Aging test method, device, equipment, medium and product of device

Abstract

The application provides an aging test method, device, equipment, medium and product of equipment, and relates to the technical field of laser tester. The method comprises the following steps: obtaining a plurality of running parameters of a target equipment; determining the equipment performance stability of the target equipment based on the plurality of running parameters; determining an aging test parameter combination of an equipment aging test phase for the target equipment according to the equipment performance stability; performing virtual verification of the aging test parameter combination on the target equipment to obtain an evaluation result; correcting the aging test parameter combination based on the evaluation result to obtain a target aging test parameter combination; and performing equipment aging test on the target equipment according to the target aging test parameter combination. Through the application, the accuracy of the equipment aging test is improved.

Description

Technical Field

[0001] This application relates to the field of laser testing technology, and in particular to an aging test method, apparatus, equipment, medium and product for a device. Background Technology

[0002] In the field of laser equipment manufacturing and application, aging testing is a crucial verification step to ensure its long-term stability, reliability, and performance degradation characteristics. With the widespread application of laser technology in various fields, the requirements for the performance and lifespan of laser equipment are increasing, making efficient and accurate aging testing an indispensable means of measuring product quality and predicting service life.

[0003] Currently, laser equipment aging tests generally employ fixed parameters or segmented time-series control strategies. In practice, testers need to pre-configure multiple aging parameters based on experience or preset rules, and a preset time-series control program executes the test tasks for each stage segment according to a preset time sequence. However, the fundamental drawback of this testing mode is that it reduces the accuracy of equipment aging tests. Summary of the Invention

[0004] This application provides aging test methods, apparatus, equipment, media, and products for improving the accuracy of equipment aging tests.

[0005] In a first aspect, this application provides an aging test method for a device, comprising:

[0006] Obtain multiple operating parameters of the target device;

[0007] Determine the equipment performance stability of the target device based on multiple operating parameters;

[0008] Based on the equipment performance stability, determine the combination of aging test parameters for the aging test phase of the target equipment;

[0009] Virtual verification of the aging test parameter combination for the target equipment is performed to obtain the evaluation results;

[0010] Based on the evaluation results, the combination of aging test parameters is adjusted to obtain the target combination of aging test parameters.

[0011] Based on the target aging test parameter combination, conduct equipment aging tests on the target equipment.

[0012] In one possible implementation, the device performance stability of the target device is determined based on multiple operating parameters, including:

[0013] Perform time-domain and / or frequency-domain analysis on multiple operating parameters to determine the characteristic quantities corresponding to the operating parameters;

[0014] Calculate the correlation matrix between different operating parameters. The correlation matrix is ​​used to characterize the degree of mutual influence between operating parameters.

[0015] Based on the correlation matrix and characteristic values, the stability of equipment performance is determined.

[0016] In one possible implementation, determining device performance stability based on a correlation matrix and characteristic quantities includes:

[0017] Determine the coupling relationships between parameters in the correlation matrix;

[0018] Based on the feature values, the stability score of the target device is determined;

[0019] The coupling relationship and stability scores are weighted to obtain the device performance stability score.

[0020] The equipment performance stability is determined based on the equipment performance stability score.

[0021] In one possible implementation, virtual verification of the target device using a combination of aging test parameters is performed to obtain evaluation results, including:

[0022] The combination of aging test parameters is input into a pre-built digital twin model to obtain virtual operating data. The digital twin model is used to simulate the operating behavior of the target device under the combination of aging test parameters.

[0023] Monitor at least one performance metric in the virtual operation data that is related to the operation behavior;

[0024] Based on performance indicators, evaluation results are generated, including a reasonableness score for aging test parameters and a warning of potential risks.

[0025] In one possible implementation, evaluation results are generated based on performance metrics, including:

[0026] Based on the device temperature rise in the performance indicators, assess whether the combination of aging test parameters leads to device overheating or thermal stress concentration, and obtain the thermal stress assessment value of the target device.

[0027] Based on the performance degradation rate in the performance indicators, predict the performance degradation of the target device under the combination of aging test parameters.

[0028] The evaluation results are generated based on the thermal stress assessment value and the performance degradation prediction value.

[0029] In one possible implementation, based on equipment performance stability, a combination of aging test parameters for the aging test phase of the target equipment is determined, including:

[0030] Based on the stability of equipment performance, the equipment aging test is divided into multiple test phases, including the initial test phase, the accelerated test phase, and the stability test phase.

[0031] For any testing phase, the aging test parameters for the target equipment are determined based on the equipment performance stability.

[0032] The aging test parameters from different testing stages are combined to form an aging test parameter set.

[0033] Secondly, this application provides an aging test apparatus for a device, comprising:

[0034] The acquisition module is used to acquire multiple operating parameters of the target device;

[0035] The determination module is used to determine the device performance stability of the target device based on multiple operating parameters; and, based on the device performance stability, to determine the combination of aging test parameters for the device aging test phase for the target device.

[0036] The verification module is used to virtually verify the combination of aging test parameters for the target device and obtain the evaluation results.

[0037] The correction module is used to correct the combination of aging test parameters based on the evaluation results to obtain the target combination of aging test parameters.

[0038] The testing module is used to perform equipment aging tests on the target equipment based on the target aging test parameter combination.

[0039] In one possible implementation, the determining module is specifically used for:

[0040] Perform time-domain and / or frequency-domain analysis on multiple operating parameters to determine the characteristic quantities corresponding to the operating parameters;

[0041] Calculate the correlation matrix between different operating parameters. The correlation matrix is ​​used to characterize the degree of mutual influence between operating parameters.

[0042] Based on the correlation matrix and characteristic values, the stability of equipment performance is determined.

[0043] In one possible implementation, the determining module is specifically used for:

[0044] Determine the coupling relationships between parameters in the correlation matrix;

[0045] Based on the feature values, the stability score of the target device is determined;

[0046] The coupling relationship and stability scores are weighted to obtain the device performance stability score.

[0047] The equipment performance stability is determined based on the equipment performance stability score.

[0048] In one possible implementation, the verification module is specifically used for:

[0049] The combination of aging test parameters is input into a pre-built digital twin model to obtain virtual operating data. The digital twin model is used to simulate the operating behavior of the target device under the combination of aging test parameters.

[0050] Monitor at least one performance metric in the virtual operation data that is related to the operation behavior;

[0051] Based on performance indicators, evaluation results are generated, including a reasonableness score for aging test parameters and a warning of potential risks.

[0052] In one possible implementation, the verification module is also used for:

[0053] Based on the device temperature rise in the performance indicators, assess whether the combination of aging test parameters leads to device overheating or thermal stress concentration, and obtain the thermal stress assessment value of the target device.

[0054] Based on the performance degradation rate in the performance indicators, predict the performance degradation of the target device under the combination of aging test parameters.

[0055] The evaluation results are generated based on the thermal stress assessment value and the performance degradation prediction value.

[0056] In one possible implementation, the determining module is specifically used for:

[0057] Based on the stability of equipment performance, the equipment aging test is divided into multiple test phases, including the initial test phase, the accelerated test phase, and the stability test phase.

[0058] For any testing phase, the aging test parameters for the target equipment are determined based on the equipment performance stability.

[0059] The aging test parameters from different testing stages are combined to form an aging test parameter set.

[0060] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0061] The memory stores instructions that the computer executes;

[0062] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0063] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0064] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0065] This application provides an aging test method, apparatus, equipment, medium, and product for laser testing technology. The method includes: acquiring multiple operating parameters of a target device; determining the device performance stability based on these parameters; determining a combination of aging test parameters for the aging test phase of the target device based on the device performance stability; performing virtual verification of the aging test parameter combination on the target device to obtain an evaluation result; modifying the aging test parameter combination based on the evaluation result to obtain a target aging test parameter combination; and performing an aging test on the target device based on the target aging test parameter combination. This application first acquires multiple operating parameters of the target device and analyzes and determines the device performance stability based on these parameters. The device performance stability is used as the basis for determining the aging test parameter combination for the aging test phase of the target device. The determined aging test parameter combination aims to effectively accelerate aging while accurately reflecting the performance degradation and failure modes of the target device during long-term operation. Subsequently, the initially determined aging test parameter combination is virtually verified. By simulating the operation of the target device under this aging test parameter combination, detailed evaluation results are obtained. These evaluation results were used to iteratively refine the aging test parameter combination until an optimal target aging test parameter combination that most accurately reflects the actual aging process was obtained. Finally, this optimized target aging test parameter combination was used to conduct aging tests on the target equipment. This method significantly improves the efficiency and accuracy of aging tests, reduces the trial-and-error costs of actual physical testing, and provides more accurate data support for equipment reliability assessment and lifespan prediction. Attached Figure Description

[0066] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0067] Figure 1 A flowchart illustrating the aging test method for the equipment provided in this application embodiment. Figure 1 ;

[0068] Figure 2 This is a flowchart illustrating the aging test system for the equipment provided in the embodiments of this application;

[0069] Figure 3 A schematic diagram of the aging test process for the equipment provided in the embodiments of this application. Figure 2 ;

[0070] Figure 4 This is a schematic diagram of the aging test apparatus for the device provided in the embodiments of this application;

[0071] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0072] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0073] 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 this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0074] To address the aforementioned issues, this application provides an aging test method for equipment. This method involves acquiring multiple operating parameters of the target equipment and analyzing these parameters to determine the equipment's performance stability. This performance stability is then used as the basis for determining the optimal combination of aging test parameters for the aging test phase. The determined combination of aging test parameters aims to effectively accelerate aging while accurately reflecting the performance degradation and failure modes of the target equipment during long-term operation. Subsequently, the initially determined combination of aging test parameters is virtually verified. By simulating the operation of the target equipment under this combination of aging test parameters, detailed evaluation results are obtained. These evaluation results are used to iteratively refine the combination of aging test parameters until an optimal combination that most accurately reflects the actual aging process is obtained. Finally, this optimized combination of target aging test parameters is used to conduct aging tests on the target equipment.

[0075] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0076] The aging test method for the device provided in this application can be executed by a computing device such as a server or a server cluster.

[0077] Figure 1 A flowchart illustrating the aging test method for the equipment provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0078] S101. Obtain multiple operating parameters of the target device.

[0079] Operating parameters refer to the physical or electrical parameters collected in real time during the operation of the target device, such as temperature, power, frequency, and load. The type of target device is not limited; it can be applied to both laser and non-laser equipment. For example, for a laser welding machine, operating parameters may include pump temperature, optical output power, and electrical load current.

[0080] S102. Determine the equipment performance stability of the target device based on multiple operating parameters.

[0081] The operating parameters of a target device are a real-time reflection of its internal state and external working environment. By continuously monitoring and analyzing the trends and fluctuations of these multi-dimensional operating parameters over time, the consistency and predictability of the target device's behavior within a specific operating cycle can be quantitatively assessed.

[0082] Specifically, the performance stability of the target equipment is reflected in the fact that its key operating parameters can be maintained within the preset normal range, with small fluctuations, no abnormal sudden changes, or obvious continuous drift. By analyzing and processing these parameters, it is possible to identify whether the target equipment has an unstable operating state, such as drastic fluctuations in parameters, abnormal values ​​exceeding thresholds, or unexpected performance degradation trends.

[0083] Therefore, by comprehensively evaluating the aggregation behavior of these parameters, it is possible to objectively determine whether the current performance of the target device is in a stable and reliable state. This provides a scientific and quantitative basis for determining a targeted combination of aging test parameters, ensuring that the aging test starts from a clear baseline state, thereby more accurately reflecting the actual degradation process of the device under accelerated testing.

[0084] S103. Based on the equipment performance stability, determine the combination of aging test parameters for the aging test phase of the target equipment.

[0085] The aging test parameter combination is based on the equipment performance stability determined in S102. Specifically, determining the aging test parameter combination for the target equipment's aging test phase, based on equipment performance stability, can be accomplished using an expert knowledge base, a preset rule engine, a fuzzy logic system, or an optimization algorithm. For example, appropriate parameter types and intensities can be intelligently selected from a predefined aging test parameter library based on the equipment performance stability level. Alternatively, an intelligent optimization algorithm can search for the optimal aging test parameter combination, including test type, test level, application order, and duration, under the premise of meeting specific aging acceleration targets and constraints.

[0086] The combination of aging test parameters selected through this step can better match the actual characteristics and performance bottlenecks of the target device, avoid blindly applying tests, and ensure the relevance and effectiveness of the aging test, thereby making the accelerated aging process more realistic and efficient.

[0087] S104. Perform virtual verification of the aging test parameter combination on the target equipment to obtain the evaluation results.

[0088] To further optimize the aging test scheme and reduce risks, this application embodiment, after initially determining the combination of aging test parameters, performs virtual verification of the target device using this combination of aging test parameters. This step, by simulating the behavior of the target device under specific tests in a virtual environment, can quickly and cost-effectively predict its performance response and potential failure modes, greatly reducing the trial-and-error costs and time consumption of actual physical testing.

[0089] S105. Based on the evaluation results, modify the combination of aging test parameters to obtain the target aging test parameter combination.

[0090] Based on the evaluation results obtained after virtual verification, the initially determined combination of aging test parameters is finely adjusted and optimized until a target aging test parameter combination that can accurately and effectively accelerate the aging process of the target equipment, while also truly reflecting its long-term performance degradation pattern, is obtained.

[0091] S106. Perform equipment aging tests on the target equipment according to the target aging test parameter combination.

[0092] After the above steps, this application embodiment has obtained an optimized combination of target aging test parameters for the target device, which has been virtual verified and iteratively corrected. This combination of target aging test parameters includes key parameters such as the type, intensity, duration, and order of various tests to be applied during aging testing.

[0093] The carefully determined and optimized combination of target aging test parameters is applied to the target equipment. The aim is to accelerate the simulation of various environmental variables and workloads that the target equipment may experience during long-term actual operation in a relatively short period of time, thereby exposing its potential defects, weaknesses, and failure modes.

[0094] Since the target aging test parameter combination used is based on the target device's own operating parameters and performance stability, and is derived through virtual verification and correction, this aging test is no longer a blind empirical test. It can accelerate the aging process of the target device more efficiently, predict the lifespan and reliability of the target device more accurately, and provide more realistic data to evaluate the verification product quality of the target device and guide subsequent product improvements.

[0095] This application first acquires multiple operating parameters of the target device and analyzes these parameters to determine the device's performance stability. This performance stability is then used as the basis for determining the aging test parameter combination for the target device's aging test phase. The determined aging test parameter combination aims to effectively accelerate aging while accurately reflecting the performance degradation and failure modes of the target device during long-term operation. Subsequently, the initially determined aging test parameter combination is virtually verified. By simulating the operation of the target device under this aging test parameter combination, detailed evaluation results are obtained. These evaluation results are used to iteratively refine the aging test parameter combination until an optimal target aging test parameter combination that most accurately reflects the actual aging process is obtained. Finally, this optimized target aging test parameter combination is used to conduct device aging tests on the target device. This method significantly improves the efficiency and accuracy of aging tests, reduces the trial-and-error costs of actual physical testing, and provides more accurate data support for device reliability assessment and lifespan prediction.

[0096] In some embodiments, determining the device performance stability of a target device based on multiple operating parameters includes: performing time-domain and / or frequency-domain analysis on the multiple operating parameters to determine the characteristic quantities corresponding to the operating parameters; calculating a correlation matrix between different operating parameters, the correlation matrix being used to characterize the degree of mutual influence between the operating parameters; and determining the device performance stability based on the correlation matrix and the characteristic quantities.

[0097] Among them, time domain analysis refers to the analysis of the characteristics of operating parameters changing over time, such as sliding window average and volatility calculation; frequency domain analysis refers to the analysis of the frequency characteristics of operating parameters, such as Fourier transform to extract periodic fluctuation characteristics; correlation matrix is ​​a statistical matrix that quantifies the linear relationship between operating parameters and is used to analyze the degree of coupling between different operating parameters, that is, to analyze the degree of mutual influence between different operating parameters.

[0098] In this embodiment, short-term fluctuation characteristics of operating parameters are extracted through time-domain analysis, and periodic abnormal signals are identified through frequency-domain analysis. The degree of mutual influence between different operating parameters is assessed by calculating the correlation matrix between them; for example, the Pearson correlation coefficient between temperature and power is calculated to assess whether a strong coupling relationship exists between the two. Based on the above analysis results, a device performance stability score is determined.

[0099] This application embodiment achieves a multi-dimensional quantitative evaluation of the device performance stability of the target device through the combined application of time-domain analysis and / or frequency-domain analysis with correlation matrix.

[0100] Furthermore, in some examples, the device performance stability is determined based on the correlation matrix and features, including: determining the coupling relationship between parameters in the correlation matrix; determining the stability score of the target device based on the features; weighting the coupling relationship and the stability score to obtain the device performance stability score; and determining the device performance stability based on the device performance stability score.

[0101] The above example, by identifying the coupling relationships between parameters in the correlation matrix, can deeply reveal the degree and pattern of mutual influence among various operating parameters within the target device. Currently, traditional stability assessments often view individual parameters in isolation. However, the identification of these coupling relationships allows the method provided in this application to capture the linkage effects and potential chain reactions between parameters, thereby forming a more comprehensive and realistic understanding of the overall operating status of the target device.

[0102] The coupling relationships can be determined through various statistical or machine learning methods. For example, Pearson correlation coefficient, Spearman rank correlation coefficient, and mutual information can be used to quantify the linear or nonlinear dependencies between different operating parameters. Furthermore, graphical models can be used to construct and analyze complex coupling structures between parameters.

[0103] Meanwhile, this application embodiment determines the stability score of the target device based on feature quantities. These feature quantities allow for a detailed evaluation of the target device's stability performance across various dimensions, resulting in a score reflecting local stability. To effectively integrate this multi-dimensional information, this application embodiment further weights the coupling relationships and stability scores to obtain a device performance stability score. The weights used in the weighting process can employ various weighting allocation strategies. For example, weights can be preset based on expert experience, device criticality, historical failure data analysis, or sensitivity analysis. Furthermore, analytic hierarchy process (AHP), entropy weighting, or machine learning methods can be used to dynamically or adaptively determine the weights to ensure the objectivity and accuracy of the weighting process.

[0104] Finally, the equipment performance stability score is used to determine the equipment's performance stability. Various classification or grading methods can be used to determine equipment performance stability based on the stability score. For example, one or more thresholds can be set to classify the equipment performance stability score into multiple levels such as high stability, medium stability, low stability, or unstable. Alternatively, classification models such as decision trees, support vector machines, or neural networks can be used to categorize the target equipment into different stability states based on the equipment performance stability score, thus providing clear guidance for subsequent decision-making.

[0105] The embodiments of this application, through the weighted comprehensive evaluation mechanism that combines the interaction between parameters and individual performance, can overcome the one-sidedness of traditional evaluation and provide a more refined, accurate, and hierarchical judgment of the device performance stability of the target device. This provides a more scientific and reliable basis for the accurate selection of aging test parameters in the subsequent aging test stage, and further improves the pertinence and effectiveness of aging tests.

[0106] Based on the above embodiments, virtual verification of the target device using aging test parameter combinations is performed to obtain evaluation results. This includes: inputting the aging test parameter combinations into a pre-built digital twin model to obtain virtual operating data. The digital twin model is used to simulate the operating behavior of the target device under the aging test parameter combinations. At least one performance indicator related to the operating behavior in the virtual operating data is monitored. Based on the performance indicators, an evaluation result is generated. The evaluation result includes a reasonableness score for the aging test parameters and a potential risk warning.

[0107] This embodiment aims to efficiently predict the effectiveness and potential problems of aging test parameter combinations through non-physical means. Specifically, virtual operating data is obtained by inputting the aging test parameter combinations into a pre-built digital twin model. In this configuration, the digital twin model, as a faithful virtual copy of the target device, can accurately simulate the operating behavior of the target device under the determined aging test parameter combinations, thereby rapidly generating a large amount of virtual operating data without actually damaging the device. This step significantly reduces the cost and time of physical testing and provides a rich data source, laying the foundation for in-depth analysis of the target device's behavior.

[0108] Subsequently, this embodiment monitors at least one performance indicator related to operational behavior in the virtual operational data. Through in-depth analysis of this virtual operational data, key performance indicators are extracted, such as performance degradation rate, key parameter drift, virtual fault occurrence frequency, and expected lifespan reduction. This allows this embodiment to transform the original simulation data into a quantitative assessment of the target device's operational status and potential degradation trends, avoiding the blind interpretation of massive amounts of raw data.

[0109] Based on the extracted performance indicators, evaluation results are further generated. These results include a rationality score for the aging test parameters and a potential risk warning. As an example, the rationality score for the aging test parameters can be a quantitative score used to measure the degree to which the selected combination of aging test parameters matches the effective acceleration of aging and accurate simulation of the target device's actual failure mode. The potential risk warning specifically identifies, during the virtual verification process, any abnormal behavior that the combination of aging test parameters might cause, such as overheating of specific components, unexpected material degradation, or failure modes inconsistent with expectations, and provides a corresponding risk level.

[0110] Optionally, AIGC (Artificial Intelligence Generated Content) technology can be used to further generate evaluation results based on the performance metrics extracted above.

[0111] This application embodiment uses a digital twin model for virtual verification, and combined with the generation of performance index monitoring and evaluation results, it can provide a comprehensive and forward-looking evaluation report of aging test parameter combinations, providing a reliable basis for subsequent accurate correction of aging test parameter combinations, thereby significantly improving the optimization efficiency and accuracy of aging test schemes.

[0112] In some examples, evaluation results are generated based on performance metrics, including: assessing whether the combination of aging test parameters leads to device overheating or thermal stress concentration based on device temperature rise in the performance metrics, and obtaining the thermal stress assessment value of the target device; predicting the performance degradation prediction value of the target device under the combination of aging test parameters based on the performance degradation rate in the performance metrics; and generating evaluation results based on the thermal stress assessment value and the performance degradation prediction value.

[0113] The above example aims to generate more instructive evaluation results by conducting a more specific and targeted analysis of the performance indicators obtained from virtual verification, providing a solid basis for the accurate correction of subsequent aging test parameter combinations. Specifically, the example assesses whether the aging test parameter combination leads to overheating or thermal stress concentration based on the device temperature rise in the performance indicators, obtaining the thermal stress assessment value of the target device. This allows the example to directly focus on one of the most common failure modes of the target device under the applied aging test parameter combination—thermal damage. By analyzing the device temperature rise reflected in the virtual operation data, it can be determined whether the aging test parameter combination will cause the device temperature to exceed the safe range, or generate unreasonable temperature gradients and thermal stress concentrations in critical parts. Thus, the example effectively avoids selecting aging test parameter combinations that may lead to unrepresentative or excessively destructive thermal failures even in the virtual environment, ensuring the physical rationality of virtual verification and the safety of testing, thereby enabling subsequent actual aging tests to more accurately simulate the thermal aging process under real-world conditions.

[0114] Furthermore, the example above also predicts the performance degradation of the target device under the aging test parameter combination based on the performance degradation rate in the performance indicators. This step focuses on quantifying the impact of the aging test parameter combination on the functional performance of the target device. By analyzing the degradation trend of key performance parameters over time in the virtual operation data, the degree and rate of performance degradation of the target device under the current aging test parameter combination can be predicted. Thus, the example above can intuitively quantify the accelerated aging effect of the aging test parameter combination, determine whether it can effectively and reasonably induce performance degradation of the target device, and provide a direct basis for evaluating the effectiveness and efficiency of the aging test parameter combination.

[0115] Finally, this application embodiment generates an evaluation result based on the thermal stress assessment value and the performance degradation prediction value. Therefore, by comprehensively considering the thermal stress assessment and these two key dimensions, this method can generate a comprehensive and balanced evaluation result. The thermal stress assessment focuses on the safety and physical rationality of the target equipment, while the performance degradation prediction focuses on the aging effect and efficiency of the target equipment. This evaluation result can not only determine whether the combination of aging test parameters will lead to unreasonable thermal damage, but also predict the extent to which it effectively accelerates performance degradation. This provides a multi-dimensional and more reliable decision-making basis for the subsequent accurate correction of the aging test parameter combination, significantly improving the optimization efficiency and accuracy of the aging test scheme.

[0116] In some embodiments, an evaluation result is generated based on the thermal stress assessment value and the performance degradation prediction value, and various fusion decision-making mechanisms can be employed. For example, the two values ​​can be combined using weighted summation, fuzzy comprehensive evaluation, decision tree algorithm, multi-objective optimization algorithm, or machine learning classifier. The evaluation result can be a comprehensive score, a detailed report including a reasonableness score and potential risk warning, a recommendation for suggested correction directions, or a flag used to determine whether the combination of aging test parameters is qualified.

[0117] Based on the above embodiments, and according to the equipment performance stability, the combination of aging test parameters for the equipment aging test stage for the target equipment is determined, including: dividing the equipment aging test stage into multiple test stages according to the equipment performance stability, the test stages include an initial test stage, an accelerated test stage, and a stable test stage; for any test stage, the aging test parameters for the target equipment are determined based on the equipment performance stability; and the aging test parameters of different test stages are combined to form an aging test parameter combination.

[0118] This embodiment divides the aging test of the target device into multiple test phases, including an initial test phase, an accelerated test phase, and a stabilization test phase. This gives the entire aging test process a clear logical structure that conforms to the natural aging process of the device. The initial test phase can use lower aging test parameters to screen out early failure issues of the target device. The accelerated test phase applies higher environmental variables to effectively shorten the test cycle and quickly evaluate the mid-term performance evolution of the target device. The stabilization test phase is used to examine the reliability and lifespan of the target device under long-term normal operating conditions.

[0119] Based on this, the solution provided in this application does not pre-set fixed aging test parameters for these stages. Instead, for each test stage, the aging test parameters to be applied at that stage are dynamically determined based on the real-time acquired device performance stability. Therefore, the application of environmental variables is no longer a blind execution detached from the actual state of the target device, but rather an adaptive adjustment closely coupled with the target device's state. For example, if extremely high device performance stability is detected in the initial test stage, it can be determined that a higher level of aging test parameters will be used in the subsequent accelerated test stage to improve test efficiency; conversely, if poor device performance stability is detected, relatively mild aging test parameters can be used to avoid atypical failure modes caused by excessive application of environmental variables, thereby ensuring that the test results truly reflect the intrinsic quality of the target device. Thus, by combining structured stage division with the dynamic determination of parameters within each stage, the entire process of generating aging test parameter combinations is both scientific and targeted. This provides a test benchmark that highly conforms to the actual situation of the target device for subsequent virtual verification and actual testing, thereby fundamentally improving the accuracy and effectiveness of the entire aging test scheme.

[0120] In some embodiments, the division of device aging test phases is not limited to initial testing, accelerated testing, and stabilization testing. For example, an intermittent recovery phase can be added to simulate performance changes of the device under discontinuous operating conditions, or an extreme testing phase can be added to explore the performance boundaries of the device. Similarly, the number of test phases can be two, four, or more, and the specific type and number of phases can be flexibly configured according to the type of target device, the expected application scenario, or specific testing objectives.

[0121] In some embodiments, the specific types of aging test parameters can vary depending on the attributes of the target device. For example, when the target device is a laser device, the aging test parameters may specifically include at least one or more combinations of laser output power, operating current, operating temperature, modulation frequency, and duty cycle. The determined combination of aging test parameters also differs for different test phases. For instance, in the accelerated testing phase, the operating current and operating temperature may be primarily increased, while in the stabilization testing phase, the focus may be more on maintaining performance stability at rated power over a long period.

[0122] In some embodiments, the specific methods for determining aging test parameters based on device performance stability are also diverse. One implementation method is to use a pre-defined lookup table, which stores the mapping relationship between different device performance stability ranges and corresponding aging test parameter values ​​or levels. Another implementation method is to use one or more mathematical function models to calculate, taking device performance stability as the input variable of the model, and obtaining the output aging test parameter values ​​through function operations. In some embodiments, a trained machine learning model can also be used, which can learn the complex nonlinear relationship between performance stability and optimal aging test parameters based on a large amount of historical test data, thereby achieving more accurate determination of aging test parameters.

[0123] The combination of aging test parameters selected in this application embodiment can better match the actual characteristics and performance bottlenecks of the target device, avoid blindly applying environmental variables, ensure the pertinence and effectiveness of the aging test, and thus make the accelerated aging process more realistic and efficient.

[0124] Furthermore, current aging tests for laser welding machines suffer from multiple technical bottlenecks: the testing process is rigid and inefficient, unable to be dynamically adjusted according to the real-time performance of the equipment, resulting in mismatched testing intensity; poor system coordination, with data forming information silos, unable to be deeply integrated with MES and SCADA, hindering production scheduling, quality traceability, and the optimization guidance of historical data for new strategies; delayed fault warnings, relying on post-event alarms based on a single threshold, lacking predictive safety capabilities based on multi-parameter correlation analysis; and the value of massive test data has not been deeply explored, failing to build an initial health model for the equipment, resulting in a disconnect between testing and full lifecycle maintenance.

[0125] To address the aforementioned deficiencies, embodiments of this application also provide a system corresponding to the aging test method for the equipment. Figure 2 This is a schematic flowchart of the aging test system for the equipment provided in an embodiment of this application. Figure 2 As shown, the system adopts a layered design, including a central control layer, an information integration layer, an enterprise information system layer, an alarm execution unit, a laser welding machine equipment layer, and a laser head. The central control layer incorporates a built-in intelligent decision-making engine and a deep collaboration mechanism with the upper-level systems. The alarm execution unit, the laser welding machine equipment layer, and the laser head constitute the aging test execution layer.

[0126] The aging test execution layer is primarily responsible for the actual aging operation and basic data acquisition of the laser equipment. Its core component is a laser welding machine fixed on a dedicated aging rack with the welding torch precisely aligned with the water tank, designed to simulate the actual working environment. This layer is controlled by a controller built into the laser welding machine. This controller has a Modbus slave interface, enabling remote setting of key parameters such as power, frequency, and duty cycle, and real-time reporting of equipment operating status data.

[0127] The central control layer is responsible for the overall control, intelligent analysis, and decision-making of aging tests. Its core is an industrial touchscreen (Human-Machine Interface, or HMI) that runs the developed aging control program. The aging control program integrates basic functions such as parameter library management, timing control, and communication protocol parsing, and incorporates four core intelligent modules: an adaptive parameter adjustment engine, used to analyze equipment performance stability in real time and dynamically adjust subsequent aging test parameters for more accurate and efficient aging tests; a dynamic test strategy generator, which dynamically optimizes and generates a multi-segment personalized aging test parameter library based on task information obtained from the MES by the information integration layer and correlated with historical test data and fault modes of the equipment model; an equipment health prediction model, which extracts performance degradation characteristic values ​​from equipment operating data, calculates and updates the initial health index of the equipment, providing baseline data for subsequent predictive maintenance; and a multi-parameter correlation analysis and early warning module, which continuously monitors the correlation between multiple key operating parameters, identifies early abnormal signs and potential risks, and achieves predictive safety interception.

[0128] The information integration layer, serving as a deep, two-way data channel between the central control layer and the enterprise information system layer, is responsible for data exchange and command transmission. Its core components include the MES intelligent interface module and the SCADA communication module. The MES intelligent interface module uses API interfaces and JSON data format to upload real-time status, quality data, alarm information, test results, and initial health indices of equipment to the upper-layer MES system, and receives test tasks and test strategy guidance optimized based on historical data. The SCADA communication module employs a dual-protocol redundancy design of OPC UA and Modbus TCP, uploading equipment status, real-time parameter curves, system health assessment results, and correlation analysis early warning signals to the upper-layer SCADA monitoring center, and receiving control commands such as remote start / stop, parameter adjustment, and alarm confirmation.

[0129] Ultimately, the enterprise information system layer receives and processes aging test data, supporting enterprise-level production management, quality traceability, and decision analysis to form a closed business loop. This layer is mainly composed of the MES system, which is responsible for receiving aging test data and health indices to achieve dynamic adjustment of production plans, enhanced quality traceability, intelligent resource allocation, and KPI monitoring. At the same time, the SCADA monitoring center centrally monitors the operating status of all aging stations, performs historical data trend analysis, intelligent early warning, and report generation.

[0130] Furthermore, through deep collaboration between the central control layer and each layer, intelligent aging testing of equipment can be achieved. Figure 3 A schematic diagram of the aging test process for the equipment provided in the embodiments of this application. Figure 2 .like Figure 3As shown, it includes the following steps:

[0131] Step 1: System Initialization and Dynamic Generation of Test Strategies

[0132] Powering on the equipment and initializing the system: The laser equipment is powered on and the aging test program is started to complete the system initialization.

[0133] Dynamic optimization of test strategy: The dynamic test strategy generator is activated to obtain information about the current aging test task, historical test data for this laser equipment model, current equipment performance stability, and failure modes from the MES interface. Based on the obtained information, the generator dynamically optimizes and generates a multi-segment personalized aging test parameter library for this aging test.

[0134] Step 2: Aging Test Initiation and Aging Test Parameter Distribution:

[0135] Operator Initiation: The operator initiates the aging test process through the human-machine interface, for example, by long-pressing the start aging button.

[0136] First Parameter Sending: The program sends the dynamically optimized first aging test parameters to the laser welding machine via the Modbus protocol.

[0137] Step 3: Real-time monitoring and data collection:

[0138] Laser equipment operation: The laser welding machine in the aging test execution layer starts working according to the issued aging test parameters.

[0139] Key data monitoring: The laser welding machine's built-in controller continuously monitors key operating data in real time, including safety status, optical core, and electrical system.

[0140] Parallel data upload: The monitored operational data is uploaded in parallel to the SCADA communication module and the MES intelligent interface module through the information integration layer. The SCADA communication module is used for high-frequency data storage, and the MES intelligent interface module is used for status and event data recording.

[0141] Intelligent module input: Operational data is synchronously input to the four intelligent modules of the central control layer, providing a data foundation for subsequent analysis.

[0142] Step 4: Intelligent Analysis and Adaptive Decision Making

[0143] Equipment performance stability analysis: The adaptive parameter adjustment engine in the central control layer analyzes the equipment performance stability of the current aging stage and provides decision instructions for the dynamic adjustment of the parameters for the next aging test.

[0144] Potential risk warning: The multi-parameter correlation analysis and early warning module in the central control layer performs multi-parameter correlation calculations to determine whether there are potential risks.

[0145] Equipment health update: The equipment health prediction model in the central control layer calculates and updates the initial health index of the equipment in real time.

[0146] Step 5: Anomaly Handling and Alarm Linkage:

[0147] Anomaly Trigger: When the central control layer detects an anomaly, such as a single parameter exceeding a threshold or a multi-parameter correlation analysis early warning module outputting an early warning signal.

[0148] Emergency Response: The system immediately performs an emergency shutdown operation on the laser welding machine in the aging test execution layer, records a detailed fault log, and triggers an audible and visual alarm.

[0149] Information Synchronization: At the same time, the system immediately synchronizes alarm information to the enterprise information system layer through the MES intelligent interface module and SCADA communication module in the information integration layer.

[0150] Step Six: Steady-State Operation and Adaptive Switching

[0151] Continuous operation and timing: If no abnormality occurs, the laser welding machine in the aging test execution layer continues to run according to the current aging test parameters, and the internal timer in the central control layer accumulates the aging time for this segment.

[0152] Segment End and Light Shutdown: When the preset time for the current aging segment is reached, the central control layer program issues a light shutdown command.

[0153] Adaptive parameter distribution: After a short interval, the central control layer program retrieves and distributes the next aging test parameters, which may have been dynamically adjusted, based on the decision results of the intelligent module in step four, and begins the next cycle of aging test.

[0154] Step 7: Process Termination and Intelligent Completion Processing:

[0155] Process termination: After all preset aging segments have been executed, the central control layer program enters the termination state.

[0156] Structured report generation: The central control layer system automatically generates structured test reports, which include the operating status of each segment, key parameter curves, alarm statistics, health index, and maintenance recommendations for the equipment.

[0157] MES Information Update: The system sends a test completion notification and the initial health index of the equipment to the MES system in the enterprise information system layer through the MES intelligent interface module of the information integration layer, so as to trigger subsequent production instructions and update the health record of the equipment.

[0158] SCADA Status Update and Data Archiving: The system updates the device status to available status in the SCADA monitoring center of the enterprise information system layer through the SCADA communication module of the information integration layer, and archives all test data.

[0159] Furthermore, before issuing the aging test parameters, the laser equipment needs to undergo virtual verification of these parameters. The detailed principle has been explained in the preceding embodiments. Even further, after the aging test parameters are issued, the system will adjust the parameters based on real-time monitored key operational data.

[0160] In summary, the embodiments of this application aim to construct an intelligent aging test system integrating automated testing, real-time monitoring, intelligent decision-making, and deep collaboration, achieving a leap from fixed-procedure automation to adaptive intelligence. To achieve this objective, the embodiments of this application propose the following mechanisms: 1) By analyzing the performance stability of equipment in real time, adaptive adjustments to aging test parameters are achieved, dynamically matching the test intensity with the actual state of the equipment to achieve precise and efficient aging; 2) Utilizing historical test and fault big data in the MES, optimized and learning-evolving personalized test strategies are dynamically generated, breaking the rigidity of test strategies and significantly improving the targeting of tests and fault detection efficiency; 3) Constructing an initial health index model for the equipment, transforming aging test data into a health baseline at the time of equipment delivery, providing a precise data starting point for predictive maintenance throughout the entire lifecycle, and achieving deep value linkage between the testing and maintenance stages; and 4) By establishing a dynamic correlation model between multiple parameters, predictive safety interception is achieved, surpassing traditional single-threshold alarms, identifying early and weak collaborative anomalies, moving from passive alarms to proactive early warning, and greatly improving the inherent safety level of the system. These mechanisms are achieved through a deeply collaborative intelligent system architecture, in which the adaptive engine, policy generator, health model, and early warning module work together within the central control unit and form a deep two-way closed loop with MES and SCADA, producing a synergistic technical effect of 1+1>2, together constituting a non-obvious, complete solution.

[0161] Therefore, the embodiments of this application bring significant beneficial effects: significantly improved testing efficiency and quality, with the average testing cycle shortened by more than 15%, and the detection efficiency of high-frequency failure modes improved by more than 50%; automatic adjustment of production sequencing based on real-time test results through the MES system enables production collaboration and resource optimization, reducing waiting time and increasing equipment utilization by 35%; predictive maintenance based on initial health indices can reduce operation and maintenance costs, reducing unplanned downtime by 30% and expected to reduce maintenance costs by 20%; a qualitative leap in security assurance capabilities, with predictive security interception mechanisms significantly reducing the probability of major failure risks, achieving a shift from treating existing problems to preventing future ones; and finally, by establishing a complete digital archive of product aging tests, the value of data assets is maximized and fully traceable, transforming test data into valuable assets that drive product design optimization, production process improvement, and long-term operation and maintenance strategies.

[0162] In addition, this application embodiment also performs virtual verification of the aging test parameter combinations generated by the laser device. Specifically, these aging test parameter combinations are input into a pre-built digital twin model for simulation, thereby assessing its performance and potential risks before actual aging tests and obtaining evaluation results, further improving the accuracy and safety of the testing strategy.

[0163] Furthermore, the system and corresponding methods provided in the above embodiments are highly versatile, especially suitable for various advanced manufacturing scenarios with extremely high requirements for reliability, traceability, and intelligence. Specifically, it can be applied not only to the factory reliability verification and life assessment of laser equipment and semiconductor manufacturing equipment, and the durability testing and health certification of key controllers in the medical device field, but also to the periodic calibration and performance verification of new energy battery production equipment and industrial robot controllers. In addition, the above methods are of great value to any advanced manufacturing field that needs to deeply apply test data to product lifecycle management, and can serve as a key data source and verification link for building digital twin systems in Industry 4.0 or lighthouse factories.

[0164] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0165] Figure 4 This is a schematic diagram of the aging test apparatus provided in the embodiments of this application, as shown below. Figure 4 As shown, the aging test apparatus for the equipment provided in this embodiment includes:

[0166] The acquisition module 401 is used to acquire multiple operating parameters of the target device;

[0167] The determination module 402 is used to determine the device performance stability of the target device based on multiple operating parameters; and, based on the device performance stability, to determine the combination of aging test parameters for the device aging test phase of the target device.

[0168] The verification module 403 is used to perform virtual verification of the aging test parameter combination on the target device and obtain the evaluation results.

[0169] The correction module 404 is used to correct the aging test parameter combination based on the evaluation results to obtain the target aging test parameter combination.

[0170] Test module 405 is used to perform equipment aging tests on the target equipment according to the target aging test parameter combination.

[0171] In one possible implementation, the determining module 402 is specifically used for:

[0172] Perform time-domain and / or frequency-domain analysis on multiple operating parameters to determine the characteristic quantities corresponding to the operating parameters;

[0173] Calculate the correlation matrix between different operating parameters. The correlation matrix is ​​used to characterize the degree of mutual influence between operating parameters.

[0174] Based on the correlation matrix and characteristic values, the stability of equipment performance is determined.

[0175] In one possible implementation, the determining module 402 is specifically used for:

[0176] Determine the coupling relationships between parameters in the correlation matrix;

[0177] Based on the feature values, the stability score of the target device is determined;

[0178] The coupling relationship and stability scores are weighted to obtain the device performance stability score.

[0179] The equipment performance stability is determined based on the equipment performance stability score.

[0180] In one possible implementation, the verification module 403 is specifically used for:

[0181] The combination of aging test parameters is input into a pre-built digital twin model to obtain virtual operating data. The digital twin model is used to simulate the operating behavior of the target device under the combination of aging test parameters.

[0182] Monitor at least one performance metric in the virtual operation data that is related to the operation behavior;

[0183] Based on performance indicators, evaluation results are generated, including a reasonableness score for aging test parameters and a warning of potential risks.

[0184] In one possible implementation, the verification module 403 is further configured to:

[0185] Based on the device temperature rise in the performance indicators, assess whether the combination of aging test parameters leads to device overheating or thermal stress concentration, and obtain the thermal stress assessment value of the target device.

[0186] Based on the performance degradation rate in the performance indicators, predict the performance degradation of the target device under the combination of aging test parameters.

[0187] The evaluation results are generated based on the thermal stress assessment value and the performance degradation prediction value.

[0188] In one possible implementation, the determining module 402 is specifically used for:

[0189] Based on the stability of equipment performance, the equipment aging test is divided into multiple test phases, including the initial test phase, the accelerated test phase, and the stability test phase.

[0190] For any testing phase, the aging test parameters for the target equipment are determined based on the equipment performance stability.

[0191] The aging test parameters from different testing stages are combined to form an aging test parameter set.

[0192] The aging test device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0193] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, a processing module can be a separate processing element, or it can be integrated into an integrated circuit within the above device. Alternatively, it can be stored as program code in the device's memory, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0194] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented by calling program code through a processing element, that processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a System-On-a-Chip (SOC).

[0195] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 500 provided in this application embodiment may include: a processor 501, and a memory 502 communicatively connected to the processor, wherein:

[0196] The memory stores instructions that the computer executes;

[0197] The processor executes computer execution instructions stored in memory to implement the method described in the foregoing method embodiments.

[0198] It should be understood that processor 501 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor. Memory 502 may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0199] Optionally, the electronic device 500 may also include a communication interface 503. In specific implementations, if the communication interface 503, memory 502, and processor 501 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0200] Optionally, in a specific implementation, if the communication interface 503, memory 502, and processor 501 are integrated on a single integrated circuit, then the communication interface 503, memory 502, and processor 501 can communicate through an internal interface.

[0201] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the methods described in any of the foregoing embodiments.

[0202] It is understood that the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0203] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an ASIC. Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic device.

[0204] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a computer-readable storage medium, include several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0205] This application also provides a computer program product, including a computer program that, when executed, implements the method described in any of the foregoing embodiments.

[0206] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0207] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0208] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as these combinations of technical features do not contradict each other, they should be considered within the scope of this specification.

[0209] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0210] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An aging test method for equipment, characterized in that, include: Obtain multiple operating parameters of the target device; Based on the multiple operating parameters, the device performance stability of the target device is determined. This determination includes: performing time-domain and / or frequency-domain analysis on the multiple operating parameters to determine the characteristic quantities corresponding to the operating parameters; calculating a correlation matrix between different operating parameters, the correlation matrix being used to characterize the degree of mutual influence between the operating parameters; determining the coupling relationship between parameters in the correlation matrix; determining the stability score of the target device based on the characteristic quantities; weighting the coupling relationship and the stability score to obtain a device performance stability score; and determining the device performance stability based on the device performance stability score. Based on the performance stability of the equipment, determine the combination of aging test parameters for the aging test phase of the target equipment; The target device is subjected to virtual verification of the aging test parameter combination to obtain the evaluation results; Based on the evaluation results, the combination of aging test parameters is modified to obtain the target combination of aging test parameters; Based on the target aging test parameter combination, the target device is subjected to an equipment aging test.

2. The method according to claim 1, characterized in that, The virtual verification of the aging test parameter combination on the target device to obtain the evaluation results includes: The combination of aging test parameters is input into a pre-built digital twin model to obtain virtual operating data. The digital twin model is used to simulate the operating behavior of the target device under the combination of aging test parameters. Monitor at least one performance metric in the virtual operation data that is related to the operation behavior; Based on the performance indicators, the evaluation results are generated, which include a reasonableness score for the aging test parameters and a warning of potential risks.

3. The method according to claim 2, characterized in that, The process of generating the evaluation result based on the performance metrics includes: Based on the device temperature rise in the performance indicators, assess whether the combination of aging test parameters leads to device overheating or thermal stress concentration, and obtain the thermal stress assessment value of the target device. Based on the performance degradation rate in the performance indicators, predict the performance degradation value of the target device under the combination of aging test parameters. The evaluation results are generated based on the thermal stress assessment value and the performance degradation prediction value.

4. The method according to claim 1, characterized in that, The step of determining the combination of aging test parameters for the aging test phase of the target device based on the device performance stability includes: Based on the performance stability of the equipment, the aging test phase of the equipment is divided into multiple test phases, including an initial test phase, an accelerated test phase, and a stable test phase. For any testing phase, the aging test parameters of the target device are determined based on the performance stability of the device. The aging test parameters from different testing stages are combined to form the aging test parameter combination.

5. An aging test apparatus for equipment, characterized in that, include: The acquisition module is used to acquire multiple operating parameters of the target device; The determination module is used to determine the device performance stability of the target device based on the multiple operating parameters; The determining module is further configured to: perform time-domain analysis and / or frequency-domain analysis on the plurality of operating parameters to determine the characteristic quantities corresponding to the operating parameters; Calculate the correlation matrix between different operating parameters, which is used to characterize the degree of mutual influence between the operating parameters; Determine the coupling relationships between the parameters in the correlation matrix; Based on the aforementioned feature values, the stability score of the target device is determined. The coupling relationship and the stability score are weighted to obtain the device performance stability score. The performance stability of the equipment is determined based on the equipment performance stability score; The verification module is used to perform virtual verification of the aging test parameter combination on the target device to obtain the evaluation result; The correction module is used to correct the aging test parameter combination based on the evaluation results to obtain the target aging test parameter combination; The testing module is used to perform equipment aging tests on the target device according to the target aging test parameter combination.

6. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1-4.

8. A computer program product, characterized in that, Includes a computer program that, when executed, implements the method described in any one of claims 1-4.

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