the subtasks are arranged to the corresponding execution units according to the scheduling method. Through mechanisms such as fine-grained task disassembly, reasonable priority sorting, real-time resource monitoring, intelligent scheduling algorithms, and comprehensive execution report generation, the optimization of task scheduling and the improvement of efficiency are achieved. This method not only improves the accuracy and efficiency of task execution, but also enhances the flexibility and scalability of the system.

[0027] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. Brief Description of the Drawings

[0028] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 Schematically shows a flowchart of a task scheduling optimization method based on collaborative analysis in an embodiment of the present application.

[0030] Figure 2 Schematically shows a flowchart of generating a task set in an embodiment of the present application.

[0031] Figure 3 Schematically shows a schematic diagram of a task scheduling optimization system based on collaborative analysis in an embodiment of the present application.

[0032] Figure 4 Shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application. Detailed implementation manners

[0033] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various 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 complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0034] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0035] The block diagrams shown in the accompanying drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can 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.

[0036] The flowcharts shown in the accompanying drawings are merely illustrative and not necessarily include all the content and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined. Therefore, the actual execution order may change according to the actual situation.

[0037] The implementation details of the technical solution of this application are elaborated in detail below:

[0038] Figure 1 The flowchart of the task scheduling optimization method based on collaborative analysis according to an embodiment of this application is shown. Refer to Figure 1 As shown, the task scheduling optimization method based on collaborative analysis includes at least steps S110 to S150, which are introduced in detail as follows:

[0039] In step S110, the task information of the target task is initialized, and based on the task information, the target task is disassembled into a task set composed of multiple subtasks.

[0040] In an embodiment of this application, initializing the task information of the target task is the starting point of the entire scheduling process. It covers core elements such as the basic description of the task, expected goals, key milestones, and necessary constraints. Subsequently, based on this detailed task information, a systematic and structured method is used to carefully disassemble the target task into a series of interrelated and relatively independent subtasks. These subtasks together form a well-organized and hierarchical task set. Each subtask inherits some attributes and goals of the target task, and at the same time clarifies more specific execution steps, expected results, responsibility attribution, and completion time limits, laying a solid foundation for subsequent task scheduling and execution.

[0041] As Figure 2 shown, in an embodiment of this application, based on the task information, disassembling the target task into a task set composed of multiple subtasks includes:

[0042] S210, based on the function information and process information in the task information, disassemble the target task in multiple dimensions to generate multiple subtasks;

[0043] S220, construct a task set based on the information of the subtasks; the information of the subtasks includes target information, result criteria, responsible person, and time node.

[0044] In an embodiment of this application, various methods such as work breakdown structure, function decomposition, and process decomposition are used to disassemble the target task into subtasks at multiple levels and in multiple dimensions. Each subtask has clear goals, result criteria, responsible persons, and time nodes.

[0045] 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 budgets, costs, etc.), and information resources (such as data, knowledge, technology, etc.).

[0046] Based on the results of the resource requirement analysis, formulate a resource allocation plan corresponding to the information of the subtasks to ensure the effective utilization of resources and avoid waste. At the same time, analyze the resource requirement conflicts between different subtasks and formulate conflict resolution strategies.

[0047] Based on the function information and process information in the task information, decompose the target task in multiple levels and dimensions, which can ensure that the degree of task refinement is appropriate, neither too trivial nor too general. This decomposition method helps to more accurately evaluate the requirements and resources of each subtask, thus providing a solid foundation for subsequent task scheduling. At the same time, construct a task set containing detailed information of the subtasks to make task management clearer and more orderly.

[0048] In step S120, based on the urgency, important parameters, and resource requirements of the subtasks, generate a weight vector; based on the execution relationship between the subtasks, generate a relationship matrix; according to the relationship matrix and the weight vector, generate a priority parameter, and sort the subtasks according to the priority parameter.

[0049] In an embodiment of the present application, during the task scheduling process, comprehensively and meticulously evaluate 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 degree or influence scope of the task to the overall goal achievement), and resource requirements (including the types of required resources, quantity, and the priority of resource use). On this basis, adopt a scientific priority sorting algorithm, comprehensively consider the interdependent relationship between tasks and the resource competition situation, and assign a clear priority order to all subtasks. This step aims to ensure that the most urgent, important, and resource - matching subtasks can be processed and resource - allocated preferentially, thereby optimizing the overall task execution process, improving resource utilization efficiency, and ensuring the smooth achievement of project goals.

[0050] In an embodiment of the present application, there is a subtask set , and each subtask has corresponding attribute values in three dimensions: urgency , important parameters , and resource requirements . Among them, i represents the subtask identifier, n represents the total number of subtasks.

[0051] Generate a judgment matrix based on the scoring results of subtasks in terms of urgency, important parameters, and resource requirements , where j represents the dimension identifier, represents the subtask i on the dimension j scoring situation.

[0052] After that, perform normalization processing on the judgment matrix to generate the elements in the normalized matrix as follows:

[0053]

[0054] Based on the elements in the normalized matrix, determine the elements in the weight vector as follows:

[0055]

[0056] After that, based on the elements in the weight vector, construct the weight vector as: .

[0057] At the same time, based on the execution relationship between subtasks, generate a relationship matrix. In this embodiment, each element in the relationship matrix represents the fuzzy relationship degree between subtask i and subtask j , that is, the correlation degree between the two tasks, which can be represented by a value between 0 and 1. The larger the value, the closer the relationship.

[0058] Finally, generate a priority parameter based on the relationship matrix and the weight vector as follows:

[0059]

[0060] After that, perform priority sorting on the subtasks according to the priority parameter.

[0061] In an embodiment of the present application, by comprehensively considering the urgency, important parameters, and resource requirements of subtasks, a weight vector and a relationship matrix are generated, and then the subtasks are sorted by priority. This method can ensure that critical tasks are processed first, and at the same time, considering the dependency relationship between tasks, avoid conflicts and delays in task execution. The priority sorting provides clear guidance for the intelligent scheduling algorithm, making the task scheduling more reasonable and efficient.

[0062] In step S130, obtain the current execution resources through the resource manager, generate a set of execution units, and generate a collaboration strategy in combination with the priority sorting of the subtasks; the collaboration strategy includes the dependency relationship and execution conditions between the subtasks.

[0063] In an embodiment of the present application, by integrating an efficient resource manager, the currently available execution resource information is captured and summarized in real time. This information covers key attributes such as the type, quantity, status, and available time period of the resources. Based on this real-time data, a set of execution units containing multiple execution units is dynamically generated. Each execution unit represents the execution ability of a specific resource combination. Then, in combination with the previously determined priority sorting of the subtasks, an algorithm and a model are used to formulate a set of collaboration strategies. This set of strategies not only details the logical dependency relationship and sequence between the subtasks, but also clarifies the specific execution conditions of each subtask, including the required resources, execution time window, and any pre- or post-tasks, etc. This step aims to maximize the utilization of existing resources, ensure that the subtasks can be executed efficiently and conflict-free in the optimal order, and thus achieve a significant improvement in the overall task scheduling performance.

[0064] In an embodiment of the present application, obtaining the current execution resources through the resource manager and generating a set of execution units includes:

[0065] Obtain the current execution resources through the resource manager, and determine the current occupancy and free amount of the execution resources;

[0066] Generate a set of execution units based on the current occupancy and free amount of the execution resources.

[0067] In an embodiment of the present application, when obtaining the current execution resources through the resource manager, the current occupancy refers to the amount of execution resources that have been occupied or are being used currently. This includes the resources occupied by the tasks that are running; the current free amount refers to the amount of execution resources that are not occupied or available for new tasks currently. This is the remaining resources that the system can use to start new tasks or execution units. Once the current occupancy and free amount of the execution resources are obtained, a set of execution units can be generated based on this information.

[0068] After generating the set of execution units, generate a collaboration strategy in combination with the priority sorting of the subtasks. Specifically, select the currently executable subtasks from the priority sorting of the subtasks. According to the system resource situation, allocate the executable subtasks to the appropriate execution units, and so on, until all the subtasks are allocated, generating the final collaboration strategy, including the execution order, dependency relationship of the subtasks, as well as the execution unit allocation and execution conditions.

[0069] In an embodiment of the present application, the current execution resource information, including the occupancy and the free amount, is obtained in real time by the resource manager, so as to generate a set of execution units. This method can ensure that the availability of resources is fully considered during task scheduling, avoiding resource conflicts and waste. At the same time, the generation of the set of execution units facilitates subsequent task allocation, enabling tasks to be quickly and accurately allocated to appropriate execution units.

[0070] In step S140, according to the priority sorting and the collaboration policy, the target values of various task scheduling methods in the intelligent scheduling algorithm are calculated, and the scheduling method is selected according to the target values.

[0071] In an embodiment of the present application, based on the priority sorting of subtasks and the collaboration policy, the intelligent scheduling algorithm is used to deeply analyze various possible task scheduling schemes. In this process, key indicators such as the time efficiency, resource utilization rate, satisfaction of task dependencies, and execution cost of each scheme are mainly evaluated. These indicators together constitute the target values 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, 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.

[0072] In an embodiment of the present application, according to the priority sorting and the collaboration policy, the target values of various task scheduling methods in the intelligent scheduling algorithm are calculated, and the scheduling method is selected according to the target values, including:

[0073] According to the priority sorting and the collaboration policy, calculate the time continuity constraint parameter and the processor load parameter corresponding to each task scheduling method in the intelligent scheduling algorithm;

[0074] Calculate the target value according to the time continuity constraint parameter and the processor load parameter;

[0075] Select the scheduling method according to the target value.

[0076] In an embodiment of the present application, according to the priority sorting and the collaboration policy, calculate the time continuity constraint parameter corresponding to each task scheduling method in the intelligent scheduling algorithm is:

[0077]

[0078] Wherein, represents the start time of the subtask of, subtask The execution time, is the subtask and the subtask the minimum interval time between; the time continuity constraint parameter is used to measure the time requirements of a subtask during execution.

[0079] After that, calculate the processor load parameter as:

[0080]

[0081] where, represents the probability that the task is assigned to the processor the processor load parameter is used to measure the current load situation of the processor.

[0082] After that, according to the time continuity constraint parameter and the processor load parameter, calculate the target value as:

[0083]

[0084] where, is a weight coefficient used to balance the numerical relationship between the completion time and the processor load.

[0085] After calculating the target value, select the scheduling method corresponding to the minimum target value and arrange the subtasks. According to the priority sorting and the cooperation strategy, calculate the time continuity constraint parameter and the processor load parameter 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 the intelligent scheduling algorithm makes task scheduling more intelligent and automated, improving the efficiency and accuracy of task execution.

[0086] In step S150, arrange the subtasks to the corresponding execution units according to the scheduling method.

[0087] In one embodiment of the present application, after determining the optimal task scheduling scheme, according to the specific requirements of the scheme, each disassembled subtask is accurately allocated to the execution unit that matches it. This step not only requires an in-depth understanding of the characteristics and requirements of each subtask, but also needs to fully consider the resource configuration, execution ability and current workload of the execution unit. Through careful matching and arrangement, it is ensured that each subtask can be allocated to the execution unit 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 to more effectively track the progress of tasks and timely discover and solve potential problems.

[0088] Arrange the subtasks to the corresponding execution units according to the said scheduling method. When the execution unit is working, monitor its execution situation and the utilization of resources in real time, and obtain the execution results of the subtasks 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 for correction. At the same time, feedback and optimization are carried out on the task scheduling method according to the execution situation, execution results and resource utilization situation, which can continuously improve the performance and efficiency of task scheduling.

[0089] In one embodiment of the present application, it further includes:

[0090] When the execution unit is working, monitor the execution situation of the execution unit and the utilization of resources, and after the task is completed, obtain the execution results of the subtasks;

[0091] According to the execution situation, the execution results and the utilization of resources, carry out feedback and optimization on the task scheduling method.

[0092] During the working period of the execution unit, it is necessary to continuously monitor the execution situation of the execution unit and the utilization of resources. Among them, the execution situation includes the execution progress of the task, whether errors or exceptions are encountered, and whether the dependency relationships between tasks are correctly maintained. The resource utilization situation involves the consumption of resources such as resource utilization rate. The system needs to monitor these resources to identify potential bottlenecks or overload situations.

[0093] Detect the completion status of the task 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. These data may include calculation results, log information, status updates, etc. Based on the monitored execution situation and resource utilization situation, as well as the collected execution results of the subtasks, the system can carry out feedback and optimization on the task scheduling method.

[0094] In one embodiment of the present application, it further includes:

[0095] After all the sub-tasks are completed, summarize the results and generate an execution report;

[0096] Send the execution report to the management terminal.

[0097] In an embodiment of the present application, after all sub-tasks are completed, the system will first collect the result data of these tasks. This data may cover calculation results, log files, status information, etc. Integrate the collected data into a unified format or data structure for subsequent analysis and report generation. Fill the integrated sub-task result data into the report template to generate the final execution report.

[0098] Identify and determine the specific location or identity of the management terminal. According to the system configuration and user preferences, the system will select an appropriate sending method, such as email, SMS notification, instant message push, or directly display on the management interface.

[0099] After all sub-tasks are completed, summarize the results and generate an execution report, and then send the report 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.

[0100] In the technical solution of the present application, initialize the task information of the target task, and based on the task information, disassemble the target task into a task set composed of multiple sub-tasks; generate a weight vector based on the urgency, important parameters, and resource requirements of the sub-tasks; generate a relationship matrix based on the execution relationship between the sub-tasks; generate a priority parameter according to the relationship matrix and the weight vector, and sort the sub-tasks according to the priority parameter; obtain the current execution resources through the resource manager, generate a set of execution units, and combine the priority sorting of the sub-tasks to generate a cooperation strategy; the cooperation strategy includes the dependency relationship and execution conditions between the sub-tasks; calculate the target value of each task scheduling method in the intelligent scheduling algorithm according to the priority sorting and the cooperation strategy, and select the scheduling method according to the target value; arrange the sub-tasks to the corresponding execution units according to the scheduling method. Through mechanisms such as fine-grained task disassembly, reasonable priority sorting, real-time resource monitoring, intelligent scheduling algorithms, and comprehensive execution report generation, the optimization and efficiency improvement of task scheduling are realized. This method not only improves the accuracy and efficiency of task execution, but also enhances the flexibility and scalability of the system, providing a new solution for task scheduling in modern computing environments and distributed systems.

[0101] The following introduces the device embodiments of the present application, which can be used to execute the task scheduling optimization method based on collaborative analysis in the above embodiments of the present application. It can be understood that the device can be a computer program (including program code) running on 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 by the embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the embodiments of the above task scheduling optimization method based on collaborative analysis of the present application.

[0102] Figure 3 The block diagram of a task scheduling optimization system based on collaborative analysis according to an embodiment of the present application is shown.

[0103] Referring to Figure 3 As shown, a task scheduling optimization system based on collaborative analysis according to an embodiment of the present application includes:

[0104] A task unit 310, configured to initialize the task information of a target task, and based on the task information, disassemble the target task into a task set composed of multiple subtasks;

[0105] A priority unit 320, configured 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 relationships between the subtasks; generate a priority parameter according to the relationship matrix and the weight vector, and perform priority sorting on the subtasks according to the priority parameter;

[0106] A collaboration unit 330, configured to obtain the current execution resources through a resource manager, generate a set of execution units, and generate a collaboration strategy in combination with the priority sorting of the subtasks; the collaboration strategy includes the dependency relationships and execution conditions between the subtasks;

[0107] A scheduling unit 340, configured to calculate the target values of various task scheduling methods in an intelligent scheduling algorithm according to the priority sorting and the collaboration strategy, and select a scheduling method according to the target values;

[0108] An execution unit 350, configured to arrange the subtasks to the corresponding execution units according to the scheduling method.

[0109] In the present application, based on the foregoing solution, the disassembling the target task into a task set composed of multiple subtasks based on the task information includes: disassembling the target task in multiple dimensions based on the function information and process information in the task information to generate multiple subtasks; constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, achievement criteria, responsible persons, and time nodes.

[0110] In this application, based on the foregoing solution, obtaining the current execution resources through the resource manager and generating an execution unit set includes: obtaining the current execution resources through the resource manager, determining the current occupancy and free amount of the execution resources; and generating an execution unit set based on the current occupancy and free amount of the execution resources.

[0111] In this application, based on the foregoing solution, calculating the target values of various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the cooperation strategy, and selecting a scheduling method according to the target values includes: calculating the time continuity constraint parameter and the processor load parameter corresponding to each task scheduling method in the intelligent scheduling algorithm according to the priority sorting and the cooperation strategy; calculating the target values according to the time continuity constraint parameter and the processor load parameter; and selecting a scheduling method according to the target values.

[0112] In this application, based on the foregoing solution, it further includes: when the execution unit is working, monitoring the execution situation of the execution unit and the utilization situation of the resources, and after the task is completed, obtaining the execution results of the subtasks; and providing feedback and optimization to the task scheduling method according to the execution situation, the execution results, and the utilization situation of the resources.

[0113] In this application, based on the foregoing solution, it further includes: after all the subtasks are executed, summarizing the results and generating an execution report; and sending the execution report to the management terminal.

[0114] In the technical solution of this application, initializing the task information of the target task, based on the task information, disassembling the target task into a task set composed 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 according to the relationship matrix and the weight vector, and performing priority sorting on the subtasks according to the priority parameter; obtaining the current execution resources through the resource manager, generating an execution unit set, and generating a cooperation strategy in combination with the priority sorting of the subtasks; the cooperation strategy includes the dependency relationship and execution conditions between the subtasks; calculating the target values of various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the cooperation strategy, and selecting a scheduling method according to the target values; and arranging the subtasks to the corresponding execution units according to the scheduling method. Through mechanisms such as fine-grained task disassembly, reasonable priority sorting, real-time resource monitoring, intelligent scheduling algorithms, 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, providing a new solution for task scheduling in modern computing environments and distributed systems.

[0115] Figure 4 The figure shows a schematic structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application.

[0116] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0117] The computer system in this embodiment 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 section 408 into the random access memory 403, such as executing the task scheduling optimization method based on collaborative analysis described in the above embodiments. In the random access memory 403, various programs and data required for system operation are also stored. The central processing unit 401, the read-only memory 402, and the random access memory 403 are connected to each other via a bus 404. The input / output interface 405 is also connected to the bus 404.

[0118] 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, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; 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 the computer program read from it can be installed into the storage section 408 as needed.

[0119] Particularly, according to the embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present application include 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 the network through the communication section 409, and / or installed from the 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.

[0120] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection having 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 disc 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 can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] The units involved in the embodiments of the present application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation to the unit itself.

[0123] According to one aspect of the present application, there is provided a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the 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 various alternative implementation manners.

[0124] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the 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 embodiments.

[0125] It should be noted that although several modules or units of the device 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 the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0126] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in a manner of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0127] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. 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 general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0128] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A task scheduling optimization method based on collaborative analysis, characterized in that, Including: Initializing the task information of the target task, and based on the task information, disassembling the target task into a task set composed 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 relationships between the subtasks; Generating a priority parameter according to the relationship matrix and the weight vector, and sorting the subtasks according to the priority parameter; The elements in the relationship matrix represent the degree of fuzzy relationship between the subtasks; Obtaining the current execution resources through a resource manager, generating a set of execution units, and generating a collaboration strategy in combination with the priority sorting of the subtasks; The collaboration strategy includes the dependency relationships and execution conditions between the subtasks; Calculating the objective values of various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the collaboration strategy, and selecting a scheduling method according to the objective values; Arranging the subtasks to the corresponding execution units according to the scheduling method; Among them, calculating the objective values of various task scheduling methods in the intelligent scheduling algorithm according to the priority sorting and the collaboration strategy, and selecting a scheduling method according to the objective values, includes: According to the said priority sorting and the said collaboration strategy, the time continuity constraint parameters corresponding to each task scheduling method in the intelligent scheduling algorithm are as follows: ， Among them, and respectively represent the start times of subtasks and ; represents the execution time of subtask ; is the minimum interval time between subtask and subtask ; the time continuity constraint parameter is used to measure the time requirement of a subtask during execution; Calculate the processor load parameter is ， Among them, represents the probability that a subtask is assigned to a processor The processor load parameter is used to measure the current load situation of the processor, n represents the number of subtasks; Calculate a target value according to the time continuity constraint parameter and the processor load parameter as follows: ， Among them, is a weight coefficient; Selecting a scheduling method according to the objective values.

2. The task scheduling optimization method based on collaborative analysis according to claim 1, wherein Based on the task information, disassembling the target task into a task set composed of multiple subtasks, includes: Based on the function information and process information in the task information, disassembling the target task in a multi-dimensional manner to generate multiple subtasks; Constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, achievement criteria, responsible persons, and time nodes.

3. The task scheduling optimization method based on collaborative analysis according to claim 1, characterized in that Obtaining the current execution resources through a resource manager, generating a set of execution units, includes: Obtaining the current execution resources through a resource manager, and determining the current occupancy and free amount of the execution resources; Generating a set of execution units based on the current occupancy and free amount of the execution resources.

4. The task scheduling optimization method based on collaborative analysis according to claim 1, characterized in that Also including: When the execution unit is working, monitoring the execution situation of the execution unit and the utilization situation of the resources, and after the task is completed, obtaining the execution results of the subtasks; Providing feedback and optimization to the task scheduling method according to the execution situation, the execution results, and the utilization situation of the resources.

5. The task scheduling optimization method based on collaborative analysis according to claim 1, characterized in that Also including: After all the subtasks are executed, summarizing the results and generating an execution report; Sending the execution report to the management terminal.

6. A task scheduling optimization system based on collaborative analysis, characterized in that, Including: A task unit, used to initialize the task information of the target task, and based on the task information, disassembling the target task into a task set composed of multiple subtasks; A priority unit, used to generate a weight vector based on the urgency, important parameters, and resource requirements of the subtasks; generating a relationship matrix based on the execution relationships between the subtasks; Generating a priority parameter according to the relationship matrix and the weight vector, and sorting the subtasks according to the priority parameter; The elements in the relationship matrix represent the degree of fuzzy relationship between the subtasks; A collaboration unit, used to obtain the current execution resources through a resource manager, generate a set of execution units, and generate a collaboration strategy in combination with the priority sorting of the subtasks; The collaborative strategy includes the dependencies and execution conditions between the subtasks; A scheduling unit, configured to calculate the objective values of the scheduling methods of each task in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy, and select a scheduling method according to the objective values; An execution unit, configured to arrange the subtasks to the corresponding execution units according to the scheduling method; Among them, calculating the objective values of the scheduling methods of each task in the intelligent scheduling algorithm according to the priority sorting and the collaborative strategy, and selecting a scheduling method according to the objective values includes: According to the priority sorting and the collaboration strategy, the time continuity constraint parameters corresponding to each task scheduling method in the intelligent scheduling algorithm are as follows: ， Among them, and respectively represent the start times of subtasks and ; represents the execution time of subtask ; is the minimum interval time between subtask and subtask ; the time continuity constraint parameter is used to measure the time requirement of a subtask during execution. Calculate the processor load parameter is ， Among them, represents the probability that the subtask is assigned to the processor The processor load parameter is used to measure the processor current load situation, n represents the number of subtasks; Calculate a target value according to the time continuity constraint parameter and the processor load parameter which is ， Among them, is a weight coefficient; Selecting a scheduling method according to the objective value.

7. The task scheduling optimization system based on collaborative analysis according to claim 6, wherein Based on the task information, disassembling the target task into a task set composed of multiple subtasks, including: Based on the function information and process information in the task information, disassembling the target task in multiple dimensions to generate multiple subtasks; Constructing a task set based on the information of the subtasks; the information of the subtasks includes target information, achievement criteria, responsible persons, and time nodes.

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