Detection and test platform for computer hardware development

Abstract

The invention relates to the field of computer system detection and fault tolerance, and discloses a detection test platform for computer hardware development, which comprises a data acquisition and interface adaptation module, a signal feature extraction and parameter modeling module, an anomaly detection and diagnosis module, a performance evaluation and optimization module and a feedback regulation and visualization module, through multi-source signal collection and protocol adaptation, in combination with a feature modeling and anomaly detection method, the platform can monitor the running state in real time in the hardware development and test process, an optimization variable set is generated through performance evaluation and optimization decision, and finally dynamic regulation and visual presentation of the test environment are achieved. According to the method, closed-loop management of the whole hardware development and test process is achieved, and stability verification and reliability analysis of computer hardware can be effectively supported.

Description

Technical Field

[0001] This invention relates to the field of computer system testing and fault tolerance, specifically to a testing platform for computer hardware development. Background Technology

[0002] With the increasing complexity of computer hardware and the continuous improvement of system integration, traditional development and testing methods are no longer sufficient to meet the verification requirements of high-reliability and high-stability hardware systems. During hardware R&D, problems such as differences in multi-source signal formats, asynchronous sampling clocks, severe noise interference, and difficulty in quickly diagnosing abnormal states exist. Common hardware test platforms often rely on a single logic analyzer or oscilloscope, which can only observe specific interfaces or limited signals, lacking unified data standardization and global analysis capabilities. This leads to a fragmented testing process and makes it difficult to form a closed-loop verification mechanism.

[0003] Furthermore, in the hardware operational status diagnosis phase, existing methods typically rely on threshold comparisons or simple statistical indicators, which are insufficient for effectively identifying complex anomaly patterns. In the performance optimization and feedback phase, traditional solutions largely depend on manual adjustments, lacking automated control capabilities and failing to support the efficiency requirements of large-scale hardware development and testing. Therefore, there is an urgent need for a complete platform capable of multi-source data acquisition, feature modeling, anomaly diagnosis, performance evaluation, and feedback control to improve the intelligence and systematization of hardware testing. Summary of the Invention

[0004] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a testing and detection platform for computer hardware development, comprising the following modules: The data acquisition and interface adaptation module is used to receive multi-source signal data from different types of sensors, bus interfaces, protocol controllers and debugging probes in real time during computer hardware development and testing, and to perform protocol adaptation and timestamp alignment processing on the data to generate standardized input data that can be directly called by subsequent analysis modules. The signal feature extraction and parameter modeling module is used to receive standardized input data, extract feature matrices representing the hardware operating state through feature decomposition, pattern recognition and parameter modeling methods, and further establish a set of parameter descriptions. The anomaly detection and diagnosis module is used to receive the feature matrix and parameter description set, identify potential abnormal operating states through anomaly measurement and clustering modeling, generate an anomaly index matrix, and output a set of diagnostic labels by combining pattern comparison methods. The performance evaluation and optimization module is used to receive the abnormal indicator matrix and the set of diagnostic labels, compare them with the system's preset set of benchmark indicators, and generate a set of optimization variables through performance measurement and optimization decision-making algorithms. The feedback control and visualization module is used to dynamically adjust the operating parameters of the hardware development and testing platform based on the set of optimization variables, and to transform the optimization process and results into visual report data, thereby achieving closed-loop management of the entire testing process.

[0005] Preferably, the data acquisition and interface adaptation module includes: The module provides unified access to signal data from different sources, including real-time signals from the debug port, bus interface, and high-speed sampling probe. Due to differences in data format, sampling frequency, and clock domain among different hardware interfaces, the module introduces a multi-protocol parsing engine at the access layer to decode data packets one by one and generate a preliminarily aligned signal stream in the buffer. Subsequently, the module uses a global timestamp synchronization mechanism to uniformly correct asynchronous signals from different sources, thereby obtaining continuous standardized input data. Meanwhile, the module has anomaly detection capabilities, which can correct noise points, packet loss and missing values ​​during the acquisition process to ensure the stability of subsequent feature extraction. By introducing a sliding window mechanism and interpolation correction strategy, the module can maintain the accuracy and continuity of data in complex testing environments, ensuring that the input data has high reliability and availability.

[0006] Preferably, the signal feature extraction and parameter modeling module includes: First, the standardized input data is decomposed into three types of representations: time domain, frequency domain, and statistical domain. This allows for the construction of a feature matrix from different signal perspectives. This feature matrix can fully reflect the state changes of the hardware during operation, including periodic components, transient fluctuations, and probability distribution characteristics. Secondly, the obtained feature matrix is ​​input into the sparsity modeling stage. In this stage, the system removes redundant and low-relevance features from the data through a stepwise screening method, retaining only the representative and stable parameters, and forming a parameter set accordingly. The generation of the parameter set follows the constraint condition, that is, while minimizing redundant information, it maintains the identifiability and integrity of the overall features. Finally, during the parametric modeling process, a projection matrix is ​​introduced to map high-dimensional features to a low-dimensional space, thereby reducing computational complexity while retaining the core elements most sensitive to hardware operation. This process enables the parameter set to be directly called later to support further analysis by the anomaly detection and diagnosis module.

[0007] Preferably, the anomaly detection and diagnosis module includes: First, the module receives the feature matrix and parameter set, and establishes an anomaly measurement mechanism in a multidimensional space. It uses an extended Mahalanobis distance formula to calculate the difference between the current test data and the reference pattern. This measurement mechanism is based on statistical distribution and distance analysis methods, and compares each input sample with the reference pattern to form an anomaly index matrix. Subsequently, the module performs a non-linear transformation on the anomaly index matrix, which allows the differences to be further amplified when the data amplitude is small. In this way, early and minor anomalies are not ignored, but are reflected with higher weight, thus maintaining high sensitivity in the anomaly identification stage. After the anomaly index matrix is ​​generated, the module summarizes and organizes the abnormal signals of different categories through pattern clustering and comparison, and generates a set of diagnostic labels accordingly. The set of diagnostic labels covers a variety of common hardware anomaly categories, including timing jitter, signal distortion and power consumption exceeding the standard. Each label corresponds to a specific anomaly pattern and is used to provide input basis for the performance evaluation module.

[0008] Preferably, the performance evaluation and optimization module includes: First, the module receives the abnormal indicator matrix and the set of diagnostic labels, and compares them with the system's preset set of benchmark indicators. During the comparison process, different weights are assigned to each type of indicator to measure its relative importance in the comprehensive evaluation. By comparing the differences between the abnormal indicators and the benchmark indicators one by one, and combining the weights, a comprehensive performance score is formed. Subsequently, after obtaining the comprehensive performance score, the module enters the optimization phase. This optimization process uses a multi-objective analysis method to consider the balance between multiple performance indicators. When generating the set of optimization variables, the system will refer to the distribution of historical best parameters and make smooth adjustments based on this to avoid instability caused by excessive parameter changes. Finally, the optimized variable set is updated through iterative calculation and kept consistent with the comprehensive performance score, so that it can be directly used as an input basis in the subsequent feedback and control stage to achieve dynamic adjustment of test conditions.

[0009] Preferably, the feedback control and visualization module includes: The module receives a set of optimized variables output by the performance evaluation and optimization module and applies them to real-time adjustments of voltage, current, frequency, and load conditions. This adjustment process is automated and can continuously correct the test environment based on the input variables. Subsequently, the module will uniformly convert the set of optimization variables, the matrix of abnormal indicators, and the comprehensive performance score into a data format that can be stored and displayed. In this process, the module establishes a mapping relationship to transform multi-dimensional numerical information into structured data records. Finally, after the module completes data processing, it generates data output including charts and reports to visually present the results of each stage. This output not only reflects the adjustment process of optimization variables, but also includes the corresponding results of anomaly diagnosis and performance evaluation, so that the data between different modules forms a closed loop and can be directly referenced in subsequent cycles.

[0010] The data acquisition and interface adaptation module is coupled with the signal feature extraction and parameter modeling module through a shared buffer and pipeline mechanism. This allows standardized input data to immediately enter the feature decomposition process after acquisition, avoiding delays caused by batch transmission and ensuring data integrity during the feature extraction stage. The anomaly detection and diagnosis module and the performance evaluation and optimization module use a shared anomaly index matrix as the core bridge. When calculating the comprehensive performance score, the performance evaluation module can dynamically use the diagnostic results of the diagnostic label set for adaptive correction, thereby avoiding misjudgments caused by a single anomaly index. The feedback control and visualization module forms a closed-loop control structure with the data acquisition module. This closed-loop structure can dynamically adjust the acquisition strategy and sampling frequency for the next round based on the adjustment results of the optimization variable set, thereby achieving continuous optimization and adaptive control during hardware testing.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: By using a data acquisition and interface adaptation module to perform unified and standardized processing of multi-source heterogeneous signals, and combining timestamp synchronization and anomaly correction mechanisms, the continuity and high reliability of input data are ensured in complex testing environments. This fundamentally solves the problems of scattered data sources, clock asynchronization, and difficulty in uniformly processing missing data in traditional hardware testing.

[0012] By cascading and coordinating modules for signal feature extraction and parameter modeling, anomaly detection and diagnosis, and performance evaluation and optimization, multi-dimensional modeling of hardware operating status and anomaly pattern recognition are achieved. Innovative formulas and multi-objective constraint methods are introduced in the parameter modeling and performance evaluation process, enabling abnormal states to be identified early and transformed into diagnostic labels, thereby providing quantifiable input basis for performance optimization.

[0013] Through the feedback control and visualization module, the optimization variables are directly applied to the real-time control of the test environment, forming a closed-loop feedback mechanism. This not only enables the test conditions to be automatically adjusted according to the optimization results, but also achieves unified visualization output of multi-dimensional data results, thereby ensuring that the hardware testing process has adaptive control capabilities and complete process traceability. Attached Figure Description

[0014] Figure 1 This application provides a schematic diagram of the system modules. Figure 2A schematic diagram of the system modules provided in this application. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] refer to Figures 1-2 This invention provides a testing and detection platform for computer hardware development, comprising the following modules: The data acquisition and interface adaptation module is used during computer hardware development and testing to process multi-source signal data from different types of sensors, bus interfaces, protocol controllers, and debug probes. The system receives data in real time and performs protocol adaptation and timestamp alignment to generate standardized input data that can be directly used by subsequent analysis modules. ; The signal feature extraction and parameter modeling module is used to receive standardized input data. By employing feature decomposition, pattern recognition, and parameter modeling methods, a feature matrix representing the hardware's operating state is extracted. And further establish a parameter description set. ; The anomaly detection and diagnosis module is used to receive the feature matrix. With parameter description set Furthermore, by using anomaly measurement functions and high-dimensional clustering models, potential abnormal operating states are identified, and an anomaly index matrix is ​​generated. And output a set of diagnostic labels by combining pattern comparison methods. ; The performance evaluation and optimization module is used to receive the abnormal indicator matrix. With diagnostic label set and compared with the system's preset set of benchmark indicators. By comparing the results and using performance deviation measurement and optimization decision-making algorithms, a set of optimization variables is generated. ; The feedback regulation and visualization module is used to optimize the set of variables. It dynamically adjusts the operating parameters of the hardware development and testing platform and transforms the optimization process and results into visual report data, thereby achieving closed-loop management of the entire testing process.

[0017] In the data acquisition and interface adaptation module, signal data from different sources are processed. Unified access is implemented for the signal data. This includes real-time signals from the JTAG debug port, I²C bus, SPI interface, and high-speed sampling probe. Due to differences in data format, sampling frequency, and clock domain among various hardware interfaces, the data acquisition and interface adaptation module employs a multi-protocol parsing engine at the access layer to ensure that data packets are decoded one by one and a preliminarily aligned signal stream is generated in the buffer. Subsequently, the module uses a global timestamp synchronization mechanism and a sliding window function to... and interpolation function To achieve unified correction of asynchronous signals, thereby obtaining continuous standardized input data. ; At the same time, through an anomaly detection mechanism, the signal data is analyzed. Noise points, packet loss, and missing values ​​are corrected to ensure the stability of subsequent feature extraction. The timestamp alignment process uses linear interpolation and weighting factors. This ensures that data points closer to the center of the sampling window have higher weights, and the core formula is as follows: ; The above formula standardizes the input data. It can maintain continuity and accuracy in a multi-clock asynchronous environment.

[0018] In the signal feature extraction and parameter modeling module, standardized input data... Multi-scale wavelet transform and spectral decomposition are performed to obtain a multi-dimensional representation of the signal, and the output is a feature matrix. It can comprehensively reflect the characteristics of hardware operation in the time domain, frequency domain, and statistical domain. Then, by compressing the high-dimensional data through a sparse modeling algorithm, a parameter description set is obtained. The parameter description set Used to reflect the stability and representativeness of key features during the testing process.

[0019] In sparse modeling algorithms, to avoid overfitting and improve the model's generalization ability in complex testing environments, the following algorithm formula is introduced: ; in, For the characteristic projection matrix, The sparsity coefficients are used to extract the sparse feature set that best represents hardware performance through this algorithm, making subsequent anomaly detection more accurate.

[0020] In the anomaly detection and diagnosis module, the first step is to receive the feature matrix. With parameter description set By constructing a high-dimensional anomaly measurement function, the degree of deviation between each sample and the reference pattern is calculated, and an anomaly index matrix is ​​obtained. Among them, the high-dimensional anomaly metric function uses an extended Mahalanobis distance formula to effectively measure the degree of difference between hardware operating data and normal patterns: ; in, For the first Class Pattern Center It is the covariance matrix; Subsequently, in order to further amplify the abnormal signal, this module introduces a nonlinear weighting function: ; in, As an adjustment factor, when the amplitude of the abnormal signal is small, the weighting effect is significant, ensuring that the system has high sensitivity to early minor anomalies; Finally, the module combines pattern clustering methods to generate a set of diagnostic labels. It can classify different types of hardware anomalies, including timing jitter, signal distortion, and excessive power consumption.

[0021] In the performance evaluation and optimization module, the module receives an anomaly index matrix. With diagnostic label set And combined with a pre-set set of benchmark indicators The performance deviation of the hardware during the testing process is measured using the following performance evaluation function: ; in, Indicates the first In a given hardware configuration scenario, the system uses a specific set of diagnostic tags. The overall performance score, Using the aforementioned performance evaluation function as the indicator weight, the platform can perform weighted evaluation of multi-dimensional performance, avoiding the excessive influence of a single indicator on the overall evaluation. In the optimization phase, the module employs a multi-objective optimization algorithm to dynamically adjust the hardware test parameters and operating environment, optimizing the set of variables. The formula for generating it is: ; in, This is the historical optimal solution. As a smoothing factor, this objective function balances performance optimization and adjustment stability, avoiding excessively frequent parameter fluctuations.

[0022] In the feedback control and visualization module, the set of optimization variables is received. By real-time control of voltage, current, frequency, and load conditions, the hardware testing environment can automatically adjust according to the optimization results, thereby ensuring that the hardware testing process runs under optimal conditions. Simultaneously, this module uses a visual mapping function to visualize the set of optimization variables. Abnormal indicator matrix and overall performance score Data is transformed into visual charts and reports, presenting complex optimization and anomaly diagnosis results to developers in an intuitive way, and supporting manual review and secondary analysis.

[0023] Meanwhile, the data acquisition and interface adaptation module and the signal feature extraction and parameter modeling module are coupled through a shared buffer and pipeline mechanism, enabling standardized input data to immediately enter the feature decomposition process after acquisition, avoiding delays caused by batch transmission, and ensuring data integrity during the feature extraction stage. The anomaly detection and diagnosis module and the performance evaluation and optimization module use a shared anomaly index matrix as the core bridge. When calculating the comprehensive performance score, the performance evaluation module can dynamically use the diagnostic results of the diagnostic label set for adaptive correction, thereby avoiding misjudgments caused by a single anomaly index. The feedback control and visualization module and the data acquisition module form a closed-loop control structure. This closed-loop structure can dynamically correct the acquisition strategy and sampling frequency for the next round based on the adjustment results of the optimization variable set, thereby achieving continuous optimization and adaptive control during hardware testing.

[0024] It should be noted that, unless otherwise specified, the embodiments and features and technical solutions in the present invention can be combined with each other.

[0025] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A testing and inspection platform for computer hardware development, characterized in that, Includes the following modules: The data acquisition and interface adaptation module is used to receive multi-source signal data from different types of sensors, bus interfaces, protocol controllers and debugging probes in real time during computer hardware development and testing, and to perform protocol adaptation and timestamp alignment processing on the data to generate standardized input data that can be directly called by subsequent analysis modules. The signal feature extraction and parameter modeling module is used to receive standardized input data, extract feature matrices representing the hardware operating state through feature decomposition, pattern recognition and parameter modeling methods, and further establish a set of parameter descriptions. The anomaly detection and diagnosis module is used to receive the feature matrix and parameter description set, identify potential abnormal operating states through anomaly measurement and clustering modeling, generate an anomaly index matrix, and output a set of diagnostic labels by combining pattern comparison methods. The performance evaluation and optimization module is used to receive the abnormal indicator matrix and the set of diagnostic labels, compare them with the system's preset set of benchmark indicators, and generate a set of optimization variables through performance measurement and optimization decision-making algorithms. The feedback control and visualization module is used to dynamically adjust the operating parameters of the hardware development and testing platform based on the set of optimization variables, and to transform the optimization process and results into visual report data, thereby achieving closed-loop management of the entire testing process.

2. The testing and inspection platform for computer hardware development according to claim 1, characterized in that, The data acquisition and interface adaptation module is used to realize parallel access and standardized processing of multi-source signals, specifically including: The module provides unified access to signal data from different sources, including real-time signals from the debug port, bus interface, and high-speed sampling probe. Due to differences in data format, sampling frequency, and clock domain among different hardware interfaces, the module introduces a multi-protocol parsing engine at the access layer to decode data packets one by one and generate a preliminarily aligned signal stream in the buffer. Subsequently, the module uses a global timestamp synchronization mechanism to uniformly correct asynchronous signals from different sources, thereby obtaining continuous standardized input data. Meanwhile, the module has anomaly detection capabilities, which can correct noise points, packet loss and missing values ​​during the acquisition process to ensure the stability of subsequent feature extraction. By introducing a sliding window mechanism and interpolation correction strategy, the module can maintain the accuracy and continuity of data in complex testing environments, ensuring that the input data has high reliability and availability.

3. The testing and inspection platform for computer hardware development according to claim 1, characterized in that, The signal feature extraction and parameter modeling module is used to perform multidimensional feature decomposition and parameter modeling on the input data, specifically including: First, the standardized input data is decomposed into three types of representations: time domain, frequency domain, and statistical domain. This allows for the construction of a feature matrix from different signal perspectives. This feature matrix can fully reflect the state changes of the hardware during operation, including periodic components, transient fluctuations, and probability distribution characteristics. Secondly, the obtained feature matrix is ​​input into the sparsity modeling stage. In this stage, the system removes redundant and low-relevance features from the data through a stepwise screening method, retaining only the representative and stable parameters, and forming a parameter set accordingly. The generation of the parameter set follows the constraint condition, that is, while minimizing redundant information, it maintains the identifiability and integrity of the overall features. Finally, during the parametric modeling process, a projection matrix is ​​introduced to map high-dimensional features to a low-dimensional space, thereby reducing computational complexity while retaining the core elements most sensitive to hardware operation. This process enables the parameter set to be directly called later to support further analysis by the anomaly detection and diagnosis module.

4. The testing and inspection platform for computer hardware development according to claim 1, characterized in that, The anomaly detection and diagnosis module is used to identify potential abnormal operating states and generate diagnostic labels, specifically including: First, the module receives the feature matrix and parameter set, and establishes an anomaly measurement mechanism in a multidimensional space. It uses an extended Mahalanobis distance formula to calculate the difference between the current test data and the reference pattern. This measurement mechanism is based on statistical distribution and distance analysis methods, and compares each input sample with the reference pattern to form an anomaly index matrix. Subsequently, the module performs a non-linear transformation on the anomaly index matrix, which allows the differences to be further amplified when the data amplitude is small. In this way, early and minor anomalies are not ignored, but are reflected with higher weight, thus maintaining high sensitivity in the anomaly identification stage. After the anomaly index matrix is ​​generated, the module summarizes and organizes the abnormal signals of different categories through pattern clustering and comparison, and generates a set of diagnostic labels accordingly. The set of diagnostic labels covers a variety of common hardware anomaly categories, including timing jitter, signal distortion and power consumption exceeding the standard. Each label corresponds to a specific anomaly pattern and is used to provide input basis for the performance evaluation module.

5. A testing and inspection platform for computer hardware development according to claim 1, characterized in that, The performance evaluation and optimization module is used to combine abnormal indicators and benchmark indicators to measure performance and generate optimization variables, specifically including: First, the module receives the abnormal indicator matrix and the set of diagnostic labels, and compares them with the system's preset set of benchmark indicators. During the comparison process, different weights are assigned to each type of indicator to measure its relative importance in the comprehensive evaluation. By comparing the differences between the abnormal indicators and the benchmark indicators one by one, and combining the weights, a comprehensive performance score is formed. Subsequently, after obtaining the comprehensive performance score, the module enters the optimization phase. This optimization process uses a multi-objective analysis method to consider the balance between multiple performance indicators. When generating the set of optimization variables, the system will refer to the distribution of historical best parameters and make smooth adjustments based on this to avoid instability caused by excessive parameter changes. Finally, the optimized variable set is updated through iterative calculation and kept consistent with the comprehensive performance score, so that it can be directly used as an input basis in the subsequent feedback and control stage to achieve dynamic adjustment of test conditions.

6. A testing and inspection platform for computer hardware development according to claim 1, characterized in that, The feedback control and visualization module is used to dynamically control the test environment based on optimization variables and generate a visualization report, specifically including: The module receives a set of optimized variables output by the performance evaluation and optimization module and applies them to real-time adjustments of voltage, current, frequency, and load conditions. This adjustment process is automated and can continuously correct the test environment based on the input variables. Subsequently, the module will uniformly convert the set of optimization variables, the matrix of abnormal indicators, and the comprehensive performance score into a data format that can be stored and displayed. In this process, the module establishes a mapping relationship to transform multi-dimensional numerical information into structured data records. Finally, after the module completes data processing, it generates data output including charts and reports to visually present the results of each stage. This output not only reflects the adjustment process of optimization variables, but also includes the corresponding results of anomaly diagnosis and performance evaluation, so that the data between different modules forms a closed loop and can be directly referenced in subsequent cycles.

7. A testing and inspection platform for computer hardware development according to any one of claims 1-6, characterized in that, The data acquisition and interface adaptation module and the signal feature extraction and parameter modeling module are coupled through a shared buffer and pipeline mechanism, which enables standardized input data to enter the feature decomposition process immediately after acquisition, avoiding delays caused by batch transmission, and ensuring data integrity in the feature extraction stage.

8. A testing and inspection platform for computer hardware development according to any one of claims 1-6, characterized in that, The anomaly detection and diagnosis module and the performance evaluation and optimization module use a shared anomaly index matrix as the core bridge. When calculating the comprehensive performance score, the performance evaluation module can dynamically use the diagnostic results of the diagnostic label set for adaptive correction, thereby avoiding misjudgment caused by a single anomaly index.

9. A testing and inspection platform for computer hardware development according to any one of claims 1-6, characterized in that, The feedback control and visualization module and the data acquisition module form a closed-loop control structure. The closed-loop structure can dynamically correct the acquisition strategy and sampling frequency for the next round based on the adjustment results of the set of optimized variables, thereby achieving continuous optimization and adaptive control during hardware testing.

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