A reliability test method and system for a foldable-screen mobile phone

Abstract

The application relates to the technical field of folding screen testing, and discloses a reliability test method and system for a folding screen mobile phone. The system comprises a test partition decision module, a dynamic test strategy module, an abnormal response analysis module and a self-adaptive reinforcement module. The test partition decision module acquires folding screen physical structure parameters and historical deformation data, and divides a screen center flexible area, a hinge stress concentration area and an edge packaging area; the dynamic test strategy module generates current and target test intensity levels for each test partition; the abnormal response analysis module calculates a partition test deviation degree according to the difference between the target and actual test intensity levels; and the self-adaptive reinforcement module generates a predicted test intensity level for the next test cycle based on the test deviation degree and historical test data. The system can improve test accuracy and efficiency, and adapt to the reliability test requirements of various areas of the folding screen.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of foldable screen testing, in particular to a reliability test method and system for foldable screen mobile phones. BACKGROUND

[0002] With the continuous development of mobile terminal technology, foldable screen mobile phones gradually become an important direction of industry development due to the advantages of deformability, portability and combination of large screen display. However, the screen structure of foldable screen mobile phones is significantly different from that of traditional rigid screens. Its core components include flexible display panels, hinge mechanisms and edge encapsulation structures. In the long-term folding and use process, the stress action, deformation demand and failure risk of different areas are obviously different. At present, the reliability test scheme for foldable screen mobile phones mostly adopts the overall test idea, that is, the same test conditions are applied to the whole screen, such as fixed number of folding cycles, fixed force pressing test, etc., without fully considering the structural characteristics and stress differences of different areas of the screen.

[0003] In actual use scenarios, the center flexible area of the screen mainly bears repeated bending deformation, the hinge stress concentration area is in a high stress alternating state due to the rotation connection of the mechanical structure, and the edge encapsulation area is prone to sealing performance degradation or display layer damage due to stretching and extrusion during the deformation process. The existing overall test scheme cannot accurately match the failure risk points of each area, and often the test strength of some areas is insufficient, which cannot expose potential faults, while the test strength of other areas is too high, which leads to unnecessary sample loss and reduces the test efficiency.

[0004] The existing test system lacks the ability to dynamically adjust the test strategy, and usually executes according to the preset fixed process during the test, which cannot adjust the test strength according to the real-time test data feedback. For example, when a test subarea has appeared a slight abnormal signal under the current intensity, the existing system cannot timely increase the test strength of the area to further verify its reliability boundary, or when a certain area has no abnormality for a long time, it also cannot appropriately reduce the intensity to save test resources. In addition, for the deviation in the test process, the existing system mostly adopts a simple threshold judgment method, lacks quantitative analysis of the deviation degree, and is difficult to accurately evaluate the actual reliability level of each subarea, thereby limiting the reference value of the test results and failing to provide accurate support for the structure optimization and quality control of foldable screen mobile phones. SUMMARY

[0005] The present application aims to provide a reliability test system for foldable screen mobile phones to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides a reliability test system for foldable screen mobile phones, which comprises:

[0007] The test partition decision module is configured to obtain physical structure parameters and historical deformation data of the foldable screen, and divide the screen into a central flexible area, a hinge stress concentration area, and an edge packaging area.

[0008] The dynamic test strategy module is configured to generate a current test intensity level and a target test intensity level for each test partition.

[0009] The abnormal response analysis module is configured to calculate a test deviation degree of each test partition according to a difference between the target test intensity level and an actual test intensity level.

[0010] The adaptive reinforcement module is configured to generate a predicted test intensity level for a next test cycle based on the test deviation degree and historical test data.

[0011] Preferably, the dynamic test strategy module includes:

[0012] The test focusing unit is configured to mark a test partition that has deformation abnormalities in consecutive test cycles as a high-sensitivity area.

[0013] The sensitivity measurement unit is configured to calculate a test sensitivity coefficient of the high-sensitivity area based on deformation amounts, temperature distributions, and stress peaks of the high-sensitivity area in historical tests.

[0014] The correlation influence unit is configured to calculate a test sensitivity coefficient of a non-high-sensitivity partition based on a physical distance between the non-high-sensitivity partition and the high-sensitivity area and local deformation rates of the non-high-sensitivity partition and the high-sensitivity area.

[0015] The intensity mapping unit is configured to map a target test intensity level of each test partition based on the test sensitivity coefficients.

[0016] Preferably, the sensitivity measurement unit performs:

[0017] Extracting micro-deformation feature points and two-dimensional coordinates of the feature points of the high-sensitivity area in a single test.

[0018] Obtaining three-dimensional stress points and two-dimensional coordinates of the stress points of the partition in a grid model in a current test cycle.

[0019] Calculating a first difference between the number of feature points and the number of stress points.

[0020] Taking the first difference and an inverse of a preset constant as a first sensitivity reference quantity.

[0021] Matching the two-dimensional coordinates of the feature points and the stress points to obtain a second number of successful matches.

[0022] Obtaining an area ratio of the partition in a test area as a second sensitivity reference quantity.

[0023] Taking a product of the second number, the first sensitivity reference quantity, and the second sensitivity reference quantity as a test sensitivity coefficient.

[0024] Preferably, the correlation influence unit performs:

[0025] calculating the minimum physical distance from the non-high sensitive partition to all high sensitive partitions;

[0026] taking the reciprocal of the preset constant as the first correlation influence quantity;

[0027] obtaining the area proportion of the partition in the test area as the second correlation influence quantity;

[0028] normalizing the product of the first correlation influence quantity and the second correlation influence quantity to obtain an initial correlation coefficient;

[0029] taking the difference between the total amount of test intensity levels and the preset constant as an adjustment base;

[0030] taking the quotient of the adjustment base divided by the total amount of test intensity levels as a weight factor;

[0031] taking the product of the weight factor and the initial correlation coefficient as a test sensitivity coefficient.

[0032] Preferably, the intensity mapping unit performs:

[0033] dividing the value range of the test sensitivity coefficient into multiple sensitive intervals, the number of intervals being equal to the total amount of test intensity levels;

[0034] establishing a mapping rule that the maximum sensitive interval corresponds to the minimum test intensity level;

[0035] when the partition test sensitivity coefficient falls into a specific sensitive interval, setting the test intensity level corresponding to the interval as the target test intensity level.

[0036] Preferably, the abnormal response analysis module performs:

[0037] calculating the cycle difference value of the target test intensity level and the actual test intensity level of the test partition in all historical test cycles;

[0038] performing negative correlation normalization processing on the cumulative result of all cycle difference values, and outputting a test deviation degree.

[0039] Preferably, the adaptive reinforcement module performs:

[0040] when the test deviation degree is greater than a preset threshold, generating a predicted test intensity level according to the current test intensity level and the historical test intensity level sequence;

[0041] when the test deviation degree is less than or equal to the preset threshold, generating a predicted test intensity level according to the current target test intensity level and the actual test intensity level.

[0042] Preferably, the adaptive reinforcement module comprises:

[0043] a sequence fitting unit that arranges historical test intensity levels in chronological order to form an intensity sequence;

[0044] a trend analysis unit that fits the intensity sequence as a linear function and sets a test intensity change factor according to the slope direction of the function;

[0045] a reinforcement output unit that takes the algebraic sum of the current test intensity level and the test intensity change factor as a predicted test intensity level.

[0046] Preferably, the adaptive reinforcement module comprises:

[0047] a strength comparison unit that sets the target test intensity level as the predicted test intensity level when the current target test intensity level of the partition is less than or equal to the actual test intensity level;

[0048] a conservative strategy unit that sets the actual test intensity level as the predicted test intensity level when the current target test intensity level of the partition is greater than the actual test intensity level.

[0049] Preferably, the present application further comprises a reliability test method for a foldable-screen mobile phone, which includes all the modules and method processes of the reliability test system for a foldable-screen mobile phone.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] The reliability test system for a foldable-screen mobile phone can accurately divide the screen center flexible area, the hinge stress concentration area and the edge packaging area based on the physical structure parameters and historical deformation data of the foldable screen by setting a test partition decision module, thereby realizing fine division of the test area. This division mode breaks the limitation of traditional overall testing that cannot match the characteristics of each area, so that subsequent testing can be carried out according to the structural characteristics and failure risk points of different areas, avoiding potential fault omission or sample overconsumption caused by insufficient test pertinence, making the test process more suitable for the actual use conditions of the foldable screen, and improving the accuracy of the test.

[0052] The setting of the dynamic test strategy module can generate the current test intensity level and the target test intensity level for each test partition, changing the mode of fixed process in traditional testing. Different partitions can execute corresponding test operations according to their target test intensity levels, for example, the hinge stress concentration area can be set to a higher stress cycle test level, while the screen center flexible area can focus on the test of bending times and deformation amplitude, so that the test intensity of each partition can match its reliability demand, ensuring the sufficiency of the test of key high-risk areas and avoiding the waste of test resources in low-risk areas, effectively improving the overall test efficiency.

[0053] The abnormal response analysis module can calculate the partition test deviation degree according to the difference between the target test intensity level and the actual test intensity level, and realize the quantitative analysis of the test deviation. By accurately calculating the deviation degree, the gap between the actual performance and the expected target of each test partition in the test process can be clearly mastered, and the simple threshold judgment is no longer relied on, so that the possible reliability problems of each partition can be more accurately identified. For example, when the test deviation degree of a certain partition is large, the actual performance of the region does not match the design expectation, which can be intuitively reflected, providing a clear direction for subsequent analysis of the problem source, and avoiding problem misjudgment or omission caused by ambiguous deviation judgment.

[0054] The adaptive reinforcement module generates the predicted test intensity level of the next test cycle based on the test deviation degree and historical test data, and gives the test system the ability of dynamic adjustment. During the test process, the system can automatically optimize the test intensity of the next cycle according to the test deviation of the previous cycle and historical data accumulation, for example, for the partition with high deviation degree and potential risk, the predicted test intensity is appropriately increased to further verify its reliability boundary; for the partition with small deviation degree and stable performance, the predicted test intensity can be appropriately reduced to save resources. This adaptive adjustment mechanism enables the test process to be continuously optimized, and as the test cycle progresses, the test strategy becomes more in line with the actual reliability status of each partition, not only improving the accuracy and reference value of the test results, but also effectively shortening the test cycle and reducing the test cost, providing more targeted test support for the structural improvement and quality improvement of the folding screen mobile phone. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A timing diagram of the reliability test system of the folding screen mobile phone described in the present application;

[0056] Figure 2 A working principle flowchart of the dynamic test strategy module;

[0057] Figure 3 A working principle flowchart of the correlation influence unit;

[0058] Figure 4 A working principle flowchart of the abnormal response analysis module. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0060] Referring to Figure 1 The application provides a reliability test method and system for a foldable-screen mobile phone, the system comprising: a test partition decision module, a dynamic test strategy module, an abnormal response analysis module and an adaptive reinforcement module.

[0061] The test partition decision module obtains physical structure parameters and historical deformation data of the foldable screen, the physical structure parameters including screen size, material properties and hinge design parameters, and the historical deformation data being derived from bending frequency and stress distribution records in previous tests. Based on these data, the module divides the screen into three zones: a screen center flexible zone, a hinge stress concentration zone and an edge packaging zone. The division is based on the mechanical property differences of the zones, the screen center flexible zone usually bearing uniform deformation, the hinge stress concentration zone being prone to stress peaks, and the edge packaging zone involving packaging processes prone to fatigue. The module analyzes the distribution pattern of the historical deformation data through an algorithm, identifies high-frequency deformation points, and combines geometric features such as radius of curvature and thickness variation in the physical structure parameters to complete the definition of the zones. The dynamic test strategy module receives the partition information and generates the current test intensity level and the target test intensity level of each test partition. The current test intensity level is set based on the initial test plan, and the target test intensity level is adjusted through internal calculation. The abnormal response analysis module monitors the actual test intensity level during the test and compares it with the target test intensity level, calculates the difference to output the partition test deviation degree. The adaptive reinforcement module uses the test deviation degree and historical test data to generate the predicted test intensity level of the next test cycle, realizing dynamic optimization of the test strategy. The system as a whole works cooperatively through data flow among the modules, the output of the test partition decision module serving as the input of the dynamic test strategy module, and the abnormal response analysis module feeding back data to the adaptive reinforcement module, forming a closed-loop control.

[0062] Embodiment 1: Referring to Figure 2 In the reliability test system for foldable-screen mobile phones, the implementation of the dynamic test strategy module involves the cooperative operation of multiple components. The test focus unit continuously monitors the deformation data of each test partition, which is derived from the bending angle, stress distribution and micro-deformation trajectory collected by high-precision sensors in real time. The determination of deformation abnormalities is based on a preset threshold range, which is determined by the statistical quantile of historical test data. If a partition exceeds the upper limit of the threshold in consecutive test cycles, the marking mechanism is triggered. In the marking process, the system uses a time series analysis algorithm to compare the data continuity of the current cycle with that of the previous cycle, thereby classifying the partitions that meet the conditions as high-sensitivity zones. The identification of high-sensitivity zones not only depends on a single parameter, but also takes into account deformation rate and cumulative fatigue index to enhance the robustness of the determination.

[0063] The sensitivity measurement unit performs in-depth analysis on the marked high sensitivity zones. The unit first extracts micro-deformation feature points from the high-frequency sampling data, which correspond to the micro-cracks or strain concentration areas on the screen surface during repeated folding. The coordinates of the feature points are calculated by digital image correlation technology, which generates a sequence of two-dimensional coordinates by comparing the pre-test and post-test microscopic images. At the same time, the system calls the finite element mesh model of the current test cycle, which is generated by simulation software and contains partitioned three-dimensional stress distribution data. The three-dimensional stress points are converted to two-dimensional coordinates through orthogonal projection to match the feature points in space. The difference between the number of feature points and the number of stress points reflects the deviation between the actual deformation and the theoretical model. This difference is multiplied by the inverse of the system's preset constant to convert it into the first sensitive reference quantity. The constant serves to adjust the numerical dimension to avoid scale distortion during calculation. Subsequently, the unit matches the coordinates of the feature points and stress points through the nearest neighbor algorithm, and the matching success is determined based on whether the Euclidean distance is lower than the tolerance threshold. The number of matching results is recorded as the second quantity, which is used together with the area ratio of the partition to calculate the importance of the partition. Finally, the product of the second quantity, the first sensitive reference quantity, and the second sensitive reference quantity is linearly normalized to output the test sensitivity coefficient of the partition. This quantity quantifies the response strength of the high sensitivity zone to external test conditions.

[0064] The correlation influence unit is responsible for evaluating the interaction between non-high sensitivity partitions and high sensitivity zones. The unit calculates the minimum physical distance from the geometric center of the non-high sensitivity partition to the geometric centers of all high sensitivity zones, based on the two-dimensional topological mapping of the screen, ignoring the three-dimensional distortion caused by the curved surface. The minimum distance value is multiplied by the inverse of the preset constant to generate the first correlation influence quantity. The larger the quantity, the smaller the direct influence of the high sensitivity zone on the non-high sensitivity partition. At the same time, the unit calculates the area ratio of the partition as the second correlation influence quantity. The larger the area, the higher the mechanical stability of the partition. The product of the first correlation influence quantity and the second correlation influence quantity is converted into an initial correlation coefficient through the range normalization method, making the result fall between zero and one. Subsequently, the system calculates an adjustment base according to the total number of test intensity levels and a preset constant, and divides the adjustment base by the total number of levels to obtain a weight factor. The weight factor is used to adjust the contribution degree of the correlation coefficient, and finally generates the test sensitivity coefficient of the non-high sensitivity partition through multiplication operation. This process ensures that even if the non-high sensitivity partition, its test strategy can be dynamically adjusted according to the state of the high sensitivity zone.

[0065] The intensity mapping unit converts the test sensitivity coefficient into a specific target test intensity level. The system pre-divides the definition domain of the test sensitivity coefficient into several continuous intervals, and the number of intervals is consistent with the total number of test intensity levels. The division method is equal-width interval, for example, when the total number of levels is five, the interval boundaries are 0.2, 0.4, 0.6 and 0.8. The mapping rule adopts reverse association design, that is, the interval with the highest test sensitivity coefficient corresponds to the lowest test intensity level, and vice versa. This design is derived from the fact that the high sensitivity area needs more moderate test conditions to avoid accelerated aging. When the partition test sensitivity coefficient falls into a certain interval, the unit directly outputs the corresponding target test intensity level through table lookup or conditional judgment logic. This level will serve as a reference parameter for test execution, guiding the intensity setting of subsequent test cycles.

[0066] The entire implementation process relies on the fusion processing of multi-source data, including real-time sensor data, historical test records and physical simulation results. Data transmission between units is realized through the bus architecture inside the system, ensuring computational efficiency and real-time performance. The calculation of the test sensitivity coefficient not only considers the independent characteristics of the partition, but also introduces spatial correlation factors, making the test strategy more adaptive and accurate. The final generated target test intensity level provides a dynamic adjustment basis for reliability testing, enabling the system to implement differentiated testing for different partitions.

[0067] Embodiment 2: refer to Figure 3 The implementation of the correlation influence unit involves quantitative evaluation of the interaction between non-high sensitivity partitions and high sensitivity areas. This unit receives high sensitivity area marking information from the test focus unit and the geometric data of each partition. The system first obtains the geometric center coordinates of all partitions, which are calculated through the digitized model of the screen. The model is constructed based on the size and curvature information in the physical structure parameters. For each non-high sensitivity partition, the unit calculates the minimum physical distance from its geometric center to the geometric centers of all high sensitivity areas. The distance calculation uses the Euclidean distance formula and is performed on the two-dimensional projection plane of the screen to eliminate the computational complexity caused by surface deformation. This minimum distance value reflects the spatial proximity of the non-high sensitivity partition to the nearest high sensitivity area.

[0068] The minimum distance value is then multiplied by the inverse of a preset constant, which is a system-level parameter whose value is set according to the total size of the screen and the test accuracy requirement, and is mainly used to adjust the dimension and value range of the distance value, so that the calculation result is more suitable for subsequent processing. The result obtained after multiplication is called the first correlation influence quantity, and the larger the value of this quantity, the smaller the direct influence of the non-highly sensitive partition by the highly sensitive area. At the same time, the unit calculates the area ratio of the non-highly sensitive partition in the overall test area. The area ratio is obtained by dividing the actual surface area of the partition by the total surface area of the screen, wherein the surface area calculation takes into account the curved surface characteristics of the screen and is realized by the curvilinear integral algorithm. The area ratio value serves as the second correlation influence quantity, reflecting the relative importance of the partition in the overall structure.

[0069] After the multiplication of the first correlation influence quantity and the second correlation influence quantity is completed, the unit normalizes the product value. The normalization process uses the minimum-maximum scaling method to linearly transform the original product value to a range of zero to one, obtaining an initial correlation coefficient. This coefficient preliminarily quantifies the degree of influence of the non-highly sensitive partition by the highly sensitive area. Then, the unit calculates an adjustment base according to the total amount of test intensity levels preset by the system and another fixed constant value. The total amount of test intensity levels represents the number of test intensity levels supported by the system, and the fixed constant is usually set to one for numerical adjustment. The adjustment base is calculated by subtracting the fixed constant from the total amount of test intensity levels.

[0070] Dividing the adjustment base by the total amount of test intensity levels gives a weight factor, which is a value between zero and one, used to adjust the contribution of the initial correlation coefficient in the final calculation result. Finally, the unit multiplies the weight factor by the initial correlation coefficient to obtain the test sensitivity coefficient of the non-highly sensitive partition. This coefficient comprehensively reflects the test sensitivity degree that the partition should have based on spatial position and area factors, providing a basis for subsequent mapping of test intensity levels.

[0071] During the entire calculation process, the system maintains a real-time updated partition database containing the geometric information, sensitive state, and intermediate results of each partition. All calculations use floating-point operations to ensure accuracy, and the final result is kept to a reasonable number of decimal places to meet the test accuracy requirement. The execution frequency of the unit is synchronized with the test period, ensuring that each test period can be calculated and updated based on the latest partition state.

[0072] The embodiment is characterized in that the influence of spatial distance and area factors on test sensitivity is comprehensively considered, and the evaluation result is gradually refined through multi-step calculation. The introduction of the weight factor enables the system to flexibly adjust the sensitivity of the calculation result according to the total scale of the test intensity level. Normalization processing ensures the comparability of the calculation results between different partitions, providing a consistent data basis for dynamic strategy adjustment of the entire test system. In this way, the correlation influence unit can accurately evaluate the degree of influence of each non-high-sensitive partition on the high-sensitive area, thereby providing a quantitative basis for the allocation of test intensity levels. This calculation method based on spatial relationships and partition characteristics enables the test system to more accurately adapt to the reliability test requirements of different regions, optimizing the allocation of test resources.

[0073] Embodiment 3: refer to Figure 4 The intensity mapping unit is responsible for converting the calculated test sensitivity coefficient into a specific executable target test intensity level. The unit receives the test sensitivity coefficient input from the sensitivity measurement unit or the correlation influence unit, which is a continuous value between zero and one, reflecting the sensitivity of a specific partition to test operations. The system pre-defines a discrete test intensity level system, usually containing several integer levels, with lower level values representing milder test intensity and higher level values representing stricter test intensity.

[0074] The intensity mapping unit first uniformly divides the value range of the test sensitivity coefficient into several continuous sub-intervals, and the number of sub-intervals is exactly the same as the total number of test intensity levels supported by the system. For example, when the total number of test intensity levels is five, the unit divides the [0, 1] interval into five equal-width sub-intervals: [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), [0.8, 1.0]. This equal division ensures consistent sub-interval widths, facilitating subsequent mapping operations.

[0075] The unit uses a reverse mapping rule to establish the correspondence between the sensitive interval and the test intensity level, according to which the highest test sensitivity coefficient interval corresponds to the lowest test intensity level, and the lowest test sensitivity coefficient interval corresponds to the highest test intensity level. This reverse design is based on the actual needs of reliability testing: high-sensitive partitions often exhibit certain fatigue characteristics and require milder test intensity to avoid accelerated damage; while low-sensitive partitions can withstand more stringent test conditions to fully verify their reliability. When the test sensitivity coefficient of a certain partition falls into a specific sensitive interval, the unit determines the corresponding target test intensity level through table lookup or conditional judgment logic. This level will serve as a reference parameter for test execution, guiding the test equipment to apply appropriate mechanical stress or folding times to the partition. The entire mapping process is performed in real time, ensuring that the test strategy is adjusted according to the latest sensitivity coefficient calculation result at each test cycle.

[0076] The anomaly response analysis module is responsible for assessing the degree of difference between the target and actual test intensity levels during test execution. It obtains the sequence of target test intensity levels and the sequence of actual test intensity levels for each test partition in historical test cycles. For each test cycle, the module calculates the absolute difference between the target level and the actual level, resulting in a cycle difference value. These cycle difference values reflect the deviations that occur during test strategy execution.

[0077] The module accumulates all historical cycle difference values to obtain a total difference amount. Then, it converts the total difference amount into a test deviation degree using negative correlation normalization processing. This value is used to quantify the accuracy of test strategy execution. The calculation formula for normalization processing is as follows:

[0078]

[0079] Where: represents the test deviation degree, represents the total number of historical test cycles, represents the target test intensity level of the th cycle, represents the actual test intensity level of the th cycle, and represent the maximum and minimum values of the test intensity levels, respectively. This formula ensures that the test deviation degree has a value range between zero and one, with a higher value indicating better alignment between test execution and the expected strategy.

[0080] During calculation, the module maintains a circular buffer to store the level data of the most recent test cycles. The buffer size is determined by system configuration. After each new test cycle ends, the oldest data is removed and the latest data is added, keeping the total amount of data constant. This design ensures that the test deviation degree is always calculated based on recent historical data, reflecting the latest state of the system. The calculation result of the test deviation degree is passed to the adaptive reinforcement module as a basis for adjusting the test strategy of the next test cycle. At the same time, this value is also recorded in the system log for long-term performance analysis and system optimization. The entire calculation process is automatically executed without human intervention, ensuring that the test system can respond to changes in test status in real time.

[0081] Through the cooperation of intensity mapping and deviation degree calculation, the system realizes dynamic optimization of the test strategy. The intensity mapping unit ensures that the test intensity matches the partition characteristics, and the anomaly response analysis module monitors the execution effect and provides feedback information. This closed-loop control mechanism enables the test system to adaptively adjust test parameters, improving the accuracy and efficiency of testing.

[0082] Example 4: The adaptive reinforcement module determines the way to generate the predicted test intensity level according to the value of test deviation degree. The module continuously receives test deviation degree data from the anomaly response analysis module, which quantifies the difference between the target test intensity level and the actual test intensity level in the historical test cycles. The system sets a fixed preset threshold to determine whether the test deviation degree is within an acceptable range. When the test deviation degree is greater than the preset threshold, it indicates that there is a significant deviation in the test execution process, and the predicted value needs to be generated based on the trend analysis of the historical test data sequence.

[0083] In this case, the adaptive reinforcement module calls the sequence fitting unit. This unit extracts the historical test intensity level data of the test partition in the last several test cycles from the system database, and arranges them in chronological order to form an intensity sequence. The length of the sequence is determined by the system configuration parameters, usually containing a sufficient number of cycles to reflect the trend changes. The trend analysis unit performs linear fitting on the intensity sequence, and calculates the slope direction of the best fitting straight line by the least squares method. The slope direction reflects the trend of the test intensity level over time: a positive slope indicates an upward trend, a negative slope indicates a downward trend, and a slope close to zero indicates a basic stability.

[0084] According to the slope direction, the trend analysis unit sets the test intensity change factor, which is an integer whose size and sign are determined by the size and direction of the slope. A positive slope corresponds to a positive change factor, a negative slope corresponds to a negative change factor, and the larger the absolute value of the slope, the larger the absolute value of the change factor. The reinforcement output unit performs algebraic addition of the test intensity level of the current test cycle and the change factor to obtain the predicted test intensity level. This process enables the predicted value to continue the historical trend and achieve dynamic adjustment of the test intensity.

[0085] When the test deviation degree is less than or equal to the preset threshold, it indicates that the test execution process is consistent with the expected strategy, and there is no need for complex analysis based on the historical sequence. At this time, the adaptive reinforcement module adopts a simplified generation strategy, directly comparing the current target test intensity level and the actual test intensity level. The intensity comparison unit first obtains the two level values, and if the target level is less than or equal to the actual level, the target level is directly set as the predicted test intensity level. This selection is based on the assumption that the target level has been optimized by the system. If the target level is greater than the actual level, the conservative strategy unit sets the actual test intensity level as the predicted value, which avoids excessive increase of the test intensity. Referring to Table 1, which shows the test intensity level data of a test partition in ten consecutive cycles, and the corresponding predicted value generation process.

[0086] Table 1: Test intensity level history record and predicted value generation example table.

[0087]

[0088] The adaptive reinforcement module maintains a state machine to manage the generation logic of the prediction value. The module regularly checks the relationship between the test deviation degree and the preset threshold, and selects the corresponding generation strategy according to the check result. All decision-making processes are recorded in the system log, including the selected generation method, the used historical data range, the calculated change factor value, and other detailed information. These records are used for subsequent system performance analysis and algorithm optimization.

[0089] The generation frequency of the predicted test intensity level is synchronized with the test cycle, and the prediction value is recalculated once at the end of each test cycle. The newly generated prediction value will be used as the initial test intensity level for the next test cycle, forming a closed-loop control system. This design enables the test system to continuously adjust the test strategy based on actual execution, gradually optimizing the test effect. The module also contains an exception handling mechanism. When the input data is abnormal or an error occurs during calculation, the system will automatically switch to a conservative mode, using the lowest test intensity level as the prediction value to avoid excessive testing due to calculation errors. At the same time, the system will issue an alert to notify the technical personnel for inspection and maintenance.

[0090] Embodiment 5: The adaptive reinforcement module implementation contains specific processing logic for cases where the test deviation degree is small. When the test deviation degree is less than or equal to the preset threshold, the module generates the predicted test intensity level using a direct comparison method based on the current test cycle data. This process is completed by the intensity comparison unit and the conservative strategy unit, without the need to call historical data sequences or perform trend analysis.

[0091] The intensity comparison unit first obtains the current target test intensity level of the test partition from the system data bus. This level is generated by the dynamic test strategy module and reflects the ideal test intensity calculated based on the partition sensitivity coefficient. At the same time, the unit reads the actual test intensity level of the same partition from the test execution unit, which represents the actual test intensity value implemented in the current test cycle. The reading process is achieved through the system's internal data interface, ensuring the real-time and accuracy of the data.

[0092] The unit compares and analyzes these two level values, and the comparison operation is based on the integer size relationship judgment, using a simple numerical comparison algorithm. If the current target test intensity level is less than or equal to the actual test intensity level, the unit directly sets the target test intensity level as the predicted test intensity level. This selection is based on the system design principle that the target level has been optimized and calculated, and the actual execution level has reached or exceeded the target value, indicating that the current test intensity is sufficient to meet the reliability test requirements.

[0093] If the current target test intensity level is greater than the actual test intensity level, the conservative strategy unit is activated. This unit sets the actual test intensity level to the predicted test intensity level. This decision is based on the conservative testing principle, avoiding excessive increase in test intensity in states with small test deviation degrees. The actual test intensity level reflects the test level that the test equipment can stably achieve in the current cycle, and using this value as the predicted value can maintain the stability of the test process.

[0094] The entire decision-making process is executed immediately after the end of a test cycle, and the time point of generating the predicted value is synchronized with the system clock. The module uses a state machine model to manage the decision-making process, automatically selecting the corresponding processing path based on the comparison results. All decision-making logic is implemented through conditional statements, ensuring that the processing efficiency meets real-time requirements.

[0095] The generated predicted test intensity level is transmitted to the test execution unit through the data bus as the initial setting value for the next test cycle. At the same time, this value is also written into the system database for subsequent auditing and analysis. The module records detailed information for each decision, including input data, comparison results, and the final predicted value, and these log data are helpful for system maintenance and algorithm optimization.

[0096] During implementation, the module also includes a data validity check mechanism. When reading the target test intensity level and the actual test intensity level, the unit verifies the value range and validity of the data. If abnormal data is found, such as values outside the preset range or missing data, the module will adopt a default handling strategy, usually setting the predicted value to the lowest test intensity level and triggering a system alarm to notify the technician.

[0097] The implementation is characterized by simple and efficient processing logic, which can complete the decision-making based on the data of the current cycle only, without complex historical data analysis or trend prediction calculations. This design reduces the system's computational load and improves response speed, especially suitable for stable test states with small test deviation degrees. At the same time, the selection of conservative strategy avoids aggressive adjustment of test intensity, helping to maintain the stability and continuity of the test process. In this way, the adaptive reinforcement module can quickly generate a reasonable predicted test intensity level under the condition of small test deviation degrees, providing accurate parameter settings for the next test cycle. The entire processing process is fully automated and does not require human intervention, ensuring that the test system can run continuously and stably.

[0098] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0099] While the embodiments of the application have been shown and described herein, it will be understood by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made to the embodiments without departing from the spirit and scope of the application, which is defined by the claims and their equivalents.

Claims

1. A reliability testing system for foldable screen mobile phones, characterized in that, include: The test partitioning decision module is used to obtain the physical structural parameters and historical deformation data of the foldable screen, and to divide the screen into the central flexible area, the hinge stress concentration area, and the edge encapsulation area. The dynamic testing strategy module is used to generate the current test intensity level and the target test intensity level for each test partition; The anomaly response analysis module is used to calculate the zonal test deviation based on the difference between the target test intensity level and the actual test intensity level. The adaptive reinforcement module is used to generate a predicted test intensity level for the next test cycle based on the test deviation and historical test data. The dynamic testing strategy module includes: The test focus unit will mark the partitions that show abnormal deformation in consecutive test cycles as high-sensitivity areas; The sensitivity measurement unit calculates the test sensitivity coefficient based on the deformation, temperature distribution, and stress peak of the high-sensitivity area in historical tests; The associated influence unit calculates the test sensitivity coefficient of the non-highly sensitive zone based on the physical distance between the non-highly sensitive zone and the highly sensitive zone and its local deformation rate; The intensity mapping unit maps the target test intensity level for each test partition based on the test sensitivity coefficient. The test focusing unit performs: Continuous monitoring of deformation data in each test zone; the deformation data comes from bending angle, stress distribution and micro deformation trajectory collected in real time by high-precision sensors. Deformation anomalies are determined based on a preset threshold range. The threshold is determined by the statistical quantile of historical test data. If a certain partition exceeds the preset threshold limit in multiple consecutive test cycles, a marking mechanism is triggered. During the labeling process, the system uses a time series analysis algorithm to compare the data continuity between the current period and previous periods, and classifies the partitions that meet the criteria as high-sensitivity areas. The sensitivity measurement unit performs: Extract the microscopic deformation feature points and two-dimensional coordinates of the feature points in the high-sensitivity area during a single test; Obtain the three-dimensional stress points and two-dimensional coordinates of the stress points in the mesh model of the current test cycle for this partition; Calculate the first difference between the number of feature points and the number of stress points; The first difference and the reciprocal of the preset constant are used as the first sensitive reference value; Match the two-dimensional coordinates of feature points and stress points to obtain the second number of successful matches; Obtain the area ratio of this partition within the test area as the second sensitive reference value; The normalized product of the second quantity, the first sensitive reference quantity, and the second sensitive reference quantity is used as the test sensitivity coefficient. The associated influence unit executes: Calculate the minimum physical distance from non-highly sensitive partitions to all highly sensitive partitions; The reciprocal of the minimum distance and the preset constant is used as the first correlation influence quantity; The area percentage of this partition within the test area is used as the second correlation influence quantity; The initial correlation coefficient is obtained by normalizing the product of the first correlation influence and the second correlation influence. The difference between the total number of test intensity levels and the preset constant is used as the adjustment base. The quotient of the adjustment base divided by the total number of test intensity levels is used as the weighting factor; The product of the weighting factor and the initial correlation coefficient is used as the test sensitivity coefficient. The intensity mapping unit performs: The range of test sensitivity coefficient values ​​is divided into multiple sensitivity intervals, and the number of intervals is equal to the total number of test intensity levels. Establish a mapping rule between the maximum sensitivity interval and the minimum test intensity level; When the sensitivity coefficient of a partition test falls into a specific sensitivity range, the test intensity level corresponding to that range is set as the target test intensity level.

2. The reliability testing system for a foldable screen phone as described in claim 1, characterized in that, The anomaly response analysis module performs the following: Calculate the periodic difference between the target test intensity level and the actual test intensity level for the test zone across all historical test periods; The cumulative results of all period differences are negatively correlated and normalized, and the output is the test deviation.

3. The reliability testing system for a foldable screen phone as described in claim 1, characterized in that, The adaptive reinforcement module executes: When the test deviation exceeds the preset threshold, a predicted test intensity level is generated based on the current test intensity level and the historical test intensity level sequence. When the test deviation is less than or equal to the preset threshold, a predicted test intensity level is generated based on the current target test intensity level and the actual test intensity level.

4. The reliability testing system for a foldable screen phone as described in claim 3, characterized in that, The adaptive reinforcement module includes: The sequence fitting unit arranges historical test intensity levels in chronological order to form an intensity sequence; The trend analysis unit fits the intensity sequence to a linear function and sets the test intensity change factor according to the slope direction of the function. The output unit is enhanced by using the algebraic sum of the current test intensity level and the test intensity change factor as the predicted test intensity level.

5. The reliability testing system for a foldable screen phone as described in claim 4, characterized in that, The adaptive reinforcement module includes: The intensity comparison unit sets the target test intensity level to the predicted test intensity level when the current target test intensity level of the partition is less than or equal to the actual test intensity level. The conservative strategy unit sets the actual test intensity level to the predicted test intensity level when the current target test intensity level of the partition is greater than the actual test intensity level.

6. A reliability testing method for a foldable screen phone, characterized in that, It includes all modules and method flows of a reliability testing system for a foldable screen mobile phone as described in any one of claims 1 to 5.

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