Full-automatic test system for single mainboard of high-efficiency server
The high-performance server single motherboard fully automated testing system realizes fully automated testing of server motherboards, solves the problems of complex testing process and low efficiency, improves testing efficiency and accuracy, adapts to the testing needs of different motherboard models, and is suitable for large-scale production.
Patent Information
- Application Number
- CN202511597652.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-04
- Publication Date
- 2026-02-13
AI Technical Summary
Existing server motherboard testing processes are complex, require extensive manual intervention, are time-consuming, rely on manual data recording and analysis, have low testing efficiency, and are difficult to process multiple test tasks simultaneously.
A high-performance server single-motherboard fully automated testing system is adopted, including a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module. It realizes fully automated testing, and through parallel processing and distributed testing technology, combined with intelligent diagnostic algorithms, it automatically analyzes abnormal data and generates diagnostic results.
It improves the automation level of testing, significantly shortens testing time, enhances testing efficiency and accuracy, adapts to the testing needs of different motherboard models, and is suitable for large-scale production scenarios.
Smart Images
Figure CN121523988A_ABST
Abstract
Description
Technical Field
[0001] This invention discloses a high-performance server single-motherboard fully automated testing system, which relates to the field of server testing technology. Background Technology
[0002] With the rapid development of information technology, high-performance servers are increasingly widely used in scientific research, industrial production, and other fields, and their performance stability directly affects the operational efficiency of these fields. As the core component of a server, the testing of the server motherboard is crucial to ensuring server quality.
[0003] Currently, server motherboard testing faces numerous challenges: the testing process is complex, requiring manual intervention in multiple stages, resulting in lengthy testing times; data recording and analysis during testing rely on manual operation, making it difficult to guarantee the accuracy of results; and existing testing methods struggle to simultaneously process multiple testing tasks, leading to low testing efficiency. Summary of the Invention
[0004] This invention addresses the problems of existing technologies by providing a high-performance, fully automated testing system for a single server motherboard. Through the collaborative work of various modules, it achieves fully automated testing of the CPU, memory, storage, network, USB, and other functions of a single server motherboard. It optimizes the testing process, improves testing efficiency and accuracy, and is suitable for motherboard testing scenarios in the server manufacturing process.
[0005] The specific solution proposed in this invention is as follows:
[0006] This invention also provides a high-performance server single-motherboard fully automated testing system, including a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module.
[0007] The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish.
[0008] The parallel computing module employs parallel processing to perform multiple test tasks simultaneously, targeting the CPU, memory, storage, network, and USB components of the server motherboard that require testing.
[0009] The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes.
[0010] The intelligent diagnostic module integrates intelligent algorithms to automatically analyze abnormal data generated during testing, quickly pinpoint the cause of the problem, and generate diagnostic results.
[0011] Data storage uses a database to store raw data, intermediate results, and final test results during the testing process, ensuring data traceability. The analysis module performs statistical analysis on the stored data and generates test pass rate reports and fault distribution reports to provide data support for production optimization.
[0012] Furthermore, the test control module of the high-performance server single motherboard fully automatic test system includes a task scheduling unit and a process control unit. The task scheduling unit allocates test tasks according to the motherboard model and test requirements, while the process control unit ensures that the test steps are executed in a preset logical order to avoid confusion in the test process.
[0013] Furthermore, the distributed testing module of the high-performance server single-motherboard fully automated testing system is configured so that each test node can be responsible for part of the testing tasks or independently test a single motherboard, thereby increasing the system's testing capacity and adapting to the testing needs of large-scale production scenarios.
[0014] Furthermore, the intelligent diagnostic module of the high-performance server single-motherboard fully automated testing system integrates intelligent algorithms, including statistical methods, machine learning methods, deep learning methods, and signal processing methods.
[0015] Statistical methods utilize statistical techniques to analyze data, uncover patterns, and identify outliers by setting thresholds or using statistical distributions.
[0016] Machine learning-based methods can predict the status of a single server motherboard and detect abnormal data by building classification or regression models.
[0017] A multi-layer neural network based on deep learning methods is used to identify abnormal data in the status of a single motherboard in a server.
[0018] The signal processing-based method extracts the fault characteristics of a single server motherboard through frequency domain and time domain analysis, and then analyzes these fault characteristics.
[0019] This invention also provides a high-performance server single-motherboard fully automated testing method, which utilizes a server single-motherboard fully automated testing system. The server single-motherboard fully automated testing system includes a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module.
[0020] The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish.
[0021] The parallel computing module enables multiple test tasks to be performed simultaneously on the server motherboard's CPU, memory, storage, network, and USB components using a parallel processing approach.
[0022] The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes.
[0023] By integrating intelligent algorithms through the intelligent diagnostic module, abnormal data generated during the testing process is automatically analyzed to quickly locate the cause of the problem and generate diagnostic results.
[0024] The database stores raw data, intermediate results, and final test results during the testing process to ensure data traceability. The analysis module performs statistical analysis on the stored data to generate test pass rate reports and fault distribution reports, providing data support for production optimization.
[0025] Furthermore, in the high-performance server single motherboard fully automatic testing method, the test control module includes a task scheduling unit and a process control unit. The task scheduling unit allocates test tasks according to the motherboard model and test requirements, while the process control unit ensures that the test steps are executed in a preset logical order to avoid confusion in the test process.
[0026] Furthermore, in the aforementioned high-performance server single-motherboard fully automated testing method, each test node is configured through a distributed testing module to be responsible for part of the testing tasks or to independently test a single motherboard, thereby increasing the system's testing capacity and adapting to the testing needs of large-scale production scenarios.
[0027] Furthermore, the intelligent algorithms integrated through the intelligent diagnostic module in the aforementioned high-performance server single-motherboard fully automated testing method include statistical methods, machine learning methods, deep learning methods, and signal processing methods.
[0028] Statistical methods utilize statistical techniques to analyze data, uncover patterns, and identify outliers by setting thresholds or using statistical distributions.
[0029] Machine learning-based methods can predict the status of a single server motherboard and detect abnormal data by building classification or regression models.
[0030] A multi-layer neural network based on deep learning methods is used to identify abnormal data in the status of a single motherboard in a server.
[0031] The signal processing-based method extracts the fault characteristics of a single server motherboard through frequency domain and time domain analysis, and then analyzes these fault characteristics.
[0032] The advantages of this invention are:
[0033] (1) High degree of automation: The test control module coordinates all aspects to achieve fully automatic execution of the test process, reducing manual intervention and human error.
[0034] (2) Improved testing efficiency: By adopting parallel computing and distributed testing technologies, multiple tasks are processed simultaneously and multiple nodes work together, which significantly shortens the testing time and improves the testing throughput.
[0035] (3) Guarantee of test accuracy: The intelligent diagnostic module automatically analyzes abnormal data, and the data storage and analysis module enables full traceability of data, ensuring the accuracy and reliability of test results.
[0036] (4) High adaptability: It can flexibly adapt to the single motherboard testing requirements of different models of high-performance servers and is suitable for large-scale production and manufacturing scenarios. Attached Figure Description
[0037] Figure 1 This is a schematic diagram of the method flow of the present invention.
[0038] Figure 2 This is a schematic diagram of a high-performance server single-motherboard fully automated testing system framework. Detailed Implementation
[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.
[0040] Example 1
[0041] This invention also provides a high-performance server single-motherboard fully automated testing system, including a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module.
[0042] The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish. The test control module includes a task scheduling unit and a process control unit. The task scheduling unit allocates test tasks based on the motherboard model and test requirements, while the process control unit ensures that test steps are executed in a preset logical order, avoiding confusion during the test process.
[0043] The parallel computing module employs parallel processing to perform multiple test tasks simultaneously, targeting the CPU, memory, storage, network, and USB components of the server motherboard that require testing.
[0044] The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes. Each test node in the distributed testing module can be configured to handle a portion of the test tasks or independently test a single motherboard, thereby increasing the system's testing capacity and adapting to the testing needs of large-scale production scenarios.
[0045] The intelligent diagnostic module integrates intelligent algorithms to automatically analyze abnormal data generated during testing, quickly pinpoint the cause of problems, and generate diagnostic results. The intelligent algorithms integrated into this module include statistical methods, machine learning methods, deep learning methods, and signal processing methods.
[0046] Statistical methods utilize statistical techniques to analyze data, uncover patterns, and identify outliers by setting thresholds or using statistical distributions.
[0047] Machine learning-based methods can predict the status of a single server motherboard and detect abnormal data by building classification or regression models.
[0048] A multi-layer neural network based on deep learning methods is used to identify abnormal data in the status of a single motherboard in a server.
[0049] The signal processing-based method extracts the fault characteristics of a single server motherboard through frequency domain and time domain analysis, and then analyzes these fault characteristics.
[0050] Data storage uses a database to store raw data during the testing process, such as test parameters, real-time monitoring data, intermediate results, and final test results, ensuring data traceability. The analysis module performs statistical analysis on the stored data and generates test pass rate reports and fault distribution reports to provide data support for production optimization.
[0051] When using the system of this invention, the workflow is as described above. Figure 2 As shown:
[0052] (1) Test preparation: Connect the high-performance server motherboard to be tested to the system. The test control module obtains the motherboard model and preset test items such as CPU function test and memory function test through the task scheduling unit.
[0053] (2) Task allocation: The test control module issues parallel test instructions to the parallel computing module, and at the same time, the distributed test module allocates the test tasks to 3 test nodes.
[0054] (3) Parallel testing: The parallel computing module starts synchronous testing of CPU, memory, storage, network and USB functions. Each test node performs test operations according to the assigned tasks, collects test data in real time and uploads it to the data storage and analysis module.
[0055] (4) Abnormal diagnosis: If a test node detects a memory function abnormality, the intelligent diagnosis module automatically retrieves the abnormal data, analyzes the cause of the abnormality through data mining algorithms, determines that it is a poor contact of the memory slot, and generates a diagnostic report.
[0056] (5) Results storage and analysis: The data storage and analysis module stores all the data from this test. The data analysis unit generates a report showing that the pass rate of this batch of motherboards is 98.5%, with memory function failures accounting for 1.2%, providing the production department with a basis for optimization.
[0057] (6) Test termination: After all test tasks are completed, the test control module issues a termination command and the system ends the test process.
[0058] Example 2
[0059] This invention also provides a high-performance server single-motherboard fully automated testing method, which utilizes a server single-motherboard fully automated testing system. The server single-motherboard fully automated testing system includes a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module.
[0060] The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish.
[0061] The parallel computing module enables multiple test tasks to be performed simultaneously on the server motherboard's CPU, memory, storage, network, and USB components using a parallel processing approach.
[0062] The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes.
[0063] By integrating intelligent algorithms through the intelligent diagnostic module, abnormal data generated during the testing process is automatically analyzed to quickly locate the cause of the problem and generate diagnostic results.
[0064] The database stores raw data, intermediate results, and final test results during the testing process to ensure data traceability. The analysis module performs statistical analysis on the stored data to generate test pass rate reports and fault distribution reports, providing data support for production optimization.
[0065] The information interaction and execution process in the above method are based on the same concept as those in the system embodiments of the present invention, and the specific details can be found in the descriptions in the system embodiments of the present invention, and will not be repeated here.
[0066] Similarly, the advantages of the method of the present invention are:
[0067] (1) Achieve a high degree of automation: By coordinating all aspects through the test control module, the test process can be fully automated, reducing manual intervention and human error.
[0068] (2) Improve testing efficiency: By adopting parallel computing and distributed testing technology, multiple tasks are processed simultaneously and multiple nodes work together, which significantly shortens the testing time and increases the testing throughput.
[0069] (3) Ensure test accuracy: The intelligent diagnostic module automatically analyzes abnormal data, and the data storage and analysis module enables full traceability of data, ensuring the accuracy and reliability of test results.
[0070] (4) High adaptability: It can flexibly adapt to the single motherboard testing requirements of different models of high-performance servers and is suitable for large-scale production and manufacturing scenarios.
[0071] It should be noted that not all steps and modules in the above processes and system structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structures described in the above embodiments can be physical or logical structures. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.
[0072] The embodiments described above are merely preferred embodiments for fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.
Claims
1. A high-performance server single-motherboard fully automated testing system, characterized in that: It includes a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module. The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish. The parallel computing module employs parallel processing to perform multiple test tasks simultaneously, targeting the CPU, memory, storage, network, and USB components of the server motherboard that require testing. The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes. The intelligent diagnostic module integrates intelligent algorithms to automatically analyze abnormal data generated during testing, quickly pinpoint the cause of the problem, and generate diagnostic results. Data storage uses a database to store raw data, intermediate results, and final test results during the testing process, ensuring data traceability. The analysis module performs statistical analysis on the stored data and generates test pass rate reports and fault distribution reports to provide data support for production optimization.
2. The high-performance server single-motherboard fully automatic testing system according to claim 1, characterized in that: The test control module includes a task scheduling unit and a process control unit. The task scheduling unit allocates test tasks based on the motherboard model and test requirements, while the process control unit ensures that the test steps are executed in a preset logical order to avoid confusion in the test process.
3. The high-performance server single-motherboard fully automatic testing system according to claim 1, characterized in that: The distributed testing module configures each test node to be responsible for part of the testing tasks or to independently test a single motherboard, thereby increasing the system's testing capacity and adapting to the testing needs of large-scale production scenarios.
4. The high-performance server single-motherboard fully automatic testing system according to claim 1, characterized in that: The intelligent diagnostic module integrates intelligent algorithms, including statistical methods, machine learning methods, deep learning methods, and signal processing methods. Statistical methods utilize statistical techniques to analyze data, uncover patterns, and identify outliers by setting thresholds or using statistical distributions. Machine learning-based methods can predict the status of a single server motherboard and detect abnormal data by building classification or regression models. A multi-layer neural network based on deep learning methods is used to identify abnormal data in the status of a single motherboard in a server. The signal processing-based method extracts the fault characteristics of a single server motherboard through frequency domain and time domain analysis, and then analyzes these fault characteristics.
5. A fully automated testing method for a single motherboard of a high-performance server, characterized by: Testing was conducted using a fully automated single-motherboard server testing system, which includes a test control module, a parallel computing module, a distributed testing module, an intelligent diagnostic module, and a data storage and analysis module. The test control module connects to other modules and is responsible for coordinating the working sequence of each module, controlling the entire test process from start to finish. The parallel computing module enables multiple test tasks to be performed simultaneously on the server motherboard's CPU, memory, storage, network, and USB components using a parallel processing approach. The distributed testing module enables multi-node collaborative work by distributing test tasks to multiple independent test nodes. By integrating intelligent algorithms through the intelligent diagnostic module, abnormal data generated during the testing process is automatically analyzed to quickly locate the cause of the problem and generate diagnostic results. The database stores raw data, intermediate results, and final test results during the testing process to ensure data traceability. The analysis module performs statistical analysis on the stored data to generate test pass rate reports and fault distribution reports, providing data support for production optimization.
6. The fully automated testing method for a single motherboard of a high-performance server according to claim 5, characterized in that: The test control module includes a task scheduling unit and a process control unit. The task scheduling unit allocates test tasks based on the motherboard model and test requirements, while the process control unit ensures that the test steps are executed in a preset logical order to avoid confusion in the test process.
7. The fully automated testing method for a single motherboard of a high-performance server according to claim 5, characterized in that: By configuring each test node through the distributed testing module, it can be responsible for part of the testing tasks or independently test a single motherboard, thereby increasing the system's testing capacity and adapting to the testing needs of large-scale production scenarios.
8. The fully automated testing method for a single motherboard of a high-performance server according to claim 5, characterized in that: The intelligent algorithms integrated through the intelligent diagnostic module include statistical methods, machine learning methods, deep learning methods, and signal processing methods. Statistical methods utilize statistical techniques to analyze data, uncover patterns, and identify outliers by setting thresholds or using statistical distributions. Machine learning-based methods can predict the status of a single server motherboard and detect abnormal data by building classification or regression models. A multi-layer neural network based on deep learning methods is used to identify abnormal data in the status of a single motherboard in a server. The signal processing-based method extracts the fault characteristics of a single server motherboard through frequency domain and time domain analysis, and then analyzes these fault characteristics.