Dynamic scheduling method and device based on task priority, equipment and storage medium

Abstract

This application relates to a dynamic scheduling method, apparatus, device, and storage medium based on task priority. The dynamic scheduling method based on task priority includes: using a multi-dimensional evaluation algorithm to dynamically prioritize test tasks to obtain their task priorities; acquiring indicator data for each test node; obtaining full-dimensional structured resource status data based on the indicator data; and obtaining a task scheduling strategy based on the task priorities and the full-dimensional structured resource status data. By introducing a dynamic scheduling mechanism based on task priority, this application can adjust the execution order and resource allocation of test tasks in real time according to factors such as the urgency of test tasks, resource requirements, and project schedule. This dynamic scheduling method can significantly improve the utilization rate of test resources, ensure the priority execution of critical test tasks, and thus improve overall testing efficiency and project delivery quality.

Description

Technical Field

[0001] This application relates to the field of task scheduling technology, and in particular to a dynamic scheduling method, apparatus, device and storage medium based on task priority. Background Technology

[0002] Current mainstream test management tools (such as TestRail and the JIRA+Selenium combination) have significant shortcomings in test asset management. While these tools provide basic test case management and defect tracking capabilities, they lack intelligent resource scheduling capabilities when handling large-scale test tasks. Especially when multiple projects are running concurrently and test resources are limited, test tasks are often executed according to a simple first-come, first-served principle, which may lead to delays in critical test tasks and low utilization of test resources. This static task scheduling method cannot meet the requirements of test efficiency and response speed in modern agile development environments. As software projects continue to grow in scale and iteration cycles continue to shorten, the efficient utilization of test resources and the timely execution of critical tasks have become core challenges that urgently need to be addressed in the field of test management. Summary of the Invention

[0003] Therefore, it is necessary to provide a dynamic scheduling method, apparatus, device, and storage medium based on task priority to address the problems in related technologies.

[0004] To achieve the above objectives, in a first aspect, this application provides a dynamic scheduling method based on task priority, the dynamic scheduling method based on task priority comprising: A multi-dimensional evaluation algorithm is used to dynamically prioritize the test tasks to obtain their task priorities; the test tasks include multiple test nodes. Obtain the metric data for each of the test nodes; Based on the aforementioned indicator data, full-dimensional structured resource status data is obtained; The task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data.

[0005] The aforementioned task-priority-based dynamic scheduling method, by introducing a dynamic scheduling mechanism, can adjust the execution order and resource allocation of test tasks in real time based on factors such as the urgency of test tasks, resource requirements, and project schedule. This dynamic scheduling approach can significantly improve the utilization rate of test resources, ensure the priority execution of critical test tasks, and thus improve overall testing efficiency and project delivery quality. It is particularly effective in mitigating resource conflicts and optimizing the testing process in situations of intense competition for test resources in multi-project parallel environments, meeting the stringent requirements of agile development for test response speed.

[0006] In some embodiments, the multi-dimensional evaluation algorithm includes project time dimension, defect impact dimension, and coverage requirement dimension.

[0007] In some embodiments, a multi-dimensional evaluation algorithm is used to dynamically prioritize the test tasks to obtain their task priorities, including: Obtain project plan data, defect data, and coverage data associated with the test tasks; The project plan data, the defect data, and the coverage data are standardized. The standardized project plan data, defect data, and coverage data are weighted to obtain the priority score of the test task. The task priority is obtained based on the priority score of the test task.

[0008] In some embodiments, full-dimensional structured resource status data is obtained based on the indicator data, including: The heartbeat fault detection algorithm is used to detect each test node to identify abnormal test nodes and normal test nodes. The indicator data of the normal test nodes are integrated with the resource status to obtain an integrated indicator dataset; Based on the integrated indicator data and the node anomaly record data of the abnormal test nodes, full-dimensional structured resource status data is obtained.

[0009] In some embodiments, after obtaining full-dimensional structured resource status data based on the indicator data, the method further includes: determining whether task scheduling is triggered based on the full-dimensional structured resource status data, and generating a task scheduling trigger signal when task scheduling is triggered. After generating the task scheduling trigger signal, a task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data.

[0010] In some embodiments, the indicator data includes equipment indicator data, personnel indicator data, and network indicator data.

[0011] In some embodiments, a task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data, including: A multi-dimensional state space is constructed, wherein the multi-dimensional state space is based on the task priority and the full-dimensional structured resource state data to obtain a multi-dimensional state vector; Construct an action space, which is an available action space based on the multidimensional state vector; A task scheduling strategy is obtained based on the improved H-PPO algorithm, the multidimensional state vector, and the available action space.

[0012] Secondly, this application also provides a dynamic scheduling device based on task priority, the dynamic scheduling device based on task priority comprising: The task priority acquisition module is used to dynamically evaluate the priority of the test task using a multi-dimensional evaluation algorithm to obtain the task priority of the test task; the test task includes multiple test nodes; The resource dynamic monitoring module is used to acquire the indicator data of each test node; and to obtain full-dimensional structured resource status data based on the indicator data. The intelligent scheduling decision module is used to obtain a task scheduling strategy based on the task priority and the full-dimensional structured resource status data.

[0013] The aforementioned task-priority-based dynamic scheduling device, by introducing a dynamic scheduling mechanism based on task priority, can adjust the execution order and resource allocation of test tasks in real time according to factors such as the urgency of test tasks, resource requirements, and project schedule. This dynamic scheduling method can significantly improve the utilization rate of test resources, ensure the priority execution of critical test tasks, thereby improving overall testing efficiency and project delivery quality. Especially in multi-project parallel environments with intense competition for test resources, it can effectively alleviate resource conflicts, optimize the testing process, and meet the stringent requirements of agile development for test response speed.

[0014] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-described dynamic scheduling methods based on task priority.

[0015] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above-described dynamic scheduling methods based on task priority. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a dynamic scheduling method based on task priority provided in one embodiment of this application; Figure 2 This is a flowchart of step S10 in a dynamic scheduling method based on task priority provided in an embodiment of this application; Figure 3 This is a flowchart of step S30 in a dynamic scheduling method based on task priority provided in an embodiment of this application; Figure 4 This is a structural block diagram of a task priority-based dynamic scheduling device provided in another embodiment of this application; Figure 5 This is an internal structural diagram of a computer device provided in another embodiment of this application.

[0018] Explanation of reference numerals in the attached diagram: 10, Task Priority Acquisition Module; 20, Resource Dynamic Monitoring Module; 30, Intelligent Scheduling Decision Module. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] In one embodiment, see Figure 1 This application also provides a dynamic scheduling method based on task priority, which may include the following steps: S10~S40.

[0021] S10: A multi-dimensional evaluation algorithm is used to dynamically prioritize the test task to obtain the task priority of the test task; the test task includes multiple test nodes.

[0022] S20: Obtain the indicator data for each of the test nodes.

[0023] S30: Obtain full-dimensional structured resource status data based on the aforementioned indicator data.

[0024] S40: Obtain a task scheduling strategy based on the task priority and the full-dimensional structured resource status data.

[0025] The aforementioned task-priority-based dynamic scheduling method, by introducing a dynamic scheduling mechanism, can adjust the execution order and resource allocation of test tasks in real time based on factors such as the urgency of test tasks, resource requirements, and project schedule. This dynamic scheduling approach can significantly improve the utilization rate of test resources, ensure the priority execution of critical test tasks, and thus improve overall testing efficiency and project delivery quality. It is particularly effective in mitigating resource conflicts and optimizing the testing process in situations of intense competition for test resources in multi-project parallel environments, meeting the stringent requirements of agile development for test response speed.

[0026] In step S10, please refer to Figure 1 In step S10, a multi-dimensional evaluation algorithm is used to dynamically prioritize the test task to obtain the task priority of the test task; the test task includes multiple test nodes.

[0027] As an example, the multi-dimensional evaluation algorithm includes project time dimension, defect impact dimension, and coverage requirement dimension.

[0028] As an example, in step S10, a multi-dimensional evaluation algorithm is used to dynamically evaluate the priority of the test task in order to obtain the task priority of the test task, which may include the following steps: S101~S104.

[0029] S101: Obtain project plan data, defect data, and coverage data associated with the test task.

[0030] S102: Standardize the project plan data, the defect data, and the coverage data.

[0031] S103: The standardized project plan data, defect data, and coverage data are weighted to obtain the priority score of the test task.

[0032] S104: Obtain the task priority based on the priority score of the test task.

[0033] Specifically, in step S101, project plan data for test tasks can be extracted from the project management system, defect data associated with test tasks can be obtained from the defect database, and coverage metadata can be read from the test case database.

[0034] Specifically, in step S102, the project plan data, the defect data, and the coverage data can be uniformly quantified into standard scores of 0 to 100 points to eliminate differences in dimensions.

[0035] Specifically, the project plan data is standardized to obtain the time urgency; the defect data is standardized to obtain the defect urgency; and the coverage data is standardized to obtain the coverage importance. The priority score of the test task is obtained by weighting the time urgency, defect urgency, and coverage importance.

[0036] It should be noted that during the weighted processing, the weights of the time urgency, the defect urgency, and the coverage importance can be dynamically adjusted according to actual needs, and no specific limitations are made here.

[0037] Specifically, regarding the project time dimension, the difference between the current date and the project milestone due date in the project plan data can be calculated and converted into a time urgency coefficient. For testing tasks on the critical path, a time weight can be automatically added. The specific calculation method is as follows: Time Urgency = (Project Milestone Date - Current Date) / Total Testing Cycle, and the result is standardized to a value between 0 and 100.

[0038] Specifically, regarding the defect impact dimension, data related to the testing task can be analyzed, which may include defect severity level (such as blocked, severe, moderate, minor, etc.) and defect repair status (such as repaired but awaiting verification, partially repaired, etc.). An impact factor can be preset for each defect level, and the defect urgency can be calculated through a defect weighting formula.

[0039] Specifically, regarding coverage requirements, the importance of test task coverage can be assessed, which can include indicators such as the business criticality of functional modules (core functions / auxiliary functions), frequency of requirement changes, and historical defect density. A coverage importance matrix can be maintained to assign basic weights to different modules.

[0040] In step S20, please refer to Figure 1 In step S20, the indicator data of each test node is obtained.

[0041] As an example, in step S20, a lightweight resource probe can be deployed on each test node. The lightweight resource probe can periodically (e.g., every 30 seconds) collect the following core indicator data: equipment indicator data, personnel indicator data, and network indicator data.

[0042] Specifically, equipment performance data may include CPU utilization (U). cpu Memory usage U mem Disk I / O wait time T io Personnel metrics data may include the current number of tasks N. task Average task time Skill matching degree S skill Network metrics data can include bandwidth utilization (B). util Delay L net and packet loss rate P loss .

[0043] In step S30, please refer to Figure 1 In step S30, full-dimensional structured resource status data is obtained based on the indicator data.

[0044] As an example, in step S30, obtaining full-dimensional structured resource status data based on the indicator data may include the following steps: S301~S303.

[0045] S301: The test nodes are detected based on the heartbeat fault detection algorithm to identify abnormal test nodes and normal test nodes.

[0046] S302: Integrate the indicator data of the normal test nodes with resource status to obtain an integrated indicator dataset.

[0047] S303: Based on the integrated indicator data and the node anomaly record data of the abnormal test node, obtain full-dimensional structured resource status data.

[0048] Specifically, in step S301, an improved φ-accrual fault detection algorithm can be used to dynamically adjust the heartbeat interval. The corresponding calculation formula can be as follows: in, The cumulative distribution function of the time it takes for the heartbeat to reach its peak; Let be the effective probability of a heartbeat at time t, with a value in the range [0,1].

[0049] Specifically, it can be based on heart rate intervals. To identify whether a test node is an abnormal or normal test node; for example, this can be done during the heartbeat interval. If the threshold is exceeded (which can be 3.0), the current test node is determined to be an abnormal test node.

[0050] As an example, in step S302, the indicator data of normal test nodes can be cleaned through time series analysis; the exponentially weighted moving average (EWMA) can be used for smoothing, and the corresponding formula is as follows: in, This is the smoothed predicted value at time t (i.e., the integrated index data at time t). This is the smoothing coefficient (i.e., the weighting coefficient), which can take a value between 0.3 and 0.5 and can be automatically adjusted based on historical variance. The original observations at time t (i.e., the index data of the normal test nodes before integration); This is the smoothed prediction value at time t-1.

[0051] As an example, in step S303, the integrated indicator data and the node anomaly record data of the abnormal test node can be updated to the resource pool based on the MVCC mechanism to form the full-dimensional structured resource status data.

[0052] As an example, in step S30, after obtaining the full-dimensional structured resource status data based on the indicator data, the method further includes: determining whether to trigger task scheduling based on the full-dimensional structured resource status data, and generating a task scheduling trigger signal when task scheduling is triggered; after generating the task scheduling trigger signal, obtaining a task scheduling strategy based on the task priority and the full-dimensional structured resource status data.

[0053] As an example, triggering conditions can be set based on the aforementioned full-dimensional structured resource status data to determine whether to trigger task scheduling. Specifically, task scheduling can be triggered when any of the following three conditions are met: Condition 1: Key resource utilization rate is consistently higher than the resource threshold (Threshold) for 3 consecutive periods. high ; Condition 2: The test node's downtime is greater than the resource downtime threshold T. unavilable ; Condition 3: Time for adding / releasing test equipment, triggered by the following logic: in, For trigger signal; The union represents the condition that at least one of the multiple indicator data sets is true; n is the number of indicator data sets. The logical "OR" means that if either of the two sets of conditions is met, the trigger condition is established. This represents the actual value of the i-th indicator data; The threshold value for the i-th indicator data; The duration of the test node failure; This represents the maximum allowable duration of the fault.

[0054] In step S40, please refer to Figure 1 In step S40, a task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data.

[0055] As an example, in step S40, the task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data, which may include the following steps: S401~S403.

[0056] S401: Construct a multi-dimensional state space, wherein the multi-dimensional state space is based on the task priority and the full-dimensional structured resource state data to obtain a multi-dimensional state vector.

[0057] S402: Construct an action space, which is an available action space based on the multidimensional state vector.

[0058] S403: A task scheduling strategy is obtained based on the improved H-PPO algorithm, the multidimensional state vector, and the available action space.

[0059] As an example, in step S401, the multidimensional state vector can be a 7-dimensional real-time state vector S. t The corresponding expression can be as follows: in, The average priority of currently running test tasks; This refers to the CPU's remaining capacity. This represents the remaining memory percentage; This represents the remaining disk I / O capacity. The number of test tasks to be scheduled; Countdown to project deadline; This represents the critical path time margin.

[0060] As an example, in step S402, the constructed action space can be a hierarchical action space; the action space is based on the multi-dimensional state vector to filter out the executable "macro strategy + micro allocation" options under the current test task, thereby obtaining the available action space.

[0061] Specifically, the macro strategy can include three types of scheduling strategy options: 1) Shortest job first, which prioritizes allocating resources to test tasks with shorter execution times; 2) Highest priority first, which prioritizes ensuring the resource needs of high-priority test tasks; 3) Resource-aware balancing, which allocates tasks with the goal of optimal resource utilization.

[0062] Specifically, the micro-allocation includes three types of fine-grained operation options: 1) Node selection, matching test tasks with suitable test resource nodes; 2) Task sorting, planning the execution order of test tasks on the same resource; 3) Preemption decision, determining whether to interrupt low-priority test tasks to make room for high-priority test tasks.

[0063] As an example, in step S403, the improved H-PPO algorithm introduces a policy hierarchical mechanism and a multi-objective reward function on the basis of the traditional PPO algorithm.

[0064] Specifically, in the strategy hierarchy mechanism, the macro-strategy network outputs the strategy type weights, while the micro-network handles the specific allocation.

[0065] Specifically, the formula for the multi-objective reward function can be as follows: in, For comprehensive scoring; Rate the priority; Score resource utilization rate; Scoring is based on the deadline; For adjustable weights, .

[0066] As an example, in step S40, a scheduling model can be constructed and trained first, which may include the following: A progressive simulation environment is built through dynamic course learning, divided into four difficulty stages: Stage 1: Single-project scheduling under fixed resource conditions; Stage 2: Introducing a resource fluctuation simulator; Stage 3: Multi-project competition environment; Stage 4: Injecting sudden failure events; Simulation data for each scenario (which can be task priorities and fully structured resource state data during the simulation process) is quantified into multi-dimensional simulation state vectors through multi-dimensional state space modeling; An adapted hierarchical action space (i.e., the available action space during the simulation process) is output based on scenario constraints through action space design; The multi-dimensional simulation state vectors and hierarchical action space are received through an improved H-PPO algorithm; The decision-making effect is evaluated through macro-strategy selection + micro-allocation strategy, combined with a multi-objective reward function, accumulating "state-action-reward" mapping experience to obtain a trained scheduling model.

[0067] As an example, in step S40, the task priority obtained in step S10 and the full-dimensional structured resource status data obtained in step S30 can be quantized into a multi-dimensional vector through a multi-dimensional state space; the action space is obtained from the multi-dimensional state space to obtain the available action space; the improved H-PPO algorithm calls the trained scheduling model, inputs the multi-dimensional vector and the available action space into the trained scheduling model for processing, and the trained scheduling model can output the current optimal task scheduling strategy.

[0068] As an example, after obtaining the trained scheduling model, the following is also included: The real-time state vector S of the current state is obtained through an online learning mechanism. t The current state's scheduling action A t The overall score of the current state (R) t and the state vector S of the next state t+1 Stored in the experience replay buffer, resulting in (S) t A t , R t , S t+1 The tuple is used to perform incremental training at a preset period (e.g., 24 hours) to update and optimize the scheduling model parameters. The corresponding formula can be as follows: in, To update and optimize the scheduling model parameters; To update the scheduling model parameters before optimization; The learning rate; The gradient operator for the model parameters; N is the number of empirical tuples sampled in the empirical playback buffer; This is the cutoff loss function for PPO.

[0069] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0070] In another embodiment, please refer to Figure 4 This application also provides a dynamic scheduling device based on task priority, comprising: a task priority acquisition module 10, a resource dynamic monitoring module 20, and an intelligent scheduling decision module 30; wherein, the task priority acquisition module 10 is used to perform dynamic priority evaluation on test tasks using a multi-dimensional evaluation algorithm to obtain the task priority of the test tasks; the test tasks include multiple test nodes; the resource dynamic monitoring module 20 is used to acquire indicator data of each test node; and obtain full-dimensional structured resource status data based on the indicator data; the intelligent scheduling decision module 30 is used to obtain a task scheduling strategy based on the task priority and the full-dimensional structured resource status data.

[0071] The aforementioned task-priority-based dynamic scheduling device, through the establishment of a task priority acquisition module 10, a resource dynamic monitoring module 20, and an intelligent scheduling decision module 30, introduces a task-priority-based dynamic scheduling mechanism. This mechanism can adjust the execution order and resource allocation of test tasks in real time based on factors such as the urgency of test tasks, resource requirements, and project schedule. This dynamic scheduling method can significantly improve the utilization rate of test resources, ensure the priority execution of critical test tasks, thereby improving overall testing efficiency and project delivery quality. Especially in multi-project parallel environments with intense competition for test resources, it can effectively alleviate resource conflicts, optimize the testing process, and meet the stringent requirements of agile development for test response speed.

[0072] The task priority-based dynamic scheduling device in this embodiment is used to execute tasks such as... Figure 1The dynamic scheduling method based on task priority described in the corresponding embodiments is also mentioned. Specific limitations on the dynamic scheduling device based on task priority can be found in the limitations on the dynamic scheduling method based on task priority described above, and will not be repeated here. Each module in the above-mentioned dynamic scheduling device based on task priority can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0073] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data such as task priorities for test tasks, indicator data for test nodes, full-dimensional structured resource status data, and task scheduling strategies. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a dynamic scheduling method based on task priorities.

[0074] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a dynamic scheduling method based on task priority. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0075] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0076] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement, for example... Figure 1 The steps of the dynamic scheduling method based on task priority in the corresponding embodiments.

[0077] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program implementing, when executed by a processor, as shown in the figure. Figure 1 The steps of the dynamic scheduling method based on task priority in the corresponding embodiments.

[0078] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0079] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method of dynamic scheduling based on task priority, characterized in that, include: A multi-dimensional evaluation algorithm is used to dynamically prioritize the test tasks in order to obtain the task priorities of the test tasks. The test task includes multiple test nodes; Obtain the metric data for each of the test nodes; Based on the aforementioned indicator data, full-dimensional structured resource status data is obtained; The task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data.

2. The method of claim 1, wherein, The multi-dimensional evaluation algorithm includes project time dimension, defect impact dimension, and coverage requirement dimension.

3. The method of claim 2, wherein, A multi-dimensional evaluation algorithm is used to dynamically prioritize the test tasks to obtain their task priorities, including: Obtain project plan data, defect data, and coverage data associated with the test tasks; The project plan data, the defect data, and the coverage data are standardized. The standardized project plan data, defect data, and coverage data are weighted to obtain the priority score of the test task. The task priority is obtained based on the priority score of the test task.

4. The dynamic scheduling method based on task priority according to claim 1, characterized in that, Based on the aforementioned indicator data, full-dimensional structured resource status data is obtained, including: The heartbeat fault detection algorithm is used to detect each test node to identify abnormal test nodes and normal test nodes. The indicator data of the normal test nodes are integrated with the resource status to obtain an integrated indicator dataset; Based on the integrated indicator data and the node anomaly record data of the abnormal test nodes, full-dimensional structured resource status data is obtained.

5. The method of claim 4, wherein, After obtaining the full-dimensional structured resource status data based on the aforementioned indicator data, the method further includes: determining whether task scheduling is triggered based on the full-dimensional structured resource status data, and generating a task scheduling trigger signal when task scheduling is triggered. After generating the task scheduling trigger signal, a task scheduling strategy is obtained based on the task priority and the full-dimensional structured resource status data.

6. The method of claim 1, wherein, The indicator data includes equipment indicator data, personnel indicator data, and network indicator data.

7. The dynamic scheduling method based on task priority according to any one of claims 1 to 6, characterized in that, Based on the task priority and the full-dimensional structured resource status data, a task scheduling strategy is obtained, including: A multi-dimensional state space is constructed, wherein the multi-dimensional state space is based on the task priority and the full-dimensional structured resource state data to obtain a multi-dimensional state vector; Construct an action space, which is the available action space based on the multidimensional state vector; A task scheduling strategy is obtained based on the improved H-PPO algorithm, the multidimensional state vector, and the available action space.

8. A task priority based dynamic scheduling apparatus, characterized by, include: The task priority acquisition module is used to dynamically evaluate the priority of the test task using a multi-dimensional evaluation algorithm to obtain the task priority of the test task. The test task includes multiple test nodes; The resource dynamic monitoring module is used to acquire the indicator data of each test node; and to obtain full-dimensional structured resource status data based on the indicator data. The intelligent scheduling decision module is used to obtain a task scheduling strategy based on the task priority and the full-dimensional structured resource status data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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