Electronic product test method and system

Abstract

The invention provides an electronic product testing method and system, and the method comprises the steps: exerting a periodic bending load on a flexible electronic product, generating a standard bending load spectrum, attaching a strain sensitive marking layer, collecting the distribution data of a strain field, and enabling a flexible circuit impedance dynamic monitoring probe to be connected with a conductive circuit of the flexible electronic product, thereby achieving the testing of the flexible electronic product. Generating an impedance dynamic response spectrum, aligning mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum, and generating a time sequence association data set; and analyzing the spatial position matching degree and the time correlation between the local strain concentration region under the preset bending phase and the impedance sudden change event of the corresponding conductive circuit in the impedance dynamic response spectrum, and determining the mapping rule of the flexible electronic product so as to judge the failure mode of the flexible electronic product under the composite bending path. The method improves the guiding value of the test result for the product design, and improves the reliability and service life of the electronic product.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of electronic testing technology, and in particular, to a testing method and system for electronic products. Background Art

[0002] With the widespread application of flexible electronic products in wearable devices, flexible displays, electronic skin and other fields, the mechanical durability and circuit functional stability of products in complex bending scenarios have become key performance indicators. For example, the hinge area of ​​foldable mobile phones needs to withstand tens of thousands of bends without failure, and implantable medical sensors need to maintain circuit functional stability under repeated bending of human joints. These scenarios require test methods that can simultaneously monitor mechanical deformation and circuit response, and establish the correlation between the two, so as to accurately locate the root cause of failure and guide design optimization;

[0003] At present, the testing scheme for flexible electronic products mainly adopts the method of independent operation of mechanical loading and circuit monitoring in time sharing. Specifically, a multi-axis bending robot is used to apply periodic bending loads to record the mechanical life and number of fractures of the product. At the same time, an impedance monitoring device is used to independently collect circuit performance data (such as impedance amplitude and phase angle changes). The two types of data are roughly correlated by manually comparing timestamps to try to analyze the potential connection between mechanical deformation and circuit failure.

[0004] The main drawback of the existing scheme is the lack of multi-technology coordination and spatiotemporal synchronization. Mechanical loading and circuit monitoring are independently operated in time-sharing, resulting in inconsistent time bases for the two types of data, making it difficult to accurately match bending phases with circuit response events. In addition, the existing scheme cannot establish spatiotemporal correlation rules between mechanical deformation and circuit failure, resulting in the inability to distinguish the root causes of substrate fracture failure and circuit functional failure; for example, when circuit failure is indirectly caused by substrate deformation, the existing scheme will misjudge it as a pure circuit defect, thereby affecting the design optimization direction. More importantly, the parameters such as mechanical life, strain gradient, and impedance threshold output by the existing scheme are isolated indicators, lacking mapping rules supported by time series correlation data sets, and cannot guide precise optimization (such as being unable to locate strain-circuit coupling areas that need to be reinforced first), limiting the overall effectiveness of the test method. Summary of the invention

[0005] The embodiments of the present application provide a testing method and system for electronic products, which are used to solve the problems in the prior art of lack of correlation analysis between mechanical deformation and circuit failure, misjudgment of failure mechanism caused by spatiotemporal asynchrony of multi-source data, and insufficient optimization guidance.

[0006] In a first aspect, an embodiment of the present application provides a method for testing an electronic product, comprising:

[0007] According to the fabric bending fatigue test standard, a multi-axis bending robotic arm is used to apply a periodic bending load to the flexible electronic product to generate a standard bending load spectrum that meets the test standard. Among them, the movement trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in three-dimensional space;

[0008] A strain-sensitive marking layer is attached to the surface of the flexible electronic product, and the dynamic strain field distribution data of the strain-sensitive marking layer under the action of the standard bending load spectrum is synchronously collected;

[0009] The preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive circuit of the flexible electronic product, and the impedance amplitude and phase angle change data of each conductive circuit are recorded during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum;

[0010] The mechanical bending parameters in the standard bending load spectrum are aligned with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum to generate a time series correlation dataset. The time series dataset includes: mechanical bending parameters, strain field spatio-temporal evolution characteristics, and impedance dynamic response characteristics;

[0011] Based on the time series correlation dataset, analyze the spatial position matching degree and time correlation between the local strain concentration region at the preset bending phase and the impedance mutation event of the corresponding conductive circuit in the impedance dynamic response spectrum to determine the mapping law between mechanical deformation and circuit failure in the flexible electronic product;

[0012] According to the mapping law, judge the failure mode of the flexible electronic product under the composite bending path. The failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0013] Optionally, based on the time series correlation dataset, analyze the spatial position matching degree and time correlation between the local strain concentration region at the preset bending phase and the impedance mutation event of the corresponding conductive circuit in the impedance dynamic response spectrum to determine the mapping law between mechanical deformation and circuit failure in the flexible electronic product, including:

[0014] Perform periodic synchronous segmentation processing on the time series correlation dataset to generate synchronous data segments that are strictly aligned with a single bending cycle. Among them, the synchronous data segments include the time series alignment data of mechanical bending parameters, strain field spatio-temporal evolution characteristics, and impedance dynamic response characteristics;

[0015] Extract the coordinates of the extreme points of the strain gradient of the dynamic strain field distribution data in the synchronous data segment to generate a feature set of strain concentration regions. Based on the strain gradient amplitudes in the feature set of strain concentration regions, screen the extreme points of the strain gradient that exceed the preset material strain threshold to generate a list of strain gradient overrun events;

[0016] Detect mutation events in the impedance dynamic response spectrum in the synchronous data segment to generate a list of effective impedance mutation events. Perform spatial grid registration on the spatial coordinates of the feature set of strain concentration regions and the conductive line topological positions in the list of effective impedance mutation events to generate a spatial coupling degree parameter;

[0017] Perform time series correlation analysis on the timestamps in the list of strain gradient overrun events and the timestamps in the list of effective impedance mutation events to generate a time series coupling degree parameter;

[0018] Input the spatial coupling degree parameter and the time series coupling degree parameter into a preset joint criterion model to generate a classification result of the mechanical deformation circuit failure association type. Based on the statistical distribution and spatial aggregation characteristics of the association type classification result, generate a mapping rule between mechanical deformation and circuit failure, where the mapping rule includes: a spatial coupling threshold, a time series coupling threshold, and a failure type determination rule.

[0019] Optionally, detecting mutation events in the impedance dynamic response spectrum in the synchronous data segment to generate a list of effective impedance mutation events, and performing spatial grid registration on the spatial coordinates of the feature set of strain concentration regions and the conductive line topological positions in the list of effective impedance mutation events to generate a spatial coupling degree parameter includes:

[0020] Perform sliding window difference calculation on the impedance dynamic response spectrum in the synchronous data segment to generate a sequence of impedance change rates. Based on the sequence of impedance change rates, identify candidate points of impedance mutation events through an adaptive threshold detection algorithm to generate an initial list of impedance mutation events;

[0021] Perform periodic verification on the candidate points in the initial list of impedance mutation events, and screen the impedance mutation events synchronized with the bending period of the mechanical bending parameters to generate a list of effective impedance mutation events. The list of effective impedance mutation events includes the timestamps of effective events, the impedance change rates, and the corresponding conductive line topological positions;

[0022] According to the conductive line topological positions in the list of effective impedance mutation events, divide the conductive lines of the flexible electronic product into independent conductive line segments associated with impedance mutation events to generate a list of conductive line segments;

[0023] Map the coordinates of the extreme points of the strain gradient concentrated in the strain concentration region feature to a preset spatial grid model to generate a strain concentration region grid distribution map, where the strain concentration region grid distribution map includes the strain gradient amplitude and spatial coordinates of each grid cell;

[0024] Project the topological positions of the conductive lines in the list of effective impedance mutation events onto the strain concentration region grid distribution map, calculate the spatial overlap area between the conductive line segments corresponding to each effective impedance mutation event and the grid cells in the strain concentration region, generate a local spatial overlap area list, and calculate the spatial coupling degree parameter of each effective impedance mutation event based on the local spatial overlap area list.

[0025] Optionally, project the topological positions of the conductive lines in the list of effective impedance mutation events onto the strain concentration region grid distribution map, calculate the spatial overlap area between the conductive line segments corresponding to each effective impedance mutation event and the grid cells in the strain concentration region, generate a local spatial overlap area list, and calculate the spatial coupling degree parameter of each effective impedance mutation event based on the local spatial overlap area list, including:

[0026] Convert each conductive line segment in the list of conductive line segments into a spatial vector line segment model to generate a conductive line vector map, where the conductive line vector map includes the starting point coordinates, ending point coordinates, and vector length of each conductive line segment;

[0027] Convert each grid cell in the strain concentration region grid distribution map into a polygon geometric model to generate a strain concentration region geometric distribution map, where the strain concentration region geometric distribution map includes the polygon vertex coordinates and strain gradient amplitude of each grid cell;

[0028] Perform geometric overlay analysis on each conductive line segment in the conductive line vector map and the grid cells in the strain concentration region geometric distribution map, calculate the intersection area between the conductive line segment vector and the grid cell polygon, and generate an initial overlap area list;

[0029] Perform normalization processing on the initial overlap area list, calculate the total coverage area of the conductive line segments according to the vector length of each conductive line segment, and generate a normalized overlap area list, where the normalized overlap area list includes the proportion of the intersection area between each conductive line segment and the corresponding grid cell;

[0030] Based on the normalized overlap area list, extract the maximum value of the proportion of the intersection area between the conductive line segments corresponding to each effective impedance mutation event and the grid cells in the strain concentration region to generate a local spatial overlap area list, and map the maximum value of the proportion of the intersection area in the local spatial overlap area list to the spatial coupling degree parameter.

[0031] Optionally, based on the time-series correlation dataset, analyze the spatial position matching degree and time correlation between the local strain concentration region under the preset bending phase and the impedance mutation events of the corresponding conductive lines in the impedance dynamic response spectrum, so as to determine the mapping law between mechanical deformation and circuit failure in the flexible electronic product, including:

[0032] Extract the spatial coupling threshold and the timing coupling threshold from the mapping law to generate a failure criterion list, and the failure criterion list contains the trigger condition combinations of the substrate fracture failure criterion and the circuit functional failure criterion;

[0033] Input the mechanical bending parameters in the time-series correlation dataset into the composite bending path simulation model to generate a path stress distribution map for each bending cycle, and the path stress distribution map contains the equivalent bending stress amplitudes of each node in the three-dimensional space;

[0034] Perform strain gradient cumulative analysis on the dynamic strain field distribution data, extract the maximum strain gradient value and its spatial coordinates within each bending cycle, and generate a strain accumulation feature list;

[0035] According to the phase angle change trend of the impedance dynamic response spectrum, identify the impedance attenuation rate of the conductive line to generate a circuit performance degradation curve, and the circuit performance degradation curve contains the corresponding relationship between the impedance attenuation rate of each conductive line and the bending cycle;

[0036] Match the maximum strain gradient value in the strain accumulation feature list with the substrate fracture failure criterion in the failure criterion list to generate a substrate fracture risk event list, and the substrate fracture risk event list contains the bending cycle number and spatial coordinates corresponding to the over-limit strain gradient value;

[0037] Match the impedance attenuation rate in the circuit performance degradation curve with the circuit functional failure criterion in the failure criterion list to generate a circuit failure risk event list, and the circuit failure risk event list contains the bending cycle number and the conductive line identifier corresponding to the over-limit impedance attenuation rate;

[0038] Perform multi-cycle spatio-temporal correlation analysis on the substrate fracture risk event list and the circuit failure risk event list, and count the spatio-temporal coincidence degree of the substrate fracture and circuit failure events within the same bending cycle to generate a composite failure correlation parameter;

[0039] Based on the composite failure correlation parameter, combined with the equivalent bending stress amplitude of the path stress distribution map, to judge the failure mode of the flexible electronic product.

[0040] Optionally, a strain-sensitive marker layer is attached to the surface of the flexible electronic product, and dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum is synchronously collected, including:

[0041] According to the thermal expansion coefficient and surface curvature of the substrate material of the flexible electronic product, the grid density and marker point size of the strain-sensitive marker layer are designed to generate strain-sensitive marker layer parameters adapted to the deformation characteristics of the flexible electronic product. The strain-sensitive marker layer parameters include the array spacing of marker points, the diameter of marker points, and the substrate adhesion strength threshold;

[0042] The marker point array corresponding to the strain-sensitive marker layer parameters is printed on the surface of the flexible substrate to generate a strain-sensitive marker layer that does not interfere with the conductive lines of the flexible electronic product. The marker point array of the strain-sensitive marker layer covers the entire bending action area and a peeling prevention buffer zone is reserved at the edge;

[0043] During the process of applying the standard bending load spectrum by the multi-axis bending robotic arm, a high-speed digital image acquisition system is used to synchronously obtain a dynamic speckle image sequence of the strain-sensitive marker layer at a sampling rate matching the bending frequency;

[0044] Perform sub-pixel level displacement field calculation on the dynamic speckle image sequence to generate an initial displacement vector field. The initial displacement vector field includes the instantaneous displacement components and displacement direction angles of each marker point in three-dimensional space;

[0045] Based on the initial displacement vector field, calculate the strain components within the neighborhood of each marker point to generate the original strain field distribution data. The original strain field distribution data includes the principal strain amplitude, shear strain component, and strain gradient direction. Perform spatial filtering and outlier removal processing on the original strain field distribution data to generate calibrated dynamic strain field distribution data.

[0046] Optionally, a preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive lines of the flexible electronic product, and impedance amplitude and phase angle change data of each conductive line are recorded during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum, including:

[0047] According to the line width, spacing, and material impedance characteristics of the conductive lines of the flexible electronic product, design the contact pressure and contact point array distribution parameters of the non-invasive probe to generate a probe configuration scheme adapted to the target conductive lines. The probe configuration scheme includes the array spacing of contact points, the single contact point pressure threshold, and the multi-point contact synchronization error range;

[0048] Based on the probe configuration scheme, the contact point array of the flexible circuit impedance dynamic monitoring probe is arranged on both sides of the target conductive line in a cross-line non-overlapping manner to form a dual-channel contact structure parallel to the conductive line direction;

[0049] During the loading process of the standard bending load spectrum, based on the dynamic contact characteristics of the dual-channel contact structure, the original impedance time-series data of each conductive line is collected at a sampling frequency synchronized with the mechanical bending phase to generate an initial impedance time-series data set. The initial impedance time-series data set includes impedance amplitude, phase angle, and time-series alignment records corresponding to the bending phase;

[0050] The initial impedance time-series data set is processed by sliding window average filtering and adaptive notch filtering to eliminate contact noise and electromagnetic interference caused by bending motion, and generate processed impedance time-series data;

[0051] Based on the processed impedance time-series data, candidate points of impedance mutation events of each conductive line are identified through a combined criterion of impedance change rate threshold detection and phase angle differentiation, and an impedance event candidate list is generated. The impedance event candidate list includes event timestamps, impedance change rates, and phase angle offsets;

[0052] The impedance event candidate list is verified for bending cycle synchronism, and valid impedance events having a fixed time delay relationship with the bending phase of the standard bending load spectrum are screened to generate a valid impedance event list. The valid impedance event list includes event timestamps, conductive line identifiers to which they belong, and event level classifications;

[0053] Based on the valid impedance event list and the processed impedance time-series data, the impedance amplitude fluctuation range, phase angle offset trend, and event trigger frequency of each conductive line within the bending cycle are statistically analyzed to generate an impedance dynamic response spectrum. The impedance dynamic response spectrum includes impedance amplitude-phase joint distribution characteristics segmented by bending phase and an event density heat map.

[0054] In a second aspect, an embodiment of the present application provides a test system for electronic products, including:

[0055] A generation module, configured to apply a periodic bending load to a flexible electronic product by using a multi-axis bending robotic arm according to a fabric bending fatigue test standard to generate a standard bending load spectrum that meets the test standard. The motion trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in three-dimensional space;

[0056] An acquisition module, configured to attach a strain-sensitive marker layer to the surface of the flexible electronic product and synchronously acquire dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum;

[0057] An access module, configured to non-invasively access the conductive lines of the flexible electronic product with a preset flexible circuit impedance dynamic monitoring probe, and record the impedance amplitude and phase angle change data of each conductive line during the action of the standard bending load spectrum, so as to generate an impedance dynamic response spectrum;

[0058] An alignment module, configured to align the mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum, so as to generate a time series correlation data set, and the time series data set includes: mechanical bending parameters, strain field spatio-temporal evolution characteristics, and impedance dynamic response characteristics;

[0059] An analysis module, configured to analyze the spatial position matching degree and time correlation between the local strain concentration region under a preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum based on the time series correlation data set, so as to determine the mapping rule between mechanical deformation and circuit failure in the flexible electronic product;

[0060] A judgment module, configured to judge the failure mode of the flexible electronic product under the composite bending path according to the mapping rule, and the failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0061] In a third aspect, an embodiment of the present application provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the test method of the electronic product according to any one of the first aspects.

[0062] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the test method of the electronic product according to any one of the first aspects is implemented.

[0063] In the embodiments of the present application, according to the fabric bending fatigue test standard, a multi-axis bending robotic arm is used to apply a periodic bending load to the flexible electronic product to generate a standard bending load spectrum that meets the test standard. Among them, the movement trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in three-dimensional space; a strain-sensitive marking layer is attached to the surface of the flexible electronic product, and the dynamic strain field distribution data of the strain-sensitive marking layer under the action of the standard bending load spectrum is synchronously collected; a preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive circuit of the flexible electronic product, and the impedance amplitude and phase angle change data of each conductive circuit are recorded during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum; the mechanical bending parameters in the standard bending load spectrum are aligned with the acquisition time of the dynamic strain field distribution data and the impedance dynamic response spectrum to generate a time series correlation data set; based on the time series correlation data set, the spatial position matching degree and time correlation between the local strain concentration region under the preset bending phase and the impedance mutation event of the corresponding conductive circuit in the impedance dynamic response spectrum are analyzed to determine the mapping rule between mechanical deformation and circuit failure in the flexible electronic product; according to the mapping rule, the failure mode of the flexible electronic product under the composite bending path is judged, and the failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0064] Through the above steps, the technical solution of the present application breaks through the limitations of traditional single physical field testing, can distinguish substrate fracture failure (triggered by local strain gradient overrun in the dynamic strain field distribution data) from circuit functional failure (independently triggered by impedance mutation events in the impedance dynamic response spectrum), and simultaneously identify the composite failure mode caused by mechanical-circuit coupling, significantly improving the guiding value of test results for product design, accurately positioning the bending-sensitive area and optimizing the material and circuit layout, thereby enhancing the reliability and service life of flexible electronic products in complex bending scenarios.

[0065] Furthermore, through periodic synchronous segmentation processing, multi-source data are strictly aligned to a single bending cycle, the strain concentration region feature set and the list of effective impedance mutation events are extracted, the spatial coupling degree parameter of the strain field and the circuit failure position is quantified by combining spatial grid registration, and the time series coupling degree parameter is generated through time series correlation analysis. Finally, the joint criterion model is used to accurately classify the correlation type between mechanical deformation and circuit failure, breaking through the limitation of isolated analysis of mechanical and circuit data in traditional testing, effectively distinguishing strain-dominated failure, independent circuit failure, and composite failure modes through spatio-temporal dual coupling criteria, being able to locate the mechanical-circuit coupling sensitive area (such as the strain-impedance synchronous abnormal area at the curvature mutation of the bending path), providing a quantitative basis for optimizing the structural design of flexible electronic products, and significantly improving the accuracy and engineering guiding value of failure mechanism analysis.

[0066] 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

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following briefly introduces the drawings required for use in 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, other drawings can be obtained based on these drawings without creative efforts.

[0068] Figure 1 It is a flowchart of a test method for an electronic product provided by an embodiment of the present application;

[0069] Figure 2 It is a schematic structural diagram of a test system for an electronic product provided by an embodiment of the present application;

[0070] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] In order to enable those skilled in the art to better understand the solutions of the present application, the following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

[0072] In some processes described in the specification, claims and the above-mentioned drawings of the present application, a plurality 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. In addition, 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" and "second" in this article 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.

[0073] The following clearly and completely describes the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of 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.

[0074] Figure 1The flowchart of the test method for electronic products provided by the embodiments of this application is as follows. Figure 1 As shown, the method includes:

[0075] Step 101: According to the fabric bending fatigue test standard, use a multi-axis bending robotic arm to apply a periodic bending load to the flexible electronic product to generate a standard bending load spectrum that meets the test standard. Among them, the motion trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in three-dimensional space.

[0076] In this step, the standard bending load spectrum refers to a set of time-series parameters including bending amplitude, frequency, phase angle, and three-dimensional path coordinates generated when a periodic bending load is applied to the flexible electronic product by the multi-axis bending robotic arm. Its data format conforms to the fabric bending fatigue test standard (such as ISO 6722) and is used to quantify the mechanical loading conditions.

[0077] In this step, first, set a composite bending path in three-dimensional space (such as a sine wave superposed with a random vibration path) for the multi-axis bending robotic arm through motion control software. Then, adjust the driving torque of the robotic arm according to the material properties of the flexible electronic product (such as thickness, modulus) to ensure that the bending stress is within the elastic range of the material. Finally, use the built-in encoder of the robotic arm to record the bending angle, angular velocity, and three-dimensional coordinates in real time, sampling once every 0.1 ms to generate a standard bending load spectrum file (including time stamp, bending amplitude, frequency, phase angle, and path coordinates).

[0078] For example, when testing the hinge area of a foldable mobile phone, set the robotic arm to execute a composite bending path: ±45° sine bending (2 Hz) in the X-axis direction, ±10° random vibration (bandwidth 0.5 - 5 Hz) in the Y-axis direction. Dynamically adjust the torque through the force feedback system to ensure that the bending stress does not exceed the yield strength of the polyimide substrate (150 MPa). Finally, generate a load spectrum containing 1000 bending cycles to provide an accurate mechanical loading benchmark for subsequent strain field and impedance data acquisition.

[0079] Step 102: Attach a strain-sensitive marker layer to the surface of the flexible electronic product and synchronously collect the dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum.

[0080] In this step, the strain-sensitive marker layer refers to an array structure composed of high-contrast micron-sized marker points (such as silica particles), which is printed on the surface of the flexible electronic product through a lithography process and is used to generate a traceable speckle pattern during optical strain measurement. The dynamic strain field distribution data refers to a spatio-temporal distribution dataset including the principal strain amplitude, shear strain, and gradient direction calculated by using the digital image correlation method (DIC) after collecting the marker layer image through a high-speed camera.

[0081] In this step, the density and diameter of the marking points are first designed according to the size of the measured area and the expected strain resolution. Then, a 500fps high-speed camera is used in conjunction with a pulsed light source (stroboscopically synchronized with the mechanical bending frequency) to capture images of the marking layer at each bending phase angle interval of 5°. Finally, the digital image correlation method (DIC) is used to calculate the displacement vector field of the marking points in adjacent images, and the dynamic strain field distribution data is generated by the strain gradient tensor algorithm (Green-Lagrange strain), including the time evolution sequence of the principal strain amplitude, shear strain, and gradient direction.

[0082] For example, in the hinge area test of the foldable mobile phone, a silicon dioxide marker array with a spacing of 0.5mm is printed on the surface. When the robot executes the composite bending path generated in step 101, the high-speed camera collects images with phase angles of 0°, 90°, 180°, and 270° as key frames, and the DIC algorithm calculates the maximum principal strain data of 0.8% in the center of the bend, and the gradient direction is consistent with the bending path.

[0083] Step 103, connecting a preset flexible circuit impedance dynamic monitoring probe to the conductive circuit of the flexible electronic product in a non-invasive manner, and recording impedance amplitude and phase angle change data of each conductive circuit during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum;

[0084] In this step, the non-invasive probe refers to a dual-channel sliding contact array made of elastic conductive polymer, and the contact pressure is dynamically adjusted to achieve continuous impedance monitoring without damaging the conductive line; the impedance dynamic response spectrum refers to a data set containing impedance phase timing curves and mutation event markers generated by real-time recording of impedance amplitude and phase angle change data of each conductive line, which is used to characterize the dynamic response characteristics of the circuit;

[0085] In this step, the contact array spacing and pressure are first designed according to the width and spacing of the conductive lines to ensure that the contact impedance fluctuation is <3%; then, an LCR meter is used to collect the impedance amplitude and phase angle at a test frequency of 1kHz and a sampling interval of 10ms, synchronously with the mechanical bending phase; finally, the raw data is subjected to sliding average filtering (window width = 5 bending cycles) and trend elimination, impedance mutation events are extracted, and an impedance dynamic response spectrum is generated;

[0086] For example, in the hinge area test of foldable mobile phones, dual-channel probes were deployed on both sides of the silver nanowire conductive line (line width 80μm). When the robotic arm performed a compound bending path to a 90° phase, it was detected that the impedance of a certain line jumped from 120Ω to 150Ω (change rate 25%), and the phase angle shifted by 8°, which was marked as an impedance mutation event.

[0087] Step 104: Align the mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum to generate a time series correlation dataset.

[0088] In this step, the time series correlation dataset refers to a fused dataset generated by aligning mechanical bending parameters, dynamic strain field distribution data, and impedance dynamic response spectrum based on a unified time reference, including spatio-temporal coupling features such as mechanical bending amplitude, strain gradient extreme value, impedance mutation event, etc.; the spatio-temporal grid mapping relationship table refers to a table that divides the three-dimensional space into 1mm×1mm grid cells and records the correlation index table of mechanical bending parameters, strain gradient extreme values, and corresponding conductive line impedance responses within each cell, which is used to quantify the multi-physical field coupling relationship.

[0089] In this step, first, a global synchronous timestamp sequence is generated based on the mechanical bending phase angle (one interval per 10°), and the strain field data (500fps) and impedance data (100Hz) are resampled to a unified frequency of 1000Hz through cubic spline interpolation; then, the product surface is divided into 1mm×1mm grid cells using the Delaunay triangulation algorithm, and the coordinates of the strain gradient extreme points and the topological positions of the conductive lines are mapped; finally, the mechanical bending amplitude, maximum strain gradient, and impedance mutation event are associated with each grid cell to generate a data matrix containing spatio-temporal coupling features (the row and column dimensions correspond to the bending cycle number and characteristic parameters respectively).

[0090] For example, in the test of the foldable mobile phone hinge, the generated composite bending path results in the detection that the maximum principal strain in the bending center area is 0.8%, and at the same time, it is captured that the impedance of the conductive line L5 jumps from 120Ω to 150Ω at the phase angle of 90°. The two are associated through the spatio-temporal grid mapping relationship table to generate a data entry: cycle 50, grid G12, mechanical bending amplitude 45°, strain gradient 0.15 / mm, impedance mutation +30Ω, spatio-temporal coincidence degree 85%.

[0091] Step 105: Based on the time series correlation dataset, analyze the spatial position matching degree and time correlation between the local strain concentration area under the preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum to determine the mapping rule between mechanical deformation and circuit failure in flexible electronic products.

[0092] In this step, the spatial position matching degree refers to the proportion of the geometric overlapping area between the strain concentration area and the impedance mutation event of the corresponding conductive line in the three-dimensional space; the time correlation refers to the time delay between the time point when the strain gradient reaches the threshold and the occurrence time of the impedance mutation event; the mapping rule refers to a set of association rules between mechanical deformation and circuit failure defined by spatio-temporal coupling criteria, including failure type determination conditions and weight distribution rules.

[0093] In this step, first, based on the spatio-temporal grid mapping relation table generated in step 104, the maximum value of the strain gradient, the amplitude of impedance mutation, and the event timestamp of each grid cell are extracted; then, the ratio of the spatial overlapping area between the conductive line segment and the strain concentration area is calculated through the geometric superposition algorithm, and the time delay distribution of the strain gradient overrun events and impedance mutation events is counted; finally, a logistic regression model is constructed, with the spatial overlapping rate, time delay, and bending amplitude-strain gradient ratio as inputs, and the failure type probability (strain-dominated type, independent circuit type, composite type) is output, generating a mapping rule including the spatial coupling threshold (>70%), the temporal coupling threshold (<5 ms), and the decision rule.

[0094] For example, in the test of the foldable mobile phone hinge, the data of the associated grid G12(X10,Y20) shows: strain gradient 0.15 / mm (overrun threshold 0.12 / mm), impedance mutation +30Ω, spatial overlapping rate 85%, time delay 3 ms. The logistic regression model determines it as a strain-dominated type of failure (probability 92%), and the mapping rule records that "when the spatial overlapping rate >80% and the time delay <5 ms, it is determined as a strain-dominated type of failure".

[0095] Step 106, according to the mapping rule, determine the failure mode of the flexible electronic product under the composite bending path, and the failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0096] In this step, substrate fracture failure refers to the material structure damage triggered by the local strain gradient in the dynamic strain field distribution data exceeding the material fracture threshold (such as polyimide film >0.15 / mm); circuit functional failure refers to the circuit performance degradation triggered by the impedance mutation event of a specific conductive line in the impedance dynamic response spectrum and having no spatio-temporal correlation with the substrate strain field; the composite failure mode refers to that when the spatio-temporal coincidence degree between the substrate fracture and the circuit failure event is >60% within 3 consecutive bending cycles, it is determined as a mechanical-circuit coupling failure.

[0097] In this step, first, based on the spatial coupling threshold (>70%) and the temporal coupling threshold (<5 ms) in the mapping rule generated in step 105, the spatio-temporal correlation events that meet the conditions are screened out; then, combined with the stress distribution map of the composite bending path (from step 101), verify whether the failure event is located in the high-stress area (equivalent bending stress >90% limit value); finally, output the result according to the failure type decision rule: if only the substrate strain exceeds the limit, it is determined as substrate fracture failure; if only the circuit impedance mutates and there is no spatio-temporal correlation, it is determined as independent circuit failure; if the spatio-temporal coincidence degree of both meets the standard, it is determined as the composite failure mode.

[0098] For example, in the test of a foldable mobile phone hinge, in step 105, it is determined that the grid G12 is a strain-dominated failure, and at the same time, a +30Ω impedance mutation of the line L5 is detected and it is located in a high-stress area. According to the mapping rule, when the spatio-temporal coincidence degree between the substrate fracture and the circuit failure is >60% within 3 consecutive cycles, it is determined as a composite failure mode.

[0099] Since in the existing test schemes for flexible electronic products, mechanical loading and circuit monitoring operate independently in a time-sharing manner, the spatio-temporal benchmarks of mechanical deformation data and circuit response data are not unified, it is impossible to accurately match the bending phase and circuit response events, and there is a lack of quantitative analysis means for the mechanical-circuit coupling failure mechanism. To solve the above problems, in some embodiments, as described in step 105, multi-source data is aligned to a single bending cycle through periodic synchronous segmentation processing, the strain gradient overrun event and the effective impedance mutation event are extracted, the spatial coupling degree parameter is generated by using spatial grid registration, the temporal coupling degree parameter is generated by combining temporal correlation analysis, and the spatio-temporal features are fused through a joint criterion model, and finally the mapping rule between mechanical deformation and circuit failure is output, including:

[0100] Step 201: Perform periodic synchronous segmentation processing on the time series correlation data set to generate a synchronous data segment that is strictly aligned with a single bending cycle, where the synchronous data segment includes the temporal alignment data of mechanical bending parameters, the spatio-temporal evolution characteristics of the strain field, and the impedance dynamic response characteristics;

[0101] In this step, the periodic synchronous segmentation processing refers to cutting the time series correlation data set into data segments that are strictly aligned with a single bending cycle according to the characteristics of the mechanical bending cycle, eliminating cross-cycle interference, and ensuring the alignment of mechanical, strain, and circuit data under the same time benchmark;

[0102] In the embodiments of the present application, first, the phase angle parameter in the standard bending load spectrum is extracted, and taking the starting phase angle (0°) of each bending cycle as the segmentation point, the continuous data stream is cut into independent cycle data segments; then, the strain field data (500Hz) and impedance data (100Hz) are resampled to a unified frequency of 1000Hz through cubic spline interpolation to generate a synchronous data segment including mechanical bending parameters (amplitude, frequency, path coordinates), spatio-temporal evolution characteristics of the strain field (principal strain amplitude, gradient direction), and impedance dynamic response characteristics (impedance amplitude, phase angle).

[0103] Step 202: Extract the coordinates of the strain gradient extreme points of the dynamic strain field distribution data in the synchronous data segment to generate a strain concentration region feature set, and based on the strain gradient amplitude in the strain concentration region feature set, screen the strain gradient extreme points that exceed the preset material strain threshold to generate a strain gradient overrun event list;

[0104] In this step, the strain gradient overrun event list refers to a set of extreme points of the strain gradient that exceed the material fracture threshold in the dynamically measured strain field data, including event timestamps, spatial coordinates, and gradient magnitudes, and is used to locate high-risk areas of mechanical deformation;

[0105] In the embodiment of the present application, first, the coordinates of the extreme points of the strain gradient of the dynamically measured strain field distribution data are extracted from the synchronized data segment, and noise interference is removed by Gaussian filtering; then, the extreme points exceeding the limit are selected based on the material fracture toughness parameters to generate a list including event timestamps, coordinates, and gradient magnitudes; finally, clustering analysis (DBSCAN algorithm) is performed on adjacent overrun events, and events of the same type with a spatial distance less than 1 mm are merged.

[0106] Step 203: Detect mutation events in the impedance dynamic response spectrum in the synchronized data segment to generate a list of valid impedance mutation events, and perform spatial grid registration on the spatial coordinates of the strain concentration region feature set and the conductive line topology positions in the list of valid impedance mutation events to generate a spatial coupling degree parameter;

[0107] In this step, the spatial coupling degree parameter refers to the ratio of the spatial overlapping area of the strain concentration region and the impedance mutation event of the conductive line calculated by spatial grid registration, and quantifies the spatial influence intensity of mechanical deformation on circuit failure;

[0108] In the embodiment of the present application, first, the conductive line topology position is projected onto a three-dimensional spatial grid (1 mm resolution) to establish a vector model of the conductive line segment; then, the geometric intersection area between the grid cells of the strain concentration region and the conductive line segment is calculated to generate a list of local overlapping areas; finally, the spatial coupling degree parameter of each impedance mutation event is obtained through normalization processing.

[0109] Step 204: Perform temporal correlation analysis on the timestamps in the strain gradient overrun event list and the timestamps in the list of valid impedance mutation events to generate a temporal coupling degree parameter;

[0110] In this step, the temporal coupling degree parameter refers to the statistical probability distribution of the time delay between the strain gradient overrun event and the impedance mutation event, and is used to quantify the time trigger correlation of mechanical deformation on circuit failure.

[0111] In the embodiment of the present application, first, the timestamps of the strain gradient overrun event and the impedance mutation event are extracted, and the time difference between the two within the same bending cycle is calculated; then, the distribution histogram of the time differences in all cycles is statistically calculated, and the probability of synchronous occurrence of events within a preset time window (such as ±10 ms) is calculated; finally, the temporal coupling degree parameter is generated through probability normalization (in the range of 0 to 1).

[0112] Step 205: Input the spatial coupling degree parameter and the timing coupling degree parameter into a preset joint criterion model to generate a classification result of the mechanical deformation circuit failure correlation type. According to the statistical distribution and spatial aggregation characteristics of the correlation type classification result, generate a mapping rule between mechanical deformation and circuit failure, where the mapping rule includes: a spatial coupling threshold, a timing coupling threshold, and a failure type determination rule;

[0113] In this step, the mapping rule refers to a set of failure type classification rules output by the joint criterion model based on the spatio-temporal coupling degree parameters, including a spatial coupling threshold, a timing coupling threshold, and a determination logic, which is used to guide the determination of failure modes;

[0114] In the embodiment of the present application, first, a logistic regression model is constructed, and the spatial coupling degree, the timing coupling degree, and the bending amplitude-strain gradient ratio are input as features; then, the model weights are fitted through training data (historical test data sets), and the failure type probabilities (strain-dominated type, independent circuit type, composite type) are output; finally, threshold rules are set according to the probability distribution (for example, when the spatial coupling > 70% and the timing coupling > 90%, it is the strain-dominated type), and a mapping rule including quantitative criteria is generated.

[0115] For example, in the bending test of the hinge area of a foldable mobile phone: the data of 1000 bending cycles are divided into independent cycles, and synchronous data segments are generated after resampling. Each cycle includes the mechanical bending amplitude (±45°), the principal strain of the strain field (0.8%), and impedance data; when the strain gradient at the hinge center area is 0.18 / mm at cycle 50 is detected, it is added to the overrun event list; then, calculate the ratio of the spatial overlap area between this area and the conductive line L5 is 85%, and generate the spatial coupling degree parameter; then, count the time delay of 3 ms between the strain gradient overrun (T = 5.235 s) and the impedance mutation of L5 (T = 5.238 s), and the timing coupling probability is 92%; finally, the logistic regression model determines it as a strain-dominated type failure (probability 95%), and the mapping rule records that "when the spatial coupling > 80% and the timing delay < 5 ms, it is determined as a strain-dominated type failure".

[0116] In summary, through cycle synchronization segmentation processing, the mechanical bending parameters, dynamic strain field data, and impedance response spectrum are strictly aligned to a single bending cycle. Strain gradient overrun events and effective impedance mutation events are extracted. The spatial coupling degree parameter between the strain concentration region and the circuit failure position is quantified using spatial grid registration, and a time delay index is generated through time series correlation analysis. Finally, based on the joint criterion model, spatio-temporal features are fused to accurately distinguish strain-dominated failures, independent circuit failures, and composite failure modes, breaking through the limitation of isolated analysis of mechanical and circuit data in traditional tests. The failure mechanism is revealed through spatio-temporal dual coupling criteria, and the mechanically-circuit coupled sensitive region (such as the curvature mutation of the bending path) can be located, providing a quantitative basis for optimizing the strength of the substrate material and improving the circuit layout. This not only improves the detection accuracy of the bending life of flexible electronic products and the engineering optimization efficiency, but also reduces the redundant design cost caused by misjudgment.

[0117] To solve the problems of large noise interference and high misjudgment rate in the existing test solutions for flexible electronic products when detecting circuit failure events, and the inability to effectively distinguish circuit failures caused by mechanical deformation from independent circuit defects, resulting in unclear optimization directions. In yet another embodiment, according to step 203, mutation event detection is performed on the impedance dynamic response spectrum in the synchronous data segment to generate a list of effective impedance mutation events. The spatial coordinates of the strain concentration region feature set are spatially grid-registered with the conductive line topology positions in the list of effective impedance mutation events to generate a spatial coupling degree parameter, including:

[0118] Step 301: Perform cycle synchronization segmentation processing on the time series correlation data set to generate a synchronous data segment that is strictly aligned with a single bending cycle, where the synchronous data segment contains time series alignment data of mechanical bending parameters, spatio-temporal evolution characteristics of the strain field, and impedance dynamic response characteristics;

[0119] In this step, the synchronous data segment refers to a time series alignment data unit generated by cutting the time series correlation data set according to a single bending cycle, and contains mechanical bending parameters (amplitude, frequency, path coordinates), spatio-temporal evolution characteristics of the strain field (principal strain amplitude, gradient direction), and impedance dynamic response characteristics (impedance amplitude, phase angle);

[0120] In the embodiment of the present application, first, the phase angle parameter of the standard bending load spectrum is extracted, and the continuous data stream is cut with the starting phase angle (0°) of each cycle as the segmentation point; then, the strain field data (500 Hz) and impedance data (100 Hz) are resampled to a unified frequency of 1000 Hz through cubic spline interpolation; finally, mechanical, strain, and circuit data are integrated to generate a synchronous data segment to ensure strict time series alignment.

[0121] Step 302: Extract the coordinate of the strain gradient extreme point of the dynamic strain field distribution data in the synchronization data segment, generate a strain concentration region feature set, and based on the strain gradient amplitude in the strain concentration region feature set, screen the strain gradient extreme points exceeding the preset material strain threshold to generate a strain gradient overrun event list;

[0122] In this step, the strain gradient overrun event list refers to the set of strain gradient extreme points screened from the dynamic strain field data that exceed the material fracture threshold, including event timestamps, spatial coordinates, and gradient amplitudes;

[0123] In the embodiment of the present application, first, the coordinate of the strain gradient extreme point of the dynamic strain field distribution data is extracted from the synchronization data segment, and noise interference is removed by Gaussian filtering; then, the overrun extreme points are screened based on the material fracture toughness parameters; finally, clustering analysis is performed on adjacent overrun events, and spatially adjacent events are merged to generate a list.

[0124] Step 303: Detect mutation events in the impedance dynamic response spectrum in the synchronization data segment to generate a list of valid impedance mutation events, perform spatial grid registration on the spatial coordinates of the strain concentration region feature set and the conductive line topology positions in the list of valid impedance mutation events to generate a spatial coupling degree parameter;

[0125] In this step, the spatial coupling degree parameter refers to the proportion of the spatial overlapping area between the strain concentration region and the impedance mutation event of the conductive line calculated by spatial grid registration, which is used to quantify the spatial influence intensity of mechanical deformation on circuit failure; in the embodiment of the present application, first, the conductive line topology position is projected onto a 1mm×1mm spatial grid model to establish a vector model of the conductive line segment; then, the geometric intersection area between the grid cells of the strain concentration region and the conductive line segment is calculated to generate a list of local overlapping areas; finally, the spatial coupling degree parameter of each impedance mutation event is obtained through normalization processing.

[0126] Step 304: Perform temporal correlation analysis on the timestamps in the strain gradient overrun event list and the timestamps in the list of valid impedance mutation events to generate a temporal coupling degree parameter;

[0127] In this step, the temporal coupling degree parameter refers to the statistical probability of the time delay distribution between the strain gradient overrun event and the impedance mutation event, which is used to quantify the time trigger correlation of mechanical deformation on circuit failure;

[0128] In the embodiment of the present application, first, the timestamps of the strain gradient overrun event and the impedance mutation event are extracted, and the time difference between the two within the same bending cycle is calculated; then, the distribution histogram of the time difference within all cycles is statistically analyzed, and the probability of synchronous occurrence of events within the preset time window is calculated; finally, the temporal coupling degree parameter is generated through probability normalization.

[0129] Step 305: Input the spatial coupling degree parameter and the timing coupling degree parameter into a preset joint criterion model to generate a classification result of the mechanical deformation circuit failure correlation type. According to the statistical distribution and spatial aggregation characteristics of the correlation type classification result, generate a mapping rule between mechanical deformation and circuit failure, where the mapping rule includes: a spatial coupling threshold, a timing coupling threshold, and a failure type determination rule;

[0130] In this step, the mapping rule refers to a set of failure type classification rules output by the joint criterion model based on the spatio-temporal coupling degree parameter, including the spatial coupling threshold, the timing coupling threshold, and the determination logic.

[0131] In the embodiment of the present application, first, a logistic regression model is constructed, and the spatial coupling degree, the timing coupling degree, and the bending amplitude-strain gradient ratio are input as features; then, the model weights are fitted through training data (historical test data sets), and the failure type probabilities (strain-dominated type, independent circuit type, composite type) are output; finally, threshold rules are set according to the probability distribution to generate a quantifiable and executable mapping rule.

[0132] For example, a certain manufacturer develops a flexible circuit in the hinge area of a foldable mobile phone. First, through periodic synchronous segmentation processing, continuous test data is cut into 1000 independent periods according to the mechanical bending period (2 Hz, period 0.5 s), and the strain field data (500 Hz) and impedance data (100 Hz) are resampled to a unified frequency of 1000 Hz to generate synchronous data segments. Each period includes the mechanical bending amplitude, the principal strain of the strain field, and the impedance baseline. It is detected that the strain gradient at coordinates X10, Y20 in the synchronous data segment of period 500 reaches 0.18 / mm (exceeding the polyimide threshold of 0.15 / mm). After clustering analysis, a list of over-limit events is generated. The conductive line L5 is projected onto a 1 mm grid model, and the ratio of the spatial overlap area between it and the grid G12 in the strain concentration area is calculated to be 85% to generate the spatial coupling degree parameter. By analyzing the timing correlation, it is found that the time stamp of the strain gradient over-limit (T = 250.235 s) is delayed by 3 ms from the time stamp of the impedance mutation of L5, and the timing coupling probability is 92%. The logistic regression model determines it as a strain-dominated type failure based on the spatio-temporal coupling parameters, and the mapping rule is set as "when the spatial coupling > 80% and the delay < 5 ms, it is a strain-dominated type failure".

[0133] To solve the problem that the existing flexible electronic product test solutions rely on Euclidean distance or simple overlap judgment when analyzing the spatial correlation between mechanical deformation and circuit failure, resulting in low spatial coupling quantization accuracy, high misjudgment rate, and inability to reflect the actual action range of deformation on the circuit. As an example, according to step 305, project the topological positions of the conductive lines in the list of effective impedance mutation events onto the grid distribution map of the strain concentration region, calculate the spatial overlap area between the conductive line segments corresponding to each effective impedance mutation event and the grid cells in the strain concentration region, generate a list of local spatial overlap areas, and based on the list of local spatial overlap areas, calculate the spatial coupling degree parameters for each effective impedance mutation event, including:

[0134] Step 401: Convert each conductive line segment in the list of conductive line segments into a spatial vector line segment model to generate a conductive line vector map, which includes the starting coordinates, ending coordinates, and vector length of each conductive line segment;

[0135] In this step, the conductive line vector map refers to the set of geometric figures generated after converting the conductive line segments into spatial vector line segment models, including the starting coordinates, ending coordinates, and vector length of each conductive line segment, and is used to support geometric overlay analysis;

[0136] In the embodiment of the present application, first generate a vector line segment model according to the topological position of the conductive line, with the direction vector pointing from the starting point to the ending point; then optimize the vector accuracy through the Bresenham algorithm to ensure the accuracy of geometric calculations; finally, integrate all conductive line segments to generate a vector map.

[0137] Step 402: Convert each grid cell in the grid distribution map of the strain concentration region into a polygon geometric model to generate a geometric distribution map of the strain concentration region, which includes the polygon vertex coordinates and strain gradient amplitude of each grid cell;

[0138] In this step, the geometric distribution map of the strain concentration region refers to the set of figures generated after converting the grid cells in the strain concentration region into polygon geometric models, including the polygon vertex coordinates and strain gradient amplitude of each grid cell, and is used to describe the spatial form of the strain field;

[0139] In the embodiment of the present application, first convert the 1mm×1mm grid cells into polygon models; then assign attribute values to each polygon according to the strain gradient amplitude; finally, integrate all grid cells to generate a geometric distribution map.

[0140] Step 403: Perform geometric overlay analysis on each conductive line segment in the conductive line vector diagram and the grid cells in the geometric distribution map of the strain concentration area, calculate the intersection area between the conductive line segment vector and the grid cell polygon, and generate an initial overlap area list;

[0141] In this step, the initial overlap area list refers to the original data set of the intersection area between the conductive line segment vector and the strain grid polygon calculated through geometric overlay analysis, and contains the intersection area values of each line segment and the grid cell;

[0142] In the embodiment of the present application, first, the Sutherland-Hodgman vector clipping algorithm is used to calculate the geometric intersection area between the conductive line segment vector and the strain grid polygon; then, all combinations of the conductive line segments and the strain grid are traversed to generate an initial overlap area list.

[0143] Step 404: Perform normalization processing on the initial overlap area list, calculate the total coverage area of the conductive line segment according to the vector length of each conductive line segment, generate a normalized overlap area list, and the normalized overlap area list contains the proportion of the intersection area of each conductive line segment and the corresponding grid cell;

[0144] In this step, the normalized overlap area list refers to the data set generated after normalizing the initial overlap area, and contains the proportion of the intersection area of each conductive line segment and the corresponding grid cell, which is used to eliminate the influence of the line length difference;

[0145] In the embodiment of the present application, first, calculate the total area of each conductive line segment; then, perform normalization processing on the initial overlap area; finally, generate a normalized overlap area list.

[0146] Step 405: Based on the normalized overlap area list, extract the maximum value of the proportion of the intersection area between the conductive line segment corresponding to each effective impedance mutation event and the grid cell in the strain concentration area, generate a local spatial overlap area list, and map the maximum value of the proportion of the intersection area in the local spatial overlap area list to a spatial coupling degree parameter;

[0147] In this step, the spatial coupling degree parameter refers to the maximum value of the proportion of the intersection area extracted based on the normalized overlap area list, which is used to quantify the spatial association strength between the conductive line segment and the strain concentration area;

[0148] In the embodiment of the present application, first, take the maximum value of the proportion of the intersection area between each conductive line segment and multiple strain grids; then, generate a local spatial overlap area list to record the maximum proportion of the intersection area corresponding to each effective impedance mutation event; finally, map the maximum value to a spatial coupling degree parameter for failure mode determination.

[0149] For example, in the flexible circuit test of the foldable mobile phone hinge area, the impedance of line L5 changes suddenly during bending. First, line L5 is converted into a vector model, and a conductive line vector diagram is generated. Then, the strain grid G12 is converted into a polygon model with a strain gradient of 0.15 / mm, and a geometric distribution diagram of the strain concentration area is generated. Subsequently, the vector clipping algorithm is used to calculate the intersection area between the L5 vector and the G12 polygon, which is 0.8 mm 2 , generating an initial overlapping area list. Then, the initial overlapping area is normalized, and the proportion of the intersection area is calculated as 0.8 / 3.0 × 100% ≈ 26.7%, generating a normalized overlapping area list. Finally, the maximum proportion of the intersection area between line L5 and the surrounding 5 strain grids is extracted as 26.7%, generating a spatial coupling degree parameter. According to the results, if the coupling degree > 50%, it is determined as a deformation-sensitive area, and line redundancy or local reinforcement needs to be increased; if the coupling degree < 30%, it is determined as an independent circuit failure, and the line material or connection process needs to be checked.

[0150] To solve the problem that the determination of failure modes in the existing flexible electronic product tests depends on a single data source and cannot accurately distinguish substrate fracture failure, circuit functional failure, and composite failure modes, in some embodiments, as described in step 106, according to the mapping rule, the failure mode of the flexible electronic product under the composite bending path is determined. The failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum, including:

[0151] Step 501: Extract the spatial coupling threshold and the temporal coupling threshold from the mapping rule to generate a failure criterion list. The failure criterion list includes the trigger condition combinations of the substrate fracture failure criterion and the circuit functional failure criterion;

[0152] In this step, the failure criterion list refers to the set of the spatial coupling threshold and the temporal coupling threshold extracted from the mapping rule, including the trigger condition combinations of the substrate fracture failure criterion and the circuit functional failure criterion, which is used to guide the determination of the failure mode.

[0153] In the embodiments of the present application, first, an initial threshold is set according to the material fracture toughness and the circuit design tolerance. Then, a dynamic adjustment rule is generated by fitting historical test data to generate a failure criterion list.

[0154] Step 502: Input the mechanical bending parameters in the time series correlation dataset into the composite bending path simulation model to generate a path stress distribution map for each bending cycle. The path stress distribution map includes the equivalent bending stress amplitudes of each node in the three-dimensional space;

[0155] In this step, the path stress distribution map refers to the distribution map of the equivalent bending stress amplitude of each node in a three-dimensional space generated by a composite bending path simulation model, which is used to locate high-stress regions;

[0156] In the embodiment of the present application, first, mechanical bending parameters (amplitude, frequency, path coordinates) are input into a finite element simulation model to calculate the equivalent bending stress of each node within each bending cycle; then, a map containing stress amplitudes and spatial coordinates is generated to support the positioning of high-stress regions.

[0157] Step 503: Perform strain gradient cumulative analysis on the dynamic strain field distribution data, extract the maximum strain gradient value and its spatial coordinates within each bending cycle, and generate a strain accumulation feature list;

[0158] In this step, the strain accumulation feature list refers to the set of the maximum strain gradient value and its spatial coordinates extracted from the dynamic strain field distribution data within each bending cycle, which is used to identify high-risk regions of mechanical deformation.

[0159] In the embodiment of the present application, first, Gaussian filtering is performed on the dynamic strain field data to remove noise; then, the extreme points of the strain gradient within each cycle are extracted, and their coordinates and gradient amplitudes are recorded; finally, a list containing cycle numbers, coordinates, and gradient values is generated.

[0160] Step 504: Identify the impedance attenuation rate of the conductive line according to the phase angle change trend of the impedance dynamic response spectrum, and generate a circuit performance degradation curve, where the circuit performance degradation curve includes the corresponding relationship between the impedance attenuation rate of each conductive line and the bending cycle;

[0161] In this step, the circuit performance degradation curve refers to the curve generated after identifying the impedance attenuation rate of the conductive line through the phase angle change trend of the impedance dynamic response spectrum, which includes the corresponding relationship between the impedance attenuation rate of each line and the bending cycle, and is used to evaluate the circuit performance degradation trend;

[0162] In the embodiment of the present application, first, sliding window difference calculation is performed on the impedance dynamic response spectrum to generate a sequence of impedance change rates; then, the impedance attenuation slope of each line is fitted by linear regression; finally, a degradation curve containing line identification, attenuation rate, and cycle number is generated.

[0163] Step 505: Perform threshold matching on the maximum strain gradient value in the strain accumulation feature list and the substrate fracture failure criterion in the failure criterion list to generate a substrate fracture risk event list, where the substrate fracture risk event list includes the bending cycle numbers and spatial coordinates corresponding to the over-limit strain gradient values;

[0164] In this step, the list of substrate fracture risk events refers to a set of over-limit strain gradient events generated by matching the strain accumulation characteristics with the failure criterion, including the bending cycle numbers and spatial coordinates corresponding to the events, and is used to locate the high-risk areas of substrate fracture;

[0165] In the embodiment of the present application, first, the maximum strain gradient value in the strain accumulation characteristic list is matched with the substrate fracture threshold in the failure criterion list; then, the over-limit events are screened to generate a list including cycle numbers, coordinates, and gradient values.

[0166] Step 506: Match the impedance attenuation rate in the circuit performance degradation curve with the circuit functional failure criterion in the failure criterion list to generate a list of circuit failure risk events, where the list of circuit failure risk events includes the bending cycle numbers corresponding to the over-limit impedance attenuation rate and the conductive line identifiers;

[0167] In this step, the list of circuit failure risk events refers to a set of over-limit impedance attenuation events generated by matching the circuit performance degradation curve with the failure criterion, including the bending cycle numbers and conductive line identifiers corresponding to the events, and is used to locate the high-risk areas of circuit functional failure;

[0168] In the embodiment of the present application, first, the impedance attenuation rate in the circuit performance degradation curve is matched with the circuit functional failure threshold in the failure criterion list; then, the over-limit events are screened to generate a list including cycle numbers, line identifiers, and attenuation rates.

[0169] Step 507: Perform multi-cycle spatio-temporal correlation analysis on the list of substrate fracture risk events and the list of circuit failure risk events, and count the spatio-temporal coincidence degree of substrate fracture and circuit failure events within the same bending cycle to generate a composite failure correlation parameter;

[0170] In this step, the composite failure correlation parameter refers to a quantitative index generated by counting the spatio-temporal coincidence degree of substrate fracture and circuit failure events, and is used to determine the composite failure mode;

[0171] In the embodiment of the present application, first, the timestamps and spatial coordinates of substrate fracture and circuit failure events are extracted, and the time difference and Euclidean distance between the two within the same cycle are calculated; then, the spatio-temporal coincidence degree (such as the coincidence area ratio > 60%) within 3 consecutive cycles is counted to generate a composite failure correlation parameter.

[0172] Step 508: Based on the composite failure correlation parameter, combined with the equivalent bending stress amplitude of the path stress distribution map, to judge the failure mode of the flexible electronic product;

[0173] In this step, the failure mode refers to the classification result of substrate fracture failure, circuit functional failure, or composite failure mode based on the equivalent bending stress amplitude of the composite failure correlation parameter and the path stress distribution map.

[0174] In the embodiment of the present application, first, it is judged whether there is a space-time coincidence event according to the composite failure correlation parameter; then, the high-stress area (equivalent bending stress > 90% limit value) is verified in combination with the path stress distribution map; finally, the failure mode determination result (such as the composite failure mode) is output.

[0175] For example, in the flexible circuit test of the hinge area of a foldable mobile phone, a certain manufacturer needs to verify its reliability under 100,000 bending tests and accurately determine the failure mode. First, a failure criterion list is extracted from the mapping rule, and the substrate fracture threshold is set to 0.15 / mm (based on the fracture toughness of the polyimide film) and the circuit functional failure threshold is set to 5Ω / cycle (based on the design tolerance of the silver nanowire circuit). Then, the mechanical bending parameters are input into the composite bending path simulation model to generate a path stress distribution map, and it is detected that the equivalent bending stress at coordinates X10, Y20 reaches 95%. Subsequently, strain gradient cumulative analysis is performed on the dynamic strain field distribution data, and the strain gradient at coordinates X10, Y20 at cycle 500 is extracted as 0.18 / mm to generate a strain accumulation feature list. At the same time, the impedance decay rate of line L5 is identified as 7Ω / cycle through the phase angle change trend of the impedance dynamic response spectrum to generate a circuit performance degradation curve. The strain accumulation characteristics are matched with the failure criteria to generate a substrate fracture risk event list; the circuit performance degradation curve is matched with the failure criteria to generate a circuit failure risk event list; multi-cycle space-time correlation analysis is performed on the substrate fracture and circuit failure events, and the space-time coincidence degree within 3 consecutive cycles is statistically 85% to generate a composite failure correlation parameter. Finally, combined with the verification of the high-stress area of the path stress distribution map, it is determined as the composite failure mode.

[0176] To solve the problems of low strain field measurement accuracy, easy peeling of the marking layer, and large data noise interference in the existing flexible electronic product tests, in some embodiments, as described in step 102, a strain-sensitive marking layer is attached to the surface of the flexible electronic product, and the dynamic strain field distribution data of the strain-sensitive marking layer under the action of the standard bending load spectrum is synchronously collected, including:

[0177] Step 601: According to the thermal expansion coefficient and surface curvature of the substrate material of the flexible electronic product, design the grid density and marking point size of the strain-sensitive marking layer, and generate strain-sensitive marking layer parameters adapted to the deformation characteristics of the flexible electronic product. The strain-sensitive marking layer parameters include the array spacing of marking points, the diameter of marking points, and the substrate adhesion strength threshold.

[0178] In this step, the parameters of the strain-sensitive marker layer refer to a set of design parameters of the marker point array that are dynamically adjusted according to the coefficient of thermal expansion and surface curvature of the substrate material of the flexible electronic product, including the spacing of the marker point array, the diameter of the marker points, and the threshold value of the substrate adhesion strength, which are used to ensure stable adhesion of the marker layer during the bending process and high measurement accuracy;

[0179] In the embodiment of the present application, first, the spacing and diameter of the marker points are calculated according to the coefficient of thermal expansion and the radius of surface curvature of the substrate material; then, the threshold value of the substrate adhesion strength is set based on the test data of the material adhesion; finally, the marker layer parameters including the spacing, diameter, and adhesion strength are generated.

[0180] Step 602: Print the marker point array corresponding to the parameters of the strain-sensitive marker layer on the surface of the flexible substrate to generate a strain-sensitive marker layer that does not interfere with the conductive circuit of the flexible electronic product. The marker point array of the strain-sensitive marker layer covers the entire bending action area and a buffer zone for preventing peeling is reserved at the edge;

[0181] In this step, the strain-sensitive marker layer refers to a measurement structure generated after printing the marker point array on the surface of the flexible substrate according to the marker layer parameters. It covers the entire bending action area and a buffer zone for preventing peeling is reserved at the edge to ensure no interference with the conductive circuit;

[0182] In the embodiment of the present application, first, the marker point array is printed on the surface of the polyimide substrate by using a photolithography process; then, a buffer zone with a width of 7.5 mm is reserved at the edge of the marker layer to prevent edge peeling during the bending process; finally, the spatial position of the marker layer and the conductive circuit is checked to ensure no interference.

[0183] Step 603: During the process of applying the standard bending load spectrum by the multi-axis bending robotic arm, use a high-speed digital image acquisition system to synchronously obtain a dynamic speckle image sequence of the strain-sensitive marker layer at a sampling rate matching the bending frequency;

[0184] In this step, the dynamic speckle image sequence refers to a set of deformed images of the marker layer taken synchronously by a high-speed digital image acquisition system at a sampling rate matching the bending frequency, which is used for subsequent displacement field calculation and strain analysis;

[0185] In the embodiment of the present application, first, set the sampling rate of the high-speed camera to 500 fps (250 times the bending frequency of 2 Hz) to ensure capturing the complete deformation process; then, cooperate with a pulsed light source (stroboscopic synchronization with the mechanical bending frequency) to take images of the marker layer at each bending phase; finally, generate a dynamic speckle image sequence containing timestamp and phase angle information.

[0186] Step 604: Calculate the sub-pixel displacement field for the dynamic speckle image sequence to generate an initial displacement vector field, where the initial displacement vector field includes the instantaneous displacement components and displacement direction angles of each marker point in three-dimensional space;

[0187] In this step, the initial displacement vector field refers to the set of instantaneous displacement data of marker points generated by calculating the sub-pixel displacement field, including the displacement components and displacement direction angles of each marker point in three-dimensional space, and is used to characterize the local displacement characteristics during the deformation process;

[0188] In the embodiment of the present application, first, the digital image correlation method is combined with the bicubic interpolation algorithm to calculate the sub-pixel displacement of the marker points in adjacent images, and then an initial displacement vector field including displacement components and direction angles is generated.

[0189] Step 605: Based on the initial displacement vector field, calculate the strain components within the neighborhood of each marker point to generate the original strain field distribution data, where the original strain field distribution data includes the principal strain amplitude, shear strain components, and strain gradient direction, and perform spatial filtering and outlier removal processing on the original strain field distribution data to generate the calibrated dynamic strain field distribution data;

[0190] In this step, the calibrated dynamic strain field distribution data refers to the high-precision strain distribution set generated after spatial filtering and outlier removal of the original strain field data, including the principal strain amplitude, shear strain components, and strain gradient direction, and is used to characterize the material deformation characteristics during the bending process;

[0191] In the embodiment of the present application, first, based on the initial displacement vector field, the principal strain amplitude, shear strain components, and gradient direction within the neighborhood of each marker point are calculated through the strain gradient tensor algorithm, then Gaussian filtering is used to remove high-frequency noise, and the abnormal strain gradient values are removed by the three-times standard deviation criterion. Finally, the calibrated dynamic strain field distribution data is generated.

[0192] For example, in the flexible circuit test of the foldable mobile phone hinge area, first, the marker layer parameters are designed according to the thermal expansion coefficient (50 ppm / °C) and the radius of curvature (50 mm) of the polyimide substrate to generate an optimized solution with a marker point spacing of 5 mm, a diameter of 2.5 mm, and an adhesion strength of 3 MPa. Then, the marker point array is printed on the substrate surface by photolithography, and a buffer band with a width of 7.5 mm is reserved to ensure no interference with the conductive circuit. Subsequently, during the process of the robotic arm executing the standard bending load spectrum (2 Hz), a high-speed camera is used to synchronously capture a sequence of dynamic speckle images at a sampling rate of 500 fps to capture the deformation details of each bending phase. The displacement of the marker points is calculated by the digital image correlation method (DIC) combined with sub-pixel interpolation to generate an initial displacement vector field with an accuracy of 0.01 pixel. Finally, the strain components are calculated based on the displacement vector field, and the calibrated dynamic strain field distribution data is generated through Gaussian filtering and outlier rejection. The maximum principal strain detected in the hinge center area is 0.8%, and the gradient direction is consistent with the bending path. This data provides high-precision input for subsequent failure mode determination and design optimization.

[0193] To solve the problems of low accuracy in circuit impedance monitoring, large contact noise interference, and high misjudgment rate of failure events in the existing flexible electronic product tests, in some embodiments, as described in step 103, a preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive circuit of the flexible electronic product, and during the action of the standard bending load spectrum, the impedance amplitude and phase angle change data of each conductive circuit are recorded to generate an impedance dynamic response spectrum, including:

[0194] Step 701: Design the contact pressure and contact point array distribution parameters of the non-invasive probe according to the line width, spacing, and material impedance characteristics of the conductive circuit of the flexible electronic product to generate a probe configuration solution adapted to the target conductive circuit. The probe configuration solution includes the contact point array spacing, the single contact point pressure threshold, and the multi-point contact synchronization error range;

[0195] In this step, the probe configuration solution refers to a set of non-invasive probe parameters designed according to the line width, spacing, and material characteristics of the conductive circuit, including the contact point array spacing, the single contact point pressure threshold, and the multi-point contact synchronization error range, which are used to ensure stable contact of the probe during bending without damaging the circuit;

[0196] In the embodiments of the present application, first, the contact point array spacing is calculated based on the line width and spacing of the conductive circuit, and the single contact point pressure threshold (0.8 N / mm 2 ) is determined by finite element simulation according to the material hardness, and then the probe configuration solution is generated through multi-point contact synchronization testing.

[0197] Step 702: Based on the probe configuration scheme, layout the contact array of the flexible circuit impedance dynamic monitoring probe on both sides of the target conductive line in a cross-line non-overlapping manner to form a dual-channel contact structure parallel to the conductive line direction;

[0198] In this step, the dual-channel contact structure refers to the contact array arranged in parallel on both sides of the conductive line, forming a symmetric contact channel through the cross-line non-overlapping method to suppress the contact impedance fluctuation during the bending process.

[0199] In the embodiment of the present application, first, the contact array is printed on the flexible substrate in a ±45° staggered arrangement manner by using the microelectromechanical system (MEMS) process, and then the edge distance between the contact array and the conductive line is ensured to be 10% of the line width through laser positioning and calibration to form a dual-channel symmetric contact.

[0200] Step 703: During the loading process of the standard bending load spectrum, based on the dynamic contact characteristics of the dual-channel contact structure, collect the original impedance time-series data of each conductive line at a sampling frequency synchronized with the mechanical bending phase to generate an initial impedance time-series data set, and the initial impedance time-series data set includes impedance amplitude, phase angle, and time-series alignment records corresponding to the bending phase;

[0201] In this step, the initial impedance time-series data set refers to the original impedance amplitude, phase angle, and time-series alignment records collected by synchronizing the mechanical bending phase, which are used for subsequent signal processing and event detection.

[0202] In the embodiment of the present application, first, set the sampling frequency of the impedance monitoring system to 1000 Hz (500 times the mechanical bending frequency of 2 Hz), and then strictly synchronize the impedance acquisition clock with the mechanical bending phase through the phase-locked loop (PLL) technology; finally, generate a time-series data set including time stamps, impedance amplitudes, and phase angles.

[0203] Step 704: Perform moving window average filtering and adaptive notch filtering on the initial impedance time-series data set to eliminate the contact noise and electromagnetic interference caused by the bending motion and generate the processed impedance time-series data;

[0204] In this step, the processed impedance time-series data refers to a high signal-to-noise ratio data set obtained by eliminating contact noise and electromagnetic interference through moving window average filtering and adaptive notch filtering, which is used to accurately identify impedance mutation events;

[0205] In the embodiment of the present application, first, perform moving window average filtering on the initial data set (window width = 5 bending cycles, that is, 2.5 seconds) to suppress the high-frequency noise caused by mechanical vibration; then use an adaptive notch filter (center frequency 500 Hz ± 10 Hz, bandwidth 20 Hz) to eliminate the motor drive interference; finally, generate the impedance time-series data.

[0206] Step 705: Based on the processed impedance time-series data, identify candidate points of impedance mutation events for each conductive line through the combined criterion of impedance change rate threshold detection and phase angle differential, and generate an impedance event candidate list, where the impedance event candidate list includes an event timestamp, an impedance change rate, and a phase angle offset;

[0207] In this step, the impedance event candidate list refers to a set of potential failure events preliminarily screened through the combined criterion of impedance change rate threshold detection and phase angle differential, including an event timestamp, an impedance change rate, and a phase angle offset;

[0208] In the embodiment of the present application, first calculate the impedance change rate and the phase angle differential, and set the combined criterion; then identify candidate events that meet the conditions and generate a candidate list including a timestamp, a change rate, and a phase offset.

[0209] Step 706: Verify the bending cycle synchronism of the impedance event candidate list, screen out effective impedance events that have a fixed time delay relationship with the bending phase of the standard bending load spectrum, and generate an effective impedance event list, where the effective impedance event list includes an event timestamp, an identification of the conductive line to which it belongs, and an event level classification;

[0210] In this step, the effective impedance event list refers to a set of failure events strongly related to mechanical loading screened through bending cycle synchronism verification, including an event timestamp, a line identification, and a level classification, and is used to exclude random interference events;

[0211] In the embodiment of the present application, first perform a fast Fourier transform (FFT) on the candidate events to verify whether their frequency components include the mechanical bending fundamental frequency (2 Hz) and its harmonics, and then screen out events with a frequency match (error < 1%), and generate an effective event list.

[0212] Step 707: Based on the effective impedance event list and the processed impedance time-series data, statistically analyze the impedance amplitude fluctuation range, phase angle offset trend, and event trigger frequency of each conductive line within the bending cycle, and generate an impedance dynamic response spectrum, where the impedance dynamic response spectrum includes an impedance amplitude-phase joint distribution feature segmented by bending phase and an event density heat map;

[0213] In this step, the impedance dynamic response spectrum refers to a multi-dimensional data map generated by statistically analyzing the impedance amplitude fluctuation, phase offset trend, and event density of each conductive line, and is used to comprehensively evaluate the dynamic response characteristics of the circuit;

[0214] In the embodiments of the present application, first, the impedance amplitude distribution and phase angle offset are statistically analyzed in segments according to the bending phase; then, an event density heat map is generated; finally, the data is integrated to generate an impedance dynamic response spectrum including time-domain, frequency-domain, and space-domain characteristics.

[0215] For example, in the test of the silver nanowire circuit in the hinge area of a foldable mobile phone, the manufacturer needs to accurately monitor the circuit failure behavior during the bending process. First, a probe configuration scheme is generated according to the line width and material characteristics, then a dual-channel contact structure is constructed through the MEMS process, and a contact array is deployed on both sides of the line L5. The contact impedance fluctuation is controlled within ±3%. Then, during the execution of the standard bending load spectrum by the robotic arm, the initial impedance data is synchronously collected at a sampling rate of 1000 Hz. The baseline impedance of 120 Ω and the phase angle of 45° are detected. Subsequently, the data is processed by sliding window average filtering and adaptive notch filtering, and the signal-to-noise ratio is increased to 22 dB. Then, through the joint criterion, it is identified that when the period is 250, the impedance of the line L5 suddenly increases to 150 Ω, and the phase angle shifts to 53°. It is added to the candidate list. After verification by FFT, the frequency of this event is 2 Hz (consistent with the mechanical bending frequency), and it is determined as an effective first-level failure event. Finally, an impedance dynamic response spectrum is generated, showing that the impedance peak at the 90° phase in the hinge area is 150 Ω, and the event density heat map indicates that this area is a high-risk area.

[0216] Figure 2 The following is a schematic structural diagram of a test system for electronic products provided by the embodiments of the present application, as Figure 2 shown. The system includes:

[0217] A generation module 21, configured to apply a periodic bending load to the flexible electronic product by using a multi-axis bending robotic arm according to the fabric bending fatigue test standard, and generate a standard bending load spectrum that meets the test standard, wherein the movement trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in a three-dimensional space;

[0218] An acquisition module 22, configured to attach a strain-sensitive marking layer to the surface of the flexible electronic product, and synchronously acquire the dynamic strain field distribution data of the strain-sensitive marking layer under the action of the standard bending load spectrum;

[0219] An access module 23, configured to non-invasively access a preset flexible circuit impedance dynamic monitoring probe to the conductive line of the flexible electronic product, and record the impedance amplitude and phase angle change data of each conductive line during the action of the standard bending load spectrum, and generate an impedance dynamic response spectrum;

[0220] An alignment module 24 is configured to align the mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum to generate a time series correlation data set, where the time series data set includes: mechanical bending parameters, spatio-temporal evolution characteristics of the strain field, and impedance dynamic response characteristics;

[0221] An analysis module 25 is configured to analyze, based on the time series correlation data set, the spatial position matching degree and time correlation between the local strain concentration region under a preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum, so as to determine the mapping rule between mechanical deformation and circuit failure in the flexible electronic product;

[0222] A judgment module 26 is configured to judge, according to the mapping rule, the failure mode of the flexible electronic product under a composite bending path, where the failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0223] Figure 2 The test system of the electronic product can execute Figure 1 the test method of the electronic product described in the illustrated embodiment. The implementation principle and technical effects will not be elaborated. For the test system of the electronic product 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 here.

[0224] In a possible design, Figure 2 the test system of the electronic product described in the illustrated embodiment can be implemented as a computing device, as Figure 3 shown, and the computing device may include a storage component 31 and a processing component 32;

[0225] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0226] The processing component 32 is used to apply a periodic bending load to the flexible electronic product by using a multi-axis bending robotic arm according to the fabric bending fatigue test standard, so as to generate a standard bending load spectrum that meets the test standard. Among them, the movement trajectory of the multi-axis bending robotic arm includes a programmable composite bending path in three-dimensional space; a strain-sensitive marker layer is attached to the surface of the flexible electronic product, and the dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum is synchronously collected; a preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive circuit of the flexible electronic product, and the impedance amplitude and phase angle change data of each conductive circuit are recorded during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum; the mechanical bending parameters in the standard bending load spectrum are aligned with the collection time of the dynamic strain field distribution data and the impedance dynamic response spectrum to generate a time-series correlation data set; based on the time-series correlation data set, the spatial position matching degree and time correlation between the local strain concentration region at a preset bending phase and the impedance mutation event of the corresponding conductive circuit in the impedance dynamic response spectrum are analyzed to determine the mapping rule between mechanical deformation and circuit failure in the flexible electronic product; according to the mapping rule, the failure mode of the flexible electronic product under the composite bending path is judged, and the failure mode includes substrate fracture failure characterized by the dynamic strain field distribution data and circuit functional failure characterized by the impedance dynamic response spectrum.

[0227] 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.

[0228] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0229] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0230] The input / output interface provides an interface between the processing component and the peripheral interface module, and the peripheral interface module may be an output device, an input device, etc.

[0231] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0232] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0233] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 test method of the electronic product in the illustrated embodiment.

[0234] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0235] 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 may be 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 labor.

[0236] 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 such an 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., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting 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 the various embodiments of the present application.

Claims

1. A method for testing electronic products, characterized in that: include: According to the fabric bending fatigue test standard, a multi-axis bending robot is used to apply a periodic bending load to the flexible electronic product to generate a standard bending load spectrum that meets the test standard, wherein the motion trajectory of the multi-axis bending robot includes a programmable composite bending path in three-dimensional space; Laminating a strain-sensitive marker layer on the surface of the flexible electronic product, and synchronously collecting dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum; Connecting a preset flexible circuit impedance dynamic monitoring probe to the conductive circuit of the flexible electronic product in a non-invasive manner, and recording impedance amplitude and phase angle change data of each conductive circuit during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum; Aligning the mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum to generate a time series correlation data set; Based on the time series correlation data set, the spatial position matching and time correlation between the local strain concentration area under the preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum are analyzed to determine the mapping law between mechanical deformation and circuit failure in flexible electronic products; According to the mapping rule, the failure mode of the flexible electronic product under the composite bending path is determined, and the failure mode includes substrate fracture failure represented by the dynamic strain field distribution data and circuit functional failure represented by the impedance dynamic response spectrum.

2. The method according to claim 1, characterized in that Based on the time series correlation data set, the spatial position matching degree and time correlation between the local strain concentration area under the preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum are analyzed to determine the mapping law between mechanical deformation and circuit failure in flexible electronic products, including: Performing periodic synchronous segmentation processing on the time series associated data set to generate a synchronous data segment strictly aligned with a single bending cycle, wherein the synchronous data segment contains time-series aligned data of mechanical bending parameters, spatiotemporal evolution characteristics of strain field, and dynamic response characteristics of impedance; Extracting the coordinates of strain gradient extreme value points of dynamic strain field distribution data in the synchronization data segment, generating a strain concentration area feature set, screening strain gradient extreme value points exceeding a preset material strain threshold based on the strain gradient amplitude in the strain concentration area feature set, and generating a strain gradient over-limit event list; Perform mutation event detection on the impedance dynamic response spectrum in the synchronization data segment to generate a list of effective impedance mutation events, perform spatial grid alignment on the spatial coordinates of the strain concentration area feature set and the conductive line topological position in the list of effective impedance mutation events to generate a spatial coupling degree parameter; Performing a time series correlation analysis on the timestamps in the strain gradient limit exceeding event list and the timestamps in the effective impedance mutation event list to generate a time series coupling degree parameter; The spatial coupling degree parameters and the temporal coupling degree parameters are input into a preset joint criterion model to generate a classification result of the mechanical deformation circuit failure association type. According to the statistical distribution and spatial aggregation characteristics of the association type classification result, a mapping law between mechanical deformation and circuit failure is generated, wherein the mapping law includes: a spatial coupling threshold, a temporal coupling threshold and a failure type judgment rule.

3. The method according to claim 2, characterized in that Perform mutation event detection on the impedance dynamic response spectrum in the synchronization data segment to generate a list of effective impedance mutation events, perform spatial grid alignment on the spatial coordinates of the strain concentration area feature set and the conductive line topological position in the effective impedance mutation event list to generate spatial coupling parameters, including: Performing sliding window difference calculation on the impedance dynamic response spectrum in the synchronization data segment to generate an impedance change rate sequence, and identifying impedance mutation event candidate points through an adaptive threshold detection algorithm based on the impedance change rate sequence to generate an initial impedance mutation event list; Performing periodic verification on candidate points in the initial impedance mutation event list, screening impedance mutation events synchronized with the bending period of the mechanical bending parameter, and generating a valid impedance mutation event list, wherein the valid impedance mutation event list includes a timestamp of a valid event, an impedance change rate, and a corresponding conductive line topological position; According to the conductive line topological positions in the effective impedance mutation event list, the conductive lines of the flexible electronic product are divided into independent conductive line segments associated with the impedance mutation events to generate a conductive line segment list; Mapping the coordinates of the strain gradient extreme value points in the strain concentration region feature set to a preset spatial grid model to generate a strain concentration region grid distribution map, wherein the strain concentration region grid distribution map includes the strain gradient amplitude and spatial coordinates of each grid unit; The topological positions of the conductive lines in the effective impedance mutation event list are projected onto the strain concentration area grid distribution map, the spatial overlapping areas between the conductive line segments corresponding to each effective impedance mutation event and the grid units in the strain concentration area are calculated, a local spatial overlapping area list is generated, and the spatial coupling degree parameters of each effective impedance mutation event are calculated based on the local spatial overlapping area list.

4. The method according to claim 3, characterized in that The conductive line topological position in the effective impedance mutation event list is projected onto the strain concentration area grid distribution map, and the spatial overlapping area between the conductive line segment corresponding to each effective impedance mutation event and the strain concentration area grid unit is calculated to generate a local spatial overlapping area list. Based on the local spatial overlapping area list, the spatial coupling degree parameter of each effective impedance mutation event is calculated, including: Convert each conductive line segment in the conductive line segment list into a space vector line segment model to generate a conductive line vector map, wherein the conductive line vector map includes the starting point coordinates, the end point coordinates and the vector length of each conductive line segment; Converting each grid unit in the strain concentration region grid distribution map into a polygonal geometric model to generate a strain concentration region geometric distribution map, wherein the strain concentration region geometric distribution map includes polygon vertex coordinates and strain gradient amplitude of each grid unit; Performing geometric superposition analysis on each conductive line segment in the conductive line vector diagram and the grid unit in the strain concentration area geometric distribution diagram, calculating the intersection area of ​​the conductive line segment vector and the grid unit polygon, and generating an initial overlapping area list; Normalizing the initial overlapping area list, calculating the total coverage area of ​​the conductive line segment according to the vector length of each conductive line segment, and generating a normalized overlapping area list, wherein the normalized overlapping area list includes the intersection area ratio of each conductive line segment and the corresponding grid unit; Based on the normalized overlapping area list, the maximum value of the intersection area ratio between the conductive line segment and the grid unit in the strain concentration area corresponding to each effective impedance mutation event is extracted to generate a local space overlapping area list, and the maximum value of the intersection area ratio in the local space overlapping area list is mapped to a spatial coupling parameter.

5. The method according to claim 1, characterized in that According to the mapping rule, the failure mode of the flexible electronic product under the composite bending path is judged, wherein the failure mode includes substrate fracture failure represented by the dynamic strain field distribution data and circuit functional failure represented by the impedance dynamic response spectrum, including: Extracting a spatial coupling threshold and a temporal coupling threshold from the mapping rule to generate a failure criterion list, wherein the failure criterion list includes a trigger condition combination of a substrate fracture failure criterion and a circuit functional failure criterion; Inputting the mechanical bending parameters in the time series correlation data set into a composite bending path simulation model to generate a path stress distribution map for each bending cycle, wherein the path stress distribution map includes an equivalent bending stress amplitude of each node in a three-dimensional space; Performing strain gradient accumulation analysis on the dynamic strain field distribution data, extracting the maximum strain gradient value and its spatial coordinates in each bending cycle, and generating a strain accumulation feature list; According to the phase angle variation trend of the impedance dynamic response spectrum, the impedance attenuation rate of the conductive circuit is identified, and a circuit performance degradation curve is generated, wherein the circuit performance degradation curve includes a corresponding relationship between the impedance attenuation rate of each conductive circuit and the bending period; Perform threshold matching on the maximum strain gradient value in the strain accumulation feature list and the substrate fracture failure criterion in the failure criterion list to generate a substrate fracture risk event list, wherein the substrate fracture risk event list includes the bending cycle number and spatial coordinates corresponding to the excessive strain gradient value; Slope matching is performed between the impedance decay rate in the circuit performance degradation curve and the circuit functional failure criteria in the failure criteria list to generate a circuit failure risk event list, wherein the circuit failure risk event list includes the bending cycle number and the conductive line identifier corresponding to the excessive impedance decay rate; Performing a multi-cycle spatiotemporal correlation analysis on the substrate fracture risk event list and the circuit failure risk event list, counting the spatiotemporal coincidence of substrate fracture and circuit failure events within the same bending cycle, and generating a composite failure correlation parameter; Based on the composite failure correlation parameter and the equivalent bending stress amplitude of the path stress distribution map, the failure mode of the flexible electronic product is determined.

6. The method according to claim 1, characterized in that A strain-sensitive marker layer is attached to the surface of the flexible electronic product, and dynamic strain field distribution data of the strain-sensitive marker layer under the standard bending load spectrum is simultaneously collected, including: According to the thermal expansion coefficient and surface curvature of the substrate material of the flexible electronic product, the grid density and the size of the marking point of the strain-sensitive marking layer are designed to generate strain-sensitive marking layer parameters adapted to the deformation characteristics of the flexible electronic product, wherein the strain-sensitive marking layer parameters include the spacing of the marking point array, the diameter of the marking point, and the substrate adhesion strength threshold; Printing a marking point array corresponding to the parameters of the strain-sensitive marking layer on the surface of the flexible substrate to generate a strain-sensitive marking layer that does not interfere with the conductive circuit of the flexible electronic product, wherein the marking point array of the strain-sensitive marking layer covers the entire bending action area and an anti-peeling buffer zone is reserved at the edge; During the process of applying a standard bending load spectrum by the multi-axis bending manipulator, a dynamic speckle image sequence of the strain sensitive marker layer is synchronously acquired by using a high-speed digital image acquisition system at a sampling rate matching the bending frequency; Performing sub-pixel displacement field calculation on the dynamic speckle image sequence to generate an initial displacement vector field, wherein the initial displacement vector field includes an instantaneous displacement component and a displacement direction angle of each marking point in a three-dimensional space; Based on the initial displacement vector field, the strain components in the neighborhood of each marking point are calculated to generate original strain field distribution data, which includes the principal strain amplitude, shear strain component and strain gradient direction. The original strain field distribution data is spatially filtered and outlier points are eliminated to generate calibrated dynamic strain field distribution data.

7. The method according to claim 1, characterized in that A preset flexible circuit impedance dynamic monitoring probe is non-invasively connected to the conductive circuit of the flexible electronic product, and the impedance amplitude and phase angle change data of each conductive circuit are recorded during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum, including: According to the line width, spacing and material impedance characteristics of the conductive circuit of the flexible electronic product, the contact pressure and contact array distribution parameters of the non-invasive probe are designed to generate a probe configuration scheme adapted to the target conductive circuit, wherein the probe configuration scheme includes the contact array spacing, the single contact pressure threshold and the multi-point contact synchronization error range; Based on the probe configuration scheme, the contact array of the flexible circuit impedance dynamic monitoring probe is arranged on both sides of the target conductive line in a non-overlapping manner across the line, forming a dual-channel contact structure parallel to the conductive line. During the loading process of the standard bending load spectrum, based on the dynamic contact characteristics of the dual-channel contact structure, the original impedance time series data of each conductive line is collected at a sampling frequency synchronized with the mechanical bending phase to generate an initial impedance time series data set, wherein the initial impedance time series data set includes impedance amplitude, phase angle, and time series alignment records of the corresponding bending phase; Performing sliding window average filtering and adaptive notch filtering on the initial impedance time series data set to eliminate contact noise and electromagnetic interference caused by bending motion, and generate processed impedance time series data; Based on the processed impedance time series data, the impedance change rate threshold detection and phase angle differential joint judgment are used to identify the impedance mutation event candidate points of each conductive line, and an impedance event candidate list is generated, wherein the impedance event candidate list includes an event timestamp, an impedance change rate, and a phase angle offset; Performing bending cycle synchronization verification on the impedance event candidate list, screening effective impedance events having a fixed time delay relationship with the bending phase of the standard bending load spectrum, and generating an effective impedance event list, wherein the effective impedance event list includes an event timestamp, an identifier of the conductive line to which it belongs, and an event level classification; Based on the effective impedance event list and the processed impedance time series data, the impedance amplitude fluctuation range, phase angle shift trend and event triggering frequency of each conductive line within the bending period are counted to generate an impedance dynamic response spectrum, which includes the impedance amplitude and phase joint distribution characteristics segmented by bending phase and an event density heat map.

8. The electronic product testing system is characterized by: include: A generation module, for applying periodic bending loads to flexible electronic products using a multi-axis bending robot according to a fabric bending fatigue test standard, and generating a standard bending load spectrum that meets the test standard, wherein the motion trajectory of the multi-axis bending robot includes a programmable composite bending path in a three-dimensional space; A collection module, used for attaching a strain-sensitive marker layer to the surface of the flexible electronic product, and synchronously collecting dynamic strain field distribution data of the strain-sensitive marker layer under the action of the standard bending load spectrum; An access module is used to connect a preset flexible circuit impedance dynamic monitoring probe to the conductive circuit of the flexible electronic product in a non-invasive manner, and record the impedance amplitude and phase angle change data of each conductive circuit during the action of the standard bending load spectrum to generate an impedance dynamic response spectrum; An alignment module, used to align the mechanical bending parameters in the standard bending load spectrum with the dynamic strain field distribution data and the acquisition time of the impedance dynamic response spectrum to generate a time series associated data set, wherein the time series data set includes: mechanical bending parameters, spatiotemporal evolution characteristics of strain field and impedance dynamic response characteristics; An analysis module, for analyzing the spatial position matching and time correlation between the local strain concentration area under a preset bending phase and the impedance mutation event of the corresponding conductive line in the impedance dynamic response spectrum based on the time series correlation data set, so as to determine the mapping law between mechanical deformation and circuit failure in the flexible electronic product; A judgment module is used to judge the failure mode of the flexible electronic product under the composite bending path according to the mapping rule, wherein the failure mode includes substrate fracture failure represented by the dynamic strain field distribution data and circuit functional failure represented by the impedance dynamic response spectrum.

9. A computing device, characterized in that It comprises 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 the electronic product testing method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the electronic product testing method according to any one of claims 1 to 7 is implemented.

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