A test bench resource scheduling method, device and equipment and storage medium

Abstract

This invention discloses a test bench resource scheduling method, apparatus, device, and storage medium. The method includes the following steps: constructing and dynamically updating a test bench profile based on real-time collected test bench status data; obtaining task requirement information for a test task to be scheduled, including task type, required configuration parameters, execution priority, and time window; matching test tasks with test bench resources using a preset multi-objective optimization scheduling algorithm based on the task requirement information and the test bench profile to determine a target bench that matches the test task to be scheduled; and assigning the test task to be scheduled to the target bench for execution. This application significantly improves test bench resource utilization and task execution efficiency, and greatly reduces anomaly response time and manual intervention costs.

Description

Technical Field

[0001] This invention relates to the field of automotive testing technology, and in particular to a test bench resource scheduling method, apparatus, equipment, and storage medium. Background Technology

[0002] With the increasing complexity of automotive electronic and electrical architectures, especially the rapid development of new energy vehicles and intelligent connected vehicles, the reliance on test bench resources for verification processes such as controller hardware-in-the-loop (HIL) testing and software-in-the-loop (Software-in-the-Loop) testing is becoming increasingly significant. Test bench resources typically include HIL test benches, driving simulators, bus simulation equipment, etc., and different test tasks have varying requirements for test bench hardware configurations, software environments, and interface types. How to efficiently and accurately allocate test tasks to suitable test bench resources has become a key issue affecting test efficiency and resource utilization.

[0003] In existing technologies, bench scheduling mainly relies on manual scheduling or automated scheduling systems based on simple rules. With manual scheduling, test engineers rely on experience to check bench occupancy and manually assign tasks, which suffers from problems such as delayed information updates, low resource utilization, and slow anomaly response.

[0004] Therefore, it is crucial to achieve precise matching between testing tasks and bench resources, avoid resource mismatch, and improve bench resource utilization. Summary of the Invention

[0005] The main objective of this invention is to provide a test bench resource scheduling method, apparatus, equipment, and storage medium that significantly improves test bench resource utilization and task execution efficiency, and greatly reduces anomaly response time and manual intervention costs.

[0006] Firstly, this application provides a test bench resource scheduling method, which includes the following steps: Based on real-time collected test bench status data, a test bench profile is constructed and dynamically updated. Obtain the task requirement information of the test task to be scheduled, including: task type, required configuration parameters, execution priority and time window; Based on the task requirement information and the test bench profile, a preset multi-objective optimization scheduling algorithm is used to match test tasks with test bench resources in order to determine the target bench that matches the test task to be scheduled. The test task to be scheduled is assigned to the target test bench for execution.

[0007] In conjunction with the first aspect mentioned above, as an optional implementation method, the task requirement information and the test bench profile are used as inputs. A multi-objective optimization scheduling algorithm that integrates genetic algorithm and reinforcement learning model is adopted to finally select the target test bench with the highest bench resource utilization, the shortest task execution cycle, and the highest task-test bench fit.

[0008] In conjunction with the first aspect mentioned above, as an optional implementation, when multiple test tasks compete for the same rack resource, scheduling is performed according to the comprehensive ranking result of the calibrated task priority and task-rack adaptability; wherein, the task priority supports dynamic adjustment.

[0009] In conjunction with the first aspect mentioned above, as an optional implementation method, the entire task execution process is monitored to automatically perform tiered processing and trigger prompts when potential anomalies are detected.

[0010] In conjunction with the first aspect mentioned above, as an optional implementation method, anomaly detection is performed on the task execution process based on preset anomaly judgment conditions, which include: test task execution timeout, bench load exceeding limit, and test data anomaly. If the abnormal detection result is that the test bench load fluctuation is within the set range, it is judged as the first level, and the test bench parameter adaptive adjustment is initiated. If the anomaly detection result is a task timeout, it is judged as Level 2, and the current test task is assigned to an appropriate idle test bench. If the anomaly detection result indicates abnormal test data or test bench malfunction, it is classified as Level 3, and the task is immediately terminated, an alarm notification is sent, and the fault information is recorded.

[0011] In conjunction with the first aspect mentioned above, as an optional implementation method, machine learning algorithms are adopted to continuously optimize scheduling algorithm parameters and bench profile models based on historical task execution data and anomaly handling results, and automatically identify scheduling bottlenecks and output resource allocation optimization suggestions for autonomous iteration of scheduling strategies.

[0012] In conjunction with the first aspect above, as an optional implementation, the test bench status data includes: operating status information and historical performance information. The operating status information includes: the current load rate of the test bench, task execution progress, and health score. The historical performance information includes: task completion rate, anomaly occurrence rate, and adapted task type. Based on the aforementioned status data, a dynamic rack profile model is established and updated in real time using big data analysis algorithms to generate profile tags that characterize the rack's current status and capabilities.

[0013] Secondly, this application provides a test bench resource scheduling device, the device comprising: The building module is used to build and dynamically update the test bench profile based on real-time acquired test bench status data; The acquisition module is also used to acquire the task requirement information of the test task to be scheduled, including: task type, required configuration parameters, execution priority and time window; The matching module is used to perform test task-test bench resource matching based on the task requirement information and the test bench profile, using a preset multi-objective optimization scheduling algorithm, so as to determine the target bench that matches the test task to be scheduled. The scheduling module is used to assign the test tasks to be scheduled to the target test bench for execution.

[0014] Thirdly, this application also provides an electronic device, the electronic device comprising: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the method described in any one of the first aspects.

[0015] Fourthly, this application also provides a computer-readable storage medium storing computer program instructions that, when executed by a computer, cause the computer to perform the method described in any of the first aspects.

[0016] This application provides a test bench resource scheduling method, apparatus, device, and storage medium. The method includes the following steps: constructing and dynamically updating a test bench profile based on real-time collected test bench status data; obtaining task requirement information for a test task to be scheduled, including task type, required configuration parameters, execution priority, and time window; matching test tasks with test bench resources using a preset multi-objective optimization scheduling algorithm based on the task requirement information and the test bench profile to determine a target bench that matches the test task to be scheduled; and assigning the test task to be scheduled to the target bench for execution. This application significantly improves test bench resource utilization and task execution efficiency, and greatly reduces anomaly response time and manual intervention costs.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit the invention. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 This is a flowchart of a test bench resource scheduling method provided in the embodiments of this application; Figure 2This is a schematic diagram of a test bench resource scheduling device provided in the embodiments of this application; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application; Figure 4 This is a schematic diagram of a computer-readable program medium provided in an embodiment of this application. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0021] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the drawings represent functional entities and do not necessarily correspond to physically or logically independent entities.

[0022] The embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0023] Reference Figure 1 , Figure 1 The diagram shown is a flowchart of a test bench resource scheduling method provided by the present invention. Figure 1 As shown, the method includes the following steps: Step S101: Based on the real-time collected test bench status data, construct and dynamically update the test bench profile.

[0024] Specifically, the test bench status data includes: operating status information and historical performance information. The operating status information includes: the current load rate of the test bench, task execution progress and health score. The historical performance information includes: task completion rate, anomaly occurrence rate and adapted task type. Based on the aforementioned status data, a dynamic rack profile model is established and updated in real time using big data analysis algorithms to generate profile tags that characterize the rack's current status and capabilities.

[0025] To make it easier to understand, examples are provided. Real-time collection of multi-dimensional data from the test bench includes hardware configuration (sensors, processors, interface types), software environment (operating system, testing tool version), running status (current load rate, task execution progress, health score), and historical performance (task completion rate, anomaly rate, and adapted task types).

[0026] Based on real-time acquisition of multi-dimensional data from test benches, a dynamic test bench profile model is established. Through big data analysis algorithms, profile tags are updated in real time (e.g., high load - suitable for three-electric performance testing; idle - support autonomous driving simulation testing; low health - requires maintenance), providing accurate basis for scheduling decisions.

[0027] Step S102: Obtain the task requirement information of the test task to be scheduled. The task requirement information includes: task type, required configuration parameters, execution priority and time window.

[0028] For example: Task A: The task type is "performance test", and the required configuration parameters include 4 CPU cores, 8GB of memory, 50GB of disk space, execution priority is "high", and the time window is from 14:00 to 16:00 on March 6, 2026.

[0029] Task B: The task type is "Functional Test". The required configuration parameters include 2 CPU cores, 4GB of memory, 20GB of disk space, execution priority is "Medium", and the time window is from 15:00 to 17:00 on March 6, 2026.

[0030] Task C: The task type is "Security Scan". The required configuration parameters include 2 CPU cores, 4GB of memory, 30GB of disk space, and the execution priority is "Low". The time window is from 16:00 to 18:00 on March 6, 2026.

[0031] Step S103: Based on the task requirement information and the test bench profile, a preset multi-objective optimization scheduling algorithm is used to match test tasks with test bench resources in order to determine the target bench that matches the test task to be scheduled.

[0032] Specifically, using the task requirement information and the test bench profile as input, a multi-objective optimization scheduling algorithm that integrates genetic algorithms and reinforcement learning models is adopted to ultimately select the target test bench with the highest bench resource utilization, the shortest task execution cycle, and the highest task-test bench fit.

[0033] To illustrate this, the scheduling algorithm takes the requirements of three test tasks (Task A, Task B, and Task C) and the profiles of five available test benches (Benchmark 1 to Benchmark 5) as input. It employs a multi-objective optimization scheduling algorithm that combines genetic algorithms and reinforcement learning models to solve the problem. The algorithm aims to maximize bench resource utilization, minimize task execution cycles, and maximize task-bench fit. After multiple generations of evolution and reinforcement learning guidance, the final optimal scheduling scheme is as follows: Task A (Performance Test, High Priority, Time Window 14:00-16:00): Scheduled to rack 3 (CPU 4 cores, memory 8GB, disk 50GB, compatibility 95%), resource utilization: 92%, execution cycle: 1.8 hours.

[0034] Task B (functional test, medium priority, time window 15:00-17:00): scheduled to rack 1 (CPU 2 cores, memory 4GB, disk 20GB, compatibility 88%), resource utilization: 85%, execution cycle: 1.5 hours.

[0035] Task C (Security Scan, Low Priority, Time Window 16:00-18:00): Scheduled to rack 5 (CPU 2 cores, memory 4GB, disk 30GB, compatibility 90%), resource utilization: 78%, execution cycle: 1.2 hours.

[0036] In one embodiment, after determining the target test bench that matches the test task to be scheduled, the process includes: when multiple test tasks compete for the same test bench resource, scheduling is performed according to the comprehensive ranking result of the calibrated task priority and task-test bench fit; wherein, the task priority supports dynamic adjustment. (It can be understood that dynamic adjustment of task priority is supported (e.g., the priority of emergency fault reproduction tasks is automatically increased), and a resource conflict coordination mechanism is provided (e.g., when multiple tasks compete for the same test bench, they are ranked according to the comprehensive ranking of "priority + fit")). To illustrate this, after identifying the target test bench that matches the test tasks to be scheduled, there are currently three test tasks (Task A, Task B, and Task C) competing for the same test bench (Test Bench X). To schedule tasks efficiently, the system first evaluates the priority of each task and its suitability for Test Bench X, and then ranks them accordingly.

[0037] Task A: Originally high priority, but dynamically upgraded to the highest priority due to the approaching deadline; Compatibility with rack X is 95% (architecture matching, configuration satisfied).

[0038] Task B: Medium priority, 80% compatibility (configuration meets requirements, but some interfaces need to be switched).

[0039] Task C: Low priority, 90% compatibility (configuration meets requirements, but the time window is late).

[0040] The system calculates a score based on a comprehensive scheduling algorithm (priority weight 0.6, fitness weight 0.4): Task A: Score = 100 × 0.6 + 95 × 0.4 = 98; Task B: Score = 60 × 0.6 + 80 × 0.4 = 68; Task C: Score = 40 × 0.6 + 90 × 0.4 = 60.

[0041] Ultimately, Task A gains scheduling rights on rack X, while Tasks B and C enter the waiting queue or are re-matched to other racks.

[0042] Understandably, the system receives test task requirements (including task type, required configuration, execution priority, and time window), combines them with dynamic bench profiles, and employs a "multi-objective optimization scheduling algorithm" (integrating genetic algorithm and reinforcement learning model) to achieve three main objectives: maximizing bench resource utilization, minimizing task execution cycle, and maximizing task-bench fit, ultimately selecting the target bench.

[0043] To illustrate this with an example: 1. A test engineer submits a new task in the system: "Voice wake-up function regression testing". The system automatically recognizes this as a high-priority task because it relates to the core product experience.

[0044] 2. Based on historical data, the system automatically calculates that this task will take approximately 10 minutes to complete. If it is a brand new task with no historical data, the system will estimate based on the average time.

[0045] 3. The system uses its "voice association engine" to determine that this task is a core function that impacts user experience. Therefore, its priority is set to P1 (highest), and the system automatically places this task at the front of the execution queue.

[0046] 5. Then the system first checks the status of all racks and filters out the available ones: A, B, and C are all online.

[0047] Then look at the number of "tasks pending" for each machine: Bench A: 2 lines to be processed (very empty) Benchmark B: 15 items pending processing (medium quality) Bench C: 30 items pending processing (very full) 6. Based on the rule of "fewer pending tasks, higher processing efficiency", the system automatically selects rack A. Because rack A is the least busy, it can complete this high-priority task the fastest.

[0048] 7. The task is automatically assigned to bench A for execution. The system updates the number of pending tasks on bench A in real time (from 2 to 3). A Lark / email notification is also sent to the test engineer.

[0049] Step S104: Assign the test task to be scheduled to the target test bench for execution.

[0050] Specifically, after the scheduled test tasks are assigned to the target test bench for execution, the entire task execution process is monitored to automatically implement tiered processing and trigger alerts when potential anomalies are detected. (This means real-time monitoring of the entire task execution process, collecting information such as task execution progress, test bench operating parameters, and test data output, and displaying this information synchronously through a visual dashboard. A task status early warning mechanism is established to trigger alerts in advance for potential problems such as execution stalls and data anomalies, allowing time for anomaly handling.) Specifically, anomaly detection is performed on the task execution process based on preset anomaly judgment conditions, which include: test task execution timeout, bench load exceeding limit, and test data anomaly. If the abnormal detection result is that the test bench load fluctuation is within the set range, it is judged as the first level, and the test bench parameter adaptive adjustment is initiated. If the anomaly detection result is a task timeout, it is judged as Level 2, and the current test task is assigned to an appropriate idle test bench. If the anomaly detection result indicates abnormal test data or test bench malfunction, it is classified as Level 3, and the task is immediately terminated, an alarm notification is sent, and the fault information is recorded.

[0051] Understandably, pre-defined multi-dimensional anomaly judgment rules (such as task execution timeout, bench load exceeding limits, and test data anomalies) will automatically execute tiered processing once an anomaly is triggered: minor anomalies (such as small-scale load fluctuations) will initiate adaptive adjustment of bench parameters; moderate anomalies (such as task timeouts) will automatically trigger task transfer (assignment to a suitable idle bench); and severe anomalies (such as bench failures) will immediately terminate the task, send an alarm notification, and record the fault information.

[0052] It supports automated recording of exception handling processes, forming an exception case library to provide data support for self-evolution.

[0053] In one embodiment, a machine learning algorithm is used to continuously optimize the scheduling algorithm parameters and bench profile model based on historical task execution data and anomaly handling results, and automatically identify scheduling bottlenecks and output resource allocation optimization suggestions to carry out autonomous iteration of scheduling strategies.

[0054] Understandably, based on historical task execution data (such as scheduling success rate, rack utilization, task completion efficiency, and anomaly handling effectiveness), machine learning algorithms are used to continuously optimize scheduling algorithm parameters and rack profile models.

[0055] It automatically identifies scheduling bottlenecks (such as insufficient compatible test benches for a certain type of task or excessively high failure rate of a certain test bench), outputs optimization suggestions (such as adding test bench configurations or adjusting task allocation weights), and realizes autonomous iteration of scheduling strategies.

[0056] In summary, this application monitors the status of all test equipment (referred to as "bench") in real time (e.g., load level, health status, configuration type) and automatically assigns tasks to the most suitable equipment based on the needs of the test tasks (e.g., task type, priority). If a device malfunctions (e.g., overload or failure), the system will immediately handle the situation in a tiered manner (e.g., transfer the task or send an alarm). More importantly, the system learns from each test, automatically improving the allocation rules, becoming more accurate with use, thereby reducing equipment idle time, shortening test waiting time, and significantly reducing the workload of manual management.

[0057] The beneficial effects of this invention include: 1. Significantly improved rack resource utilization: Intelligent scheduling algorithms achieve precise matching of tasks and racks, increasing the average rack utilization rate from the traditional 50%-60% to over 85%, reducing resource idleness and waste.

[0058] 2. Significantly optimized task execution efficiency: Task waiting time is reduced by more than 40%, and the average execution cycle of a single task is reduced by 25%-30%, supporting rapid iteration in the testing of new energy vehicles.

[0059] 3. Timely response to anomalies: The automated anomaly detection and hierarchical processing mechanism reduces the anomaly response time from more than 1 hour to less than 5 minutes, and reduces the task interruption rate by 70%.

[0060] 4. Continuous optimization of scheduling strategies: The self-evolution capability continuously improves the system's adaptability. When faced with new task types or rack resources, the scheduling adaptation success rate increases to over 95%, eliminating the need for frequent manual adjustments to rules.

[0061] 5. Significantly reduced management costs: Full-process automated management reduces manual scheduling and monitoring workload by more than 80%, while data accumulation forms a rack resource management knowledge base, providing decision support for rack maintenance and expansion.

[0062] Reference Figure 2 , Figure 2 The diagram shown is a schematic of a test bench resource scheduling device provided by the present invention. Figure 2 As shown, the device includes: Module 201: It is used to build and dynamically update the test bench profile based on the real-time acquired test bench status data.

[0063] Acquisition module 202: It is used to acquire the task requirement information of the test task to be scheduled. The task requirement information includes: task type, required configuration parameters, execution priority and time window.

[0064] Matching module 203: It is used to perform test task-test bench resource matching based on the task requirement information and the test bench profile, through a preset multi-objective optimization scheduling algorithm, so as to determine the target bench that matches the test task to be scheduled.

[0065] Scheduling module 204: It is used to assign the test task to be scheduled to the target test bench for execution.

[0066] Furthermore, in one possible implementation, the matching module is also used to take the task requirement information and the test bench profile as input, and use a multi-objective optimization scheduling algorithm that integrates genetic algorithm and reinforcement learning model to finally select the target test bench with the highest bench resource utilization, the shortest task execution cycle, and the highest task-test bench fit.

[0067] Furthermore, in one possible implementation, the scheduling module is also used to schedule tasks according to the comprehensive ranking result of the calibrated task priority and task-rack adaptability when multiple test tasks are competing for the same rack resource; wherein, the task priority supports dynamic adjustment.

[0068] Furthermore, in one possible implementation, the scheduling module is also used to monitor the entire task execution process so as to automatically perform hierarchical processing and trigger prompts when potential anomalies are detected.

[0069] Furthermore, in one possible implementation, the scheduling module is also used to detect anomalies in the task execution process based on preset anomaly judgment conditions, the anomaly judgment conditions including: test task execution timeout, bench load exceeding limit, and test data anomalies; If the abnormal detection result is that the test bench load fluctuation is within the set range, it is judged as the first level, and the test bench parameter adaptive adjustment is initiated. If the anomaly detection result is a task timeout, it is judged as Level 2, and the current test task is assigned to an appropriate idle test bench. If the anomaly detection result indicates abnormal test data or test bench malfunction, it is classified as Level 3, and the task is immediately terminated, an alarm notification is sent, and the fault information is recorded.

[0070] Furthermore, in one possible implementation, the scheduling module is also used to employ machine learning algorithms to continuously optimize scheduling algorithm parameters and bench profile models based on historical task execution data and anomaly handling results, and automatically identify scheduling bottlenecks, output resource allocation optimization suggestions, so as to perform autonomous iteration of scheduling strategies.

[0071] Furthermore, in one possible implementation, the construction module is also used to include the test bench status data as: operating status information and historical performance information, wherein the operating status information includes: the current load rate of the test bench, task execution progress and health score, and the historical performance information includes: task completion rate, anomaly occurrence rate and adapted task type; Based on the aforementioned status data, a dynamic rack profile model is established and updated in real time using big data analysis algorithms to generate profile tags that characterize the rack's current status and capabilities.

[0072] The following reference Figure 3 To describe an electronic device 300 according to this embodiment of the present invention. Figure 3 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0073] like Figure 3 As shown, the electronic device 300 is presented in the form of a general-purpose computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, and a bus 330 connecting different system components (including storage unit 320 and processing unit 310).

[0074] The storage unit stores program code that can be executed by the processing unit 310, causing the processing unit 310 to perform the steps described in the "Embodiment Methods" section of this specification according to various exemplary embodiments of the present invention.

[0075] Storage unit 320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0076] Storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0077] Bus 330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0078] Electronic device 300 can also communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 300, and / or any device that enables electronic device 300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 350. Furthermore, electronic device 300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 360. As shown, network adapter 360 communicates with other modules of electronic device 300 via bus 330. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0079] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure 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, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0080] According to the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the present invention can also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the present invention described in the "Exemplary Methods" section above.

[0081] refer to Figure 4 As shown, a program product 400 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0083] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0084] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0085] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0086] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0087] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

Claims

1. A test bench resource scheduling method, characterized in that, include: Based on real-time collected test bench status data, a test bench profile is constructed and dynamically updated. Obtain the task requirement information of the test task to be scheduled, including: task type, required configuration parameters, execution priority and time window; Based on the task requirement information and the test bench profile, a preset multi-objective optimization scheduling algorithm is used to match test tasks with test bench resources in order to determine the target bench that matches the test task to be scheduled. The test task to be scheduled is assigned to the target test bench for execution.

2. The method according to claim 1, characterized in that, The step of matching test tasks with test bench resources based on the task requirement information and the test bench profile, using a preset multi-objective optimization scheduling algorithm, to determine the target test bench that matches the test task to be scheduled includes: Using the task requirements information and the test bench profile as input, a multi-objective optimization scheduling algorithm that integrates genetic algorithm and reinforcement learning model is adopted to finally select the target test bench with the highest bench resource utilization, the shortest task execution cycle, and the highest task-test bench fit.

3. The method according to claim 1, characterized in that, After determining the target test bench that matches the test task to be scheduled, the process includes: When multiple test tasks compete for the same rack resource, scheduling is performed according to the combined ranking result of the calibrated task priority and task-rack adaptability; wherein, the task priority supports dynamic adjustment.

4. The method according to claim 1, characterized in that, After assigning the test task to be scheduled to the target test bench for execution, the process includes: Monitor the entire task execution process to automatically perform tiered processing and trigger prompts when potential anomalies are detected.

5. The method according to claim 4, characterized in that, The hierarchical processing includes: Anomalies are detected in the task execution process based on preset anomaly detection conditions, including: test task execution timeout, bench load exceeding limit, and test data anomalies. If the abnormal detection result is that the test bench load fluctuation is within the set range, it is judged as the first level, and the test bench parameter adaptive adjustment is initiated. If the anomaly detection result is a task timeout, it is judged as Level 2, and the current test task is assigned to an appropriate idle test bench. If the anomaly detection result indicates abnormal test data or test bench malfunction, it is classified as Level 3, and the task is immediately terminated, an alarm notification is sent, and the fault information is recorded.

6. The method according to claim 5, characterized in that, Also includes: Using machine learning algorithms, based on historical task execution data and anomaly handling results, the scheduling algorithm parameters and bench profile model are continuously optimized, and scheduling bottlenecks are automatically identified, outputting resource allocation optimization suggestions for autonomous iteration of scheduling strategies.

7. The method according to claim 1, characterized in that, The process of constructing and dynamically updating a test bench profile based on real-time acquired test bench status data includes: The test bench status data includes: operating status information and historical performance information. The operating status information includes: the current load rate of the test bench, task execution progress, and health score. The historical performance information includes: task completion rate, anomaly occurrence rate, and adapted task type. Based on the aforementioned status data, a dynamic rack profile model is established and updated in real time using big data analysis algorithms to generate profile tags that characterize the rack's current status and capabilities.

8. A test bench resource scheduling device, characterized in that, include: The building module is used to build and dynamically update the test bench profile based on real-time acquired test bench status data; The acquisition module is used to acquire the task requirement information of the test task to be scheduled. The task requirement information includes: task type, required configuration parameters, execution priority and time window. The matching module is used to perform test task-test bench resource matching based on the task requirement information and the test bench profile, using a preset multi-objective optimization scheduling algorithm, so as to determine the target bench that matches the test task to be scheduled. The scheduling module is used to assign the test tasks to be scheduled to the target test bench for execution.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer program instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 7.

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