Key delay test method and system

Abstract

The embodiment of the invention provides a key delay test method and system, and the method comprises the steps: capturing a hardware interrupt signal generated by each physical key response in real time through simulating a preset operation event, measuring first delay time, and evaluating the first delay time according to a preset performance threshold value, analyzing existing potential external interference factors and potential system internal anomalies, adjusting initial test environment parameters, obtaining an optimized test environment, repeatedly simulating operation events, capturing hardware interrupt signals generated by response of each physical key, and calculating average delay time as a response characteristic index of the physical key; predicting the use reliability and the aging effect of the physical key by responding to the change trend of the characteristic index, and generating a trend analysis report; according to the technical scheme provided by the embodiment of the invention, the test precision and reliability are improved, a user can take preventive measures in advance, and service interruption or equipment damage caused by key faults is avoided.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of electronic devices, and in particular, to a method and system for testing button delay. Background Art

[0002] In modern electronic devices, physical buttons, as one of the important interfaces for user-device interaction, their response performance directly affects the user experience and the reliability of the device. Especially in application scenarios with extremely high requirements for response time, such as industrial control, medical devices, and vehicle-mounted systems, it is crucial to accurately measure and optimize the delay of physical buttons. To ensure that physical buttons in these critical applications can quickly and stably respond to user operations, an accurate and reliable test method is needed to evaluate the response characteristics of the buttons and provide dynamic monitoring and pre-maintenance functions to prevent potential problems from occurring;

[0003] Currently, there are already some methods and technologies for testing the delay of physical buttons in the market. The common approach is to trigger physical buttons by simply simulating operation events and record the corresponding response time. However, these methods usually only focus on the first delay time of a single measurement and lack the ability to comprehensively analyze the results of multiple measurements. In addition, existing solutions often fail to fully consider the influence of external environmental factors (such as electromagnetic interference, temperature changes) and internal system anomalies (such as software conflicts, resource contention) on the measurement results, resulting in insufficient test accuracy and unable to truly reflect the response performance of physical buttons under actual usage conditions. Summary of the Invention

[0004] Embodiments of the present application provide a method and system for testing button delay to solve the problems in the prior art that only relying on single or limited measurements cannot capture the long-term change trend of button response characteristics, it is difficult to predict the aging effect of buttons and its impact on reliability, and the influence of external environmental factors (such as electromagnetic interference, temperature changes) and internal system anomalies (such as software conflicts, resource contention) on the measurement results is not fully considered, resulting in insufficient test accuracy.

[0005] In a first aspect, embodiments of the present application provide a method for testing button delay, including:

[0006] Simulate a preset operation event in a preset initial test environment, the operation event triggers the physical button response of the device, and synchronously record the timestamp generated by the operation event;

[0007] Instantaneously capture the hardware interrupt signal generated by each physical button response, and measure the first delay time from the issuance of the operation event to the reception of the hardware interrupt signal;

[0008] Evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential internal system anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources outside the preset target;

[0009] Use the optimized test environment to repeat the simulation of operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button;

[0010] By comparing the change trends of the response characteristic indexes obtained in different test cycles, predict the reliability and aging effect of the physical button usage, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance.

[0011] Optionally, evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential internal system anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources outside the preset target, including:

[0012] Use the preset performance threshold to compare and process the first delay time measured each time to obtain the detection result of the delay exceeding the standard;

[0013] Based on the detection result, when it is found that the first delay time exceeds the preset performance threshold, start a comprehensive diagnosis process, use a multi-stage inspection mechanism to analyze and process the existing potential external interference factors, potential internal system anomalies, as well as the interaction effects and cumulative effects between different operation events, and generate a preliminary problem source identification report;

[0014] According to the preliminary problem source identification report, use a machine learning algorithm to predict the probability distribution of the existing potential external interference factors and potential internal system anomalies, generate an interference source analysis report, and based on the interference source analysis report, adjust the corresponding initial test environment parameters and configurations to generate an adjusted optimized test environment;

[0015] Based on the optimized test environment, implement a verification measurement cycle, repeat the simulation of operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button to obtain a verification measurement result;

[0016] When the first delay time within a preset number of times of the verification measurement result meets the preset performance threshold, it is confirmed that the optimized test environment reaches the best state. When the first delay time within a preset number of times of the verification measurement result does not meet the preset performance threshold, the optimized test environment is dynamically adjusted using the adaptive adjustment module based on historical data and real-time feedback information until it is confirmed that all interference sources are eliminated.

[0017] Optionally, according to the preliminary problem source identification report, use a machine learning algorithm to predict the probability distribution of existing potential external interference factors and potential system internal abnormal conditions, generate an interference source analysis report, and based on the interference source analysis report, adjust the corresponding initial test environment parameters and configurations to generate an optimized test environment, including:

[0018] Use the information provided in the preliminary problem source identification report to classify and organize the identified potential external interference factors, potential system internal abnormal conditions, and the interaction effects and cumulative effects between different operation events to obtain a classification and organization result;

[0019] Based on the classification and organization result, input it into a pre-trained machine learning model to predict the occurrence probability of each potential external interference factor and potential system internal abnormal condition, and evaluate the degree of influence of each potential external interference factor and potential system internal abnormal condition on the first delay time to generate a probability distribution prediction result;

[0020] According to the probability distribution prediction result, combined with the output of the machine learning model, estimate the occurrence probability of each potential external interference factor and potential system internal abnormal condition and the specific value of the increase in the first delay time caused by each potential external interference factor and potential system internal abnormal condition to obtain an interference source analysis report;

[0021] According to the suggestions in the interference source analysis report, for the potential external interference factors and potential system internal abnormal conditions confirmed to be higher than the preset probability threshold, adjust the relevant parameters and configurations of the initial test environment to generate an adjusted optimized test environment setting scheme to obtain an optimized test environment.

[0022] Optionally, according to the suggestions in the interference source analysis report, for the potential external interference factors and potential system internal abnormal conditions confirmed to be higher than the preset probability threshold, adjust the relevant parameters and configurations of the initial test environment to generate an adjusted optimized test environment setting scheme to obtain an optimized test environment, including:

[0023] Use the probability distribution prediction result provided in the interference source analysis report to identify and process the potential external interference factors and potential system internal abnormal conditions confirmed to be higher than the preset probability threshold to obtain a list of interference factors;

[0024] Based on the list of interference factors, using a predefined adjustment strategy, perform targeted adjustment processing on the relevant parameters and configurations of the initial test environment to obtain an adjustment result;

[0025] According to the adjustment result, record the measures and parameter changes for each adjustment to obtain an adjustment record, and generate an optimized test environment setting plan after adjustment based on the adjustment record;

[0026] Use the optimized test environment setting plan after adjustment to implement a verification measurement cycle, repeatedly simulate operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data for each measurement, and calculate the average delay time as the response characteristic index of the physical button to obtain an optimized test environment.

[0027] Optionally, using the optimized test environment, repeatedly simulate operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data for each measurement, and calculate the average delay time as the response characteristic index of the physical button, including:

[0028] Use the optimized test environment to perform repeated simulation processing on preset operation events for a preset number of rounds, ensuring that each round of simulation triggers the physical button response of the device under the same strictly controlled conditions to obtain simulated operation events;

[0029] Based on the simulated operation events, immediately capture the hardware interrupt signals generated by each physical button response, and measure the first delay time between the issuance of the simulated operation event and the receipt of the hardware interrupt signal to obtain first delay time data points;

[0030] Accumulate the first delay time data points for each measurement to generate a measurement result data set, and add a time stamp and environmental parameter record to the measurement result data set to generate a first delay time data set with time series and condition labels;

[0031] Use the first delay time data set with time series and condition labels to perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report. Use the average delay time and other statistics in the statistical report as the initial response characteristic index of the physical button;

[0032] Based on the first delay time data set with time series and condition labels, implement a cross-validation step. Randomly extract several subsets from the first delay time data set with time series and condition labels, independently calculate the response characteristic indexes of each subset, and compare the differences between different subsets to obtain the verified target response characteristic index.

[0033] Optionally, using the first delay time data set with timing and condition tags, perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report. Use the average delay time and other statistics in the statistical report as the initial response characteristic indicators of the physical button, including:

[0034] Using the first delay time data set with timing and condition tags, perform multi-dimensional statistical analysis processing on all first delay time data points to obtain multi-dimensional statistical analysis results;

[0035] Based on the multi-dimensional statistical analysis results, calculate the average value of the first delay time data points to obtain the average delay time representing the typical response performance of the physical button under interference-free conditions, and compare the average value with historical data to evaluate the consistency and stability of the multi-dimensional statistical analysis results, and generate a consistency evaluation report;

[0036] Calculate the standard deviation, minimum value, and maximum value of the first delay time data points, and calculate the coefficient of variation to evaluate the distribution and fluctuation range of the physical button response time to obtain a set of statistics;

[0037] Generate a statistical report containing the average delay time, standard deviation, minimum value, maximum value, and coefficient of variation according to the set of statistics;

[0038] Define the initial response characteristic indicators of the physical button according to the average delay time and other statistics in the detailed statistical report.

[0039] Optionally, by comparing the change trends of the response characteristic indicators obtained in different test cycles, predict the reliability and aging effect of the physical button usage, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance, including:

[0040] Using the response characteristic indicator data accumulated in different test cycles, summarize the average delay time, standard deviation, minimum value, maximum value, and other statistics for each test cycle, and add environmental parameter records for each test cycle to generate a periodic response characteristic summary table;

[0041] Based on the periodic response characteristic summary table, use time series analysis methods and combine machine learning algorithms to model the change trends of the response characteristic indicators in different test cycles to obtain a response characteristic change trend model;

[0042] Using the response characteristic change trend model, identify the trend pattern and evaluate the influence degree of the trend pattern on the reliability and aging effect of the physical button usage to generate a trend analysis report;

[0043] Using the trend analysis report information, combined with a preset reliability threshold and aging warning criteria, classify the current state of the physical buttons, determine pre-maintenance measures, and for physical buttons approaching the reliability threshold, trigger preventive maintenance reminders to plan maintenance actions in advance.

[0044] In a second aspect, an embodiment of the present application provides a button delay test system, including:

[0045] A simulation module for simulating a preset operation event in a preset initial test environment, where the operation event triggers the physical button response of the device, and synchronously records the timestamps generated by the operation event;

[0046] A measurement module for immediately capturing the hardware interrupt signals generated by each physical button response and measuring the first delay time from the issuance of the operation event to the reception of the hardware interrupt signal;

[0047] An adjustment module for evaluating the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential internal system anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources other than the preset target;

[0048] A calculation module for, after confirming that all interference sources have been excluded from the test environment, repeating the simulation of the operation event and capturing the hardware interrupt signals generated by each physical button response, accumulating the first delay time data measured each time, and calculating the average delay time as the response characteristic index of the physical button;

[0049] A prediction module for predicting the reliability and aging effect of the physical button usage by comparing the change trends of the response characteristic indexes obtained in different test cycles, generating a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance.

[0050] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a button delay test method as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a button delay test method as described in the first aspect.

[0052] In the embodiment of the present application, a preset operation event is simulated in a preset initial test environment. The operation event triggers the physical button response of the device, and the time stamp generated by the operation event is synchronously recorded. The hardware interruption signal generated by each physical button response is captured immediately, and the first delay time between the emission of the operation event and the reception of the hardware interruption signal is measured. The first delay time is evaluated according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, the existing potential external interference factors and potential internal system anomalies are analyzed, and the initial test environment parameters are adjusted to obtain an optimized test environment to exclude interference sources other than the preset target. Using the optimized test environment, the operation event is repeatedly simulated and the hardware interruption signal generated by each physical button response is captured. The first delay time data measured each time is accumulated, and the average delay time is calculated as the response characteristic index of the physical button. By comparing the change trends of the response characteristic indexes obtained in different test cycles, the reliability and aging effect of the physical button use are predicted, and a trend analysis report is generated to realize the dynamic monitoring and pre-maintenance of the button response performance. The technical solution provided by the present invention improves the test accuracy and reliability, enabling users to take preventive measures in advance to avoid service interruption or equipment damage caused by button failures;

[0053] Furthermore, a multi-stage inspection mechanism and a machine learning algorithm are used to comprehensively analyze the existing potential external interference factors and internal abnormal conditions, and a detailed preliminary problem source identification report is generated. This method not only improves the speed of problem diagnosis but also enhances the accuracy of problem location. The test environment parameters are adjusted based on the interference source analysis report to generate an optimized test environment. If the verification measurement results still show an over-standard situation, the adaptive adjustment module is used to further optimize the test environment until all interference sources are effectively eliminated. This process realizes the automatic optimization of the test environment and ensures the stability and consistency of the test results. By starting the comprehensive diagnosis process and implementing the verification measurement cycle, multiple measurements can be completed in a short time, and it can be quickly confirmed whether the optimized test environment reaches the best state. This greatly shortens the test cycle and improves the test efficiency and quality;

[0054] Further, using the probability distribution prediction results in the interference source analysis report, potential external interference factors and internal abnormal conditions higher than a preset probability threshold are identified to form a list of interference factors. This method helps to accurately locate the factors most likely to affect the test results, thus providing a clear direction for subsequent adjustments; based on a predefined adjustment strategy, targeted adjustment processing is carried out for each high-risk factor. This strategy not only considers the characteristics of specific problems but also combines historical data and real-time feedback information to ensure the effectiveness and applicability of the adjustment measures; record the specific measures and parameter changes of each adjustment to form an adjustment record, and generate an optimized test environment setting plan based on these records. This method not only ensures the transparency and traceability of the adjustment process but also provides valuable reference for future tests; using the optimized test environment setting plan after adjustment, the verification measurement cycle is implemented again, accumulating measurement data and calculating the average delay time as the response characteristic index. In this way, the test environment is continuously verified and optimized to ensure that it is always in the best state, thus improving the reliability and accuracy of the overall test.

[0055] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 The flowchart of a key delay test method provided by the present application is shown;

[0058] Figure 2 The structural schematic diagram of a key delay test system provided by the present application is shown;

[0059] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0061] In some of the processes described in the specification, claims, and the above-mentioned drawings of this application, a number of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The operation numbers, such as 101, 102, etc., are only used to distinguish different operations, and the numbers themselves do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0062] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0063] Figure 1 The following is a flowchart of a method for testing button delay provided for an embodiment of the present application. As Figure 1 shown, the method includes:

[0064] Step 101: Simulate a preset operation event in a preset initial test environment, where the operation event triggers the physical button response of the device, and synchronously record the timestamp generated by the operation event;

[0065] In this step, the preset initial test environment refers to the basic conditions and parameters set before the formal test starts, including but not limited to the location of the test device, system resource allocation, the version of the driver used, and whether there are significant interference sources around the physical button, etc. These conditions ensure the consistency and repeatability of the test process. The operation event refers to simulating an operation by the user on the device, such as pressing a certain physical button, which triggers the internal response mechanism of the device and synchronously generates a timestamp to record the specific moment when the operation occurs;

[0066] In the preset initial test environment, a preset operation event is simulated through software or hardware simulation tools, and these events trigger the physical button response of the device. Each time an operation event occurs, the system synchronously records the timestamp generated by the operation event. This step provides an accurate time reference point for the subsequent measurement of the first delay time;

[0067] In the test of a typical in-vehicle entertainment system, the engineer sets up the initial test environment to ensure that all external electromagnetic interferences have been shielded and the temperature remains constant. Then, an automated test platform is used to simulate a driver pressing the volume adjustment key of the in-vehicle entertainment system. Whenever a key press operation is simulated, the test platform records a high-precision timestamp accurate to the microsecond level to ensure the accuracy of the time reference in subsequent analysis.

[0068] Step 102: Immediately capture the hardware interrupt signal generated by each physical key response, and measure the first delay time between the issuance of the operation event and the reception of the hardware interrupt signal;

[0069] In this step, the hardware interrupt signal is a signal sent by the hardware circuit after a physical key is pressed, used to notify the operating system or other control systems that a key event has occurred. The first delay time is the time difference between the issuance of the operation event (i.e., the simulated key action) and the reception of the hardware interrupt signal, which directly reflects the response speed of the physical key;

[0070] Once the operation event triggers a physical key response, the system immediately captures the hardware interrupt signal generated by each physical key response and accurately measures the first delay time between the issuance of the operation event and the reception of the hardware interrupt signal. This measured value is one of the key indicators for evaluating the physical key response performance;

[0071] Continuing with the above example of the in-vehicle entertainment system, after the simulated key operation, the system monitors the interrupt signal at the hardware level in real time. When the hardware interrupt signal is detected, the time difference from the occurrence moment of the operation event recorded by the timestamp to this moment is calculated to obtain the first delay time. In this process, a high-performance timer is used to ensure the accuracy of time measurement, usually requiring a precision of up to the nanosecond level.

[0072] Step 103: Evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential internal system anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources outside the preset target;

[0073] In this step, the performance threshold is a preset standard used to determine whether the first delay time is within an acceptable range. Potential external interference factors refer to external factors that may affect the test results, such as electromagnetic interference, environmental noise, temperature changes, etc.; potential internal system anomalies involve internal problems such as software conflicts, resource contention, driver errors, etc.;

[0074] Evaluate the first delay time according to a preset performance threshold. If it is found to exceed the threshold, start an integrated diagnostic process, and use a multi-stage inspection mechanism to identify potential external interference factors and internal abnormal conditions. Based on the preliminary problem source identification report, use machine learning algorithms to predict the probability distribution, and adjust the initial test environment parameters to exclude unexpected interference sources, and finally form an optimized test environment;

[0075] In the in-vehicle entertainment system test, if the first delay time exceeds the preset performance threshold, the system will automatically enter the diagnostic mode. First, quickly conduct a preliminary check to confirm whether there are obvious electromagnetic interferences or temperature fluctuations. If the problem is not solved, further in-depth investigation will be carried out, such as checking whether there are interferences generated by other electronic devices, or whether there are software conflicts. Subsequently, adjust the test environment parameters according to the interference source analysis report, such as repositioning the test equipment away from potential electromagnetic interference sources, or optimizing the system resource allocation to avoid software conflicts. After multiple verification measurement cycles, confirm that the new test environment has reached the optimal state.

[0076] Step 104: Use the optimized test environment to repeat simulating operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button;

[0077] In this step, the response characteristic index is the result obtained through statistical processing of the first delay time data measured multiple times, and usually includes statistical quantities such as the average value, standard deviation, minimum value, and maximum value. These indexes comprehensively reflect the general response behavior and its stability of the physical button under specific conditions;

[0078] Use the optimized test environment to repeat simulating operation events and capture the hardware interrupt signals generated by each physical button response, and accumulate the first delay time data measured each time. Calculate the average delay time as the response characteristic index of the physical button through statistical analysis. This step provides a quantitative description of the button response characteristics;

[0079] In the in-vehicle entertainment system test, in the optimized test environment, perform a series of simulated button operations again. After each operation, the system will capture the hardware interrupt signal and record the first delay time. These data are accumulated to form a data set containing multiple measurement results. Next, calculate the average delay time and other related statistics, such as the standard deviation, minimum value, and maximum value, through statistical analysis to generate a detailed statistical report. These statistical reports not only provide comprehensive information about the button response characteristics, but also lay a foundation for subsequent trend analysis.

[0080] Step 105: By comparing the changing trends of the response characteristic indicators obtained in different test cycles, predict the reliability and aging effect of the physical button usage, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance;

[0081] In this step, the trend analysis report is a document generated through comparative analysis of the changing trends of the response characteristic indicators obtained in different test cycles. It can help predict the reliability and aging effect of the physical button, thereby achieving dynamic monitoring and pre-maintenance;

[0082] By comparing the changing trends of the response characteristic indicators in different test cycles, generate a trend analysis report to predict the reliability and aging effect of the physical button usage. This method can not only detect potential problems in advance but also provide a scientific basis for future maintenance to ensure that the physical button is always in the best working condition;

[0083] For the long-term monitoring of the in-vehicle entertainment system, the system regularly collects the latest response characteristic indicator data and compares it with the historical data to update the response characteristic change trend model. When it detects that the response characteristic indicator exceeds the preset reliability threshold or an aging warning appears, the system automatically generates an alarm notification to prompt the relevant personnel to take corresponding pre-maintenance actions. For example, if the trend analysis shows that the response time of a certain button gradually increases, it may indicate the aging of the button, and the system will recommend replacing the button in advance to prevent failures. At the same time, the system will continuously optimize the pre-maintenance strategy to improve the service life of the button and the user experience.

[0084] The button delay test method provided by the present invention achieves precise evaluation, dynamic monitoring, and pre-maintenance of the physical button response performance through the above key steps. First, simulate operation events and record timestamps in a preset initial test environment to ensure the consistency and repeatability of the test conditions. Second, immediately capture the hardware interrupt signal and measure the first delay time to provide key data directly reflecting the button response speed. Third, by evaluating the first delay time and optimizing the test environment, effectively exclude unexpected interference sources and improve the accuracy of the test results. Fourth, accumulate data and calculate the average delay time as the response characteristic indicator to provide a reliable quantitative basis for subsequent analysis. Finally, through trend analysis and dynamic monitoring, predict the reliability and aging effect of the button to achieve preventive maintenance and ensure the efficient and stable operation of the physical button in various application scenarios. This method not only improves the test efficiency and quality but also enhances the prediction ability for the long-term use of the physical button.

[0085] To solve the problem that potential interference sources and internal abnormal conditions are difficult to identify and eliminate in key press delay testing, and to improve the accuracy and reliability of test results, in some embodiments, in step 103, the first delay time is evaluated according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold, potential external interference factors and potential internal system abnormalities are analyzed, and the initial test environment parameters are adjusted to obtain an optimized test environment to exclude interference sources outside the preset target, specifically including:

[0086] The first delay time measured each time is compared and processed using the preset performance threshold to obtain the detection result of the delay exceeding the standard; based on this detection result, when it is found that the first delay time exceeds the preset performance threshold, a comprehensive diagnosis process is started. Using a multi-stage inspection mechanism, potential external interference factors, potential internal system abnormal conditions, and the interaction effects and cumulative effects between different operation events are analyzed and processed to generate a preliminary problem source identification report; according to the preliminary problem source identification report, a machine learning algorithm is used to predict the probability distribution of potential external interference factors and potential internal system abnormal conditions, generating an interference source analysis report. Based on the interference source analysis report, the corresponding initial test environment parameters and configurations are adjusted to generate an adjusted optimized test environment; based on the optimized test environment, a verification measurement cycle is implemented, repeating the simulation of operation events and capturing the hardware interrupt signals generated by each physical key response, accumulating the first delay time data measured each time, and calculating the average delay time as the response characteristic index of the physical key to obtain the verification measurement result; when the first delay time within the preset number of times of the verification measurement result meets the preset performance threshold, it is confirmed that the optimized test environment reaches the best state. When the first delay time within the preset number of times of the verification measurement result does not meet the preset performance threshold, the optimized test environment is dynamically adjusted using the adaptive adjustment module based on historical data and real-time feedback information until it is confirmed that all interference sources are eliminated;

[0087] In this embodiment, the performance threshold is a preset standard for judging whether the first delay time is within an acceptable range. The comprehensive diagnosis process is a multi-level inspection process aimed at comprehensively checking various factors that may affect the test results. The preliminary problem source identification report is a summary and analysis of all potential problems, providing basic data for the subsequent machine learning algorithm prediction. The adaptive adjustment module is a key tool for dynamically adjusting the optimized test environment when the verification measurement result does not meet expectations. It uses historical data and real-time feedback information to continuously improve the test conditions;

[0088] In the embodiments of the present application, according to the preliminary problem source identification report, a machine learning algorithm is used to predict the probability distribution of existing potential external interference factors and potential internal system abnormal conditions, and an interference source analysis report is generated. Then, based on this interference source analysis report, the corresponding initial test environment parameters and configurations are adjusted to generate an adjusted optimized test environment. Next, based on the optimized test environment, a verification measurement loop is implemented, the simulated operation events are repeated, and the hardware interruption signals generated by capturing the responses of each physical button are captured. The first delay time data of each measurement is accumulated, and the average delay time is calculated as the response characteristic index of the physical button to obtain the verification measurement result. If the verification measurement result shows that the first delay time within the preset number of times meets the preset performance threshold, it is confirmed that the optimized test environment reaches the best state; if not, the adaptive adjustment module is continuously used to dynamically adjust the optimized test environment until all interference sources are completely eliminated.

[0089] To improve the optimization efficiency and accuracy of the test environment, according to the preliminary problem source identification report described in the previous embodiment, a machine learning algorithm is used to predict the probability distribution of existing potential external interference factors and potential internal system abnormal conditions, and an interference source analysis report is generated. Based on the interference source analysis report, the corresponding initial test environment parameters and configurations are adjusted to generate an adjusted optimized test environment, specifically including:

[0090] Using the information provided in the preliminary problem source identification report, the identified potential external interference factors, potential internal system abnormal conditions, and the interaction effects and cumulative effects between different operation events are classified and sorted to obtain a classification and sorting result; based on the classification and sorting result, it is input into a pre-trained machine learning model to predict the occurrence probability of each potential external interference factor and potential internal system abnormal condition, and evaluate the influence degree of each potential external interference factor and potential internal system abnormal condition on the first delay time to generate a probability distribution prediction result; according to the probability distribution prediction result, combined with the output of the machine learning model, the occurrence probability of each potential external interference factor and potential internal system abnormal condition and the specific value of the increase in the first delay time caused by each potential external interference factor and potential internal system abnormal condition are estimated to obtain an interference source analysis report; according to the suggestions in the interference source analysis report, for the potential external interference factors and potential internal system abnormal conditions confirmed to be higher than the preset probability threshold, the relevant parameters and configurations of the initial test environment are adjusted to generate an adjusted optimized test environment setting scheme to obtain an optimized test environment;

[0091] In this embodiment, the classification and sorting result is to systematically classify and summarize all potential problems in the preliminary problem source identification report, aiming to reveal the relevance and importance among different problems. The probability distribution prediction result is a probability distribution graph output by a machine learning model, which shows the probabilities of different interference factors occurring and their influencing degrees on the key response performance. The interference source analysis report combines the probability distribution prediction result and the output of the machine learning model, and details the occurrence probabilities of each potential external interference factor and potential internal system anomaly and the specific values of the increased first delay time caused, providing a scientific basis for subsequent adjustments;

[0092] In the embodiment of the present application, according to the suggestions in the interference source analysis report, for the potential external interference factors and potential internal system anomalies confirmed to be higher than the preset probability threshold, the relevant parameters and configurations of the initial test environment are adjusted specifically to generate an adjusted optimized test environment setting plan to obtain an optimized test environment. This method not only considers the characteristics of specific problems but also combines historical data and real-time feedback information, ensuring the effectiveness and applicability of the adjustment measures. Finally, through the verification measurement loop, the test environment is continuously optimized until it is confirmed that all interference sources are effectively eliminated, ensuring the stability and reliability of the test results;

[0093] For example, in the key delay test of medical devices, engineers first classified and sorted the identified potential external interference factors such as electromagnetic interference and temperature fluctuations according to the preliminary problem source identification report. Then, these classification and sorting results were input into a pre-trained machine learning model, and it was predicted that the probability of electromagnetic interference occurring was high and it had a significant impact on the first delay time. According to the probability distribution prediction result, the interference source analysis report detailed the specific value of the increased first delay time caused by electromagnetic interference. Based on this report, engineers adjusted the test environment, such as adding shielding materials to reduce electromagnetic interference and reconfiguring the system's heat dissipation mechanism to cope with temperature changes. After multiple verification measurement loops, the key response time in the new test environment always meets the performance requirements, proving that the optimization measures are effective. In addition, the system also has an adaptive adjustment ability and can dynamically adjust the test conditions according to real-time data to ensure long-term stable operation.

[0094] To improve the identification accuracy of potential external interference factors and internal anomalies and the optimization effect of response performance in the prior art, according to the previous embodiment, based on the preliminary problem source identification report, the probability distribution of the existing potential external interference factors and potential internal system anomalies is predicted using a machine learning algorithm to generate an interference source analysis report, and based on the interference source analysis report, the corresponding initial test environment parameters and configurations are adjusted to generate an adjusted optimized test environment, specifically including:

[0095] Using the probability distribution prediction results provided in the interference source analysis report, identify and process potential external interference factors and potential internal system anomalies that are confirmed to be higher than the preset probability threshold to obtain a list of interference factors; based on the list of interference factors, use a predefined adjustment strategy to perform targeted adjustment processing on the relevant parameters and configurations of the initial test environment to obtain an adjustment result; according to the adjustment result, record the measures and parameter changes for each adjustment to obtain an adjustment record, and generate an optimized test environment setting plan after adjustment based on the adjustment record; use the optimized test environment setting plan after adjustment to implement a verification measurement cycle, repeat the simulated operation event and capture the hardware interrupt signal generated by each physical button response, accumulate the first delay time data for each measurement, and calculate the average delay time as the response characteristic index of the physical button to obtain an optimized test environment;

[0096] In this embodiment, the machine learning model is a mathematical model constructed after being trained with a large amount of historical data, and is used to predict the probabilities of various interference factors and abnormal conditions occurring under specific conditions and their impacts on the button response time. The probability distribution prediction result is a quantitative description of all possible interference sources and their influence degrees, providing a scientific basis for subsequent adjustments. The interference source analysis report combines the probability distribution prediction result and the output of the machine learning model, and details the occurrence probabilities of each potential external interference factor and potential internal system anomaly and the specific values of the increase in the first delay time caused by them, providing clear guidance for optimizing the test environment;

[0097] In the embodiment of the present application, according to the suggestions in the interference source analysis report, the relevant parameters and configurations of the initial test environment are adjusted for potential external interference factors and potential internal system anomalies that are confirmed to be higher than the preset probability threshold, and an optimized test environment setting plan after adjustment is generated to obtain an optimized test environment. This adjustment not only considers the specific characteristics of the interference source, but also combines real-time feedback information to ensure that each adjustment can minimize the impact of unexpected factors on the test results, and finally form a stable and reliable test environment;

[0098] For example, in a physical button response performance test of a medical device, engineers identified through a preliminary problem source identification report that there might be multiple external interference factors and internal abnormal conditions. By classifying and organizing these factors and inputting them into a pre-trained machine learning model, the system predicted that electromagnetic interference and resource contention were the main problems and evaluated their specific impacts on the first delay time. According to the predicted results of the generated probability distribution, the interference source analysis report recommended adding shielding materials to reduce electromagnetic interference and optimizing the resource allocation strategy to alleviate resource contention. Following these recommendations, the engineers adjusted the relevant parameters and configurations of the test environment to form an adjusted optimized test environment setting plan. Subsequently, after multiple verification measurement cycles, it was confirmed that the button response time in the new test environment always met the performance threshold requirements, proving that the optimization measures were effective. In addition, the system can also dynamically adjust the test conditions according to real-time data to ensure long-term stable operation, significantly improving the test accuracy and reliability.

[0099] To improve the accuracy and reliability of button response performance evaluation and to address the problem in the prior art that test results are susceptible to fluctuations in environmental and operating conditions, as another embodiment, according to step 104, using the optimized test environment, repeat the simulation of operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button, specifically including:

[0100] Using the optimized test environment, perform repeated simulation processing of a preset number of rounds on the preset operation events to ensure that each round of simulation triggers the physical button response of the device under the same strictly controlled conditions to obtain simulated operation events; based on the simulated operation events, immediately capture the hardware interrupt signals generated by each physical button response and measure the first delay time between the issuance of the simulated operation event and the receipt of the hardware interrupt signal to obtain the first delay time data points; accumulate the first delay time data points measured each time to generate a measurement result data set, and add a time stamp and environmental parameter record to the measurement result data set to generate a first delay time data set with time series and condition labels; use the first delay time data set with time series and condition labels for statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report, and use the average delay time and other statistics in the statistical report as the initial response characteristic index of the physical button; based on the first delay time data set with time series and condition labels, implement a cross-validation step to randomly extract several subsets from the first delay time data set with time series and condition labels, independently calculate the response characteristic indexes of each subset, and compare the differences between different subsets to obtain the verified target response characteristic index;

[0101] In this embodiment, the preset number of rounds refers to the pre-determined number of repeated tests, ensuring a sufficient sample size for statistical analysis; the simulated operation event is a user operation repeatedly executed according to predetermined conditions in an optimized test environment, such as a key press; the first delay time data point is the specific delay value measured each time; the first delay time data set with time sequence and condition tags not only contains the delay time, but also includes the time stamp and environmental parameters of each measurement, facilitating the consideration of the influence of time and environmental factors during subsequent analysis; the statistical report is a document generated after statistical analysis of all the first delay time data points, providing comprehensive information about the key response characteristics; the cross-validation step verifies the consistency and reliability of the test results by randomly extracting subsets from the data set and independently calculating their response characteristic indicators.

[0102] In the embodiment of the present application, by repeatedly simulating operation events and capturing hardware interrupt signals in an optimized test environment, the system can accumulate a large number of first delay time data points, thus providing a richer sample basis for statistical analysis. This approach not only improves the accuracy of the average delay time calculation, but also enables statistical analysis to cover more variables, such as standard deviation, minimum value, and maximum value, and then generates a more detailed statistical report. The cross-validation step further ensures the reliability and stability of the response characteristic indicators, avoiding result deviations caused by single measurement errors.

[0103] To improve the accuracy and stability of the evaluation of the physical key response performance in the prior art, according to the previous embodiment, using the first delay time data set with time sequence and condition tags, perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report. Use the average delay time and other statistics in the statistical report as the initial response characteristic indicators of the physical key, specifically including:

[0104] Using the first delay time data set with time sequence and condition tags, perform multi-dimensional statistical analysis processing on all the first delay time data points to obtain multi-dimensional statistical analysis results; based on the multi-dimensional statistical analysis results, calculate the average value of the first delay time data points to obtain the average delay time representing the typical response performance of the physical key under interference-free conditions, and compare the average value with historical data to evaluate the consistency and stability of the multi-dimensional statistical analysis results, generating a consistency evaluation report; calculate the standard deviation, minimum value, and maximum value of the first delay time data points, and calculate the coefficient of variation to evaluate the distribution and fluctuation range of the physical key response time, obtaining a set of statistics; according to the set of statistics, generate a statistical report including the average delay time, standard deviation, minimum value, maximum value, and coefficient of variation.

[0105] In this embodiment, multi-dimensional statistical analysis refers to considering the impacts of multiple factors (such as time and environmental conditions) on data points simultaneously to obtain more comprehensive data characteristics. The coefficient of variation quantifies the degree of change in response time by calculating the ratio of the standard deviation to the mean, helping to evaluate the stability and fluctuation range of the response time. The set of statistics includes a series of numerical values used to describe the distribution characteristics of data, such as the mean, standard deviation, minimum value, maximum value, and coefficient of variation. The detailed statistical report is a summary of all statistics, providing detailed data support for subsequent analysis. The initial response characteristic index comprehensively reflects the basic response performance and its stability of the physical button, providing a benchmark for comparing changes within different test cycles;

[0106] In the embodiment of the present application, the system further calculates the standard deviation, minimum value, and maximum value of the first delay time data points, and calculates the coefficient of variation to comprehensively evaluate the distribution and fluctuation range of the physical button response time, obtaining a set of statistics. Based on this set of statistics, a detailed statistical report including the average delay time, standard deviation, minimum value, maximum value, and coefficient of variation is generated. Finally, according to the average delay time and other statistics in the detailed statistical report, the initial response characteristic index of the physical button is defined to ensure a unified and reliable reference standard for each test. This method can not only accurately capture the true response characteristics of the physical button but also provide in-depth understanding of the button behavior through in-depth analysis of these statistics;

[0107] For example, in the physical button response performance test of a medical device, the engineer performed multi-dimensional statistical analysis using the first delay time data set with time sequence and condition labels. The system calculated the mean of each first delay time data point and found that the average delay time was 15 milliseconds, which was consistent with the historical data, indicating that the test environment optimization was effective. Then, the system calculated the standard deviation, minimum value, maximum value, and the coefficient of variation. The results showed that the fluctuation range of the response time was small, indicating that the button response performance was stable. Finally, the system generated a detailed statistical report including the average delay time, standard deviation, minimum value, maximum value, and coefficient of variation. These data together defined the initial response characteristic index of the physical button. Based on this report, the engineer confirmed the reliability of the button response performance in the test environment and provided a solid foundation for future long-term monitoring and maintenance. In addition, through the cross-validation step, the system randomly selected several subsets from the data set for independent calculation to verify the consistency and reliability of the response characteristic index, ensuring the accuracy of the test results.

[0108] Define the initial response characteristic index of the physical button according to the average delay time and other statistics in the detailed statistical report.

[0109] To solve the problems of large fluctuations in test results and difficulty in accurately evaluating the response performance of physical buttons in the prior art, and to improve the precise evaluation effect of the button response characteristics in the prior art, as another embodiment, according to step 105, using the first delay time data set with timing and condition tags, perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value, generate a statistical report, and use the average delay time and other statistics in the statistical report as the initial response characteristic indicators of the physical button, specifically including:

[0110] Using the response characteristic index data accumulated in different test cycles, summarize the average delay time, standard deviation, minimum value, maximum value, and other statistics of each test cycle, and add environmental parameter records for each test cycle to generate a periodic response characteristic summary table; based on the periodic response characteristic summary table, use time series analysis methods and combine machine learning algorithms to model the change trend of the response characteristic indicators in different test cycles to obtain a response characteristic change trend model; use the response characteristic change trend model to identify trend patterns and evaluate the influence degree of the trend patterns on the use reliability and aging effect of the physical button to generate a trend analysis report; use the information in the trend analysis report, combine the pre-set reliability threshold and aging warning standard to classify the current state of the physical button, determine pre-maintenance measures, and trigger preventive maintenance reminders for physical buttons close to the reliability threshold to plan maintenance actions in advance;

[0111] In this embodiment, multi-dimensional statistical analysis refers to a comprehensive analysis method that simultaneously considers the influence of multiple variables (such as time series, environmental parameters) on test results; a consistency evaluation report is a document that evaluates the stability and consistency of test results by comparing the current test results with historical data; the coefficient of variation (CV) is a statistic used to quantify the degree of change in response time, which helps to evaluate the relative volatility of response time; the statistic set contains a variety of statistics that describe the distribution characteristics of data and is used to comprehensively evaluate the response characteristics of physical buttons; the initial response characteristic indicator is a key performance indicator defined based on statistical analysis results and is used to characterize the basic response behavior and its stability of physical buttons;

[0112] In the embodiments of the present application, through multi-dimensional statistical analysis of the first delay time dataset with time sequence and condition tags, the system can more accurately capture the key response characteristics under different environmental conditions, reducing the influence of a single factor on the test results. The calculated average delay time and other statistics provide a quantitative description of the key response performance, while the coefficient of variation further reveals the degree of variation in the response time. The generated detailed statistical report not only contains key statistical data but also provides intuitive data visualization support, such as probability distribution graphs, cumulative distribution function (CDF) graphs, and box plots, facilitating subsequent analysis. In addition, the defined initial response characteristic indicators provide a solid foundation for long-term tracking and comparison, ensuring a unified and reliable reference standard for each test.

[0113] Figure 2 The following is a schematic structural diagram of a key delay test device (or system) provided by an embodiment of the present application, as Figure 2 shown. The device includes:

[0114] A simulation module 21, configured to simulate a preset operation event in a preset initial test environment, where the operation event triggers the physical key response of the device and synchronously records the timestamps generated by the operation event;

[0115] A measurement module 22, configured to immediately capture the hardware interrupt signal generated by each physical key response and measure the first delay time from the occurrence of the operation event to the reception of the hardware interrupt signal;

[0116] An adjustment module 23, configured to evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential internal system anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources other than the preset target;

[0117] A calculation module 24, configured to, after confirming that all interference sources have been excluded from the test environment, repeat the simulation of the operation event and capture the hardware interrupt signal generated by each physical key response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic indicator of the physical key;

[0118] A prediction module 25, configured to predict the reliability and aging effect of the physical key usage by comparing the change trends of the response characteristic indicators obtained in different test cycles, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the key response performance.

[0119] Figure 2 The described key delay test device can execute Figure 1A key delay test method described in the illustrated embodiment, its implementation principle and technical effects will not be elaborated further. For the key delay test device in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated herein.

[0120] In a possible design, Figure 2 A key delay test device of the illustrated embodiment can be implemented as a computing device, such as Figure 3 as shown, the computing device may include a storage component 31 and a processing component 32;

[0121] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are for the processing component 32 to call and execute.

[0122] The processing component 32 is used for the Figure 1 A key delay test method of the above

[0123] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0124] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component may 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 memory, flash memory, magnetic disk or optical disk.

[0125] Of course, the computing device will certainly also include other components, such as input / output interfaces, display components, communication components, etc.

[0126] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0127] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0128] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0129] An embodiment of the present application also provides a computer storage medium storing a computer program, which when executed by a computer can implement the Figure 1 key press delay test method shown in the above embodiment.

[0130] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A key delay test method, characterized in that, Including: Simulate a preset operation event in a preset initial test environment, where the operation event triggers the physical button response of the device, and synchronously record the timestamp generated by the operation event; Instantaneously capture the hardware interrupt signal generated by each physical button response, and measure the first delay time between the issuance of the operation event and the reception of the hardware interrupt signal; Evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential system internal anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources other than the preset target; Utilize the optimized test environment to repeatedly simulate operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button; By comparing the change trends of the response characteristic indexes obtained in different test cycles, predict the reliability and aging effect of the physical button usage, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance.

2. The method according to claim 1, wherein Evaluate the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential system internal anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources other than the preset target, including: Use the preset performance threshold to compare and process the first delay time measured each time to obtain the detection result of the delay exceeding the standard; Based on the detection result, when it is found that the first delay time exceeds the preset performance threshold, start a comprehensive diagnosis process, and use a multi-stage inspection mechanism to analyze and process the existing potential external interference factors, potential system internal anomaly conditions, and the interaction effects and cumulative effects between different operation events, and generate a preliminary problem source identification report; According to the preliminary problem source identification report, use a machine learning algorithm to predict the probability distribution of the existing potential external interference factors and potential system internal anomaly conditions, generate an interference source analysis report, and based on the interference source analysis report, adjust the corresponding initial test environment parameters and configurations to generate an adjusted optimized test environment; Based on the optimized test environment, implement a verification measurement loop, repeatedly simulate operation events and capture the hardware interrupt signals generated by each physical button response, accumulate the first delay time data measured each time, and calculate the average delay time as the response characteristic index of the physical button to obtain a verification measurement result; When the first delay time within the preset number of times of the verification measurement result meets the preset performance threshold, confirm that the optimized test environment reaches the best state. When the first delay time within the preset number of times of the verification measurement result does not meet the preset performance threshold, then based on historical data and real-time feedback information, use an adaptive adjustment module to dynamically adjust the optimized test environment until it is confirmed that all interference sources are eliminated.

3. The method according to claim 2, characterized in that, According to the preliminary problem source identification report, use a machine learning algorithm to predict the probability distribution of existing potential external interference factors and potential internal system anomalies, generate an interference source analysis report, and based on the interference source analysis report, adjust the corresponding initial test environment parameters and configurations to generate an optimized test environment, including: Use the information provided in the preliminary problem source identification report to classify and organize the identified potential external interference factors, potential internal system anomalies, and the interaction effects and cumulative effects between different operation events to obtain the classification and organization results; Based on the classification and organization results, input them into a pre-trained machine learning model to predict the occurrence probability of each potential external interference factor and potential internal system anomaly, and evaluate the degree of influence of each potential external interference factor and potential internal system anomaly on the first delay time to generate a probability distribution prediction result; According to the probability distribution prediction result, combined with the output of the machine learning model, estimate the occurrence probability of each potential external interference factor and potential internal system anomaly and the specific value of the increase in the first delay time caused by each potential external interference factor and potential internal system anomaly to obtain an interference source analysis report; According to the suggestions in the interference source analysis report, for the potential external interference factors and potential internal system anomalies confirmed to be higher than the preset probability threshold, adjust the relevant parameters and configurations of the initial test environment to generate an adjusted optimized test environment setting plan to obtain an optimized test environment.

4. The method according to any one of claims 1 to 3, characterized in that, According to the suggestions in the interference source analysis report, for the potential external interference factors and potential internal system anomalies confirmed to be higher than the preset probability threshold, adjust the relevant parameters and configurations of the initial test environment to generate an adjusted optimized test environment setting plan to obtain an optimized test environment, including: Use the probability distribution prediction result provided in the interference source analysis report to identify and process the potential external interference factors and potential internal system anomalies confirmed to be higher than the preset probability threshold to obtain a list of interference factors; Based on the list of interference factors, use a predefined adjustment strategy to perform targeted adjustment processing on the relevant parameters and configurations of the initial test environment to obtain an adjustment result; According to the adjustment result, record the measures and parameter changes of each adjustment to obtain an adjustment record, and generate an adjusted optimized test environment setting plan based on the adjustment record; Use the adjusted optimized test environment setting plan to implement a verification measurement cycle, repeat simulating operation events and capturing the hardware interrupt signals generated by each physical button response, accumulate the first delay time data of each measurement, and calculate the average delay time as the response characteristic index of the physical button to obtain an optimized test environment.

5. The method according to any one of claims 1 to 3, characterized in that, Use the optimized test environment to repeat simulating operation events and capturing the hardware interrupt signals generated by each physical button response, accumulate the first delay time data of each measurement, and calculate the average delay time as the response characteristic index of the physical button, including: Using the optimized test environment, perform repeated simulation processing on a preset operation event for a preset number of rounds, ensuring that each round of simulation triggers the physical button response of the device under the same strictly controlled conditions, so as to obtain simulated operation events; Based on the simulated operation events, immediately capture the hardware interrupt signals generated by each physical button response, and measure the first delay time from the issuance of the simulated operation event to the receipt of the hardware interrupt signal to obtain the first delay time data points; Accumulate the first delay time data points measured each time to generate a measurement result data set, and add a time stamp and environmental parameter record to the measurement result data set to generate a first delay time data set with time series and condition labels; Using the first delay time data set with time series and condition labels, perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report. Use the average delay time and other statistics in the statistical report as the initial response characteristic indicators of the physical button; Based on the first delay time data set with time series and condition labels, implement a cross-validation step. Randomly extract several subsets from the first delay time data set with time series and condition labels, independently calculate the response characteristic indicators of each subset, and compare the differences between different subsets to obtain the verified target response characteristic indicators.

6. The method according to claim 5, wherein Using the first delay time data set with time series and condition labels, perform statistical analysis processing, calculate the average value of the first delay time data points to obtain the average delay time, and calculate the standard deviation, minimum value, and maximum value to generate a statistical report. Use the average delay time and other statistics in the statistical report as the initial response characteristic indicators of the physical button, including: Using the first delay time data set with time series and condition labels, perform multi-dimensional statistical analysis processing on all the first delay time data points to obtain multi-dimensional statistical analysis results; Based on the multi-dimensional statistical analysis results, calculate the average value of the first delay time data points to obtain the average delay time representing the typical response performance of the physical button under interference-free conditions, and compare the average value with historical data to evaluate the consistency and stability of the multi-dimensional statistical analysis results and generate a consistency evaluation report; Calculate the standard deviation, minimum value, and maximum value of the first delay time data points, and calculate the coefficient of variation to evaluate the distribution and fluctuation range of the physical button response time to obtain a set of statistics; According to the set of statistics, generate a statistical report including the average delay time, standard deviation, minimum value, maximum value, and coefficient of variation; According to the average delay time and other statistics in the detailed statistical report, define the initial response characteristic indicators of the physical button.

7. The method according to claim 1, characterized in that, By comparing the change trends of the response characteristic indicators obtained in different test cycles, predict the reliability and aging effect of the physical button usage, and generate a trend analysis report to achieve dynamic monitoring and pre-maintenance of the button response performance, including: Using the response characteristic index data accumulated within different test cycles, summarize the average delay time, standard deviation, minimum value, maximum value, and other statistics for each test cycle, and add environmental parameter records for each test cycle to generate a periodic response characteristic summary table; Based on the periodic response characteristic summary table, use time series analysis methods and combine with machine learning algorithms to model the change trends of the response characteristic indexes within different test cycles to obtain a response characteristic change trend model; Using the response characteristic change trend model, identify trend patterns and evaluate the influence degree of the trend patterns on the use reliability and aging effect of the physical keys to generate a trend analysis report; Using the information in the trend analysis report, combine with the preset reliability threshold and aging warning criteria to classify the current state of the physical keys, determine pre-maintenance measures, and trigger preventive maintenance reminders for physical keys close to the reliability threshold to plan maintenance actions in advance.

8. A key delay test system, characterized in that, Including: A simulation module for simulating preset operation events in a preset initial test environment, where the operation events trigger the physical key responses of the device and synchronously record the timestamps generated by the operation events; A measurement module for immediately capturing the hardware interruption signals generated by each physical key response and measuring the first delay time from the issuance of the operation event to the receipt of the hardware interruption signal; An adjustment module for evaluating the first delay time according to a preset performance threshold. When it is detected that the first delay time exceeds the preset performance threshold evaluation, analyze the existing potential external interference factors and potential system internal anomalies, and adjust the initial test environment parameters to obtain an optimized test environment to exclude interference sources other than the preset targets; A calculation module for, after confirming that all interference sources have been excluded from the test environment, repeating the simulation of operation events and capturing the hardware interruption signals generated by each physical key response, accumulating the first delay time data measured each time, and calculating the average delay time as the response characteristic index of the physical key; A prediction module for predicting the use reliability and aging effect of the physical keys by comparing the change trends of the response characteristic indexes obtained within different test cycles, generating a trend analysis report to achieve dynamic monitoring and pre-maintenance of the key response performance.

9. A computing device, characterized in that, Including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a key delay test method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stored with a computer program, when the computer program is executed by the computer, it implements a key delay test method as described in any one of claims 1 to 7.

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