A fatigue strength testing method and system based on self-feedback image acquisition

Abstract

The present invention discloses a fatigue strength testing method and system based on self-feedback image acquisition. The testing method includes: based on a preset tensile force signal period t0, determining a set of sampling points with λt0 as the sampling period, where λ>1 and λ is a non-integer so that any two sampling points are within different periods of the tensile force signal and at different phases of different periods; controlling the start of the tensile force machine based on the parameter t0, and simultaneously controlling the acquisition card to acquire the actual tensile force signal of the tensile force machine and the camera to acquire images of the test piece based on the sampling point time; and obtaining the stress-strain relationship of the test piece at different phases within the same period of the tensile force signal based on the images and tensile force signals acquired at the set of sampling point times. The present invention achieves complete acquisition of high-frequency tensile force data and efficient and accurate analysis of strain parameter changes during the high-frequency tensile process of the test piece through low-frequency sampling.

Description

Technical Field

[0001] The present invention relates to the technical field of fatigue failure testing, and in particular to a fatigue strength testing method and system based on acquired images. Background Art

[0002] Fatigue damage is one of the most common failure modes in engineering structures. According to statistics, 80% to 90% of the total damage to various mechanical parts is caused by fatigue fracture, and the direct economic losses caused account for about 6% to 8% of the annual gross national product of countries such as the United States, Japan, and the European Community.

[0003] In order to give full play to the material performance and ensure the reliability of the structure, it is necessary to carry out fatigue tests on the material under alternating loads and accurately evaluate the fatigue performance of the material.

[0004] For high-cycle fatigue, in traditional fatigue experiments, since the entire experimental process is very long and the frequency of actual material stretching is also relatively high, if it is necessary to ensure that the complete stretching process data can be collected, the camera sampling frequency must also be relatively high. In this way, after the entire experiment is completed, a large amount of image data will be collected, requiring a huge amount of storage space. At the same time, a large amount of image data will also make subsequent analysis more difficult. Summary of the Invention

[0005] In response to the problems of the above-mentioned prior art, the present invention provides a fatigue strength testing method and system based on image acquisition, which can realize the analysis of the high-frequency stretching process of the stretching machine by low-frequency image acquisition. The technical solution is as follows:

[0006] In a first aspect, a fatigue strength testing method based on acquired images is provided, comprising the following steps:

[0007] Based on the preset tensile force signal period t0, a set of sampling points is determined with λt0 as the sampling period, where λ>1 and λ is a non-integer so that any two sampling points are in different periods of the tensile force signal and are located at different phases of different periods;

[0008] The stretching machine is started based on the parameter t0, and the acquisition card is controlled to collect the actual tensile force signal of the stretching machine and the camera is controlled to collect the image of the test piece based on the sampling point time;

[0009] Based on the images of the test piece and the tension signal collected at a set of sampling points, the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal is obtained.

[0010] Here, the above λ=n+1 / m, where n and m are integers.

[0011] Among them, the above m is not less than 4.

[0012] The method of controlling the acquisition card to acquire the actual tensile force signal of the stretching machine and controlling the camera to acquire the image of the test piece based on the sampling point time includes:

[0013] Analyze the stress-strain relationship of the test piece under the tensile signal at the current sampling point based on the image collected at the current sampling point;

[0014] Analyze the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the stretching machine to obtain the accurate time of the next sampling point;

[0015] Based on the accurate time of the next sampling point, the acquisition card is controlled to acquire the actual tensile force signal of the stretching machine and the camera is controlled to acquire the image of the tested piece.

[0016] The analysis of the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the stretching machine to obtain the accurate next sampling point time includes:

[0017] If the actual tension signal cycle of the stretching machine changes from t0, the time of the next sampling point is re-determined based on the changed tension signal cycle t1 with λt1 as the sampling cycle.

[0018] The changed tension signal period t1 is determined based on a trained tension signal period prediction model.

[0019] The method of controlling the acquisition card to acquire the actual tensile force signal of the stretching machine and controlling the camera to acquire the image of the test piece based on the sampling point time includes:

[0020] Analyze the stress-strain relationship of the test piece under the tensile signal at the current sampling point based on the image collected at the current sampling point;

[0021] Strain parameter change data is obtained based on the strain parameters obtained at the previous sampling point and the strain parameters obtained at the current sampling point, and it is determined based on the strain parameter change data whether to add multiple phase sub-sampling points between the current sampling point phase and the next sampling point phase.

[0022] The step of determining whether to add multiple sub-sampling points between the current sampling point phase and the next sampling point phase based on the strain parameter change data includes:

[0023] When the strain parameter change between the previous sampling point and the current sampling point exceeds a first preset threshold range or the strain parameter of the current sampling point exceeds a second preset threshold range, it is determined that the material characteristics of the test piece are destroyed, and it is determined to add multiple sub-sampling points between the current sampling point and the next sampling point.

[0024] In a second aspect, a fatigue strength testing system based on acquired images is provided, comprising:

[0025] a signal acquisition parameter determination unit, configured to determine a set of sampling points for the tension signal based on a preset tensioning machine tension signal period t0 and with λt0 as a sampling period, where λ>1 and λ is a non-integer so that any two sampling points are within different periods of the tension signal and are located at different phases of different periods;

[0026] The signal acquisition control unit is used to control the start of the stretching machine based on the parameter t0, and at the same time control the acquisition card to collect the actual tensile force signal of the stretching machine and control the camera to collect the image of the test piece based on the sampling point time;

[0027] The stress-strain analysis unit is used to obtain the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal based on the test piece image and tension signal collected at a set of sampling points.

[0028] In a third aspect, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the fatigue strength testing method as described in the first aspect are implemented.

[0029] The fatigue strength testing method and system based on acquired images of the present invention have the following beneficial effects:

[0030] With λt as the sampling period, where λ>1, the camera captures images at a frequency (1 / λt) lower than the stretching frequency (1 / t) of the stretcher, thereby enabling low-frequency image acquisition and analysis of the high-frequency stretching process. This method achieves complete acquisition of high-frequency tensile force data and efficient and accurate analysis of strain parameter changes during the high-frequency stretching of the test piece through low-frequency sampling, reducing the amount of collected image data, avoiding the complexity of analyzing large amounts of image data, and reducing the storage space required for the collected data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a schematic diagram of the equipment position structure in the fatigue strength test scenario of this application;

[0032] Figure 2 This is an embodiment of a tension signal and a sampling point in the present application;

[0033] Figure 3 This is a flow chart of a fatigue strength test method based on collected images in this application

[0034] Figure 4 This is a structural diagram of a fatigue strength testing system based on acquired images in this application. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] The present application provides a fatigue strength testing method based on self-feedback image acquisition, comprising the following steps:

[0037] Based on the preset tensile force signal period t0, a set of sampling points is determined with λt0 as the sampling period, where λ>1 and λ is a non-integer so that any two sampling points are in different periods of the tensile force signal and are located at different phases of different periods;

[0038] The stretching machine is started based on the parameter t0, and the acquisition card is controlled to collect the actual tensile force signal of the stretching machine and the camera is controlled to collect the image of the test piece based on the sampling point time;

[0039] Based on the images of the test piece and the tension signal collected at a set of sampling points, the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal is obtained.

[0040] In the embodiment of the present application, λt0 is used as the sampling period, λ>1, so that the camera can be stretched at a frequency lower than the stretching frequency of the stretching machine 1 / t0. Image acquisition is performed, thereby realizing the analysis of the high-frequency stretching process of the stretching machine by low-frequency image acquisition.

[0041] Furthermore, in the present application, λ is a non-integer, which enables any two sampling points in a set of sampling points to be in different cycles of the tension signal and at different phases of different cycles. Based on such a set of sampling points, the tension data of different phases of the tension signal collected by a set of sampling points in different cycles are translated to the same cycle, which is equivalent to obtaining sampling points at different phase positions in the same cycle of the tension signal. Correspondingly, based on the image of the test piece collected at a set of sampling point times, the strain parameters of the test piece at each sampling point time are analyzed, and based on the amplitude of the tension signal collected at a set of sampling point times, the stress-strain relationship of the test piece under different tensions is obtained.

[0042] Furthermore, the number of a group of sampling points should be sufficient to reconstruct a periodic tension signal corresponding to a group of sampling points based on the tension signal collected at each sampling point, thereby reconstructing a periodic tension signal and simultaneously fitting the strain parameter change curve of the test piece under a periodic tension signal based on the strain parameters of the test piece obtained from the image collected by a group of sampling points.

[0043] Thus, the present application realizes the complete acquisition of high-frequency tensile data by low-frequency sampling and the efficient and accurate analysis of the changes in strain parameters of the test piece during high-frequency stretching, reduces the amount of collected image data, avoids the complexity of analyzing a large amount of image data, and reduces the storage space for collected data.

[0044] Specifically, in the fatigue strength test scenario of the embodiment of the present application, the equipment position layout is as follows: Figure 1 As shown in the figure, the system mainly consists of five parts: a camera, an acquisition card, a computer, a tensile machine, and a light source. The tensile machine applies a long-term fatigue load to the test piece and outputs a tensile force signal corresponding to the load. The acquisition card collects the tensile force signal and transmits an image acquisition clock signal to the camera. The clock signal format is generated by the user inputting relevant parameters into the computer through an algorithm. The image captured by the camera is transmitted to the computer and stored, and its phase corresponds to the tensile force signal collected at the same time by the tensile machine.

[0045] Preferably, λ=n+1 / m, wherein n and m are integers.

[0046] In the embodiment of the present application, λ is in the form of λ=n+1 / m, so as to realize the cyclic variation of the sampling phase of each group of sampling points, so as to facilitate obtaining the phase of the tension signal collected at each sampling point based on a group of sampling points in the λt0 sampling period. For example,

[0047] λ=n+1 / m, assuming n=5, m=8, the camera starts collecting data at the 4th t+1 / 8th t of the periodic tension signal, and the subsequent sampling points are: 9t+2 / 8t, 14t+3 / 8t, 19t+4 / 8t, 24t+5 / 8t, 29t+6 / 8t, 34t+7 / 8t, 39t+8 / 8t, ..., the schematic diagram of the periodic tension signal and the sampling points is shown in Figure 2 As shown in , the phases of the sampling points are: 1 / 8t, 2 / 8t, 3 / 8t, 4 / 8t, 5 / 8t, 6 / 8t, 7 / 8t, 8 / 8t, and the phases change periodically in this order. Figure 2 It can be seen that only 8 sampling points need to be collected at a low frequency in the tension signal of n*m=40 cycles, which is equivalent to the camera collecting images at a low frequency realizing the collection of 8 sampling points of the tension signal of 1 cycle in the process of 40 cycles of the tension signal, realizing the analysis of the high-frequency stretching process of the stretching machine by low-frequency image collection. The 8 sampling points are a group of sampling points of the tension periodic signal of 1 cycle.

[0048] The number m of a group of sampling points should satisfy that the tension signals collected at m sampling points can reconstruct a tension signal of one cycle corresponding to the group of sampling points. In the embodiment of the present application, preferably, m is not less than 4.

[0049] In one embodiment, the above-mentioned control of the acquisition card based on the sampling point time to collect the actual tensile force signal of the stretching machine and the control of the camera to collect the image of the test piece includes:

[0050] Analyze the stress-strain relationship of the test piece under the tensile signal at the current sampling point based on the image collected at the current sampling point;

[0051] Analyze the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the stretching machine to obtain the accurate time of the next sampling point;

[0052] Based on the accurate time of the next sampling point, the acquisition card is controlled to acquire the actual tensile force signal of the stretching machine and the camera is controlled to acquire the image of the tested piece.

[0053] In an embodiment of the present application, when the period of the tension signal is t0, a group of sampling points determined with λt0 as the sampling period can determine the phase of the tension signal corresponding to each sampling point. Combined with the tension amplitude data collected at the sampling point, the tension signal of a period with a period of t0 can be restored. However, considering that the tension signal of the stretching machine is not necessarily output uniformly, if a fixed group of sampling points set according to the initial period t0 is continuously collected, the phase of the tension signal actually collected at the sampling point time will change, that is, the phase and amplitude data of the tension signal obtained at the sampling point time are not truly corresponding, resulting in unusable collected data.

[0054] This application proposes a mechanism for real-time prediction and monitoring of the tensile force signal output by a stretching machine. During actual use, the stretching machine is subject to interference from various factors, including ambient temperature, humidity, magnetic fields, mechanical structure vibrations, and so on. This can lead to a cumulative error in the period of the stretching machine signal. However, generally speaking, the accumulation of errors follows certain patterns. Therefore, a time series data prediction mechanism is employed for the tensile force signal. Based on the actual periodic changes in the tensile force signal, the future period of the tensile force signal is predicted. When the period of the tensile force signal changes, the time of the next sampling point is re-determined.

[0055] Furthermore, the accuracy of the next sampling point time is analyzed based on the periodic change of the actual tensile force signal of the stretching machine to obtain the accurate next sampling point time, including:

[0056] If the actual tension signal cycle of the stretching machine changes from t0, the time of the next sampling point is re-determined based on the changed tension signal cycle t1 with λt1 as the sampling cycle.

[0057] In an embodiment of the present application, if the actual tensile force signal period of the stretching machine changes from t0, the time of the next sampling point is re-determined based on the changed tensile force signal period t1 with λt1 as the sampling period. At this time, the tensile force signal phase of the re-determined next sampling point is consistent with the tensile force signal phase of the sampling point previously determined with λt0 as the sampling period.

[0058] Furthermore, it can be considered that when the change of the tension signal period t0 exceeds the preset threshold range, the next sampling point time is re-determined based on the changed tension signal period t1 with λt1 as the sampling period. Considering that when the deviation between the changed tension signal period t1 and the initial period t0 is less than the preset threshold, the error between the multiple sampling points of the tension signal determined based on the period t1 with λt1 as the sampling period and the group of sampling points originally determined with the period t1 is very small and can be ignored.

[0059] The changed tension signal period t1 is determined based on the trained tension signal period prediction model.

[0060] Specifically, the tension signal period prediction model is trained based on the LSTM neural network. The training process includes:

[0061] A 4-layer network is adopted, including 1 input layer, 2 hidden layers and 1 output layer. The 2 hidden layers are connected in series to realize deep extraction of input data features. The optimization algorithm adopts the momentum gradient descent algorithm, and the transfer function is the sigmoid function. In the embodiment of the present application, multiple hidden layers are adopted to improve the prediction accuracy of the periodic change of the tension signal. At the same time, in order to avoid the overfitting problem caused by the multiple hidden layers, the Dropout algorithm is added to the loss function used in the training process. For the initialization parameters used in the initial stage of the training process, such as the number of hidden layer nodes, the particle swarm optimization algorithm is used to optimize and obtain the best model training hyperparameter matching strategy, and the model training process is carried out to improve the model training efficiency and model accuracy.

[0062] In one embodiment, the above-mentioned control of the acquisition card to collect the actual tensile force signal of the stretching machine and the control of the camera to collect the image of the test piece based on the sampling point time includes:

[0063] Analyze the stress-strain relationship of the test piece under the tensile signal at the current sampling point based on the image collected at the current sampling point;

[0064] Strain parameter change data is obtained based on the strain parameters obtained at the previous sampling point and the strain parameters obtained at the current sampling point, and it is determined based on the strain parameter change data whether to add multiple phase sub-sampling points between the current sampling point phase and the next sampling point phase.

[0065] When the strain parameter changes between the previous sampling point and the current sampling point are abnormal, it indicates that the material properties of the test piece have been destroyed during the fatigue test. In this case, in the embodiment of the present application, the sampling frequency is increased during the process of material property destruction by reducing the sampling interval to accurately analyze the cause of the problem. Specifically, adding multiple sub-sampling points between the phase of the current sampling point and the phase of the next sampling point can be achieved by reducing the value of n and increasing the value of m. Taking the phase change sequence of 1 / 8t, 2 / 8t, 3 / 8t, 4 / 8t, 5 / 8t, 6 / 8t, 7 / 8t, and 8 / 8t as an example, if analysis shows that multiple sub-sampling points need to be added between 9t+2 / 8t (current sampling point) and 14t+3 / 8t (next sampling point), a sampling point of 10t+3 / 16t can be added. That is, n=5 becomes n=1, and m=8 becomes m=16. A sub-sampling point is added at the phase of the tension signal of the 11th cycle at 3 / 16t.

[0066] Furthermore, the above-mentioned determining whether to add a plurality of sub-sampling points of phases between the current sampling point phase and the next sampling point phase based on the strain parameter change data includes:

[0067] When the strain parameter change between the previous sampling point and the current sampling point exceeds a first preset threshold range or the strain parameter of the current sampling point exceeds a second preset threshold range, it is determined that the material characteristics of the test piece are destroyed, and it is determined to add multiple sub-sampling points between the current sampling point and the next sampling point.

[0068] In the embodiment of the present application, an abnormal change in the strain parameters between the previous sampling point and the current sampling point may be caused by the difference between the strain parameters of the previous sampling point and the current sampling point exceeding a preset strain change threshold range, or the strain parameter of the current sampling point exceeding a preset strain parameter threshold range, or the strain parameter of the current sampling point being abnormal due to a change pattern inconsistent with that of the previous strain parameter. In this case, considering that the material properties of the test piece are destroyed, multiple sub-sampling points are added between the current sampling point and the next sampling point to facilitate a detailed analysis of the change process of the test piece in the time period between the previous sampling point and the next sampling point.

[0069] In addition, in the above-mentioned re-correction of the time of the next sampling point based on the periodic change of the actual tensile machine tension signal or determination of whether to add multiple sub-sampling points between the current sampling point and the next sampling point based on the strain parameter change data of the previous sampling point and the current sampling point, in an embodiment of the present application, it can be performed simultaneously. Specifically, the time of the next sampling point is first re-corrected based on the periodic change of the actual tensile machine tension signal to determine whether the phase of the next sampling point is the correct phase, such as the phase change sequence set above 1 / 8t, 2 / 8t, 3 / 8t, 4 / 8t, 5 / 8t, 6 / 8t, 7 / 8t, 8 / 8t. On this basis, based on the strain parameter change data of the previous sampling point and the current sampling point, it is determined whether it is necessary to add multiple sub-sampling points before the next sampling point.

[0070] Specifically, the process of revising the next sampling point time or adding multiple sub-sampling points includes: after the system is started, a set of sampling points is determined based on the preset tensile machine tension signal period t0, with λt0 as the sampling period, where λ = n + 1 / m. After transmitting the determined sampling point parameters to the acquisition card, the system begins the fatigue test process. During the fatigue test phase, the system simultaneously performs real-time stress and strain analysis on the collected images of the test piece and predicts and monitors the tensile force signal output by the tensile machine in real time. When the actual tensile machine tension signal period changes, the sampling time of the next sampling point phase is revised. When the strain parameter change data is normal, the tension signal acquisition and image acquisition continue at the preset sampling phase. Otherwise, multiple sub-sampling points are added before the next sampling point.

[0071] The present application also provides a fatigue strength testing system for collecting images based on self-feedback, which is characterized by comprising:

[0072] a signal acquisition parameter determination unit, configured to determine a set of sampling points for the tension signal based on a preset tensioning machine tension signal period t0 and with λt0 as a sampling period, where λ>1 and λ is a non-integer so that any two sampling points are within different periods of the tension signal and are located at different phases of different periods;

[0073] The signal acquisition control unit is used to control the start of the stretching machine based on the parameter t0, and at the same time control the acquisition card to collect the actual tensile force signal of the stretching machine and control the camera to collect the image of the test piece based on the sampling point time;

[0074] The stress-strain analysis unit is used to obtain the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal based on the test piece image and tension signal collected at a set of sampling points.

[0075] In some embodiments, a fatigue strength testing system based on acquired images provided by an embodiment of the present invention can be implemented by a combination of software and hardware. As an example, a fatigue strength testing system based on acquired images provided by an embodiment of the present invention can be directly embodied as a combination of software modules executed by a processor. The software module can be located in a storage medium, and the storage medium is located in a memory. The processor reads the executable instructions included in the software module in the memory, and combines with necessary hardware (for example, including a processor and other components connected to a bus) to complete a fatigue strength testing method based on acquired images provided by an embodiment of the present invention.

[0076] In addition, the fatigue strength testing system based on acquired images provided in this embodiment and the fatigue strength testing method based on acquired images provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the fatigue strength testing method based on acquired images, which will not be repeated here.

[0077] The present invention is not limited to the above-mentioned specific implementation methods. Various changes made by ordinary technicians in this field based on the above-mentioned concept without creative work are all within the scope of protection of the present invention.

Claims

1. A fatigue strength testing method based on self-feedback image acquisition, characterized in that: The steps include: Based on the preset tensile force signal period t0, a set of sampling points is determined with λt0 as the sampling period, where λ>1 and λ is a non-integer so that any two sampling points are in different periods of the tensile force signal and are located at different phases of different periods; The stretching machine is started based on the parameter t0, and the acquisition card is controlled to collect the actual tensile force signal of the stretching machine and the camera is controlled to collect the image of the test piece based on the sampling point time; Based on the images of the test piece and the tension signal collected at a set of sampling points, the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal is obtained; The method of controlling the acquisition card to acquire the actual tensile force signal of the tensile machine and controlling the camera to acquire the image of the test piece based on the sampling point time includes: analyzing the stress-strain relationship of the test piece under the tensile force signal at the current sampling point based on the image acquired at the current sampling point; analyzing the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the tensile machine to obtain the accurate time of the next sampling point; and controlling the acquisition card to acquire the actual tensile force signal of the tensile machine and controlling the camera to acquire the image of the test piece based on the accurate time of the next sampling point.

2. The fatigue strength testing method based on self-feedback image acquisition according to claim 1, characterized in that: λ=n+1 / m, where n and m are integers.

3. The fatigue strength testing method based on self-feedback image acquisition according to claim 1, characterized in that: m is not less than 4.

4. The fatigue strength testing method based on self-feedback image acquisition according to claim 1, characterized in that: The analysis of the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the stretching machine to obtain the accurate next sampling point time includes: If the actual tension signal cycle of the stretching machine changes from t0, the time of the next sampling point is re-determined based on the changed tension signal cycle t1 with λt1 as the sampling cycle.

5. The fatigue strength testing method based on self-feedback image acquisition according to claim 4, characterized in that: The changed tension signal period t1 is determined based on a trained tension signal period prediction model.

6. A fatigue strength testing method based on self-feedback image acquisition according to any one of claims 1 to 3, characterized in that: The method of controlling the acquisition card to acquire the actual tensile force signal of the stretching machine and controlling the camera to acquire the image of the test piece based on the sampling point time includes: Analyze the stress-strain relationship of the test piece under the tensile signal at the current sampling point based on the image collected at the current sampling point; Strain parameter change data is obtained based on the strain parameters obtained at the previous sampling point and the strain parameters obtained at the current sampling point, and it is determined based on the strain parameter change data whether to add multiple phase sub-sampling points between the current sampling point phase and the next sampling point phase.

7. The fatigue strength testing method based on self-feedback image acquisition according to claim 6, characterized in that: Determining whether to add multiple phase sub-sampling points between the current sampling point phase and the next sampling point phase based on the strain parameter change data includes: When the strain parameter change between the previous sampling point and the current sampling point exceeds a first preset threshold range or the strain parameter of the current sampling point exceeds a second preset threshold range, it is determined that the material characteristics of the test piece are destroyed, and it is determined to add multiple sub-sampling points between the current sampling point and the next sampling point.

8. A fatigue strength testing system based on self-feedback image acquisition, characterized in that: include: a signal acquisition parameter determination unit, configured to determine a set of sampling points for the tension signal based on a preset tensioning machine tension signal period t0 and with λt0 as a sampling period, where λ>1 and λ is a non-integer so that any two sampling points are within different periods of the tension signal and are located at different phases of different periods; The signal acquisition control unit is used to control the start of the stretching machine based on the parameter t0, and at the same time control the acquisition card to collect the actual tensile force signal of the stretching machine and control the camera to collect the image of the test piece based on the sampling point time; The method of controlling the acquisition card to acquire the actual tensile force signal of the tensile machine and controlling the camera to acquire the image of the test piece based on the sampling point time includes: analyzing the stress-strain relationship of the test piece under the tensile force signal at the current sampling point based on the image acquired at the current sampling point; analyzing the accuracy of the next sampling point time based on the periodic change of the actual tensile force signal of the tensile machine to obtain the accurate time of the next sampling point; and controlling the acquisition card to acquire the actual tensile force signal of the tensile machine and controlling the camera to acquire the image of the test piece based on the accurate time of the next sampling point; The stress-strain analysis unit is used to obtain the stress-strain relationship of the test piece at different phases within the same cycle of the tension signal based on the test piece image and tension signal collected at a set of sampling points.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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