Test frequency conversion measurement method and system, equipment and storage medium

Abstract

The invention belongs to the technical field of composite material test, and provides a test frequency conversion measurement method and system, equipment and a storage medium, and the method comprises the following steps: obtaining temperature data and image data of a sample in a composite material test process; processing the temperature data and the image data to generate integrated data reflecting the real-time temperature state of the sample; establishing an interconnection relationship between the temperature integration data and the test frequency, and dynamically adjusting the test frequency according to the change trend of the temperature data; and setting a closed-loop control mechanism. The temperature data and the image data of the sample are synchronously acquired, the effective detection area is determined based on the image data, the temperature change of the core detection area is focused, the temperature space distribution model is established through mean filtering and data fusion, and the generated integrated data can accurately reflect the real-time temperature state of the sample. The hysteresis quality of indirect temperature control of the environment box is avoided, the real-time performance and accuracy of temperature monitoring are ensured, and therefore it is ensured that test data truly reflect the material performance.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of composite material test, and particularly relates to a test variable-frequency measurement method and system, equipment and a storage medium. BACKGROUND

[0002] Composite materials are widely used in industrial production and social life due to their characteristics of light weight, high strength, high modulus and unique function. The materials are formed by two or more than two materials with different properties through special design and process combination, which not only maintains the performance advantages of each component material, but also realizes performance complementation through composite process to obtain the optimal performance of the combined material. Therefore, the performance research is particularly important.

[0003] Unlike metal materials, the heat generation phenomenon of composite materials during mechanical property testing (such as fatigue testing, tensile testing, etc.) is much higher than that of metal materials. The excessively high temperature directly changes the microstructure of the material, resulting in distorted test data and unable to accurately reflect the real performance of the material.

[0004] Currently, the industry indirectly controls the temperature of the test sample to ensure the stability of the test temperature, that is, to maintain the constant temperature of the test space. However, this method has obvious defects: 1. Temperature control lag: the environmental box cannot respond to the heat change of the test sample in real time. When the test sample continuously generates heat during the test process, the cooling or temperature control speed of the environmental box cannot match the temperature rising speed of the test sample, which easily leads to local temperature exceeding of the test sample.

[0005] 2. The existing method does not establish a linkage relationship between the "test sample temperature" and the "test frequency". SUMMARY

[0006] In order to solve the problems in the background art, the present application provides a test variable-frequency measurement method and system, equipment and a storage medium.

[0007] In order to achieve the above purpose, the present application adopts the following technical solutions: The present application provides a test variable-frequency measurement method, comprising the following steps: Obtaining temperature data and image data of the test sample during the test of the composite material; Processing the temperature data and image data to generate integrated data reflecting the real-time temperature state of the test sample; Establishing an interconnection relationship between the temperature integrated data and the test frequency, and dynamically adjusting the test frequency according to the change trend of the temperature data; Setting a closed-loop control mechanism to compare the temperature of the test sample with the preset temperature threshold in real time; if the temperature of the test sample does not exceed the preset temperature threshold, the test frequency is dynamically adjusted according to the temperature change; if the temperature of the test sample exceeds the preset temperature threshold, the test frequency remains a fixed value or gradually decreases to a set value.

[0008] Furthermore, acquiring temperature data and image data of the specimens during the composite material testing process includes the following steps: Acquire initial image data of the sample, determine the effective detection area of ​​the sample based on the initial image data, and remove the edge and background areas of the sample from the effective detection area. Within the effective detection area, temperature data of the sample is collected at preset time intervals, and image data of the sample is collected simultaneously. The temperature data includes real-time temperature values ​​at different detection points of the sample, and the image data includes the outline, position and surface texture information of the sample.

[0009] Furthermore, the temperature and image data are processed to generate integrated data reflecting the real-time temperature state of the sample, including the following steps: The mean filtering algorithm is used to remove random noise from the temperature data, and the image data is denoised. The temperature data with random noise removed is fused with the noise-reduced image data; Based on the pixel coordinates of the image data, a spatial distribution model of the temperature data is established, and the average temperature value, the highest temperature value, and the temperature gradient distribution within the effective detection area are calculated. The calculated data are integrated into a unified dataset that reflects the real-time temperature state of the sample.

[0010] Furthermore, an interconnection is established between integrated temperature data and test frequency, and the test frequency is dynamically adjusted based on the changing trends of the temperature data, including the following steps: Establish the interconnection between the average temperature value and the test frequency in the integrated temperature data, set the threshold range for the change of the average temperature value, and match the corresponding test frequency for each threshold range. When the average temperature value is within a certain threshold range, adjust the corresponding test frequency in real time.

[0011] The present invention also provides a test frequency conversion measurement system, comprising: A machine vision unit is used to acquire image data of the specimens during composite material testing. Infrared thermometer unit, used to acquire temperature data of the sample during composite material testing; The temperature control unit is used to process temperature data and image data to generate integrated data reflecting the real-time temperature status of the sample. The automatic frequency conversion unit is used to establish the interconnection between integrated temperature data and test frequency, and dynamically adjust the test frequency according to the changing trend of temperature data; A closed-loop control unit is configured to set a closed-loop control mechanism, compare the temperature of the sample with a preset temperature threshold in real time, dynamically adjust the test frequency according to the temperature change if the temperature of the sample does not exceed the preset temperature threshold, and keep the test frequency at a fixed value or gradually reduce the test frequency to a set value if the temperature of the sample exceeds the preset temperature threshold.

[0012] Further, the mechanical vision unit comprises: A mechanical vision module is configured to acquire initial image data of the sample, determine an effective detection area of the sample based on the initial image data, and remove the edge and background area of the sample from the effective detection area. An infrared temperature measurement unit is configured to collect temperature data of the sample in the effective detection area at a preset time interval, and the mechanical vision unit is further configured to synchronously collect image data of the sample, wherein the temperature data comprises real-time temperature values of different detection points of the sample, and the image data comprises the contour, position and surface texture information of the sample.

[0013] Further, the temperature measurement control unit comprises: A processing module is configured to remove random noise in the temperature data by using a mean filtering algorithm, and perform noise reduction processing on the image data. A fusion module is configured to fuse the temperature data after removing the random noise and the image data after the noise reduction processing. A calculation module is configured to establish a spatial distribution model of the temperature data based on the pixel coordinates of the image data, and calculate the average temperature value, the maximum temperature value and the temperature gradient distribution in the effective detection area. An integration module is configured to integrate the calculated data into integrated data reflecting the real-time temperature state of the sample.

[0014] Further, the automatic frequency conversion unit comprises: An automatic frequency conversion module is configured to establish an interconnection relationship between the average temperature value in the temperature integrated data and the test frequency, set a change threshold interval of the average temperature value, and match a corresponding test frequency for each change threshold interval, and adjust the corresponding test frequency in real time when the average temperature value is in a certain threshold interval.

[0015] The application further provides an electronic device comprising: A memory is configured to store a computer program. A processor is configured to execute the computer program stored on the memory to implement the test frequency conversion measurement method.

[0016] The application further provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the test frequency conversion measurement method.

[0017] The application has the following advantages: 1. The application synchronously acquires temperature data and image data of the sample, determines the temperature change of the effective detection area and the focused core detection area based on the image data, and establishes a temperature spatial distribution model through mean filtering and data fusion. The integrated data accurately reflects the real-time temperature state of the sample, avoids the hysteresis of indirect temperature control between environmental boxes, ensures the real-time and accuracy of temperature monitoring, and thus ensures that the test data truly reflects the material performance. 2. The application establishes the interconnection relationship between temperature integrated data and test frequency, matches the corresponding frequency for different temperature change threshold intervals, appropriately increases the frequency to shorten the test period when the temperature is low, and timely reduces the frequency to inhibit overheating when the temperature rises. This not only avoids overheating of the sample caused by fixed high frequency, but also solves the problem of low efficiency caused by fixed low frequency, improves the efficiency while ensuring the safety of the test.

[0018] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be achieved and obtained by the structure indicated in the specification and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0020] Figure 1 A flowchart of a test variable frequency measurement method of the present application is shown; Figure 2 A framework diagram of a test variable frequency measurement system of the present application is shown. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0022] As shown in Figure 1 is a test variable frequency measurement method, which includes the following steps: S1: acquiring temperature data and image data of the sample in the composite material test process.

[0023] S2: processing the temperature data and the image data to generate integrated data reflecting the real-time temperature state of the sample.

[0024] S3: establishing an interconnection relationship between the temperature integrated data and the test frequency, and dynamically adjusting the test frequency according to the change trend of the temperature data.

[0025] S4: setting a closed-loop control mechanism to compare the temperature of the sample with a preset temperature threshold in real time; if the temperature of the sample does not exceed the preset temperature threshold, the test frequency is dynamically adjusted according to the temperature change; if the temperature of the sample exceeds the preset temperature threshold, the test frequency remains a fixed value or gradually decreases to a set value.

[0026] It should be noted that the above steps of synchronously acquiring sample temperature data and image data and integrating can more comprehensively reflect the real-time state of the sample, avoiding the limitations of single data monitoring. Secondly, the interconnection relationship between temperature and test frequency is established, and the frequency is dynamically adjusted according to the temperature change trend, which prevents low temperature from causing low test efficiency and avoids abnormal temperature rise affecting data accuracy. Finally, relying on the closed-loop control mechanism, when the temperature does not exceed the preset threshold, the frequency is continuously dynamically adjusted, and once the limit is exceeded, the frequency is fixed or reduced by force, which ensures the safety and integrity of the test from the mechanism, and finally realizes the intelligentization, high efficiency and safety control of the test process.

[0027] It should be further noted that in the above test, the test frequency represents the number of times the sample completes the cyclic fatigue loading in a unit of time (usually 1 second). Generally, the frequency of the sample during the test process needs to be kept constant. However, due to the dynamic change of the frequency of the composite material at high frequency, the sample will generate heat, which will not cause the temperature of the sample to be too high, so the frequency needs to be adjusted in real time to adjust the temperature until the sample stabilizes to a certain frequency and does not exceed the temperature.

[0028] For example, the maximum allowed temperature of the sample is set to 30℃, which is the core threshold for triggering frequency adjustment, and the loading process is as follows: 1) During the test process, the temperature of the sample is monitored in real time (usually through contact thermocouples or non-contact infrared temperature measurement equipment). When the sample temperature is monitored to reach 30.1℃ (i.e. exceeding the preset upper limit by 0.1℃), the frequency down program is immediately started, and the down adjustment amplitude is 1Hz each time (for example, if the current frequency is 10Hz, then the adjusted frequency is 9Hz).

[0029] 2) After the frequency is down-regulated, the current frequency is maintained for 2-3 minutes, and the temperature change is continuously monitored during this period. If the sample temperature does not show a downward trend (still maintained at 30.1°C and above) after 2-3 minutes, the frequency is continuously down-regulated at an amplitude of 1 Hz; if the temperature decreases but does not decrease to below 30°C, the current frequency is still maintained for observation until the temperature is stable or the frequency is again triggered to decrease; if the temperature decreases to 30°C and below, the current frequency is maintained for further testing without further adjustment.

[0030] 3) The above process needs to be repeatedly performed until the sample temperature is stable at 30°C and below, and the temperature does not rise again above the threshold when the current frequency is continuously run, at which time the frequency is maintained to complete the subsequent test.

[0031] S1 specifically includes the following steps: S101: Obtain the initial image data of the sample, perform edge segmentation and background region elimination processing on the initial image through image recognition technology, and finally determine the effective detection area of the sample. This area only retains the sample body part, completely excluding the edge blur area and irrelevant background area.

[0032] S102: In the effective detection area, collect the temperature data of the sample at a preset time interval, and simultaneously collect the image data of the sample. The temperature data includes the real-time temperature values of different detection points of the sample, and the image data includes the contour, position and surface texture information of the sample.

[0033] It should be noted that in material detection (especially performance testing and analysis of composite materials, metal materials, etc.), the effective detection area refers to a specific area that can truly reflect the intrinsic characteristics of the material and is suitable for subsequent detection and analysis, which is selected from the initial image or physical space of the sample through technical means. Its core function is to exclude interference information to ensure the accuracy, relevance and repeatability of the detection data, and it is a key link connecting the original state of the sample and subsequent precise detection.

[0034] In the detection scene, the sample is usually placed on the test table, clamp or other support structure. These background objects (such as metal clamps, rubber gaskets, experimental table surfaces) have different physical properties (such as temperature, texture, composition) from the sample, and if mixed into the detection area, it will interfere with data collection. The effective detection area needs to be completely isolated from these irrelevant backgrounds through image segmentation or spatial positioning, and only the space occupied by the sample itself is retained.

[0035] In addition, the definition of the effective detection area needs to be related to the specific detection purpose. If the uniformity of the material is concerned, the area with the most representative structure in the sample needs to be selected; if the evolution of local defects (such as the cracking of the fiber-resin interface in composite materials) is studied, the characteristic part where defects are prone to occur (such as the transition zone between the fiber dense area and the resin rich area) needs to be focused on. The size and position of the effective detection area need to serve the detection target to ensure that the temperature and image data collected later can directly reflect the changes in the characteristics of the research object.

[0036] For example, in the high-temperature aging detection of composite materials, if the target is to analyze the thermal oxidation rate of the resin matrix, the effective detection area will preferentially select the area with continuous resin distribution and less fiber interference, rather than the "hard point" area with dense fiber bundles.

[0037] S2 specifically includes the following steps: S201: Adopting mean filter algorithm to remove random noise in temperature data, and performing noise reduction processing on image data.

[0038] S202: Fusing the temperature data with the random noise removed and the image data after noise reduction processing.

[0039] S203: Establishing a spatial distribution model of temperature data based on the pixel coordinates of image data, calculating the average temperature value, the maximum temperature value and the temperature gradient distribution in the effective detection area.

[0040] S204: Integrating the calculated data into integrated data reflecting the real-time temperature state of the sample.

[0041] It should be noted that the mean filter algorithm is used to process the temperature data in S201. The size of the sliding window is set, such as 5 consecutive collection periods, and the arithmetic mean of all temperature values in the window is used to replace the original value of each detection point to smooth the random fluctuations (such as the jump value caused by instantaneous misreading of the sensor. In addition, when performing image processing, if it is Gaussian noise, Gaussian filtering is used; if it is salt and pepper noise, median filtering is used. After processing, the outline edges and surface texture details of the image need to be preserved to avoid information loss caused by excessive blurring.

[0042] It should be further noted that in S203: The temperature spatial distribution model is established by taking the pixel coordinates (x-axis in the horizontal direction and y-axis in the vertical direction) of the image data as the spatial positioning reference, mapping the temperature value corresponding to each pixel in S202 to its coordinate position, and constructing a two-dimensional or three-dimensional temperature spatial distribution model. The two-dimensional model can be presented in the form of a heat map to intuitively reflect the temperature differences at different coordinate positions (for example, red represents a high-temperature area and blue represents a low-temperature area) by color gradient. The three-dimensional model takes the x and y coordinates as the plane axes and the temperature value as the vertical axis to form a three-dimensional surface, quantitatively displaying the continuous change trend of the temperature with the spatial position. The model needs to completely cover the coordinate range of the effective detection area to ensure the spatial integrity of the temperature distribution.

[0043] The calculation of the temperature characteristic parameters is based on the above-mentioned model, and the temperature data in the effective detection area is statistically calculated to obtain the following key parameters: Average temperature value: the arithmetic mean of all temperature data participating in modeling in the effective detection area, the calculation formula is: average temperature = the sum of temperature values participating in calculation / the number of temperature values participating in calculation.

[0044] Maximum temperature value and corresponding position: screening the maximum temperature value in the effective detection area, and recording the specific position (x, y) or (x, y, z) of the temperature value in the image coordinate system to locate the local area with the highest temperature.

[0045] Temperature gradient distribution: for adjacent pixels or points in the effective detection area, the temperature gradient value is calculated, the formula is: temperature gradient = temperature difference of two points / distance of straight line between two points; by traversing all adjacent point pairs, a spatial distribution matrix of temperature gradient is formed to quantitatively reflect the change rate of temperature in the local area and identify the boundary or transition zone with sharp temperature change.

[0046] S3 specifically includes the following steps: The interconnection relationship between the average temperature value in the temperature integrated data and the test frequency is established, the change threshold interval of the average temperature value is set, and the corresponding test frequency is matched for each change threshold interval. When the average temperature value is in a certain threshold interval, the corresponding test frequency is adjusted in real time.

[0047] It should be noted that, in order to ensure the accuracy of the test frequency adjustment, S3 can also introduce a duration determination mechanism. For example, when it is monitored that the average temperature value enters a certain threshold interval, the frequency is not immediately adjusted, but the timer is started. If the temperature state lasts for a preset time length (such as 30 seconds), it is confirmed that it is a stable change rather than an accidental fluctuation, and then the test frequency is automatically adjusted to the matching value of the interval, so as to avoid frequent adjustment caused by accidental temperature disturbance.

[0048] For example, Figure 2As shown, it is a test variable frequency measurement system, including a mechanical vision unit, an infrared temperature measurement unit, a temperature measurement control unit, an automatic variable frequency unit and a closed-loop control unit. The mechanical vision unit is used to obtain image data of the sample during the test process of the composite material; the infrared temperature measurement unit is used to obtain temperature data of the sample during the test process of the composite material; the temperature measurement control unit is used to process the temperature data and the image data, and generate integrated data reflecting the real-time temperature state of the sample; the automatic variable frequency unit is used to establish the interconnection relationship between the temperature integrated data and the test frequency, and dynamically adjust the test frequency according to the change trend of the temperature data; the closed-loop control unit is used to set the closed-loop control mechanism, and compare the temperature of the sample with the preset temperature threshold in real time; if the temperature of the sample does not exceed the preset temperature threshold, the test frequency is dynamically adjusted according to the temperature change; if the temperature of the sample exceeds the preset temperature threshold, the test frequency remains a fixed value or gradually decreases to a set value.

[0049] It should be noted that for the system, a signal output unit and a controller can also be provided. In operation, the infrared temperature measurement unit transmits the recognized temperature information to the temperature measurement control unit, which will interact with the automatic variable frequency unit based on the collected information, control the output frequency through the change of temperature, and transmit the control information to the controller through the signal output unit. When the test temperature exceeds the set value, the closed-loop control unit will control the system to maintain a fixed test frequency.

[0050] The mechanical vision unit includes a mechanical vision module. The mechanical vision module is used to obtain initial image data of the sample, determine an effective detection area of the sample based on the initial image data, and remove the edge and background area of the sample from the effective detection area. The infrared temperature measurement unit is used to collect temperature data of the sample in the effective detection area at a preset time interval, and the mechanical vision unit is also used to synchronously collect image data of the sample. The temperature data includes real-time temperature values of different detection points of the sample, and the image data includes contour, position and surface texture information of the sample.

[0051] The temperature measurement control unit includes a processing module, a fusion module, a calculation module and an integration module. Specifically, the processing module is used to remove random noise in the temperature data by using a mean filter algorithm, and to perform noise reduction processing on the image data; the fusion module is used to fuse the temperature data after removing random noise and the image data after noise reduction processing; the calculation module is used to establish a spatial distribution model of the temperature data based on the pixel coordinates of the image data, calculate the average temperature value, the highest temperature value and the temperature gradient distribution in the effective detection area; and the integration module is used to integrate the calculated data into integrated data reflecting the real-time temperature state of the sample.

[0052] In addition, the temperature measurement control module further comprises focusing, calibration, lens, temperature range, and frame frequency, and the temperature measurement control module exchanges data with the infrared temperature measurement module and the mechanical vision module, and accurately detects the temperature of the sample through parameters of the device, such as focal length, lens, and the like, to realize input of data.

[0053] The automatic frequency conversion unit comprises an automatic frequency conversion module, which is configured to establish a correlation between the average temperature value in the temperature integration data and the test frequency, set a variation threshold interval of the average temperature value, and match a corresponding test frequency for each variation threshold interval, and adjust the corresponding test frequency in real time when the average temperature value is in a certain threshold interval.

[0054] An electronic device comprises: a memory for storing a computer program; a processor for executing the computer program stored in the memory to implement the test frequency conversion measurement method.

[0055] It should be noted that the memory can include a random access memory (RAM) and a non-volatile memory, such as at least one disk memory.

[0056] The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., and can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0057] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the test frequency conversion measurement method.

[0058] It should be noted that the computer-readable storage medium can be included in the device / apparatus described in the above embodiments; or can exist separately and not be assembled into the device / apparatus. The computer-readable storage medium carries one or more programs, which when executed, implement the test frequency conversion measurement method according to the embodiments of the present application.

[0059] According to embodiments of the present application, the computer readable storage medium can be a non-transitory computer readable storage medium, and can include, for example, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0060] Although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various modifications can be made to the foregoing embodiments, or some of the technical features thereof can be equivalently replaced, without departing from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A test frequency conversion measurement method, characterized in that, Includes the following steps: Acquire temperature and image data of the specimens during composite material testing; The temperature data and image data are processed to generate integrated data reflecting the real-time temperature state of the sample. Establish an interconnection between integrated temperature data and test frequency, and dynamically adjust the test frequency based on the changing trends of temperature data; A closed-loop control mechanism is set up to compare the temperature of the sample with the preset temperature threshold in real time. If the temperature of the sample does not exceed the preset temperature threshold, the test frequency is dynamically adjusted according to the temperature change. If the temperature of the sample exceeds the preset temperature threshold, the test frequency is kept at a fixed value or gradually reduced to the set value.

2. The experimental frequency conversion measurement method according to claim 1, characterized in that, Acquiring temperature and image data of the specimens during composite material testing includes the following steps: Acquire initial image data of the sample, determine the effective detection area of ​​the sample based on the initial image data, and remove the edge and background areas of the sample from the effective detection area. Within the effective detection area, temperature data of the sample is collected at preset time intervals, and image data of the sample is collected simultaneously. The temperature data includes real-time temperature values ​​at different detection points of the sample, and the image data includes the outline, position, and surface texture information of the sample.

3. The experimental frequency conversion measurement method and apparatus according to claim 1, characterized in that, The temperature and image data are processed to generate integrated data reflecting the real-time temperature state of the sample, including the following steps: The mean filtering algorithm is used to remove random noise from the temperature data, and the image data is denoised. The temperature data with random noise removed is fused with the noise-reduced image data; Based on the pixel coordinates of the image data, a spatial distribution model of the temperature data is established, and the average temperature value, the highest temperature value, and the temperature gradient distribution within the effective detection area are calculated. The calculated data are integrated into a unified dataset that reflects the real-time temperature state of the sample.

4. The experimental frequency conversion measurement method according to claim 1, characterized in that, Establishing an interconnection between integrated temperature data and test frequency, and dynamically adjusting the test frequency based on the changing trends of temperature data, includes the following steps: Establish the interconnection between the average temperature value and the test frequency in the integrated temperature data, set the threshold range for the change of the average temperature value, and match the corresponding test frequency for each threshold range. When the average temperature value is within a certain threshold range, adjust the corresponding test frequency in real time.

5. A test frequency conversion measurement system, characterized in that, include: A machine vision unit is used to acquire image data of the specimens during composite material testing. Infrared thermometer unit, used to acquire temperature data of the sample during composite material testing; The temperature control unit is used to process temperature data and image data to generate integrated data reflecting the real-time temperature status of the sample. The automatic frequency conversion unit is used to establish the interconnection between integrated temperature data and test frequency, and dynamically adjust the test frequency according to the changing trend of temperature data; The closed-loop control unit is used to set the closed-loop control mechanism and compare the temperature of the sample with the preset temperature threshold in real time. If the temperature of the sample does not exceed the preset temperature threshold, the test frequency is dynamically adjusted according to the temperature change; if the temperature of the sample exceeds the preset temperature threshold, the test frequency remains fixed or gradually decreases to the set value.

6. The experimental frequency conversion measurement system according to claim 5, characterized in that, The machine vision unit includes: The machine vision module is used to acquire initial image data of the sample and determine the effective detection area of ​​the sample based on the initial image data. The effective detection area removes the edges and background areas of the sample. The infrared temperature measurement unit is used to collect temperature data of the sample at preset time intervals within the effective detection area. At the same time, the mechanical vision unit is also used to synchronously collect image data of the sample. The temperature data includes real-time temperature values ​​at different detection points of the sample, and the image data includes the outline, position and surface texture information of the sample.

7. The test frequency conversion measurement system according to claim 5, characterized in that, The temperature measurement and control unit includes: The processing module is used to remove random noise from temperature data using a mean filtering algorithm, and to perform noise reduction processing on image data. The fusion module is used to fuse temperature data with random noise removed with the noise-reduced image data; The calculation module is used to establish a spatial distribution model of temperature data based on the pixel coordinates of image data, and to calculate the average temperature value, the highest temperature value, and the temperature gradient distribution within the effective detection area. The integration module is used to integrate the calculated data into integrated data that reflects the real-time temperature state of the sample.

8. The test frequency conversion measurement system according to claim 5, characterized in that, The automatic frequency conversion unit includes: The automatic frequency conversion module is used to establish the interconnection between the average temperature value and the test frequency in the integrated temperature data, set the threshold range of the average temperature value, and match the corresponding test frequency for each threshold range. When the average temperature value is within a certain threshold range, the corresponding test frequency is adjusted in real time.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a computer program stored in a memory, implements the experimental frequency conversion measurement method according to any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the experimental frequency conversion measurement method according to any one of claims 1-4.

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